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Notebook 9_5_Augmentation - Removed duplicate weight initialization

.editorconfig

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+root = true
+
+[*.{js,jsx,ts,tsx,md,mdx,json,cjs,mjs,css}]
+indent_style = space
+indent_size = 4
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+max_line_length = 100

.eslintrc.cjs

@@ -0,0 +1,18 @@
+module.exports = {
+    root: true,
+    env: { browser: true, es2020: true, node: true },
+    extends: [
+        "eslint:recommended",
+        "plugin:react/recommended",
+        "plugin:react/jsx-runtime",
+        "plugin:react-hooks/recommended",
+    ],
+    ignorePatterns: ["build", ".eslintrc.cjs"],
+    parserOptions: { ecmaVersion: "latest", sourceType: "module" },
+    settings: { react: { version: "18.2" } },
+    plugins: ["react-refresh"],
+    rules: {
+        "react/jsx-no-target-blank": "off",
+        "react-refresh/only-export-components": ["warn", { allowConstantExport: true }],
+    },
+};

.gitignore

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+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# production
+/dist
+
+# ENV
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# IDE
+.idea
+.vscode
+
+# macOS
+.DS_Store

.prettierignore

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+# ignore these directories when formatting the repo
+/Blogs
+/CM20315
+/CM20315_2023
+/Notebooks
+/PDFFigures
+/Slides

.prettierrc.cjs

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+/** @type {import("prettier").Config} */
+const prettierConfig = {
+    trailingComma: "all",
+    tabWidth: 4,
+    useTabs: false,
+    semi: true,
+    singleQuote: false,
+    bracketSpacing: true,
+    printWidth: 100,
+    endOfLine: "lf",
+    plugins: [require.resolve("prettier-plugin-organize-imports")],
+};
+
+module.exports = prettierConfig;

Blogs/BorealisGradientFlow.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyP9fLqBQPgcYJB1KXs3Scp/",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Blogs/BorealisGradientFlow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Gradient flow\n",
+        "\n",
+        "This notebook replicates some of the results in the Borealis AI [blog](https://www.borealisai.com/research-blogs/gradient-flow/) on gradient flow.  \n"
+      ],
+      "metadata": {
+        "id": "ucrRRJ4dq8_d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import relevant libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.linalg import expm\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap"
+      ],
+      "metadata": {
+        "id": "_IQFHZEMZE8T"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Create the three data points that are used to train the linear model in the blog.  Each input point is a column in $\\mathbf{X}$ and consists of the $x$ position in the plot and the value 1, which is used to allow the model to fit bias terms neatly."
+      ],
+      "metadata": {
+        "id": "NwgUP3MSriiJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cJNZ2VIcYsD8"
+      },
+      "outputs": [],
+      "source": [
+        "X = np.array([[0.2, 0.4, 0.8],[1,1,1]])\n",
+        "y = np.array([[-0.1],[0.15],[0.3]])\n",
+        "D = X.shape[0]\n",
+        "I  = X.shape[1]\n",
+        "\n",
+        "print(\"X=\\n\",X)\n",
+        "print(\"y=\\n\",y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the three data points\n",
+        "fig, ax  = plt.subplots()\n",
+        "ax.plot(X[0:1,:],y.T,'ro')\n",
+        "ax.set_xlim([0,1]); ax.set_ylim([-0.5,0.5])\n",
+        "ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "FpFlD4nUZDRt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Compute the evolution of the residuals, loss, and parameters as a function of time."
+      ],
+      "metadata": {
+        "id": "H2LBR1DasQej"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Discretized time to evaluate quantities at\n",
+        "t_all = np.arange(0,20,0.01)\n",
+        "nT = t_all.shape[0]\n",
+        "\n",
+        "# Initial parameters, and initial function output at training points\n",
+        "phi_0 = np.array([[-0.05],[-0.4]])\n",
+        "f_0 = X.T @ phi_0\n",
+        "\n",
+        "# Precompute pseudoinverse term (not a very sensible numerical implementation, but it works...)\n",
+        "XXTInvX = np.linalg.inv(X@X.T)@X\n",
+        "\n",
+        "# Create arrays to hold function at data points over time, residual over time, parameters over time\n",
+        "f_all = np.zeros((I,nT))\n",
+        "f_minus_y_all = np.zeros((I,nT))\n",
+        "phi_t_all = np.zeros((D,nT))\n",
+        "\n",
+        "# For each time, compute function, residual, and parameters at each time.\n",
+        "for t in range(len(t_all)):\n",
+        "  f = y + expm(-X.T@X * t_all[t]) @ (f_0-y)\n",
+        "  f_all[:,t:t+1] = f\n",
+        "  f_minus_y_all[:,t:t+1] = f-y\n",
+        "  phi_t_all[:,t:t+1] = phi_0 - XXTInvX @ (np.identity(3)-expm(-X.T@X * t_all[t])) @ (f_0-y)"
+      ],
+      "metadata": {
+        "id": "wfF_oTS5Z4Wi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plot the results that were calculated in the previous cell"
+      ],
+      "metadata": {
+        "id": "9jSjOOFutJUE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot function at data points\n",
+        "fig, ax  = plt.subplots()\n",
+        "ax.plot(t_all,np.squeeze(f_all[0,:]),'r-', label='$f[x_{0},\\phi]$')\n",
+        "ax.plot(t_all,np.squeeze(f_all[1,:]),'g-', label='$f[x_{1},\\phi]$')\n",
+        "ax.plot(t_all,np.squeeze(f_all[2,:]),'b-', label='$f[x_{2},\\phi]$')\n",
+        "ax.set_xlim([0,np.max(t_all)]); ax.set_ylim([-0.5,0.5])\n",
+        "ax.set_xlabel('t'); ax.set_ylabel('f')\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot residual\n",
+        "fig, ax  = plt.subplots()\n",
+        "ax.plot(t_all,np.squeeze(f_minus_y_all[0,:]),'r-', label='$f[x_{0},\\phi]-y_{0}$')\n",
+        "ax.plot(t_all,np.squeeze(f_minus_y_all[1,:]),'g-', label='$f[x_{1},\\phi]-y_{1}$')\n",
+        "ax.plot(t_all,np.squeeze(f_minus_y_all[2,:]),'b-', label='$f[x_{2},\\phi]-y_{2}$')\n",
+        "ax.set_xlim([0,np.max(t_all)]); ax.set_ylim([-0.5,0.5])\n",
+        "ax.set_xlabel('t'); ax.set_ylabel('f-y')\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot loss (sum of residuals)\n",
+        "fig, ax  = plt.subplots()\n",
+        "square_error = 0.5 * np.sum(f_minus_y_all * f_minus_y_all, axis=0)\n",
+        "ax.plot(t_all, square_error,'k-')\n",
+        "ax.set_xlim([0,np.max(t_all)]); ax.set_ylim([-0.0,0.25])\n",
+        "ax.set_xlabel('t'); ax.set_ylabel('Loss')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot parameters\n",
+        "fig, ax  = plt.subplots()\n",
+        "ax.plot(t_all, np.squeeze(phi_t_all[0,:]),'c-',label='$\\phi_{0}$')\n",
+        "ax.plot(t_all, np.squeeze(phi_t_all[1,:]),'m-',label='$\\phi_{1}$')\n",
+        "ax.set_xlim([0,np.max(t_all)]); ax.set_ylim([-1,1])\n",
+        "ax.set_xlabel('t'); ax.set_ylabel('$\\phi$')\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "G9IwgwKltHz5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the model and the loss function"
+      ],
+      "metadata": {
+        "id": "N6VaUq2swa8D"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Model is just a straight line with intercept phi[0] and slope phi[1]\n",
+        "def model(phi,x):\n",
+        "  y_pred = phi[0]+phi[1] * x\n",
+        "  return y_pred\n",
+        "\n",
+        "# Loss function is 0.5 times sum of squares of residuals for training data\n",
+        "def compute_loss(data_x, data_y, model, phi):\n",
+        "  pred_y = model(phi, data_x)\n",
+        "  loss = 0.5 * np.sum((pred_y-data_y)*(pred_y-data_y))\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "LGHEVUWWiB4f"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Draw the loss function"
+      ],
+      "metadata": {
+        "id": "hr3hs7pKwo0g"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def draw_loss_function(compute_loss, X, y,  model, phi_iters):\n",
+        "  # Define pretty colormap\n",
+        "  my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "  my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "  r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "  g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "  b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "  my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "  # Make grid of intercept/slope values to plot\n",
+        "  intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(-1.0,1.0,0.005), np.arange(-1.0,1.0,0.005))\n",
+        "  loss_mesh = np.zeros_like(slopes_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(slopes_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss(X, y, model, np.array([[intercepts_mesh[idslope]], [slope]]))\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n",
+        "  ax.set_ylim([1,-1]); ax.set_xlim([-1,1])\n",
+        "\n",
+        "  ax.plot(phi_iters[1,:], phi_iters[0,:],'g-')\n",
+        "  ax.set_xlabel('Intercept'); ax.set_ylabel('Slope')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "UCxa3tZ8a9kz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_loss_function(compute_loss, X[0:1,:], y.T, model, phi_t_all)"
+      ],
+      "metadata": {
+        "id": "pXLLBaSaiI2A"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Draw the evolution of the function"
+      ],
+      "metadata": {
+        "id": "ZsremHW-xFi5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax  = plt.subplots()\n",
+        "ax.plot(X[0:1,:],y.T,'ro')\n",
+        "x_vals = np.arange(0,1,0.001)\n",
+        "ax.plot(x_vals, phi_t_all[0,0]*x_vals + phi_t_all[1,0],'r-', label='t=0.00')\n",
+        "ax.plot(x_vals, phi_t_all[0,10]*x_vals + phi_t_all[1,10],'g-', label='t=0.10')\n",
+        "ax.plot(x_vals, phi_t_all[0,30]*x_vals + phi_t_all[1,30],'b-', label='t=0.30')\n",
+        "ax.plot(x_vals, phi_t_all[0,200]*x_vals + phi_t_all[1,200],'c-', label='t=2.00')\n",
+        "ax.plot(x_vals, phi_t_all[0,1999]*x_vals + phi_t_all[1,1999],'y-', label='t=20.0')\n",
+        "ax.set_xlim([0,1]); ax.set_ylim([-0.5,0.5])\n",
+        "ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "cv9ZrUoRkuhI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute MAP and ML solutions\n",
+        "MLParams = np.linalg.inv(X@X.T)@X@y\n",
+        "sigma_sq_p = 3.0\n",
+        "sigma_sq = 0.05\n",
+        "MAPParams = np.linalg.inv(X@X.T+np.identity(X.shape[0])*sigma_sq/sigma_sq_p)@X@y"
+      ],
+      "metadata": {
+        "id": "OU9oegSOof-o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we predict both the mean and the uncertainty in the fitted model as a function of time"
+      ],
+      "metadata": {
+        "id": "Ul__XvOgyYSA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define x positions to make predictions (appending a 1 to each column)\n",
+        "x_predict = np.arange(0,1,0.01)[None,:]\n",
+        "x_predict = np.concatenate((x_predict,np.ones_like(x_predict)))\n",
+        "nX = x_predict.shape[1]\n",
+        "\n",
+        "# Create variables to store evolution of mean and variance of prediction over time\n",
+        "predict_mean_all = np.zeros((nT,nX))\n",
+        "predict_var_all = np.zeros((nT,nX))\n",
+        "\n",
+        "# Initial covariance\n",
+        "sigma_sq_p = 2.0\n",
+        "cov_init = sigma_sq_p * np.identity(2)\n",
+        "\n",
+        "# Run through each time computing a and b and hence mean and variance of prediction\n",
+        "for t in range(len(t_all)):\n",
+        "  a = x_predict.T @(XXTInvX @ (np.identity(3)-expm(-X.T@X * t_all[t])) @ y)\n",
+        "  b = x_predict.T -x_predict.T@XXTInvX @ (np.identity(3)-expm(-X.T@X * t_all[t])) @ X.T\n",
+        "  predict_mean_all[t:t+1,:] = a.T\n",
+        "  predict_cov  = b@ cov_init @b.T\n",
+        "  # We just want the diagonal of the covariance to plot the uncertainty\n",
+        "  predict_var_all[t:t+1,:] = np.reshape(np.diag(predict_cov),(1,nX))"
+      ],
+      "metadata": {
+        "id": "aMPADCuByKWr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plot the mean and variance at various times"
+      ],
+      "metadata": {
+        "id": "PZTj93KK7QH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t, sigma_sq = 0.00001):\n",
+        "  fig, ax  = plt.subplots()\n",
+        "  ax.plot(X[0:1,:],y.T,'ro')\n",
+        "  ax.plot(x_predict[0:1,:].T, predict_mean_all[this_t:this_t+1,:].T,'r-')\n",
+        "  lower = np.squeeze(predict_mean_all[this_t:this_t+1,:].T-np.sqrt(predict_var_all[this_t:this_t+1,:].T+np.sqrt(sigma_sq)))\n",
+        "  upper = np.squeeze(predict_mean_all[this_t:this_t+1,:].T+np.sqrt(predict_var_all[this_t:this_t+1,:].T+np.sqrt(sigma_sq)))\n",
+        "  ax.fill_between(np.squeeze(x_predict[0:1,:]), lower, upper, color='lightgray')\n",
+        "  ax.set_xlim([0,1]); ax.set_ylim([-0.5,0.5])\n",
+        "  ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "  plt.show()\n",
+        "\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=0)\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=40)\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=80)\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=200)\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=500)\n",
+        "plot_mean_var(X,y,x_predict, predict_mean_all, predict_var_all, this_t=1000)"
+      ],
+      "metadata": {
+        "id": "bYAFxgB880-v"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

CM20315/CM20315_Convolution_I.ipynb

@@ -429,4 +429,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Convolution_II.ipynb

@@ -105,7 +105,7 @@
       "cell_type": "code",
       "source": [
         "\n",
-        "# TODO Create a model with the folowing layers\n",
+        "# TODO Create a model with the following layers\n",
         "# 1. Convolutional layer, (input=length 40 and 1 channel, kernel size 3x3, stride 2, padding=\"valid\", 15 output channels ) \n",
         "# 2. ReLU\n",
         "# 3. Convolutional layer, (input=length 19 and 15 channels, kernel size 3x3, stride 2, padding=\"valid\", 15 output channels )\n",
@@ -120,7 +120,7 @@
         "# https://pytorch.org/docs/1.13/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear\n",
         "\n",
         "# Replace the following function which just runs a standard fully connected network\n",
-        "# The flatten at the beginning is becuase we are passing in the data in a slightly different format.\n",
+        "# The flatten at the beginning is because we are passing in the data in a slightly different format.\n",
         "model = nn.Sequential(\n",
         "nn.Flatten(),\n",
         "nn.Linear(40, 100),\n",
@@ -250,4 +250,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Convolution_III.ipynb

@@ -148,7 +148,7 @@
         "# 8. A flattening operation\n",
         "# 9. A fully connected layer mapping from (whatever dimensions we are at-- find out using .shape) to 50 \n",
         "# 10. A ReLU\n",
-        "# 11. A fully connected layer mappiing from 50 to 10 dimensions\n",
+        "# 11. A fully connected layer mapping from 50 to 10 dimensions\n",
         "# 12. A softmax function.\n",
         "\n",
         "# Replace this class which implements a minimal network (which still does okay)\n",
@@ -645,4 +645,4 @@
       }
     }
   ]
-}
+}

CM20315/CM20315_Coursework_I.ipynb

@@ -444,4 +444,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Coursework_II.ipynb

@@ -289,4 +289,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Coursework_III.ipynb

@@ -287,4 +287,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Coursework_IV.ipynb

@@ -128,7 +128,7 @@
         "\n",
         "In part (b) of the practical we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  You will find that the volume decreases to almost nothing in high dimensions.  All of the volume is in the corners of the unit hypercube (which always has volume 1). Double weird.\n",
         "\n",
-        "Note that you you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+        "Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
       ],
       "metadata": {
         "id": "b2FYKV1SL4Z7"
@@ -219,4 +219,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Deep.ipynb

@@ -445,4 +445,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Deep2.ipynb

@@ -314,4 +314,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Gradients_I.ipynb

@@ -417,4 +417,4 @@
       }
     }
   ]
-}
+}

CM20315/CM20315_Gradients_II.ipynb

@@ -32,7 +32,7 @@
       "source": [
         "# Gradients II: Backpropagation algorithm\n",
         "\n",
-        "In this practical, we'll investigate the backpropagation algoritithm.  This computes the gradients of the loss with respect to all of the parameters (weights and biases) in the network.  We'll use these gradients when we run stochastic gradient descent."
+        "In this practical, we'll investigate the backpropagation algorithm.  This computes the gradients of the loss with respect to all of the parameters (weights and biases) in the network.  We'll use these gradients when we run stochastic gradient descent."
       ],
       "metadata": {
         "id": "L6chybAVFJW2"
@@ -53,7 +53,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "First let's define a neural network.  We'll just choose the weights and biaes randomly for now"
+        "First let's define a neural network.  We'll just choose the weights and biases randomly for now"
       ],
       "metadata": {
         "id": "nnUoI0m6GyjC"
@@ -178,7 +178,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Now let's define a loss function.  We'll just use the least squaures loss function. We'll also write a function to compute dloss_doutpu"
+        "Now let's define a loss function.  We'll just use the least squares loss function. We'll also write a function to compute dloss_doutpu"
       ],
       "metadata": {
         "id": "SxVTKp3IcoBF"
@@ -347,4 +347,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Gradients_III.ipynb

@@ -53,7 +53,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "First let's define a neural network.  We'll just choose the weights and biaes randomly for now"
+        "First let's define a neural network.  We'll just choose the weights and biases randomly for now"
       ],
       "metadata": {
         "id": "nnUoI0m6GyjC"
@@ -204,7 +204,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Now let's define a loss function.  We'll just use the least squaures loss function. We'll also write a function to compute dloss_doutput\n"
+        "Now let's define a loss function.  We'll just use the least squares loss function. We'll also write a function to compute dloss_doutput\n"
       ],
       "metadata": {
         "id": "SxVTKp3IcoBF"
@@ -348,4 +348,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Intro.ipynb

@@ -176,7 +176,7 @@
         "# Color represents y value (brighter = higher value)\n",
         "# Black = -10 or less, White = +10 or more\n",
         "# 0 = mid orange\n",
-        "# Lines are conoturs where value is equal\n",
+        "# Lines are contours where value is equal\n",
         "draw_2D_function(x1,x2,y)\n",
         "\n",
         "# TODO\n",
@@ -295,4 +295,4 @@
       }
     }
   ]
-}
+}

CM20315/CM20315_Intro_Answers.ipynb

@@ -215,7 +215,7 @@
         "# Color represents y value (brighter = higher value)\n",
         "# Black = -10 or less, White = +10 or more\n",
         "# 0 = mid orange\n",
-        "# Lines are conoturs where value is equal\n",
+        "# Lines are contours where value is equal\n",
         "draw_2D_function(x1,x2,y)\n",
         "\n",
         "# TODO\n",
@@ -451,4 +451,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Loss.ipynb

@@ -36,7 +36,7 @@
         "\n",
         "We'll compute loss functions for maximum likelihood, minimum negative log likelihood, and least squares and show that they all imply that we should use the same parameter values\n",
         "\n",
-        "In part II, we'll investigate binary classification (where the output data is 0 or 1).  This will be based on the Bernouilli distribution\n",
+        "In part II, we'll investigate binary classification (where the output data is 0 or 1).  This will be based on the Bernoulli distribution\n",
         "\n",
         "In part III we'll investigate multiclass classification (where the output data is 0,1, or, 2).  This will be based on the categorical distribution."
       ],
@@ -178,7 +178,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The blue line i sthe mean prediction of the model and the gray area represents plus/minus two standardard deviations.  This model fits okay, but could be improved. Let's compute the loss.  We'll compute the  the least squares error, the likelihood, the negative log likelihood."
+        "The blue line is the mean prediction of the model and the gray area represents plus/minus two standard deviations.  This model fits okay, but could be improved. Let's compute the loss.  We'll compute the  the least squares error, the likelihood, the negative log likelihood."
       ],
       "metadata": {
         "id": "MvVX6tl9AEXF"
@@ -276,7 +276,7 @@
         "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
         "# Use our neural network to predict the mean of the Gaussian\n",
         "mu_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
-        "# Set the standard devation to something reasonable\n",
+        "# Set the standard deviation to something reasonable\n",
         "sigma = 0.2\n",
         "# Compute the likelihood\n",
         "likelihood = compute_likelihood(y_train, mu_pred, sigma)\n",
@@ -292,7 +292,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of sveral probabilities, which are all quite small themselves.\n",
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
         "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
         "\n",
         "This is why we use negative log likelihood"
@@ -326,7 +326,7 @@
         "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
         "# Use our neural network to predict the mean of the Gaussian\n",
         "mu_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
-        "# Set the standard devation to something reasonable\n",
+        "# Set the standard deviation to something reasonable\n",
         "sigma = 0.2\n",
         "# Compute the log likelihood\n",
         "nll = compute_negative_log_likelihood(y_train, mu_pred, sigma)\n",
@@ -397,7 +397,7 @@
       "source": [
         "# Define a range of values for the parameter\n",
         "beta_1_vals = np.arange(0,1.0,0.01)\n",
-        "# Create some arrays to store the likelihoods, negative log likehoos and sum of squares\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods and sum of squares\n",
         "likelihoods = np.zeros_like(beta_1_vals)\n",
         "nlls = np.zeros_like(beta_1_vals)\n",
         "sum_squares = np.zeros_like(beta_1_vals)\n",
@@ -482,7 +482,7 @@
       "source": [
         "# Define a range of values for the parameter\n",
         "sigma_vals = np.arange(0.1,0.5,0.005)\n",
-        "# Create some arrays to store the likelihoods, negative log likehoos and sum of squares\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods and sum of squares\n",
         "likelihoods = np.zeros_like(sigma_vals)\n",
         "nlls = np.zeros_like(sigma_vals)\n",
         "sum_squares = np.zeros_like(sigma_vals)\n",
@@ -561,4 +561,4 @@
       }
     }
   ]
-}
+}

CM20315/CM20315_Loss_II.ipynb

@@ -34,7 +34,7 @@
         "\n",
         "This practical investigates loss functions.  In part I we investigated univariate regression (where the output data $y$ is continuous.  Our formulation was based on the normal/Gaussian distribution.\n",
         "\n",
-        "In this notebook, we investigate binary classification (where the output data is 0 or 1).  This will be based on the Bernouilli distribution\n",
+        "In this notebook, we investigate binary classification (where the output data is 0 or 1).  This will be based on the Bernoulli distribution\n",
         "\n",
         "In part III we'll investigate multiclass classification (where the outputs data can take multiple values 1,... K.\n",
         "\n",
@@ -199,7 +199,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The left is model output and the right is the model output after the sigmoid has been applied, so it now lies in the range [0,1] and represents the probabiilty, that y=1.  The black dots show the training data.  We'll compute the the likelihood and the negative log likelihood."
+        "The left is model output and the right is the model output after the sigmoid has been applied, so it now lies in the range [0,1] and represents the probability, that y=1.  The black dots show the training data.  We'll compute the likelihood and the negative log likelihood."
       ],
       "metadata": {
         "id": "MvVX6tl9AEXF"
@@ -210,7 +210,7 @@
       "source": [
         "# Return probability under Bernoulli distribution for input x\n",
         "def bernoulli_distribution(y, lambda_param):\n",
-        "    # TODO-- write in the equation for the Bernoullid distribution \n",
+        "    # TODO-- write in the equation for the Bernoulli distribution \n",
         "    # Equation 5.17 from the notes (you will need np.power)\n",
         "    # Replace the line below\n",
         "    prob = np.zeros_like(y)\n",
@@ -249,7 +249,7 @@
       "source": [
         "# Return the likelihood of all of the data under the model\n",
         "def compute_likelihood(y_train, lambda_param):\n",
-        "  # TODO -- compute the likelihood of the data -- the product of the Bernoullis probabilities for each data point\n",
+        "  # TODO -- compute the likelihood of the data -- the product of the Bernoulli's probabilities for each data point\n",
         "  # Top line of equation 5.3 in the notes\n",
         "  # You will need np.prod() and the bernoulli_distribution function you used above\n",
         "  # Replace the line below\n",
@@ -284,7 +284,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of sveral probabilities, which are all quite small themselves.\n",
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
         "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
         "\n",
         "This is why we use negative log likelihood"
@@ -317,7 +317,7 @@
         "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
         "# Use our neural network to predict the mean of the Gaussian\n",
         "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
-        "# Set the standard devation to something reasonable\n",
+        "# Set the standard deviation to something reasonable\n",
         "lambda_train = sigmoid(model_out)\n",
         "# Compute the log likelihood\n",
         "nll = compute_negative_log_likelihood(y_train, lambda_train)\n",
@@ -362,7 +362,7 @@
       "source": [
         "# Define a range of values for the parameter\n",
         "beta_1_vals = np.arange(-2,6.0,0.1)\n",
-        "# Create some arrays to store the likelihoods, negative log likehoods\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods\n",
         "likelihoods = np.zeros_like(beta_1_vals)\n",
         "nlls = np.zeros_like(beta_1_vals)\n",
         "\n",
@@ -450,4 +450,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Loss_III.ipynb

@@ -33,7 +33,7 @@
         "# Loss functions part III\n",
         "\n",
         "This practical investigates loss functions.  In part I we investigated univariate regression (where the output data $y$ is continuous.  Our formulation was based on the normal/Gaussian distribution.\n",
-        "In part II we investigated binary classification (where the output data is 0 or 1).  This will be based on the Bernouilli distribution.<br><br>\n",
+        "In part II we investigated binary classification (where the output data is 0 or 1).  This will be based on the Bernoulli distribution.<br><br>\n",
         "\n",
         "Now we'll investigate multiclass classification (where the outputs data can take multiple values 1,... K, which is based on the categorical distribution\n",
         "\n",
@@ -218,7 +218,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probabiilty, that y=0 (red), 1 (green) and 2 (blue)   The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dotsmand the blue curve to be high where there are blue dots  We'll compute the the likelihood and the negative log likelihood."
+        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue)   The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dotsmand the blue curve to be high where there are blue dots  We'll compute the likelihood and the negative log likelihood."
       ],
       "metadata": {
         "id": "MvVX6tl9AEXF"
@@ -228,7 +228,7 @@
       "cell_type": "code",
       "source": [
         "# Return probability under Bernoulli distribution for input x\n",
-        "# Complicated code to commpute it but just take value from row k of lambda param where y =k, \n",
+        "# Complicated code to compute it but just take value from row k of lambda param where y =k, \n",
         "def categorical_distribution(y, lambda_param):\n",
         "    prob = np.zeros_like(y)\n",
         "    for row_index in range(lambda_param.shape[0]):\n",
@@ -305,7 +305,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of sveral probabilities, which are all quite small themselves.\n",
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
         "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
         "\n",
         "This is why we use negative log likelihood"
@@ -338,7 +338,7 @@
         "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
         "# Use our neural network to predict the mean of the Gaussian\n",
         "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
-        "# Set the standard devation to something reasonable\n",
+        "# Set the standard deviation to something reasonable\n",
         "lambda_train = softmax(model_out)\n",
         "# Compute the log likelihood\n",
         "nll = compute_negative_log_likelihood(y_train, lambda_train)\n",
@@ -365,7 +365,7 @@
       "source": [
         "# Define a range of values for the parameter\n",
         "beta_1_vals = np.arange(-2,6.0,0.1)\n",
-        "# Create some arrays to store the likelihoods, negative log likehoods\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods\n",
         "likelihoods = np.zeros_like(beta_1_vals)\n",
         "nlls = np.zeros_like(beta_1_vals)\n",
         "\n",
@@ -444,4 +444,4 @@
       }
     }
   ]
-}
+}

CM20315/CM20315_Shallow.ipynb

@@ -233,7 +233,7 @@
         "# TODO\n",
         "# 1. Predict what effect changing phi_0 will have on the network.  \n",
         "#   Answer:\n",
-        "# 2. Predict what effect multplying phi_1, phi_2, phi_3 by 0.5 would have.  Check if you are correct\n",
+        "# 2. Predict what effect multiplying phi_1, phi_2, phi_3 by 0.5 would have.  Check if you are correct\n",
         "#   Answer:\n",
         "# 3. Predict what effect multiplying phi_1 by -1 will have.  Check if you are correct.\n",
         "#   Answer:\n",
@@ -500,7 +500,7 @@
         "print(\"Loss = %3.3f\"%(loss))\n",
         "\n",
         "# TODO.  Manipulate the parameters (by hand!) to make the function \n",
-        "# fit the data better and try to reduct the loss to as small a number \n",
+        "# fit the data better and try to reduce the loss to as small a number \n",
         "# as possible.  The best that I could do was 0.181\n",
         "# Tip... start by manipulating phi_0.\n",
         "# It's not that easy, so don't spend too much time on this!"
@@ -626,4 +626,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Training_I.ipynb

@@ -108,7 +108,7 @@
       "source": [
         "def line_search(loss_function, thresh=.0001, max_iter = 10, draw_flag = False):\n",
         "\n",
-        "    # Initialize four points along the rnage we are going to search\n",
+        "    # Initialize four points along the range we are going to search\n",
         "    a = 0\n",
         "    b = 0.33\n",
         "    c = 0.66\n",
@@ -139,7 +139,7 @@
         "        # Rule #2 If point b is less than point c then\n",
         "        #                     then point d becomes point c, and\n",
         "        #                     point b becomes 1/3 between a and new d\n",
-        "        #                     point c beocome 2/3 between a and new d \n",
+        "        #                     point c becomes 2/3 between a and new d \n",
         "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
         "        if (0):\n",
         "          continue;\n",
@@ -147,7 +147,7 @@
         "        # Rule #3 If point c is less than point b then\n",
         "        #                     then point a becomes point b, and\n",
         "        #                     point b becomes 1/3 between new a and d\n",
-        "        #                     point c beocome 2/3 between new a and d \n",
+        "        #                     point c becomes 2/3 between new a and d \n",
         "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
         "        if(0):\n",
         "          continue\n",
@@ -187,4 +187,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Training_II.ipynb

@@ -114,7 +114,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Initialize the parmaeters and draw the model\n",
+        "# Initialize the parameters and draw the model\n",
         "phi = np.zeros((2,1))\n",
         "phi[0] = 0.6      # Intercept\n",
         "phi[1] = -0.2      # Slope\n",
@@ -314,7 +314,7 @@
         "  return compute_loss(data[0,:], data[1,:], model, phi_start+ gradient * dist_prop)\n",
         "\n",
         "def line_search(data, model, phi, gradient, thresh=.00001, max_dist = 0.1, max_iter = 15, verbose=False):\n",
-        "    # Initialize four points along the rnage we are going to search\n",
+        "    # Initialize four points along the range we are going to search\n",
         "    a = 0\n",
         "    b = 0.33 * max_dist\n",
         "    c = 0.66 * max_dist\n",
@@ -345,7 +345,7 @@
         "        # Rule #2 If point b is less than point c then\n",
         "        #                     then point d becomes point c, and\n",
         "        #                     point b becomes 1/3 between a and new d\n",
-        "        #                     point c beocome 2/3 between a and new d \n",
+        "        #                     point c becomes 2/3 between a and new d \n",
         "        if lossb < lossc:\n",
         "          d = c\n",
         "          b = a+ (d-a)/3\n",
@@ -355,7 +355,7 @@
         "        # Rule #2 If point c is less than point b then\n",
         "        #                     then point a becomes point b, and\n",
         "        #                     point b becomes 1/3 between new a and d\n",
-        "        #                     point c beocome 2/3 between new a and d \n",
+        "        #                     point c becomes 2/3 between new a and d \n",
         "        a = b\n",
         "        b = a+ (d-a)/3\n",
         "        c = a+ 2*(d-a)/3\n",
@@ -424,4 +424,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Training_III.ipynb

@@ -340,7 +340,7 @@
         "  return compute_loss(data[0,:], data[1,:], model, phi_start+ gradient * dist_prop)\n",
         "\n",
         "def line_search(data, model, phi, gradient, thresh=.00001, max_dist = 0.1, max_iter = 15, verbose=False):\n",
-        "    # Initialize four points along the rnage we are going to search\n",
+        "    # Initialize four points along the range we are going to search\n",
         "    a = 0\n",
         "    b = 0.33 * max_dist\n",
         "    c = 0.66 * max_dist\n",
@@ -371,7 +371,7 @@
         "        # Rule #2 If point b is less than point c then\n",
         "        #                     then point d becomes point c, and\n",
         "        #                     point b becomes 1/3 between a and new d\n",
-        "        #                     point c beocome 2/3 between a and new d \n",
+        "        #                     point c becomes 2/3 between a and new d \n",
         "        if lossb < lossc:\n",
         "          d = c\n",
         "          b = a+ (d-a)/3\n",
@@ -381,7 +381,7 @@
         "        # Rule #2 If point c is less than point b then\n",
         "        #                     then point a becomes point b, and\n",
         "        #                     point b becomes 1/3 between new a and d\n",
-        "        #                     point c beocome 2/3 between new a and d \n",
+        "        #                     point c becomes 2/3 between new a and d \n",
         "        a = b\n",
         "        b = a+ (d-a)/3\n",
         "        c = a+ 2*(d-a)/3\n",
@@ -582,4 +582,4 @@
       "outputs": []
     }
   ]
-}
+}

CM20315/CM20315_Transformers.ipynb

@@ -175,7 +175,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# TODO Modify the code below by changeing the number of tokens generated and the initial sentence\n",
+        "# TODO Modify the code below by changing the number of tokens generated and the initial sentence\n",
         "# to get a feel for how well this works.  Since I didn't reset the seed, it will give a different\n",
         "# answer every time that you run it.\n",
         "\n",
@@ -253,7 +253,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# TODO Modify the code below by changeing the number of tokens generated and the initial sentence\n",
+        "# TODO Modify the code below by changing the number of tokens generated and the initial sentence\n",
         "# to get a feel for how well this works.  \n",
         "\n",
         "# TODO Experiment with changing this line:\n",
@@ -471,7 +471,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# This routine reutnrs the k'th most likely next token.\n",
+        "# This routine returns the k'th most likely next token.\n",
         "# If k =0 then it returns the most likely token, if k=1 it returns the next most likely and so on\n",
         "# We will need this for beam search\n",
         "def get_kth_most_likely_token(input_tokens, model, tokenizer, k):\n",
@@ -631,4 +631,4 @@
       }
     }
   ]
-}
+}

CM20315/Data/Info.txt

@@ -0,0 +1 @@
+Data for CM20315 practical

CM20315/Info.txt

@@ -0,0 +1 @@
+Practicals from CM20315 course taught at University of Bath, Fall 2022

CM20315_2023/CM20315_Coursework_I.ipynb

@@ -0,0 +1,280 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPNASgWoh4kBvxFP0xkN/I4",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_I.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Coursework I -- Model hyperparameters\n",
+        "\n",
+        "The goal of the coursework is to modify a simple bit of numpy code that trains a network and measures the performance on a validation set for the MNIST 1D dataset.\n",
+        "\n",
+        "In this coursework, you need to modify the **model hyperparameters** (only) to improve the performance over the current attempt.  This could mean the number of layers, the number of hidden units per layer, or the type of activation function, or any combination of the three.\n",
+        "\n",
+        "The only constraint is that you MUST use a fully connected network (no convolutional networks for now if you have read ahead in the book).\n",
+        "\n",
+        "You must improve the performance by at least 2% to get full marks.\n",
+        "\n",
+        "You will need to upload three things to Moodle:\n",
+        "1.   The image that this notebook saves (click the folder icon on the left on colab to download it)\n",
+        "2.   The lines of code you changed\n",
+        "3.   The whole notebook as a .ipynb file.  You can do this on the File menu\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "import gdown"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Run this once to copy the train and validation data to your CoLab environment\n",
+        "# or download from my github to your local machine if you are doing this locally\n",
+        "if not os.path.exists('./Data.zip'):\n",
+        "  !gdown 1HtnCrncY6dFCYqzgPf1HtPVAerTpwFRm\n",
+        "  !unzip Data.zip"
+      ],
+      "metadata": {
+        "id": "wScBGXXFVadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = np.load('train_data_x.npy')\n",
+        "val_data_y = np.load('val_data_y.npy')\n",
+        "train_data_y = np.load('train_data_y.npy')\n",
+        "val_data_x = np.load('val_data_x.npy')\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# YOU SHOULD ONLY CHANGE THIS CELL!\n",
+        "\n",
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "# Number of hidden units in layers 1 and 2\n",
+        "D_1 = 100\n",
+        "D_2 = 100\n",
+        "\n",
+        "# create model with two hidden layers\n",
+        "model = nn.Sequential(\n",
+        "nn.Linear(D_i, D_1),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_1, D_2),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_2, D_o))"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You need all this stuff to ensure that PyTorch is deterministic\n",
+        "def set_seed(seed):\n",
+        "    torch.manual_seed(seed)\n",
+        "    torch.cuda.manual_seed_all(seed)\n",
+        "    torch.backends.cudnn.deterministic = True\n",
+        "    torch.backends.cudnn.benchmark = False\n",
+        "    np.random.seed(seed)\n",
+        "    random.seed(seed)\n",
+        "    os.environ['PYTHONHASHSEED'] = str(seed)"
+      ],
+      "metadata": {
+        "id": "zXRmxCQNnL_M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so always get same result (do not change)\n",
+        "set_seed(1)\n",
+        "\n",
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the lss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_val = model(x_val)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='validation')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Part I: Validation Result %3.2f'%(errors_val[-1]))\n",
+        "ax.legend()\n",
+        "ax.plot([0,n_epoch],[37.45, 37.45],'k:') # Original results. You should be better than this!\n",
+        "plt.savefig('Coursework_I_Results.png',format='png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Leave this all commented for now\n",
+        "# We'll see how well you did on the test data after the coursework is submitted\n",
+        "\n",
+        "# # I haven't given you this yet, leave commented\n",
+        "# test_data_x = np.load('test_data_x.npy')\n",
+        "# test_data_y = np.load('test_data_y.npy')\n",
+        "# x_test = torch.tensor(test_data_x.transpose().astype('float32'))\n",
+        "# y_test = torch.tensor(test_data_y.astype('long'))\n",
+        "# pred_test = model(x_test)\n",
+        "# _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "# errors_test = 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "# print(\"Test error = %3.3f\"%(errors_test))"
+      ],
+      "metadata": {
+        "id": "O7nBz-R84QdJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

CM20315_2023/CM20315_Coursework_II.ipynb

@@ -0,0 +1,276 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyM+iKos5DEoHUxL8+9oxA2A",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_II.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Coursework II -- Training hyperparameters\n",
+        "\n",
+        "The goal of the coursework is to modify a simple bit of numpy code that trains a network and measures the performance on a validation set for the MNist 1D dataset.\n",
+        "\n",
+        "In this coursework, you need to modify the **training hyperparameters** (only) to improve the performance over the current attempt. This could mean the training algorithm, learning rate, learning rate schedule, momentum term, initialization etc.  \n",
+        "\n",
+        "You must improve the performance by at least 2% to get full marks.\n",
+        "\n",
+        "You will need to upload three things to Moodle:\n",
+        "1.   The image that this notebook saves (click the folder icon on the left on colab to download it)\n",
+        "2.   The lines of code you changed\n",
+        "3.   The whole notebook as a .ipynb file.  You can do this on the File menu"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "import gdown"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this once to copy the train and validation data to your CoLab environment\n",
+        "if not os.path.exists('./Data.zip'):\n",
+        "  !gdown 1HtnCrncY6dFCYqzgPf1HtPVAerTpwFRm\n",
+        "  !unzip Data.zip"
+      ],
+      "metadata": {
+        "id": "wScBGXXFVadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = np.load('train_data_x.npy',allow_pickle=True)\n",
+        "train_data_y = np.load('train_data_y.npy',allow_pickle=True)\n",
+        "val_data_x = np.load('val_data_x.npy',allow_pickle=True)\n",
+        "val_data_y = np.load('val_data_y.npy',allow_pickle=True)\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# YOU SHOULD NOT CHANGE THIS CELL!\n",
+        "\n",
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "# Number of hidden units in layers 1 and 2\n",
+        "D_1 = 100\n",
+        "D_2 = 100\n",
+        "\n",
+        "# create model with two hidden layers\n",
+        "model = nn.Sequential(\n",
+        "nn.Linear(D_i, D_1),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_1, D_2),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_2, D_o))"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You need all this stuff to ensure that PyTorch is deterministic\n",
+        "def set_seed(seed):\n",
+        "    torch.manual_seed(seed)\n",
+        "    torch.cuda.manual_seed_all(seed)\n",
+        "    torch.backends.cudnn.deterministic = True\n",
+        "    torch.backends.cudnn.benchmark = False\n",
+        "    np.random.seed(seed)\n",
+        "    random.seed(seed)\n",
+        "    os.environ['PYTHONHASHSEED'] = str(seed)"
+      ],
+      "metadata": {
+        "id": "zXRmxCQNnL_M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so always get same result (do not change)\n",
+        "set_seed(1)\n",
+        "\n",
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "print(x_train.shape)\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))\n",
+        "print(y_train.shape)\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the lss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_val = model(x_val)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='validation')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Part II: Validation Result %3.2f'%(errors_val[-1]))\n",
+        "ax.legend()\n",
+        "ax.plot([0,n_epoch],[37.45, 37.45],'k:') # Original results. You should be better than this!\n",
+        "plt.savefig('Coursework_II_Results.png',format='png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Leave this all commented for now\n",
+        "# We'll see how well you did on the test data after the coursework is submitted\n",
+        "\n",
+        "# # I haven't given you this yet, leave commented\n",
+        "# test_data_x = np.load('test_data_x.npy')\n",
+        "# test_data_y = np.load('test_data_y.npy')\n",
+        "# x_test = torch.tensor(test_data_x.transpose().astype('float32'))\n",
+        "# y_test = torch.tensor(test_data_y.astype('long'))\n",
+        "# pred_test = model(x_test)\n",
+        "# _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "# errors_test = 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "# print(\"Test error = %3.3f\"%(errors_test))"
+      ],
+      "metadata": {
+        "id": "O7nBz-R84QdJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

CM20315_2023/CM20315_Coursework_III.ipynb

@@ -0,0 +1,275 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNDH1z3I76jjglu3o0LSlZc",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_III.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Coursework III -- Regularization\n",
+        "\n",
+        "The goal of the coursework is to modify a simple bit of numpy code that trains a network and measures the performance on a validation set for the MNist 1D dataset.\n",
+        "\n",
+        "In this coursework, you need add **regularization** of some kind to improve the performance.  Anything from chapter 9 of the book or anything else you can find is fine *except* early stopping.  You must not change the model hyperparameters or the training algorithm.\n",
+        "\n",
+        "You must improve the performance by at least 2% to get full marks.\n",
+        "\n",
+        "You will need to upload three things to Moodle:\n",
+        "1.   The image that this notebook saves (click the folder icon on the left on colab to download it)\n",
+        "2.   The lines of code you changed\n",
+        "3.   The whole notebook as a .ipynb file.  You can do this on the File menu\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "import gdown"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this once to copy the train and validation data to your CoLab environment\n",
+        "if not os.path.exists('./Data.zip'):\n",
+        "  !gdown 1HtnCrncY6dFCYqzgPf1HtPVAerTpwFRm\n",
+        "  !unzip Data.zip"
+      ],
+      "metadata": {
+        "id": "wScBGXXFVadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = np.load('train_data_x.npy')\n",
+        "train_data_y = np.load('train_data_y.npy')\n",
+        "val_data_x = np.load('val_data_x.npy')\n",
+        "val_data_y = np.load('val_data_y.npy')\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "# Number of hidden units in layers 1 and 2\n",
+        "D_1 = 100\n",
+        "D_2 = 100\n",
+        "\n",
+        "# create model with two hidden layers\n",
+        "model = nn.Sequential(\n",
+        "nn.Linear(D_i, D_1),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_1, D_2),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_2, D_o))"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You need all this stuff to ensure that PyTorch is deterministic\n",
+        "def set_seed(seed):\n",
+        "    torch.manual_seed(seed)\n",
+        "    torch.cuda.manual_seed_all(seed)\n",
+        "    torch.backends.cudnn.deterministic = True\n",
+        "    torch.backends.cudnn.benchmark = False\n",
+        "    np.random.seed(seed)\n",
+        "    random.seed(seed)\n",
+        "    os.environ['PYTHONHASHSEED'] = str(seed)"
+      ],
+      "metadata": {
+        "id": "zXRmxCQNnL_M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so always get same result (do not change)\n",
+        "set_seed(1)\n",
+        "\n",
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the lss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_val = model(x_val)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='validation')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Part III: Validation Result %3.2f'%(errors_val[-1]))\n",
+        "ax.legend()\n",
+        "ax.plot([0,n_epoch],[37.45, 37.45],'k:') # Original results. You should be better than this!\n",
+        "plt.savefig('Coursework_III_Results.png',format='png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Leave this all commented for now\n",
+        "# We'll see how well you did on the test data after the coursework is submitted\n",
+        "\n",
+        "\n",
+        "# # I haven't given you this yet, leave commented\n",
+        "# test_data_x = np.load('test_data_x.npy')\n",
+        "# test_data_y = np.load('test_data_y.npy')\n",
+        "# x_test = torch.tensor(test_data_x.transpose().astype('float32'))\n",
+        "# y_test = torch.tensor(test_data_y.astype('long'))\n",
+        "# pred_test = model(x_test)\n",
+        "# _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "# errors_test = 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "# print(\"Test error = %3.3f\"%(errors_test))"
+      ],
+      "metadata": {
+        "id": "O7nBz-R84QdJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

CM20315_2023/CM20315_Coursework_IV.ipynb

@@ -0,0 +1,212 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMrWYwQrwgJvDza1vhYK9WQ",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_IV.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Coursework IV\n",
+        "\n",
+        "This coursework explores the geometry of high dimensional spaces.  It doesn't behave how you would expect and all your intuitions are wrong!  You will write code and it will give you three numerical answers that you need to type into Moodle."
+      ],
+      "metadata": {
+        "id": "EjLK-kA1KnYX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4ESMmnkYEVAb"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import scipy.special as sci"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Part (a)\n",
+        "\n",
+        "In part (a) of the practical, we investigate how close random points are in 2D, 100D, and 1000D.   In each case, we generate 1000 points and calculate the Euclidean distance between each pair.  You should find that in 1000D, the furthest two points are only slightly further apart than the nearest points.  Weird!"
+      ],
+      "metadata": {
+        "id": "MonbPEitLNgN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fix the random seed so we all have the same random numbers\n",
+        "np.random.seed(0)\n",
+        "n_data = 1000\n",
+        "# Create 1000 data examples (columns) each with 2 dimensions (rows)\n",
+        "n_dim = 2\n",
+        "x_2D = np.random.normal(size=(n_dim,n_data))\n",
+        "# Create 1000 data examples (columns) each with 100 dimensions (rows)\n",
+        "n_dim = 100\n",
+        "x_100D = np.random.normal(size=(n_dim,n_data))\n",
+        "# Create 1000 data examples (columns) each with 1000 dimensions (rows)\n",
+        "n_dim = 1000\n",
+        "x_1000D = np.random.normal(size=(n_dim,n_data))\n",
+        "\n",
+        "# These values should be the same, otherwise your answer will be wrong\n",
+        "# Get in touch if they are not!\n",
+        "print('Sum of your data is %3.3f, Should be %3.3f'%(np.sum(x_1000D),1036.321))"
+      ],
+      "metadata": {
+        "id": "vZSHVmcWEk14"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def distance_ratio(x):\n",
+        "  # TODO -- replace the two lines below to calculate the largest and smallest Euclidean distance between\n",
+        "  # the data points in the columns of x.  DO NOT include the distance between the data point\n",
+        "  # and itself (which is obviously zero)\n",
+        "  smallest_dist = 1.0\n",
+        "  largest_dist = 1.0\n",
+        "\n",
+        "  # Calculate the ratio and return\n",
+        "  dist_ratio = largest_dist / smallest_dist\n",
+        "  return dist_ratio"
+      ],
+      "metadata": {
+        "id": "PhVmnUs8ErD9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Ratio of largest to smallest distance 2D: %3.3f'%(distance_ratio(x_2D)))\n",
+        "print('Ratio of largest to smallest distance 100D: %3.3f'%(distance_ratio(x_100D)))\n",
+        "print('Ratio of largest to smallest distance 1000D: %3.3f'%(distance_ratio(x_1000D)))\n",
+        "print('**Note down the last of these three numbers, you will need to submit it for your coursework**')"
+      ],
+      "metadata": {
+        "id": "0NdPxfn5GQuJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Part (b)\n",
+        "\n",
+        "In part (b) of the practical we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  You will find that the volume decreases to almost nothing in high dimensions.  All of the volume is in the corners of the unit hypercube (which always has volume 1). Double weird.\n",
+        "\n",
+        "Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+      ],
+      "metadata": {
+        "id": "b2FYKV1SL4Z7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def volume_of_hypersphere(diameter, dimensions):\n",
+        "  # Formula given in Problem 8.7 of the notes\n",
+        "  # You will need sci.gamma()\n",
+        "  # Check out:    https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html\n",
+        "  # Also use this value for pi\n",
+        "  pi = np.pi\n",
+        "  # TODO replace this code with formula for the volume of a hypersphere\n",
+        "  volume = 1.0\n",
+        "\n",
+        "  return volume\n"
+      ],
+      "metadata": {
+        "id": "CZoNhD8XJaHR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "diameter = 1.0\n",
+        "for c_dim in range(1,11):\n",
+        "  print(\"Volume of unit diameter hypersphere in %d dimensions is %3.3f\"%(c_dim, volume_of_hypersphere(diameter, c_dim)))\n",
+        "print('**Note down the last of these ten numbers, you will need to submit it for your coursework**')"
+      ],
+      "metadata": {
+        "id": "fNTBlg_GPEUh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Part (c)\n",
+        "\n",
+        "In part (c) of the coursework, you will calculate what proportion of the volume of a hypersphere is in the outer 1% of the radius/diameter.  Calculate the volume of a hypersphere and then the volume of a hypersphere with 0.99 of the radius and then figure out the proportion (a number between 0 and 1).  You'll see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent.  Extremely weird!"
+      ],
+      "metadata": {
+        "id": "GdyMeOBmoXyF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_prop_of_volume_in_outer_1_percent(dimension):\n",
+        "  # TODO -- replace this line\n",
+        "  proportion = 1.0\n",
+        "\n",
+        "  return proportion"
+      ],
+      "metadata": {
+        "id": "8_CxZ2AIpQ8w"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# While we're here, let's look at how much of the volume is in the outer 1% of the radius\n",
+        "for c_dim in [1,2,10,20,50,100,150,200,250,300]:\n",
+        "  print('Proportion of volume in outer 1 percent of radius in %d dimensions =%3.3f'%(c_dim, get_prop_of_volume_in_outer_1_percent(c_dim)))\n",
+        "print('**Note down the last of these ten numbers, you will need to submit it for your coursework**')"
+      ],
+      "metadata": {
+        "id": "LtMDIn2qPVfJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

CM20315_2023/CM20315_Coursework_V_2023.ipynb

@@ -0,0 +1,525 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyN7KaQQ63bf52r+b5as0MkK",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_V_2023.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Coursework V: Backpropagation in Toy Model**\n",
+        "\n",
+        "This notebook computes the derivatives of a toy function similar (but different from) that in section 7.3 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.  At various points, you will get an answer that you need to copy into Moodle to be marked.\n",
+        "\n",
+        "Post to the content forum if you find any mistakes or need to clarify something."
+      ],
+      "metadata": {
+        "id": "pOZ6Djz0dhoy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Problem setting\n",
+        "\n",
+        "We're going to investigate how to take the derivatives of functions where one operation is composed with another, which is composed with a third and so on.  For example, consider the model:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "     \\mbox{f}[x,\\boldsymbol\\phi] = \\beta_3+\\omega_3\\cdot\\mbox{PReLU}\\Bigl[\\gamma, \\beta_2+\\omega_2\\cdot\\mbox{PReLU}\\bigl[\\gamma, \\beta_1+\\omega_1\\cdot\\mbox{PReLU}[\\gamma, \\beta_0+\\omega_0x]\\bigr]\\Bigr],\n",
+        "\\end{equation}\n",
+        "\n",
+        "with parameters $\\boldsymbol\\phi=\\{\\beta_0,\\omega_0,\\beta_1,\\omega_1,\\beta_2,\\omega_2,\\beta_3,\\omega_3\\}$, where\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mbox{PReLU}[\\gamma, z] = \\begin{cases} \\gamma\\cdot z & \\quad z \\leq0 \\\\ z & \\quad z> 0\\end{cases}.\n",
+        "\\end{equation}\n",
+        "\n",
+        "Suppose that we have a binary cross-entropy loss function (equation 5.20 from the book):\n",
+        "\n",
+        "\\begin{equation*}\n",
+        "\\ell_i  = -(1-y_{i})\\log\\Bigl[1-\\mbox{sig}[\\mbox{f}[\\mathbf{x}_i,\\boldsymbol\\phi]]\\Bigr] - y_{i}\\log\\Bigl[\\mbox{sig}[\\mbox{f}[\\mathbf{x}_i,\\boldsymbol\\phi]]\\Bigr].\n",
+        "\\end{equation*}\n",
+        "\n",
+        "Assume that we know the current values of $\\beta_{0},\\beta_{1},\\beta_{2},\\beta_{3},\\omega_{0},\\omega_{1},\\omega_{2},\\omega_{3}$, $\\gamma$, $x_i$ and $y_i$. We want to know how $\\ell_i$ changes when we make a small change to $\\beta_{0},\\beta_{1},\\beta_{2},\\beta_{3},\\omega_{0},\\omega_{1},\\omega_{2}$, or $\\omega_{3}$.  In other words, we want to compute the eight derivatives:\n",
+        "\n",
+        "\\begin{eqnarray*}\n",
+        "\\frac{\\partial \\ell_i}{\\partial \\beta_{0}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\beta_{1}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\beta_{2}}, \\quad \\frac{\\partial \\ell_i }{\\partial \\beta_{3}},  \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{0}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{1}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{2}},  \\quad\\mbox{and} \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{3}}.\n",
+        "\\end{eqnarray*}"
+      ],
+      "metadata": {
+        "id": "1DmMo2w63CmT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import library\n",
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "RIPaoVN834Lj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's first define the original function and the loss term:"
+      ],
+      "metadata": {
+        "id": "32-ufWhc3v2c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AakK_qen3BpU"
+      },
+      "outputs": [],
+      "source": [
+        "# Defines the activation function\n",
+        "def paramReLU(gamma,x):\n",
+        "  if x > 0:\n",
+        "    return x\n",
+        "  else:\n",
+        "    return x * gamma\n",
+        "\n",
+        "# Defines the main function\n",
+        "def fn(x, beta0, beta1, beta2, beta3, omega0, omega1, omega2, omega3, gamma):\n",
+        "  return beta3+omega3 * paramReLU(gamma, beta2 + omega2 * paramReLU(gamma, beta1 + omega1 * paramReLU(gamma, beta0 + omega0 * x)))\n",
+        "\n",
+        "# Logistic sigmoid\n",
+        "def sig(z):\n",
+        "  return 1./(1+np.exp(-z))\n",
+        "\n",
+        "# The loss function (equation 5.20 from book)\n",
+        "def loss(f,y):\n",
+        "  sig_net_out = sig(f)\n",
+        "  l = -(1-y) * np.log(1-sig_net_out) - y * np.log(sig_net_out)\n",
+        "  return l"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll choose some values for the betas and the omegas and x and compute the output of the function:"
+      ],
+      "metadata": {
+        "id": "y7tf0ZMt5OXt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "beta0 = 1.0; beta1 = -2.0; beta2 = -3.0; beta3 = 0.4\n",
+        "omega0 = 0.1; omega1 = -0.4; omega2 = 2.0; omega3 = -3.0\n",
+        "gamma = 0.2\n",
+        "x = 2.3; y =1.0\n",
+        "f_val = fn(x,beta0,beta1,beta2,beta3,omega0,omega1,omega2,omega3, gamma)\n",
+        "l_i_func = loss(f_val, y)\n",
+        "print('Loss full function = %3.3f'%l_i_func)"
+      ],
+      "metadata": {
+        "id": "pwvOcCxr41X_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Forward pass\n",
+        "\n",
+        "We compute a series of intermediate values $f_0, h_0, f_1, h_1, f_2, h_2, f_3$, and finally the loss $\\ell$"
+      ],
+      "metadata": {
+        "id": "W6ZP62T5fU64"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = 2.3; y =1.0\n",
+        "gamma = 0.2\n",
+        "# Compute all the f_k and h_k terms\n",
+        "# I've done the first two for you\n",
+        "f0 = beta0+omega0 * x\n",
+        "h1 = paramReLU(gamma, f0)\n",
+        "\n",
+        "\n",
+        "# TODO:  Replace the code below\n",
+        "f1 = 0\n",
+        "h2 = 0\n",
+        "f2 = 0\n",
+        "h3 = 0\n",
+        "f3 = 0\n",
+        "\n",
+        "\n",
+        "# Compute the loss and print\n",
+        "# The answer should be the same as when we computed the full function above\n",
+        "l_i = loss(f3, y)\n",
+        "print(\"Loss forward pass = %3.3f\"%(l_i))\n"
+      ],
+      "metadata": {
+        "id": "z-BckTpMf5PL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Backward pass:  Derivative of loss function with respect to function output\n",
+        "\n",
+        "Now, we'll compute the derivative $\\frac{dl}{df_3}$ of the loss function with respect to the network output $f_3$.  In other words, we are asking how does the loss change as we make a small change in the network output.\n",
+        "\n",
+        "Since the loss it itself a function of $\\mbox{sig}[f_3]$ we'll compute this using the chain rule:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{dl}{df_3} = \\frac{d\\mbox{sig}[f_3]}{df_3}\\cdot \\frac{dl}{d\\mbox{sig}[f_3]}\n",
+        "\\end{equation}\n",
+        "\n",
+        "Your job is to compute the two quantities on the right hand side.\n"
+      ],
+      "metadata": {
+        "id": "TbFbxz64Xz4I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute the derivative of the loss with respect to the function output f_val\n",
+        "def dl_df(f_val,y):\n",
+        "  # Compute sigmoid of network output\n",
+        "  sig_f_val = sig(f_val)\n",
+        "  # Compute the derivative of loss with respect to network output using chain rule\n",
+        "  dl_df_val = dsig_df(f_val) * dl_dsigf(sig_f_val, y)\n",
+        "  # Return the derivative\n",
+        "  return dl_df_val"
+      ],
+      "metadata": {
+        "id": "ZWKAq6HC90qV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# MOODLE ANSWER # Notebook V 1a: Copy this code when you have finished it.\n",
+        "\n",
+        "# Compute the derivative of the logistic sigmoid function with respect to its input (as a closed form solution)\n",
+        "def dsig_df(f_val):\n",
+        "  # TODO Write this function\n",
+        "  # Replace this line:\n",
+        "  return 1\n",
+        "\n",
+        "# Compute the derivative of the loss with respect to the logistic sigmoid (as a closed form solution)\n",
+        "def dl_dsigf(sig_f_val, y):\n",
+        "  # TODO Write this function\n",
+        "  # Replace this line:\n",
+        "  return 1"
+      ],
+      "metadata": {
+        "id": "lIngYAgPq-5I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's run that for some f_val, y.  Check previous practicals to see how you can check whether your answer is correct."
+      ],
+      "metadata": {
+        "id": "Q-j-i8khXzbK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = 0.0\n",
+        "dl_df3 = dl_df(f3,y)\n",
+        "print(\"Moodle Answer Notebook V 1b: dldh3=%3.3f\"%(dl_df3))\n",
+        "\n",
+        "y= 1.0\n",
+        "dl_df3 = dl_df(f3,y)\n",
+        "print(\"Moodle Answer Notebook V 1c: dldh3=%3.3f\"%(dl_df3))"
+      ],
+      "metadata": {
+        "id": "Z7Lb5BibY50H"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Backward pass:  Derivative of activation function with respect to preactivations\n",
+        "\n",
+        "Write a function to compute the derivative $\\frac{\\partial h}{\\partial f}$ of the activation function (parametric ReLU) with respect to its input.\n"
+      ],
+      "metadata": {
+        "id": "BA7QaOzejzZw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# MOODLE ANSWER Notebook V 2a: Copy this code when you have finished it.\n",
+        "\n",
+        "def dh_df(gamma, f_val):\n",
+        "  # TODO:  Write this function\n",
+        "  # Replace this line:\n",
+        "  return 1\n"
+      ],
+      "metadata": {
+        "id": "bBPfPg04j-Qw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's run that for some values of f_val.  Check previous practicals to see how you can check whether your answer is correct."
+      ],
+      "metadata": {
+        "id": "QRNCM0EGk9-w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f_val_test = 0.6\n",
+        "dh_df_val = dh_df(gamma, f_val_test)\n",
+        "print(\"Moodle Answer Notebook V 2b: dhdf=%3.3f\"%(dh_df_val))\n",
+        "\n",
+        "f_val_test = -0.4\n",
+        "dh_df_val = dh_df(gamma, f_val_test)\n",
+        "print(\"Moodle Answer Notebook V 2c: dhdf=%3.3f\"%(dh_df_val))"
+      ],
+      "metadata": {
+        "id": "bql8VZIGk8Wy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " # Backward pass:  Compute the derivatives of $l_i$ with respect to the intermediate quantities but in reverse order:\n",
+        "\n",
+        "\\begin{eqnarray}\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_3}, \\quad \\frac{\\partial \\ell_i}{\\partial f_2}, \\quad\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_2}, \\quad \\frac{\\partial \\ell_i}{\\partial f_1}, \\quad\\frac{\\partial \\ell_i}{\\partial h_1},  \\quad\\mbox{and} \\quad \\frac{\\partial \\ell_i}{\\partial f_0}.\n",
+        "\\end{eqnarray}\n",
+        "\n",
+        "The first of these derivatives can be calculated using the chain rule:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{3}} =\\frac{\\partial f_{3}}{\\partial h_{3}} \\frac{\\partial \\ell_i}{\\partial f_{3}} .\n",
+        "\\end{equation}\n",
+        "\n",
+        "The left-hand side asks how $\\ell_i$ changes when $h_{3}$ changes.  The right-hand side says we can decompose this into (i) how $\\ell_i$ changes when $f_{3}$ changes and how $f_{3}$ changes when $h_{3}$ changes.  So you get a chain of events happening:  $h_{3}$ changes $f_{3}$, which changes $\\ell_i$, and the derivatives represent the effects of this chain.  Notice that we computed the first of these derivatives already.  The second term is the derivative of $\\beta_{3} + \\omega_{3}h_{3}$ with respect to $h_3$ which is simply $\\omega_3$.  \n",
+        "\n",
+        "We can continue in this way, computing the derivatives of the output with respect to these intermediate quantities:\n",
+        "\n",
+        "\\begin{eqnarray}\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{2}} &=& \\frac{\\partial h_{3}}{\\partial f_{2}}\\left(\n",
+        "\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\n",
+        "\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{2}} &=& \\frac{\\partial f_{2}}{\\partial h_{2}}\\left(\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}}\\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{1}} &=& \\frac{\\partial h_{2}}{\\partial f_{1}}\\left( \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{1}} &=& \\frac{\\partial f_{1}}{\\partial h_{1}}\\left(\\frac{\\partial h_{2}}{\\partial f_{1}} \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{0}} &=& \\frac{\\partial h_{1}}{\\partial f_{0}}\\left(\\frac{\\partial f_{1}}{\\partial h_{1}}\\frac{\\partial h_{2}}{\\partial f_{1}} \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right).\n",
+        "\\end{eqnarray}\n",
+        "\n",
+        "In each case, we have already computed all of the terms except the last one in the previous step, and the last term is simple to evaluate.  This is called the **backward pass**."
+      ],
+      "metadata": {
+        "id": "jay8NYWdFHuZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = 2.3; y =1.0\n",
+        "dldf3 = dl_df(f3,y)"
+      ],
+      "metadata": {
+        "id": "RSC_2CIfKF1b"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# MOODLE ANSWER Notebook V 3a: Copy this code when you have finished it.\n",
+        "# TODO -- Compute the derivatives of the output with respect\n",
+        "# to the intermediate computations h_k and f_k (i.e, run the backward pass)\n",
+        "# I've done the first two for you.  You replace the code below:\n",
+        "# Replace the code below\n",
+        "dldh3 = 1\n",
+        "dldf2 = 1\n",
+        "dldh2 = 1\n",
+        "dldf1 = 1\n",
+        "dldh1 = 1\n",
+        "dldf0 = 1"
+      ],
+      "metadata": {
+        "id": "gCQJeI--Egdl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we consider how the loss~$\\ell_{i}$ changes when we change the parameters $\\beta_{\\bullet}$ and $\\omega_{\\bullet}$. Once more, we apply the chain rule:\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\\begin{eqnarray}\n",
+        "\\frac{\\partial \\ell_i}{\\partial \\beta_{k}} &=& \\frac{\\partial f_{k}}{\\partial \\beta_{k}}\\frac{\\partial \\ell_i}{\\partial f_{k}}\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial \\omega_{k}} &=& \\frac{\\partial f_{k}}{\\partial \\omega_{k}}\\frac{\\partial \\ell_i}{\\partial f_{k}}.\n",
+        "\\end{eqnarray}\n",
+        "\n",
+        "\\noindent In each case, the second term on the right-hand side was computed in step 2. When $k>0$, we have~$f_{k}=\\beta_{k}+\\omega_k \\cdot h_{k}$, so:\n",
+        "\n",
+        "\\begin{eqnarray}\n",
+        "\\frac{\\partial f_{k}}{\\partial \\beta_{k}} = 1 \\quad\\quad\\mbox{and}\\quad \\quad \\frac{\\partial f_{k}}{\\partial \\omega_{k}} &=& h_{k}.\n",
+        "\\end{eqnarray}"
+      ],
+      "metadata": {
+        "id": "FlzlThQPGpkU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# MOODLE ANSWER Notebook V 3b: Copy this code when you have finished it.\n",
+        "# TODO -- Calculate the final derivatives with respect to the beta and omega terms\n",
+        "# Replace these terms\n",
+        "dldbeta3 = 1\n",
+        "dldomega3 = 1\n",
+        "dldbeta2 = 1\n",
+        "dldomega2 = 1\n",
+        "dldbeta1 = 1\n",
+        "dldomega1 = 1\n",
+        "dldbeta0 = 1\n",
+        "dldomega0 = 1"
+      ],
+      "metadata": {
+        "id": "1I2BhqZhGMK6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Print the last two values out (enter these into Moodle).  Again, think about how you can test whether these are correct.\n",
+        "print('Moodle Answer Notebook V 3c: dldbeta0=%3.3f'%(dldbeta0))\n",
+        "print('Moodle Answer Notebook V 3d: dldOmega0=%3.3f'%(dldomega0))"
+      ],
+      "metadata": {
+        "id": "38eiOn2aHgHI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Compute the derivatives of  $\\ell_i$  with respect to the parmeter $\\gamma$ of the parametric ReLU function.  \n",
+        "\n",
+        "In other words, compute:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{d\\ell_i}{d\\gamma}\n",
+        "\\end{equation}\n",
+        "\n",
+        "Along the way, we will need to compute derivatives\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{dh_k(\\gamma,f_{k-1})}{d\\gamma}\n",
+        "\\end{equation}\n",
+        "\n",
+        "This is quite difficult and not worth many marks, so don't spend too much time on it if you are confused!"
+      ],
+      "metadata": {
+        "id": "lhD5AoUHx3DS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Computes how an activation changes with a small change in gamma assuming preactivations are f\n",
+        "# MOODLE ANSWER # Notebook V 4a: Copy this code when you have finished it.\n",
+        "def dhdgamma(gamma, f):\n",
+        "  # TODO -- Write this function\n",
+        "  # Replace this line\n",
+        "  return 1"
+      ],
+      "metadata": {
+        "id": "yC-9MTQevliP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute how the loss changes with gamma\n",
+        "# Replace this line:\n",
+        "# MOODLE ANSWER # Notebook V 4b: Copy this code when you have finished it.\n",
+        "dldgamma = 1"
+      ],
+      "metadata": {
+        "id": "DiNQrveoLuHR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Moodle Answer Notebook V 4c: dldgamma = %3.3f\"%(dldgamma))"
+      ],
+      "metadata": {
+        "id": "YHxmAEnxzy3O"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

LICENSE

@@ -1,346 +1,346 @@
-Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
+Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
-International Public License
+International Public License
-
+
-By exercising the Licensed Rights (defined below), You accept and agree
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-to be bound by the terms and conditions of this Creative Commons
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-Attribution-NonCommercial-NoDerivatives 4.0 International Public
+Attribution-NonCommercial-NoDerivatives 4.0 International Public
-License ("Public License"). To the extent this Public License may be
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+interpreted as a contract, You are granted the Licensed Rights in
-consideration of Your acceptance of these terms and conditions, and the
+consideration of Your acceptance of these terms and conditions, and the
-Licensor grants You such rights in consideration of benefits the
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-Licensor receives from making the Licensed Material available under
+Licensor receives from making the Licensed Material available under
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+these terms and conditions.
-
+
-
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-Section 1 -- Definitions.
+Section 1 -- Definitions.
-
+
-  a. Adapted Material means material subject to Copyright and Similar
+  a. Adapted Material means material subject to Copyright and Similar
-     Rights that is derived from or based upon the Licensed Material
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+     permission under the Copyright and Similar Rights held by the
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+     Licensor. For purposes of this Public License, where the Licensed
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+     Material is a musical work, performance, or sound recording,
-     Adapted Material is always produced where the Licensed Material is
+     Adapted Material is always produced where the Licensed Material is
-     synched in timed relation with a moving image.
+     synched in timed relation with a moving image.
-
+
-  b. Copyright and Similar Rights means copyright and/or similar rights
+  b. Copyright and Similar Rights means copyright and/or similar rights
-     closely related to copyright including, without limitation,
+     closely related to copyright including, without limitation,
-     performance, broadcast, sound recording, and Sui Generis Database
+     performance, broadcast, sound recording, and Sui Generis Database
-     Rights, without regard to how the rights are labeled or
+     Rights, without regard to how the rights are labeled or
-     categorized. For purposes of this Public License, the rights
+     categorized. For purposes of this Public License, the rights
-     specified in Section 2(b)(1)-(2) are not Copyright and Similar
+     specified in Section 2(b)(1)-(2) are not Copyright and Similar
-     Rights.
+     Rights.
-
+
-  c. Effective Technological Measures means those measures that, in the
+  c. Effective Technological Measures means those measures that, in the
-     absence of proper authority, may not be circumvented under laws
+     absence of proper authority, may not be circumvented under laws
-     fulfilling obligations under Article 11 of the WIPO Copyright
+     fulfilling obligations under Article 11 of the WIPO Copyright
-     Treaty adopted on December 20, 1996, and/or similar international
+     Treaty adopted on December 20, 1996, and/or similar international
-     agreements.
+     agreements.
-
+
-  d. Exceptions and Limitations means fair use, fair dealing, and/or
+  d. Exceptions and Limitations means fair use, fair dealing, and/or
-     any other exception or limitation to Copyright and Similar Rights
+     any other exception or limitation to Copyright and Similar Rights
-     that applies to Your use of the Licensed Material.
+     that applies to Your use of the Licensed Material.
-
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-  e. Licensed Material means the artistic or literary work, database,
+  e. Licensed Material means the artistic or literary work, database,
-     or other material to which the Licensor applied this Public
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-     License.
+     License.
-
+
-  f. Licensed Rights means the rights granted to You subject to the
+  f. Licensed Rights means the rights granted to You subject to the
-     terms and conditions of this Public License, which are limited to
+     terms and conditions of this Public License, which are limited to
-     all Copyright and Similar Rights that apply to Your use of the
+     all Copyright and Similar Rights that apply to Your use of the
-     Licensed Material and that the Licensor has authority to license.
+     Licensed Material and that the Licensor has authority to license.
-
+
-  g. Licensor means the individual(s) or entity(ies) granting rights
+  g. Licensor means the individual(s) or entity(ies) granting rights
-     under this Public License.
+     under this Public License.
-
+
-  h. NonCommercial means not primarily intended for or directed towards
+  h. NonCommercial means not primarily intended for or directed towards
-     commercial advantage or monetary compensation. For purposes of
+     commercial advantage or monetary compensation. For purposes of
-     this Public License, the exchange of the Licensed Material for
+     this Public License, the exchange of the Licensed Material for
-     other material subject to Copyright and Similar Rights by digital
+     other material subject to Copyright and Similar Rights by digital
-     file-sharing or similar means is NonCommercial provided there is
+     file-sharing or similar means is NonCommercial provided there is
-     no payment of monetary compensation in connection with the
+     no payment of monetary compensation in connection with the
-     exchange.
+     exchange.
-
+
-  i. Share means to provide material to the public by any means or
+  i. Share means to provide material to the public by any means or
-     process that requires permission under the Licensed Rights, such
+     process that requires permission under the Licensed Rights, such
-     as reproduction, public display, public performance, distribution,
+     as reproduction, public display, public performance, distribution,
-     dissemination, communication, or importation, and to make material
+     dissemination, communication, or importation, and to make material
-     available to the public including in ways that members of the
+     available to the public including in ways that members of the
-     public may access the material from a place and at a time
+     public may access the material from a place and at a time
-     individually chosen by them.
+     individually chosen by them.
-
+
-  j. Sui Generis Database Rights means rights other than copyright
+  j. Sui Generis Database Rights means rights other than copyright
-     resulting from Directive 96/9/EC of the European Parliament and of
+     resulting from Directive 96/9/EC of the European Parliament and of
-     the Council of 11 March 1996 on the legal protection of databases,
+     the Council of 11 March 1996 on the legal protection of databases,
-     as amended and/or succeeded, as well as other essentially
+     as amended and/or succeeded, as well as other essentially
-     equivalent rights anywhere in the world.
+     equivalent rights anywhere in the world.
-
+
-  k. You means the individual or entity exercising the Licensed Rights
+  k. You means the individual or entity exercising the Licensed Rights
-     under this Public License. Your has a corresponding meaning.
+     under this Public License. Your has a corresponding meaning.
-
+
-
+
-Section 2 -- Scope.
+Section 2 -- Scope.
-
+
-  a. License grant.
+  a. License grant.
-
+
-       1. Subject to the terms and conditions of this Public License,
+       1. Subject to the terms and conditions of this Public License,
-          the Licensor hereby grants You a worldwide, royalty-free,
+          the Licensor hereby grants You a worldwide, royalty-free,
-          non-sublicensable, non-exclusive, irrevocable license to
+          non-sublicensable, non-exclusive, irrevocable license to
-          exercise the Licensed Rights in the Licensed Material to:
+          exercise the Licensed Rights in the Licensed Material to:
-
+
-            a. reproduce and Share the Licensed Material, in whole or
+            a. reproduce and Share the Licensed Material, in whole or
-               in part, for NonCommercial purposes only; and
+               in part, for NonCommercial purposes only; and
-
+
-            b. produce and reproduce, but not Share, Adapted Material
+            b. produce and reproduce, but not Share, Adapted Material
-               for NonCommercial purposes only.
+               for NonCommercial purposes only.
-
+
-       2. Exceptions and Limitations. For the avoidance of doubt, where
+       2. Exceptions and Limitations. For the avoidance of doubt, where
-          Exceptions and Limitations apply to Your use, this Public
+          Exceptions and Limitations apply to Your use, this Public
-          License does not apply, and You do not need to comply with
+          License does not apply, and You do not need to comply with
-          its terms and conditions.
+          its terms and conditions.
-
+
-       3. Term. The term of this Public License is specified in Section
+       3. Term. The term of this Public License is specified in Section
-          6(a).
+          6(a).
-
+
-       4. Media and formats; technical modifications allowed. The
+       4. Media and formats; technical modifications allowed. The
-          Licensor authorizes You to exercise the Licensed Rights in
+          Licensor authorizes You to exercise the Licensed Rights in
-          all media and formats whether now known or hereafter created,
+          all media and formats whether now known or hereafter created,
-          and to make technical modifications necessary to do so. The
+          and to make technical modifications necessary to do so. The
-          Licensor waives and/or agrees not to assert any right or
+          Licensor waives and/or agrees not to assert any right or
-          authority to forbid You from making technical modifications
+          authority to forbid You from making technical modifications
-          necessary to exercise the Licensed Rights, including
+          necessary to exercise the Licensed Rights, including
-          technical modifications necessary to circumvent Effective
+          technical modifications necessary to circumvent Effective
-          Technological Measures. For purposes of this Public License,
+          Technological Measures. For purposes of this Public License,
-          simply making modifications authorized by this Section 2(a)
+          simply making modifications authorized by this Section 2(a)
-          (4) never produces Adapted Material.
+          (4) never produces Adapted Material.
-
+
-       5. Downstream recipients.
+       5. Downstream recipients.
-
+
-            a. Offer from the Licensor -- Licensed Material. Every
+            a. Offer from the Licensor -- Licensed Material. Every
-               recipient of the Licensed Material automatically
+               recipient of the Licensed Material automatically
-               receives an offer from the Licensor to exercise the
+               receives an offer from the Licensor to exercise the
-               Licensed Rights under the terms and conditions of this
+               Licensed Rights under the terms and conditions of this
-               Public License.
+               Public License.
-
+
-            b. No downstream restrictions. You may not offer or impose
+            b. No downstream restrictions. You may not offer or impose
-               any additional or different terms or conditions on, or
+               any additional or different terms or conditions on, or
-               apply any Effective Technological Measures to, the
+               apply any Effective Technological Measures to, the
-               Licensed Material if doing so restricts exercise of the
+               Licensed Material if doing so restricts exercise of the
-               Licensed Rights by any recipient of the Licensed
+               Licensed Rights by any recipient of the Licensed
-               Material.
+               Material.
-
+
-       6. No endorsement. Nothing in this Public License constitutes or
+       6. No endorsement. Nothing in this Public License constitutes or
-          may be construed as permission to assert or imply that You
+          may be construed as permission to assert or imply that You
-          are, or that Your use of the Licensed Material is, connected
+          are, or that Your use of the Licensed Material is, connected
-          with, or sponsored, endorsed, or granted official status by,
+          with, or sponsored, endorsed, or granted official status by,
-          the Licensor or others designated to receive attribution as
+          the Licensor or others designated to receive attribution as
-          provided in Section 3(a)(1)(A)(i).
+          provided in Section 3(a)(1)(A)(i).
-
+
-  b. Other rights.
+  b. Other rights.
-
+
-       1. Moral rights, such as the right of integrity, are not
+       1. Moral rights, such as the right of integrity, are not
-          licensed under this Public License, nor are publicity,
+          licensed under this Public License, nor are publicity,
-          privacy, and/or other similar personality rights; however, to
+          privacy, and/or other similar personality rights; however, to
-          the extent possible, the Licensor waives and/or agrees not to
+          the extent possible, the Licensor waives and/or agrees not to
-          assert any such rights held by the Licensor to the limited
+          assert any such rights held by the Licensor to the limited
-          extent necessary to allow You to exercise the Licensed
+          extent necessary to allow You to exercise the Licensed
-          Rights, but not otherwise.
+          Rights, but not otherwise.
-
+
-       2. Patent and trademark rights are not licensed under this
+       2. Patent and trademark rights are not licensed under this
-          Public License.
+          Public License.
-
+
-       3. To the extent possible, the Licensor waives any right to
+       3. To the extent possible, the Licensor waives any right to
-          collect royalties from You for the exercise of the Licensed
+          collect royalties from You for the exercise of the Licensed
-          Rights, whether directly or through a collecting society
+          Rights, whether directly or through a collecting society
-          under any voluntary or waivable statutory or compulsory
+          under any voluntary or waivable statutory or compulsory
-          licensing scheme. In all other cases the Licensor expressly
+          licensing scheme. In all other cases the Licensor expressly
-          reserves any right to collect such royalties, including when
+          reserves any right to collect such royalties, including when
-          the Licensed Material is used other than for NonCommercial
+          the Licensed Material is used other than for NonCommercial
-          purposes.
+          purposes.
-
+
-
+
-Section 3 -- License Conditions.
+Section 3 -- License Conditions.
-
+
-Your exercise of the Licensed Rights is expressly made subject to the
+Your exercise of the Licensed Rights is expressly made subject to the
-following conditions.
+following conditions.
-
+
-  a. Attribution.
+  a. Attribution.
-
+
-       1. If You Share the Licensed Material, You must:
+       1. If You Share the Licensed Material, You must:
-
+
-            a. retain the following if it is supplied by the Licensor
+            a. retain the following if it is supplied by the Licensor
-               with the Licensed Material:
+               with the Licensed Material:
-
+
-                 i. identification of the creator(s) of the Licensed
+                 i. identification of the creator(s) of the Licensed
-                    Material and any others designated to receive
+                    Material and any others designated to receive
-                    attribution, in any reasonable manner requested by
+                    attribution, in any reasonable manner requested by
-                    the Licensor (including by pseudonym if
+                    the Licensor (including by pseudonym if
-                    designated);
+                    designated);
-
+
-                ii. a copyright notice;
+                ii. a copyright notice;
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+
-               iii. a notice that refers to this Public License;
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+
-                iv. a notice that refers to the disclaimer of
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+
-            b. indicate if You modified the Licensed Material and
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-               Public License, and include the text of, or the URI or
+               Public License, and include the text of, or the URI or
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+               hyperlink to, this Public License.
-
+
-          For the avoidance of doubt, You do not have permission under
+          For the avoidance of doubt, You do not have permission under
-          this Public License to Share Adapted Material.
+          this Public License to Share Adapted Material.
-
+
-       2. You may satisfy the conditions in Section 3(a)(1) in any
+       2. You may satisfy the conditions in Section 3(a)(1) in any
-          reasonable manner based on the medium, means, and context in
+          reasonable manner based on the medium, means, and context in
-          which You Share the Licensed Material. For example, it may be
+          which You Share the Licensed Material. For example, it may be
-          reasonable to satisfy the conditions by providing a URI or
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+          information.
-
+
-       3. If requested by the Licensor, You must remove any of the
+       3. If requested by the Licensor, You must remove any of the
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-          reasonably practicable.
+          reasonably practicable.
-
+
-
+
-Section 4 -- Sui Generis Database Rights.
+Section 4 -- Sui Generis Database Rights.
-
+
-Where the Licensed Rights include Sui Generis Database Rights that
+Where the Licensed Rights include Sui Generis Database Rights that
-apply to Your use of the Licensed Material:
+apply to Your use of the Licensed Material:
-
+
-  a. for the avoidance of doubt, Section 2(a)(1) grants You the right
+  a. for the avoidance of doubt, Section 2(a)(1) grants You the right
-     to extract, reuse, reproduce, and Share all or a substantial
+     to extract, reuse, reproduce, and Share all or a substantial
-     portion of the contents of the database for NonCommercial purposes
+     portion of the contents of the database for NonCommercial purposes
-     only and provided You do not Share Adapted Material;
+     only and provided You do not Share Adapted Material;
-
+
-  b. if You include all or a substantial portion of the database
+  b. if You include all or a substantial portion of the database
-     contents in a database in which You have Sui Generis Database
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-     Rights (but not its individual contents) is Adapted Material; and
+     Rights (but not its individual contents) is Adapted Material; and
-
+
-  c. You must comply with the conditions in Section 3(a) if You Share
+  c. You must comply with the conditions in Section 3(a) if You Share
-     all or a substantial portion of the contents of the database.
+     all or a substantial portion of the contents of the database.
-
+
-For the avoidance of doubt, this Section 4 supplements and does not
+For the avoidance of doubt, this Section 4 supplements and does not
-replace Your obligations under this Public License where the Licensed
+replace Your obligations under this Public License where the Licensed
-Rights include other Copyright and Similar Rights.
+Rights include other Copyright and Similar Rights.
-
+
-
+
-Section 5 -- Disclaimer of Warranties and Limitation of Liability.
+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
-
+
-  a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
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-     EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
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+     ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
-     KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
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-     ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
+     ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
-
+
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+  b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
-     TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
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-     NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
+     NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
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+     ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
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+     DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
-     IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
+     IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
-
+
-  c. The disclaimer of warranties and limitation of liability provided
+  c. The disclaimer of warranties and limitation of liability provided
-     above shall be interpreted in a manner that, to the extent
+     above shall be interpreted in a manner that, to the extent
-     possible, most closely approximates an absolute disclaimer and
+     possible, most closely approximates an absolute disclaimer and
-     waiver of all liability.
+     waiver of all liability.
-
+
-
+
-Section 6 -- Term and Termination.
+Section 6 -- Term and Termination.
-
+
-  a. This Public License applies for the term of the Copyright and
+  a. This Public License applies for the term of the Copyright and
-     Similar Rights licensed here. However, if You fail to comply with
+     Similar Rights licensed here. However, if You fail to comply with
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+     terminate automatically.
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+  b. Where Your right to use the Licensed Material has terminated under
-     Section 6(a), it reinstates:
+     Section 6(a), it reinstates:
-
+
-       1. automatically as of the date the violation is cured, provided
+       1. automatically as of the date the violation is cured, provided
-          it is cured within 30 days of Your discovery of the
+          it is cured within 30 days of Your discovery of the
-          violation; or
+          violation; or
-
+
-       2. upon express reinstatement by the Licensor.
+       2. upon express reinstatement by the Licensor.
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Notebooks/Chap01/1_1_BackgroundMathematics.ipynb

@@ -0,0 +1,412 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s5zzKSOusPOB"
+      },
+      "source": [
+        "\n",
+        "# **Notebook 1.1 -- Background Mathematics**\n",
+        "\n",
+        "The purpose of this Python notebook is to make sure you can use CoLab and to familiarize yourself with some of the background mathematical concepts that you are going to need to understand deep learning. <br><br> It's not meant to be difficult and it may be that you know some or all of this information already.<br><br> Math is *NOT* a spectator sport.  You won't learn it by just listening to lectures or reading books.  It really helps to interact with it and explore yourself. <br><br> Work through the cells below, running each cell in turn.  In various places you will see the words **\"TO DO\"**. Follow the instructions at these places and write code to complete the functions.  There are also questions interspersed in the text.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aUAjBbqzivMY"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WV2Dl6owme2d"
+      },
+      "source": [
+        "**Linear functions**<br> We will be using the term *linear equation* to mean a weighted sum of inputs plus an offset. If there is just one input $x$, then this is a straight line:\n",
+        "\n",
+        "\\begin{equation}y=\\beta+\\omega x,\\end{equation}\n",
+        "\n",
+        "where $\\beta$ is the y-intercept of the linear and $\\omega$ is the slope of the line. When there are two inputs $x_{1}$ and $x_{2}$, then this becomes:\n",
+        "\n",
+        "\\begin{equation}y=\\beta+\\omega_1 x_1 + \\omega_2 x_2.\\end{equation}\n",
+        "\n",
+        "Any other functions are by definition **non-linear**.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WeFK4AvTotd8"
+      },
+      "outputs": [],
+      "source": [
+        "# Define a linear function with just one input, x\n",
+        "def linear_function_1D(x,beta,omega):\n",
+        "  # TODO -- replace the code line below with formula for 1D linear equation\n",
+        "  y = x\n",
+        "\n",
+        "  return y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eimhJ8_jpmEp"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the 1D linear function\n",
+        "\n",
+        "# Define an array of x values from 0 to 10 with increments of 0.01\n",
+        "# https://numpy.org/doc/stable/reference/generated/numpy.arange.html\n",
+        "x = np.arange(0.0,10.0, 0.01)\n",
+        "# Compute y using the function you filled in above\n",
+        "beta = 0.0; omega = 1.0\n",
+        "\n",
+        "y = linear_function_1D(x,beta,omega)\n",
+        "\n",
+        "# Plot this function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(x,y,'r-')\n",
+        "ax.set_ylim([0,10]);ax.set_xlim([0,10])\n",
+        "ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "plt.show()\n",
+        "\n",
+        "# TODO -- experiment with changing the values of beta and omega\n",
+        "# to understand what they do.  Try to make a line\n",
+        "# that crosses the y-axis at y=10 and the x-axis at x=5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AedfvD9dxShZ"
+      },
+      "source": [
+        "Now let's investigate a 2D linear function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "57Gvkk-Ir_7b"
+      },
+      "outputs": [],
+      "source": [
+        "# Code to draw 2D function -- read it so you know what is going on, but you don't have to change it\n",
+        "def draw_2D_function(x1_mesh, x2_mesh, y):\n",
+        "    fig, ax = plt.subplots()\n",
+        "    fig.set_size_inches(7,7)\n",
+        "    pos = ax.contourf(x1_mesh, x2_mesh, y, levels=256 ,cmap = 'hot', vmin=-10,vmax=10.0)\n",
+        "    fig.colorbar(pos, ax=ax)\n",
+        "    ax.set_xlabel('x1');ax.set_ylabel('x2')\n",
+        "    levels = np.arange(-10,10,1.0)\n",
+        "    ax.contour(x1_mesh, x2_mesh, y, levels, cmap='winter')\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YxeNhrXMzkZR"
+      },
+      "outputs": [],
+      "source": [
+        "# Define a linear function with two inputs, x1 and x2\n",
+        "def linear_function_2D(x1,x2,beta,omega1,omega2):\n",
+        "  # TODO -- replace the code line below with formula for 2D linear equation\n",
+        "  y = x1\n",
+        "\n",
+        "  return y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rn_UBRDBysmR"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the 2D function\n",
+        "\n",
+        "# Make 2D array of x and y points\n",
+        "x1 = np.arange(0.0, 10.0, 0.1)\n",
+        "x2 = np.arange(0.0, 10.0, 0.1)\n",
+        "x1,x2 = np.meshgrid(x1,x2)  # https://www.geeksforgeeks.org/numpy-meshgrid-function/\n",
+        "\n",
+        "# Compute the 2D function for given values of omega1, omega2\n",
+        "beta = 0.0; omega1 = 1.0; omega2 = -0.5\n",
+        "y  = linear_function_2D(x1,x2,beta, omega1, omega2)\n",
+        "\n",
+        "# Draw the function.\n",
+        "# Color represents y value (brighter = higher value)\n",
+        "# Black = -10 or less, White = +10 or more\n",
+        "# 0 = mid orange\n",
+        "# Lines are contours where value is equal\n",
+        "draw_2D_function(x1,x2,y)\n",
+        "\n",
+        "# TODO\n",
+        "# Predict what this plot will look like if you set omega_1 to zero\n",
+        "# Change the code and see if you are right.\n",
+        "\n",
+        "# TODO\n",
+        "# Predict what this plot will look like if you set omega_2 to zero\n",
+        "# Change the code and see if you are right.\n",
+        "\n",
+        "# TODO\n",
+        "# Predict what this plot will look like if you set beta to -5\n",
+        "# Change the code and see if you are correct\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i8tLwpls476R"
+      },
+      "source": [
+        "Often we will want to compute many linear functions at the same time.  For example, we might have three inputs, $x_1$, $x_2$, and $x_3$ and want to compute two linear functions giving $y_1$ and $y_2$. Of course, we could do this by just running each equation separately,<br><br>\n",
+        "\n",
+        "\\begin{align}y_1 &=& \\beta_1 + \\omega_{11} x_1 + \\omega_{12} x_2 + \\omega_{13} x_3\\\\\n",
+        "y_2 &=& \\beta_2 + \\omega_{21} x_1 + \\omega_{22} x_2 + \\omega_{23} x_3.\n",
+        "\\end{align}\n",
+        "\n",
+        "However, we can write it more compactly with vectors and matrices:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\begin{bmatrix} y_1\\\\ y_2 \\end{bmatrix} = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\end{bmatrix}+ \\begin{bmatrix}\\omega_{11}&\\omega_{12}&\\omega_{13}\\\\\\omega_{21}&\\omega_{22}&\\omega_{23}\\end{bmatrix}\\begin{bmatrix}x_{1}\\\\x_{2}\\\\x_{3}\\end{bmatrix},\n",
+        "\\end{equation}\n",
+        "or\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mathbf{y} = \\boldsymbol\\beta +\\boldsymbol\\Omega\\mathbf{x}.\n",
+        "\\end{equation}\n",
+        "\n",
+        "for short.  Here, lowercase bold symbols are used for vectors.  Upper case bold symbols are used for matrices.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MjHXMavh9IUz"
+      },
+      "outputs": [],
+      "source": [
+        "# Define a linear function with three inputs, x1, x2, and x_3\n",
+        "def linear_function_3D(x1,x2,x3,beta,omega1,omega2,omega3):\n",
+        "  # TODO -- replace the code below with formula for a single 3D linear equation\n",
+        "  y = x1\n",
+        "\n",
+        "  return y"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fGzVJQ6N-mHJ"
+      },
+      "source": [
+        "Let's compute two linear equations, using both the individual equations and the vector / matrix form and check they give the same result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Swd_bFIE9p2n"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the parameters\n",
+        "beta1 = 0.5; beta2 = 0.2\n",
+        "omega11 =  -1.0 ; omega12 = 0.4; omega13 = -0.3\n",
+        "omega21 =  0.1  ; omega22 = 0.1; omega23 = 1.2\n",
+        "\n",
+        "# Define the inputs\n",
+        "x1 = 4 ; x2 =-1; x3 = 2\n",
+        "\n",
+        "# Compute using the individual equations\n",
+        "y1 = linear_function_3D(x1,x2,x3,beta1,omega11,omega12,omega13)\n",
+        "y2 = linear_function_3D(x1,x2,x3,beta2,omega21,omega22,omega23)\n",
+        "print(\"Individual equations\")\n",
+        "print('y1 = %3.3f\\ny2 = %3.3f'%((y1,y2)))\n",
+        "\n",
+        "# Define vectors and matrices\n",
+        "beta_vec = np.array([[beta1],[beta2]])\n",
+        "omega_mat = np.array([[omega11,omega12,omega13],[omega21,omega22,omega23]])\n",
+        "x_vec = np.array([[x1], [x2], [x3]])\n",
+        "\n",
+        "# Compute with vector/matrix form\n",
+        "y_vec = beta_vec+np.matmul(omega_mat, x_vec)\n",
+        "print(\"Matrix/vector form\")\n",
+        "print('y1= %3.3f\\ny2 = %3.3f'%((y_vec[0][0],y_vec[1][0])))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3LGRoTMLU8ZU"
+      },
+      "source": [
+        "# Questions\n",
+        "\n",
+        "1.  A single linear equation with three inputs (i.e. **linear_function_3D()**) associates a value y with each point in a 3D space ($x_1$,$x_2$,$x_3$).  Is it possible to visualize this?   What value is at position (0,0,0)?\n",
+        "\n",
+        "2.  Write code to compute three linear equations with two inputs ($x_1$, $x_2$) using both the individual equations and the matrix form (you can make up any values for the inputs $\\beta_{i}$ and the slopes $\\omega_{ij}$."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7Y5zdKtKZAB2"
+      },
+      "source": [
+        "# Special functions\n",
+        "\n",
+        "Throughout the book, we'll be using some special functions (see Appendix B.1.3).  The most important of these are the logarithm and exponential functions.  Let's investigate their properties.\n",
+        "\n",
+        "We'll start with the exponential function $y=\\exp[x]=e^x$ which maps the real line $[-\\infty,+\\infty]$ to non-negative numbers $[0,+\\infty]$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c_GkjiY9IWCu"
+      },
+      "outputs": [],
+      "source": [
+        "# Draw the exponential function\n",
+        "\n",
+        "# Define an array of x values from -5 to 5 with increments of 0.01\n",
+        "x = np.arange(-5.0,5.0, 0.01)\n",
+        "y = np.exp(x) ;\n",
+        "\n",
+        "# Plot this function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(x,y,'r-')\n",
+        "ax.set_ylim([0,100]);ax.set_xlim([-5,5])\n",
+        "ax.set_xlabel('x'); ax.set_ylabel('exp[x]')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XyrT8257IWCu"
+      },
+      "source": [
+        "# Questions\n",
+        "\n",
+        "1. What is $\\exp[0]$?  \n",
+        "2. What is $\\exp[1]$?\n",
+        "3. What is $\\exp[-\\infty]$?\n",
+        "4. What is $\\exp[+\\infty]$?\n",
+        "5. A function is convex if we can draw a straight line between any two points on the function, and the line lies above the function everywhere between these two points. Similarly, a function is concave if a straight line between any two points lies below the function everywhere between these two points.  Is the exponential function convex or concave or neither?\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R6A4e5IxIWCu"
+      },
+      "source": [
+        "Now let's consider the logarithm function $y=\\log[x]$. Throughout the book we always use natural (base $e$) logarithms. The log function maps non-negative numbers $[0,\\infty]$ to real numbers $[-\\infty,\\infty]$.  It is the inverse of the exponential function.  So when we compute $\\log[x]$ we are really asking \"What is the number $y$ so that $e^y=x$?\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fOR7v2iXIWCu"
+      },
+      "outputs": [],
+      "source": [
+        "# Draw the logarithm function\n",
+        "\n",
+        "# Define an array of x values from -5 to 5 with increments of 0.01\n",
+        "x = np.arange(0.01,5.0, 0.01)\n",
+        "y = np.log(x) ;\n",
+        "\n",
+        "# Plot this function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(x,y,'r-')\n",
+        "ax.set_ylim([-5,5]);ax.set_xlim([0,5])\n",
+        "ax.set_xlabel('x'); ax.set_ylabel('$\\log[x]$')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yYWrL5AXIWCv"
+      },
+      "source": [
+        "# Questions\n",
+        "\n",
+        "1. What is $\\log[0]$?  \n",
+        "2. What is $\\log[1]$?\n",
+        "3. What is $\\log[e]$?\n",
+        "4. What is $\\log[\\exp[3]]$?\n",
+        "5. What is $\\exp[\\log[4]]$?\n",
+        "6. What is $\\log[-1]$?\n",
+        "7. Is the logarithm function concave or convex?\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap02/2_1_Supervised_Learning.ipynb

@@ -0,0 +1,252 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Notebook 2.1 Supervised Learning\n",
+        "\n",
+        "The purpose of this notebook is to explore the linear regression model discussed in Chapter 2 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "sfB2oX2RNvuF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uoYl2Gn3Nr52"
+      },
+      "outputs": [],
+      "source": [
+        "# Math library\n",
+        "import numpy as np\n",
+        "# Plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create some input / output data\n",
+        "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n",
+        "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])\n",
+        "\n",
+        "print(x)\n",
+        "print(y)"
+      ],
+      "metadata": {
+        "id": "MUbTD4znORtd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define 1D linear regression model\n",
+        "def f(x, phi0, phi1):\n",
+        "  # TODO :  Replace this line with the linear regression model (eq 2.4)\n",
+        "  y = x\n",
+        "\n",
+        "  return y"
+      ],
+      "metadata": {
+        "id": "lw2dCRHwSW9a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to help plot the data\n",
+        "def plot(x, y, phi0, phi1):\n",
+        "    fig,ax = plt.subplots()\n",
+        "    ax.scatter(x,y)\n",
+        "    plt.xlim([0,2.0])\n",
+        "    plt.ylim([0,2.0])\n",
+        "    ax.set_xlabel('Input, $x$')\n",
+        "    ax.set_ylabel('Output, $y$')\n",
+        "    # Draw line\n",
+        "    x_line = np.arange(0,2,0.01)\n",
+        "    y_line = f(x_line, phi0, phi1)\n",
+        "    plt.plot(x_line, y_line,'b-',lw=2)\n",
+        "\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "VT4F3xxSOt8C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the intercept and slope as in figure 2.2b\n",
+        "phi0 = 0.4 ; phi1 = 0.2\n",
+        "# Plot the data and the model\n",
+        "plot(x,y,phi0,phi1)"
+      ],
+      "metadata": {
+        "id": "AkdZdmhHWuVR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to calculate the loss\n",
+        "def compute_loss(x,y,phi0,phi1):\n",
+        "\n",
+        "  # TODO Replace this line with the loss calculation (equation 2.5)\n",
+        "  loss = 0\n",
+        "\n",
+        "\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "1-GW218wX44b"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute the loss for our current model\n",
+        "loss = compute_loss(x,y,phi0,phi1)\n",
+        "print(f'Your Loss = {loss:3.2f}, Ground truth =7.07')"
+      ],
+      "metadata": {
+        "id": "Hgw7_GzBZ8tX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the intercept and slope as in figure 2.2c\n",
+        "phi0 = 1.60 ; phi1 =-0.8\n",
+        "# Plot the data and the model\n",
+        "plot(x,y,phi0,phi1)\n",
+        "loss = compute_loss(x,y,phi0,phi1)\n",
+        "print(f'Your Loss = {loss:3.2f}, Ground truth =10.28')"
+      ],
+      "metadata": {
+        "id": "_vZS28-FahGP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TO DO -- Change the parameters manually to fit the model\n",
+        "# First fix phi1 and try changing phi0 until you can't make the loss go down any more\n",
+        "# Then fix phi0 and try changing phi1 until you can't make the loss go down any more\n",
+        "# Repeat this process until you find a set of parameters that fit the model as in figure 2.2d\n",
+        "# You can either do this by hand, or if you want to get fancy, write code to descent automatically in this way\n",
+        "# Start at these values:\n",
+        "phi0 = 1.60 ; phi1 =-0.8\n",
+        "\n",
+        "plot(x,y,phi0,phi1)\n",
+        "print(f'Your Loss = {compute_loss(x,y,phi0,phi1):3.2f}')"
+      ],
+      "metadata": {
+        "id": "VzpnzdW5d9vj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Visualizing the loss function\n",
+        "\n",
+        "The above process is equivalent to descending coordinate wise on the loss function<br>\n",
+        "\n",
+        "Now let's plot that function"
+      ],
+      "metadata": {
+        "id": "MNC4qEZognEe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make a 2D grid of possible phi0 and phi1 values\n",
+        "phi0_mesh, phi1_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.02))\n",
+        "\n",
+        "# Make a 2D array for the losses\n",
+        "all_losses = np.zeros_like(phi1_mesh)\n",
+        "# Run through each 2D combination of phi0, phi1 and compute loss\n",
+        "for indices,temp in np.ndenumerate(phi1_mesh):\n",
+        "    all_losses[indices] = compute_loss(x,y, phi0_mesh[indices], phi1_mesh[indices])\n"
+      ],
+      "metadata": {
+        "id": "ATrU8sqqg2hJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the loss function as a heatmap\n",
+        "fig = plt.figure()\n",
+        "ax = plt.axes()\n",
+        "fig.set_size_inches(7,7)\n",
+        "levels = 256\n",
+        "ax.contourf(phi0_mesh, phi1_mesh, all_losses ,levels)\n",
+        "levels = 40\n",
+        "ax.contour(phi0_mesh, phi1_mesh, all_losses ,levels, colors=['#80808080'])\n",
+        "ax.set_ylim([1,-1])\n",
+        "ax.set_xlabel(r'Intercept, $\\phi_0$')\n",
+        "ax.set_ylabel(r'Slope, $\\phi_1$')\n",
+        "\n",
+        "# Plot the position of your best fitting line on the loss function\n",
+        "# It should be close to the minimum\n",
+        "ax.plot(phi0,phi1,'ro')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "6OXAjx5xfQkl"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb

@@ -0,0 +1,293 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 3.2 -- Shallow neural networks II**\n",
+        "\n",
+        "The purpose of this notebook is to gain some familiarity with shallow neural networks with 2D inputs.  It works through an example similar to figure 3.8 and experiments with different activation functions. <br><br>\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "1Z6LB4Ybn1oN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hAM55ZjSncOk"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code to draw 2D function -- read it so you know what is going on, but you don't have to change it\n",
+        "def draw_2D_function(ax, x1_mesh, x2_mesh, y):\n",
+        "    pos = ax.contourf(x1_mesh, x2_mesh, y, levels=256 ,cmap = 'hot', vmin=-10,vmax=10.0)\n",
+        "    ax.set_xlabel('x1');ax.set_ylabel('x2')\n",
+        "    levels = np.arange(-10,10,1.0)\n",
+        "    ax.contour(x1_mesh, x2_mesh, y, levels, cmap='winter')\n",
+        "\n",
+        "# Plot the shallow neural network.  We'll assume input in is range [0,10],[0,10] and output [-10,10]\n",
+        "def plot_neural_2_inputs(x1,x2, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3):\n",
+        "\n",
+        "  fig, ax = plt.subplots(3,3)\n",
+        "  fig.set_size_inches(8.5, 8.5)\n",
+        "  fig.tight_layout(pad=3.0)\n",
+        "  draw_2D_function(ax[0,0], x1,x2,pre_1); ax[0,0].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[0,1], x1,x2,pre_2); ax[0,1].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[0,2], x1,x2,pre_3); ax[0,2].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[1,0], x1,x2,act_1); ax[1,0].set_title('Activation')\n",
+        "  draw_2D_function(ax[1,1], x1,x2,act_2); ax[1,1].set_title('Activation')\n",
+        "  draw_2D_function(ax[1,2], x1,x2,act_3); ax[1,2].set_title('Activation')\n",
+        "  draw_2D_function(ax[2,0], x1,x2,w_act_1); ax[2,0].set_title('Weighted Act')\n",
+        "  draw_2D_function(ax[2,1], x1,x2,w_act_2); ax[2,1].set_title('Weighted Act')\n",
+        "  draw_2D_function(ax[2,2], x1,x2,w_act_3); ax[2,2].set_title('Weighted Act')\n",
+        "  plt.show()\n",
+        "\n",
+        "  fig, ax = plt.subplots()\n",
+        "  draw_2D_function(ax,x1,x2,y)\n",
+        "  ax.set_title('Network output, $y$')\n",
+        "  ax.set_aspect(1.0)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "IHtCP0t2HC4c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "Lw71laEeJgKs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a shallow neural network with, two input, one output, and three hidden units\n",
+        "def shallow_2_1_3(x1,x2, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11,\\\n",
+        "                  theta_12, theta_20, theta_21, theta_22, theta_30, theta_31, theta_32):\n",
+        "  # TODO Replace the lines below to compute the three initial linear functions\n",
+        "  # (figure 3.8a-c) from the theta parameters.  These are the preactivations\n",
+        "  pre_1 = np.zeros_like(x1)\n",
+        "  pre_2 = np.zeros_like(x1)\n",
+        "  pre_3 = np.zeros_like(x1)\n",
+        "\n",
+        "  # Pass these through the ReLU function to compute the activations as in\n",
+        "  # figure 3.8 d-f\n",
+        "  act_1 = activation_fn(pre_1)\n",
+        "  act_2 = activation_fn(pre_2)\n",
+        "  act_3 = activation_fn(pre_3)\n",
+        "\n",
+        "  # TODO Replace the code below to weight the activations using phi1, phi2 and phi3\n",
+        "  # To create the equivalent of figure 3.8 g-i\n",
+        "  w_act_1 = np.zeros_like(x1)\n",
+        "  w_act_2 = np.zeros_like(x1)\n",
+        "  w_act_3 = np.zeros_like(x1)\n",
+        "\n",
+        "  # TODO Replace the code below to combing the weighted activations and add\n",
+        "  # phi_0 to create the output as in figure 3.8j\n",
+        "  y = np.zeros_like(x1)\n",
+        "\n",
+        "  # Return everything we have calculated\n",
+        "  return y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3"
+      ],
+      "metadata": {
+        "id": "VIZA8HywIjfl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now lets define some parameters and run the neural network\n",
+        "theta_10 =  -4.0 ;  theta_11 = 0.9; theta_12 = 0.0\n",
+        "theta_20 =  5.0  ; theta_21 = -0.9 ; theta_22 = -0.5\n",
+        "theta_30 =  -7  ; theta_31 = 0.5; theta_32 = 0.9\n",
+        "phi_0 = 0.0; phi_1 = -2.0; phi_2 = 2.0; phi_3 = 1.5\n",
+        "\n",
+        "x1 = np.arange(0.0, 10.0, 0.1)\n",
+        "x2 = np.arange(0.0, 10.0, 0.1)\n",
+        "x1,x2 = np.meshgrid(x1,x2)  # https://www.geeksforgeeks.org/numpy-meshgrid-function/\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3 = \\\n",
+        "    shallow_2_1_3(x1,x2, ReLU, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_12, theta_20, theta_21, theta_22, theta_30, theta_31, theta_32)\n",
+        "# And then plot it\n",
+        "plot_neural_2_inputs(x1,x2, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3)"
+      ],
+      "metadata": {
+        "id": "51lvc9bfIrs4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "How many different linear polytopes are made by this model?  Identify each in the network output."
+      ],
+      "metadata": {
+        "id": "j62IizIfMYZK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll extend this model to have two outputs $y_1$ and $y_2$, each of which can be visualized with a separate heatmap.  You will now have sets of parameters $\\phi_{10}, \\phi_{11}, \\phi_{12}, \\phi_{13}$ and $\\phi_{20}, \\phi_{21}, \\phi_{22}, \\phi_{23}$ that correspond to each of these outputs."
+      ],
+      "metadata": {
+        "id": "Xl6LcrUyM7Lh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the shallow neural network.  We'll assume input in is range [0,10],[0,10] and output [-10,10]\n",
+        "def plot_neural_2_inputs_2_outputs(x1,x2, y1, y2, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_11, w_act_12, w_act_13, w_act_21, w_act_22, w_act_23):\n",
+        "\n",
+        "  # Plot intermediate plots if flag set\n",
+        "  fig, ax = plt.subplots(4,3)\n",
+        "  fig.set_size_inches(8.5, 8.5)\n",
+        "  fig.tight_layout(pad=3.0)\n",
+        "  draw_2D_function(ax[0,0], x1,x2,pre_1); ax[0,0].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[0,1], x1,x2,pre_2); ax[0,1].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[0,2], x1,x2,pre_3); ax[0,2].set_title('Preactivation')\n",
+        "  draw_2D_function(ax[1,0], x1,x2,act_1); ax[1,0].set_title('Activation')\n",
+        "  draw_2D_function(ax[1,1], x1,x2,act_2); ax[1,1].set_title('Activation')\n",
+        "  draw_2D_function(ax[1,2], x1,x2,act_3); ax[1,2].set_title('Activation')\n",
+        "  draw_2D_function(ax[2,0], x1,x2,w_act_11); ax[2,0].set_title('Weighted Act 1')\n",
+        "  draw_2D_function(ax[2,1], x1,x2,w_act_12); ax[2,1].set_title('Weighted Act 1')\n",
+        "  draw_2D_function(ax[2,2], x1,x2,w_act_13); ax[2,2].set_title('Weighted Act 1')\n",
+        "  draw_2D_function(ax[3,0], x1,x2,w_act_21); ax[3,0].set_title('Weighted Act 2')\n",
+        "  draw_2D_function(ax[3,1], x1,x2,w_act_22); ax[3,1].set_title('Weighted Act 2')\n",
+        "  draw_2D_function(ax[3,2], x1,x2,w_act_23); ax[3,2].set_title('Weighted Act 2')\n",
+        "  plt.show()\n",
+        "\n",
+        "  fig, ax = plt.subplots()\n",
+        "  draw_2D_function(ax,x1,x2,y1)\n",
+        "  ax.set_title('Network output, $y_1$')\n",
+        "  ax.set_aspect(1.0)\n",
+        "  plt.show()\n",
+        "\n",
+        "  fig, ax = plt.subplots()\n",
+        "  draw_2D_function(ax,x1,x2,y2)\n",
+        "  ax.set_title('Network output, $y_2$')\n",
+        "  ax.set_aspect(1.0)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "DlznqZWdPtjI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Define a shallow neural network with, two inputs, two outputs, and three hidden units\n",
+        "def shallow_2_2_3(x1,x2, activation_fn, phi_10,phi_11,phi_12,phi_13, phi_20,phi_21,phi_22,phi_23, theta_10, theta_11,\\\n",
+        "                  theta_12, theta_20, theta_21, theta_22, theta_30, theta_31, theta_32):\n",
+        "\n",
+        "  # TODO -- write this function -- replace the dummy code below\n",
+        "  pre_1 = np.zeros_like(x1)\n",
+        "  pre_2 = np.zeros_like(x1)\n",
+        "  pre_3 = np.zeros_like(x1)\n",
+        "  act_1 = np.zeros_like(x1)\n",
+        "  act_2 = np.zeros_like(x1)\n",
+        "  act_3 = np.zeros_like(x1)\n",
+        "  w_act_11 = np.zeros_like(x1)\n",
+        "  w_act_12 = np.zeros_like(x1)\n",
+        "  w_act_13 = np.zeros_like(x1)\n",
+        "  w_act_21 = np.zeros_like(x1)\n",
+        "  w_act_22 = np.zeros_like(x1)\n",
+        "  w_act_23 = np.zeros_like(x1)\n",
+        "  y1 = np.zeros_like(x1)\n",
+        "  y2 = np.zeros_like(x1)\n",
+        "\n",
+        "\n",
+        "  # Return everything we have calculated\n",
+        "  return y1,y2, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_11, w_act_12, w_act_13, w_act_21, w_act_22, w_act_23\n"
+      ],
+      "metadata": {
+        "id": "m8KAhwr4QWro"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now lets define some parameters and run the neural network\n",
+        "theta_10 =  -4.0 ;  theta_11 = 0.9; theta_12 = 0.0\n",
+        "theta_20 =  5.0  ; theta_21 = -0.9 ; theta_22 = -0.5\n",
+        "theta_30 =  -7  ; theta_31 = 0.5; theta_32 = 0.9\n",
+        "phi_10 = 0.0; phi_11 = -2.0; phi_12 = 2.0; phi_13 = 1.5\n",
+        "phi_20 = -2.0; phi_21 = -1.0; phi_22 = -2.0; phi_23 = 0.8\n",
+        "\n",
+        "x1 = np.arange(0.0, 10.0, 0.1)\n",
+        "x2 = np.arange(0.0, 10.0, 0.1)\n",
+        "x1,x2 = np.meshgrid(x1,x2)  # https://www.geeksforgeeks.org/numpy-meshgrid-function/\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y1, y2, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_11, w_act_12, w_act_13, w_act_21, w_act_22, w_act_23 = \\\n",
+        "    shallow_2_2_3(x1,x2, ReLU, phi_10,phi_11,phi_12,phi_13, phi_20,phi_21,phi_22,phi_23, theta_10, theta_11, theta_12, theta_20, theta_21, theta_22, theta_30, theta_31, theta_32)\n",
+        "# And then plot it\n",
+        "plot_neural_2_inputs_2_outputs(x1,x2, y1, y2, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_11, w_act_12, w_act_13, w_act_21, w_act_22, w_act_23)"
+      ],
+      "metadata": {
+        "id": "ms4YTqbYUeRV"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb

@@ -0,0 +1,259 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNioITtfAcfxEfM3UOfQyb9",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 3.3 -- Shallow network regions**\n",
+        "\n",
+        "The purpose of this notebook is to compute the maximum possible number of linear regions as seen in figure 3.9 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "DCTC8fQ6cp-n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Imports math library\n",
+        "import math"
+      ],
+      "metadata": {
+        "id": "W3C1ZA1gcpq_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The number of regions $N$ created by a shallow neural network with $D_i$ inputs and $D$ hidden units is given by Zaslavsky's formula:\n",
+        "\n",
+        "\\begin{equation}N = \\sum_{j=0}^{D_{i}}\\binom{D}{j}=\\sum_{j=0}^{D_{i}} \\frac{D!}{(D-j)!j!} \\end{equation} <br>\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TbfanfXBe84L"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4UQ2n0RWcgOb"
+      },
+      "outputs": [],
+      "source": [
+        "def number_regions(Di, D):\n",
+        "  # TODO -- implement Zaslavsky's formula\n",
+        "  # You can use math.comb() https://www.w3schools.com/python/ref_math_comb.asp\n",
+        "  # Replace this code\n",
+        "  N = 1;\n",
+        "\n",
+        "  return N"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the number of regions for 2D input (Di=2) and 3 hidden units (D=3) as in figure 3.8j\n",
+        "N = number_regions(2, 3)\n",
+        "print(f\"Di=2, D=3, Number of regions = {int(N)}, True value = 7\")"
+      ],
+      "metadata": {
+        "id": "AqSUfuJDigN9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the number of regions for 10D input (Di=10) and 50 hidden units (D=50)\n",
+        "N = number_regions(10, 50)\n",
+        "print(f\"Di=10, D=50, Number of regions = {int(N)}, True value = 13432735556\")"
+      ],
+      "metadata": {
+        "id": "krNKPV9gjCu-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This works but there is a complication. If the number of hidden units $D$ is fewer than the number of input dimensions $D_i$ , the formula will fail.  When this is the case, there are just $2^D$ regions (see figure 3.10 to understand why).\n",
+        "\n",
+        "Let's demonstrate this:"
+      ],
+      "metadata": {
+        "id": "rk1a2LqGkO9u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Depending on how you implemented it, the calculation may fail when $D_i > D$ (not to worry...)\n",
+        "try:\n",
+        "  N = number_regions(10, 8)\n",
+        "  print(f\"Di=10, D=8, Number of regions = {int(N)}, True value = 256\")\n",
+        "except Exception as error:\n",
+        "    print(\"An exception occurred:\", error)\n"
+      ],
+      "metadata": {
+        "id": "uq5IeAZTkIMg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's do the calculation properly when D<Di (see figure 3.10 from the book)\n",
+        "D = 8; Di = 10\n",
+        "N = np.power(2,D)\n",
+        "# We can equivalently do this by calling number_regions with the D twice\n",
+        "# Think about why this works\n",
+        "N2 = number_regions (D,D)\n",
+        "print(f\"Di=10, D=8, Number of regions = {int(N)}, Number of regions = {int(N2)}, True value = 256\")"
+      ],
+      "metadata": {
+        "id": "Ig8Kg_ADjoQd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's plot the graph from figure 3.9a\n",
+        "dims = np.array([1,5,10,50,100])\n",
+        "regions = np.zeros((dims.shape[0], 1000))\n",
+        "for c_dim in range(dims.shape[0]):\n",
+        "    D_i = dims[c_dim]\n",
+        "    print (f\"Counting regions for {D_i} input dimensions\")\n",
+        "    for D in range(1000):\n",
+        "        regions[c_dim, D] = number_regions(np.min([D_i,D]), D)\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.semilogy(regions[0,:],'k-')\n",
+        "ax.semilogy(regions[1,:],'b-')\n",
+        "ax.semilogy(regions[2,:],'m-')\n",
+        "ax.semilogy(regions[3,:],'c-')\n",
+        "ax.semilogy(regions[4,:],'y-')\n",
+        "ax.legend(['$D_i$=1', '$D_i$=5', '$D_i$=10', '$D_i$=50', '$D_i$=100'])\n",
+        "ax.set_xlabel(\"Number of hidden units, D\")\n",
+        "ax.set_ylabel(\"Number of regions, N\")\n",
+        "plt.xlim([0,1000])\n",
+        "plt.ylim([1e1,1e150])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "5XnEOp0Bj_QK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's compute and plot the number of regions as a function of the number of parameters as in figure 3.9b\n",
+        "# First let's write a function that computes the number of parameters as a function of the input dimension and number of hidden units (assuming just one output)\n",
+        "\n",
+        "def number_parameters(D_i, D):\n",
+        "  # TODO -- replace this code with the proper calculation\n",
+        "  N = 1\n",
+        "\n",
+        "  return N ;"
+      ],
+      "metadata": {
+        "id": "Pav1OsCnpm6P"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's test the code\n",
+        "N = number_parameters(10, 8)\n",
+        "print(f\"Di=10, D=8, Number of parameters = {int(N)}, True value = 97\")"
+      ],
+      "metadata": {
+        "id": "VbhDmZ1gwkQj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's plot the graph from figure 3.9a (takes ~1min)\n",
+        "dims = np.array([1,5,10,50,100])\n",
+        "regions = np.zeros((dims.shape[0], 200))\n",
+        "params = np.zeros((dims.shape[0], 200))\n",
+        "\n",
+        "# We'll compute the five lines separately this time to make it faster\n",
+        "for c_dim in range(dims.shape[0]):\n",
+        "    D_i = dims[c_dim]\n",
+        "    print (f\"Counting regions for {D_i} input dimensions\")\n",
+        "    for c_hidden in range(1, 200):\n",
+        "        # Iterate over different ranges of number hidden variables for different input sizes\n",
+        "        D = int(c_hidden * 500 / D_i)\n",
+        "        params[c_dim, c_hidden] =  D_i * D +D + D +1\n",
+        "        regions[c_dim, c_hidden] = number_regions(np.min([D_i,D]), D)\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.semilogy(params[0,:], regions[0,:],'k-')\n",
+        "ax.semilogy(params[1,:], regions[1,:],'b-')\n",
+        "ax.semilogy(params[2,:], regions[2,:],'m-')\n",
+        "ax.semilogy(params[3,:], regions[3,:],'c-')\n",
+        "ax.semilogy(params[4,:], regions[4,:],'y-')\n",
+        "ax.legend(['$D_i$=1', '$D_i$=5', '$D_i$=10', '$D_i$=50', '$D_i$=100'])\n",
+        "ax.set_xlabel(\"Number of parameters, D\")\n",
+        "ax.set_ylabel(\"Number of regions, N\")\n",
+        "plt.xlim([0,100000])\n",
+        "plt.ylim([1e1,1e150])\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "AH4nA50Au8-a"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap03/3_4_Activation_Functions.ipynb

@@ -0,0 +1,435 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mn0F56yY8ohX"
+      },
+      "source": [
+        "# **Notebook 3.4 -- Activation functions**\n",
+        "\n",
+        "The purpose of this practical is to experiment with different activation functions. <br>\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2GaDML3I8Yx4"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AeHzflFt9Tgn"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the shallow neural network.  We'll assume input in is range [0,1] and output [-1,1]\n",
+        "# If the plot_all flag is set to true, then we'll plot all the intermediate stages as in Figure 3.3\n",
+        "def plot_neural(x, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3, plot_all=False, x_data=None, y_data=None):\n",
+        "\n",
+        "  # Plot intermediate plots if flag set\n",
+        "  if plot_all:\n",
+        "    fig, ax = plt.subplots(3,3)\n",
+        "    fig.set_size_inches(8.5, 8.5)\n",
+        "    fig.tight_layout(pad=3.0)\n",
+        "    ax[0,0].plot(x,pre_1,'r-'); ax[0,0].set_ylabel('Preactivation')\n",
+        "    ax[0,1].plot(x,pre_2,'b-'); ax[0,1].set_ylabel('Preactivation')\n",
+        "    ax[0,2].plot(x,pre_3,'g-'); ax[0,2].set_ylabel('Preactivation')\n",
+        "    ax[1,0].plot(x,act_1,'r-'); ax[1,0].set_ylabel('Activation')\n",
+        "    ax[1,1].plot(x,act_2,'b-'); ax[1,1].set_ylabel('Activation')\n",
+        "    ax[1,2].plot(x,act_3,'g-'); ax[1,2].set_ylabel('Activation')\n",
+        "    ax[2,0].plot(x,w_act_1,'r-'); ax[2,0].set_ylabel('Weighted Act')\n",
+        "    ax[2,1].plot(x,w_act_2,'b-'); ax[2,1].set_ylabel('Weighted Act')\n",
+        "    ax[2,2].plot(x,w_act_3,'g-'); ax[2,2].set_ylabel('Weighted Act')\n",
+        "\n",
+        "    for plot_y in range(3):\n",
+        "      for plot_x in range(3):\n",
+        "        ax[plot_y,plot_x].set_xlim([0,1]);ax[plot_x,plot_y].set_ylim([-1,1])\n",
+        "        ax[plot_y,plot_x].set_aspect(0.5)\n",
+        "      ax[2,plot_y].set_xlabel('Input, $x$');\n",
+        "    plt.show()\n",
+        "\n",
+        "  fig, ax = plt.subplots()\n",
+        "  ax.plot(x,y)\n",
+        "  ax.set_xlabel('Input, $x$'); ax.set_ylabel('Output, $y$')\n",
+        "  ax.set_xlim([0,1]);ax.set_ylim([-1,1])\n",
+        "  ax.set_aspect(0.5)\n",
+        "  if x_data is not None:\n",
+        "    ax.plot(x_data, y_data, 'mo')\n",
+        "    for i in range(len(x_data)):\n",
+        "      ax.plot(x_data[i], y_data[i],)\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7qeIUrh19AkH"
+      },
+      "outputs": [],
+      "source": [
+        "# Define a shallow neural network with, one input, one output, and three hidden units\n",
+        "def shallow_1_1_3(x, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31):\n",
+        "  pre_1 = theta_10 + theta_11 * x\n",
+        "  pre_2 = theta_20 + theta_21 * x\n",
+        "  pre_3 = theta_30 + theta_31 * x\n",
+        "  # Pass these through the ReLU function to compute the activations as in\n",
+        "  # figure 3.3 d-f\n",
+        "  act_1 = activation_fn(pre_1)\n",
+        "  act_2 = activation_fn(pre_2)\n",
+        "  act_3 = activation_fn(pre_3)\n",
+        "\n",
+        "  w_act_1 = phi_1 * act_1\n",
+        "  w_act_2 = phi_2 * act_2\n",
+        "  w_act_3 = phi_3 * act_3\n",
+        "\n",
+        "  y = phi_0 + w_act_1 + w_act_2 + w_act_3\n",
+        "\n",
+        "  # Return everything we have calculated\n",
+        "  return y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cwTp__Fk9YUx"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "INQkRzyn9kVC"
+      },
+      "source": [
+        "First, let's run the network with a ReLU functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jT9QuKou9i0_"
+      },
+      "outputs": [],
+      "source": [
+        "# Now lets define some parameters and run the neural network\n",
+        "theta_10 =  0.3 ; theta_11 = -1.0\n",
+        "theta_20 = -1.0  ; theta_21 = 2.0\n",
+        "theta_30 = -0.5  ; theta_31 = 0.65\n",
+        "phi_0 = -0.3; phi_1 = 2.0; phi_2 = -1.0; phi_3 = 7.0\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x = np.arange(0,1,0.01)\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3 = \\\n",
+        "    shallow_1_1_3(x, ReLU, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(x, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3, plot_all=True)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-I8N7r1o9HYf"
+      },
+      "source": [
+        "# Sigmoid activation function\n",
+        "\n",
+        "The ReLU isn't the only kind of activation function.  For a long time, people used sigmoid functions.  A logistic sigmoid function is defined by the equation\n",
+        "\n",
+        "\\begin{equation}\n",
+        "f[z] = \\frac{1}{1+\\exp{[-10 z ]}}\n",
+        "\\end{equation}\n",
+        "\n",
+        "(Note that the factor of 10 is not standard -- but it allow us to plot on the same axes as the ReLU examples)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hgkioNyr975Y"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the sigmoid function\n",
+        "def sigmoid(preactivation):\n",
+        "  # TODO write code to implement the sigmoid function and compute the activation at the\n",
+        "  # hidden unit from the preactivation.  Use the np.exp() function.\n",
+        "  activation = np.zeros_like(preactivation);\n",
+        "\n",
+        "  return activation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "94HIXKJH97ve"
+      },
+      "outputs": [],
+      "source": [
+        "# Make an array of inputs\n",
+        "z = np.arange(-1,1,0.01)\n",
+        "sig_z = sigmoid(z)\n",
+        "\n",
+        "# Plot the sigmoid function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(z,sig_z,'r-')\n",
+        "ax.set_xlim([-1,1]);ax.set_ylim([0,1])\n",
+        "ax.set_xlabel('z'); ax.set_ylabel('sig[z]')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p3zQNXhj-J-o"
+      },
+      "source": [
+        "Let's see what happens when we use this activation function in a neural network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1dASr9L-GNt"
+      },
+      "outputs": [],
+      "source": [
+        "theta_10 =  0.3 ; theta_11 = -1.0\n",
+        "theta_20 = -1.0  ; theta_21 = 2.0\n",
+        "theta_30 = -0.5  ; theta_31 = 0.65\n",
+        "phi_0 = 0.3; phi_1 = 0.5; phi_2 = -1.0; phi_3 = 0.9\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x = np.arange(0,1,0.01)\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3 = \\\n",
+        "    shallow_1_1_3(x, sigmoid, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(x, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3, plot_all=True)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uuam_DewA9fH"
+      },
+      "source": [
+        "You probably notice that this gives nice smooth curves.  So why don't we use this?  Aha... it's not obvious right now, but we will get to it when we learn to fit models."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C9WKkcMUABze"
+      },
+      "source": [
+        "# Heaviside activation function\n",
+        "\n",
+        "The Heaviside function is defined as:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\text{heaviside}[z] = \\begin{cases} 0 & \\quad z <0 \\\\ 1 & \\quad z\\geq 0\\end{cases}\n",
+        "\\end{equation}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-1qFkdOL-NPc"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the heaviside function\n",
+        "def heaviside(preactivation):\n",
+        "  # TODO write code to implement the heaviside function and compute the activation at the\n",
+        "  # hidden unit from the preactivation.  Depending on your implementation you may need to\n",
+        "  # convert a Boolean array to an array of ones and zeros.  To do this, use .astype(int)\n",
+        "  activation = np.zeros_like(preactivation);\n",
+        "\n",
+        "\n",
+        "  return activation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mSPyp7iA-44H"
+      },
+      "outputs": [],
+      "source": [
+        "# Make an array of inputs\n",
+        "z = np.arange(-1,1,0.01)\n",
+        "heav_z = heaviside(z)\n",
+        "\n",
+        "# Plot the heaviside function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(z,heav_z,'r-')\n",
+        "ax.set_xlim([-1,1]);ax.set_ylim([-2,2])\n",
+        "ax.set_xlabel('z'); ax.set_ylabel('heaviside[z]')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t99K2lSl--Mq"
+      },
+      "outputs": [],
+      "source": [
+        "theta_10 =  0.3 ; theta_11 = -1.0\n",
+        "theta_20 = -1.0  ; theta_21 = 2.0\n",
+        "theta_30 = -0.5  ; theta_31 = 0.65\n",
+        "phi_0 = 0.3; phi_1 = 0.5; phi_2 = -1.0; phi_3 = 0.9\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x = np.arange(0,1,0.01)\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3 = \\\n",
+        "    shallow_1_1_3(x, heaviside, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(x, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3, plot_all=True)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T65MRtM-BCQA"
+      },
+      "source": [
+        "This can approximate any function, but the output is discontinuous, and there are also reasons not to use it that we will discover when we learn more about model fitting."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RkB-XZMLBTaR"
+      },
+      "source": [
+        "# Linear activation functions\n",
+        "\n",
+        "Neural networks don't work if the activation function is linear.  For example, consider what would happen if the activation function was:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\text{lin}[z] = a + bz\n",
+        "\\end{equation}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q59v3saj_jq1"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the linear activation function\n",
+        "def lin(preactivation):\n",
+        "  a =0\n",
+        "  b =1\n",
+        "  # Compute linear function\n",
+        "  activation = a+b * preactivation\n",
+        "  # Return\n",
+        "  return activation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IwodsBr0BkDn"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO\n",
+        "# 1. The linear activation function above just returns the input: (0+1*z) = z\n",
+        "# Before running the code Make a prediction about what the ten panels of the drawing will look like\n",
+        "# Now run the code below to see if you were right. What family of functions can this represent?\n",
+        "\n",
+        "# 2. What happens if you change the parameters (a,b) to different values?\n",
+        "# Try a=0.5, b=-0.4 Don't forget to run the cell again to update the function\n",
+        "\n",
+        "theta_10 =  0.3 ; theta_11 = -1.0\n",
+        "theta_20 = -1.0  ; theta_21 = 2.0\n",
+        "theta_30 = -0.5  ; theta_31 = 0.65\n",
+        "phi_0 = 0.3; phi_1 = 0.5; phi_2 = -1.0; phi_3 = 0.9\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x = np.arange(0,1,0.01)\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3 = \\\n",
+        "    shallow_1_1_3(x, lin, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(x, y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3, plot_all=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyOmxhh3ymYWX+1HdZ91I6zU",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap04/4_1_Composing_Networks.ipynb

@@ -0,0 +1,361 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Notebook 4.1 -- Composing networks\n",
+        "\n",
+        "The purpose of this notebook is to understand what happens when we feed one neural network into another. It works through an example similar to 4.1 and varies both networks\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions"
+      ],
+      "metadata": {
+        "id": "MaKn8CFlzN8E"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8ClURpZQzI6L"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "YdmveeAUz4YG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a shallow neural network with, one input, one output, and three hidden units\n",
+        "def shallow_1_1_3(x, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31):\n",
+        "  # Initial lines\n",
+        "  pre_1 = theta_10 + theta_11 * x\n",
+        "  pre_2 = theta_20 + theta_21 * x\n",
+        "  pre_3 = theta_30 + theta_31 * x\n",
+        "  # Activation functions\n",
+        "  act_1 = activation_fn(pre_1)\n",
+        "  act_2 = activation_fn(pre_2)\n",
+        "  act_3 = activation_fn(pre_3)\n",
+        "  # Weight activations\n",
+        "  w_act_1 = phi_1 * act_1\n",
+        "  w_act_2 = phi_2 * act_2\n",
+        "  w_act_3 = phi_3 * act_3\n",
+        "  # Combine weighted activation and add y offset\n",
+        "  y = phi_0 + w_act_1 + w_act_2 + w_act_3\n",
+        "  # Return everything we have calculated\n",
+        "  return y"
+      ],
+      "metadata": {
+        "id": "ximCLwIfz8kj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Plot two shallow neural networks and the composition of the two\n",
+        "def plot_neural_two_components(x_in, net1_out, net2_out, net12_out=None):\n",
+        "\n",
+        "  # Plot the two networks separately\n",
+        "  fig, ax = plt.subplots(1,2)\n",
+        "  fig.set_size_inches(8.5, 8.5)\n",
+        "  fig.tight_layout(pad=3.0)\n",
+        "  ax[0].plot(x_in, net1_out,'r-')\n",
+        "  ax[0].set_xlabel('Net 1 input'); ax[0].set_ylabel('Net 1 output')\n",
+        "  ax[0].set_xlim([-1,1]);ax[0].set_ylim([-1,1])\n",
+        "  ax[0].set_aspect(1.0)\n",
+        "  ax[1].plot(x_in, net2_out,'b-')\n",
+        "  ax[1].set_xlabel('Net 2 input'); ax[1].set_ylabel('Net 2 output')\n",
+        "  ax[1].set_xlim([-1,1]);ax[1].set_ylim([-1,1])\n",
+        "  ax[1].set_aspect(1.0)\n",
+        "  plt.show()\n",
+        "\n",
+        "  if net12_out is not None:\n",
+        "    # Plot their composition\n",
+        "    fig, ax = plt.subplots()\n",
+        "    ax.plot(x_in ,net12_out,'g-')\n",
+        "    ax.set_xlabel('Net 1 Input'); ax.set_ylabel('Net 2 Output')\n",
+        "    ax.set_xlim([-1,1]);ax.set_ylim([-1,1])\n",
+        "    ax.set_aspect(1.0)\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "ZB2HTalOE40X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's define two networks.  We'll put the prefixes n1_ and n2_ before all the variables to make it clear which network is which.  We'll just consider the inputs and outputs over the range [-1,1]."
+      ],
+      "metadata": {
+        "id": "LxBJCObC-NTY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now lets define some parameters and run the first neural network\n",
+        "n1_theta_10 = 0.0   ; n1_theta_11 = -1.0\n",
+        "n1_theta_20 = 0     ; n1_theta_21 = 1.0\n",
+        "n1_theta_30 = -0.67 ; n1_theta_31 =  1.0\n",
+        "n1_phi_0 = 1.0; n1_phi_1 = -2.0; n1_phi_2 = -3.0; n1_phi_3 = 9.3\n",
+        "\n",
+        "# Now lets define some parameters and run the second neural network\n",
+        "n2_theta_10 =  -0.6 ; n2_theta_11 = -1.0\n",
+        "n2_theta_20 =  0.2  ; n2_theta_21 = 1.0\n",
+        "n2_theta_30 =  -0.5  ; n2_theta_31 =  1.0\n",
+        "n2_phi_0 = 0.5; n2_phi_1 = -1.0; n2_phi_2 = -1.5; n2_phi_3 = 2.0\n",
+        "\n",
+        "# Display the two inputs\n",
+        "x = np.arange(-1,1,0.001)\n",
+        "# We run the first  and second neural networks for each of these input values\n",
+        "net1_out = shallow_1_1_3(x, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "net2_out = shallow_1_1_3(x, ReLU, n2_phi_0, n2_phi_1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "# Plot both graphs\n",
+        "plot_neural_two_components(x, net1_out, net2_out)"
+      ],
+      "metadata": {
+        "id": "JRebvurv22pT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Take a piece of paper and draw what you think will happen when we feed the\n",
+        "# output of the first network into the second one.  Draw the relationship between\n",
+        "# the input of the first network and the output of the second one."
+      ],
+      "metadata": {
+        "id": "NUQVop9-Xta1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's see if your predictions were right\n",
+        "\n",
+        "# TODO feed the output of first network into second network (replace this line)\n",
+        "net12_out = np.zeros_like(x)\n",
+        "\n",
+        "# Plot all three graphs\n",
+        "plot_neural_two_components(x, net1_out, net2_out, net12_out)"
+      ],
+      "metadata": {
+        "id": "Yq7GH-MCIyPI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now we'll change things a up a bit.  What happens if we change the second network? (note the *-1 change)\n",
+        "net1_out = shallow_1_1_3(x, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "net2_out = shallow_1_1_3(x, ReLU, n2_phi_0, n2_phi_1*-1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out)"
+      ],
+      "metadata": {
+        "id": "BMlLkLbdEuPu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Take a piece of paper and draw what you think will happen when we feed the\n",
+        "# output of the first network into the modified second network.  Draw the relationship between\n",
+        "# the input of the first network and the output of the second one."
+      ],
+      "metadata": {
+        "id": "Of6jVXLTJ688"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# When you have a prediction, run this code to see if you were right\n",
+        "net12_out = shallow_1_1_3(net1_out, ReLU, n2_phi_0, n2_phi_1*-1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out, net12_out)"
+      ],
+      "metadata": {
+        "id": "PbbSCaSeK6SM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's change things again.  What happens if we change the first network? (note the changes)\n",
+        "net1_out = shallow_1_1_3(x, ReLU, n1_phi_0, n1_phi_1*0.5, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "net2_out = shallow_1_1_3(x, ReLU, n2_phi_0, n2_phi_1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out)"
+      ],
+      "metadata": {
+        "id": "b39mcSGFK9Fd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Take a piece of paper and draw what you think will happen when we feed the\n",
+        "# output of the modified first network into the original second network.  Draw the relationship between\n",
+        "# the input of the first network and the output of the second one."
+      ],
+      "metadata": {
+        "id": "MhO40cC_LW9I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# When you have a prediction, run this code to see if you were right\n",
+        "net12_out = shallow_1_1_3(net1_out, ReLU, n2_phi_0, n2_phi_1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out, net12_out)"
+      ],
+      "metadata": {
+        "id": "Akwo-hnPLkNr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's change things again.  What happens if the first network and second networks are the same?\n",
+        "net1_out = shallow_1_1_3(x, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "net2_out_new = shallow_1_1_3(x, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out_new)"
+      ],
+      "metadata": {
+        "id": "TJ7wXKpRLl_E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Take a piece of paper and draw what you think will happen when we feed the\n",
+        "# output of the first network into the a copy of itself.  Draw the relationship between\n",
+        "# the input of the first network and the output of the second one."
+      ],
+      "metadata": {
+        "id": "dJbbh6R7NG9k"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# When you have a prediction, run this code to see if you were right\n",
+        "net12_out = shallow_1_1_3(net1_out, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "plot_neural_two_components(x, net1_out, net2_out_new, net12_out)"
+      ],
+      "metadata": {
+        "id": "BiZZl3yNM2Bq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Contemplate what you think will happen when we feed the\n",
+        "# output of the original first network into a second copy of the original first network, and then\n",
+        "# the output of that into the original second network (so now we have a three layer network)\n",
+        "# How many total linear regions will we have in the output?\n",
+        "net123_out = shallow_1_1_3(net12_out, ReLU, n2_phi_0, n2_phi_1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "plot_neural_two_components(x, net12_out, net2_out, net123_out)"
+      ],
+      "metadata": {
+        "id": "BSd51AkzNf7-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TO DO\n",
+        "# How many linear regions would there be if we ran N copies of the first network, feeding the result of the first\n",
+        "# into the second, the second into the third and so on, and then passed the result into the original second\n",
+        "# network (blue curve above)\n",
+        "\n",
+        "# Take away conclusion:  with very few parameters, we can make A LOT of linear regions, but\n",
+        "# they depend on one another in complex ways that quickly become too difficult to understand intuitively."
+      ],
+      "metadata": {
+        "id": "HqzePCLOVQK7"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap04/4_2_Clipping_functions.ipynb

@@ -0,0 +1,219 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPZzptvvf7OPZai8erQ/0xT",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Notebook 4.2 -- Clipping functions\n",
+        "\n",
+        "The purpose of this notebook is to understand how a neural network with two hidden layers build more complicated functions by clipping and recombining the representations at the intermediate hidden variables.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions"
+      ],
+      "metadata": {
+        "id": "MaKn8CFlzN8E"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8ClURpZQzI6L"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "YdmveeAUz4YG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a deep neural network with, one input, one output, two hidden layers and three hidden units (eqns 4.7-4.9)\n",
+        "# To make this easier, we store the parameters in ndarrays, so phi_0 = phi[0] and psi_3,3 = psi[3,3] etc.\n",
+        "def shallow_1_1_3_3(x, activation_fn, phi, psi, theta):\n",
+        "\n",
+        "  # TODO -- You write this function\n",
+        "  # Replace the skeleton code below.\n",
+        "\n",
+        "  # ANSWER\n",
+        "  # Preactivations at layer 1 (terms in brackets in equation 4.7)\n",
+        "  layer1_pre_1 = np.zeros_like(x) ;\n",
+        "  layer1_pre_2 = np.zeros_like(x) ;\n",
+        "  layer1_pre_3 = np.zeros_like(x) ;\n",
+        "\n",
+        "  # Activation functions (rest of equation 4.7)\n",
+        "  h1 = activation_fn(layer1_pre_1)\n",
+        "  h2 = activation_fn(layer1_pre_2)\n",
+        "  h3 = activation_fn(layer1_pre_3)\n",
+        "\n",
+        "  # Preactivations at layer 2 (terms in brackets in equation 4.8)\n",
+        "  layer2_pre_1 = np.zeros_like(x) ;\n",
+        "  layer2_pre_2 = np.zeros_like(x) ;\n",
+        "  layer2_pre_3 = np.zeros_like(x) ;\n",
+        "\n",
+        "  # Activation functions (rest of equation 4.8)\n",
+        "  h1_prime = activation_fn(layer2_pre_1)\n",
+        "  h2_prime = activation_fn(layer2_pre_2)\n",
+        "  h3_prime = activation_fn(layer2_pre_3)\n",
+        "\n",
+        "  # Weighted outputs by phi (three last terms of equation 4.9)\n",
+        "  phi1_h1_prime = np.zeros_like(x) ;\n",
+        "  phi2_h2_prime = np.zeros_like(x) ;\n",
+        "  phi3_h3_prime = np.zeros_like(x) ;\n",
+        "\n",
+        "  # Combine weighted activation and add y offset (summing terms of equation 4.9)\n",
+        "  y = np.zeros_like(x) ;\n",
+        "\n",
+        "\n",
+        "  # Return everything we have calculated\n",
+        "  return y, layer2_pre_1, layer2_pre_2, layer2_pre_3, h1_prime, h2_prime, h3_prime, phi1_h1_prime, phi2_h2_prime, phi3_h3_prime"
+      ],
+      "metadata": {
+        "id": "ximCLwIfz8kj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Plot two layer neural network as in figure 4.5\n",
+        "def plot_neural_two_layers(x, y, layer2_pre_1, layer2_pre_2, layer2_pre_3, h1_prime, h2_prime, h3_prime, phi1_h1_prime, phi2_h2_prime, phi3_h3_prime):\n",
+        "\n",
+        "    fig, ax = plt.subplots(3,3)\n",
+        "    fig.set_size_inches(8.5, 8.5)\n",
+        "    fig.tight_layout(pad=3.0)\n",
+        "    ax[0,0].plot(x,layer2_pre_1,'r-'); ax[0,0].set_ylabel(r'$\\psi_{10}+\\psi_{11}h_{1}+\\psi_{12}h_{2}+\\psi_{13}h_3$')\n",
+        "    ax[0,1].plot(x,layer2_pre_2,'b-'); ax[0,1].set_ylabel(r'$\\psi_{20}+\\psi_{21}h_{1}+\\psi_{22}h_{2}+\\psi_{23}h_3$')\n",
+        "    ax[0,2].plot(x,layer2_pre_3,'g-'); ax[0,2].set_ylabel(r'$\\psi_{30}+\\psi_{31}h_{1}+\\psi_{32}h_{2}+\\psi_{33}h_3$')\n",
+        "    ax[1,0].plot(x,h1_prime,'r-'); ax[1,0].set_ylabel(r\"$h_{1}^{'}$\")\n",
+        "    ax[1,1].plot(x,h2_prime,'b-'); ax[1,1].set_ylabel(r\"$h_{2}^{'}$\")\n",
+        "    ax[1,2].plot(x,h3_prime,'g-'); ax[1,2].set_ylabel(r\"$h_{3}^{'}$\")\n",
+        "    ax[2,0].plot(x,phi1_h1_prime,'r-'); ax[2,0].set_ylabel(r\"$\\phi_1 h_{1}^{'}$\")\n",
+        "    ax[2,1].plot(x,phi2_h2_prime,'b-'); ax[2,1].set_ylabel(r\"$\\phi_2 h_{2}^{'}$\")\n",
+        "    ax[2,2].plot(x,phi3_h3_prime,'g-'); ax[2,2].set_ylabel(r\"$\\phi_3 h_{3}^{'}$\")\n",
+        "\n",
+        "    for plot_y in range(3):\n",
+        "      for plot_x in range(3):\n",
+        "        ax[plot_y,plot_x].set_xlim([0,1]);ax[plot_x,plot_y].set_ylim([-1,1])\n",
+        "        ax[plot_y,plot_x].set_aspect(0.5)\n",
+        "      ax[2,plot_y].set_xlabel(r'Input, $x$');\n",
+        "    plt.show()\n",
+        "\n",
+        "    fig, ax = plt.subplots()\n",
+        "    ax.plot(x,y)\n",
+        "    ax.set_xlabel(r'Input, $x$'); ax.set_ylabel(r'Output, $y$')\n",
+        "    ax.set_xlim([0,1]);ax.set_ylim([-1,1])\n",
+        "    ax.set_aspect(0.5)\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "ZB2HTalOE40X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define the parameters and visualize the network"
+      ],
+      "metadata": {
+        "id": "LxBJCObC-NTY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define parameters (note first dimension of theta and phi is padded to make indices match\n",
+        "# notation in book)\n",
+        "theta = np.zeros([4,2])\n",
+        "psi = np.zeros([4,4])\n",
+        "phi = np.zeros([4,1])\n",
+        "\n",
+        "theta[1,0] =  0.3 ; theta[1,1] = -1.0\n",
+        "theta[2,0]= -1.0  ; theta[2,1] = 2.0\n",
+        "theta[3,0] = -0.5  ; theta[3,1] = 0.65\n",
+        "psi[1,0] = 0.3;  psi[1,1] = 2.0; psi[1,2] = -1.0; psi[1,3]=7.0\n",
+        "psi[2,0] = -0.2;  psi[2,1] = 2.0; psi[2,2] = 1.2; psi[2,3]=-8.0\n",
+        "psi[3,0] = 0.3;  psi[3,1] = -2.3; psi[3,2] = -0.8; psi[3,3]=2.0\n",
+        "phi[0] = 0.0; phi[1] = 0.5; phi[2] = -1.5; phi [3] = 2.2\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x = np.arange(0,1,0.01)\n",
+        "\n",
+        "# Run the neural network\n",
+        "y, layer2_pre_1, layer2_pre_2, layer2_pre_3, h1_prime, h2_prime, h3_prime, phi1_h1_prime, phi2_h2_prime, phi3_h3_prime \\\n",
+        "    = shallow_1_1_3_3(x, ReLU, phi, psi, theta)\n",
+        "\n",
+        "# And then plot it\n",
+        "plot_neural_two_layers(x, y, layer2_pre_1, layer2_pre_2, layer2_pre_3, h1_prime, h2_prime, h3_prime, phi1_h1_prime, phi2_h2_prime, phi3_h3_prime)"
+      ],
+      "metadata": {
+        "id": "JRebvurv22pT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To do:  To test your understanding of this, consider:\n",
+        "\n",
+        "1.   What would happen if we increase $\\psi_{1,0}$?\n",
+        "2.   What would happen if we multiplied $\\psi_{2,0}, \\psi_{2,1}, \\psi_{2,2},  \\psi_{2,3}$ by -1?\n",
+        "3.  What would happen if set $\\phi_{3}$ to -1?\n",
+        "\n",
+        "You can rerun the code to see if you were correct.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "GcjUUHbXf25D"
+      }
+    }
+  ]
+}

Notebooks/Chap04/4_3_Deep_Networks.ipynb

@@ -0,0 +1,321 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyO2DaD75p+LGi7WgvTzjrk1",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 4.3 Deep neural networks**\n",
+        "\n",
+        "This network investigates converting neural networks to matrix form.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "MaKn8CFlzN8E"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8ClURpZQzI6L"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "YdmveeAUz4YG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a shallow neural network with, one input, one output, and three hidden units\n",
+        "def shallow_1_1_3(x, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31):\n",
+        "  # Initial lines\n",
+        "  pre_1 = theta_10 + theta_11 * x\n",
+        "  pre_2 = theta_20 + theta_21 * x\n",
+        "  pre_3 = theta_30 + theta_31 * x\n",
+        "  # Activation functions\n",
+        "  act_1 = activation_fn(pre_1)\n",
+        "  act_2 = activation_fn(pre_2)\n",
+        "  act_3 = activation_fn(pre_3)\n",
+        "  # Weight activations\n",
+        "  w_act_1 = phi_1 * act_1\n",
+        "  w_act_2 = phi_2 * act_2\n",
+        "  w_act_3 = phi_3 * act_3\n",
+        "  # Combine weighted activation and add y offset\n",
+        "  y = phi_0 + w_act_1 + w_act_2 + w_act_3\n",
+        "  # Return everything we have calculated\n",
+        "  return y, pre_1, pre_2, pre_3, act_1, act_2, act_3, w_act_1, w_act_2, w_act_3"
+      ],
+      "metadata": {
+        "id": "ximCLwIfz8kj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Plot the shallow neural network.  We'll assume input in is range [-1,1] and output [-1,1]\n",
+        "def plot_neural(x, y):\n",
+        "  fig, ax = plt.subplots()\n",
+        "  ax.plot(x.T,y.T)\n",
+        "  ax.set_xlabel('Input'); ax.set_ylabel('Output')\n",
+        "  ax.set_xlim([-1,1]);ax.set_ylim([-1,1])\n",
+        "  ax.set_aspect(1.0)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "btrt7BX20gKD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's define a network.  We'll just consider the inputs and outputs over the range [-1,1]."
+      ],
+      "metadata": {
+        "id": "LxBJCObC-NTY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now lets define some parameters and run the first neural network\n",
+        "n1_theta_10 = 0.0   ; n1_theta_11 = -1.0\n",
+        "n1_theta_20 = 0     ; n1_theta_21 = 1.0\n",
+        "n1_theta_30 = -0.67 ; n1_theta_31 =  1.0\n",
+        "n1_phi_0 = 1.0; n1_phi_1 = -2.0; n1_phi_2 = -3.0; n1_phi_3 = 9.3\n",
+        "\n",
+        "# Define a range of input values\n",
+        "n1_in = np.arange(-1,1,0.01).reshape([1,-1])\n",
+        "\n",
+        "# We run the neural network for each of these input values\n",
+        "n1_out, *_ = shallow_1_1_3(n1_in, ReLU, n1_phi_0, n1_phi_1, n1_phi_2, n1_phi_3, n1_theta_10, n1_theta_11, n1_theta_20, n1_theta_21, n1_theta_30, n1_theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(n1_in, n1_out)"
+      ],
+      "metadata": {
+        "id": "JRebvurv22pT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll define the same neural network, but this time, we will  use matrix form.  When you get this right, it will draw the same plot as above."
+      ],
+      "metadata": {
+        "id": "XCJqo_AjfAra"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "beta_0 = np.zeros((3,1))\n",
+        "Omega_0 = np.zeros((3,1))\n",
+        "beta_1 = np.zeros((1,1))\n",
+        "Omega_1 = np.zeros((1,3))\n",
+        "\n",
+        "# TODO Fill in the values of the beta and Omega matrices with the n1_theta and n1_phi parameters that define the network above\n",
+        "# !!! NOTE THAT MATRICES ARE CONVENTIONALLY INDEXED WITH a_11 IN THE TOP LEFT CORNER, BUT NDARRAYS START AT [0,0]\n",
+        "# To get you started I've filled in a couple:\n",
+        "beta_0[0,0] = n1_theta_10\n",
+        "Omega_0[0,0] = n1_theta_11\n",
+        "\n",
+        "# Make sure that input data matrix has different inputs in its columns\n",
+        "n_data = n1_in.size\n",
+        "n_dim_in = 1\n",
+        "n1_in_mat = np.reshape(n1_in,(n_dim_in,n_data))\n",
+        "\n",
+        "# This runs the network for ALL of the inputs, x at once so we can draw graph\n",
+        "h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(Omega_0,n1_in_mat))\n",
+        "n1_out = np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(Omega_1,h1)\n",
+        "\n",
+        "# Draw the network and check that it looks the same as the non-matrix case\n",
+        "plot_neural(n1_in, n1_out)"
+      ],
+      "metadata": {
+        "id": "MR0AecZYfACR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll feed the output of the first network into the second one."
+      ],
+      "metadata": {
+        "id": "qOcj2Rof-o20"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now lets define some parameters and run the second neural network\n",
+        "n2_theta_10 =  -0.6 ; n2_theta_11 = -1.0\n",
+        "n2_theta_20 =  0.2  ; n2_theta_21 = 1.0\n",
+        "n2_theta_30 =  -0.5  ; n2_theta_31 =  1.0\n",
+        "n2_phi_0 = 0.5; n2_phi_1 = -1.0; n2_phi_2 = -1.5; n2_phi_3 = 2.0\n",
+        "\n",
+        "# Define a range of input values\n",
+        "n2_in = np.arange(-1,1,0.01)\n",
+        "\n",
+        "# We run the second neural network on the output of the first network\n",
+        "n2_out, *_ = \\\n",
+        "    shallow_1_1_3(n1_out, ReLU, n2_phi_0, n2_phi_1, n2_phi_2, n2_phi_3, n2_theta_10, n2_theta_11, n2_theta_20, n2_theta_21, n2_theta_30, n2_theta_31)\n",
+        "# And then plot it\n",
+        "plot_neural(n1_in, n2_out)"
+      ],
+      "metadata": {
+        "id": "ZRjWu8i9239X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "beta_0 = np.zeros((3,1))\n",
+        "Omega_0 = np.zeros((3,1))\n",
+        "beta_1 = np.zeros((3,1))\n",
+        "Omega_1 = np.zeros((3,3))\n",
+        "beta_2 = np.zeros((1,1))\n",
+        "Omega_2 = np.zeros((1,3))\n",
+        "\n",
+        "# TODO Fill in the values of the beta and Omega matrices for the n1_theta, n1_phi, n2_theta, and n2_phi parameters\n",
+        "# that define the composition of the two networks above (see eqn 4.5 for Omega1 and beta1 albeit in different notation)\n",
+        "# !!! NOTE THAT MATRICES ARE CONVENTIONALLY INDEXED WITH a_11 IN THE TOP LEFT CORNER, BUT NDARRAYS START AT [0,0] SO EVERYTHING IS OFFSET\n",
+        "# To get you started I've filled in a few:\n",
+        "beta_0[0,0] = n1_theta_10\n",
+        "Omega_0[0,0] = n1_theta_11\n",
+        "beta_1[0,0] = n2_theta_10 + n2_theta_11 * n1_phi_0\n",
+        "Omega_1[0,0] = n2_theta_11 * n1_phi_1\n",
+        "\n",
+        "\n",
+        "# Make sure that input data matrix has different inputs in its columns\n",
+        "n_data = n1_in.size\n",
+        "n_dim_in = 1\n",
+        "n1_in_mat = np.reshape(n1_in,(n_dim_in,n_data))\n",
+        "\n",
+        "# This runs the network for ALL of the inputs, x at once so we can draw graph (hence extra np.ones term)\n",
+        "h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(Omega_0,n1_in_mat))\n",
+        "h2 = ReLU(np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(Omega_1,h1))\n",
+        "n1_out = np.matmul(beta_2,np.ones((1,n_data))) + np.matmul(Omega_2,h2)\n",
+        "\n",
+        "# Draw the network and check that it looks the same as the non-matrix version\n",
+        "plot_neural(n1_in, n1_out)"
+      ],
+      "metadata": {
+        "id": "ZB2HTalOE40X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's make a deep network with 3 hidden layers.  It will have $D_i=4$ inputs, $D_1=5$ neurons  in the first layer, $D_2=2$ neurons in the second layer and $D_3=4$ neurons in the third layer, and $D_o = 1$ output.  Consult figure 4.6 and equations 4.15 for guidance."
+      ],
+      "metadata": {
+        "id": "0VANqxH2kyS4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# define sizes\n",
+        "D_i=4; D_1=5; D_2=2; D_3=4; D_o=1\n",
+        "# We'll choose the inputs and parameters of this network randomly using np.random.normal\n",
+        "# For example, we'll set the input using\n",
+        "n_data = 4;\n",
+        "x = np.random.normal(size=(D_i, n_data))\n",
+        "# TODO initialize the parameters randomly with the correct sizes\n",
+        "# Replace the lines below\n",
+        "beta_0 = np.random.normal(size=(1,1))\n",
+        "Omega_0 = np.random.normal(size=(1,1))\n",
+        "beta_1 = np.random.normal(size=(1,1))\n",
+        "Omega_1 = np.random.normal(size=(1,1))\n",
+        "beta_2 = np.random.normal(size=(1,1))\n",
+        "Omega_2 = np.random.normal(size=(1,1))\n",
+        "beta_3 = np.random.normal(size=(1,1))\n",
+        "Omega_3 = np.random.normal(size=(1,1))\n",
+        "\n",
+        "\n",
+        "# If you set the parameters to the correct sizes, the following code will run\n",
+        "h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(Omega_0,x));\n",
+        "h2 = ReLU(np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(Omega_1,h1));\n",
+        "h3 = ReLU(np.matmul(beta_2,np.ones((1,n_data))) + np.matmul(Omega_2,h2));\n",
+        "y = np.matmul(beta_3,np.ones((1,n_data))) + np.matmul(Omega_3,h3)\n",
+        "\n",
+        "if h1.shape[0] is not D_1 or h1.shape[1] is not n_data:\n",
+        "  print(\"h1 is wrong shape\")\n",
+        "if h2.shape[0] is not D_2 or h1.shape[1] is not n_data:\n",
+        "  print(\"h2 is wrong shape\")\n",
+        "if h3.shape[0] is not D_3 or h1.shape[1] is not n_data:\n",
+        "  print(\"h3 is wrong shape\")\n",
+        "if y.shape[0] is not D_o or h1.shape[1] is not n_data:\n",
+        "  print(\"Output is wrong shape\")\n",
+        "\n",
+        "# Print the inputs and outputs\n",
+        "print(\"Input data points\")\n",
+        "print(x)\n",
+        "print (\"Output data points\")\n",
+        "print(y)"
+      ],
+      "metadata": {
+        "id": "RdBVAc_Rj22-"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb

@@ -0,0 +1,593 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 5.1: Least Squares Loss**\n",
+        "\n",
+        "This notebook investigates the least squares loss and the equivalence of maximum likelihood and minimum negative log likelihood.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "jSlFkICHwHQF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PYMZ1x-Pv1ht"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Import math Library\n",
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "# Define a shallow neural network\n",
+        "def shallow_nn(x, beta_0, omega_0, beta_1, omega_1):\n",
+        "    # Make sure that input data is (1 x n_data) array\n",
+        "    n_data = x.size\n",
+        "    x = np.reshape(x,(1,n_data))\n",
+        "\n",
+        "    # This runs the network for ALL of the inputs, x at once so we can draw graph\n",
+        "    h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(omega_0,x))\n",
+        "    y = np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(omega_1,h1)\n",
+        "    return y"
+      ],
+      "metadata": {
+        "id": "Fv7SZR3tv7mV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get parameters for model -- we can call this function to easily reset them\n",
+        "def get_parameters():\n",
+        "  # And we'll create a network that approximately fits it\n",
+        "  beta_0 = np.zeros((3,1));  # formerly theta_x0\n",
+        "  omega_0 = np.zeros((3,1)); # formerly theta_x1\n",
+        "  beta_1 = np.zeros((1,1));  # formerly phi_0\n",
+        "  omega_1 = np.zeros((1,3)); # formerly phi_x\n",
+        "\n",
+        "  beta_0[0,0] = 0.3; beta_0[1,0] = -1.0; beta_0[2,0] = -0.5\n",
+        "  omega_0[0,0] = -1.0; omega_0[1,0] = 1.8; omega_0[2,0] = 0.65\n",
+        "  beta_1[0,0] = 0.1;\n",
+        "  omega_1[0,0] = -2.0; omega_1[0,1] = -1.0; omega_1[0,2] = 7.0\n",
+        "\n",
+        "  return beta_0, omega_0, beta_1, omega_1"
+      ],
+      "metadata": {
+        "id": "pUT9Ain_HRim"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Utility function for plotting data\n",
+        "def plot_univariate_regression(x_model, y_model, x_data = None, y_data = None, sigma_model = None, title= None):\n",
+        "  # Make sure model data are 1D arrays\n",
+        "  x_model = np.squeeze(x_model)\n",
+        "  y_model = np.squeeze(y_model)\n",
+        "\n",
+        "  fig, ax = plt.subplots()\n",
+        "  ax.plot(x_model,y_model)\n",
+        "  if sigma_model is not None:\n",
+        "    ax.fill_between(x_model, y_model-2*sigma_model, y_model+2*sigma_model, color='lightgray')\n",
+        "  ax.set_xlabel(r'Input, $x$'); ax.set_ylabel(r'Output, $y$')\n",
+        "  ax.set_xlim([0,1]);ax.set_ylim([-1,1])\n",
+        "  ax.set_aspect(0.5)\n",
+        "  if title is not None:\n",
+        "    ax.set_title(title)\n",
+        "  if x_data is not None:\n",
+        "    ax.plot(x_data, y_data, 'ko')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "NRR67ri_1TzN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Univariate regression\n",
+        "\n",
+        "We'll investigate a simple univariate regression situation with a single input $x$ and a single output $y$ as pictured in figures 5.4 and 5.5b."
+      ],
+      "metadata": {
+        "id": "PsgLZwsPxauP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's create some 1D training data\n",
+        "x_train = np.array([0.09291784,0.46809093,0.93089486,0.67612654,0.73441752,0.86847339,\\\n",
+        "                   0.49873225,0.51083168,0.18343972,0.99380898,0.27840809,0.38028817,\\\n",
+        "                   0.12055708,0.56715537,0.92005746,0.77072270,0.85278176,0.05315950,\\\n",
+        "                   0.87168699,0.58858043])\n",
+        "y_train = np.array([-0.25934537,0.18195445,0.651270150,0.13921448,0.09366691,0.30567674,\\\n",
+        "                    0.372291170,0.20716968,-0.08131792,0.51187806,0.16943738,0.3994327,\\\n",
+        "                    0.019062570,0.55820410,0.452564960,-0.1183121,0.02957665,-1.24354444, \\\n",
+        "                    0.248038840,0.26824970])\n",
+        "\n",
+        "# Get parameters for the model\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "sigma = 0.2\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x_model = np.arange(0,1,0.01)\n",
+        "# Run the model to get values to plot and plot it.\n",
+        "y_model = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "plot_univariate_regression(x_model, y_model, x_train, y_train, sigma_model = sigma)\n"
+      ],
+      "metadata": {
+        "id": "VWzNOt1swFVd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The blue line is the mean prediction of the model and the gray area represents plus/minus two standard deviations.  This model fits okay, but could be improved. Let's compute the loss.  We'll compute the  the least squares error, the likelihood, the negative log likelihood."
+      ],
+      "metadata": {
+        "id": "MvVX6tl9AEXF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return probability under normal distribution\n",
+        "def normal_distribution(y, mu, sigma):\n",
+        "    # TODO-- write in the equation for the normal distribution\n",
+        "    # Equation 5.7 from the notes (you will need np.sqrt() and np.exp(), and math.pi)\n",
+        "    # Don't use the numpy version -- that's cheating!\n",
+        "    # Replace the line below\n",
+        "    prob = np.zeros_like(y)\n",
+        "\n",
+        "    return prob"
+      ],
+      "metadata": {
+        "id": "YaLdRlEX0FkU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.119,normal_distribution(1,-1,2.3)))"
+      ],
+      "metadata": {
+        "id": "4TSL14dqHHbV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's plot the Gaussian distribution.\n",
+        "y_gauss = np.arange(-5,5,0.1)\n",
+        "mu = 0; sigma = 1.0\n",
+        "gauss_prob = normal_distribution(y_gauss, mu, sigma)\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(y_gauss, gauss_prob)\n",
+        "ax.set_xlabel(r'Input, $y$'); ax.set_ylabel(r'Probability $Pr(y)$')\n",
+        "ax.set_xlim([-5,5]);ax.set_ylim([0,1.0])\n",
+        "plt.show()\n",
+        "\n",
+        "# TODO\n",
+        "# 1. Predict what will happen if we change to mu=1 and leave sigma=1\n",
+        "# Now change the code above and see if you were correct.\n",
+        "\n",
+        "# 2. Predict what will happen if we leave mu = 0 and change sigma to 2.0\n",
+        "\n",
+        "# 3. Predict what will happen if we leave mu = 0 and change sigma to 0.5"
+      ],
+      "metadata": {
+        "id": "A2HcmNfUMIlj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the likelihood using this function"
+      ],
+      "metadata": {
+        "id": "R5z_0dzQMF35"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return the likelihood of all of the data under the model\n",
+        "def compute_likelihood(y_train, mu, sigma):\n",
+        "  # TODO -- compute the likelihood of the data -- the product of the normal probabilities for each data point\n",
+        "  # Top line of equation 5.3 in the notes\n",
+        "  # You will need np.prod() and the normal_distribution function you used above\n",
+        "  # Replace the line below\n",
+        "  likelihood = 0\n",
+        "\n",
+        "  return likelihood"
+      ],
+      "metadata": {
+        "id": "zpS7o6liCx7f"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this for a homoscedastic (constant sigma) model\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the mean of the Gaussian\n",
+        "mu_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "# Set the standard deviation to something reasonable\n",
+        "sigma = 0.2\n",
+        "# Compute the likelihood\n",
+        "likelihood = compute_likelihood(y_train, mu_pred, sigma)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(0.000010624,likelihood))"
+      ],
+      "metadata": {
+        "id": "1hQxBLoVNlr2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
+        "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
+        "\n",
+        "This is why we use negative log likelihood"
+      ],
+      "metadata": {
+        "id": "HzphKgPfOvlk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return the negative log likelihood of the data under the model\n",
+        "def compute_negative_log_likelihood(y_train, mu, sigma):\n",
+        "  # TODO -- compute the negative log likelihood of the data without using a product\n",
+        "  # In other words, compute minus one times the sum of the log probabilities\n",
+        "  # Equation 5.4 in the notes\n",
+        "  # You will need np.sum(), np.log()\n",
+        "  # Replace the line below\n",
+        "  nll = 0\n",
+        "\n",
+        "  return nll"
+      ],
+      "metadata": {
+        "id": "dsT0CWiKBmTV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this for a homoscedastic (constant sigma) model\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the mean of the Gaussian\n",
+        "mu_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "# Set the standard deviation to something reasonable\n",
+        "sigma = 0.2\n",
+        "# Compute the negative log likelihood\n",
+        "nll = compute_negative_log_likelihood(y_train, mu_pred, sigma)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(11.452419564,nll))"
+      ],
+      "metadata": {
+        "id": "nVxUXg9rQmwI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For good measure, let's compute the sum of squares as well"
+      ],
+      "metadata": {
+        "id": "-S8bXApoWVLG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return the squared distance between the observed data (y_train) and the prediction of the model (y_pred)\n",
+        "def compute_sum_of_squares(y_train, y_pred):\n",
+        "  # TODO -- compute the sum of squared distances between the training data and the model prediction\n",
+        "  # Eqn 5.10 in the notes.  Make sure that you understand this, and ask questions if you don't\n",
+        "  # Replace the line below\n",
+        "  sum_of_squares = 0;\n",
+        "\n",
+        "  return sum_of_squares"
+      ],
+      "metadata": {
+        "id": "I1pjFdHCF4JZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this again\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the mean of the Gaussian, which is out best prediction of y\n",
+        "y_pred = mu_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "# Compute the sum of squares\n",
+        "sum_of_squares = compute_sum_of_squares(y_train, y_pred)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(2.020992572,sum_of_squares))"
+      ],
+      "metadata": {
+        "id": "2C40fskIHBx7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's investigate finding the maximum likelihood / minimum negative log likelihood / least squares solution.  For simplicity, we'll assume that all the parameters are correct except one and look at how the likelihood, negative log likelihood, and sum of squares change as we manipulate the last parameter.  We'll start with overall y offset, beta_1 (formerly phi_0)"
+      ],
+      "metadata": {
+        "id": "OgcRojvPWh4V"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a range of values for the parameter\n",
+        "beta_1_vals = np.arange(0,1.0,0.01)\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods and sum of squares\n",
+        "likelihoods = np.zeros_like(beta_1_vals)\n",
+        "nlls = np.zeros_like(beta_1_vals)\n",
+        "sum_squares = np.zeros_like(beta_1_vals)\n",
+        "\n",
+        "# Initialise the parameters\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "sigma = 0.2\n",
+        "for count in range(len(beta_1_vals)):\n",
+        "  # Set the value for the parameter\n",
+        "  beta_1[0,0] = beta_1_vals[count]\n",
+        "  # Run the network with new parameters\n",
+        "  mu_pred = y_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "  # Compute and store the three values\n",
+        "  likelihoods[count] = compute_likelihood(y_train, mu_pred, sigma)\n",
+        "  nlls[count] = compute_negative_log_likelihood(y_train, mu_pred, sigma)\n",
+        "  sum_squares[count] = compute_sum_of_squares(y_train, y_pred)\n",
+        "  # Draw the model for every 20th parameter setting\n",
+        "  if count % 20 == 0:\n",
+        "    # Run the model to get values to plot and plot it.\n",
+        "    y_model = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "    plot_univariate_regression(x_model, y_model, x_train, y_train, sigma_model = sigma, title=\"beta1=%3.3f\"%(beta_1[0,0]))\n"
+      ],
+      "metadata": {
+        "id": "pFKtDaAeVU4U"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's plot the likelihood, negative log likelihood, and least squares as a function of the value of the offset beta1\n",
+        "fig, ax = plt.subplots(1,2)\n",
+        "fig.set_size_inches(10.5, 5.5)\n",
+        "fig.tight_layout(pad=10.0)\n",
+        "likelihood_color = 'tab:red'\n",
+        "nll_color = 'tab:blue'\n",
+        "\n",
+        "ax[0].set_xlabel('beta_1[0]')\n",
+        "ax[0].set_ylabel('likelihood', color = likelihood_color)\n",
+        "ax[0].plot(beta_1_vals, likelihoods, color = likelihood_color)\n",
+        "ax[0].tick_params(axis='y', labelcolor=likelihood_color)\n",
+        "\n",
+        "ax00 = ax[0].twinx()\n",
+        "ax00.plot(beta_1_vals, nlls, color = nll_color)\n",
+        "ax00.set_ylabel('negative log likelihood', color = nll_color)\n",
+        "ax00.tick_params(axis='y', labelcolor = nll_color)\n",
+        "\n",
+        "plt.axvline(x = beta_1_vals[np.argmax(likelihoods)], linestyle='dotted')\n",
+        "\n",
+        "ax[1].plot(beta_1_vals, sum_squares); ax[1].set_xlabel('beta_1[0]'); ax[1].set_ylabel('sum of squares')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "UHXeTa9MagO6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Hopefully, you can see that the maximum of the likelihood fn is at the same position as the minimum negative log likelihood\n",
+        "# and the least squares solutions\n",
+        "# Let's check that:\n",
+        "print(\"Maximum likelihood = %3.3f, at beta_1=%3.3f\"%( (likelihoods[np.argmax(likelihoods)],beta_1_vals[np.argmax(likelihoods)])))\n",
+        "print(\"Minimum negative log likelihood = %3.3f, at beta_1=%3.3f\"%( (nlls[np.argmin(nlls)],beta_1_vals[np.argmin(nlls)])))\n",
+        "print(\"Least squares = %3.3f, at beta_1=%3.3f\"%( (sum_squares[np.argmin(sum_squares)],beta_1_vals[np.argmin(sum_squares)])))\n",
+        "\n",
+        "# Plot the best model\n",
+        "beta_1[0,0] = beta_1_vals[np.argmin(sum_squares)]\n",
+        "y_model = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "plot_univariate_regression(x_model, y_model, x_train, y_train, sigma_model = sigma, title=\"beta1=%3.3f\"%(beta_1[0,0]))"
+      ],
+      "metadata": {
+        "id": "aDEPhddNdN4u"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "They all give the same answer. But you can see from the three plots above that the likelihood is very small unless the parameters are almost correct.  So in practice, we would work with the negative log likelihood or the least squares.<br>\n",
+        "\n",
+        "Let's do the same thing with the standard deviation parameter of our network.  This is not an output of the network (unless we choose to make that the case), but it still affects the likelihood.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "771G8N1Vk5A2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a range of values for the parameter\n",
+        "sigma_vals = np.arange(0.1,0.5,0.005)\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods and sum of squares\n",
+        "likelihoods = np.zeros_like(sigma_vals)\n",
+        "nlls = np.zeros_like(sigma_vals)\n",
+        "sum_squares = np.zeros_like(sigma_vals)\n",
+        "\n",
+        "# Initialise the parameters\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Might as well set to the best offset\n",
+        "beta_1[0,0] = 0.27\n",
+        "for count in range(len(sigma_vals)):\n",
+        "  # Set the value for the parameter\n",
+        "  sigma = sigma_vals[count]\n",
+        "  # Run the network with new parameters\n",
+        "  mu_pred = y_pred = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "  # Compute and store the three values\n",
+        "  likelihoods[count] = compute_likelihood(y_train, mu_pred, sigma)\n",
+        "  nlls[count] = compute_negative_log_likelihood(y_train, mu_pred, sigma)\n",
+        "  sum_squares[count] = compute_sum_of_squares(y_train, y_pred)\n",
+        "  # Draw the model for every 20th parameter setting\n",
+        "  if count % 20 == 0:\n",
+        "    # Run the model to get values to plot and plot it.\n",
+        "    y_model = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "    plot_univariate_regression(x_model, y_model, x_train, y_train, sigma_model=sigma, title=\"sigma=%3.3f\"%(sigma))"
+      ],
+      "metadata": {
+        "id": "dMNAr0R8gg82"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's plot the likelihood, negative log likelihood, and least squares as a function of the value of the standard deviation sigma\n",
+        "fig, ax = plt.subplots(1,2)\n",
+        "fig.set_size_inches(10.5, 5.5)\n",
+        "fig.tight_layout(pad=10.0)\n",
+        "likelihood_color = 'tab:red'\n",
+        "nll_color = 'tab:blue'\n",
+        "\n",
+        "\n",
+        "ax[0].set_xlabel('sigma')\n",
+        "ax[0].set_ylabel('likelihood', color = likelihood_color)\n",
+        "ax[0].plot(sigma_vals, likelihoods, color = likelihood_color)\n",
+        "ax[0].tick_params(axis='y', labelcolor=likelihood_color)\n",
+        "\n",
+        "ax00 = ax[0].twinx()\n",
+        "ax00.plot(sigma_vals, nlls, color = nll_color)\n",
+        "ax00.set_ylabel('negative log likelihood', color = nll_color)\n",
+        "ax00.tick_params(axis='y', labelcolor = nll_color)\n",
+        "\n",
+        "plt.axvline(x = sigma_vals[np.argmax(likelihoods)], linestyle='dotted')\n",
+        "\n",
+        "ax[1].plot(sigma_vals, sum_squares); ax[1].set_xlabel('sigma'); ax[1].set_ylabel('sum of squares')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "l9jduyHLDAZC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Hopefully, you can see that the maximum of the likelihood fn is at the same position as the minimum negative log likelihood\n",
+        "# The least squares solution does not depend on sigma, so it's just flat -- no use here.\n",
+        "# Let's check that:\n",
+        "print(\"Maximum likelihood = %3.3f, at sigma=%3.3f\"%( (likelihoods[np.argmax(likelihoods)],sigma_vals[np.argmax(likelihoods)])))\n",
+        "print(\"Minimum negative log likelihood = %3.3f, at sigma=%3.3f\"%( (nlls[np.argmin(nlls)],sigma_vals[np.argmin(nlls)])))\n",
+        "# Plot the best model\n",
+        "sigma= sigma_vals[np.argmin(nlls)]\n",
+        "y_model = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "plot_univariate_regression(x_model, y_model, x_train, y_train, sigma_model = sigma, title=\"beta_1=%3.3f, sigma =%3.3f\"%(beta_1[0,0],sigma))"
+      ],
+      "metadata": {
+        "id": "XH7yER52Dxt5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Obviously, to fit the full neural model we would vary all of the 10 parameters of the network in $\\boldsymbol\\beta_{0},\\boldsymbol\\Omega_{0},\\boldsymbol\\beta_{1},\\boldsymbol\\Omega_{1}$ (and maybe $\\sigma$) until we find the combination that have the maximum likelihood / minimum negative log likelihood / least squares.<br><br>\n",
+        "\n",
+        "Here we just varied one at a time as it is easier to see what is going on.  This is known as **coordinate descent**.\n"
+      ],
+      "metadata": {
+        "id": "q_KeGNAHEbIt"
+      }
+    }
+  ]
+}

Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb

@@ -0,0 +1,440 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 5.2 Binary Cross-Entropy Loss**\n",
+        "\n",
+        "This notebook investigates the binary cross-entropy loss.  It follows from applying the formula in section 5.2 to a loss function based on the Bernoulli distribution.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "jSlFkICHwHQF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PYMZ1x-Pv1ht"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Import math Library\n",
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "# Define a shallow neural network\n",
+        "def shallow_nn(x, beta_0, omega_0, beta_1, omega_1):\n",
+        "    # Make sure that input data is (1 x n_data) array\n",
+        "    n_data = x.size\n",
+        "    x = np.reshape(x,(1,n_data))\n",
+        "\n",
+        "    # This runs the network for ALL of the inputs, x at once so we can draw graph\n",
+        "    h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(omega_0,x))\n",
+        "    model_out = np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(omega_1,h1)\n",
+        "    return model_out"
+      ],
+      "metadata": {
+        "id": "Fv7SZR3tv7mV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get parameters for model -- we can call this function to easily reset them\n",
+        "def get_parameters():\n",
+        "  # And we'll create a network that approximately fits it\n",
+        "  beta_0 = np.zeros((3,1));  # formerly theta_x0\n",
+        "  omega_0 = np.zeros((3,1)); # formerly theta_x1\n",
+        "  beta_1 = np.zeros((1,1));  # formerly phi_0\n",
+        "  omega_1 = np.zeros((1,3)); # formerly phi_x\n",
+        "\n",
+        "  beta_0[0,0] = 0.3; beta_0[1,0] = -1.0; beta_0[2,0] = -0.5\n",
+        "  omega_0[0,0] = -1.0; omega_0[1,0] = 1.8; omega_0[2,0] = 0.65\n",
+        "  beta_1[0,0] = 2.6;\n",
+        "  omega_1[0,0] = -24.0; omega_1[0,1] = -8.0; omega_1[0,2] = 50.0\n",
+        "\n",
+        "  return beta_0, omega_0, beta_1, omega_1"
+      ],
+      "metadata": {
+        "id": "pUT9Ain_HRim"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Utility function for plotting data\n",
+        "def plot_binary_classification(x_model, out_model, lambda_model, x_data = None, y_data = None, title= None):\n",
+        "  # Make sure model data are 1D arrays\n",
+        "  x_model = np.squeeze(x_model)\n",
+        "  out_model = np.squeeze(out_model)\n",
+        "  lambda_model = np.squeeze(lambda_model)\n",
+        "\n",
+        "  fig, ax = plt.subplots(1,2)\n",
+        "  fig.set_size_inches(7.0, 3.5)\n",
+        "  fig.tight_layout(pad=3.0)\n",
+        "  ax[0].plot(x_model,out_model)\n",
+        "  ax[0].set_xlabel(r'Input, $x$'); ax[0].set_ylabel(r'Model output')\n",
+        "  ax[0].set_xlim([0,1]);ax[0].set_ylim([-4,4])\n",
+        "  if title is not None:\n",
+        "    ax[0].set_title(title)\n",
+        "  ax[1].plot(x_model,lambda_model)\n",
+        "  ax[1].set_xlabel(r'Input, $x$'); ax[1].set_ylabel(r'$\\lambda$ or Pr(y=1|x)')\n",
+        "  ax[1].set_xlim([0,1]);ax[1].set_ylim([-0.05,1.05])\n",
+        "  if title is not None:\n",
+        "    ax[1].set_title(title)\n",
+        "  if x_data is not None:\n",
+        "    ax[1].plot(x_data, y_data, 'ko')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "NRR67ri_1TzN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Binary classification\n",
+        "\n",
+        "In binary classification tasks, the network predicts the probability of the output belonging to class 1.  Since probabilities must lie in [0,1] and the network can output arbitrary values, we map the network through a sigmoid function that ensures the range is valid."
+      ],
+      "metadata": {
+        "id": "PsgLZwsPxauP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Sigmoid function that maps [-infty,infty] to [0,1]\n",
+        "def sigmoid(model_out):\n",
+        "  # TODO -- implement the logistic sigmoid function from equation 5.18\n",
+        "  # Replace this line:\n",
+        "  sig_model_out = np.zeros_like(model_out)\n",
+        "\n",
+        "  return sig_model_out"
+      ],
+      "metadata": {
+        "id": "uFb8h-9IXnIe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's create some 1D training data\n",
+        "x_train = np.array([0.09291784,0.46809093,0.93089486,0.67612654,0.73441752,0.86847339,\\\n",
+        "                   0.49873225,0.51083168,0.18343972,0.99380898,0.27840809,0.38028817,\\\n",
+        "                   0.12055708,0.56715537,0.92005746,0.77072270,0.85278176,0.05315950,\\\n",
+        "                   0.87168699,0.58858043])\n",
+        "y_train = np.array([0,1,1,0,0,1,\\\n",
+        "                    1,0,0,1,0,1,\\\n",
+        "                    0,1,1,0,1,0, \\\n",
+        "                    1,1])\n",
+        "\n",
+        "# Get parameters for the model\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x_model = np.arange(0,1,0.01)\n",
+        "# Run the model to get values to plot and plot it.\n",
+        "model_out= shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_model = sigmoid(model_out)\n",
+        "plot_binary_classification(x_model, model_out, lambda_model, x_train, y_train)\n"
+      ],
+      "metadata": {
+        "id": "VWzNOt1swFVd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The left is model output and the right is the model output after the sigmoid has been applied, so it now lies in the range [0,1] and represents the probability, that y=1.  The black dots show the training data.  We'll compute the likelihood and the negative log likelihood."
+      ],
+      "metadata": {
+        "id": "MvVX6tl9AEXF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return probability under Bernoulli distribution for observed class y\n",
+        "def bernoulli_distribution(y, lambda_param):\n",
+        "    # TODO-- write in the equation for the Bernoulli distribution\n",
+        "    # Equation 5.17 from the notes (you will need np.power)\n",
+        "    # Replace the line below\n",
+        "    prob = np.zeros_like(y)\n",
+        "\n",
+        "    return prob"
+      ],
+      "metadata": {
+        "id": "YaLdRlEX0FkU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.8,bernoulli_distribution(0,0.2)))\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.2,bernoulli_distribution(1,0.2)))"
+      ],
+      "metadata": {
+        "id": "4TSL14dqHHbV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the likelihood using this function"
+      ],
+      "metadata": {
+        "id": "R5z_0dzQMF35"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return the likelihood of all of the data under the model\n",
+        "def compute_likelihood(y_train, lambda_param):\n",
+        "  # TODO -- compute the likelihood of the data -- the product of the Bernoulli probabilities for each data point\n",
+        "  # Top line of equation 5.3 in the notes\n",
+        "  # You will need np.prod() and the bernoulli_distribution function you used above\n",
+        "  # Replace the line below\n",
+        "  likelihood = 0\n",
+        "\n",
+        "  return likelihood"
+      ],
+      "metadata": {
+        "id": "zpS7o6liCx7f"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the Bernoulli parameter lambda\n",
+        "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_train = sigmoid(model_out)\n",
+        "# Compute the likelihood\n",
+        "likelihood = compute_likelihood(y_train, lambda_train)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(0.000070237,likelihood))"
+      ],
+      "metadata": {
+        "id": "1hQxBLoVNlr2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
+        "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
+        "\n",
+        "This is why we use negative log likelihood"
+      ],
+      "metadata": {
+        "id": "HzphKgPfOvlk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Return the negative log likelihood of the data under the model\n",
+        "def compute_negative_log_likelihood(y_train, lambda_param):\n",
+        "  # TODO -- compute the likelihood of the data -- don't use the likelihood function above -- compute the negative sum of the log probabilities\n",
+        "  # You will need np.sum(), np.log()\n",
+        "  # Replace the line below\n",
+        "  nll = 0\n",
+        "\n",
+        "  return nll"
+      ],
+      "metadata": {
+        "id": "dsT0CWiKBmTV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the mean of the Gaussian\n",
+        "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "# Pass through the sigmoid function\n",
+        "lambda_train = sigmoid(model_out)\n",
+        "# Compute the log likelihood\n",
+        "nll = compute_negative_log_likelihood(y_train, lambda_train)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(9.563639387,nll))"
+      ],
+      "metadata": {
+        "id": "nVxUXg9rQmwI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's investigate finding the maximum likelihood / minimum negative log likelihood solution.  For simplicity, we'll assume that all the parameters are fixed except one and look at how the likelihood and negative log likelihood change as we manipulate the last parameter.  We'll start with overall y_offset, beta_1 (formerly phi_0)"
+      ],
+      "metadata": {
+        "id": "OgcRojvPWh4V"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a range of values for the parameter\n",
+        "beta_1_vals = np.arange(-2,6.0,0.1)\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods\n",
+        "likelihoods = np.zeros_like(beta_1_vals)\n",
+        "nlls = np.zeros_like(beta_1_vals)\n",
+        "\n",
+        "# Initialise the parameters\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "for count in range(len(beta_1_vals)):\n",
+        "  # Set the value for the parameter\n",
+        "  beta_1[0,0] = beta_1_vals[count]\n",
+        "  # Run the network with new parameters\n",
+        "  model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "  lambda_train = sigmoid(model_out)\n",
+        "  # Compute and store the two values\n",
+        "  likelihoods[count] = compute_likelihood(y_train,lambda_train)\n",
+        "  nlls[count] = compute_negative_log_likelihood(y_train, lambda_train)\n",
+        "  # Draw the model for every 20th parameter setting\n",
+        "  if count % 20 == 0:\n",
+        "    # Run the model to get values to plot and plot it.\n",
+        "    model_out = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "    lambda_model = sigmoid(model_out)\n",
+        "    plot_binary_classification(x_model, model_out, lambda_model, x_train, y_train, title=\"beta_1[0]=%3.3f\"%(beta_1[0,0]))\n"
+      ],
+      "metadata": {
+        "id": "pFKtDaAeVU4U"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's plot the likelihood and negative log likelihood as a function of the value of the offset beta1\n",
+        "fig, ax = plt.subplots()\n",
+        "fig.tight_layout(pad=5.0)\n",
+        "likelihood_color = 'tab:red'\n",
+        "nll_color = 'tab:blue'\n",
+        "\n",
+        "\n",
+        "ax.set_xlabel('beta_1[0]')\n",
+        "ax.set_ylabel('likelihood', color = likelihood_color)\n",
+        "ax.plot(beta_1_vals, likelihoods, color = likelihood_color)\n",
+        "ax.tick_params(axis='y', labelcolor=likelihood_color)\n",
+        "\n",
+        "ax1 = ax.twinx()\n",
+        "ax1.plot(beta_1_vals, nlls, color = nll_color)\n",
+        "ax1.set_ylabel('negative log likelihood', color = nll_color)\n",
+        "ax1.tick_params(axis='y', labelcolor = nll_color)\n",
+        "\n",
+        "plt.axvline(x = beta_1_vals[np.argmax(likelihoods)], linestyle='dotted')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "UHXeTa9MagO6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Hopefully, you can see that the maximum of the likelihood fn is at the same position as the minimum negative log likelihood\n",
+        "# Let's check that:\n",
+        "print(\"Maximum likelihood = %f, at beta_1=%3.3f\"%( (likelihoods[np.argmax(likelihoods)],beta_1_vals[np.argmax(likelihoods)])))\n",
+        "print(\"Minimum negative log likelihood = %f, at beta_1=%3.3f\"%( (nlls[np.argmin(nlls)],beta_1_vals[np.argmin(nlls)])))\n",
+        "\n",
+        "# Plot the best model\n",
+        "beta_1[0,0] = beta_1_vals[np.argmin(nlls)]\n",
+        "model_out = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_model = sigmoid(model_out)\n",
+        "plot_binary_classification(x_model, model_out, lambda_model, x_train, y_train, title=\"beta_1[0]=%3.3f\"%(beta_1[0,0]))\n"
+      ],
+      "metadata": {
+        "id": "aDEPhddNdN4u"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "They both give the same answer. But you can see from the likelihood above that the likelihood is very small unless the parameters are almost correct.  So in practice, we would work with the negative log likelihood.<br><br>\n",
+        "\n",
+        "Again, to fit the full neural model we would vary all of the 10 parameters of the network in the $\\boldsymbol\\beta_{0},\\boldsymbol\\Omega_{0},\\boldsymbol\\beta_{1},\\boldsymbol\\Omega_{1}$ until we find the combination that have the maximum likelihood / minimum negative log likelihood.<br><br>\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "771G8N1Vk5A2"
+      }
+    }
+  ]
+}

Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb

@@ -0,0 +1,463 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jSlFkICHwHQF"
+      },
+      "source": [
+        "# **Notebook 5.3 Multiclass Cross-Entropy Loss**\n",
+        "\n",
+        "This notebook investigates the multi-class cross-entropy loss.  It follows from applying the formula in section 5.2 to a loss function based on the Categorical distribution.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PYMZ1x-Pv1ht"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports math library\n",
+        "import numpy as np\n",
+        "# Used for repmat\n",
+        "import numpy.matlib\n",
+        "# Imports plotting library\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Import math Library\n",
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fv7SZR3tv7mV"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "# Define a shallow neural network\n",
+        "def shallow_nn(x, beta_0, omega_0, beta_1, omega_1):\n",
+        "    # Make sure that input data is (1 x n_data) array\n",
+        "    n_data = x.size\n",
+        "    x = np.reshape(x,(1,n_data))\n",
+        "\n",
+        "    # This runs the network for ALL of the inputs, x at once so we can draw graph\n",
+        "    h1 = ReLU(np.matmul(beta_0,np.ones((1,n_data))) + np.matmul(omega_0,x))\n",
+        "    model_out = np.matmul(beta_1,np.ones((1,n_data))) + np.matmul(omega_1,h1)\n",
+        "    return model_out"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pUT9Ain_HRim"
+      },
+      "outputs": [],
+      "source": [
+        "# Get parameters for model -- we can call this function to easily reset them\n",
+        "def get_parameters():\n",
+        "  # And we'll create a network that approximately fits it\n",
+        "  beta_0 = np.zeros((3,1));  # formerly theta_x0\n",
+        "  omega_0 = np.zeros((3,1)); # formerly theta_x1\n",
+        "  beta_1 = np.zeros((3,1));  # NOTE -- there are three outputs now (one for each class, so three output biases)\n",
+        "  omega_1 = np.zeros((3,3)); # NOTE -- there are three outputs now (one for each class, so nine output weights, connecting 3 hidden units to 3 outputs)\n",
+        "\n",
+        "  beta_0[0,0] = 0.3; beta_0[1,0] = -1.0; beta_0[2,0] = -0.5\n",
+        "  omega_0[0,0] = -1.0; omega_0[1,0] = 1.8; omega_0[2,0] = 0.65\n",
+        "  beta_1[0,0] = 2.0; beta_1[1,0] = -2; beta_1[2,0] = 0.0\n",
+        "  omega_1[0,0] = -24.0; omega_1[0,1] = -8.0; omega_1[0,2] = 50.0\n",
+        "  omega_1[1,0] = -2.0; omega_1[1,1] = 8.0; omega_1[1,2] = -30.0\n",
+        "  omega_1[2,0] = 16.0; omega_1[2,1] = -8.0; omega_1[2,2] =-8\n",
+        "\n",
+        "  return beta_0, omega_0, beta_1, omega_1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NRR67ri_1TzN"
+      },
+      "outputs": [],
+      "source": [
+        "# Utility function for plotting data\n",
+        "def plot_multiclass_classification(x_model, out_model, lambda_model, x_data = None, y_data = None, title= None):\n",
+        "  # Make sure model data are 1D arrays\n",
+        "  n_data = len(x_model)\n",
+        "  n_class = 3\n",
+        "  x_model = np.squeeze(x_model)\n",
+        "  out_model = np.reshape(out_model, (n_class,n_data))\n",
+        "  lambda_model = np.reshape(lambda_model, (n_class,n_data))\n",
+        "\n",
+        "  fig, ax = plt.subplots(1,2)\n",
+        "  fig.set_size_inches(7.0, 3.5)\n",
+        "  fig.tight_layout(pad=3.0)\n",
+        "  ax[0].plot(x_model,out_model[0,:],'r-')\n",
+        "  ax[0].plot(x_model,out_model[1,:],'g-')\n",
+        "  ax[0].plot(x_model,out_model[2,:],'b-')\n",
+        "  ax[0].set_xlabel('Input, $x$'); ax[0].set_ylabel('Model outputs')\n",
+        "  ax[0].set_xlim([0,1]);ax[0].set_ylim([-4,4])\n",
+        "  if title is not None:\n",
+        "    ax[0].set_title(title)\n",
+        "  ax[1].plot(x_model,lambda_model[0,:],'r-')\n",
+        "  ax[1].plot(x_model,lambda_model[1,:],'g-')\n",
+        "  ax[1].plot(x_model,lambda_model[2,:],'b-')\n",
+        "  ax[1].set_xlabel('Input, $x$'); ax[1].set_ylabel('$\\lambda$ or Pr(y=k|x)')\n",
+        "  ax[1].set_xlim([0,1]);ax[1].set_ylim([-0.1,1.05])\n",
+        "  if title is not None:\n",
+        "    ax[1].set_title(title)\n",
+        "  if x_data is not None:\n",
+        "    for i in range(len(x_data)):\n",
+        "      if y_data[i] ==0:\n",
+        "        ax[1].plot(x_data[i],-0.05, 'r.')\n",
+        "      if y_data[i] ==1:\n",
+        "        ax[1].plot(x_data[i],-0.05, 'g.')\n",
+        "      if y_data[i] ==2:\n",
+        "        ax[1].plot(x_data[i],-0.05, 'b.')\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PsgLZwsPxauP"
+      },
+      "source": [
+        "# Multiclass classification\n",
+        "\n",
+        "For multiclass classification, the network must predict the probability of $K$ classes, using $K$ outputs.  However, these probability must be non-negative and sum to one, and the network outputs can take arbitrary values.  Hence, we pass the outputs through a softmax function which maps $K$ arbitrary values to $K$ non-negative values that sum to one."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uFb8h-9IXnIe"
+      },
+      "outputs": [],
+      "source": [
+        "# Softmax function that maps a vector of arbitrary values to a vector of values that are positive and sum to one.\n",
+        "def softmax(model_out):\n",
+        "  # This operation has to be done separately for every column of the input\n",
+        "  # Compute exponentials of all the elements\n",
+        "  # TODO: compute the softmax function (eq 5.22)\n",
+        "  # Replace this skeleton code\n",
+        "\n",
+        "  # Compute the exponential of the model outputs\n",
+        "  exp_model_out = np.zeros_like(model_out) ;\n",
+        "  # Compute the sum of the exponentials (denominator of equation 5.22)\n",
+        "  sum_exp_model_out = np.zeros_like(model_out) ;\n",
+        "  # Normalize the exponentials (np.matlib.repmat might be useful here)\n",
+        "  softmax_model_out = np.ones_like(model_out)/ exp_model_out.shape[0]\n",
+        "\n",
+        "  return softmax_model_out"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VWzNOt1swFVd"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Let's create some 1D training data\n",
+        "x_train = np.array([0.09291784,0.46809093,0.93089486,0.67612654,0.73441752,0.86847339,\\\n",
+        "                   0.49873225,0.51083168,0.18343972,0.99380898,0.27840809,0.38028817,\\\n",
+        "                   0.12055708,0.56715537,0.92005746,0.77072270,0.85278176,0.05315950,\\\n",
+        "                   0.87168699,0.58858043])\n",
+        "y_train = np.array([2,0,1,2,1,0,\\\n",
+        "                    0,2,2,0,2,0,\\\n",
+        "                    2,0,1,2,1,2, \\\n",
+        "                    1,0])\n",
+        "\n",
+        "# Get parameters for the model\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "\n",
+        "# Define a range of input values\n",
+        "x_model = np.arange(0,1,0.01)\n",
+        "# Run the model to get values to plot and plot it.\n",
+        "model_out= shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_model = softmax(model_out)\n",
+        "plot_multiclass_classification(x_model, model_out, lambda_model, x_train, y_train)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MvVX6tl9AEXF"
+      },
+      "source": [
+        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue).  The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dots, and the blue curve to be high where there are blue dots  We'll compute the likelihood and the negative log likelihood."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YaLdRlEX0FkU"
+      },
+      "outputs": [],
+      "source": [
+        "# Return probability under categorical distribution for observed class y\n",
+        "# Just take value from row k of lambda param where y =k,\n",
+        "def categorical_distribution(y, lambda_param):\n",
+        "    return np.array([lambda_param[row, i] for i, row in enumerate (y)])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4TSL14dqHHbV"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.2,categorical_distribution(np.array([[0]]),np.array([[0.2],[0.5],[0.3]]))))\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.5,categorical_distribution(np.array([[1]]),np.array([[0.2],[0.5],[0.3]]))))\n",
+        "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.3,categorical_distribution(np.array([[2]]),np.array([[0.2],[0.5],[0.3]]))))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5z_0dzQMF35"
+      },
+      "source": [
+        "Now let's compute the likelihood using this function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zpS7o6liCx7f"
+      },
+      "outputs": [],
+      "source": [
+        "# Return the likelihood of all of the data under the model\n",
+        "def compute_likelihood(y_train, lambda_param):\n",
+        "  # TODO -- compute the likelihood of the data -- the product of the categorical probabilities for each data point\n",
+        "  # Top line of equation 5.3 in the notes\n",
+        "  # You will need np.prod() and the categorical_distribution function you used above\n",
+        "  # Replace the line below\n",
+        "  likelihood = 0\n",
+        "\n",
+        "  return likelihood"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1hQxBLoVNlr2"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's test this\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the parameters of the categorical distribution\n",
+        "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_train = softmax(model_out)\n",
+        "# Compute the likelihood\n",
+        "likelihood = compute_likelihood(y_train, lambda_train)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(0.000000041,likelihood))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HzphKgPfOvlk"
+      },
+      "source": [
+        "You can see that this gives a very small answer, even for this small 1D dataset, and with the model fitting quite well.  This is because it is the product of several probabilities, which are all quite small themselves.\n",
+        "This will get out of hand pretty quickly with real datasets -- the likelihood will get so small that we can't represent it with normal finite-precision math\n",
+        "\n",
+        "This is why we use negative log likelihood"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dsT0CWiKBmTV"
+      },
+      "outputs": [],
+      "source": [
+        "# Return the negative log likelihood of the data under the model\n",
+        "def compute_negative_log_likelihood(y_train, lambda_param):\n",
+        "  # TODO -- compute the negative log likelihood of the data -- don't use the likelihood function above -- compute the negative sum of the log probabilities\n",
+        "  # You will need np.sum(), np.log()\n",
+        "  # Replace the line below\n",
+        "  nll = 0\n",
+        "\n",
+        "  return nll"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nVxUXg9rQmwI"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's test this\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "# Use our neural network to predict the parameters of the categorical distribution\n",
+        "model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "# Pass the outputs through the softmax function\n",
+        "lambda_train = softmax(model_out)\n",
+        "# Compute the negative log likelihood\n",
+        "nll = compute_negative_log_likelihood(y_train, lambda_train)\n",
+        "# Let's double check we get the right answer before proceeding\n",
+        "print(\"Correct answer = %9.9f, Your answer = %9.9f\"%(17.015457867,nll))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OgcRojvPWh4V"
+      },
+      "source": [
+        "Now let's investigate finding the maximum likelihood / minimum negative log likelihood solution.  For simplicity, we'll assume that all the parameters are fixed except one and look at how the likelihood and negative log likelihood change as we manipulate the last parameter.  We'll start with overall y_offset, $\\beta_1$ (formerly $\\phi_0$)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pFKtDaAeVU4U"
+      },
+      "outputs": [],
+      "source": [
+        "# Define a range of values for the parameter\n",
+        "beta_1_vals = np.arange(-2,6.0,0.1)\n",
+        "# Create some arrays to store the likelihoods, negative log likelihoods\n",
+        "likelihoods = np.zeros_like(beta_1_vals)\n",
+        "nlls = np.zeros_like(beta_1_vals)\n",
+        "\n",
+        "# Initialise the parameters\n",
+        "beta_0, omega_0, beta_1, omega_1 = get_parameters()\n",
+        "for count in range(len(beta_1_vals)):\n",
+        "  # Set the value for the parameter\n",
+        "  beta_1[0,0] = beta_1_vals[count]\n",
+        "  # Run the network with new parameters\n",
+        "  model_out = shallow_nn(x_train, beta_0, omega_0, beta_1, omega_1)\n",
+        "  lambda_train = softmax(model_out)\n",
+        "  # Compute and store the two values\n",
+        "  likelihoods[count] = compute_likelihood(y_train,lambda_train)\n",
+        "  nlls[count] = compute_negative_log_likelihood(y_train, lambda_train)\n",
+        "  # Draw the model for every 20th parameter setting\n",
+        "  if count % 20 == 0:\n",
+        "    # Run the model to get values to plot and plot it.\n",
+        "    model_out = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "    lambda_model = softmax(model_out)\n",
+        "    plot_multiclass_classification(x_model, model_out, lambda_model, x_train, y_train, title=\"beta1[0,0]=%3.3f\"%(beta_1[0,0]))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UHXeTa9MagO6"
+      },
+      "outputs": [],
+      "source": [
+        "# Now let's plot the likelihood and negative log likelihood as a function of the value of the offset beta1\n",
+        "fig, ax = plt.subplots()\n",
+        "fig.tight_layout(pad=5.0)\n",
+        "likelihood_color = 'tab:red'\n",
+        "nll_color = 'tab:blue'\n",
+        "\n",
+        "\n",
+        "ax.set_xlabel('beta_1[0, 0]')\n",
+        "ax.set_ylabel('likelihood', color = likelihood_color)\n",
+        "ax.plot(beta_1_vals, likelihoods, color = likelihood_color)\n",
+        "ax.tick_params(axis='y', labelcolor=likelihood_color)\n",
+        "\n",
+        "ax1 = ax.twinx()\n",
+        "ax1.plot(beta_1_vals, nlls, color = nll_color)\n",
+        "ax1.set_ylabel('negative log likelihood', color = nll_color)\n",
+        "ax1.tick_params(axis='y', labelcolor = nll_color)\n",
+        "\n",
+        "plt.axvline(x = beta_1_vals[np.argmax(likelihoods)], linestyle='dotted')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aDEPhddNdN4u"
+      },
+      "outputs": [],
+      "source": [
+        "# Hopefully, you can see that the maximum of the likelihood fn is at the same position as the minimum negative log likelihood solution\n",
+        "# Let's check that:\n",
+        "print(\"Maximum likelihood = %f, at beta_1=%3.3f\"%( (likelihoods[np.argmax(likelihoods)],beta_1_vals[np.argmax(likelihoods)])))\n",
+        "print(\"Minimum negative log likelihood = %f, at beta_1=%3.3f\"%( (nlls[np.argmin(nlls)],beta_1_vals[np.argmin(nlls)])))\n",
+        "\n",
+        "# Plot the best model\n",
+        "beta_1[0,0] = beta_1_vals[np.argmin(nlls)]\n",
+        "model_out = shallow_nn(x_model, beta_0, omega_0, beta_1, omega_1)\n",
+        "lambda_model = softmax(model_out)\n",
+        "plot_multiclass_classification(x_model, model_out, lambda_model, x_train, y_train, title=\"beta1[0,0]=%3.3f\"%(beta_1[0,0]))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "771G8N1Vk5A2"
+      },
+      "source": [
+        "They both give the same answer. But you can see from the likelihood above that the likelihood is very small unless the parameters are almost correct.  So in practice, we would work with the negative log likelihood.<br><br>\n",
+        "\n",
+        "Again, to fit the full neural model we would vary all of the 16 parameters of the network in the $\\boldsymbol\\beta_{0},\\boldsymbol\\Omega_{0},\\boldsymbol\\beta_{1},\\boldsymbol\\Omega_{1}$ until we find the combination that have the maximum likelihood / minimum negative log likelihood.<br><br>\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap06/6_1_Line_Search.ipynb

@@ -0,0 +1,192 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 6.1: Line search**\n",
+        "\n",
+        "This notebook investigates how to find the minimum of a 1D function using line search as described in Figure 6.10.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's create a simple 1D function\n",
+        "def loss_function(phi):\n",
+        "  return 1- 0.5 * np.exp(-(phi-0.65)*(phi-0.65)/0.1) - 0.45 *np.exp(-(phi-0.35)*(phi-0.35)/0.02)\n",
+        "\n",
+        "def draw_function(loss_function,a=None, b=None, c=None, d=None):\n",
+        "  # Plot the function\n",
+        "  phi_plot = np.arange(0,1,0.01);\n",
+        "  fig,ax = plt.subplots()\n",
+        "  ax.plot(phi_plot,loss_function(phi_plot),'r-')\n",
+        "  ax.set_xlim(0,1); ax.set_ylim(0,1)\n",
+        "  ax.set_xlabel(r'$\\phi$'); ax.set_ylabel(r'$L[\\phi]$')\n",
+        "  if a is not None and b is not None and c is not None and d is not None:\n",
+        "      plt.axvspan(a, d, facecolor='k', alpha=0.2)\n",
+        "      ax.plot([a,a],[0,1],'b-')\n",
+        "      ax.plot([b,b],[0,1],'b-')\n",
+        "      ax.plot([c,c],[0,1],'b-')\n",
+        "      ax.plot([d,d],[0,1],'b-')\n",
+        "  plt.show()\n"
+      ],
+      "metadata": {
+        "id": "qFRe9POHF2le"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw this function\n",
+        "draw_function(loss_function)"
+      ],
+      "metadata": {
+        "id": "TXx1Tpd1Tl-I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now lets create a line search procedure to find the minimum in the range 0,1"
+      ],
+      "metadata": {
+        "id": "QU5mdGvpTtEG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def line_search(loss_function, thresh=.0001, max_iter = 10, draw_flag = False):\n",
+        "\n",
+        "    # Initialize four points along the range we are going to search\n",
+        "    a = 0\n",
+        "    b = 0.33\n",
+        "    c = 0.66\n",
+        "    d = 1.0\n",
+        "    n_iter = 0\n",
+        "\n",
+        "    # While we haven't found the minimum closely enough\n",
+        "    while np.abs(b-c) > thresh and n_iter < max_iter:\n",
+        "        # Increment iteration counter (just to prevent an infinite loop)\n",
+        "        n_iter = n_iter+1\n",
+        "\n",
+        "        # Calculate all four points\n",
+        "        lossa = loss_function(a)\n",
+        "        lossb = loss_function(b)\n",
+        "        lossc = loss_function(c)\n",
+        "        lossd = loss_function(d)\n",
+        "\n",
+        "        if draw_flag:\n",
+        "          draw_function(loss_function, a,b,c,d)\n",
+        "\n",
+        "        print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
+        "\n",
+        "        # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then move them to they are half\n",
+        "        # as far from A as they start\n",
+        "        # i.e. bring them closer to the original point\n",
+        "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
+        "        if (0):\n",
+        "          continue;\n",
+        "\n",
+        "\n",
+        "        # Rule #2 If the HEIGHT at point b is less than the HEIGHT at point c then\n",
+        "        #                     point d becomes point c, and\n",
+        "        #                     point b becomes 1/3 between a and new d\n",
+        "        #                     point c becomes 2/3 between a and new d\n",
+        "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
+        "        if (0):\n",
+        "          continue;\n",
+        "\n",
+        "        # Rule #3 If the HEIGHT at point c is less than the HEIGHT at point b then\n",
+        "        #                     point a becomes point b, and\n",
+        "        #                     point b becomes 1/3 between new a and d\n",
+        "        #                     point c becomes 2/3 between new a and d\n",
+        "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
+        "        if(0):\n",
+        "          continue\n",
+        "\n",
+        "    # TODO -- FINAL SOLUTION IS AVERAGE OF B and C\n",
+        "    # REPLACE THIS LINE\n",
+        "    soln = 1\n",
+        "\n",
+        "\n",
+        "    return soln"
+      ],
+      "metadata": {
+        "id": "K-NTHpAAHlCl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "soln = line_search(loss_function, draw_flag=True)\n",
+        "print('Soln = %3.3f, loss = %3.3f'%(soln,loss_function(soln)))"
+      ],
+      "metadata": {
+        "id": "YVq6rmaWRD2M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "tOLd0gtdRLLS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap06/6_2_Gradient_Descent.ipynb

@@ -0,0 +1,420 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "el8l05WQEO46"
+      },
+      "source": [
+        "# **Notebook 6.2 Gradient descent**\n",
+        "\n",
+        "This notebook recreates the gradient descent algorithm as shown in figure 6.1.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4cRkrh9MZ58Z"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's create our training data 12 pairs {x_i, y_i}\n",
+        "# We'll try to fit the straight line model to these data\n",
+        "data = np.array([[0.03,0.19,0.34,0.46,0.78,0.81,1.08,1.18,1.39,1.60,1.65,1.90],\n",
+        "                 [0.67,0.85,1.05,1.00,1.40,1.50,1.30,1.54,1.55,1.68,1.73,1.60]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WQUERmb2erAe"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's define our model -- just a straight line with intercept phi[0] and slope phi[1]\n",
+        "def model(phi,x):\n",
+        "  y_pred = phi[0]+phi[1] * x\n",
+        "  return y_pred"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qFRe9POHF2le"
+      },
+      "outputs": [],
+      "source": [
+        "# Draw model\n",
+        "def draw_model(data,model,phi,title=None):\n",
+        "  x_model = np.arange(0,2,0.01)\n",
+        "  y_model = model(phi,x_model)\n",
+        "\n",
+        "  fix, ax = plt.subplots()\n",
+        "  ax.plot(data[0,:],data[1,:],'bo')\n",
+        "  ax.plot(x_model,y_model,'m-')\n",
+        "  ax.set_xlim([0,2]);ax.set_ylim([0,2])\n",
+        "  ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "  ax.set_aspect('equal')\n",
+        "  if title is not None:\n",
+        "    ax.set_title(title)\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TXx1Tpd1Tl-I"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize the parameters to some arbitrary values and draw the model\n",
+        "phi = np.zeros((2,1))\n",
+        "phi[0] = 0.6      # Intercept\n",
+        "phi[1] = -0.2      # Slope\n",
+        "draw_model(data,model,phi, \"Initial parameters\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QU5mdGvpTtEG"
+      },
+      "source": [
+        "Now let's compute the sum of squares loss for the training data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I7dqTY2Gg7CR"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_loss(data_x, data_y, model, phi):\n",
+        "  # TODO -- Write this function -- replace the line below\n",
+        "  # First make model predictions from data x\n",
+        "  # Then compute the squared difference between the predictions and true y values\n",
+        "  # Then sum them all and return\n",
+        "  pred_y = 0\n",
+        "  loss = 0\n",
+        "\n",
+        "  return loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eB5DQvU5hYNx"
+      },
+      "source": [
+        "Let's just test that we got that right"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ty05UtEEg9tc"
+      },
+      "outputs": [],
+      "source": [
+        "loss = compute_loss(data[0,:],data[1,:],model,np.array([[0.6],[-0.2]]))\n",
+        "print('Your loss = %3.3f, Correct loss = %3.3f'%(loss, 12.367))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F3trnavPiHpH"
+      },
+      "source": [
+        "Now let's plot the whole loss function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K-NTHpAAHlCl"
+      },
+      "outputs": [],
+      "source": [
+        "def draw_loss_function(compute_loss, data,  model, phi_iters = None):\n",
+        "  # Define pretty colormap\n",
+        "  my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "  my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "  r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "  g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "  b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "  my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "  # Make grid of intercept/slope values to plot\n",
+        "  intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.002))\n",
+        "  loss_mesh = np.zeros_like(slopes_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(slopes_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss(data[0,:], data[1,:], model, np.array([[intercepts_mesh[idslope]], [slope]]))\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n",
+        "  if phi_iters is not None:\n",
+        "    ax.plot(phi_iters[0,:], phi_iters[1,:],'go-')\n",
+        "  ax.set_ylim([1,-1])\n",
+        "  ax.set_xlabel('Intercept $\\phi_{0}$'); ax.set_ylabel('Slope, $\\phi_{1}$')\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l8HbvIupnTME"
+      },
+      "outputs": [],
+      "source": [
+        "draw_loss_function(compute_loss, data, model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s9Duf05WqqSC"
+      },
+      "source": [
+        "Now let's compute the gradient vector for a given set of parameters:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial L}{\\partial \\boldsymbol\\phi} = \\begin{bmatrix}\\frac{\\partial L}{\\partial \\phi_0} \\\\\\frac{\\partial L}{\\partial \\phi_1} \\end{bmatrix}.\n",
+        "\\end{equation}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UpswmkL2qwBT"
+      },
+      "outputs": [],
+      "source": [
+        "# These are in the lecture slides and notes, but worth trying to calculate them yourself to\n",
+        "# check that you get them right.  Write out the expression for the sum of squares loss and take the\n",
+        "# derivative with respect to phi0 and phi1\n",
+        "def compute_gradient(data_x, data_y, phi):\n",
+        "    # TODO -- write this function, replacing the lines below\n",
+        "    dl_dphi0 = 0.0\n",
+        "    dl_dphi1 = 0.0\n",
+        "\n",
+        "    # Return the gradient\n",
+        "    return np.array([[dl_dphi0],[dl_dphi1]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RS1nEcYVuEAM"
+      },
+      "source": [
+        "We can check we got this right using a trick known as **finite differences**.  If we evaluate the function and then change one of the parameters by a very small amount and normalize by that amount, we get an approximation to the gradient, so:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial L}{\\partial \\phi_{0}}&\\approx & \\frac{L[\\phi_0+\\delta, \\phi_1]-L[\\phi_0, \\phi_1]}{\\delta}\\\\\n",
+        "\\frac{\\partial L}{\\partial \\phi_{1}}&\\approx & \\frac{L[\\phi_0, \\phi_1+\\delta]-L[\\phi_0, \\phi_1]}{\\delta}\n",
+        "\\end{align}\n",
+        "\n",
+        "We can't do this when there are many parameters;  for a million parameters, we would have to evaluate the loss function one million plus one times, and usually computing the gradients directly is much more efficient."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QuwAHN7yt-gi"
+      },
+      "outputs": [],
+      "source": [
+        "# Compute the gradient using your function\n",
+        "gradient = compute_gradient(data[0,:],data[1,:], phi)\n",
+        "print(\"Your gradients: (%3.3f,%3.3f)\"%(gradient[0],gradient[1]))\n",
+        "# Approximate the gradients with finite differences\n",
+        "delta = 0.0001\n",
+        "dl_dphi0_est = (compute_loss(data[0,:],data[1,:],model,phi+np.array([[delta],[0]])) - \\\n",
+        "                    compute_loss(data[0,:],data[1,:],model,phi))/delta\n",
+        "dl_dphi1_est = (compute_loss(data[0,:],data[1,:],model,phi+np.array([[0],[delta]])) - \\\n",
+        "                    compute_loss(data[0,:],data[1,:],model,phi))/delta\n",
+        "print(\"Approx gradients: (%3.3f,%3.3f)\"%(dl_dphi0_est,dl_dphi1_est))\n",
+        "# There might be small differences in the last significant figure because finite gradients is an approximation\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5EIjMM9Fw2eT"
+      },
+      "source": [
+        "Now we are ready to perform gradient descent.  We'll need to use our line search routine from notebook 6.1, which I've reproduced here plus the helper function loss_function_1D that maps the search along the negative gradient direction in 2D space to a 1D problem (distance along this direction)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XrJ2gQjfw1XP"
+      },
+      "outputs": [],
+      "source": [
+        "def loss_function_1D(dist_prop, data, model, phi_start, search_direction):\n",
+        "  # Return the loss after moving this far\n",
+        "  return compute_loss(data[0,:], data[1,:], model, phi_start+ search_direction * dist_prop)\n",
+        "\n",
+        "def line_search(data, model, phi, gradient, thresh=.00001, max_dist = 0.1, max_iter = 15, verbose=False):\n",
+        "    # Initialize four points along the range we are going to search\n",
+        "    a = 0\n",
+        "    b = 0.33 * max_dist\n",
+        "    c = 0.66 * max_dist\n",
+        "    d = 1.0 * max_dist\n",
+        "    n_iter = 0\n",
+        "\n",
+        "    # While we haven't found the minimum closely enough\n",
+        "    while np.abs(b-c) > thresh and n_iter < max_iter:\n",
+        "        # Increment iteration counter (just to prevent an infinite loop)\n",
+        "        n_iter = n_iter+1\n",
+        "        # Calculate all four points\n",
+        "        lossa = loss_function_1D(a, data, model, phi,gradient)\n",
+        "        lossb = loss_function_1D(b, data, model, phi,gradient)\n",
+        "        lossc = loss_function_1D(c, data, model, phi,gradient)\n",
+        "        lossd = loss_function_1D(d, data, model, phi,gradient)\n",
+        "\n",
+        "        if verbose:\n",
+        "          print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
+        "          print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
+        "\n",
+        "        # Rule #1 If point A is less than points B, C, and D then halve distance from A to points B,C, and D\n",
+        "        if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
+        "          b = a+ (b-a)/2\n",
+        "          c = a+ (c-a)/2\n",
+        "          d = a+ (d-a)/2\n",
+        "          continue;\n",
+        "\n",
+        "        # Rule #2 If point b is less than point c then\n",
+        "        #                     point d becomes point c, and\n",
+        "        #                     point b becomes 1/3 between a and new d\n",
+        "        #                     point c becomes 2/3 between a and new d\n",
+        "        if lossb < lossc:\n",
+        "          d = c\n",
+        "          b = a+ (d-a)/3\n",
+        "          c = a+ 2*(d-a)/3\n",
+        "          continue\n",
+        "\n",
+        "        # Rule #2 If point c is less than point b then\n",
+        "        #                     point a becomes point b, and\n",
+        "        #                     point b becomes 1/3 between new a and d\n",
+        "        #                     point c becomes 2/3 between new a and d\n",
+        "        a = b\n",
+        "        b = a+ (d-a)/3\n",
+        "        c = a+ 2*(d-a)/3\n",
+        "\n",
+        "    # Return average of two middle points\n",
+        "    return (b+c)/2.0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YVq6rmaWRD2M"
+      },
+      "outputs": [],
+      "source": [
+        "def gradient_descent_step(phi, data,  model):\n",
+        "  # TODO -- update Phi with the gradient descent step (equation 6.3)\n",
+        "  # 1. Compute the gradient (you wrote this function above)\n",
+        "  # 2. Find the best step size alpha using line search function (above) -- use negative gradient as going downhill\n",
+        "  # 3. Update the parameters phi based on the gradient and the step size alpha.\n",
+        "\n",
+        "  return phi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tOLd0gtdRLLS"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize the parameters and draw the model\n",
+        "n_steps = 10\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = 1.6\n",
+        "phi_all[1,0] = -0.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "# Repeatedly take gradient descent steps\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step(phi_all[:,c_step:c_step+1],data, model)\n",
+        "  # Measure loss and draw model\n",
+        "  loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "  draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "# Draw the trajectory on the loss function\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb

@@ -0,0 +1,585 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "el8l05WQEO46"
+      },
+      "source": [
+        "# **Notebook 6.3: Stochastic gradient descent**\n",
+        "\n",
+        "This notebook investigates gradient descent and stochastic gradient descent and recreates figure 6.5 from the book\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4cRkrh9MZ58Z"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's create our training data of 30 pairs {x_i, y_i}\n",
+        "# We'll try to fit the Gabor model to these data\n",
+        "data = np.array([[-1.920e+00,-1.422e+01,1.490e+00,-1.940e+00,-2.389e+00,-5.090e+00,\n",
+        "                 -8.861e+00,3.578e+00,-6.010e+00,-6.995e+00,3.634e+00,8.743e-01,\n",
+        "                 -1.096e+01,4.073e-01,-9.467e+00,8.560e+00,1.062e+01,-1.729e-01,\n",
+        "                  1.040e+01,-1.261e+01,1.574e-01,-1.304e+01,-2.156e+00,-1.210e+01,\n",
+        "                 -1.119e+01,2.902e+00,-8.220e+00,-1.179e+01,-8.391e+00,-4.505e+00],\n",
+        "                  [-1.051e+00,-2.482e-02,8.896e-01,-4.943e-01,-9.371e-01,4.306e-01,\n",
+        "                  9.577e-03,-7.944e-02 ,1.624e-01,-2.682e-01,-3.129e-01,8.303e-01,\n",
+        "                  -2.365e-02,5.098e-01,-2.777e-01,3.367e-01,1.927e-01,-2.222e-01,\n",
+        "                  6.352e-02,6.888e-03,3.224e-02,1.091e-02,-5.706e-01,-5.258e-02,\n",
+        "                  -3.666e-02,1.709e-01,-4.805e-02,2.008e-01,-1.904e-01,5.952e-01]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WQUERmb2erAe"
+      },
+      "outputs": [],
+      "source": [
+        "# Let's define our model\n",
+        "def model(phi,x):\n",
+        "  sin_component = np.sin(phi[0] + 0.06 * phi[1] * x)\n",
+        "  gauss_component = np.exp(-(phi[0] + 0.06 * phi[1] * x) * (phi[0] + 0.06 * phi[1] * x) / 32)\n",
+        "  y_pred= sin_component * gauss_component\n",
+        "  return y_pred"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qFRe9POHF2le"
+      },
+      "outputs": [],
+      "source": [
+        "# Draw model\n",
+        "def draw_model(data,model,phi,title=None):\n",
+        "  x_model = np.arange(-15,15,0.1)\n",
+        "  y_model = model(phi,x_model)\n",
+        "\n",
+        "  fix, ax = plt.subplots()\n",
+        "  ax.plot(data[0,:],data[1,:],'bo')\n",
+        "  ax.plot(x_model,y_model,'m-')\n",
+        "  ax.set_xlim([-15,15]);ax.set_ylim([-1,1])\n",
+        "  ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "  if title is not None:\n",
+        "    ax.set_title(title)\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TXx1Tpd1Tl-I"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize the parameters and draw the model\n",
+        "phi = np.zeros((2,1))\n",
+        "phi[0] =  -5     # Horizontal offset\n",
+        "phi[1] =  25     # Frequency\n",
+        "draw_model(data,model,phi, \"Initial parameters\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QU5mdGvpTtEG"
+      },
+      "source": [
+        "Now let's compute the sum of squares loss for the training data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I7dqTY2Gg7CR"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_loss(data_x, data_y, model, phi):\n",
+        "  # TODO -- Write this function -- replace the line below\n",
+        "  # TODO -- First make model predictions from data x\n",
+        "  # TODO -- Then compute the squared difference between the predictions and true y values\n",
+        "  # TODO -- Then sum them all and return\n",
+        "  loss = 0\n",
+        "\n",
+        "  return loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eB5DQvU5hYNx"
+      },
+      "source": [
+        "Let's just test that we got that right"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ty05UtEEg9tc"
+      },
+      "outputs": [],
+      "source": [
+        "loss = compute_loss(data[0,:],data[1,:],model,np.array([[0.6],[-0.2]]))\n",
+        "print('Your loss = %3.3f, Correct loss = %3.3f'%(loss, 16.419))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F3trnavPiHpH"
+      },
+      "source": [
+        "Now let's plot the whole loss function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K-NTHpAAHlCl"
+      },
+      "outputs": [],
+      "source": [
+        "def draw_loss_function(compute_loss, data,  model, phi_iters = None):\n",
+        "  # Define pretty colormap\n",
+        "  my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "  my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "  r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "  g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "  b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "  my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "  # Make grid of offset/frequency values to plot\n",
+        "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
+        "  loss_mesh = np.zeros_like(freqs_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(freqs_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss(data[0,:], data[1,:], model, np.array([[offsets_mesh[idslope]], [slope]]))\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(offsets_mesh,freqs_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(offsets_mesh,freqs_mesh,loss_mesh,20,colors=['#80808080'])\n",
+        "  if phi_iters is not None:\n",
+        "    ax.plot(phi_iters[0,:], phi_iters[1,:],'go-')\n",
+        "  ax.set_ylim([2.5,22.5])\n",
+        "  ax.set_xlabel('Offset $\\phi_{0}$'); ax.set_ylabel('Frequency, $\\phi_{1}$')\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l8HbvIupnTME"
+      },
+      "outputs": [],
+      "source": [
+        "draw_loss_function(compute_loss, data, model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s9Duf05WqqSC"
+      },
+      "source": [
+        "Now let's compute the gradient vector for a given set of parameters:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial L}{\\partial \\boldsymbol\\phi} = \\begin{bmatrix}\\frac{\\partial L}{\\partial \\phi_0} \\\\\\frac{\\partial L}{\\partial \\phi_1} \\end{bmatrix}.\n",
+        "\\end{equation}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UpswmkL2qwBT"
+      },
+      "outputs": [],
+      "source": [
+        "# These came from writing out the expression for the sum of squares loss and taking the\n",
+        "# derivative with respect to phi0 and phi1. It was a lot of hassle to get it right!\n",
+        "def gabor_deriv_phi0(data_x,data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = cos_component * gauss_component - sin_component * gauss_component * x / 16\n",
+        "    deriv = 2* deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def gabor_deriv_phi1(data_x, data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = 0.06 * data_x * cos_component * gauss_component - 0.06 * data_x*sin_component * gauss_component * x / 16\n",
+        "    deriv = 2*deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def compute_gradient(data_x, data_y, phi):\n",
+        "    dl_dphi0 = gabor_deriv_phi0(data_x, data_y, phi[0],phi[1])\n",
+        "    dl_dphi1 = gabor_deriv_phi1(data_x, data_y, phi[0],phi[1])\n",
+        "    # Return the gradient\n",
+        "    return np.array([[dl_dphi0],[dl_dphi1]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RS1nEcYVuEAM"
+      },
+      "source": [
+        "We can check we got this right using a trick known as **finite differences**.  If we evaluate the function and then change one of the parameters by a very small amount and normalize by that amount, we get an approximation to the gradient, so:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial L}{\\partial \\phi_{0}}&\\approx & \\frac{L[\\phi_0+\\delta, \\phi_1]-L[\\phi_0, \\phi_1]}{\\delta}\\\\\n",
+        "\\frac{\\partial L}{\\partial \\phi_{1}}&\\approx & \\frac{L[\\phi_0, \\phi_1+\\delta]-L[\\phi_0, \\phi_1]}{\\delta}\n",
+        "\\end{align}\n",
+        "\n",
+        "We can't do this when there are many parameters;  for a million parameters, we would have to evaluate the loss function two million times, and usually computing the gradients directly is much more efficient."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QuwAHN7yt-gi"
+      },
+      "outputs": [],
+      "source": [
+        "# Compute the gradient using your function\n",
+        "gradient = compute_gradient(data[0,:],data[1,:], phi)\n",
+        "print(\"Your gradients: (%3.3f,%3.3f)\"%(gradient[0],gradient[1]))\n",
+        "# Approximate the gradients with finite differences\n",
+        "delta = 0.0001\n",
+        "dl_dphi0_est = (compute_loss(data[0,:],data[1,:],model,phi+np.array([[delta],[0]])) - \\\n",
+        "                    compute_loss(data[0,:],data[1,:],model,phi))/delta\n",
+        "dl_dphi1_est = (compute_loss(data[0,:],data[1,:],model,phi+np.array([[0],[delta]])) - \\\n",
+        "                    compute_loss(data[0,:],data[1,:],model,phi))/delta\n",
+        "print(\"Approx gradients: (%3.3f,%3.3f)\"%(dl_dphi0_est,dl_dphi1_est))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5EIjMM9Fw2eT"
+      },
+      "source": [
+        "Now we are ready to perform gradient descent.  We'll need to use our line search routine from Notebook 6.1, which I've reproduced here plus the helper function loss_function_1D that converts from a 2D problem to a 1D problem"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XrJ2gQjfw1XP"
+      },
+      "outputs": [],
+      "source": [
+        "def loss_function_1D(dist_prop, data, model, phi_start, gradient):\n",
+        "  # Return the loss after moving this far\n",
+        "  return compute_loss(data[0,:], data[1,:], model, phi_start+ gradient * dist_prop)\n",
+        "\n",
+        "def line_search(data, model, phi, gradient, thresh=.00001, max_dist = 0.1, max_iter = 15, verbose=False):\n",
+        "    # Initialize four points along the range we are going to search\n",
+        "    a = 0\n",
+        "    b = 0.33 * max_dist\n",
+        "    c = 0.66 * max_dist\n",
+        "    d = 1.0 * max_dist\n",
+        "    n_iter = 0\n",
+        "\n",
+        "    # While we haven't found the minimum closely enough\n",
+        "    while np.abs(b-c) > thresh and n_iter < max_iter:\n",
+        "        # Increment iteration counter (just to prevent an infinite loop)\n",
+        "        n_iter = n_iter+1\n",
+        "        # Calculate all four points\n",
+        "        lossa = loss_function_1D(a, data, model, phi,gradient)\n",
+        "        lossb = loss_function_1D(b, data, model, phi,gradient)\n",
+        "        lossc = loss_function_1D(c, data, model, phi,gradient)\n",
+        "        lossd = loss_function_1D(d, data, model, phi,gradient)\n",
+        "\n",
+        "        if verbose:\n",
+        "          print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
+        "          print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
+        "\n",
+        "        # Rule #1 If point A is less than points B, C, and D then change B,C,D so they are half their current distance from A\n",
+        "        if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
+        "          b = a+ (b-a)/2\n",
+        "          c = a+ (c-a)/2\n",
+        "          d = a+ (d-a)/2\n",
+        "          continue;\n",
+        "\n",
+        "        # Rule #2 If point b is less than point c then\n",
+        "        #                     point d becomes point c, and\n",
+        "        #                     point b becomes 1/3 between a and new d\n",
+        "        #                     point c becomes 2/3 between a and new d\n",
+        "        if lossb < lossc:\n",
+        "          d = c\n",
+        "          b = a+ (d-a)/3\n",
+        "          c = a+ 2*(d-a)/3\n",
+        "          continue\n",
+        "\n",
+        "        # Rule #2 If point c is less than point b then\n",
+        "        #                     point a becomes point b, and\n",
+        "        #                     point b becomes 1/3 between new a and d\n",
+        "        #                     point c becomes 2/3 between new a and d\n",
+        "        a = b\n",
+        "        b = a+ (d-a)/3\n",
+        "        c = a+ 2*(d-a)/3\n",
+        "\n",
+        "    # Return average of two middle points\n",
+        "    return (b+c)/2.0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YVq6rmaWRD2M"
+      },
+      "outputs": [],
+      "source": [
+        "def gradient_descent_step(phi, data,  model):\n",
+        "  # Step 1:  Compute the gradient\n",
+        "  gradient = compute_gradient(data[0,:],data[1,:], phi)\n",
+        "  # Step 2:  Update the parameters -- note we want to search in the negative (downhill direction)\n",
+        "  alpha = line_search(data, model, phi, gradient*-1, max_dist = 2.0)\n",
+        "  phi = phi - alpha * gradient\n",
+        "  return phi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tOLd0gtdRLLS"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize the parameters\n",
+        "n_steps = 21\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = -1.5\n",
+        "phi_all[1,0] = 8.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step(phi_all[:,c_step:c_step+1],data, model)\n",
+        "  # Measure loss and draw model every 4th step\n",
+        "  if c_step % 4 == 0:\n",
+        "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Oi8ZlH0ptLqA"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO Experiment with starting the optimization in the previous cell in different places\n",
+        "# and show that it heads to a local minimum if we don't start it in the right valley"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4l-ueLk-oAxV"
+      },
+      "outputs": [],
+      "source": [
+        "def gradient_descent_step_fixed_learning_rate(phi, data, alpha):\n",
+        "  # TODO -- fill in this routine so that we take a fixed size step of size alpha without using line search\n",
+        "\n",
+        "  return phi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oi9MX_GRpM41"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize the parameters\n",
+        "n_steps = 21\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = -1.5\n",
+        "phi_all[1,0] = 8.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step_fixed_learning_rate(phi_all[:,c_step:c_step+1],data, alpha =0.2)\n",
+        "  # Measure loss and draw model every 4th step\n",
+        "  if c_step % 4 == 0:\n",
+        "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "In6sQ5YCpMqn"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO Experiment with the learning rate, alpha.\n",
+        "# What happens if you set it too large?\n",
+        "# What happens if you set it too small?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VKTC9-1Gpm3N"
+      },
+      "outputs": [],
+      "source": [
+        "def stochastic_gradient_descent_step(phi, data, alpha, batch_size):\n",
+        "  # TODO -- fill in this routine so that we take a fixed size step of size alpha but only using a subset (batch) of the data\n",
+        "  # at each step\n",
+        "  # You can use the function np.random.permutation to generate a random permutation of the n_data = data.shape[1] indices\n",
+        "  # and then just choose the first n=batch_size of these indices.  Then compute the gradient update\n",
+        "  # from just the data with these indices.   More properly, you should sample without replacement, but this will do for now.\n",
+        "\n",
+        "\n",
+        "  return phi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "469OP_UHskJ4"
+      },
+      "outputs": [],
+      "source": [
+        "# Set the random number generator so you always get same numbers (disable if you don't want this)\n",
+        "np.random.seed(1)\n",
+        "# Initialize the parameters\n",
+        "n_steps = 41\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = 3.5\n",
+        "phi_all[1,0] = 6.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = stochastic_gradient_descent_step(phi_all[:,c_step:c_step+1],data, alpha =0.8, batch_size=5)\n",
+        "  # Measure loss and draw model every 8th step\n",
+        "  if c_step % 8 == 0:\n",
+        "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LxE2kTa3s29p"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO -- Experiment with different learning rates, starting points, batch sizes, number of steps.  Get a feel for this."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lw4QPOaQTh5e"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO -- Add a learning rate schedule.  Reduce the learning rate by a factor of beta every M iterations"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap06/6_4_Momentum.ipynb

@@ -0,0 +1,389 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 6.4: Momentum**\n",
+        "\n",
+        "This notebook investigates the use of momentum as illustrated in figure 6.7 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's create our training data of 30 pairs {x_i, y_i}\n",
+        "# We'll try to fit the Gabor model to these data\n",
+        "data = np.array([[-1.920e+00,-1.422e+01,1.490e+00,-1.940e+00,-2.389e+00,-5.090e+00,\n",
+        "                 -8.861e+00,3.578e+00,-6.010e+00,-6.995e+00,3.634e+00,8.743e-01,\n",
+        "                 -1.096e+01,4.073e-01,-9.467e+00,8.560e+00,1.062e+01,-1.729e-01,\n",
+        "                  1.040e+01,-1.261e+01,1.574e-01,-1.304e+01,-2.156e+00,-1.210e+01,\n",
+        "                 -1.119e+01,2.902e+00,-8.220e+00,-1.179e+01,-8.391e+00,-4.505e+00],\n",
+        "                  [-1.051e+00,-2.482e-02,8.896e-01,-4.943e-01,-9.371e-01,4.306e-01,\n",
+        "                  9.577e-03,-7.944e-02 ,1.624e-01,-2.682e-01,-3.129e-01,8.303e-01,\n",
+        "                  -2.365e-02,5.098e-01,-2.777e-01,3.367e-01,1.927e-01,-2.222e-01,\n",
+        "                  6.352e-02,6.888e-03,3.224e-02,1.091e-02,-5.706e-01,-5.258e-02,\n",
+        "                  -3.666e-02,1.709e-01,-4.805e-02,2.008e-01,-1.904e-01,5.952e-01]])"
+      ],
+      "metadata": {
+        "id": "4cRkrh9MZ58Z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's define our model\n",
+        "def model(phi,x):\n",
+        "  sin_component = np.sin(phi[0] + 0.06 * phi[1] * x)\n",
+        "  gauss_component = np.exp(-(phi[0] + 0.06 * phi[1] * x) * (phi[0] + 0.06 * phi[1] * x) / 32)\n",
+        "  y_pred= sin_component * gauss_component\n",
+        "  return y_pred"
+      ],
+      "metadata": {
+        "id": "WQUERmb2erAe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw model\n",
+        "def draw_model(data,model,phi,title=None):\n",
+        "  x_model = np.arange(-15,15,0.1)\n",
+        "  y_model = model(phi,x_model)\n",
+        "\n",
+        "  fix, ax = plt.subplots()\n",
+        "  ax.plot(data[0,:],data[1,:],'bo')\n",
+        "  ax.plot(x_model,y_model,'m-')\n",
+        "  ax.set_xlim([-15,15]);ax.set_ylim([-1,1])\n",
+        "  ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "  if title is not None:\n",
+        "    ax.set_title(title)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "qFRe9POHF2le"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initialize the parameters and draw the model\n",
+        "phi = np.zeros((2,1))\n",
+        "phi[0] =  -5     # Horizontal offset\n",
+        "phi[1] =  25     # Frequency\n",
+        "draw_model(data,model,phi, \"Initial parameters\")\n"
+      ],
+      "metadata": {
+        "id": "TXx1Tpd1Tl-I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the sum of squares loss for the training data and plot the loss function"
+      ],
+      "metadata": {
+        "id": "QU5mdGvpTtEG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_loss(data_x, data_y, model, phi):\n",
+        "  pred_y = model(phi, data_x)\n",
+        "  loss = np.sum((pred_y-data_y)*(pred_y-data_y))\n",
+        "  return loss\n",
+        "\n",
+        "def draw_loss_function(compute_loss, data,  model, phi_iters = None):\n",
+        "  # Define pretty colormap\n",
+        "  my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "  my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "  r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "  g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "  b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "  my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "  # Make grid of offset/frequency values to plot\n",
+        "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
+        "  loss_mesh = np.zeros_like(freqs_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(freqs_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss(data[0,:], data[1,:], model, np.array([[offsets_mesh[idslope]], [slope]]))\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(offsets_mesh,freqs_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(offsets_mesh,freqs_mesh,loss_mesh,20,colors=['#80808080'])\n",
+        "  if phi_iters is not None:\n",
+        "    ax.plot(phi_iters[0,:], phi_iters[1,:],'go-')\n",
+        "  ax.set_ylim([2.5,22.5])\n",
+        "  ax.set_xlabel('Offset $\\phi_{0}$'); ax.set_ylabel('Frequency, $\\phi_{1}$')\n",
+        "  plt.show()\n",
+        "\n",
+        "draw_loss_function(compute_loss, data, model)"
+      ],
+      "metadata": {
+        "id": "I7dqTY2Gg7CR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As before, we compute the gradient vector for a given set of parameters:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial L}{\\partial \\boldsymbol\\phi} = \\begin{bmatrix}\\frac{\\partial L}{\\partial \\phi_0} \\\\\\frac{\\partial L}{\\partial \\phi_1} \\end{bmatrix}.\n",
+        "\\end{equation}"
+      ],
+      "metadata": {
+        "id": "s9Duf05WqqSC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# These came from writing out the expression for the sum of squares loss and taking the\n",
+        "# derivative with respect to phi0 and phi1. It was a lot of hassle to get it right!\n",
+        "def gabor_deriv_phi0(data_x,data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = cos_component * gauss_component - sin_component * gauss_component * x / 16\n",
+        "    deriv = 2* deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def gabor_deriv_phi1(data_x, data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = 0.06 * data_x * cos_component * gauss_component - 0.06 * data_x*sin_component * gauss_component * x / 16\n",
+        "    deriv = 2*deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def compute_gradient(data_x, data_y, phi):\n",
+        "    dl_dphi0 = gabor_deriv_phi0(data_x, data_y, phi[0],phi[1])\n",
+        "    dl_dphi1 = gabor_deriv_phi1(data_x, data_y, phi[0],phi[1])\n",
+        "    # Return the gradient\n",
+        "    return np.array([[dl_dphi0],[dl_dphi1]])"
+      ],
+      "metadata": {
+        "id": "UpswmkL2qwBT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's first run standard stochastic gradient descent."
+      ],
+      "metadata": {
+        "id": "7Tv3d4zqAdZR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the random number generator so you always get same numbers (disable if you don't want this)\n",
+        "np.random.seed(1)\n",
+        "# Initialize the parameters\n",
+        "n_steps = 81\n",
+        "batch_size = 5\n",
+        "alpha = 0.6\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = -1.5\n",
+        "phi_all[1,0] = 6.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Choose random batch indices\n",
+        "  batch_index = np.random.permutation(data.shape[1])[0:batch_size]\n",
+        "  # Compute the gradient\n",
+        "  gradient = compute_gradient(data[0,batch_index], data[1,batch_index], phi_all[:,c_step:c_step+1] )\n",
+        "  # Update the parameters\n",
+        "  phi_all[:,c_step+1:c_step+2] = phi_all[:,c_step:c_step+1] - alpha * gradient\n",
+        "\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)"
+      ],
+      "metadata": {
+        "id": "469OP_UHskJ4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's add momentum (equation 6.11)"
+      ],
+      "metadata": {
+        "id": "nMILovgMFpdI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the random number generator so you always get same numbers (disable if you don't want this)\n",
+        "np.random.seed(1)\n",
+        "# Initialize the parameters\n",
+        "n_steps = 81\n",
+        "batch_size = 5\n",
+        "alpha = 0.6\n",
+        "beta = 0.6\n",
+        "momentum = np.zeros([2,1])\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = -1.5\n",
+        "phi_all[1,0] = 6.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Choose random batch indices\n",
+        "  batch_index = np.random.permutation(data.shape[1])[0:batch_size]\n",
+        "  # Compute the gradient\n",
+        "  gradient = compute_gradient(data[0,batch_index], data[1,batch_index],  phi_all[:,c_step:c_step+1])\n",
+        "  # TODO -- calculate momentum - replace the line below\n",
+        "  momentum = np.zeros([2,1])\n",
+        "\n",
+        "  # Update the parameters\n",
+        "  phi_all[:,c_step+1:c_step+2] = phi_all[:,c_step:c_step+1] - alpha * momentum\n",
+        "\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)"
+      ],
+      "metadata": {
+        "id": "dWBU8ZbSFny9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we'll try Nesterov momentum"
+      ],
+      "metadata": {
+        "id": "nYIAomA-KPkU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the random number generator so you always get same numbers (disable if you don't want this)\n",
+        "np.random.seed(1)\n",
+        "# Initialize the parameters\n",
+        "n_steps = 81\n",
+        "batch_size = 5\n",
+        "alpha = 0.6\n",
+        "beta = 0.6\n",
+        "momentum = np.zeros([2,1])\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = -1.5\n",
+        "phi_all[1,0] = 6.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Choose random batch indices\n",
+        "  batch_index = np.random.permutation(data.shape[1])[0:batch_size]\n",
+        "  # TODO -- calculate Nesterov momentum - replace the lines below\n",
+        "  gradient = np.zeros([2,1])\n",
+        "  momentum = np.zeros([2,1])\n",
+        "\n",
+        "  # Update the parameters\n",
+        "  phi_all[:,c_step+1:c_step+2] = phi_all[:,c_step:c_step+1] - alpha * momentum\n",
+        "\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "draw_loss_function(compute_loss, data, model,phi_all)"
+      ],
+      "metadata": {
+        "id": "XtwWeCZ5HLLh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Note that for this case, Nesterov momentum does not improve the result."
+      ],
+      "metadata": {
+        "id": "F-As4hS8s2nm"
+      }
+    }
+  ]
+}

Notebooks/Chap06/6_5_Adam.ipynb

@@ -0,0 +1,287 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 6.5: Adam**\n",
+        "\n",
+        "This notebook investigates the Adam algorithm as illustrated in figure 6.9 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "ysg9OHZq07YC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hi_t5nCk01tx"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define function that we wish to find the minimum of (normally would be defined implicitly by data and loss)\n",
+        "def loss(phi0, phi1):\n",
+        "    height = np.exp(-0.5 * (phi1 * phi1)*4.0)\n",
+        "    height = height * np. exp(-0.5* (phi0-0.7) *(phi0-0.7)/4.0)\n",
+        "    return 1.0-height\n",
+        "\n",
+        "# Compute the gradients of this function (for simplicity, I just used finite differences)\n",
+        "def get_loss_gradient(phi0, phi1):\n",
+        "    delta_phi = 0.00001;\n",
+        "    gradient = np.zeros((2,1));\n",
+        "    gradient[0] = (loss(phi0+delta_phi/2.0, phi1) - loss(phi0-delta_phi/2.0, phi1))/delta_phi\n",
+        "    gradient[1] = (loss(phi0, phi1+delta_phi/2.0) - loss(phi0, phi1-delta_phi/2.0))/delta_phi\n",
+        "    return gradient[:,0];\n",
+        "\n",
+        "# Compute the loss function at a range of values of phi0 and phi1 for plotting\n",
+        "def get_loss_function_for_plot():\n",
+        "  grid_values = np.arange(-1.0,1.0,0.01);\n",
+        "  phi0mesh, phi1mesh = np.meshgrid(grid_values, grid_values)\n",
+        "  loss_function = np.zeros((grid_values.size, grid_values.size))\n",
+        "  for idphi0, phi0 in enumerate(grid_values):\n",
+        "      for idphi1, phi1 in enumerate(grid_values):\n",
+        "          loss_function[idphi0, idphi1] = loss(phi1,phi0)\n",
+        "  return loss_function, phi0mesh, phi1mesh"
+      ],
+      "metadata": {
+        "id": "GTrgOKhp16zw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define fancy colormap\n",
+        "my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "my_colormap_vals = np.vstack((r,g,b)).transpose()/255.0\n",
+        "my_colormap = ListedColormap(my_colormap_vals)\n",
+        "\n",
+        "# Plotting function\n",
+        "def draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, opt_path):\n",
+        "    fig = plt.figure();\n",
+        "    ax = plt.axes();\n",
+        "    fig.set_size_inches(7,7)\n",
+        "    ax.contourf(phi0mesh, phi1mesh, loss_function, 256, cmap=my_colormap);\n",
+        "    ax.contour(phi0mesh, phi1mesh, loss_function, 20, colors=['#80808080'])\n",
+        "    ax.plot(opt_path[0,:], opt_path[1,:],'-', color='#a0d9d3ff')\n",
+        "    ax.plot(opt_path[0,:], opt_path[1,:],'.', color='#a0d9d3ff',markersize=10)\n",
+        "    ax.set_xlabel(r\"$\\phi_{0}$\")\n",
+        "    ax.set_ylabel(r\"$\\phi_{1}$\")\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "YKijFyuH4ZJD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Simple fixed step size gradient descent\n",
+        "def grad_descent(start_posn, n_steps, alpha):\n",
+        "    grad_path = np.zeros((2, n_steps+1));\n",
+        "    grad_path[:,0] = start_posn[:,0];\n",
+        "    for c_step in range(n_steps):\n",
+        "        this_grad = get_loss_gradient(grad_path[0,c_step], grad_path[1,c_step]);\n",
+        "        grad_path[:,c_step+1] = grad_path[:,c_step] - alpha * this_grad\n",
+        "    return grad_path;"
+      ],
+      "metadata": {
+        "id": "Afxr7RqR8s7Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll start by running gradient descent with a fixed step size for this loss function."
+      ],
+      "metadata": {
+        "id": "MXZL8lu3-EUF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss_function, phi0mesh, phi1mesh = get_loss_function_for_plot() ;\n",
+        "\n",
+        "start_posn = np.zeros((2,1));\n",
+        "start_posn[0,0] = -0.7; start_posn[1,0] = -0.9\n",
+        "\n",
+        "# Run gradient descent\n",
+        "grad_path1 = grad_descent(start_posn, n_steps=200, alpha = 0.08)\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path1)\n",
+        "grad_path2 = grad_descent(start_posn, n_steps=40, alpha= 1.0)\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path2)"
+      ],
+      "metadata": {
+        "id": "fgkwVEal8stH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Because the function changes much faster in $\\phi_1$ than in $\\phi_0$, there is no great step size to choose.  If we set the step size so that it makes sensible progress in the $\\phi_1$ direction, then it takes many iterations to converge.  If we set the step size so that we make sensible progress in the $\\phi_0$ direction, then the path oscillates in the $\\phi_1$ direction.  \n",
+        "\n",
+        "This motivates Adam.  At the core of Adam is the idea that we should just determine which way is downhill along each axis (i.e. left/right for $\\phi_0$ or up/down for $\\phi_1$) and move a fixed distance in that direction."
+      ],
+      "metadata": {
+        "id": "AN2uNxaa-bRX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def normalized_gradients(start_posn, n_steps, alpha,  epsilon=1e-20):\n",
+        "    grad_path = np.zeros((2, n_steps+1));\n",
+        "    grad_path[:,0] = start_posn[:,0];\n",
+        "    for c_step in range(n_steps):\n",
+        "        # Measure the gradient as in equation 6.13 (first line)\n",
+        "        m = get_loss_gradient(grad_path[0,c_step], grad_path[1,c_step]);\n",
+        "        # TO DO -- compute the squared gradient as in equation 6.13 (second line)\n",
+        "        # Replace this line:\n",
+        "        v = np.ones_like(grad_path[:,0])\n",
+        "\n",
+        "        # TO DO -- apply the update rule (equation 6.14)\n",
+        "        # Replace this line:\n",
+        "        grad_path[:,c_step+1] = grad_path[:,c_step]\n",
+        "\n",
+        "    return grad_path;"
+      ],
+      "metadata": {
+        "id": "IqX2zP_29gLF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's try out normalized gradients\n",
+        "start_posn = np.zeros((2,1));\n",
+        "start_posn[0,0] = -0.7; start_posn[1,0] = -0.9\n",
+        "\n",
+        "# Run gradient descent\n",
+        "grad_path1 = normalized_gradients(start_posn, n_steps=40, alpha = 0.08)\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path1)"
+      ],
+      "metadata": {
+        "id": "wxe-dKW5Chv3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This moves towards the minimum at a sensible speed, but we never actually converge -- the solution just bounces back and forth between the last two points.  To make it converge, we add momentum to both the estimates of the gradient and the pointwise squared gradient.  We also modify the statistics by a factor that depends on the time to make sure the progress is not slow to start with."
+      ],
+      "metadata": {
+        "id": "_6KoKBJdGGI4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def adam(start_posn, n_steps, alpha,  beta=0.9, gamma=0.99, epsilon=1e-20):\n",
+        "    grad_path = np.zeros((2, n_steps+1));\n",
+        "    grad_path[:,0] = start_posn[:,0];\n",
+        "    m = np.zeros_like(grad_path[:,0])\n",
+        "    v = np.zeros_like(grad_path[:,0])\n",
+        "    for c_step in range(n_steps):\n",
+        "        # Measure the gradient\n",
+        "        grad = get_loss_gradient(grad_path[0,c_step], grad_path[1,c_step])\n",
+        "        # TODO -- Update the momentum based gradient estimate equation 6.15 (first line)\n",
+        "        # Replace this line:\n",
+        "        m = m;\n",
+        "\n",
+        "\n",
+        "        # TODO -- update the momentum based squared gradient estimate as in equation 6.15 (second line)\n",
+        "        # Replace this line:\n",
+        "        v = v\n",
+        "\n",
+        "        # TODO -- Modify the statistics according to equation 6.16\n",
+        "        # You will need the function np.power\n",
+        "        # Replace these lines\n",
+        "        m_tilde = m\n",
+        "        v_tilde = v\n",
+        "\n",
+        "\n",
+        "        # TO DO -- apply the update rule (equation 6.17)\n",
+        "        # Replace this line:\n",
+        "        grad_path[:,c_step+1] = grad_path[:,c_step]\n",
+        "\n",
+        "    return grad_path;"
+      ],
+      "metadata": {
+        "id": "BKUhZSGgDEm0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's try out our Adam algorithm\n",
+        "start_posn = np.zeros((2,1));\n",
+        "start_posn[0,0] = -0.7; start_posn[1,0] = -0.9\n",
+        "\n",
+        "# Run gradient descent\n",
+        "grad_path1 = adam(start_posn, n_steps=60, alpha = 0.05)\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path1)"
+      ],
+      "metadata": {
+        "id": "sg5X18P3IbYo"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb

@@ -0,0 +1,483 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pOZ6Djz0dhoy"
+      },
+      "source": [
+        "# **Notebook 7.1: Backpropagation in Toy Model**\n",
+        "\n",
+        "This notebook computes the derivatives of the toy function discussed in section 7.3 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1DmMo2w63CmT"
+      },
+      "source": [
+        "We're going to investigate how to take the derivatives of functions where one operation is composed with another, which is composed with a third and so on.  For example, consider the model:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "     \\text{f}[x,\\boldsymbol\\phi] = \\beta_3+\\omega_3\\cdot\\cos\\Bigl[\\beta_2+\\omega_2\\cdot\\exp\\bigl[\\beta_1+\\omega_1\\cdot\\sin[\\beta_0+\\omega_0x]\\bigr]\\Bigr],\n",
+        "\\end{equation}\n",
+        "\n",
+        "with parameters $\\boldsymbol\\phi=\\{\\beta_0,\\omega_0,\\beta_1,\\omega_1,\\beta_2,\\omega_2,\\beta_3,\\omega_3\\}$.<br>\n",
+        "\n",
+        "This is a composition of the functions $\\cos[\\bullet],\\exp[\\bullet],\\sin[\\bullet]$.   I chose these just because you probably already know the derivatives of these functions:\n",
+        "\n",
+        "\\begin{align}\n",
+        " \\frac{\\partial \\cos[z]}{\\partial z} = -\\sin[z] \\quad\\quad \\frac{\\partial \\exp[z]}{\\partial z} = \\exp[z] \\quad\\quad \\frac{\\partial \\sin[z]}{\\partial z} = \\cos[z].\n",
+        "\\end{align}\n",
+        "\n",
+        "Suppose that we have a least squares loss function:\n",
+        "\n",
+        "\\begin{equation*}\n",
+        "\\ell_i = (\\text{f}[x_i,\\boldsymbol\\phi]-y_i)^2,\n",
+        "\\end{equation*}\n",
+        "\n",
+        "Assume that we know the current values of $\\beta_{0},\\beta_{1},\\beta_{2},\\beta_{3},\\omega_{0},\\omega_{1},\\omega_{2},\\omega_{3}$, $x_i$ and $y_i$. We could obviously calculate $\\ell_i$.   But we also want to know how $\\ell_i$ changes when we make a small change to $\\beta_{0},\\beta_{1},\\beta_{2},\\beta_{3},\\omega_{0},\\omega_{1},\\omega_{2}$, or $\\omega_{3}$.  In other words, we want to compute the eight derivatives:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial \\ell_i}{\\partial \\beta_{0}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\beta_{1}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\beta_{2}}, \\quad \\frac{\\partial \\ell_i }{\\partial \\beta_{3}},  \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{0}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{1}}, \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{2}},  \\quad\\text{and} \\quad \\frac{\\partial \\ell_i}{\\partial \\omega_{3}}.\n",
+        "\\end{align}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RIPaoVN834Lj"
+      },
+      "outputs": [],
+      "source": [
+        "# import library\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "32-ufWhc3v2c"
+      },
+      "source": [
+        "Let's first define the original function for $y$ and the loss term:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AakK_qen3BpU"
+      },
+      "outputs": [],
+      "source": [
+        "def fn(x, beta0, beta1, beta2, beta3, omega0, omega1, omega2, omega3):\n",
+        "  return beta3+omega3 * np.cos(beta2 + omega2 * np.exp(beta1 + omega1 * np.sin(beta0 + omega0 * x)))\n",
+        "\n",
+        "def loss(x, y, beta0, beta1, beta2, beta3, omega0, omega1, omega2, omega3):\n",
+        "  diff = fn(x, beta0, beta1, beta2, beta3, omega0, omega1, omega2, omega3) - y\n",
+        "  return diff * diff"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7tf0ZMt5OXt"
+      },
+      "source": [
+        "Now we'll choose some values for the betas and the omegas and x and compute the output of the function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pwvOcCxr41X_",
+        "outputId": "9541922c-dfc4-4b2e-dfa3-3298812155ce"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "l_i=0.139\n"
+          ]
+        }
+      ],
+      "source": [
+        "beta0 = 1.0; beta1 = 2.0; beta2 = -3.0; beta3 = 0.4\n",
+        "omega0 = 0.1; omega1 = -0.4; omega2 = 2.0; omega3 = 3.0\n",
+        "x = 2.3; y = 2.0\n",
+        "l_i_func = loss(x,y,beta0,beta1,beta2,beta3,omega0,omega1,omega2,omega3)\n",
+        "print('l_i=%3.3f'%l_i_func)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5w69NeT64yV"
+      },
+      "source": [
+        "# Computing derivatives by hand\n",
+        "\n",
+        "We could compute expressions for the derivatives by hand and write code to compute them directly but some have very complex expressions, even for this relatively simple original equation. For example:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial \\ell_i}{\\partial \\omega_{0}} &=& -2 \\left( \\beta_3+\\omega_3\\cdot\\cos\\Bigl[\\beta_2+\\omega_2\\cdot\\exp\\bigl[\\beta_1+\\omega_1\\cdot\\sin[\\beta_0+\\omega_0\\cdot x_i]\\bigr]\\Bigr]-y_i\\right)\\nonumber \\\\\n",
+        "&&\\hspace{0.5cm}\\cdot \\omega_1\\omega_2\\omega_3\\cdot x_i\\cdot\\cos[\\beta_0+\\omega_0 \\cdot x_i]\\cdot\\exp\\Bigl[\\beta_1 + \\omega_1 \\cdot \\sin[\\beta_0+\\omega_0\\cdot x_i]\\Bigr]\\nonumber\\\\\n",
+        "&& \\hspace{1cm}\\cdot \\sin\\biggl[\\beta_2+\\omega_2\\cdot \\exp\\Bigl[\\beta_1 + \\omega_1 \\cdot \\sin[\\beta_0+\\omega_0\\cdot x_i]\\Bigr]\\biggr].\n",
+        "\\end{align}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7t22hALp5zkq"
+      },
+      "outputs": [],
+      "source": [
+        "dldbeta3_func = 2 * (beta3 +omega3 * np.cos(beta2 + omega2 * np.exp(beta1+omega1 * np.sin(beta0+omega0 * x)))-y)\n",
+        "dldomega0_func = -2 *(beta3 +omega3 * np.cos(beta2 + omega2 * np.exp(beta1+omega1 * np.sin(beta0+omega0 * x)))-y) * \\\n",
+        "              omega1 * omega2 * omega3 * x * np.cos(beta0 + omega0 * x) * np.exp(beta1 +omega1 * np.sin(beta0 + omega0 * x)) *\\\n",
+        "              np.sin(beta2 + omega2 * np.exp(beta1+ omega1* np.sin(beta0+omega0 * x)))"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iRh4hnu3-H3n"
+      },
+      "source": [
+        "Let's make sure this is correct using finite differences:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1O3XmXMx-HlZ",
+        "outputId": "389ed78e-9d8d-4e8b-9e6b-5f20c21407e8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dydomega0: Function value = 5.246, Finite difference value = 5.246\n"
+          ]
+        }
+      ],
+      "source": [
+        "dldomega0_fd = (loss(x,y,beta0,beta1,beta2,beta3,omega0+0.00001,omega1,omega2,omega3)-loss(x,y,beta0,beta1,beta2,beta3,omega0,omega1,omega2,omega3))/0.00001\n",
+        "\n",
+        "print('dydomega0: Function value = %3.3f, Finite difference value = %3.3f'%(dldomega0_func,dldomega0_fd))"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wS4IPjZAKWTN"
+      },
+      "source": [
+        "The code to calculate $\\partial l_i/ \\partial \\omega_0$ is a bit of a nightmare.  It's easy to make mistakes, and you can see that some parts of it are repeated (for example, the $\\sin[\\bullet]$ term), which suggests some kind of redundancy in the calculations.  The goal of this practical is to compute the derivatives in a much simpler way.  There will be three steps:"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8UWhvDeNDudz"
+      },
+      "source": [
+        "**Step 1:** Write the original equations as a series of intermediate calculations.\n",
+        "\n",
+        "\\begin{align}\n",
+        "f_{0} &=& \\beta_{0} + \\omega_{0} x_i\\nonumber\\\\\n",
+        "h_{1} &=& \\sin[f_{0}]\\nonumber\\\\\n",
+        "f_{1} &=& \\beta_{1} + \\omega_{1}h_{1}\\nonumber\\\\\n",
+        "h_{2} &=& \\exp[f_{1}]\\nonumber\\\\\n",
+        "f_{2} &=& \\beta_{2} + \\omega_{2} h_{2}\\nonumber\\\\\n",
+        "h_{3} &=& \\cos[f_{2}]\\nonumber\\\\\n",
+        "f_{3} &=& \\beta_{3} + \\omega_{3}h_{3}\\nonumber\\\\\n",
+        "l_i &=& (f_3-y_i)^2\n",
+        "\\end{align}\n",
+        "\n",
+        "and compute and store the values of all of these intermediate values.  We'll need them to compute the derivatives.<br>  This is called the **forward pass**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZWKAq6HC90qV"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO compute all the f_k and h_k terms\n",
+        "# Replace the code below\n",
+        "\n",
+        "f0 = 0\n",
+        "h1 = 0\n",
+        "f1 = 0\n",
+        "h2 = 0\n",
+        "f2 = 0\n",
+        "h3 = 0\n",
+        "f3 = 0\n",
+        "l_i = 0\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ibxXw7TUW4Sx",
+        "outputId": "4575e3eb-2b16-4e0b-c84e-9c22b443c3ce"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "f0: true value = 1.230, your value = 0.000\n",
+            "h1: true value = 0.942, your value = 0.000\n",
+            "f1: true value = 1.623, your value = 0.000\n",
+            "h2: true value = 5.068, your value = 0.000\n",
+            "f2: true value = 7.137, your value = 0.000\n",
+            "h3: true value = 0.657, your value = 0.000\n",
+            "f3: true value = 2.372, your value = 0.000\n",
+            "l_i original = 0.139, l_i from forward pass = 0.000\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Let's check we got that right:\n",
+        "print(\"f0: true value = %3.3f, your value = %3.3f\"%(1.230, f0))\n",
+        "print(\"h1: true value = %3.3f, your value = %3.3f\"%(0.942, h1))\n",
+        "print(\"f1: true value = %3.3f, your value = %3.3f\"%(1.623, f1))\n",
+        "print(\"h2: true value = %3.3f, your value = %3.3f\"%(5.068, h2))\n",
+        "print(\"f2: true value = %3.3f, your value = %3.3f\"%(7.137, f2))\n",
+        "print(\"h3: true value = %3.3f, your value = %3.3f\"%(0.657, h3))\n",
+        "print(\"f3: true value = %3.3f, your value = %3.3f\"%(2.372, f3))\n",
+        "print(\"l_i original = %3.3f, l_i from forward pass = %3.3f\"%(l_i_func, l_i))\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jay8NYWdFHuZ"
+      },
+      "source": [
+        "**Step 2:** Compute the derivatives of $\\ell_i$ with respect to the intermediate quantities that we just calculated, but in reverse order:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\quad \\frac{\\partial \\ell_i}{\\partial f_3}, \\quad \\frac{\\partial \\ell_i}{\\partial h_3}, \\quad \\frac{\\partial \\ell_i}{\\partial f_2}, \\quad\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_2}, \\quad \\frac{\\partial \\ell_i}{\\partial f_1}, \\quad \\frac{\\partial \\ell_i}{\\partial h_1},  \\quad\\text{and} \\quad \\frac{\\partial \\ell_i}{\\partial f_0}.\n",
+        "\\end{align}\n",
+        "\n",
+        "The first of these derivatives is straightforward:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{3}} = 2 (f_3-y).\n",
+        "\\end{equation}\n",
+        "\n",
+        "The second derivative can be calculated using the chain rule:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{3}} =\\frac{\\partial f_{3}}{\\partial h_{3}} \\frac{\\partial \\ell_i}{\\partial f_{3}} .\n",
+        "\\end{equation}\n",
+        "\n",
+        "The left-hand side asks how $\\ell_i$ changes when $h_{3}$ changes.  The right-hand side says we can decompose this into (i) how $\\ell_i$ changes when $f_{3}$ changes and how $f_{3}$ changes when $h_{3}$ changes.  So you get a chain of events happening:  $h_{3}$ changes $f_{3}$, which changes $\\ell_i$, and the derivatives represent the effects of this chain.  Notice that we computed the first of these derivatives already and is  $2 (f_3-y)$. We calculated $f_{3}$ in step 1.  The second term is the derivative of $\\beta_{3} + \\omega_{3}h_{3}$ with respect to $h_3$ which is simply $\\omega_3$.  \n",
+        "\n",
+        "We can continue in this way, computing the derivatives of the output with respect to these intermediate quantities:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{2}} &=& \\frac{\\partial h_{3}}{\\partial f_{2}}\\left(\n",
+        "\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\n",
+        "\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{2}} &=& \\frac{\\partial f_{2}}{\\partial h_{2}}\\left(\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}}\\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{1}} &=& \\frac{\\partial h_{2}}{\\partial f_{1}}\\left( \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial h_{1}} &=& \\frac{\\partial f_{1}}{\\partial h_{1}}\\left(\\frac{\\partial h_{2}}{\\partial f_{1}} \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right)\\nonumber \\\\\n",
+        "\\frac{\\partial \\ell_i}{\\partial f_{0}} &=& \\frac{\\partial h_{1}}{\\partial f_{0}}\\left(\\frac{\\partial f_{1}}{\\partial h_{1}}\\frac{\\partial h_{2}}{\\partial f_{1}} \\frac{\\partial f_{2}}{\\partial h_{2}}\\frac{\\partial h_{3}}{\\partial f_{2}}\\frac{\\partial f_{3}}{\\partial h_{3}}\\frac{\\partial \\ell_i}{\\partial f_{3}} \\right).\n",
+        "\\end{align}\n",
+        "\n",
+        "In each case, we have already computed all of the terms except the last one in the previous step, and the last term is simple to evaluate.  This is called the **backward pass**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gCQJeI--Egdl"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO -- Compute the derivatives of the output with respect\n",
+        "# to the intermediate computations h_k and f_k (i.e, run the backward pass)\n",
+        "# I've done the first two for you.  You replace the code below:\n",
+        "dldf3 = 2* (f3 - y)\n",
+        "dldh3 = omega3 * dldf3\n",
+        "# Replace the code below\n",
+        "dldf2 = 1\n",
+        "dldh2 = 1\n",
+        "dldf1 = 1\n",
+        "dldh1 = 1\n",
+        "dldf0 = 1\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dS1OrLtlaFr7",
+        "outputId": "414f0862-ae36-4a0e-b68f-4758835b0e23"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dldf3: true value = 0.745, your value = -4.000\n",
+            "dldh3: true value = 2.234, your value = -12.000\n",
+            "dldf2: true value = -1.683, your value = 1.000\n",
+            "dldh2: true value = -3.366, your value = 1.000\n",
+            "dldf1: true value = -17.060, your value = 1.000\n",
+            "dldh1: true value = 6.824, your value = 1.000\n",
+            "dldf0: true value = 2.281, your value = 1.000\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Let's check we got that right\n",
+        "print(\"dldf3: true value = %3.3f, your value = %3.3f\"%(0.745, dldf3))\n",
+        "print(\"dldh3: true value = %3.3f, your value = %3.3f\"%(2.234, dldh3))\n",
+        "print(\"dldf2: true value = %3.3f, your value = %3.3f\"%(-1.683, dldf2))\n",
+        "print(\"dldh2: true value = %3.3f, your value = %3.3f\"%(-3.366, dldh2))\n",
+        "print(\"dldf1: true value = %3.3f, your value = %3.3f\"%(-17.060, dldf1))\n",
+        "print(\"dldh1: true value = %3.3f, your value = %3.3f\"%(6.824, dldh1))\n",
+        "print(\"dldf0: true value = %3.3f, your value = %3.3f\"%(2.281, dldf0))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1I2BhqZhGMK6"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO -- Calculate the final derivatives with respect to the beta and omega terms\n",
+        "\n",
+        "dldbeta3 = 1\n",
+        "dldomega3 = 1\n",
+        "dldbeta2 = 1\n",
+        "dldomega2 = 1\n",
+        "dldbeta1 = 1\n",
+        "dldomega1 = 1\n",
+        "dldbeta0 = 1\n",
+        "dldomega0 = 1\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "38eiOn2aHgHI",
+        "outputId": "1a67a636-e832-471e-e771-54824363158a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dldbeta3: Your value = 1.000, True value = 0.745\n",
+            "dldomega3: Your value = 1.000, True value = 0.489\n",
+            "dldbeta2: Your value = 1.000, True value = -1.683\n",
+            "dldomega2: Your value = 1.000, True value = -8.530\n",
+            "dldbeta1: Your value = 1.000, True value = -17.060\n",
+            "dldomega1: Your value = 1.000, True value = -16.079\n",
+            "dldbeta0: Your value = 1.000, True value = 2.281\n",
+            "dldomega0: Your value = 1.000, Function value = 5.246, Finite difference value = 5.246\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Let's check we got them right\n",
+        "print('dldbeta3: Your value = %3.3f, True value = %3.3f'%(dldbeta3, 0.745))\n",
+        "print('dldomega3: Your value = %3.3f, True value = %3.3f'%(dldomega3, 0.489))\n",
+        "print('dldbeta2: Your value = %3.3f, True value = %3.3f'%(dldbeta2, -1.683))\n",
+        "print('dldomega2: Your value = %3.3f, True value = %3.3f'%(dldomega2, -8.530))\n",
+        "print('dldbeta1: Your value = %3.3f, True value = %3.3f'%(dldbeta1, -17.060))\n",
+        "print('dldomega1: Your value = %3.3f, True value = %3.3f'%(dldomega1, -16.079))\n",
+        "print('dldbeta0: Your value = %3.3f, True value = %3.3f'%(dldbeta0, 2.281))\n",
+        "print('dldomega0: Your value = %3.3f, Function value = %3.3f, Finite difference value = %3.3f'%(dldomega0, dldomega0_func, dldomega0_fd))"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N2ZhrR-2fNa1"
+      },
+      "source": [
+        "Using this method, we can compute the derivatives quite easily without needing to compute very complicated expressions.  In the next practical, we'll apply this same method to a deep neural network."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyN7JeDgslwtZcwRCOuGuPFt",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap07/7_2_Backpropagation.ipynb

@@ -0,0 +1,356 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyM2kkHLr00J4Jeypw41sTkQ",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 7.2: Backpropagation**\n",
+        "\n",
+        "This notebook runs the backpropagation algorithm on a deep neural network as described in section 7.4 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LdIDglk1FFcG"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First let's define a neural network.  We'll just choose the weights and biases randomly for now"
+      ],
+      "metadata": {
+        "id": "nnUoI0m6GyjC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so we always get the same random numbers\n",
+        "np.random.seed(0)\n",
+        "\n",
+        "# Number of layers\n",
+        "K = 5\n",
+        "# Number of neurons per layer\n",
+        "D = 6\n",
+        "# Input layer\n",
+        "D_i = 1\n",
+        "# Output layer\n",
+        "D_o = 1\n",
+        "\n",
+        "# Make empty lists\n",
+        "all_weights = [None] * (K+1)\n",
+        "all_biases = [None] * (K+1)\n",
+        "\n",
+        "# Create input and output layers\n",
+        "all_weights[0] = np.random.normal(size=(D, D_i))\n",
+        "all_weights[-1] = np.random.normal(size=(D_o, D))\n",
+        "all_biases[0] = np.random.normal(size =(D,1))\n",
+        "all_biases[-1]= np.random.normal(size =(D_o,1))\n",
+        "\n",
+        "# Create intermediate layers\n",
+        "for layer in range(1,K):\n",
+        "  all_weights[layer] = np.random.normal(size=(D,D))\n",
+        "  all_biases[layer] = np.random.normal(size=(D,1))"
+      ],
+      "metadata": {
+        "id": "WVM4Tc_jGI0Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "jZh-7bPXIDq4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's run our random network.  The weight matrices $\\boldsymbol\\Omega_{1\\ldots K}$ are the entries of the list \"all_weights\" and the biases $\\boldsymbol\\beta_{1\\ldots K}$ are the entries of the list \"all_biases\"\n",
+        "\n",
+        "We know that we will need the preactivations $\\mathbf{f}_{0\\ldots K}$ and the activations $\\mathbf{h}_{1\\ldots K}$ for the forward pass of backpropagation, so we'll store and return these as well.\n"
+      ],
+      "metadata": {
+        "id": "5irtyxnLJSGX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_network_output(net_input, all_weights, all_biases):\n",
+        "\n",
+        "  # Retrieve number of layers\n",
+        "  K = len(all_weights) -1\n",
+        "\n",
+        "  # We'll store the pre-activations at each layer in a list \"all_f\"\n",
+        "  # and the activations in a second list \"all_h\".\n",
+        "  all_f = [None] * (K+1)\n",
+        "  all_h = [None] * (K+1)\n",
+        "\n",
+        "  #For convenience, we'll set\n",
+        "  # all_h[0] to be the input, and all_f[K] will be the output\n",
+        "  all_h[0] = net_input\n",
+        "\n",
+        "  # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
+        "  for layer in range(K):\n",
+        "      # Update preactivations and activations at this layer according to eqn 7.16\n",
+        "      # Remember to use np.matmul for matrix multiplications\n",
+        "      # TODO -- Replace the lines below\n",
+        "      all_f[layer] = all_h[layer]\n",
+        "      all_h[layer+1] = all_f[layer]\n",
+        "\n",
+        "  # Compute the output from the last hidden layer\n",
+        "  # TO DO -- Replace the line below\n",
+        "  all_f[K] = np.zeros_like(all_biases[-1])\n",
+        "\n",
+        "  # Retrieve the output\n",
+        "  net_output = all_f[K]\n",
+        "\n",
+        "  return net_output, all_f, all_h"
+      ],
+      "metadata": {
+        "id": "LgquJUJvJPaN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define input\n",
+        "net_input = np.ones((D_i,1)) * 1.2\n",
+        "# Compute network output\n",
+        "net_output, all_f, all_h = compute_network_output(net_input,all_weights, all_biases)\n",
+        "print(\"True output = %3.3f, Your answer = %3.3f\"%(1.907, net_output[0,0]))"
+      ],
+      "metadata": {
+        "id": "IN6w5m2ZOhnB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define a loss function.  We'll just use the least squares loss function. We'll also write a function to compute dloss_doutput"
+      ],
+      "metadata": {
+        "id": "SxVTKp3IcoBF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def least_squares_loss(net_output, y):\n",
+        "  return np.sum((net_output-y) * (net_output-y))\n",
+        "\n",
+        "def d_loss_d_output(net_output, y):\n",
+        "    return 2*(net_output -y);"
+      ],
+      "metadata": {
+        "id": "6XqWSYWJdhQR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = np.ones((D_o,1)) * 20.0\n",
+        "loss = least_squares_loss(net_output, y)\n",
+        "print(\"y = %3.3f Loss = %3.3f\"%(y, loss))"
+      ],
+      "metadata": {
+        "id": "njF2DUQmfttR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the derivatives of the network.  We already computed the forward pass.  Let's compute the backward pass."
+      ],
+      "metadata": {
+        "id": "98WmyqFYWA-0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We'll need the indicator function\n",
+        "def indicator_function(x):\n",
+        "  x_in = np.array(x)\n",
+        "  x_in[x_in>=0] = 1\n",
+        "  x_in[x_in<0] = 0\n",
+        "  return x_in\n",
+        "\n",
+        "# Main backward pass routine\n",
+        "def backward_pass(all_weights, all_biases, all_f, all_h, y):\n",
+        "  # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
+        "  all_dl_dweights = [None] * (K+1)\n",
+        "  all_dl_dbiases = [None] * (K+1)\n",
+        "  # And we'll store the derivatives of the loss with respect to the activation and preactivations in lists\n",
+        "  all_dl_df = [None] * (K+1)\n",
+        "  all_dl_dh = [None] * (K+1)\n",
+        "  # Again for convenience we'll stick with the convention that all_h[0] is the net input and all_f[k] in the net output\n",
+        "\n",
+        "  # Compute derivatives of the loss with respect to the network output\n",
+        "  all_dl_df[K] = np.array(d_loss_d_output(all_f[K],y))\n",
+        "\n",
+        "  # Now work backwards through the network\n",
+        "  for layer in range(K,-1,-1):\n",
+        "    # TODO Calculate the derivatives of the loss with respect to the biases at layer from all_dl_df[layer]. (eq 7.21)\n",
+        "    # NOTE!  To take a copy of matrix X, use Z=np.array(X)\n",
+        "    # REPLACE THIS LINE\n",
+        "    all_dl_dbiases[layer] = np.zeros_like(all_biases[layer])\n",
+        "\n",
+        "    # TODO Calculate the derivatives of the loss with respect to the weights at layer from all_dl_df[layer] and all_h[layer] (eq 7.22)\n",
+        "    # Don't forget to use np.matmul\n",
+        "    # REPLACE THIS LINE\n",
+        "    all_dl_dweights[layer] = np.zeros_like(all_weights[layer])\n",
+        "\n",
+        "    # TODO: calculate the derivatives of the loss with respect to the activations from weight and derivatives of next preactivations (second part of last line of eq 7.24)\n",
+        "    # REPLACE THIS LINE\n",
+        "    all_dl_dh[layer] = np.zeros_like(all_h[layer])\n",
+        "\n",
+        "\n",
+        "    if layer > 0:\n",
+        "      # TODO Calculate the derivatives of the loss with respect to the pre-activation f (use derivative of ReLu function, first part of last line of eq. 7.24)\n",
+        "      # REPLACE THIS LINE\n",
+        "      all_dl_df[layer-1] = np.zeros_like(all_f[layer-1])\n",
+        "\n",
+        "  return all_dl_dweights, all_dl_dbiases"
+      ],
+      "metadata": {
+        "id": "LJng7WpRPLMz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "all_dl_dweights, all_dl_dbiases = backward_pass(all_weights, all_biases, all_f, all_h, y)"
+      ],
+      "metadata": {
+        "id": "9A9MHc4sQvbp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.set_printoptions(precision=3)\n",
+        "# Make space for derivatives computed by finite differences\n",
+        "all_dl_dweights_fd = [None] * (K+1)\n",
+        "all_dl_dbiases_fd = [None] * (K+1)\n",
+        "\n",
+        "# Let's test if we have the derivatives right using finite differences\n",
+        "delta_fd = 0.000001\n",
+        "\n",
+        "# Test the dervatives of the bias vectors\n",
+        "for layer in range(K):\n",
+        "  dl_dbias  = np.zeros_like(all_dl_dbiases[layer])\n",
+        "  # For every element in the bias\n",
+        "  for row in range(all_biases[layer].shape[0]):\n",
+        "    # Take copy of biases  We'll change one element each time\n",
+        "    all_biases_copy = [np.array(x) for x in all_biases]\n",
+        "    all_biases_copy[layer][row] += delta_fd\n",
+        "    network_output_1, *_ = compute_network_output(net_input, all_weights, all_biases_copy)\n",
+        "    network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n",
+        "    dl_dbias[row] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n",
+        "  all_dl_dbiases_fd[layer] = np.array(dl_dbias)\n",
+        "  print(\"-----------------------------------------------\")\n",
+        "  print(\"Bias %d, derivatives from backprop:\"%(layer))\n",
+        "  print(all_dl_dbiases[layer])\n",
+        "  print(\"Bias %d, derivatives from finite differences\"%(layer))\n",
+        "  print(all_dl_dbiases_fd[layer])\n",
+        "  if np.allclose(all_dl_dbiases_fd[layer],all_dl_dbiases[layer],rtol=1e-05, atol=1e-08, equal_nan=False):\n",
+        "    print(\"Success!  Derivatives match.\")\n",
+        "  else:\n",
+        "    print(\"Failure!  Derivatives different.\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Test the derivatives of the weights matrices\n",
+        "for layer in range(K):\n",
+        "  dl_dweight  = np.zeros_like(all_dl_dweights[layer])\n",
+        "  # For every element in the bias\n",
+        "  for row in range(all_weights[layer].shape[0]):\n",
+        "    for col in range(all_weights[layer].shape[1]):\n",
+        "      # Take copy of biases  We'll change one element each time\n",
+        "      all_weights_copy = [np.array(x) for x in all_weights]\n",
+        "      all_weights_copy[layer][row][col] += delta_fd\n",
+        "      network_output_1, *_ = compute_network_output(net_input, all_weights_copy, all_biases)\n",
+        "      network_output_2, *_ = compute_network_output(net_input, all_weights, all_biases)\n",
+        "      dl_dweight[row][col] = (least_squares_loss(network_output_1, y) - least_squares_loss(network_output_2,y))/delta_fd\n",
+        "  all_dl_dweights_fd[layer] = np.array(dl_dweight)\n",
+        "  print(\"-----------------------------------------------\")\n",
+        "  print(\"Weight %d, derivatives from backprop:\"%(layer))\n",
+        "  print(all_dl_dweights[layer])\n",
+        "  print(\"Weight %d, derivatives from finite differences\"%(layer))\n",
+        "  print(all_dl_dweights_fd[layer])\n",
+        "  if np.allclose(all_dl_dweights_fd[layer],all_dl_dweights[layer],rtol=1e-05, atol=1e-08, equal_nan=False):\n",
+        "    print(\"Success!  Derivatives match.\")\n",
+        "  else:\n",
+        "    print(\"Failure!  Derivatives different.\")"
+      ],
+      "metadata": {
+        "id": "PK-UtE3hreAK"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap07/7_3_Initialization.ipynb

@@ -0,0 +1,356 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 7.3: Initialization**\n",
+        "\n",
+        "This notebook explores weight initialization in deep neural networks as described in section 7.5 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LdIDglk1FFcG"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First let's define a neural network.  We'll just choose the weights and biases randomly for now"
+      ],
+      "metadata": {
+        "id": "nnUoI0m6GyjC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def init_params(K, D, sigma_sq_omega):\n",
+        "  # Set seed so we always get the same random numbers\n",
+        "  np.random.seed(0)\n",
+        "\n",
+        "  # Input layer\n",
+        "  D_i = 1\n",
+        "  # Output layer\n",
+        "  D_o = 1\n",
+        "\n",
+        "  # Make empty lists\n",
+        "  all_weights = [None] * (K+1)\n",
+        "  all_biases = [None] * (K+1)\n",
+        "\n",
+        "  # Create input and output layers\n",
+        "  all_weights[0] = np.random.normal(size=(D, D_i))*np.sqrt(sigma_sq_omega)\n",
+        "  all_weights[-1] = np.random.normal(size=(D_o, D)) * np.sqrt(sigma_sq_omega)\n",
+        "  all_biases[0] = np.zeros((D,1))\n",
+        "  all_biases[-1]= np.zeros((D_o,1))\n",
+        "\n",
+        "  # Create intermediate layers\n",
+        "  for layer in range(1,K):\n",
+        "    all_weights[layer] = np.random.normal(size=(D,D))*np.sqrt(sigma_sq_omega)\n",
+        "    all_biases[layer] = np.zeros((D,1))\n",
+        "\n",
+        "  return all_weights, all_biases"
+      ],
+      "metadata": {
+        "id": "WVM4Tc_jGI0Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation"
+      ],
+      "metadata": {
+        "id": "jZh-7bPXIDq4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_network_output(net_input, all_weights, all_biases):\n",
+        "\n",
+        "  # Retrieve number of layers\n",
+        "  K = len(all_weights)-1\n",
+        "\n",
+        "  # We'll store the pre-activations at each layer in a list \"all_f\"\n",
+        "  # and the activations in a second list \"all_h\".\n",
+        "  all_f = [None] * (K+1)\n",
+        "  all_h = [None] * (K+1)\n",
+        "\n",
+        "  #For convenience, we'll set\n",
+        "  # all_h[0] to be the input, and all_f[K] will be the output\n",
+        "  all_h[0] = net_input\n",
+        "\n",
+        "  # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
+        "  for layer in range(K):\n",
+        "      # Update preactivations and activations at this layer according to eqn 7.5\n",
+        "      all_f[layer] = all_biases[layer] + np.matmul(all_weights[layer], all_h[layer])\n",
+        "      all_h[layer+1] = ReLU(all_f[layer])\n",
+        "\n",
+        "  # Compute the output from the last hidden layer\n",
+        "  all_f[K] = all_biases[K] + np.matmul(all_weights[K], all_h[K])\n",
+        "\n",
+        "  # Retrieve the output\n",
+        "  net_output = all_f[K]\n",
+        "\n",
+        "  return net_output, all_f, all_h"
+      ],
+      "metadata": {
+        "id": "LgquJUJvJPaN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's investigate how the size of the outputs vary as we change the initialization variance:\n"
+      ],
+      "metadata": {
+        "id": "bIUrcXnOqChl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Number of layers\n",
+        "K = 5\n",
+        "# Number of neurons per layer\n",
+        "D = 8\n",
+        "# Input layer\n",
+        "D_i = 1\n",
+        "# Output layer\n",
+        "D_o = 1\n",
+        "# Set variance of initial weights to 1\n",
+        "sigma_sq_omega = 1.0\n",
+        "# Initialize parameters\n",
+        "all_weights, all_biases = init_params(K,D,sigma_sq_omega)\n",
+        "\n",
+        "n_data = 1000\n",
+        "data_in = np.random.normal(size=(1,n_data))\n",
+        "net_output, all_f, all_h = compute_network_output(data_in, all_weights, all_biases)\n",
+        "\n",
+        "for layer in range(1,K+1):\n",
+        "  print(\"Layer %d, std of hidden units = %3.3f\"%(layer, np.std(all_h[layer])))"
+      ],
+      "metadata": {
+        "id": "A55z3rKBqO7M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You can see that the values of the hidden units are increasing on average (the variance is across all hidden units at the layer\n",
+        "# and the 1000 training examples\n",
+        "\n",
+        "# TO DO\n",
+        "# Change this to 50 layers with 80 hidden units per layer\n",
+        "\n",
+        "# TO DO\n",
+        "# Now experiment with sigma_sq_omega to try to stop the variance of the forward computation exploding"
+      ],
+      "metadata": {
+        "id": "VL_SO4tar3DC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define a loss function.  We'll just use the least squares loss function. We'll also write a function to compute dloss_doutput\n"
+      ],
+      "metadata": {
+        "id": "SxVTKp3IcoBF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def least_squares_loss(net_output, y):\n",
+        "  return np.sum((net_output-y) * (net_output-y))\n",
+        "\n",
+        "def d_loss_d_output(net_output, y):\n",
+        "    return 2*(net_output -y);"
+      ],
+      "metadata": {
+        "id": "6XqWSYWJdhQR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Here's the code for the backward pass"
+      ],
+      "metadata": {
+        "id": "98WmyqFYWA-0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We'll need the indicator function\n",
+        "def indicator_function(x):\n",
+        "  x_in = np.array(x)\n",
+        "  x_in[x_in>=0] = 1\n",
+        "  x_in[x_in<0] = 0\n",
+        "  return x_in\n",
+        "\n",
+        "# Main backward pass routine\n",
+        "def backward_pass(all_weights, all_biases, all_f, all_h, y):\n",
+        "  # Retrieve number of layers\n",
+        "  K = len(all_weights) - 1\n",
+        "\n",
+        "  # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
+        "  all_dl_dweights = [None] * (K+1)\n",
+        "  all_dl_dbiases = [None] * (K+1)\n",
+        "  # And we'll store the derivatives of the loss with respect to the activation and preactivations in lists\n",
+        "  all_dl_df = [None] * (K+1)\n",
+        "  all_dl_dh = [None] * (K+1)\n",
+        "  # Again for convenience we'll stick with the convention that all_h[0] is the net input and all_f[k] in the net output\n",
+        "\n",
+        "  # Compute derivatives of net output with respect to loss\n",
+        "  all_dl_df[K] = np.array(d_loss_d_output(all_f[K],y))\n",
+        "\n",
+        "  # Now work backwards through the network\n",
+        "  for layer in range(K,-1,-1):\n",
+        "    # Calculate the derivatives of biases at layer from all_dl_df[K]. (eq 7.13, line 1)\n",
+        "    all_dl_dbiases[layer] = np.array(all_dl_df[layer])\n",
+        "    # Calculate the derivatives of weight at layer from all_dl_df[K] and all_h[K] (eq 7.13, line 2)\n",
+        "    all_dl_dweights[layer] = np.matmul(all_dl_df[layer], all_h[layer].transpose())\n",
+        "\n",
+        "    # Calculate the derivatives of activations from weight and derivatives of next preactivations (eq 7.13, line 3 second part)\n",
+        "    all_dl_dh[layer] = np.matmul(all_weights[layer].transpose(), all_dl_df[layer])\n",
+        "    # Calculate the derivatives of the pre-activation f with respect to activation h (eq 7.13, line 3, first part)\n",
+        "    if layer > 0:\n",
+        "      all_dl_df[layer-1] = indicator_function(all_f[layer-1]) * all_dl_dh[layer]\n",
+        "\n",
+        "  return all_dl_dweights, all_dl_dbiases, all_dl_dh, all_dl_df"
+      ],
+      "metadata": {
+        "id": "LJng7WpRPLMz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's look at what happens to the magnitude of the gradients on the way back."
+      ],
+      "metadata": {
+        "id": "phFnbthqwhFi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Number of layers\n",
+        "K = 5\n",
+        "# Number of neurons per layer\n",
+        "D = 8\n",
+        "# Input layer\n",
+        "D_i = 1\n",
+        "# Output layer\n",
+        "D_o = 1\n",
+        "# Set variance of initial weights to 1\n",
+        "sigma_sq_omega = 1.0\n",
+        "# Initialize parameters\n",
+        "all_weights, all_biases = init_params(K,D,sigma_sq_omega)\n",
+        "\n",
+        "# For simplicity we'll just consider the gradients of the weights and biases between the first and last hidden layer\n",
+        "n_data = 100\n",
+        "aggregate_dl_df = [None] * (K+1)\n",
+        "for layer in range(1,K):\n",
+        "  # These 3D arrays will store the gradients for every data point\n",
+        "  aggregate_dl_df[layer] = np.zeros((D,n_data))\n",
+        "\n",
+        "\n",
+        "# We'll have to compute the derivatives of the parameters for each data point separately\n",
+        "for c_data in range(n_data):\n",
+        "  data_in = np.random.normal(size=(1,1))\n",
+        "  y = np.zeros((1,1))\n",
+        "  net_output, all_f, all_h = compute_network_output(data_in, all_weights, all_biases)\n",
+        "  all_dl_dweights, all_dl_dbiases, all_dl_dh, all_dl_df = backward_pass(all_weights, all_biases, all_f, all_h, y)\n",
+        "  for layer in range(1,K):\n",
+        "    aggregate_dl_df[layer][:,c_data] = np.squeeze(all_dl_df[layer])\n",
+        "\n",
+        "for layer in range(1,K):\n",
+        "  print(\"Layer %d, std of dl_dh = %3.3f\"%(layer, np.std(aggregate_dl_df[layer].ravel())))\n"
+      ],
+      "metadata": {
+        "id": "9A9MHc4sQvbp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You can see that the gradients of the hidden units are increasing on average (the standard deviation is across all hidden units at the layer\n",
+        "# and the 100 training examples\n",
+        "\n",
+        "# TO DO\n",
+        "# Change this to 50 layers with 80 hidden units per layer\n",
+        "\n",
+        "# TO DO\n",
+        "# Now experiment with sigma_sq_omega to try to stop the variance of the gradients exploding\n"
+      ],
+      "metadata": {
+        "id": "gtokc0VX0839"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb

@@ -0,0 +1,237 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      },
+      "source": [
+        "# **Notebook 8.1: MNIST_1D_Performance**\n",
+        "\n",
+        "This notebook runs a simple neural network on the MNIST1D dataset as in figure 8.2a. It uses code from https://github.com/greydanus/mnist1d to generate the data.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ifVjS4cTOqKz"
+      },
+      "outputs": [],
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "%pip install git+https://github.com/greydanus/mnist1d"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qyE7G1StPIqO"
+      },
+      "outputs": [],
+      "source": [
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F7LNq72SP6jO"
+      },
+      "source": [
+        "Let's generate a training and test dataset using the MNIST1D code.  The dataset gets saved as a .pkl file so it doesn't have to be regenerated each time."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YLxf7dJfPaqw"
+      },
+      "outputs": [],
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FxaB5vc0uevl"
+      },
+      "outputs": [],
+      "source": [
+        "D_i = 40    # Input dimensions\n",
+        "D_k = 100   # Hidden dimensions\n",
+        "D_o = 10    # Output dimensions\n",
+        "# TO DO:\n",
+        "# Define a model with two hidden layers of size 100\n",
+        "# And ReLU activations between them\n",
+        "# Replace this line (see Figure 7.8 of book for help):\n",
+        "model = torch.nn.Sequential(torch.nn.Linear(D_i, D_o));\n",
+        "\n",
+        "\n",
+        "def weights_init(layer_in):\n",
+        "  # TO DO:\n",
+        "  # Initialize the parameters with He initialization\n",
+        "  # Replace this line (see figure 7.8 of book for help)\n",
+        "  print(\"Initializing layer\")\n",
+        "\n",
+        "\n",
+        "# Call the function you just defined\n",
+        "model.apply(weights_init)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_rX6N3VyyQTY"
+      },
+      "outputs": [],
+      "source": [
+        "# choose cross entropy loss function (equation 5.24)\n",
+        "loss_function = torch.nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "x_train = torch.tensor(data['x'].astype('float32'))\n",
+        "y_train = torch.tensor(data['y'].transpose().astype('int64'))\n",
+        "x_test= torch.tensor(data['x_test'].astype('float32'))\n",
+        "y_test = torch.tensor(data['y_test'].astype('int64'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_test = np.zeros((n_epoch))\n",
+        "errors_test = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, batch in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = batch\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_test = model(x_test)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_test[epoch]= 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_test[epoch]= loss_function(pred_test, y_test).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  test loss {losses_test[epoch]:.6f}, test error {errors_test[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yI-l6kA_EH9G"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_test,'b-',label='test')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('TrainError %3.2f, Test Error %3.2f'%(errors_train[-1],errors_test[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(losses_train,'r-',label='train')\n",
+        "ax.plot(losses_test,'b-',label='test')\n",
+        "ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Loss')\n",
+        "ax.set_title('Train loss %3.2f, Test loss %3.2f'%(losses_train[-1],losses_test[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q-yT6re6GZS4"
+      },
+      "source": [
+        "**TO DO**\n",
+        "\n",
+        "Play with the model -- try changing the number of layers, hidden units, learning rate, batch size, momentum or anything else you like.  See if you can improve the test results.\n",
+        "\n",
+        "Is it a good idea to optimize the hyperparameters in this way?  Will the final result be a good estimate of the true test performance?"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "authorship_tag": "ABX9TyOuKMUcKfOIhIL2qTX9jJCy",
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb

@@ -0,0 +1,347 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 8.2: Bias-Variance Trade-Off**\n",
+        "\n",
+        "This notebook investigates the bias-variance trade-off for the toy model used throughout chapter 8 and reproduces the bias/variance trade off curves seen in figure 8.9.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "01Cu4SGZOVAi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The true function that we are trying to estimate, defined on [0,1]\n",
+        "def true_function(x):\n",
+        "    y = np.exp(np.sin(x*(2*3.1413)))\n",
+        "    return y"
+      ],
+      "metadata": {
+        "id": "bSK2_EGyOgHu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate some data points with or without noise\n",
+        "def generate_data(n_data, sigma_y=0.3):\n",
+        "    # Generate x values quasi uniformly\n",
+        "    x = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
+        "\n",
+        "    # y value from running through function and adding noise\n",
+        "    y = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        y[i] = true_function(x[i])\n",
+        "        y[i] += np.random.normal(0, sigma_y, 1)\n",
+        "    return x,y\n"
+      ],
+      "metadata": {
+        "id": "yzZr2tcJO5pq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
+        "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
+        "\n",
+        "    fig,ax = plt.subplots()\n",
+        "    ax.plot(x_func, y_func, 'k-')\n",
+        "    if sigma_func is not None:\n",
+        "      ax.fill_between(x_func, y_func-2*sigma_func, y_func+2*sigma_func, color='lightgray')\n",
+        "\n",
+        "    if x_data is not None:\n",
+        "        ax.plot(x_data, y_data, 'o', color='#d18362')\n",
+        "\n",
+        "    if x_model is not None:\n",
+        "        ax.plot(x_model, y_model, '-', color='#7fe7de')\n",
+        "\n",
+        "    if sigma_model is not None:\n",
+        "      ax.fill_between(x_model, y_model-2*sigma_model, y_model+2*sigma_model, color='lightgray')\n",
+        "\n",
+        "    ax.set_xlim(0,1)\n",
+        "    ax.set_xlabel('Input, $x$')\n",
+        "    ax.set_ylabel('Output, $y$')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "xfq1SD_ZOi6G"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate true function\n",
+        "x_func = np.linspace(0, 1.0, 100)\n",
+        "y_func = true_function(x_func);\n",
+        "\n",
+        "# Generate some data points\n",
+        "np.random.seed(1)\n",
+        "sigma_func = 0.3\n",
+        "n_data = 15\n",
+        "x_data,y_data = generate_data(n_data, sigma_func)\n",
+        "\n",
+        "# Plot the functinon, data and uncertainty\n",
+        "plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
+      ],
+      "metadata": {
+        "id": "2tP-p7B6Qnuf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define model -- beta is a scalar and omega has size n_hidden,1\n",
+        "def network(x, beta, omega):\n",
+        "    # Retrieve number of hidden units\n",
+        "    n_hidden = omega.shape[0]\n",
+        "\n",
+        "    y = np.zeros_like(x)\n",
+        "    for c_hidden in range(n_hidden):\n",
+        "        # Evaluate activations based on shifted lines (figure 8.4b-d)\n",
+        "        line_vals =  x  - c_hidden/n_hidden\n",
+        "        h =  line_vals * (line_vals > 0)\n",
+        "        # Weight activations by omega parameters and sum\n",
+        "        y = y + omega[c_hidden] * h\n",
+        "    # Add bias, beta\n",
+        "    y = y + beta\n",
+        "\n",
+        "    return y"
+      ],
+      "metadata": {
+        "id": "zYMLtS3nT-0y"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This fits the n_hidden+1 parameters (see fig 8.4a) in closed form.\n",
+        "# If you have studied linear algebra, then you will know it is a least\n",
+        "# squares solution of the form (A^TA)^-1A^Tb.  If you don't recognize that,\n",
+        "# then just take it on trust that this gives you the best possible solution.\n",
+        "def fit_model_closed_form(x,y,n_hidden):\n",
+        "  n_data = len(x)\n",
+        "  A = np.ones((n_data, n_hidden+1))\n",
+        "  for i in range(n_data):\n",
+        "      for j in range(1,n_hidden+1):\n",
+        "          A[i,j] = x[i]-(j-1)/n_hidden\n",
+        "          if A[i,j] < 0:\n",
+        "              A[i,j] = 0;\n",
+        "\n",
+        "  beta_omega = np.linalg.lstsq(A, y, rcond=None)[0]\n",
+        "\n",
+        "  beta = beta_omega[0]\n",
+        "  omega = beta_omega[1:]\n",
+        "\n",
+        "  return beta, omega\n"
+      ],
+      "metadata": {
+        "id": "MinJxLh1XTHx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Closed form solution\n",
+        "beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=3)\n",
+        "\n",
+        "# Get prediction for model across graph range\n",
+        "x_model = np.linspace(0,1,100);\n",
+        "y_model = network(x_model, beta, omega)\n",
+        "\n",
+        "# Draw the function and the model\n",
+        "plot_function(x_func, y_func, x_data,y_data, x_model, y_model)"
+      ],
+      "metadata": {
+        "id": "HP7fiwNFSfWz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run the model many times with different datasets and return the mean and variance\n",
+        "def get_model_mean_variance(n_data, n_datasets, n_hidden, sigma_func):\n",
+        "\n",
+        "  # Create array that stores model results in rows\n",
+        "  y_model_all = np.zeros((n_datasets, x_model.shape[0]))\n",
+        "\n",
+        "  for c_dataset in range(n_datasets):\n",
+        "    # TODO -- Generate n_data x,y, pairs with standard deviation sigma_func\n",
+        "    # Replace this line\n",
+        "    x_data,y_data = np.zeros([1,n_data]),np.zeros([1,n_data])\n",
+        "\n",
+        "    # TODO -- Fit the model\n",
+        "    # Replace this line:\n",
+        "    beta = 0; omega = np.zeros([n_hidden,1])\n",
+        "\n",
+        "    # TODO -- Run the fitted model on x_model\n",
+        "    # Replace this line\n",
+        "    y_model = np.zeros_like(x_model);\n",
+        "\n",
+        "    # Store the model results\n",
+        "    y_model_all[c_dataset,:] = y_model\n",
+        "\n",
+        "  # Get mean and standard deviation of model\n",
+        "  mean_model = np.mean(y_model_all,axis=0)\n",
+        "  std_model = np.std(y_model_all,axis=0)\n",
+        "\n",
+        "  # Return the mean and standard deviation of the fitted model\n",
+        "  return mean_model, std_model"
+      ],
+      "metadata": {
+        "id": "bL553uSaYidy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's generate N random data sets, fit the model N times and look the mean and variance\n",
+        "n_datasets = 100\n",
+        "n_data = 15\n",
+        "sigma_func = 0.3\n",
+        "n_hidden = 5\n",
+        "\n",
+        "# Get mean and variance of fitted model\n",
+        "np.random.seed(1)\n",
+        "mean_model, std_model = get_model_mean_variance(n_data, n_datasets, n_hidden, sigma_func) ;\n",
+        "\n",
+        "# Plot the results\n",
+        "plot_function(x_func, y_func, x_model=x_model, y_model=mean_model, sigma_model=std_model)"
+      ],
+      "metadata": {
+        "id": "Wxk64t2SoX9c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- Experiment with changing the number of data points and the number of hidden variables\n",
+        "# in the model.  Get a feeling for what happens in terms of the bias (squared deviation between cyan and black lines)\n",
+        "# and the variance (gray region) as we manipulate these quantities."
+      ],
+      "metadata": {
+        "id": "QO6mFaKNJ3J_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the noise, bias and variance as a function of capacity\n",
+        "hidden_variables = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
+        "bias = np.zeros((len(hidden_variables),1)) ;\n",
+        "variance = np.zeros((len(hidden_variables),1)) ;\n",
+        "\n",
+        "n_datasets = 100\n",
+        "n_data = 15\n",
+        "sigma_func = 0.3\n",
+        "n_hidden = 5\n",
+        "\n",
+        "# Set random seed so that we get the same result every time\n",
+        "np.random.seed(1)\n",
+        "\n",
+        "for c_hidden in range(len(hidden_variables)):\n",
+        "  # Get mean and variance of fitted model\n",
+        "  mean_model, std_model = get_model_mean_variance(n_data, n_datasets, hidden_variables[c_hidden], sigma_func) ;\n",
+        "  # TODO -- Estimate bias and variance\n",
+        "  # Replace these lines\n",
+        "\n",
+        "  # Compute variance -- average of the model variance (average squared deviation of fitted models around mean fitted model)\n",
+        "  variance[c_hidden] = 0\n",
+        "  # Compute bias (average squared deviation of mean fitted model around true function)\n",
+        "  bias[c_hidden] = 0\n",
+        "\n",
+        "# Plot the results\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(hidden_variables, variance, 'k-')\n",
+        "ax.plot(hidden_variables, bias, 'r-')\n",
+        "ax.plot(hidden_variables, variance+bias, 'g-')\n",
+        "ax.set_xlim(0,12)\n",
+        "ax.set_ylim(0,0.5)\n",
+        "ax.set_xlabel(\"Model capacity\")\n",
+        "ax.set_ylabel(\"Variance\")\n",
+        "ax.legend(['Variance', 'Bias', 'Bias + Variance'])\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "ICKjqAlx3Ka9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "WKUyOAywL_b2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap08/8_3_Double_Descent.ipynb

@@ -0,0 +1,320 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 8.3: Double Descent**\n",
+        "\n",
+        "This notebook investigates double descent as described in section 8.4 of the book.\n",
+        "\n",
+        "It uses the MNIST-1D database which can be found at https://github.com/greydanus/mnist1d\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
+      ],
+      "metadata": {
+        "id": "fn9BP5N5TguP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d\n",
+        "import random\n",
+        "random.seed(0)\n",
+        "\n",
+        "# Try attaching to GPU -- Use \"Change Runtime Type to change to GPUT\"\n",
+        "DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n",
+        "print('Using:', DEVICE)"
+      ],
+      "metadata": {
+        "id": "hFxuHpRqTgri"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "args.num_samples = 8000\n",
+        "args.train_split = 0.5\n",
+        "args.corr_noise_scale = 0.25\n",
+        "args.iid_noise_scale=2e-2\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=True)\n",
+        "\n",
+        "# Add 15% noise to training labels\n",
+        "for c_y in range(len(data['y'])):\n",
+        "    random_number = random.random()\n",
+        "    if random_number < 0.15 :\n",
+        "        random_int = int(random.random() * 10)\n",
+        "        data['y'][c_y] = random_int\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Dimensionality of each example: {}\".format(data['x'].shape[-1]))"
+      ],
+      "metadata": {
+        "id": "PW2gyXL5UkLU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initialize the parameters with He initialization\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)\n",
+        "\n",
+        "# Return an initialized model with two hidden layers and n_hidden hidden units at each\n",
+        "def get_model(n_hidden):\n",
+        "\n",
+        "  D_i = 40    # Input dimensions\n",
+        "  D_k = n_hidden   # Hidden dimensions\n",
+        "  D_o = 10    # Output dimensions\n",
+        "\n",
+        "  # Define a model with two hidden layers\n",
+        "  # And ReLU activations between them\n",
+        "  model = nn.Sequential(\n",
+        "  nn.Linear(D_i, D_k),\n",
+        "  nn.ReLU(),\n",
+        "  nn.Linear(D_k, D_k),\n",
+        "  nn.ReLU(),\n",
+        "  nn.Linear(D_k, D_o))\n",
+        "\n",
+        "  # Call the function you just defined\n",
+        "  model.apply(weights_init)\n",
+        "\n",
+        "  # Return the model\n",
+        "  return model ;"
+      ],
+      "metadata": {
+        "id": "hAIvZOAlTnk9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def fit_model(model, data, n_epoch):\n",
+        "\n",
+        "  # choose cross entropy loss function (equation 5.24)\n",
+        "  loss_function = torch.nn.CrossEntropyLoss()\n",
+        "  # construct SGD optimizer and initialize learning rate and momentum\n",
+        "  # optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+        "  optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum=0.9)\n",
+        "\n",
+        "\n",
+        "  x_train = torch.tensor(data['x'].astype('float32'))\n",
+        "  y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
+        "  x_test= torch.tensor(data['x_test'].astype('float32'))\n",
+        "  y_test = torch.tensor(data['y_test'].astype('long'))\n",
+        "\n",
+        "  # load the data into a class that creates the batches\n",
+        "  data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "  for epoch in range(n_epoch):\n",
+        "    # loop over batches\n",
+        "    for i, batch in enumerate(data_loader):\n",
+        "      # retrieve inputs and labels for this batch\n",
+        "      x_batch, y_batch = batch\n",
+        "      # zero the parameter gradients\n",
+        "      optimizer.zero_grad()\n",
+        "      # forward pass -- calculate model output\n",
+        "      pred = model(x_batch)\n",
+        "      # compute the loss\n",
+        "      loss = loss_function(pred, y_batch)\n",
+        "      # backward pass\n",
+        "      loss.backward()\n",
+        "      # SGD update\n",
+        "      optimizer.step()\n",
+        "\n",
+        "    # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "    pred_train = model(x_train)\n",
+        "    pred_test = model(x_test)\n",
+        "    _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "    _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "    errors_train = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "    errors_test= 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "    losses_train = loss_function(pred_train, y_train).item()\n",
+        "    losses_test= loss_function(pred_test, y_test).item()\n",
+        "    if epoch%100 ==0 :\n",
+        "      print(f'Epoch {epoch:5d}, train loss {losses_train:.6f}, train error {errors_train:3.2f},  test loss {losses_test:.6f}, test error {errors_test:3.2f}')\n",
+        "\n",
+        "  return errors_train, errors_test\n"
+      ],
+      "metadata": {
+        "id": "AazlQhheWmHk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def count_parameters(model):\n",
+        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
+      ],
+      "metadata": {
+        "id": "AQNCmFNV6JpV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The following code produces the double descent curve by training the model with different numbers of hidden units and plotting the test error.\n",
+        "\n",
+        "TO DO:\n",
+        "\n",
+        "*Before* you run the code, and considering that there are 4000 training examples predict:<br>\n",
+        "\n",
+        "1.    At what capacity do you think the training error will become zero?\n",
+        "2.   At what capacity do you expect the first minima of the double descent curve to appear?\n",
+        "3. At what capacity do you expect the maximum of the double descent curve to appear?"
+      ],
+      "metadata": {
+        "id": "IcP4UPMudxPS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This code will take a while (~30 mins on GPU) to run!  Go and make a cup of coffee!\n",
+        "\n",
+        "hidden_variables = np.array([2,4,6,8,10,14,18,22,26,30,35,40,45,50,55,60,70,80,90,100,120,140,160,180,200,250,300,400]) ;\n",
+        "\n",
+        "errors_train_all = np.zeros_like(hidden_variables)\n",
+        "errors_test_all = np.zeros_like(hidden_variables)\n",
+        "total_weights_all = np.zeros_like(hidden_variables)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 1000\n",
+        "\n",
+        "# For each hidden variable size\n",
+        "for c_hidden in range(len(hidden_variables)):\n",
+        "    print(f'Training model with {hidden_variables[c_hidden]:3d} hidden variables')\n",
+        "    # Get a model\n",
+        "    model = get_model(hidden_variables[c_hidden]) ;\n",
+        "    # Count and store number of weights\n",
+        "    total_weights_all[c_hidden] = count_parameters(model)\n",
+        "    # Train the model\n",
+        "    errors_train, errors_test = fit_model(model, data, n_epoch)\n",
+        "    # Store the results\n",
+        "    errors_train_all[c_hidden] = errors_train\n",
+        "    errors_test_all[c_hidden]= errors_test\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "K4OmBZGHWXpk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Assuming data['y'] is available and contains the training examples\n",
+        "num_training_examples = len(data['y'])\n",
+        "\n",
+        "# Find the index where total_weights_all is closest to num_training_examples\n",
+        "closest_index = np.argmin(np.abs(np.array(total_weights_all) - num_training_examples))\n",
+        "\n",
+        "# Get the corresponding value of hidden variables\n",
+        "hidden_variable_at_num_training_examples = hidden_variables[closest_index]\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(hidden_variables, errors_train_all, 'r-', label='train')\n",
+        "ax.plot(hidden_variables, errors_test_all, 'b-', label='test')\n",
+        "\n",
+        "# Add a vertical line at the point where total weights equal the number of training examples\n",
+        "ax.axvline(x=hidden_variable_at_num_training_examples, color='g', linestyle='--', label='N(weights) = N(train)')\n",
+        "\n",
+        "ax.set_ylim(0, 100)\n",
+        "ax.set_xlabel('No. hidden variables')\n",
+        "ax.set_ylabel('Error')\n",
+        "ax.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "Rw-iRboTXbck"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "KT4X8_hE5NFb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "iGKZSfVF2r4z"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb

@@ -0,0 +1,236 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPAKqlf9VxztHXKylyJwqe8",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 8.4: High-dimensional spaces**\n",
+        "\n",
+        "This notebook investigates the strange properties of high-dimensional spaces as discussed in the notes at the end of chapter 8.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "EjLK-kA1KnYX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4ESMmnkYEVAb"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import scipy.special as sci"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# How close are points in high dimensions?\n",
+        "\n",
+        "In this part of the notebook, we investigate how close random points are in 2D, 100D, and 1000D.   In each case, we generate 1000 points and calculate the Euclidean distance between each pair.  "
+      ],
+      "metadata": {
+        "id": "MonbPEitLNgN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fix the random seed so we all have the same random numbers\n",
+        "np.random.seed(0)\n",
+        "n_data = 1000\n",
+        "# Create 1000 data examples (columns) each with 2 dimensions (rows)\n",
+        "n_dim = 2\n",
+        "x_2D = np.random.normal(size=(n_dim,n_data))\n",
+        "# Create 1000 data examples (columns) each with 100 dimensions (rows)\n",
+        "n_dim = 100\n",
+        "x_100D = np.random.normal(size=(n_dim,n_data))\n",
+        "# Create 1000 data examples (columns) each with 1000 dimensions (rows)\n",
+        "n_dim = 1000\n",
+        "x_1000D = np.random.normal(size=(n_dim,n_data))"
+      ],
+      "metadata": {
+        "id": "vZSHVmcWEk14"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def distance_ratio(x):\n",
+        "  # TODO -- replace the two lines below to calculate the largest and smallest Euclidean distance between\n",
+        "  # the data points in the columns of x.  DO NOT include the distance between the data point\n",
+        "  # and itself (which is obviously zero)\n",
+        "  smallest_dist = 1.0\n",
+        "  largest_dist = 1.0\n",
+        "\n",
+        "  # Calculate the ratio and return\n",
+        "  dist_ratio = largest_dist / smallest_dist\n",
+        "  return dist_ratio"
+      ],
+      "metadata": {
+        "id": "PhVmnUs8ErD9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Ratio of largest to smallest distance 2D: %3.3f'%(distance_ratio(x_2D)))\n",
+        "print('Ratio of largest to smallest distance 100D: %3.3f'%(distance_ratio(x_100D)))\n",
+        "print('Ratio of largest to smallest distance 1000D: %3.3f'%(distance_ratio(x_1000D)))\n"
+      ],
+      "metadata": {
+        "id": "0NdPxfn5GQuJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "If you did this right, you will see that the distance between the nearest and farthest two points in high dimensions is almost the same.  "
+      ],
+      "metadata": {
+        "id": "uT68c0k8uBxs"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Volume of a hypersphere\n",
+        "\n",
+        "In the second part of this notebook we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+      ],
+      "metadata": {
+        "id": "b2FYKV1SL4Z7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def volume_of_hypersphere(diameter, dimensions):\n",
+        "  # Formula given in Problem 8.7 of the book\n",
+        "  # You will need sci.gamma()\n",
+        "  # Check out:    https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html\n",
+        "  # Also use this value for pi\n",
+        "  pi = np.pi\n",
+        "  # TODO replace this code with formula for the volume of a hypersphere\n",
+        "  volume = 1.0\n",
+        "\n",
+        "  return volume\n"
+      ],
+      "metadata": {
+        "id": "CZoNhD8XJaHR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "diameter = 1.0\n",
+        "for c_dim in range(1,11):\n",
+        "  print(\"Volume of unit diameter hypersphere in %d dimensions is %3.3f\"%(c_dim, volume_of_hypersphere(diameter, c_dim)))"
+      ],
+      "metadata": {
+        "id": "fNTBlg_GPEUh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You should see that the volume decreases to almost nothing in high dimensions.  All of the volume is in the corners of the unit hypercube (which always has volume 1)."
+      ],
+      "metadata": {
+        "id": "PtaeGSNBunJl"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Proportion of hypersphere in outer shell\n",
+        "\n",
+        "In the third part of the notebook you will calculate what proportion of the volume of a hypersphere is in the outer 1% of the radius/diameter.  Calculate the volume of a hypersphere and then the volume of a hypersphere with 0.99 of the radius and then figure out the ratio.  "
+      ],
+      "metadata": {
+        "id": "GdyMeOBmoXyF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_prop_of_volume_in_outer_1_percent(dimension):\n",
+        "  # TODO -- replace this line\n",
+        "  proportion = 1.0\n",
+        "\n",
+        "  return proportion"
+      ],
+      "metadata": {
+        "id": "8_CxZ2AIpQ8w"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# While we're here, let's look at how much of the volume is in the outer 1% of the radius\n",
+        "for c_dim in [1,2,10,20,50,100,150,200,250,300]:\n",
+        "  print('Proportion of volume in outer 1 percent of radius in %d dimensions =%3.3f'%(c_dim, get_prop_of_volume_in_outer_1_percent(c_dim)))"
+      ],
+      "metadata": {
+        "id": "LtMDIn2qPVfJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You should see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent. <br><br>\n",
+        "\n",
+        "The conclusion of all of this is that in high dimensions you should be sceptical of your intuitions about how things work.  I have tried to visualize many things in one or two dimensions in the book, but you should also be sceptical about these visualizations!"
+      ],
+      "metadata": {
+        "id": "n_FVeb-ZwzxD"
+      }
+    }
+  ]
+}

Notebooks/Chap09/9_1_L2_Regularization.ipynb

@@ -0,0 +1,538 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPJzymRTuvoWggIskM2Kamc",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 9.1: L2 Regularization**\n",
+        "\n",
+        "This notebook investigates adding L2 regularization to the loss function for the Gabor model as in figure 9.1.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's create our training data 30 pairs {x_i, y_i}\n",
+        "# We'll try to fit the Gabor model to these data\n",
+        "data = np.array([[-1.920e+00,-1.422e+01,1.490e+00,-1.940e+00,-2.389e+00,-5.090e+00,\n",
+        "                 -8.861e+00,3.578e+00,-6.010e+00,-6.995e+00,3.634e+00,8.743e-01,\n",
+        "                 -1.096e+01,4.073e-01,-9.467e+00,8.560e+00,1.062e+01,-1.729e-01,\n",
+        "                  1.040e+01,-1.261e+01,1.574e-01,-1.304e+01,-2.156e+00,-1.210e+01,\n",
+        "                 -1.119e+01,2.902e+00,-8.220e+00,-1.179e+01,-8.391e+00,-4.505e+00],\n",
+        "                  [-1.051e+00,-2.482e-02,8.896e-01,-4.943e-01,-9.371e-01,4.306e-01,\n",
+        "                  9.577e-03,-7.944e-02 ,1.624e-01,-2.682e-01,-3.129e-01,8.303e-01,\n",
+        "                  -2.365e-02,5.098e-01,-2.777e-01,3.367e-01,1.927e-01,-2.222e-01,\n",
+        "                  6.352e-02,6.888e-03,3.224e-02,1.091e-02,-5.706e-01,-5.258e-02,\n",
+        "                  -3.666e-02,1.709e-01,-4.805e-02,2.008e-01,-1.904e-01,5.952e-01]])"
+      ],
+      "metadata": {
+        "id": "4cRkrh9MZ58Z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Gabor model definition\n",
+        "def model(phi,x):\n",
+        "  sin_component = np.sin(phi[0] + 0.06 * phi[1] * x)\n",
+        "  gauss_component = np.exp(-(phi[0] + 0.06 * phi[1] * x) * (phi[0] + 0.06 * phi[1] * x) / 32)\n",
+        "  y_pred= sin_component * gauss_component\n",
+        "  return y_pred"
+      ],
+      "metadata": {
+        "id": "WQUERmb2erAe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw model\n",
+        "def draw_model(data,model,phi,title=None):\n",
+        "  x_model = np.arange(-15,15,0.1)\n",
+        "  y_model = model(phi,x_model)\n",
+        "\n",
+        "  fix, ax = plt.subplots()\n",
+        "  ax.plot(data[0,:],data[1,:],'bo')\n",
+        "  ax.plot(x_model,y_model,'m-')\n",
+        "  ax.set_xlim([-15,15]);ax.set_ylim([-1,1])\n",
+        "  ax.set_xlabel('x'); ax.set_ylabel('y')\n",
+        "  if title is not None:\n",
+        "    ax.set_title(title)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "qFRe9POHF2le"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initialize the parameters and draw the model\n",
+        "phi = np.zeros((2,1))\n",
+        "phi[0] =  -5     # Horizontal offset\n",
+        "phi[1] =  25     # Frequency\n",
+        "draw_model(data,model,phi, \"Initial parameters\")\n"
+      ],
+      "metadata": {
+        "id": "TXx1Tpd1Tl-I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's\n",
+        "compute the sum of squares loss for the training data"
+      ],
+      "metadata": {
+        "id": "QU5mdGvpTtEG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_loss(data_x, data_y, model, phi):\n",
+        "  pred_y = model(phi, data_x)\n",
+        "  loss = np.sum((pred_y-data_y)*(pred_y-data_y))\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "I7dqTY2Gg7CR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's plot the whole loss function"
+      ],
+      "metadata": {
+        "id": "F3trnavPiHpH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define pretty colormap\n",
+        "my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "def draw_loss_function(compute_loss, data,  model, my_colormap, phi_iters = None):\n",
+        "\n",
+        "  # Make grid of offset/frequency values to plot\n",
+        "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
+        "  loss_mesh = np.zeros_like(freqs_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(freqs_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss(data[0,:], data[1,:], model, np.array([[offsets_mesh[idslope]], [slope]]))\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(offsets_mesh,freqs_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(offsets_mesh,freqs_mesh,loss_mesh,20,colors=['#80808080'])\n",
+        "  if phi_iters is not None:\n",
+        "    ax.plot(phi_iters[0,:], phi_iters[1,:],'go-')\n",
+        "  ax.set_ylim([2.5,22.5])\n",
+        "  ax.set_xlabel('Offset $\\phi_{0}$'); ax.set_ylabel('Frequency, $\\phi_{1}$')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "K-NTHpAAHlCl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_loss_function(compute_loss, data, model, my_colormap)"
+      ],
+      "metadata": {
+        "id": "l8HbvIupnTME"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the gradient vector for a given set of parameters:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\frac{\\partial L}{\\partial \\boldsymbol\\phi} = \\begin{bmatrix}\\frac{\\partial L}{\\partial \\phi_0} \\\\\\frac{\\partial L}{\\partial \\phi_1} \\end{bmatrix}.\n",
+        "\\end{equation}"
+      ],
+      "metadata": {
+        "id": "s9Duf05WqqSC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# These came from writing out the expression for the sum of squares loss and taking the\n",
+        "# derivative with respect to phi0 and phi1. It was a lot of hassle to get it right!\n",
+        "def gabor_deriv_phi0(data_x,data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = cos_component * gauss_component - sin_component * gauss_component * x / 16\n",
+        "    deriv = 2* deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def gabor_deriv_phi1(data_x, data_y,phi0, phi1):\n",
+        "    x = 0.06 * phi1 * data_x + phi0\n",
+        "    y = data_y\n",
+        "    cos_component = np.cos(x)\n",
+        "    sin_component = np.sin(x)\n",
+        "    gauss_component = np.exp(-0.5 * x *x / 16)\n",
+        "    deriv = 0.06 * data_x * cos_component * gauss_component - 0.06 * data_x*sin_component * gauss_component * x / 16\n",
+        "    deriv = 2*deriv * (sin_component * gauss_component - y)\n",
+        "    return np.sum(deriv)\n",
+        "\n",
+        "def compute_gradient(data_x, data_y, phi):\n",
+        "    dl_dphi0 = gabor_deriv_phi0(data_x, data_y, phi[0],phi[1])\n",
+        "    dl_dphi1 = gabor_deriv_phi1(data_x, data_y, phi[0],phi[1])\n",
+        "    # Return the gradient\n",
+        "    return np.array([[dl_dphi0],[dl_dphi1]])"
+      ],
+      "metadata": {
+        "id": "UpswmkL2qwBT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we are ready to find the minimum.  For simplicity, we'll just use regular (non-stochastic) gradient descent with a fixed learning rate."
+      ],
+      "metadata": {
+        "id": "5EIjMM9Fw2eT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def gradient_descent_step(phi, data,  model):\n",
+        "  # Step 1:  Compute the gradient\n",
+        "  gradient = compute_gradient(data[0,:],data[1,:], phi)\n",
+        "  # Step 2:  Update the parameters -- note we want to search in the negative (downhill direction)\n",
+        "  alpha = 0.1\n",
+        "  phi = phi - alpha * gradient\n",
+        "  return phi"
+      ],
+      "metadata": {
+        "id": "YVq6rmaWRD2M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initialize the parameters\n",
+        "n_steps = 41\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = 2.6\n",
+        "phi_all[1,0] = 8.5\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,0:1])\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step(phi_all[:,c_step:c_step+1],data, model)\n",
+        "  # Measure loss and draw model every 8th step\n",
+        "  if c_step % 8 == 0:\n",
+        "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
+        "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "draw_loss_function(compute_loss, data, model, my_colormap, phi_all)\n"
+      ],
+      "metadata": {
+        "id": "tOLd0gtdRLLS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Unfortunately, when we start from this position, the solution descends to a local minimum and the final model doesn't fit well.<br><br>\n",
+        "\n",
+        "But what if we had some weak knowledge that the solution was in the vicinity of $\\phi_0=0.0$, $\\phi_{1} = 12.5$ (the center of the plot)?\n",
+        "\n",
+        "Let's add a term to the loss function that penalizes solutions that deviate from this point.  \n",
+        "\n",
+        "\\begin{equation}\n",
+        "L'[\\boldsymbol\\phi] = L[\\boldsymbol\\phi]+ \\lambda\\cdot \\Bigl(\\phi_{0}^2+(\\phi_1-12.5)^2\\Bigr)\n",
+        "\\end{equation}\n",
+        "\n",
+        "where $\\lambda$ controls the relative importance of the original loss and the regularization term"
+      ],
+      "metadata": {
+        "id": "3kKW2D5vEwhA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Computes the regularization term\n",
+        "def compute_reg_term(phi0,phi1):\n",
+        "  # TODO compute the regularization term (term in large brackets in the above equation)\n",
+        "  # Replace this line\n",
+        "  reg_term = 0.0\n",
+        "\n",
+        "  return reg_term ;\n",
+        "\n",
+        "# Define the loss function\n",
+        "# Note I called the weighting lambda_ to avoid confusing it with python lambda functions\n",
+        "def compute_loss2(data_x, data_y, model, phi, lambda_):\n",
+        "  pred_y = model(phi, data_x)\n",
+        "  loss = np.sum((pred_y-data_y)*(pred_y-data_y))\n",
+        "  # Add the new term to the loss\n",
+        "  loss = loss + lambda_ * compute_reg_term(phi[0],phi[1])\n",
+        "\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "4IgsQelgDdQ-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code to draw the regularization function\n",
+        "def draw_reg_function():\n",
+        "\n",
+        "  # Make grid of offset/frequency values to plot\n",
+        "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
+        "  loss_mesh = np.zeros_like(freqs_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(freqs_mesh):\n",
+        "     loss_mesh[idslope] = compute_reg_term(offsets_mesh[idslope], slope)\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(offsets_mesh,freqs_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(offsets_mesh,freqs_mesh,loss_mesh,20,colors=['#80808080'])\n",
+        "  ax.set_ylim([2.5,22.5])\n",
+        "  ax.set_xlabel('Offset $\\phi_{0}$'); ax.set_ylabel('Frequency, $\\phi_{1}$')\n",
+        "  plt.show()\n",
+        "\n",
+        "# Draw the regularization function.  It should look similar to figure 9.1b\n",
+        "draw_reg_function()"
+      ],
+      "metadata": {
+        "id": "PFl9zWzLNjuK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code to draw loss function with regularization\n",
+        "def draw_loss_function_reg(data,  model, lambda_, my_colormap, phi_iters = None):\n",
+        "\n",
+        "  # Make grid of offset/frequency values to plot\n",
+        "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
+        "  loss_mesh = np.zeros_like(freqs_mesh)\n",
+        "  # Compute loss for every set of parameters\n",
+        "  for idslope, slope in np.ndenumerate(freqs_mesh):\n",
+        "     loss_mesh[idslope] = compute_loss2(data[0,:], data[1,:], model, np.array([[offsets_mesh[idslope]], [slope]]), lambda_)\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(8,8)\n",
+        "  ax.contourf(offsets_mesh,freqs_mesh,loss_mesh,256,cmap=my_colormap)\n",
+        "  ax.contour(offsets_mesh,freqs_mesh,loss_mesh,20,colors=['#80808080'])\n",
+        "  if phi_iters is not None:\n",
+        "    ax.plot(phi_iters[0,:], phi_iters[1,:],'go-')\n",
+        "  ax.set_ylim([2.5,22.5])\n",
+        "  ax.set_xlabel('Offset $\\phi_{0}$'); ax.set_ylabel('Frequency, $\\phi_{1}$')\n",
+        "  plt.show()\n",
+        "\n",
+        "# This should look something like figure 9.1c\n",
+        "draw_loss_function_reg(data, model, 0.2, my_colormap)"
+      ],
+      "metadata": {
+        "id": "mQdEWCQdN5Mt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- Experiment with different values of the regularization weight lambda_\n",
+        "# What do you predict will happen when it is very small (e.g. 0.01)?\n",
+        "# What do you predict will happen when it is large (e.g, 1.0)?\n",
+        "# What happens to the loss at the global minimum when we add the regularization term?\n",
+        "# Does it go up?  Go down?  Stay the same?"
+      ],
+      "metadata": {
+        "id": "da047xjZQqj6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll compute the derivatives $\\frac{\\partial L'}{\\partial\\phi_0}$ and $\\frac{\\partial L'}{\\partial\\phi_1}$ of the regularized loss function:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "L'[\\boldsymbol\\phi] = L[\\boldsymbol\\phi]+ \\lambda\\cdot \\Bigl(\\phi_{0}^2+(\\phi_1-12.5)^2\\Bigr)\n",
+        "\\end{equation}\n",
+        "\n",
+        "so that we can perform gradient descent."
+      ],
+      "metadata": {
+        "id": "z7k0QHRNRwtD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def dldphi0(phi, lambda_):\n",
+        "  # TODO compute the derivative with respect to phi0\n",
+        "  # Replace this line:]\n",
+        "  deriv = 0\n",
+        "\n",
+        "  return deriv\n",
+        "\n",
+        "def dldphi1(phi, lambda_):\n",
+        "  # TODO compute the derivative with respect to phi1\n",
+        "  # Replace this line:]\n",
+        "  deriv = 0\n",
+        "\n",
+        "\n",
+        "  return deriv\n",
+        "\n",
+        "\n",
+        "def compute_gradient2(data_x, data_y, phi, lambda_):\n",
+        "    dl_dphi0 = gabor_deriv_phi0(data_x, data_y, phi[0],phi[1])+dldphi0(np.squeeze(phi), lambda_)\n",
+        "    dl_dphi1 = gabor_deriv_phi1(data_x, data_y, phi[0],phi[1])+dldphi1(np.squeeze(phi), lambda_)\n",
+        "    # Return the gradient\n",
+        "    return np.array([[dl_dphi0],[dl_dphi1]])\n",
+        "\n",
+        "def gradient_descent_step2(phi, lambda_, data,  model):\n",
+        "  # Step 1:  Compute the gradient\n",
+        "  gradient = compute_gradient2(data[0,:],data[1,:], phi, lambda_)\n",
+        "  # Step 2:  Update the parameters -- note we want to search in the negative (downhill direction)\n",
+        "  alpha = 0.1\n",
+        "  phi = phi - alpha * gradient\n",
+        "  return phi"
+      ],
+      "metadata": {
+        "id": "0OStdqo3Rv0a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Finally, let's run gradient descent and draw the result\n",
+        "# Initialize the parameters\n",
+        "n_steps = 41\n",
+        "phi_all = np.zeros((2,n_steps+1))\n",
+        "phi_all[0,0] = 2.6\n",
+        "phi_all[1,0] = 8.5\n",
+        "lambda_ = 0.2\n",
+        "\n",
+        "# Measure loss and draw initial model\n",
+        "loss =  compute_loss2(data[0,:], data[1,:], model, phi_all[:,0:1], lambda_)\n",
+        "draw_model(data,model,phi_all[:,0:1], \"Initial parameters, Loss = %f\"%(loss))\n",
+        "\n",
+        "for c_step in range (n_steps):\n",
+        "  # Do gradient descent step\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step2(phi_all[:,c_step:c_step+1],lambda_, data, model)\n",
+        "  # Measure loss and draw model every 8th step\n",
+        "  if c_step % 8 == 0:\n",
+        "    loss =  compute_loss2(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2], lambda_)\n",
+        "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
+        "\n",
+        "draw_loss_function_reg(data, model, lambda_, my_colormap, phi_all)"
+      ],
+      "metadata": {
+        "id": "c_V-Gv5hWgTE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You should see that the gradient descent algorithm now finds the correct minimum.  By applying a tiny bit of domain knowledge (the parameter phi0 tends to be near zero and the parameter phi1 tends to be near 12.5), we get a better solution.  However, the cost is that this solution is slightly biased towards this prior knowledge."
+      ],
+      "metadata": {
+        "id": "wrszSLrqZG4k"
+      }
+    }
+  ]
+}

Notebooks/Chap09/9_2_Implicit_Regularization.ipynb

@@ -0,0 +1,337 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 9.2: Implicit Regularization**\n",
+        "\n",
+        "This notebook investigates how the finite step sizes in gradient descent cause the trajectory to deviate and how this can be explained by adding an implicit regularization term.  It recreates figure 9.3 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib.colors import ListedColormap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Create colormap\n",
+        "my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "my_colormap_vals = np.vstack((r,g,b)).transpose()/255.0\n",
+        "my_colormap = ListedColormap(my_colormap_vals)"
+      ],
+      "metadata": {
+        "id": "RSrv2Ab7hbsY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# define main function\n",
+        "def loss(phi0, phi1):\n",
+        "    phi1_std = np.exp(-0.5 * (phi0 * phi0)*4.0)\n",
+        "    return 1.0-np.exp(-0.5 * (phi1 * phi1)/(phi1_std * phi1_std))\n",
+        "\n",
+        "# Compute the gradient (just done with finite differences for simplicity)\n",
+        "def get_loss_gradient(phi0, phi1):\n",
+        "    delta_phi = 0.00001;\n",
+        "    gradient = np.zeros((2,1));\n",
+        "    gradient[0] = (loss(phi0+delta_phi/2.0, phi1) - loss(phi0-delta_phi/2.0, phi1))/delta_phi\n",
+        "    gradient[1] = (loss(phi0, phi1+delta_phi/2.0) - loss(phi0, phi1-delta_phi/2.0))/delta_phi\n",
+        "    return gradient;"
+      ],
+      "metadata": {
+        "id": "Wzi8xmOmhkGF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# define grid to plot function\n",
+        "grid_values = np.arange(-0.8,0.5,0.01);\n",
+        "phi0mesh, phi1mesh = np.meshgrid(grid_values, grid_values)\n",
+        "loss_function = np.zeros((grid_values.size, grid_values.size))\n",
+        "for idphi0, phi0 in enumerate(grid_values):\n",
+        "    for idphi1, phi1 in enumerate(grid_values):\n",
+        "        loss_function[idphi0, idphi1] = loss(phi1,phi0)\n"
+      ],
+      "metadata": {
+        "id": "SFJgLbH5iAZe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform gradient descent n_steps times and return path\n",
+        "def grad_descent(start_posn, n_steps, step_size):\n",
+        "    grad_path = np.zeros((2, n_steps+1));\n",
+        "    grad_path[:,0] = start_posn[:,0];\n",
+        "    for c_step in range(n_steps):\n",
+        "        this_grad = get_loss_gradient(grad_path[0,c_step], grad_path[1,c_step]);\n",
+        "        grad_path[:,c_step+1] = grad_path[:,c_step] - step_size * this_grad[:,0]\n",
+        "    return grad_path;"
+      ],
+      "metadata": {
+        "id": "1BKt38kOh5TU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the loss function and the trajectories on it\n",
+        "def draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path_tiny_lr=None, grad_path_typical_lr=None):\n",
+        "    fig = plt.figure();\n",
+        "    ax = plt.axes();\n",
+        "    fig.set_size_inches(7,7)\n",
+        "    ax.contourf(phi0mesh, phi1mesh, loss_function, 256, cmap=my_colormap);\n",
+        "    ax.contour(phi0mesh, phi1mesh, loss_function, 20, colors=['#80808080'])\n",
+        "    ax.set_xlabel(r'$\\phi_{0}$'); ax.set_ylabel(r'$\\phi_{1}$')\n",
+        "\n",
+        "    if grad_path_typical_lr is not None:\n",
+        "        ax.plot(grad_path_typical_lr[0,:], grad_path_typical_lr[1,:],'ro-')\n",
+        "    if grad_path_tiny_lr is not None:\n",
+        "        ax.plot(grad_path_tiny_lr[0,:], grad_path_tiny_lr[1,:],'b-')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "KMoEZIiEiQ33"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the start position\n",
+        "start_posn = np.zeros((2,1)); start_posn[0,0] = -0.7; start_posn[1,0] = -0.75\n",
+        "\n",
+        "# Run the gradient descent with a very small learning rate to simulate continuous case\n",
+        "grad_path_tiny_lr = grad_descent(start_posn, 10000, 0.001)\n",
+        "# Run the gradient descent with a typical sized learning rate\n",
+        "grad_path_typical_lr = grad_descent(start_posn, 100, 0.05)\n",
+        "\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function, my_colormap, grad_path_tiny_lr, grad_path_typical_lr)\n"
+      ],
+      "metadata": {
+        "id": "FYpJ3cB4iy_B"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that the two solutions do not converge to the same place.  The ideal continuous solution is in blue, but in practice, we run the gradient set with as large a learning rate as possible so that it converges quickly (red curve). <br>\n",
+        "\n",
+        "It turns out that using a large learning rate often gives better generalization results (figure 9.5a from book), and presumably, this is because we converge to a different (and better) place.\n",
+        "\n",
+        "But how can we characterize the effect of the large learning rate?  One way is to consider what regularization term we would have to add to the original loss function so that the continuous solution converges to the same place as the discrete version with the large learning rate did on the original curve."
+      ],
+      "metadata": {
+        "id": "cZI8FfnVj9PT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute the implicit regularization term (second term in equation 9.8 in the book)\n",
+        "def get_reg_term(phi0, phi1, alpha):\n",
+        "  # TODO -- compute this term\n",
+        "  # You can use the function get_loss_gradient(phi0, phi1) that was defined above\n",
+        "  # Replace this line:\n",
+        "  reg_term = 0.0;\n",
+        "\n",
+        "  return reg_term;\n",
+        "\n",
+        "\n",
+        "# Compute modified loss function (equation 9.8)\n",
+        "def loss_reg(phi0, phi1, alpha):\n",
+        "    # The original function\n",
+        "    phi1_std = np.exp(-0.5 * (phi0 * phi0)*4.0)\n",
+        "    loss_out =  1.0-np.exp(-0.5 * (phi1 * phi1)/(phi1_std * phi1_std))\n",
+        "\n",
+        "    # Add the regularization term that you just calculated above\n",
+        "    loss_out = loss_out + get_reg_term(phi0, phi1,alpha)\n",
+        "    return loss_out ;\n",
+        "\n",
+        "# Compute gradient of modified loss function for gradient descent\n",
+        "def get_loss_gradient_reg(phi0, phi1,alpha):\n",
+        "    delta_phi = 0.00001;\n",
+        "    gradient = np.zeros((2,1));\n",
+        "    gradient[0] = (loss_reg(phi0+delta_phi/2.0, phi1, alpha) - loss_reg(phi0-delta_phi/2.0, phi1, alpha))/delta_phi\n",
+        "    gradient[1] = (loss_reg(phi0, phi1+delta_phi/2.0, alpha) - loss_reg(phi0, phi1-delta_phi/2.0, alpha))/delta_phi\n",
+        "    return gradient;"
+      ],
+      "metadata": {
+        "id": "dv6CvhDwjKAm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's visualize the regularization term\n",
+        "alpha = 0.1\n",
+        "reg_term = np.zeros((grid_values.size, grid_values.size))\n",
+        "for idphi0, phi0 in enumerate(grid_values):\n",
+        "    for idphi1, phi1 in enumerate(grid_values):\n",
+        "        reg_term[idphi0, idphi1] = get_reg_term(phi1,phi0, alpha)\n",
+        "\n",
+        "\n",
+        "draw_function(phi0mesh, phi1mesh, reg_term, my_colormap)"
+      ],
+      "metadata": {
+        "id": "6dRiS07DtpzU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As you would expect, the regularization term is largest where the magnitude or the gradient of the original loss function was biggest (i.e., where it was steepest)"
+      ],
+      "metadata": {
+        "id": "dTzRD0L1u9u8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We'll also visualize the loss function plus the regularization term\n",
+        "alpha = 0.1\n",
+        "loss_function_reg = np.zeros((grid_values.size, grid_values.size))\n",
+        "for idphi0, phi0 in enumerate(grid_values):\n",
+        "    for idphi1, phi1 in enumerate(grid_values):\n",
+        "        loss_function_reg[idphi0, idphi1] = loss_reg (phi1,phi0, alpha)\n",
+        "\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function_reg, my_colormap)"
+      ],
+      "metadata": {
+        "id": "B3cDTP8MwkGc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "It looks pretty similar to the original loss function, but you can see from the contours that it is slightly different."
+      ],
+      "metadata": {
+        "id": "H2HP4VLyxQ9d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform gradient descent n_steps times on modified loss function and return path\n",
+        "# Alpha is the step size for the gradient descent\n",
+        "# Alpha reg is the step size used to calculate the regularization term\n",
+        "def grad_descent_reg(start_posn, n_steps, alpha, alpha_reg):\n",
+        "    grad_path = np.zeros((2, n_steps+1));\n",
+        "    grad_path[:,0] = start_posn[:,0];\n",
+        "    for c_step in range(n_steps):\n",
+        "        this_grad = get_loss_gradient_reg(grad_path[0,c_step], grad_path[1,c_step],alpha_reg);\n",
+        "        grad_path[:,c_step+1] = grad_path[:,c_step] - alpha * this_grad[:,0]\n",
+        "    return grad_path;"
+      ],
+      "metadata": {
+        "id": "Szod9XEhto7q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the start position\n",
+        "start_posn = np.zeros((2,1)); start_posn[0,0] = -0.7; start_posn[1,0] = -0.75\n",
+        "\n",
+        "# TODO:  Run the gradient descent on the modified loss\n",
+        "# function with 10000 steps and alpha_reg = 0.05, and a very small learning rate alpha of 0.001\n",
+        "# Replace this line:\n",
+        "grad_path_tiny_lr = None ;\n",
+        "\n",
+        "\n",
+        "# TODO:  Run the gradient descent on the unmodified loss\n",
+        "# function with 100 steps and a very small learning rate alpha of 0.05\n",
+        "# Replace this line:\n",
+        "grad_path_typical_lr = None ;\n",
+        "\n",
+        "\n",
+        "# Draw the functions\n",
+        "draw_function(phi0mesh, phi1mesh, loss_function_reg, my_colormap, grad_path_tiny_lr, grad_path_typical_lr)"
+      ],
+      "metadata": {
+        "id": "44hpwMm0v2y0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now the two trajectories align.  The red curve runs gradient descent with a typical step size on the original loss function.  The blue curve simulates continuous gradient descent on the regularized loss function."
+      ],
+      "metadata": {
+        "id": "0wyIgvKF1QIq"
+      }
+    }
+  ]
+}

Notebooks/Chap09/9_3_Ensembling.ipynb

@@ -0,0 +1,328 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOAC7YLEqN5qZhJXqRj+aHB",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 9.3: Ensembling**\n",
+        "\n",
+        "This notebook investigates how ensembling can improve the performance of models. We'll work with the simplified neural network model (figure 8.4 of book) which we can fit in closed form, and so we can eliminate any errors due to not finding the global maximum.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Define seed to get same results each time\n",
+        "np.random.seed(1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The true function that we are trying to estimate, defined on [0,1]\n",
+        "def true_function(x):\n",
+        "    y = np.exp(np.sin(x*(2*3.1413)))\n",
+        "    return y"
+      ],
+      "metadata": {
+        "id": "3hpqmFyQNrbt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate some data points with or without noise\n",
+        "def generate_data(n_data, sigma_y=0.3):\n",
+        "    # Generate x values quasi uniformly\n",
+        "    x = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
+        "\n",
+        "    # y value from running through function and adding noise\n",
+        "    y = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        y[i] = true_function(x[i])\n",
+        "        y[i] += np.random.normal(0, sigma_y, 1)\n",
+        "    return x,y"
+      ],
+      "metadata": {
+        "id": "skZMM5TbNwq4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
+        "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
+        "\n",
+        "    fig,ax = plt.subplots()\n",
+        "    ax.plot(x_func, y_func, 'k-')\n",
+        "    if sigma_func is not None:\n",
+        "      ax.fill_between(x_func, y_func-2*sigma_func, y_func+2*sigma_func, color='lightgray')\n",
+        "\n",
+        "    if x_data is not None:\n",
+        "        ax.plot(x_data, y_data, 'o', color='#d18362')\n",
+        "\n",
+        "    if x_model is not None:\n",
+        "        ax.plot(x_model, y_model, '-', color='#7fe7de')\n",
+        "\n",
+        "    if sigma_model is not None:\n",
+        "      ax.fill_between(x_model, y_model-2*sigma_model, y_model+2*sigma_model, color='lightgray')\n",
+        "\n",
+        "    ax.set_xlim(0,1)\n",
+        "    ax.set_xlabel('Input, $x$')\n",
+        "    ax.set_ylabel('Output, $y$')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "ziwD_R7lN0DY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate true function\n",
+        "x_func = np.linspace(0, 1.0, 100)\n",
+        "y_func = true_function(x_func);\n",
+        "\n",
+        "# Generate some data points\n",
+        "np.random.seed(1)\n",
+        "sigma_func = 0.3\n",
+        "n_data = 15\n",
+        "x_data,y_data = generate_data(n_data, sigma_func)\n",
+        "\n",
+        "# Plot the function, data and uncertainty\n",
+        "plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
+      ],
+      "metadata": {
+        "id": "2CgKanwaN3NM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define model -- beta is a scalar and omega has size n_hidden,1\n",
+        "def network(x, beta, omega):\n",
+        "    # Retrieve number of hidden units\n",
+        "    n_hidden = omega.shape[0]\n",
+        "\n",
+        "    y = np.zeros_like(x)\n",
+        "    for c_hidden in range(n_hidden):\n",
+        "        # Evaluate activations based on shifted lines (figure 8.4b-d)\n",
+        "        line_vals =  x  - c_hidden/n_hidden\n",
+        "        h =  line_vals * (line_vals > 0)\n",
+        "        # Weight activations by omega parameters and sum\n",
+        "        y = y + omega[c_hidden] * h\n",
+        "    # Add bias, beta\n",
+        "    y = y + beta\n",
+        "\n",
+        "    return y"
+      ],
+      "metadata": {
+        "id": "gorZ6i97N7AR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This fits the n_hidden+1 parameters (see fig 8.4a) in closed form.\n",
+        "# If you have studied linear algebra, then you will know it is a least\n",
+        "# squares solution of the form (A^TA)^-1A^Tb.  If you don't recognize that,\n",
+        "# then just take it on trust that this gives you the best possible solution.\n",
+        "def fit_model_closed_form(x,y,n_hidden):\n",
+        "  n_data = len(x)\n",
+        "  A = np.ones((n_data, n_hidden+1))\n",
+        "  for i in range(n_data):\n",
+        "      for j in range(1,n_hidden+1):\n",
+        "          # Compute preactivation\n",
+        "          A[i,j] = x[i]-(j-1)/n_hidden\n",
+        "          # Apply the ReLU function\n",
+        "          if A[i,j] < 0:\n",
+        "              A[i,j] = 0;\n",
+        "\n",
+        "  # Add a tiny bit of regularization\n",
+        "  reg_value = 0.00001\n",
+        "  regMat = reg_value * np.identity(n_hidden+1)\n",
+        "  regMat[0,0] = 0\n",
+        "\n",
+        "  ATA = np.matmul(np.transpose(A), A) +regMat\n",
+        "  ATAInv = np.linalg.inv(ATA)\n",
+        "  ATAInvAT = np.matmul(ATAInv, np.transpose(A))\n",
+        "  beta_omega = np.matmul(ATAInvAT,y)\n",
+        "  beta = beta_omega[0]\n",
+        "  omega = beta_omega[1:]\n",
+        "\n",
+        "  return beta, omega"
+      ],
+      "metadata": {
+        "id": "bMrLZUIqOwiM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Closed form solution\n",
+        "beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=14)\n",
+        "\n",
+        "# Get prediction for model across graph range\n",
+        "x_model = np.linspace(0,1,100);\n",
+        "y_model = network(x_model, beta, omega)\n",
+        "\n",
+        "# Draw the function and the model\n",
+        "plot_function(x_func, y_func, x_data,y_data, x_model, y_model)\n",
+        "\n",
+        "# Compute the mean squared error between the fitted model (cyan) and the true curve (black)\n",
+        "mean_sq_error = np.mean((y_model-y_func) * (y_model-y_func))\n",
+        "print(f\"Mean square error = {mean_sq_error:3.3f}\")"
+      ],
+      "metadata": {
+        "id": "mzmtdY8DOz16"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's resample the data with replacement four times.\n",
+        "n_model = 4\n",
+        "# Array to store the prediction from all of our models\n",
+        "all_y_model = np.zeros((n_model, len(y_model)))\n",
+        "\n",
+        "# For each model\n",
+        "for c_model in range(n_model):\n",
+        "    # TODO Sample data indices with replacement (use np.random.choice)\n",
+        "    # Replace this line\n",
+        "    resampled_indices = np.arange(0,n_data,1);\n",
+        "\n",
+        "    # Extract the resampled x and y data\n",
+        "    x_data_resampled = x_data[resampled_indices]\n",
+        "    y_data_resampled = y_data[resampled_indices]\n",
+        "\n",
+        "    # Fit the model\n",
+        "    beta, omega = fit_model_closed_form(x_data_resampled,y_data_resampled,n_hidden=14)\n",
+        "\n",
+        "    # Run the model\n",
+        "    y_model_resampled = network(x_model, beta, omega)\n",
+        "\n",
+        "    # Store the results\n",
+        "    all_y_model[c_model,:] = y_model_resampled\n",
+        "\n",
+        "    # Draw the function and the model\n",
+        "    plot_function(x_func, y_func, x_data,y_data, x_model, y_model_resampled)\n",
+        "\n",
+        "    # Compute the mean squared error between the fitted model (cyan) and the true curve (black)\n",
+        "    mean_sq_error = np.mean((y_model_resampled-y_func) * (y_model_resampled-y_func))\n",
+        "    print(f\"Mean square error = {mean_sq_error:3.3f}\")"
+      ],
+      "metadata": {
+        "id": "UKrAOEiKO8Go"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the median of the results\n",
+        "# TODO -- find the median prediction\n",
+        "# Replace this line\n",
+        "y_model_median = all_y_model[0,:]\n",
+        "\n",
+        "# Draw the function and the model\n",
+        "plot_function(x_func, y_func, x_data,y_data, x_model, y_model_median)\n",
+        "\n",
+        "# Compute the mean squared error between the fitted model (cyan) and the true curve (black)\n",
+        "mean_sq_error = np.mean((y_model_median-y_func) * (y_model_median-y_func))\n",
+        "print(f\"Mean square error = {mean_sq_error:3.3f}\")"
+      ],
+      "metadata": {
+        "id": "cUTaW_GMS6nb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the mean of the results\n",
+        "# TODO -- find the mean prediction\n",
+        "# Replace this line\n",
+        "y_model_mean = all_y_model[0,:]\n",
+        "\n",
+        "# Draw the function and the model\n",
+        "plot_function(x_func, y_func, x_data,y_data, x_model, y_model_mean)\n",
+        "\n",
+        "# Compute the mean squared error between the fitted model (cyan) and the true curve (black)\n",
+        "mean_sq_error = np.mean((y_model_mean-y_func) * (y_model_mean-y_func))\n",
+        "print(f\"Mean square error = {mean_sq_error:3.3f}\")"
+      ],
+      "metadata": {
+        "id": "2MKxwVxuRvCx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You should see that both the median and mean models are better than any of the individual models. We have improved our performance at the cost of four times as much training time, storage, and inference time."
+      ],
+      "metadata": {
+        "id": "K-jDZrfjWwBa"
+      }
+    }
+  ]
+}

Notebooks/Chap09/9_4_Bayesian_Approach.ipynb

@@ -0,0 +1,426 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "el8l05WQEO46"
+      },
+      "source": [
+        "# **Notebook 9.4: Bayesian approach**\n",
+        "\n",
+        "This notebook investigates the Bayesian approach to model fitting and reproduces figure 9.11 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xhmIOLiZELV_"
+      },
+      "outputs": [],
+      "source": [
+        "# import libraries\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Define seed to get same results each time\n",
+        "np.random.seed(1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3hpqmFyQNrbt"
+      },
+      "outputs": [],
+      "source": [
+        "# The true function that we are trying to estimate, defined on [0,1]\n",
+        "def true_function(x):\n",
+        "    y = np.exp(np.sin(x*(2*3.1413)))\n",
+        "    return y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "skZMM5TbNwq4"
+      },
+      "outputs": [],
+      "source": [
+        "# Generate some data points with or without noise\n",
+        "def generate_data(n_data, sigma_y=0.3):\n",
+        "    # Generate x values quasi uniformly\n",
+        "    x = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
+        "\n",
+        "    # y value from running through function and adding noise\n",
+        "    y = np.ones(n_data)\n",
+        "    for i in range(n_data):\n",
+        "        y[i] = true_function(x[i])\n",
+        "        y[i] += np.random.normal(0, sigma_y, 1)\n",
+        "    return x,y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ziwD_R7lN0DY"
+      },
+      "outputs": [],
+      "source": [
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
+        "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
+        "\n",
+        "    fig,ax = plt.subplots()\n",
+        "    ax.plot(x_func, y_func, 'k-')\n",
+        "    if sigma_func is not None:\n",
+        "      ax.fill_between(x_func, y_func-2*sigma_func, y_func+2*sigma_func, color='lightgray')\n",
+        "\n",
+        "    if x_data is not None:\n",
+        "        ax.plot(x_data, y_data, 'o', color='#d18362')\n",
+        "\n",
+        "    if x_model is not None:\n",
+        "        ax.plot(x_model, y_model, '-', color='#7fe7de')\n",
+        "\n",
+        "    if sigma_model is not None:\n",
+        "      ax.fill_between(x_model, y_model-2*sigma_model, y_model+2*sigma_model, color='lightgray')\n",
+        "\n",
+        "    ax.set_xlim(0,1)\n",
+        "    ax.set_xlabel('Input, $x$')\n",
+        "    ax.set_ylabel('Output, $y$')\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2CgKanwaN3NM"
+      },
+      "outputs": [],
+      "source": [
+        "# Generate true function\n",
+        "x_func = np.linspace(0, 1.0, 100)\n",
+        "y_func = true_function(x_func);\n",
+        "\n",
+        "# Generate some data points\n",
+        "np.random.seed(1)\n",
+        "sigma_func = 0.3\n",
+        "n_data = 15\n",
+        "x_data,y_data = generate_data(n_data, sigma_func)\n",
+        "\n",
+        "# Plot the function, data and uncertainty\n",
+        "plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gorZ6i97N7AR"
+      },
+      "outputs": [],
+      "source": [
+        "# Define model -- beta is a scalar and omega has size n_hidden,1\n",
+        "def network(x, beta, omega):\n",
+        "    # Retrieve number of hidden units\n",
+        "    n_hidden = omega.shape[0]\n",
+        "\n",
+        "    y = np.zeros_like(x)\n",
+        "    for c_hidden in range(n_hidden):\n",
+        "        # Evaluate activations based on shifted lines (figure 8.4b-d)\n",
+        "        line_vals =  x  - c_hidden/n_hidden\n",
+        "        h =  line_vals * (line_vals > 0)\n",
+        "        # Weight activations by omega parameters and sum\n",
+        "        y = y + omega[c_hidden] * h\n",
+        "    # Add bias, beta\n",
+        "    y = y + beta\n",
+        "\n",
+        "    return y"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i8T_QduzeBmM"
+      },
+      "source": [
+        "Now let's compute a probability distribution over the model parameters using Bayes's rule:\n",
+        "\n",
+        "\\begin{equation}\n",
+        " Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) = \\frac{\\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi) Pr(\\boldsymbol\\phi)}{\\int \\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi) Pr(\\boldsymbol\\phi)d\\boldsymbol\\phi } ,\n",
+        "\\end{equation}\n",
+        "\n",
+        "We'll define the prior $Pr(\\boldsymbol\\phi)$ as:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "Pr(\\boldsymbol\\phi) = \\text{Norm}_{\\boldsymbol\\phi}\\bigl[\\mathbf{0},\\sigma^2_p\\mathbf{I}\\bigr]\n",
+        "\\end{equation}\n",
+        "\n",
+        "where $\\phi=[\\omega_1,\\omega_2\\ldots \\omega_n, \\beta]^T$ and $\\sigma^2_{p}$  is the prior variance.\n",
+        "\n",
+        "The likelihood term $\\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi)$ is given by:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi) &=& \\prod_{i=1}^{I} \\text{Norm}_{y_i}\\bigl[\\text{f}[\\mathbf{x}_{i},\\boldsymbol\\phi],\\sigma_d^2\\bigr]\\\\\n",
+        "&=& \\prod_{i=1}^{I} \\text{Norm}_{y_i}\\bigl[\\boldsymbol\\omega\\mathbf{h}_i+\\beta,\\sigma_d^2\\bigr]\\\\\n",
+        "&=& \\text{Norm}_{\\mathbf{y}}\\bigl[\\mathbf{H}^T\\boldsymbol\\phi,\\sigma^2\\mathbf{I}\\bigr].\n",
+        "\\end{align}\n",
+        "\n",
+        "where $\\sigma^2$ is the measurement noise and $\\mathbf{h}_{i}$ is the column vector of hidden variables for the $i^{th}$ input.  Here the vector $\\mathbf{y}$ and matrix $\\mathbf{H}$ are defined as:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "\\mathbf{y} = \\begin{bmatrix}y_1\\\\y_2\\\\\\vdots\\\\y_{I}\\end{bmatrix}\\quad\\text{and}\\quad \\mathbf{H} = \\begin{bmatrix}\\mathbf{h}_{1}&\\mathbf{h}_{2}&\\cdots&\\mathbf{h}_{I}\\\\1&1&\\cdots &1\\end{bmatrix}.\n",
+        "\\end{equation}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JojV6ueRk49G"
+      },
+      "source": [
+        "To make progress we use the change of variable relation (Appendix C.3.4 of the book) to rewrite the likelihood term as a normal distribution in the parameters $\\boldsymbol\\phi$:\n",
+        "\n",
+        "\\begin{align}\n",
+        "\\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi+\\beta)\n",
+        "&=& \\text{Norm}_{\\mathbf{y}}\\bigl[\\mathbf{H}^T\\boldsymbol\\phi,\\sigma^2\\bigr]\\\\\n",
+        "&\\propto& \\text{Norm}_{\\boldsymbol\\phi}\\bigl[(\\mathbf{H}\\mathbf{H}^T)^{-1}\\mathbf{H}\\mathbf{y},\\sigma^2(\\mathbf{H}\\mathbf{H}^t)^{-1}\\bigr]\n",
+        "\\end{align}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YX0O_Ciwp4W1"
+      },
+      "source": [
+        "Finally, we can combine the likelihood and prior terms using the product of two normal distributions relation (Appendix C.3.3).\n",
+        "\n",
+        "\\begin{align}\n",
+        " Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) &\\propto& \\prod_{i=1}^{I} Pr(\\mathbf{y}_{i}|\\mathbf{x}_{i},\\boldsymbol\\phi) Pr(\\boldsymbol\\phi)\\\\\n",
+        " &\\propto&\\text{Norm}_{\\boldsymbol\\phi}\\bigl[(\\mathbf{H}\\mathbf{H}^T)^{-1}\\mathbf{H}\\mathbf{y},\\sigma^2(\\mathbf{H}\\mathbf{H}^T)^{-1}\\bigr] \\text{Norm}_{\\boldsymbol\\phi}\\bigl[\\mathbf{0},\\sigma^2_p\\mathbf{I}\\bigr]\\\\\n",
+        " &\\propto&\\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr].\n",
+        "\\end{align}\n",
+        "\n",
+        "In fact, since this is already a normal distribution, the constant of proportionality must be one and we can write\n",
+        "\n",
+        "\\begin{align}\n",
+        " Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) &=& \\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr].\n",
+        "\\end{align}\n",
+        "\n",
+        "TODO -- On a piece of paper, use the relations in Appendix C.3.3 and C.3.4 to fill in the missing steps and establish that this is the correct formula for the posterior."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nF1AcgNDwm4t"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_H(x_data, n_hidden):\n",
+        "  psi1 = np.ones((n_hidden+1,1));\n",
+        "  psi0 = np.linspace(0.0, 1.0, num=n_hidden, endpoint=False) * -1\n",
+        "\n",
+        "  n_data = x_data.size\n",
+        "  # First compute the hidden variables\n",
+        "  H = np.ones((n_hidden+1, n_data))\n",
+        "  for i in range(n_hidden):\n",
+        "    for j in range(n_data):\n",
+        "      # Compute preactivation\n",
+        "      H[i,j] = psi1[i] * x_data[j]+psi0[i]\n",
+        "      # Apply ReLU to get activation\n",
+        "      if H[i,j] < 0:\n",
+        "        H[i,j] = 0;\n",
+        "\n",
+        "  return H\n",
+        "\n",
+        "def compute_param_mean_covar(x_data, y_data, n_hidden, sigma_sq, sigma_p_sq):\n",
+        "  # Retrieve the matrix containing the hidden variables\n",
+        "  H = compute_H(x_data, n_hidden) ;\n",
+        "\n",
+        "  # TODO -- Compute the covariance matrix (you will need np.transpose(), np.matmul(), np.linalg.inv())\n",
+        "  # Replace this line\n",
+        "  phi_covar = np.identity(n_hidden+1)\n",
+        "\n",
+        "\n",
+        "  # TODO -- Compute the mean matrix\n",
+        "  # Replace this line\n",
+        "  phi_mean = np.zeros((n_hidden+1,1))\n",
+        "\n",
+        "\n",
+        "  return phi_mean, phi_covar"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GjPnlG4q0UFK"
+      },
+      "source": [
+        "Now we can draw samples from this distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K4vYc82D0BMq"
+      },
+      "outputs": [],
+      "source": [
+        "# Define parameters\n",
+        "n_hidden = 5\n",
+        "sigma_sq = sigma_func * sigma_func\n",
+        "# Arbitrary large value reflecting the fact we are uncertain about the\n",
+        "# parameters before we see any data\n",
+        "sigma_p_sq = 1000\n",
+        "\n",
+        "# Compute the mean and covariance matrix\n",
+        "phi_mean, phi_covar = compute_param_mean_covar(x_data, y_data, n_hidden, sigma_sq, sigma_p_sq)\n",
+        "\n",
+        "# Let's draw the mean model\n",
+        "x_model = x_func\n",
+        "y_model_mean = network(x_model, phi_mean[-1], phi_mean[0:n_hidden])\n",
+        "plot_function(x_func, y_func, x_data, y_data, x_model, y_model_mean)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TVIjhubkSw-R"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO Draw two samples from the normal distribution over the parameters\n",
+        "# Replace these lines\n",
+        "phi_sample1 = np.zeros((n_hidden+1,1))\n",
+        "phi_sample2 = np.zeros((n_hidden+1,1))\n",
+        "\n",
+        "\n",
+        "# Run the network for these two sample sets of parameters\n",
+        "y_model_sample1 = network(x_model, phi_sample1[-1], phi_sample1[0:n_hidden])\n",
+        "y_model_sample2 = network(x_model, phi_sample2[-1], phi_sample2[0:n_hidden])\n",
+        "\n",
+        "# Draw the two models\n",
+        "plot_function(x_func, y_func, x_data, y_data, x_model, y_model_sample1)\n",
+        "plot_function(x_func, y_func, x_data, y_data, x_model, y_model_sample2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GiNg5EroUiUb"
+      },
+      "source": [
+        "Now we need to perform inference for a new data points $\\mathbf{x}^*$ with corresponding hidden values $\\mathbf{h}^*$.  Instead of having a single estimate of the parameters, we have a distribution over the possible parameters.  So we marginalize (integrate) over this distribution to account for all possible values:\n",
+        "\n",
+        "\\begin{align}\n",
+        "Pr(y^*|\\mathbf{x}^*)  &= \\int Pr(y^{*}|\\mathbf{x}^*,\\boldsymbol\\phi)Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) d\\boldsymbol\\phi\\\\\n",
+        "&= \\int \\text{Norm}_{y^*}\\bigl[[\\mathbf{h}^{*T},1]\\boldsymbol\\phi,\\sigma^2\\bigr]\\cdot\\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr]d\\boldsymbol\\phi\\\\\n",
+        "&= \\text{Norm}_{y^*}\\biggl[\\frac{1}{\\sigma^2} [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},  [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\n",
+        "[\\mathbf{h}^*;1]\\biggr],\n",
+        "\\end{align}\n",
+        "\n",
+        "where the notation $[\\mathbf{h}^{*T},1]$ is a row vector containing $\\mathbf{h}^{T}$ with a one appended to the end and $[\\mathbf{h};1 ]$ is a column vector containing $\\mathbf{h}$ with a one appended to the end.\n",
+        "\n",
+        "\n",
+        "To compute this, we reformulated the integrand using the relations from appendices C.3.3 and C.3.4 as the product of a normal distribution in $\\boldsymbol\\phi$ and a constant with respect\n",
+        "to $\\boldsymbol\\phi$. The integral of the normal distribution must be one, and so the final result is just the constant. This constant is itself a normal distribution in $y^*$. <br>\n",
+        "\n",
+        "If you feel so inclined you can work through the math of this yourself.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ILxT4EfW2lUm"
+      },
+      "outputs": [],
+      "source": [
+        "# Predict mean and variance of y_star from x_star\n",
+        "def inference(x_star, x_data, y_data, sigma_sq, sigma_p_sq, n_hidden):\n",
+        "\n",
+        "  # Compute hidden variables\n",
+        "  h_star = compute_H(x_star, n_hidden);\n",
+        "  H = compute_H(x_data, n_hidden);\n",
+        "\n",
+        "  # TODO: Compute mean and variance of y*\n",
+        "  # Replace these lines:\n",
+        "  y_star_mean = 0\n",
+        "  y_star_var =  1\n",
+        "\n",
+        "  return y_star_mean, y_star_var"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "87cjUjMaixHZ"
+      },
+      "outputs": [],
+      "source": [
+        "x_model = x_func\n",
+        "y_model = np.zeros_like(x_model)\n",
+        "y_model_std = np.zeros_like(x_model)\n",
+        "for c_model in range(len(x_model)):\n",
+        "  y_star_mean, y_star_var = inference(x_model[c_model]*np.ones((1,1)), x_data, y_data, sigma_sq, sigma_p_sq, n_hidden)\n",
+        "  y_model[c_model] = y_star_mean\n",
+        "  y_model_std[c_model] = np.sqrt(y_star_var)\n",
+        "\n",
+        "# Draw the model\n",
+        "plot_function(x_func, y_func, x_data, y_data, x_model, y_model, sigma_model=y_model_std)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8Hcbe_16sK0F"
+      },
+      "source": [
+        "TODO:\n",
+        "\n",
+        "1.  Experiment running this again with different numbers of hidden units.  Make a prediction for what will happen when you increase / decrease them.\n",
+        "2.  Experiment with what happens if you make the prior variance $\\sigma^2_p$ to a smaller value like 1.  How do you explain the results?"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Notebooks/Chap09/9_5_Augmentation.ipynb

@@ -0,0 +1,342 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 9.5: Augmentation**\n",
+        "\n",
+        "This notebook investigates data augmentation for the MNIST-1D model.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
+      ],
+      "metadata": {
+        "id": "syvgxgRr3myY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d\n",
+        "import random"
+      ],
+      "metadata": {
+        "id": "ckrNsYd13pMe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+      ],
+      "metadata": {
+        "id": "D_Woo9U730lZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "D_i = 40    # Input dimensions\n",
+        "D_k = 200   # Hidden dimensions\n",
+        "D_o = 10    # Output dimensions\n",
+        "\n",
+        "# Define a model with two hidden layers of size 200\n",
+        "# And ReLU activations between them\n",
+        "model = nn.Sequential(\n",
+        "nn.Linear(D_i, D_k),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_k, D_k),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_k, D_o))\n",
+        "\n",
+        "def weights_init(layer_in):\n",
+        "  # Initialize the parameters with He initialization\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)\n"
+      ],
+      "metadata": {
+        "id": "JfIFWFIL33eF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# choose cross entropy loss function (equation 5.24)\n",
+        "loss_function = torch.nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(data['x'].astype('float32'))\n",
+        "y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
+        "x_test= torch.tensor(data['x_test'].astype('float32'))\n",
+        "y_test = torch.tensor(data['y_test'].astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "errors_test = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, batch in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = batch\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_test = model(x_test)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_test[epoch]= 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "  print(f'Epoch {epoch:5d}, train error {errors_train[epoch]:3.2f}, test error {errors_test[epoch]:3.2f}')"
+      ],
+      "metadata": {
+        "id": "YFfVbTPE4BkJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_test,'b-',label='test')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Train Error %3.2f, Test Error %3.2f'%(errors_train[-1],errors_test[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "FmGDd4vB8LyM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The best test performance is about 33%.  Let's see if we can improve on that by augmenting the data."
+      ],
+      "metadata": {
+        "id": "55XvoPDO8Qp-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def augment(input_vector):\n",
+        "  # Create output vector\n",
+        "  data_out = np.zeros_like(input_vector)\n",
+        "\n",
+        "  # TODO:  Shift the input data by a random offset\n",
+        "  # (rotating, so points that would go off the end, are added back to the beginning)\n",
+        "  # Replace this line:\n",
+        "  data_out = np.zeros_like(input_vector) ;\n",
+        "\n",
+        "  # TODO:    # Randomly scale the data by a factor drawn from a uniform distribution over [0.8,1.2]\n",
+        "  # Replace this line:\n",
+        "  data_out = np.array(data_out)\n",
+        "\n",
+        "  return data_out"
+      ],
+      "metadata": {
+        "id": "IP6z2iox8MOF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_data_orig = data['x'].shape[0]\n",
+        "# We'll double the amount of data\n",
+        "n_data_augment = n_data_orig+4000\n",
+        "augmented_x = np.zeros((n_data_augment, D_i))\n",
+        "augmented_y = np.zeros(n_data_augment)\n",
+        "# First n_data_orig rows are original data\n",
+        "augmented_x[0:n_data_orig,:] = data['x']\n",
+        "augmented_y[0:n_data_orig] = data['y']\n",
+        "\n",
+        "# Fill in rest of with augmented data\n",
+        "for c_augment in range(n_data_orig, n_data_augment):\n",
+        "  # Choose a data point randomly\n",
+        "  random_data_index = random.randint(0, n_data_orig-1)\n",
+        "  # Augment the point and store\n",
+        "  augmented_x[c_augment,:] = augment(data['x'][random_data_index,:])\n",
+        "  augmented_y[c_augment] = data['y'][random_data_index]\n"
+      ],
+      "metadata": {
+        "id": "bzN0lu5J95AJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# choose cross entropy loss function (equation 5.24)\n",
+        "loss_function = torch.nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 50 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(augmented_x.astype('float32'))\n",
+        "y_train = torch.tensor(augmented_y.transpose().astype('long'))\n",
+        "x_test= torch.tensor(data['x_test'].astype('float32'))\n",
+        "y_test = torch.tensor(data['y_test'].astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "errors_train_aug = np.zeros((n_epoch))\n",
+        "errors_test_aug = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, batch in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = batch\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_test = model(x_test)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "  errors_train_aug[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_test_aug[epoch]= 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "  print(f'Epoch {epoch:5d}, train error {errors_train_aug[epoch]:3.2f}, test error {errors_test_aug[epoch]:3.2f}')"
+      ],
+      "metadata": {
+        "id": "hZUNrXpS_kRs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_test,'b-',label='test')\n",
+        "ax.plot(errors_test_aug,'g-',label='test (augmented)')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('TrainError %3.2f, Test Error %3.2f'%(errors_train_aug[-1],errors_test_aug[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "IcnAW4ixBnuc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Hopefully, you should see an improvement in performance when we augment the data."
+      ],
+      "metadata": {
+        "id": "jgsR7ScJHc9b"
+      }
+    }
+  ]
+}

Notebooks/Chap10/10_1_1D_Convolution.ipynb

@@ -0,0 +1,387 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyML7rfAGE4gvmNUEiK5x3PS",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 10.1: 1D Convolution**\n",
+        "\n",
+        "This notebook investigates 1D convolutional layers.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "el8l05WQEO46"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "NOTE!!\n",
+        "\n",
+        "If you have the first edition of the printed book, it mistakenly refers to a convolutional filter with no spaces between the elements (i.e. a normal filter without dilation) as having dilation zero.  Actually, the convention is (weirdly) that this has dilation one.  And when there is one space between the elements, this is dilation two.   This notebook reflects the correct convention and so will be out of sync with the printed book.  If this is confusing, check the [errata](https://github.com/udlbook/udlbook/blob/main/UDL_Errata.pdf) document."
+      ],
+      "metadata": {
+        "id": "ggQrHkFZcUiV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "nw7k5yCtOzoK"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a signal that we can apply convolution to\n",
+        "x = [5.2, 5.3, 5.4, 5.1, 10.1, 10.3, 9.9, 10.3, 3.2, 3.4, 3.3, 3.1]"
+      ],
+      "metadata": {
+        "id": "lSSHuoEqO3Ly"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the signal\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, 'k-')\n",
+        "ax.set_xlim(0,11)\n",
+        "ax.set_ylim(0, 12)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "zVssv_wiREc2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's define a zero-padded convolution operation\n",
+        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 1\n",
+        "# as in figure 10.2a-c.  Write it yourself, don't call a library routine!\n",
+        "# Don't forget that Python arrays are indexed from zero, not from 1 as in the book figures\n",
+        "def conv_3_1_1_zp(x_in, omega):\n",
+        "    x_out = np.zeros_like(x_in)\n",
+        "    # TODO -- write this function\n",
+        "    # replace this line\n",
+        "    x_out = x_out\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return x_out"
+      ],
+      "metadata": {
+        "id": "MmfXED12RvNq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's see what kind of things convolution can do\n",
+        "First, it can average nearby values, smoothing the function:"
+      ],
+      "metadata": {
+        "id": "Fof_Rs98Zovq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "omega = [0.33,0.33,0.33]\n",
+        "h = conv_3_1_1_zp(x, omega)\n",
+        "\n",
+        "# Check that you have computed this correctly\n",
+        "print(f\"Sum of output is {np.sum(h):3.3}, should be 71.1\")\n",
+        "\n",
+        "# Draw the signal\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, 'k-',label='before')\n",
+        "ax.plot(h, 'r-',label='after')\n",
+        "ax.set_xlim(0,11)\n",
+        "ax.set_ylim(0, 12)\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "HOcPZR6iWXsa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Notice how the red function is a smoothed version of the black one as it has averaged adjacent values.  The first and last outputs are considerably lower than the original curve though.  Make sure that you understand why!<br><br>\n",
+        "\n",
+        "With different weights, the convolution can be used to find sharp changes in the function:"
+      ],
+      "metadata": {
+        "id": "PBkNKUylZr-k"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "omega = [-0.5,0,0.5]\n",
+        "h2 = conv_3_1_1_zp(x, omega)\n",
+        "\n",
+        "# Draw the signal\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, 'k-',label='before')\n",
+        "ax.plot(h2, 'r-',label='after')\n",
+        "ax.set_xlim(0,11)\n",
+        "# ax.set_ylim(0, 12)\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "o8T5WKeuZrgS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Notice that the convolution has a peak where the original function went up and trough where it went down.  It is roughly zero where the function is locally flat.  This convolution approximates a derivative. <br> <br>\n",
+        "\n",
+        "Now let's define the convolutions from figure 10.3.  "
+      ],
+      "metadata": {
+        "id": "ogfCVThJgtPx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's define a zero-padded convolution operation\n",
+        "# with a convolution kernel size of 3, a stride of 2, and a dilation of 1\n",
+        "# as in figure 10.3a-b.  Write it yourself, don't call a library routine!\n",
+        "def conv_3_2_1_zp(x_in, omega):\n",
+        "    x_out = np.zeros(int(np.ceil(len(x_in)/2)))\n",
+        "    # TODO -- write this function\n",
+        "    # replace this line\n",
+        "    x_out = x_out\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return x_out"
+      ],
+      "metadata": {
+        "id": "5QYrQmFMiDBj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "omega = [0.33,0.33,0.33]\n",
+        "h3 = conv_3_2_1_zp(x, omega)\n",
+        "\n",
+        "# If you have done this right, the output length should be six and it should\n",
+        "# contain every other value from the original convolution with stride 1\n",
+        "print(h)\n",
+        "print(h3)"
+      ],
+      "metadata": {
+        "id": "CD96lnDHX72A"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's define a zero-padded convolution operation\n",
+        "# with a convolution kernel size of 5, a stride of 1, and a dilation of 1\n",
+        "# as in figure 10.3c.  Write it yourself, don't call a library routine!\n",
+        "def conv_5_1_1_zp(x_in, omega):\n",
+        "    x_out = np.zeros_like(x_in)\n",
+        "    # TODO -- write this function\n",
+        "    # replace this line\n",
+        "    x_out = x_out\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return x_out"
+      ],
+      "metadata": {
+        "id": "lw46-gNUjDw7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "omega2 = [0.2, 0.2, 0.2, 0.2, 0.2]\n",
+        "h4 = conv_5_1_1_zp(x, omega2)\n",
+        "\n",
+        "# Check that you have computed this correctly\n",
+        "print(f\"Sum of output is {np.sum(h4):3.3}, should be 69.6\")\n",
+        "\n",
+        "# Draw the signal\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, 'k-',label='before')\n",
+        "ax.plot(h4, 'r-',label='after')\n",
+        "ax.set_xlim(0,11)\n",
+        "ax.set_ylim(0, 12)\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "JkKBL-nFk4bf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Finally let's define a zero-padded convolution operation\n",
+        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 2\n",
+        "# as in figure 10.3d.  Write it yourself, don't call a library routine!\n",
+        "# Don't forget that Python arrays are indexed from zero, not from 1 as in the book figures\n",
+        "def conv_3_1_2_zp(x_in, omega):\n",
+        "    x_out = np.zeros_like(x_in)\n",
+        "    # TODO -- write this function\n",
+        "    # replace this line\n",
+        "    x_out = x_out\n",
+        "\n",
+        "\n",
+        "    return x_out"
+      ],
+      "metadata": {
+        "id": "_aBcW46AljI0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "omega = [0.33,0.33,0.33]\n",
+        "h5 = conv_3_1_2_zp(x, omega)\n",
+        "\n",
+        "# Check that you have computed this correctly\n",
+        "print(f\"Sum of output is {np.sum(h5):3.3}, should be 68.3\")\n",
+        "\n",
+        "# Draw the signal\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, 'k-',label='before')\n",
+        "ax.plot(h5, 'r-',label='after')\n",
+        "ax.set_xlim(0,11)\n",
+        "ax.set_ylim(0, 12)\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "En-ByCqWlvMI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, let's investigate representing convolutions as full matrices, and show we get the same answer."
+      ],
+      "metadata": {
+        "id": "loBwu125lXx1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute matrix in figure 10.4 d\n",
+        "def get_conv_mat_3_1_1_zp(n_out, omega):\n",
+        "  omega_mat = np.zeros((n_out,n_out))\n",
+        "  # TODO Fill in this matrix\n",
+        "  # Replace this line:\n",
+        "  omega_mat = omega_mat\n",
+        "\n",
+        "\n",
+        "\n",
+        "  return omega_mat"
+      ],
+      "metadata": {
+        "id": "U2RFWfGgs72j"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run original convolution\n",
+        "omega = np.array([-1.0,0.5,-0.2])\n",
+        "h6 = conv_3_1_1_zp(x, omega)\n",
+        "print(h6)\n",
+        "\n",
+        "# If you have done this right, you should get the same answer\n",
+        "omega_mat = get_conv_mat_3_1_1_zp(len(x), omega)\n",
+        "h7 = np.matmul(omega_mat, x)\n",
+        "print(h7)\n"
+      ],
+      "metadata": {
+        "id": "20IYxku8lMty"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO:  What do you expect to happen if we apply the last convolution twice?  Can this be represented as a single convolution?  If so, then what is it?"
+      ],
+      "metadata": {
+        "id": "rYoQVhBfu8R4"
+      }
+    }
+  ]
+}

Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb

@@ -0,0 +1,256 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNb46PJB/CC1pcHGfjpUUZg",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 10.2: Convolution for MNIST-1D**\n",
+        "\n",
+        "This notebook investigates a 1D convolutional network for MNIST-1D as in figure 10.7 and 10.8a.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
+      ],
+      "metadata": {
+        "id": "D5yLObtZCi9J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d\n",
+        "import random"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+      ],
+      "metadata": {
+        "id": "twI72ZCrCt5z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = data['x'].transpose()\n",
+        "train_data_y = data['y']\n",
+        "val_data_x = data['x_test'].transpose()\n",
+        "val_data_y = data['y_test']\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "\n",
+        "# TODO Create a model with the following layers\n",
+        "# 1. Convolutional layer, (input=length 40 and 1 channel, kernel size 3, stride 2, padding=\"valid\", 15 output channels )\n",
+        "# 2. ReLU\n",
+        "# 3. Convolutional layer, (input=length 19 and 15 channels, kernel size 3, stride 2, padding=\"valid\", 15 output channels )\n",
+        "# 4. ReLU\n",
+        "# 5. Convolutional layer, (input=length 9 and 15 channels, kernel size 3, stride 2, padding=\"valid\", 15 output channels)\n",
+        "# 6. ReLU\n",
+        "# 7. Flatten (converts 4x15) to length 60\n",
+        "# 8. Linear layer (input size = 60, output size = 10)\n",
+        "# References:\n",
+        "# https://pytorch.org/docs/1.13/generated/torch.nn.Conv1d.html?highlight=conv1d#torch.nn.Conv1d\n",
+        "# https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html\n",
+        "# https://pytorch.org/docs/1.13/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear\n",
+        "\n",
+        "# NOTE THAT THE CONVOLUTIONAL LAYERS NEED TO TAKE THE NUMBER OF INPUT CHANNELS AS A PARAMETER\n",
+        "# AND NOT THE INPUT SIZE.\n",
+        "\n",
+        "# Replace the following function:\n",
+        "model = nn.Sequential(\n",
+        "nn.Flatten(),\n",
+        "nn.Linear(40, 100),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(100, 100),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(100, 10))\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 20 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=20, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "y_train = torch.tensor(train_data_y.astype('long')).long()\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long')).long()\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 100\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch[:,None,:])\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train[:,None,:])\n",
+        "  pred_val = model(x_val[:,None,:])\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='validation')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Part I: Validation Result %3.2f'%(errors_val[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap10/10_3_2D_Convolution.ipynb

@@ -0,0 +1,436 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNDaU2KKZDyY9Ea7vm/fNxo",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 10.3: 2D Convolution**\n",
+        "\n",
+        "This notebook investigates the 2D convolution operation.  It asks you to hand code the convolution so we can be sure that we are computing the same thing as in PyTorch.  The next notebook uses the convolutional layers in PyTorch directly.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "VB_crnDGASX-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import torch\n",
+        "# Set to print in reasonable form\n",
+        "np.set_printoptions(precision=3, floatmode=\"fixed\")\n",
+        "torch.set_printoptions(precision=3)"
+      ],
+      "metadata": {
+        "id": "YAoWDUb_DezG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This routine performs convolution in PyTorch"
+      ],
+      "metadata": {
+        "id": "eAwYWXzAElHG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform convolution in PyTorch\n",
+        "def conv_pytorch(image, conv_weights, stride=1, pad =1):\n",
+        "  # Convert image and kernel to tensors\n",
+        "  image_tensor = torch.from_numpy(image) # (batchSize, channelsIn, imageHeightIn, =imageWidthIn)\n",
+        "  conv_weights_tensor = torch.from_numpy(conv_weights) # (channelsOut, channelsIn, kernelHeight, kernelWidth)\n",
+        "  # Do the convolution\n",
+        "  output_tensor = torch.nn.functional.conv2d(image_tensor, conv_weights_tensor, stride=stride, padding=pad)\n",
+        "  # Convert back from PyTorch and return\n",
+        "  return(output_tensor.numpy()) # (batchSize channelsOut imageHeightOut imageHeightIn)"
+      ],
+      "metadata": {
+        "id": "xsmUIN-3BlWr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First we'll start with the simplest 2D convolution.  Just one channel in and one channel out.  A single image in the batch."
+      ],
+      "metadata": {
+        "id": "A3Sm8bUWtDNO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform convolution in numpy\n",
+        "def conv_numpy_1(image, weights, pad=1):\n",
+        "\n",
+        "    # Perform zero padding\n",
+        "    if pad != 0:\n",
+        "        image = np.pad(image, ((0, 0), (0 ,0), (pad, pad), (pad, pad)),'constant')\n",
+        "\n",
+        "    # Get sizes of image array and kernel weights\n",
+        "    batchSize,  channelsIn, imageHeightIn, imageWidthIn = image.shape\n",
+        "    channelsOut, channelsIn, kernelHeight, kernelWidth = weights.shape\n",
+        "\n",
+        "    # Get size of output arrays\n",
+        "    imageHeightOut = np.floor(1 + imageHeightIn - kernelHeight).astype(int)\n",
+        "    imageWidthOut = np.floor(1 + imageWidthIn - kernelWidth).astype(int)\n",
+        "\n",
+        "    # Create output\n",
+        "    out = np.zeros((batchSize, channelsOut, imageHeightOut, imageWidthOut), dtype=np.float32)\n",
+        "\n",
+        "    # !!!!!! NOTE THERE IS A SUBTLETY HERE !!!!!!!!\n",
+        "    # I have padded the image with zeros above, so it is surrouned by a \"ring\" of zeros\n",
+        "    # That means that the image indexes are all off by one\n",
+        "    # This actually makes your code simpler\n",
+        "\n",
+        "    for c_y in range(imageHeightOut):\n",
+        "      for c_x in range(imageWidthOut):\n",
+        "        for c_kernel_y in range(kernelHeight):\n",
+        "          for c_kernel_x in range(kernelWidth):\n",
+        "            # TODO -- Retrieve the image pixel and the weight from the convolution\n",
+        "            # Only one image in batch, one input channel and one output channel, so these indices should all be zero\n",
+        "            # Replace the two lines below\n",
+        "            this_pixel_value = 1.0\n",
+        "            this_weight = 1.0\n",
+        "\n",
+        "\n",
+        "            # Multiply these together and add to the output at this position\n",
+        "            out[0, 0, c_y, c_x] += np.sum(this_pixel_value * this_weight)\n",
+        "\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "EF8FWONVLo1Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set random seed so we always get same answer\n",
+        "np.random.seed(1)\n",
+        "n_batch = 1\n",
+        "image_height = 4\n",
+        "image_width = 6\n",
+        "channels_in = 1\n",
+        "kernel_size = 3\n",
+        "channels_out = 1\n",
+        "\n",
+        "# Create random input image\n",
+        "input_image= np.random.normal(size=(n_batch, channels_in, image_height, image_width))\n",
+        "# Create random convolution kernel weights\n",
+        "conv_weights = np.random.normal(size=(channels_out, channels_in, kernel_size, kernel_size))\n",
+        "\n",
+        "# Perform convolution using PyTorch\n",
+        "conv_results_pytorch = conv_pytorch(input_image, conv_weights, stride=1, pad=1)\n",
+        "print(\"PyTorch Results\")\n",
+        "print(conv_results_pytorch)\n",
+        "\n",
+        "# Perform convolution in numpy\n",
+        "print(\"Your results\")\n",
+        "conv_results_numpy = conv_numpy_1(input_image, conv_weights)\n",
+        "print(conv_results_numpy)"
+      ],
+      "metadata": {
+        "id": "iw9KqXZTHN8v"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's now add in the possibility of using different strides"
+      ],
+      "metadata": {
+        "id": "IYj_lxeGzaHX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform convolution in numpy\n",
+        "def conv_numpy_2(image, weights, stride=1, pad=1):\n",
+        "\n",
+        "    # Perform zero padding\n",
+        "    if pad != 0:\n",
+        "        image = np.pad(image, ((0, 0), (0 ,0), (pad, pad), (pad, pad)),'constant')\n",
+        "\n",
+        "    # Get sizes of image array and kernel weights\n",
+        "    batchSize,  channelsIn, imageHeightIn, imageWidthIn = image.shape\n",
+        "    channelsOut, channelsIn, kernelHeight, kernelWidth = weights.shape\n",
+        "\n",
+        "    # Get size of output arrays\n",
+        "    imageHeightOut = np.floor(1 + (imageHeightIn - kernelHeight) / stride).astype(int)\n",
+        "    imageWidthOut = np.floor(1 + (imageWidthIn - kernelWidth) / stride).astype(int)\n",
+        "\n",
+        "    # Create output\n",
+        "    out = np.zeros((batchSize, channelsOut, imageHeightOut, imageWidthOut), dtype=np.float32)\n",
+        "\n",
+        "    for c_y in range(imageHeightOut):\n",
+        "      for c_x in range(imageWidthOut):\n",
+        "        for c_kernel_y in range(kernelHeight):\n",
+        "          for c_kernel_x in range(kernelWidth):\n",
+        "            # TODO -- Retrieve the image pixel and the weight from the convolution\n",
+        "            # Only one image in batch, one input channel and one output channel, so these indices should all be zero\n",
+        "            # Replace the two lines below\n",
+        "            this_pixel_value = 1.0\n",
+        "            this_weight = 1.0\n",
+        "\n",
+        "\n",
+        "            # Multiply these together and add to the output at this position\n",
+        "            out[0, 0, c_y, c_x] += np.sum(this_pixel_value * this_weight)\n",
+        "\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "GiujmLhqHN1F"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set random seed so we always get same answer\n",
+        "np.random.seed(1)\n",
+        "n_batch = 1\n",
+        "image_height = 12\n",
+        "image_width = 10\n",
+        "channels_in = 1\n",
+        "kernel_size = 3\n",
+        "channels_out = 1\n",
+        "stride = 2\n",
+        "\n",
+        "# Create random input image\n",
+        "input_image= np.random.normal(size=(n_batch, channels_in, image_height, image_width))\n",
+        "# Create random convolution kernel weights\n",
+        "conv_weights = np.random.normal(size=(channels_out, channels_in, kernel_size, kernel_size))\n",
+        "\n",
+        "# Perform convolution using PyTorch\n",
+        "conv_results_pytorch = conv_pytorch(input_image, conv_weights, stride, pad=1)\n",
+        "print(\"PyTorch Results\")\n",
+        "print(conv_results_pytorch)\n",
+        "\n",
+        "# Perform convolution in numpy\n",
+        "print(\"Your results\")\n",
+        "conv_results_numpy = conv_numpy_2(input_image, conv_weights, stride, pad=1)\n",
+        "print(conv_results_numpy)"
+      ],
+      "metadata": {
+        "id": "FeJy6Bvozgxq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll introduce multiple input and output channels"
+      ],
+      "metadata": {
+        "id": "3flq1Wan2gX-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform convolution in numpy\n",
+        "def conv_numpy_3(image, weights, stride=1, pad=1):\n",
+        "\n",
+        "    # Perform zero padding\n",
+        "    if pad != 0:\n",
+        "        image = np.pad(image, ((0, 0), (0 ,0), (pad, pad), (pad, pad)),'constant')\n",
+        "\n",
+        "    # Get sizes of image array and kernel weights\n",
+        "    batchSize,  channelsIn, imageHeightIn, imageWidthIn = image.shape\n",
+        "    channelsOut, channelsIn, kernelHeight, kernelWidth = weights.shape\n",
+        "\n",
+        "    # Get size of output arrays\n",
+        "    imageHeightOut = np.floor(1 + (imageHeightIn - kernelHeight) / stride).astype(int)\n",
+        "    imageWidthOut = np.floor(1 + (imageWidthIn - kernelWidth) / stride).astype(int)\n",
+        "\n",
+        "    # Create output\n",
+        "    out = np.zeros((batchSize, channelsOut, imageHeightOut, imageWidthOut), dtype=np.float32)\n",
+        "\n",
+        "    for c_y in range(imageHeightOut):\n",
+        "      for c_x in range(imageWidthOut):\n",
+        "        for c_channel_out in range(channelsOut):\n",
+        "          for c_channel_in in range(channelsIn):\n",
+        "            for c_kernel_y in range(kernelHeight):\n",
+        "              for c_kernel_x in range(kernelWidth):\n",
+        "                  # TODO -- Retrieve the image pixel and the weight from the convolution\n",
+        "                  # Only one image in batch so this index should be zero\n",
+        "                  # Replace the two lines below\n",
+        "                  this_pixel_value = 1.0\n",
+        "                  this_weight = 1.0\n",
+        "\n",
+        "                  # Multiply these together and add to the output at this position\n",
+        "                  out[0, c_channel_out, c_y, c_x] += np.sum(this_pixel_value * this_weight)\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "AvdRWGiU2ppX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set random seed so we always get same answer\n",
+        "np.random.seed(1)\n",
+        "n_batch = 1\n",
+        "image_height = 4\n",
+        "image_width = 6\n",
+        "channels_in = 5\n",
+        "kernel_size = 3\n",
+        "channels_out = 2\n",
+        "\n",
+        "# Create random input image\n",
+        "input_image= np.random.normal(size=(n_batch, channels_in, image_height, image_width))\n",
+        "# Create random convolution kernel weights\n",
+        "conv_weights = np.random.normal(size=(channels_out, channels_in, kernel_size, kernel_size))\n",
+        "\n",
+        "# Perform convolution using PyTorch\n",
+        "conv_results_pytorch = conv_pytorch(input_image, conv_weights, stride=1, pad=1)\n",
+        "print(\"PyTorch Results\")\n",
+        "print(conv_results_pytorch)\n",
+        "\n",
+        "# Perform convolution in numpy\n",
+        "print(\"Your results\")\n",
+        "conv_results_numpy = conv_numpy_3(input_image, conv_weights, stride=1, pad=1)\n",
+        "print(conv_results_numpy)"
+      ],
+      "metadata": {
+        "id": "mdSmjfvY4li2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we'll do the full convolution with multiple images (batch size > 1), and multiple input channels, multiple output channels."
+      ],
+      "metadata": {
+        "id": "Q2MUFebdsJbH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform convolution in numpy\n",
+        "def conv_numpy_4(image, weights, stride=1, pad=1):\n",
+        "\n",
+        "    # Perform zero padding\n",
+        "    if pad != 0:\n",
+        "        image = np.pad(image, ((0, 0), (0 ,0), (pad, pad), (pad, pad)),'constant')\n",
+        "\n",
+        "    # Get sizes of image array and kernel weights\n",
+        "    batchSize,  channelsIn, imageHeightIn, imageWidthIn = image.shape\n",
+        "    channelsOut, channelsIn, kernelHeight, kernelWidth = weights.shape\n",
+        "\n",
+        "    # Get size of output arrays\n",
+        "    imageHeightOut = np.floor(1 + (imageHeightIn - kernelHeight) / stride).astype(int)\n",
+        "    imageWidthOut = np.floor(1 + (imageWidthIn - kernelWidth) / stride).astype(int)\n",
+        "\n",
+        "    # Create output\n",
+        "    out = np.zeros((batchSize, channelsOut, imageHeightOut, imageWidthOut), dtype=np.float32)\n",
+        "\n",
+        "    for c_batch in range(batchSize):\n",
+        "      for c_y in range(imageHeightOut):\n",
+        "        for c_x in range(imageWidthOut):\n",
+        "          for c_channel_out in range(channelsOut):\n",
+        "            for c_channel_in in range(channelsIn):\n",
+        "              for c_kernel_y in range(kernelHeight):\n",
+        "                for c_kernel_x in range(kernelWidth):\n",
+        "                    # TODO -- Retrieve the image pixel and the weight from the convolution\n",
+        "                    # Replace the two lines below\n",
+        "                    this_pixel_value = 1.0\n",
+        "                    this_weight = 1.0\n",
+        "\n",
+        "\n",
+        "\n",
+        "                    # Multiply these together and add to the output at this position\n",
+        "                    out[c_batch, c_channel_out, c_y, c_x] += np.sum(this_pixel_value * this_weight)\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "5WePF-Y-sC1y"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1w2GEBtqAM2P"
+      },
+      "outputs": [],
+      "source": [
+        "# Set random seed so we always get same answer\n",
+        "np.random.seed(1)\n",
+        "n_batch = 2\n",
+        "image_height = 4\n",
+        "image_width = 6\n",
+        "channels_in = 5\n",
+        "kernel_size = 3\n",
+        "channels_out = 2\n",
+        "\n",
+        "# Create random input image\n",
+        "input_image= np.random.normal(size=(n_batch, channels_in, image_height, image_width))\n",
+        "# Create random convolution kernel weights\n",
+        "conv_weights = np.random.normal(size=(channels_out, channels_in, kernel_size, kernel_size))\n",
+        "\n",
+        "# Perform convolution using PyTorch\n",
+        "conv_results_pytorch = conv_pytorch(input_image, conv_weights, stride=1, pad=1)\n",
+        "print(\"PyTorch Results\")\n",
+        "print(conv_results_pytorch)\n",
+        "\n",
+        "# Perform convolution in numpy\n",
+        "print(\"Your results\")\n",
+        "conv_results_numpy = conv_numpy_4(input_image, conv_weights, stride=1, pad=1)\n",
+        "print(conv_results_numpy)"
+      ]
+    }
+  ]
+}

Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb

@@ -0,0 +1,520 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMbSR8fzpXvO6TIQdO7bI0H",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 10.4: Downsampling and Upsampling**\n",
+        "\n",
+        "This notebook investigates the down sampling and downsampling methods discussed in section 10.4 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from PIL import Image\n",
+        "from numpy import asarray"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define 4 by 4 original patch\n",
+        "orig_4_4 = np.array([[1, 3, 5,3 ], [6,2,0,8], [4,6,1,4], [2,8,0,3]])\n",
+        "print(orig_4_4)"
+      ],
+      "metadata": {
+        "id": "WPRoJcC_JXE2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def subsample(x_in):\n",
+        "  x_out = np.zeros(( int(np.ceil(x_in.shape[0]/2)), int(np.ceil(x_in.shape[1]/2)) ))\n",
+        "  # TO DO -- write the subsampling routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "qneyOiZRJubi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_4_4)\n",
+        "print(\"Subsampled:\")\n",
+        "print(subsample(orig_4_4))"
+      ],
+      "metadata": {
+        "id": "O_i0y72_JwGZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's try that on an image to get a feel for how it works:"
+      ],
+      "metadata": {
+        "id": "AobyC8IILbCO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://raw.githubusercontent.com/udlbook/udlbook/main/Notebooks/Chap10/test_image.png"
+      ],
+      "metadata": {
+        "id": "3dJEo-6DM-Py"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# load the image\n",
+        "image = Image.open('test_image.png')\n",
+        "# convert image to numpy array\n",
+        "data = asarray(image)\n",
+        "data_subsample = subsample(data);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_subsample, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_subsample2 = subsample(data_subsample)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_subsample2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_subsample3 = subsample(data_subsample2)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_subsample3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "HCZVutk6NB6B"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's try max-pooling\n",
+        "def maxpool(x_in):\n",
+        "  x_out = np.zeros(( int(np.floor(x_in.shape[0]/2)), int(np.floor(x_in.shape[1]/2)) ))\n",
+        "  # TO DO -- write the maxpool routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "Z99uYehaPtJa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_4_4)\n",
+        "print(\"Maxpooled:\")\n",
+        "print(maxpool(orig_4_4))"
+      ],
+      "metadata": {
+        "id": "J4KMTMmG9P44"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's see what Rick looks like:\n",
+        "data_maxpool = maxpool(data);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_maxpool, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_maxpool2 = maxpool(data_maxpool)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_maxpool2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_maxpool3 = maxpool(data_maxpool2)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_maxpool3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "0ES0sB8t9Wyv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that the stripes on his shirt gradually turn to white because we keep retaining the brightest local pixels."
+      ],
+      "metadata": {
+        "id": "nMtSdBGlAktq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Finally, let's try mean pooling\n",
+        "def meanpool(x_in):\n",
+        "  x_out = np.zeros(( int(np.floor(x_in.shape[0]/2)), int(np.floor(x_in.shape[1]/2)) ))\n",
+        "  # TO DO -- write the meanpool routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "ZQBjBtmB_aGQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_4_4)\n",
+        "print(\"Meanpooled:\")\n",
+        "print(meanpool(orig_4_4))"
+      ],
+      "metadata": {
+        "id": "N4VDlWNt_8dp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's see what Rick looks like:\n",
+        "data_meanpool = meanpool(data);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_meanpool, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_meanpool2 = meanpool(data_maxpool)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_meanpool2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_meanpool3 = meanpool(data_meanpool2)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_meanpool3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "Lkg5zUYo_-IV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Notice that the three low resolution images look quite different. <br>\n",
+        "\n",
+        "Now let's upscale them again"
+      ],
+      "metadata": {
+        "id": "J7VssF4pBf2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define 2 by 2 original patch\n",
+        "orig_2_2 = np.array([[6, 8], [8,4]])\n",
+        "print(orig_2_2)"
+      ],
+      "metadata": {
+        "id": "Q4N7i76FA_YH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's first use the duplication method\n",
+        "def duplicate(x_in):\n",
+        "  x_out = np.zeros(( x_in.shape[0]*2, x_in.shape[1]*2 ))\n",
+        "  # TO DO -- write the duplication routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "6eSjnl3cB5g4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_2_2)\n",
+        "print(\"Duplicated:\")\n",
+        "print(duplicate(orig_2_2))"
+      ],
+      "metadata": {
+        "id": "4FtRcvXrFLg7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's re-upsample, sub-sampled rick\n",
+        "data_duplicate = duplicate(data_subsample3);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_subsample3, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_duplicate, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_duplicate2 = duplicate(data_duplicate)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_duplicate2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_duplicate3 = duplicate(data_duplicate2)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_duplicate3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "agq0YN34FQfA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "They look the same, but if you look at the axes, you'll see that the pixels are just duplicated."
+      ],
+      "metadata": {
+        "id": "bCQrJ_M8GUFs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's try max pooling back up\n",
+        "# The input x_high_res is the original high res image, from which you can deduce the position of the maximum index\n",
+        "def max_unpool(x_in, x_high_res):\n",
+        "  x_out = np.zeros(( x_in.shape[0]*2, x_in.shape[1]*2 ))\n",
+        "  # TO DO -- write the subsampling routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "uDUDChmBF71_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_2_2)\n",
+        "print(\"Max unpooled:\")\n",
+        "print(max_unpool(orig_2_2,orig_4_4))"
+      ],
+      "metadata": {
+        "id": "EmjptCVNHq74"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's re-upsample, sub-sampled rick\n",
+        "data_max_unpool= max_unpool(data_maxpool3,data_maxpool2);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_maxpool3, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_max_unpool, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_max_unpool2 = max_unpool(data_max_unpool, data_maxpool)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_max_unpool2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_max_unpool3 = max_unpool(data_max_unpool2, data)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_max_unpool3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "SSPhTuV6H4ZH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we'll try upsampling using bilinear interpolation.  We'll treat the positions off the image as zeros by padding the original image and round fractional values upwards using np.ceil()"
+      ],
+      "metadata": {
+        "id": "sBx36bvbJHrK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def bilinear(x_in):\n",
+        "  x_out = np.zeros(( x_in.shape[0]*2, x_in.shape[1]*2 ))\n",
+        "  x_in_pad = np.zeros((x_in.shape[0]+1, x_in.shape[1]+1))\n",
+        "  x_in_pad[0:x_in.shape[0],0:x_in.shape[1]] = x_in\n",
+        "  # TO DO -- write the duplication routine\n",
+        "  # Replace this line\n",
+        "  x_out = x_out\n",
+        "\n",
+        "  return x_out"
+      ],
+      "metadata": {
+        "id": "00XpfQo3Ivdf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Original:\")\n",
+        "print(orig_2_2)\n",
+        "print(\"Bilinear:\")\n",
+        "print(bilinear(orig_2_2))"
+      ],
+      "metadata": {
+        "id": "qI5oRVCCNRob"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's re-upsample, sub-sampled rick\n",
+        "data_bilinear = bilinear(data_meanpool3);\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_meanpool3, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_bilinear, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_bilinear2 = bilinear(data_bilinear)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_bilinear2, cmap='gray')\n",
+        "plt.show()\n",
+        "\n",
+        "data_bilinear3 = duplicate(data_bilinear2)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(data_bilinear3, cmap='gray')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4m0bkhdmNRec"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb

@@ -0,0 +1,310 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNELb86uz5qbhEKH81UqFKT",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 10.5: Convolution for MNIST**\n",
+        "\n",
+        "This notebook builds a proper network for 2D convolution.  It works with the MNIST dataset (figure 15.15a), which was the original classic dataset for classifying images.  The network will take a 28x28 grayscale image and classify it into one of 10 classes representing a digit.\n",
+        "\n",
+        "The code is adapted from https://nextjournal.com/gkoehler/pytorch-mnist\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torchvision\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torch.optim as optim\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this once to load the train and test data straight into a dataloader class\n",
+        "# that will provide the batches\n",
+        "\n",
+        "# (It may complain that some files are missing because the files seem to have been\n",
+        "# reorganized on the underlying website, but it still seems to work). If everything is working\n",
+        "# properly, then the whole notebook should run to the end without further problems\n",
+        "# even before you make changes.\n",
+        "batch_size_train = 64\n",
+        "batch_size_test = 1000\n",
+        "train_loader = torch.utils.data.DataLoader(\n",
+        "  torchvision.datasets.MNIST('/files/', train=True, download=True,\n",
+        "                             transform=torchvision.transforms.Compose([\n",
+        "                               torchvision.transforms.ToTensor(),\n",
+        "                               torchvision.transforms.Normalize(\n",
+        "                                 (0.1307,), (0.3081,))\n",
+        "                             ])),\n",
+        "  batch_size=batch_size_train, shuffle=True)\n",
+        "\n",
+        "test_loader = torch.utils.data.DataLoader(\n",
+        "  torchvision.datasets.MNIST('/files/', train=False, download=True,\n",
+        "                             transform=torchvision.transforms.Compose([\n",
+        "                               torchvision.transforms.ToTensor(),\n",
+        "                               torchvision.transforms.Normalize(\n",
+        "                                 (0.1307,), (0.3081,))\n",
+        "                             ])),\n",
+        "  batch_size=batch_size_test, shuffle=True)"
+      ],
+      "metadata": {
+        "id": "wScBGXXFVadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "YGwbxJDEm88i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's draw some of the training data\n",
+        "examples = enumerate(test_loader)\n",
+        "batch_idx, (example_data, example_targets) = next(examples)\n",
+        "\n",
+        "fig = plt.figure()\n",
+        "for i in range(6):\n",
+        "  plt.subplot(2,3,i+1)\n",
+        "  plt.tight_layout()\n",
+        "  plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
+        "  plt.title(\"Ground Truth: {}\".format(example_targets[i]))\n",
+        "  plt.xticks([])\n",
+        "  plt.yticks([])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network.  This is a more typical way to define a network than the sequential structure.  We define a class for the network, and define the parameters in the constructor.  Then we use a function called forward to actually run the network.  It's easy to see how you might use residual connections in this format."
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from os import X_OK\n",
+        "# TODO Change this class to implement\n",
+        "# 1. A valid convolution with kernel size 5, 1 input channel and 10 output channels\n",
+        "# 2. A max pooling operation over a 2x2 area\n",
+        "# 3. A Relu\n",
+        "# 4. A valid convolution with kernel size 5, 10 input channels and 20 output channels\n",
+        "# 5. A 2D Dropout layer\n",
+        "# 6. A max pooling operation over a 2x2 area\n",
+        "# 7. A relu\n",
+        "# 8. A flattening operation\n",
+        "# 9. A fully connected layer mapping from (whatever dimensions we are at-- find out using .shape) to 50\n",
+        "# 10. A ReLU\n",
+        "# 11. A fully connected layer mapping from 50 to 10 dimensions\n",
+        "# 12. A softmax function.\n",
+        "\n",
+        "# Replace this class which implements a minimal network (which still does okay)\n",
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(Net, self).__init__()\n",
+        "        # Valid convolution, 1 channel in, 2 channels out, stride 1, kernel size = 3\n",
+        "        self.conv1 = nn.Conv2d(1, 2, kernel_size=3)\n",
+        "        # Dropout for convolutions\n",
+        "        self.drop = nn.Dropout2d()\n",
+        "        # Fully connected layer\n",
+        "        self.fc1 = nn.Linear(338, 10)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.conv1(x)\n",
+        "        x = self.drop(x)\n",
+        "        x = F.max_pool2d(x,2)\n",
+        "        x = F.relu(x)\n",
+        "        x = x.flatten(1)\n",
+        "        x = self.fc1(x)\n",
+        "        x = F.log_softmax(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "EQkvw2KOPVl7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "qWZtkCZcU_dg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create network\n",
+        "model = Net()\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "# Define optimizer\n",
+        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Main training routine\n",
+        "def train(epoch):\n",
+        "  model.train()\n",
+        "  # Get each\n",
+        "  for batch_idx, (data, target) in enumerate(train_loader):\n",
+        "    optimizer.zero_grad()\n",
+        "    output = model(data)\n",
+        "    loss = F.nll_loss(output, target)\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "    # Store results\n",
+        "    if batch_idx % 10 == 0:\n",
+        "      print('Train Epoch: {} [{}/{}]\\tLoss: {:.6f}'.format(\n",
+        "        epoch, batch_idx * len(data), len(train_loader.dataset), loss.item()))"
+      ],
+      "metadata": {
+        "id": "xKQd9PzkQ766"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run on test data\n",
+        "def test():\n",
+        "  model.eval()\n",
+        "  test_loss = 0\n",
+        "  correct = 0\n",
+        "  with torch.no_grad():\n",
+        "    for data, target in test_loader:\n",
+        "      output = model(data)\n",
+        "      test_loss += F.nll_loss(output, target, size_average=False).item()\n",
+        "      pred = output.data.max(1, keepdim=True)[1]\n",
+        "      correct += pred.eq(target.data.view_as(pred)).sum()\n",
+        "  test_loss /= len(test_loader.dataset)\n",
+        "  print('\\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
+        "    test_loss, correct, len(test_loader.dataset),\n",
+        "    100. * correct / len(test_loader.dataset)))"
+      ],
+      "metadata": {
+        "id": "Byn-f7qWRLxX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get initial performance\n",
+        "test()\n",
+        "# Train for three epochs\n",
+        "n_epochs = 3\n",
+        "for epoch in range(1, n_epochs + 1):\n",
+        "  train(epoch)\n",
+        "  test()"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run network on data we got before and show predictions\n",
+        "output = model(example_data)\n",
+        "\n",
+        "fig = plt.figure()\n",
+        "for i in range(10):\n",
+        "  plt.subplot(5,5,i+1)\n",
+        "  plt.tight_layout()\n",
+        "  plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
+        "  plt.title(\"Prediction: {}\".format(\n",
+        "    output.data.max(1, keepdim=True)[1][i].item()))\n",
+        "  plt.xticks([])\n",
+        "  plt.yticks([])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "o7fRUAy9Se1B"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap11/11_1_Shattered_Gradients.ipynb

@@ -0,0 +1,392 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyP3VmRg51U+7NCfSYjRRrgv",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 11.1: Shattered gradients**\n",
+        "\n",
+        "This notebook investigates the phenomenon of shattered gradients as discussed in section 11.1.1.  It replicates some of the experiments in [Balduzzi et al. (2017)](https://arxiv.org/abs/1702.08591).\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "pOZ6Djz0dhoy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "iaFyNGhU21VJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First let's define a neural network. We'll initialize both the weights and biases randomly with Glorot initialization (He initialization without the factor of two)"
+      ],
+      "metadata": {
+        "id": "YcNlAxnE3XXn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# K is width, D is number of hidden units in each layer\n",
+        "def init_params(K, D):\n",
+        "  # Set seed so we always get the same random numbers\n",
+        "  np.random.seed(1)\n",
+        "\n",
+        "  # Input layer\n",
+        "  D_i = 1\n",
+        "  # Output layer\n",
+        "  D_o = 1\n",
+        "\n",
+        "  # Glorot initialization\n",
+        "  sigma_sq_omega = 1.0/D\n",
+        "\n",
+        "  # Make empty lists\n",
+        "  all_weights = [None] * (K+1)\n",
+        "  all_biases = [None] * (K+1)\n",
+        "\n",
+        "  # Create parameters for input and output layers\n",
+        "  all_weights[0] = np.random.normal(size=(D, D_i))*np.sqrt(sigma_sq_omega)\n",
+        "  all_weights[-1] = np.random.normal(size=(D_o, D)) * np.sqrt(sigma_sq_omega)\n",
+        "  all_biases[0] = np.random.normal(size=(D,1))* np.sqrt(sigma_sq_omega)\n",
+        "  all_biases[-1]= np.random.normal(size=(D_o,1))* np.sqrt(sigma_sq_omega)\n",
+        "\n",
+        "  # Create intermediate layers\n",
+        "  for layer in range(1,K):\n",
+        "    all_weights[layer] = np.random.normal(size=(D,D))*np.sqrt(sigma_sq_omega)\n",
+        "    all_biases[layer] = np.random.normal(size=(D,1))* np.sqrt(sigma_sq_omega)\n",
+        "\n",
+        "  return all_weights, all_biases"
+      ],
+      "metadata": {
+        "id": "kr-q7hc23Bn9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The next two functions define the forward pass of the algorithm"
+      ],
+      "metadata": {
+        "id": "kwcn5z7-dq_1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "def forward_pass(net_input, all_weights, all_biases):\n",
+        "\n",
+        "  # Retrieve number of layers\n",
+        "  K = len(all_weights) -1\n",
+        "\n",
+        "  # We'll store the pre-activations at each layer in a list \"all_f\"\n",
+        "  # and the activations in a second list[all_h].\n",
+        "  all_f = [None] * (K+1)\n",
+        "  all_h = [None] * (K+1)\n",
+        "\n",
+        "  #For convenience, we'll set\n",
+        "  # all_h[0] to be the input, and all_f[K] will be the output\n",
+        "  all_h[0] = net_input\n",
+        "\n",
+        "  # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
+        "  for layer in range(K):\n",
+        "      # Update preactivations and activations at this layer according to eqn 7.5\n",
+        "      all_f[layer] = all_biases[layer] + np.matmul(all_weights[layer], all_h[layer])\n",
+        "      all_h[layer+1] = ReLU(all_f[layer])\n",
+        "\n",
+        "  # Compute the output from the last hidden layer\n",
+        "  all_f[K] = all_biases[K] + np.matmul(all_weights[K], all_h[K])\n",
+        "\n",
+        "  # Retrieve the output\n",
+        "  net_output = all_f[K]\n",
+        "\n",
+        "  return net_output, all_f, all_h"
+      ],
+      "metadata": {
+        "id": "_2w-Tr7G3sYq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The next two functions compute the gradient of the output with respect to the input using the back propagation algorithm."
+      ],
+      "metadata": {
+        "id": "aM2l7QafeC8T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We'll need the indicator function\n",
+        "def indicator_function(x):\n",
+        "  x_in = np.array(x)\n",
+        "  x_in[x_in>=0] = 1\n",
+        "  x_in[x_in<0] = 0\n",
+        "  return x_in\n",
+        "\n",
+        "# Main backward pass routine\n",
+        "def calc_input_output_gradient(x_in, all_weights, all_biases):\n",
+        "\n",
+        "  # Run the forward pass\n",
+        "  y, all_f, all_h = forward_pass(x_in, all_weights, all_biases)\n",
+        "\n",
+        "  # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
+        "  all_dl_dweights = [None] * (K+1)\n",
+        "  all_dl_dbiases = [None] * (K+1)\n",
+        "  # And we'll store the derivatives of the loss with respect to the activation and preactivations in lists\n",
+        "  all_dl_df = [None] * (K+1)\n",
+        "  all_dl_dh = [None] * (K+1)\n",
+        "  # Again for convenience we'll stick with the convention that all_h[0] is the net input and all_f[k] in the net output\n",
+        "\n",
+        "  # Compute derivatives of net output with respect to loss\n",
+        "  all_dl_df[K] = np.ones_like(all_f[K])\n",
+        "\n",
+        "  # Now work backwards through the network\n",
+        "  for layer in range(K,-1,-1):\n",
+        "    all_dl_dbiases[layer] = np.array(all_dl_df[layer])\n",
+        "    all_dl_dweights[layer] = np.matmul(all_dl_df[layer], all_h[layer].transpose())\n",
+        "\n",
+        "    all_dl_dh[layer] = np.matmul(all_weights[layer].transpose(), all_dl_df[layer])\n",
+        "\n",
+        "    if layer > 0:\n",
+        "      all_dl_df[layer-1] = indicator_function(all_f[layer-1]) * all_dl_dh[layer]\n",
+        "\n",
+        "\n",
+        "  return all_dl_dh[0],y"
+      ],
+      "metadata": {
+        "id": "DwR3eGMgV8bl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Double check we have the gradient correct using finite differences"
+      ],
+      "metadata": {
+        "id": "Ar_VmraReSWe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "D = 200; K = 3\n",
+        "# Initialize parameters\n",
+        "all_weights, all_biases = init_params(K,D)\n",
+        "\n",
+        "x = np.ones((1,1))\n",
+        "dydx,y = calc_input_output_gradient(x, all_weights, all_biases)\n",
+        "\n",
+        "# Offset for finite gradients\n",
+        "delta = 0.00000001\n",
+        "x1 = x\n",
+        "y1,*_ = forward_pass(x1, all_weights, all_biases)\n",
+        "x2 = x+delta\n",
+        "y2,*_ = forward_pass(x2, all_weights, all_biases)\n",
+        "# Finite difference calculation\n",
+        "dydx_fd = (y2-y1)/delta\n",
+        "\n",
+        "print(\"Gradient calculation=%f, Finite difference gradient=%f\"%(dydx.squeeze(),dydx_fd.squeeze()))\n"
+      ],
+      "metadata": {
+        "id": "KJpQPVd36Haq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Helper function that computes the derivatives for a 1D array of input values and plots them."
+      ],
+      "metadata": {
+        "id": "YC-LAYRKtbxp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_derivatives(K, D):\n",
+        "\n",
+        "  # Initialize parameters\n",
+        "  all_weights, all_biases = init_params(K,D)\n",
+        "\n",
+        "  x_in = np.arange(-2,2, 4.0/256.0)\n",
+        "  x_in = np.resize(x_in, (1,len(x_in)))\n",
+        "  dydx,y = calc_input_output_gradient(x_in, all_weights, all_biases)\n",
+        "\n",
+        "  fig,ax = plt.subplots()\n",
+        "  ax.plot(np.squeeze(x_in), np.squeeze(dydx), 'b-')\n",
+        "  ax.set_xlim(-2,2)\n",
+        "  ax.set_xlabel(r'Input, $x$')\n",
+        "  ax.set_ylabel(r'Gradient, $dy/dx$')\n",
+        "  ax.set_title('No layers = %d'%(K))\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "uJr5eDe648jF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Build a model with one hidden layer and 200 neurons and plot derivatives\n",
+        "D = 200; K = 1\n",
+        "plot_derivatives(K,D)\n",
+        "\n",
+        "# TODO -- Interpret this result\n",
+        "# Why does the plot have some flat regions?\n",
+        "\n",
+        "# TODO -- Add code to plot the derivatives for models with 24 and 50 hidden layers\n",
+        "# with 200 neurons per layer\n",
+        "\n",
+        "# TODO -- Why does this graph not have visible flat regions?\n",
+        "\n",
+        "# TODO -- Why does the magnitude of the gradients decrease as we increase the number\n",
+        "# of hidden layers\n",
+        "\n",
+        "# TODO -- Do you find this a convincing replication of the experiment in the original paper? (I don't)\n",
+        "# Can you help me find why I have failed to replicate this result?  udlbookmail@gmail.com"
+      ],
+      "metadata": {
+        "id": "56gTMTCb49KO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's look at the autocorrelation function now"
+      ],
+      "metadata": {
+        "id": "f_0zjQbxuROQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def autocorr(dydx):\n",
+        "    # TODO -- compute the autocorrelation function\n",
+        "    # Use the numpy function \"correlate\" with the mode set to \"same\"\n",
+        "    # Replace this line:\n",
+        "    ac = np.ones((256,1))\n",
+        "\n",
+        "    return ac"
+      ],
+      "metadata": {
+        "id": "ggnO8hfoRN1e"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Helper function to plot the autocorrelation function and normalize so correlation is one with offset of zero"
+      ],
+      "metadata": {
+        "id": "EctWSV1RuddK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_autocorr(K, D):\n",
+        "\n",
+        "  # Initialize parameters\n",
+        "  all_weights, all_biases = init_params(K,D)\n",
+        "\n",
+        "  x_in = np.arange(-2.0,2.0, 4.0/256)\n",
+        "  x_in = np.resize(x_in, (1,len(x_in)))\n",
+        "  dydx,y = calc_input_output_gradient(x_in, all_weights, all_biases)\n",
+        "  ac = autocorr(np.squeeze(dydx))\n",
+        "  ac = ac / ac[128]\n",
+        "\n",
+        "  y = ac[128:]\n",
+        "  x = np.squeeze(x_in)[128:]\n",
+        "  fig,ax = plt.subplots()\n",
+        "  ax.plot(x,y, 'b-')\n",
+        "  ax.set_xlim([0,2])\n",
+        "  ax.set_xlabel('Distance')\n",
+        "  ax.set_ylabel('Autocorrelation')\n",
+        "  ax.set_title('No layers = %d'%(K))\n",
+        "  plt.show()\n"
+      ],
+      "metadata": {
+        "id": "2LKlZ9u_WQXN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the autocorrelation functions\n",
+        "D = 200; K =1\n",
+        "plot_autocorr(K,D)\n",
+        "D = 200; K =50\n",
+        "plot_autocorr(K,D)\n",
+        "\n",
+        "# TODO -- Do you find this a convincing replication of the experiment in the original paper? (I don't)\n",
+        "# Can you help me find why I have failed to replicate this result?"
+      ],
+      "metadata": {
+        "id": "RD9JTdjNWw6p"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap11/11_2_Residual_Networks.ipynb

@@ -0,0 +1,277 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNIY8tswL9e48d5D53aSmHO",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 11.2: Residual Networks**\n",
+        "\n",
+        "This notebook adapts the networks for MNIST1D to use residual connections.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
+      ],
+      "metadata": {
+        "id": "D5yLObtZCi9J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d\n",
+        "import random"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+      ],
+      "metadata": {
+        "id": "twI72ZCrCt5z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = data['x'].transpose()\n",
+        "train_data_y = data['y']\n",
+        "val_data_x = data['x_test'].transpose()\n",
+        "val_data_y = data['y_test']\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "\n",
+        "# We will adapt this model to have residual connections around the linear layers\n",
+        "# This is the same model we used in practical 8.1, but we can't use the sequential\n",
+        "# class for residual networks (which aren't strictly sequential).  Hence, I've rewritten\n",
+        "# it as a model that inherits from a base class\n",
+        "\n",
+        "class ResidualNetwork(torch.nn.Module):\n",
+        "  def __init__(self, input_size, output_size, hidden_size=100):\n",
+        "    super(ResidualNetwork, self).__init__()\n",
+        "    self.linear1 = nn.Linear(input_size, hidden_size)\n",
+        "    self.linear2 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear3 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear4 = nn.Linear(hidden_size, output_size)\n",
+        "    print(\"Initialized MLPBase model with {} parameters\".format(self.count_params()))\n",
+        "\n",
+        "  def count_params(self):\n",
+        "    return sum([p.view(-1).shape[0] for p in self.parameters()])\n",
+        "\n",
+        "# TODO -- Add residual connections to this model\n",
+        "# The order of operations within each block should similar to figure 11.5b\n",
+        "# ie., linear1 first, ReLU+linear2 in first residual block, ReLU+linear3 in second residual block), linear4 at end\n",
+        "# Replace this function\n",
+        "  def forward(self, x):\n",
+        "    h1 = self.linear1(x).relu()\n",
+        "    h2 = self.linear2(h1).relu()\n",
+        "    h3 = self.linear3(h2).relu()\n",
+        "    return self.linear4(h3)\n"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Define the model\n",
+        "model = ResidualNetwork(40, 10)\n",
+        "\n",
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 20 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=20, gamma=0.5)\n",
+        "# convert data to torch tensors\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 100\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_val = model(x_val)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='test')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('TrainError %3.2f, Val Error %3.2f'%(errors_train[-1],errors_val[-1]))\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "CcP_VyEmE2sv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The primary motivation of residual networks is to allow training of much deeper networks.   \n",
+        "\n",
+        "TODO: Try running this network with and without the residual connections.  Does adding the residual connections change the performance?"
+      ],
+      "metadata": {
+        "id": "wMmqhmxuAx0M"
+      }
+    }
+  ]
+}

Notebooks/Chap11/11_3_Batch_Normalization.ipynb

@@ -0,0 +1,330 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPx2mM2zTHmDJeKeiE1RymT",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 11.3: Batch normalization**\n",
+        "\n",
+        "This notebook investigates the use of batch normalization in residual networks.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
+      ],
+      "metadata": {
+        "id": "D5yLObtZCi9J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mnist1d\n",
+        "import random"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = mnist1d.data.get_dataset_args()\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "\n",
+        "# The training and test input and outputs are in\n",
+        "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+      ],
+      "metadata": {
+        "id": "twI72ZCrCt5z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = data['x'].transpose()\n",
+        "train_data_y = data['y']\n",
+        "val_data_x = data['x_test'].transpose()\n",
+        "val_data_y = data['y_test']\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def print_variance(name, data):\n",
+        "  # First dimension(rows) is batch elements\n",
+        "  # Second dimension(columns) is neurons.\n",
+        "  np_data = data.detach().numpy()\n",
+        "  # Compute variance across neurons and average these variances over members of the batch\n",
+        "  neuron_variance = np.mean(np.var(np_data, axis=0))\n",
+        "  # Print out the name and the variance\n",
+        "  print(\"%s variance=%f\"%(name,neuron_variance))"
+      ],
+      "metadata": {
+        "id": "3bBpJIV-N-lt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def run_one_step_of_model(model, x_train, y_train):\n",
+        "  # choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "  loss_function = nn.CrossEntropyLoss()\n",
+        "  # construct SGD optimizer and initialize learning rate and momentum\n",
+        "  optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "\n",
+        "  # load the data into a class that creates the batches\n",
+        "  data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=200, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "  # Initialize model weights\n",
+        "  model.apply(weights_init)\n",
+        "\n",
+        "  # Get a batch\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the loss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "    # Break out of this loop -- we just want to see the first\n",
+        "    # iteration, but usually we would continue\n",
+        "    break"
+      ],
+      "metadata": {
+        "id": "DFlu45pORQEz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# convert training data to torch tensors\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))"
+      ],
+      "metadata": {
+        "id": "i7Q0ScWgRe4G"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This is a simple residual model with 5 residual branches in a row\n",
+        "class ResidualNetwork(torch.nn.Module):\n",
+        "  def __init__(self, input_size, output_size, hidden_size=100):\n",
+        "    super(ResidualNetwork, self).__init__()\n",
+        "    self.linear1 = nn.Linear(input_size, hidden_size)\n",
+        "    self.linear2 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear3 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear4 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear5 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear6 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear7 = nn.Linear(hidden_size, output_size)\n",
+        "\n",
+        "  def count_params(self):\n",
+        "    return sum([p.view(-1).shape[0] for p in self.parameters()])\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    print_variance(\"Input\",x)\n",
+        "    f = self.linear1(x)\n",
+        "    print_variance(\"First preactivation\",f)\n",
+        "    res1 = f+ self.linear2(f.relu())\n",
+        "    print_variance(\"After first residual connection\",res1)\n",
+        "    res2 = res1 + self.linear3(res1.relu())\n",
+        "    print_variance(\"After second residual connection\",res2)\n",
+        "    res3 = res2 + self.linear4(res2.relu())\n",
+        "    print_variance(\"After third residual connection\",res3)\n",
+        "    res4 = res3 + self.linear5(res3.relu())\n",
+        "    print_variance(\"After fourth residual connection\",res4)\n",
+        "    res5 = res4 + self.linear6(res4.relu())\n",
+        "    print_variance(\"After fifth residual connection\",res5)\n",
+        "    return self.linear7(res5)"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the model and run for one step\n",
+        "# Monitoring the variance at each point in the network\n",
+        "n_hidden = 100\n",
+        "n_input = 40\n",
+        "n_output = 10\n",
+        "model = ResidualNetwork(n_input, n_output, n_hidden)\n",
+        "run_one_step_of_model(model, x_train, y_train)"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Notice that the variance roughly doubles at each step so it increases exponentially as in figure 11.6b in the book."
+      ],
+      "metadata": {
+        "id": "0kZUlWkkW8jE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Adapt the residual network below to add a batch norm operation\n",
+        "# before the contents of each residual link as in figure 11.6c in the book\n",
+        "# Use the torch function nn.BatchNorm1d\n",
+        "class ResidualNetworkWithBatchNorm(torch.nn.Module):\n",
+        "  def __init__(self, input_size, output_size, hidden_size=100):\n",
+        "    super(ResidualNetworkWithBatchNorm, self).__init__()\n",
+        "    self.linear1 = nn.Linear(input_size, hidden_size)\n",
+        "    self.linear2 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear3 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear4 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear5 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear6 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear7 = nn.Linear(hidden_size, output_size)\n",
+        "\n",
+        "  def count_params(self):\n",
+        "    return sum([p.view(-1).shape[0] for p in self.parameters()])\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    print_variance(\"Input\",x)\n",
+        "    f = self.linear1(x)\n",
+        "    print_variance(\"First preactivation\",f)\n",
+        "    res1 = f+ self.linear2(f.relu())\n",
+        "    print_variance(\"After first residual connection\",res1)\n",
+        "    res2 = res1 + self.linear3(res1.relu())\n",
+        "    print_variance(\"After second residual connection\",res2)\n",
+        "    res3 = res2 + self.linear4(res2.relu())\n",
+        "    print_variance(\"After third residual connection\",res3)\n",
+        "    res4 = res3 + self.linear5(res3.relu())\n",
+        "    print_variance(\"After fourth residual connection\",res4)\n",
+        "    res5 = res4 + self.linear6(res4.relu())\n",
+        "    print_variance(\"After fifth residual connection\",res5)\n",
+        "    return self.linear7(res5)"
+      ],
+      "metadata": {
+        "id": "5JvMmaRITKGd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the model\n",
+        "n_hidden = 100\n",
+        "n_input = 40\n",
+        "n_output = 10\n",
+        "model = ResidualNetworkWithBatchNorm(n_input, n_output, n_hidden)\n",
+        "run_one_step_of_model(model, x_train, y_train)"
+      ],
+      "metadata": {
+        "id": "2U3DnlH9Uw6c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Note that the variance now increases linearly as in figure 11.6c."
+      ],
+      "metadata": {
+        "id": "R_ucFq9CXq8D"
+      }
+    }
+  ]
+}

Notebooks/Chap12/12_1_Self_Attention.ipynb

@@ -0,0 +1,375 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOKrX9gmuhl9+KwscpZKr3u",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 12.1: Self Attention**\n",
+        "\n",
+        "This notebook builds a self-attention mechanism from scratch, as discussed in section 12.2 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The self-attention mechanism maps $N$ inputs $\\mathbf{x}_{n}\\in\\mathbb{R}^{D}$ and returns $N$ outputs $\\mathbf{x}'_{n}\\in \\mathbb{R}^{D}$.  \n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "9OJkkoNqCVK2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so we get the same random numbers\n",
+        "np.random.seed(3)\n",
+        "# Number of inputs\n",
+        "N = 3\n",
+        "# Number of dimensions of each input\n",
+        "D = 4\n",
+        "# Create an empty list\n",
+        "all_x = []\n",
+        "# Create elements x_n and append to list\n",
+        "for n in range(N):\n",
+        "  all_x.append(np.random.normal(size=(D,1)))\n",
+        "# Print out the list\n",
+        "print(all_x)\n"
+      ],
+      "metadata": {
+        "id": "oAygJwLiCSri"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll also need the weights and biases for the keys, queries, and values (equations 12.2 and 12.4)"
+      ],
+      "metadata": {
+        "id": "W2iHFbtKMaDp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so we get the same random numbers\n",
+        "np.random.seed(0)\n",
+        "\n",
+        "# Choose random values for the parameters\n",
+        "omega_q = np.random.normal(size=(D,D))\n",
+        "omega_k = np.random.normal(size=(D,D))\n",
+        "omega_v = np.random.normal(size=(D,D))\n",
+        "beta_q = np.random.normal(size=(D,1))\n",
+        "beta_k = np.random.normal(size=(D,1))\n",
+        "beta_v = np.random.normal(size=(D,1))"
+      ],
+      "metadata": {
+        "id": "79TSK7oLMobe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the queries, keys, and values for each input"
+      ],
+      "metadata": {
+        "id": "VxaKQtP3Ng6R"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make three lists to store queries, keys, and values\n",
+        "all_queries = []\n",
+        "all_keys = []\n",
+        "all_values = []\n",
+        "# For every input\n",
+        "for x in all_x:\n",
+        "  # TODO -- compute the keys, queries and values.\n",
+        "  # Replace these three lines\n",
+        "  query = np.ones_like(x)\n",
+        "  key = np.ones_like(x)\n",
+        "  value = np.ones_like(x)\n",
+        "\n",
+        "  all_queries.append(query)\n",
+        "  all_keys.append(key)\n",
+        "  all_values.append(value)"
+      ],
+      "metadata": {
+        "id": "TwDK2tfdNmw9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll need a softmax function (equation 12.5) -- here, it will take a list of arbitrary numbers and return a list where the elements are non-negative and sum to one\n"
+      ],
+      "metadata": {
+        "id": "Se7DK6PGPSUk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def softmax(items_in):\n",
+        "\n",
+        "  # TODO Compute the elements of items_out\n",
+        "  # Replace this line\n",
+        "  items_out = items_in.copy()\n",
+        "\n",
+        "  return items_out ;"
+      ],
+      "metadata": {
+        "id": "u93LIcE5PoiM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now compute the self attention values:"
+      ],
+      "metadata": {
+        "id": "8aJVhbKDW7lm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create emptymlist for output\n",
+        "all_x_prime = []\n",
+        "\n",
+        "# For each output\n",
+        "for n in range(N):\n",
+        "  # Create list for dot products of query N with all keys\n",
+        "  all_km_qn = []\n",
+        "  # Compute the dot products\n",
+        "  for key in all_keys:\n",
+        "    # TODO -- compute the appropriate dot product\n",
+        "    # Replace this line\n",
+        "    dot_product = 1\n",
+        "\n",
+        "    # Store dot product\n",
+        "    all_km_qn.append(dot_product)\n",
+        "\n",
+        "  # Compute dot product\n",
+        "  attention = softmax(all_km_qn)\n",
+        "  # Print result (should be positive sum to one)\n",
+        "  print(\"Attentions for output \", n)\n",
+        "  print(attention)\n",
+        "\n",
+        "  # TODO: Compute a weighted sum of all of the values according to the attention\n",
+        "  # (equation 12.3)\n",
+        "  # Replace this line\n",
+        "  x_prime = np.zeros((D,1))\n",
+        "\n",
+        "  all_x_prime.append(x_prime)\n",
+        "\n",
+        "\n",
+        "# Print out true values to check you have it correct\n",
+        "print(\"x_prime_0_calculated:\", all_x_prime[0].transpose())\n",
+        "print(\"x_prime_0_true: [[ 0.94744244 -0.24348429 -0.91310441 -0.44522983]]\")\n",
+        "print(\"x_prime_1_calculated:\", all_x_prime[1].transpose())\n",
+        "print(\"x_prime_1_true: [[ 1.64201168 -0.08470004  4.02764044  2.18690791]]\")\n",
+        "print(\"x_prime_2_calculated:\", all_x_prime[2].transpose())\n",
+        "print(\"x_prime_2_true: [[ 1.61949281 -0.06641533  3.96863308  2.15858316]]\")\n"
+      ],
+      "metadata": {
+        "id": "yimz-5nCW6vQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the same thing, but using matrix calculations.  We'll store the $N$ inputs $\\mathbf{x}_{n}\\in\\mathbb{R}^{D}$ in the columns of a $D\\times N$ matrix, using equations 12.6 and 12.7/8.\n",
+        "\n",
+        "Note:  The book uses column vectors (for compatibility with the rest of the text), but in the wider literature it is more normal to store the inputs in the rows of a matrix;  in this case, the computation is the same, but all the matrices are transposed and the operations proceed in the reverse order."
+      ],
+      "metadata": {
+        "id": "PJ2vCQ_7C38K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define softmax operation that works independently on each column\n",
+        "def softmax_cols(data_in):\n",
+        "  # Exponentiate all of the values\n",
+        "  exp_values = np.exp(data_in) ;\n",
+        "  # Sum over columns\n",
+        "  denom = np.sum(exp_values, axis = 0);\n",
+        "  # Replicate denominator to N rows\n",
+        "  denom = np.matmul(np.ones((data_in.shape[0],1)), denom[np.newaxis,:])\n",
+        "  # Compute softmax\n",
+        "  softmax = exp_values / denom\n",
+        "  # return the answer\n",
+        "  return softmax"
+      ],
+      "metadata": {
+        "id": "obaQBdUAMXXv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " # Now let's compute self attention in matrix form\n",
+        "def self_attention(X,omega_v, omega_q, omega_k, beta_v, beta_q, beta_k):\n",
+        "\n",
+        "  # TODO -- Write this function\n",
+        "  # 1. Compute queries, keys, and values\n",
+        "  # 2. Compute dot products\n",
+        "  # 3. Apply softmax to calculate attentions\n",
+        "  # 4. Weight values by attentions\n",
+        "  # Replace this line\n",
+        "  X_prime = np.zeros_like(X);\n",
+        "\n",
+        "\n",
+        "  return X_prime"
+      ],
+      "metadata": {
+        "id": "gb2WvQ3SiH8r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Copy data into matrix\n",
+        "X = np.zeros((D, N))\n",
+        "X[:,0] = np.squeeze(all_x[0])\n",
+        "X[:,1] = np.squeeze(all_x[1])\n",
+        "X[:,2] = np.squeeze(all_x[2])\n",
+        "\n",
+        "# Run the self attention mechanism\n",
+        "X_prime = self_attention(X,omega_v, omega_q, omega_k, beta_v, beta_q, beta_k)\n",
+        "\n",
+        "# Print out the results\n",
+        "print(X_prime)"
+      ],
+      "metadata": {
+        "id": "MUOJbgJskUpl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "If you did this correctly, the values should be the same as above.\n",
+        "\n",
+        "TODO:  \n",
+        "\n",
+        "Print out the attention matrix\n",
+        "You will see that the values are quite extreme (one is very close to one and the others are very close to zero.  Now we'll fix this problem by using scaled dot-product attention."
+      ],
+      "metadata": {
+        "id": "as_lRKQFpvz0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's compute self attention in matrix form\n",
+        "def scaled_dot_product_self_attention(X,omega_v, omega_q, omega_k, beta_v, beta_q, beta_k):\n",
+        "\n",
+        "  # TODO -- Write this function\n",
+        "  # 1. Compute queries, keys, and values\n",
+        "  # 2. Compute dot products\n",
+        "  # 3. Scale the dot products as in equation 12.9\n",
+        "  # 4. Apply softmax to calculate attentions\n",
+        "  # 5. Weight values by attentions\n",
+        "  # Replace this line\n",
+        "  X_prime = np.zeros_like(X);\n",
+        "\n",
+        "  return X_prime"
+      ],
+      "metadata": {
+        "id": "kLU7PUnnqvIh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run the self attention mechanism\n",
+        "X_prime = scaled_dot_product_self_attention(X,omega_v, omega_q, omega_k, beta_v, beta_q, beta_k)\n",
+        "\n",
+        "# Print out the results\n",
+        "print(X_prime)"
+      ],
+      "metadata": {
+        "id": "n18e3XNzmVgL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO -- Investigate whether the self-attention mechanism is covariant with respect to permutation.\n",
+        "If it is, when we permute the columns of the input matrix $\\mathbf{X}$, the columns of the output matrix $\\mathbf{X}'$ will also be permuted.\n"
+      ],
+      "metadata": {
+        "id": "QDEkIrcgrql-"
+      }
+    }
+  ]
+}

Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb

@@ -0,0 +1,209 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 12.2: Multihead Self-Attention**\n",
+        "\n",
+        "This notebook builds a multihead self-attention mechanism as in figure 12.6\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The multihead self-attention mechanism maps $N$ inputs $\\mathbf{x}_{n}\\in\\mathbb{R}^{D}$ and returns $N$ outputs $\\mathbf{x}'_{n}\\in \\mathbb{R}^{D}$.  \n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "9OJkkoNqCVK2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so we get the same random numbers\n",
+        "np.random.seed(3)\n",
+        "# Number of inputs\n",
+        "N = 6\n",
+        "# Number of dimensions of each input\n",
+        "D = 8\n",
+        "# Create an empty list\n",
+        "X = np.random.normal(size=(D,N))\n",
+        "# Print X\n",
+        "print(X)"
+      ],
+      "metadata": {
+        "id": "oAygJwLiCSri"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll use two heads.  We'll need the weights and biases for the keys, queries, and values (equations 12.2 and 12.4).  We'll use two heads, and (as in the figure), we'll make the queries keys and values of size D/H"
+      ],
+      "metadata": {
+        "id": "W2iHFbtKMaDp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Number of heads\n",
+        "H = 2\n",
+        "# QDV dimension\n",
+        "H_D = int(D/H)\n",
+        "\n",
+        "# Set seed so we get the same random numbers\n",
+        "np.random.seed(0)\n",
+        "\n",
+        "# Choose random values for the parameters for the first head\n",
+        "omega_q1 = np.random.normal(size=(H_D,D))\n",
+        "omega_k1 = np.random.normal(size=(H_D,D))\n",
+        "omega_v1 = np.random.normal(size=(H_D,D))\n",
+        "beta_q1 = np.random.normal(size=(H_D,1))\n",
+        "beta_k1 = np.random.normal(size=(H_D,1))\n",
+        "beta_v1 = np.random.normal(size=(H_D,1))\n",
+        "\n",
+        "# Choose random values for the parameters for the second head\n",
+        "omega_q2 = np.random.normal(size=(H_D,D))\n",
+        "omega_k2 = np.random.normal(size=(H_D,D))\n",
+        "omega_v2 = np.random.normal(size=(H_D,D))\n",
+        "beta_q2 = np.random.normal(size=(H_D,1))\n",
+        "beta_k2 = np.random.normal(size=(H_D,1))\n",
+        "beta_v2 = np.random.normal(size=(H_D,1))\n",
+        "\n",
+        "# Choose random values for the parameters\n",
+        "omega_c = np.random.normal(size=(D,D))"
+      ],
+      "metadata": {
+        "id": "79TSK7oLMobe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compute the multiscale self-attention"
+      ],
+      "metadata": {
+        "id": "VxaKQtP3Ng6R"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define softmax operation that works independently on each column\n",
+        "def softmax_cols(data_in):\n",
+        "  # Exponentiate all of the values\n",
+        "  exp_values = np.exp(data_in) ;\n",
+        "  # Sum over columns\n",
+        "  denom = np.sum(exp_values, axis = 0);\n",
+        "  # Compute softmax (numpy broadcasts denominator to all rows automatically)\n",
+        "  softmax = exp_values / denom\n",
+        "  # return the answer\n",
+        "  return softmax"
+      ],
+      "metadata": {
+        "id": "obaQBdUAMXXv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " # Now let's compute self attention in matrix form\n",
+        "def multihead_scaled_self_attention(X,omega_v1, omega_q1, omega_k1, beta_v1, beta_q1, beta_k1, omega_v2, omega_q2, omega_k2, beta_v2, beta_q2, beta_k2, omega_c):\n",
+        "\n",
+        "  # TODO Write the multihead scaled self-attention mechanism.\n",
+        "  # Replace this line\n",
+        "  X_prime = np.zeros_like(X) ;\n",
+        "\n",
+        "\n",
+        "  return X_prime"
+      ],
+      "metadata": {
+        "id": "gb2WvQ3SiH8r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run the self attention mechanism\n",
+        "X_prime = multihead_scaled_self_attention(X,omega_v1, omega_q1, omega_k1, beta_v1, beta_q1, beta_k1, omega_v2, omega_q2, omega_k2, beta_v2, beta_q2, beta_k2, omega_c)\n",
+        "\n",
+        "# Print out the results\n",
+        "np.set_printoptions(precision=3)\n",
+        "print(\"Your answer:\")\n",
+        "print(X_prime)\n",
+        "\n",
+        "print(\"True values:\")\n",
+        "print(\"[[-21.207  -5.373 -20.933  -9.179 -11.319 -17.812]\")\n",
+        "print(\" [ -1.995   7.906 -10.516   3.452   9.863  -7.24 ]\")\n",
+        "print(\" [  5.479   1.115   9.244   0.453   5.656   7.089]\")\n",
+        "print(\" [ -7.413  -7.416   0.363  -5.573  -6.736  -0.848]\")\n",
+        "print(\" [-11.261  -9.937  -4.848  -8.915 -13.378  -5.761]\")\n",
+        "print(\" [  3.548  10.036  -2.244   1.604  12.113  -2.557]\")\n",
+        "print(\" [  4.888  -5.814   2.407   3.228  -4.232   3.71 ]\")\n",
+        "print(\" [  1.248  18.894  -6.409   3.224  19.717  -5.629]]\")\n",
+        "\n",
+        "# If your answers don't match, then make sure that you are doing the scaling, and make sure the scaling value is correct"
+      ],
+      "metadata": {
+        "id": "MUOJbgJskUpl"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap12/12_3_Tokenization.ipynb

@@ -0,0 +1,341 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyP0/KodWM9Dtr2x+8MdXXH1",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 12.3: Tokenization**\n",
+        "\n",
+        "This notebook builds set of tokens from a text string as in figure 12.8 of the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "I adapted this code from *SOMEWHERE*.  If anyone recognizes it, can you let me know and I will give the proper attribution or rewrite if the license is not permissive.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re, collections"
+      ],
+      "metadata": {
+        "id": "3_WkaFO3OfLi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text = \"a sailor went to sea sea sea \"+\\\n",
+        "                  \"to see what he could see see see \"+\\\n",
+        "                  \"but all that he could see see see \"+\\\n",
+        "                  \"was the bottom of the deep blue sea sea sea\""
+      ],
+      "metadata": {
+        "id": "tVZVuauIXmJk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Tokenize the input sentence To begin with the tokens are the individual letters and the </w> whitespace token. So, we represent each word in terms of these tokens with spaces between the tokens to delineate them.\n",
+        "\n",
+        "The tokenized text is stored in a structure that represents each word as tokens together with the count of how often that word occurs.  We'll call this the *vocabulary*."
+      ],
+      "metadata": {
+        "id": "fF2RBrouWV5w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def initialize_vocabulary(text):\n",
+        "  vocab = collections.defaultdict(int)\n",
+        "  words = text.strip().split()\n",
+        "  for word in words:\n",
+        "      vocab[' '.join(list(word)) + ' </w>'] += 1\n",
+        "  return vocab"
+      ],
+      "metadata": {
+        "id": "OfvXkLSARk4_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vocab = initialize_vocabulary(text)\n",
+        "print('Vocabulary: {}'.format(vocab))\n",
+        "print('Size of vocabulary: {}'.format(len(vocab)))"
+      ],
+      "metadata": {
+        "id": "aydmNqaoOpSm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Find all the tokens in the current vocabulary and their frequencies"
+      ],
+      "metadata": {
+        "id": "fJAiCjphWsI9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_tokens_and_frequencies(vocab):\n",
+        "  tokens = collections.defaultdict(int)\n",
+        "  for word, freq in vocab.items():\n",
+        "      word_tokens = word.split()\n",
+        "      for token in word_tokens:\n",
+        "          tokens[token] += freq\n",
+        "  return tokens"
+      ],
+      "metadata": {
+        "id": "qYi6F_K3RYsW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokens = get_tokens_and_frequencies(vocab)\n",
+        "print('Tokens: {}'.format(tokens))\n",
+        "print('Number of tokens: {}'.format(len(tokens)))"
+      ],
+      "metadata": {
+        "id": "Y4LCVGnvXIwp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Find each pair of adjacent tokens in the vocabulary\n",
+        "and count them.  We will subsequently merge the most frequently occurring pair."
+      ],
+      "metadata": {
+        "id": "_-Rh1mD_Ww3b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_pairs_and_counts(vocab):\n",
+        "    pairs = collections.defaultdict(int)\n",
+        "    for word, freq in vocab.items():\n",
+        "        symbols = word.split()\n",
+        "        for i in range(len(symbols)-1):\n",
+        "            pairs[symbols[i],symbols[i+1]] += freq\n",
+        "    return pairs"
+      ],
+      "metadata": {
+        "id": "OqJTB3UFYubH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pairs = get_pairs_and_counts(vocab)\n",
+        "print('Pairs: {}'.format(pairs))\n",
+        "print('Number of distinct pairs: {}'.format(len(pairs)))\n",
+        "\n",
+        "most_frequent_pair = max(pairs, key=pairs.get)\n",
+        "print('Most frequent pair: {}'.format(most_frequent_pair))"
+      ],
+      "metadata": {
+        "id": "d-zm0JBcZSjS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Merge the instances of the best pair in the vocabulary"
+      ],
+      "metadata": {
+        "id": "pcborzqIXQFS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def merge_pair_in_vocabulary(pair, vocab_in):\n",
+        "    vocab_out = {}\n",
+        "    bigram = re.escape(' '.join(pair))\n",
+        "    p = re.compile(r'(?<!\\S)' + bigram + r'(?!\\S)')\n",
+        "    for word in vocab_in:\n",
+        "        word_out = p.sub(''.join(pair), word)\n",
+        "        vocab_out[word_out] = vocab_in[word]\n",
+        "    return vocab_out"
+      ],
+      "metadata": {
+        "id": "xQI6NALdWQZX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vocab = merge_pair_in_vocabulary(most_frequent_pair, vocab)\n",
+        "print('Vocabulary: {}'.format(vocab))\n",
+        "print('Size of vocabulary: {}'.format(len(vocab)))"
+      ],
+      "metadata": {
+        "id": "TRYeBZI3ZULu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Update the tokens, which now include the best token 'se'"
+      ],
+      "metadata": {
+        "id": "bkhUx3GeXwba"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokens = get_tokens_and_frequencies(vocab)\n",
+        "print('Tokens: {}'.format(tokens))\n",
+        "print('Number of tokens: {}'.format(len(tokens)))"
+      ],
+      "metadata": {
+        "id": "Fqj-vQWeXxQi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's write the full tokenization routine"
+      ],
+      "metadata": {
+        "id": "K_hKp2kSXXS1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- write this routine by filling in this missing parts,\n",
+        "# calling the above routines\n",
+        "def tokenize(text, num_merges):\n",
+        "  # Initialize the vocabulary from the input text\n",
+        "  # vocab = (your code here)\n",
+        "\n",
+        "  for i in range(num_merges):\n",
+        "    # Find the tokens and how often they occur in the vocabulary\n",
+        "    # tokens = (your code here)\n",
+        "\n",
+        "    # Find the pairs of adjacent tokens and their counts\n",
+        "    # pairs = (your code here)\n",
+        "\n",
+        "    # Find the most frequent pair\n",
+        "    # most_frequent_pair = (your code here)\n",
+        "    print('Most frequent pair: {}'.format(most_frequent_pair))\n",
+        "\n",
+        "    # Merge the code in the vocabulary\n",
+        "    # vocab = (your code here)\n",
+        "\n",
+        "  # Find the tokens and how often they occur in the vocabulary one last time\n",
+        "  # tokens = (your code here)\n",
+        "\n",
+        "  return tokens, vocab"
+      ],
+      "metadata": {
+        "id": "U_1SkQRGQ8f3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokens, vocab = tokenize(text, num_merges=22)"
+      ],
+      "metadata": {
+        "id": "w0EkHTrER_-I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Tokens: {}'.format(tokens))\n",
+        "print('Number of tokens: {}'.format(len(tokens)))\n",
+        "print('Vocabulary: {}'.format(vocab))\n",
+        "print('Size of vocabulary: {}'.format(len(vocab)))"
+      ],
+      "metadata": {
+        "id": "moqDtTzIb-NG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO - Consider the input text:\n",
+        "\n",
+        "\"How much wood could a woodchuck chuck if a woodchuck could chuck wood\"\n",
+        "\n",
+        "How many tokens will there be initially and what will they be?\n",
+        "How many tokens will there be if we run the tokenization routine for the maximum number of iterations (merges)?\n",
+        "\n",
+        "When you've made your predictions, run the code and see if you are correct."
+      ],
+      "metadata": {
+        "id": "jOW_HJtMdAxd"
+      }
+    }
+  ]
+}

Notebooks/Chap12/12_4_Decoding_Strategies.ipynb

@@ -0,0 +1,648 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPsZjfqVeHYh95Hzt+hCIO7",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 12.4: Decoding strategies**\n",
+        "\n",
+        "This practical investigates neural decoding from transformer models.  \n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "RnIUiieJWu6e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install transformers"
+      ],
+      "metadata": {
+        "id": "7abjZ9pMVj3k"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed\n",
+        "import torch\n",
+        "import torch.nn.functional as F\n",
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "sMOyD0zem2Ef"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load model and tokenizer\n",
+        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
+        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')"
+      ],
+      "metadata": {
+        "id": "pZgfxbzKWNSR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Decoding from GPT2\n",
+        "\n",
+        "This tutorial investigates how to use GPT2 (the forerunner of GPT3) to generate text.  There are a number of ways to do this that trade-off the realism of the text against the amount of variation.\n",
+        "\n",
+        "At every stage, GPT2 takes an input string and returns a probability for each of the possible subsequent tokens.  We can choose what to do with these probability.  We could always *greedily choose* the most likely next token, or we could draw a *sample* randomly according to the probabilities.  There are also intermediate strategies such as *top-k sampling* and *nucleus sampling*, that have some controlled randomness.\n",
+        "\n",
+        "We'll also investigate *beam search* -- the idea is that rather than greedily take the next best token at each stage, we maintain a set of hypotheses  (beams)as we add each subsequent token and return the most likely overall hypothesis.  This is not necessarily the same result we get from greedily choosing the next token."
+      ],
+      "metadata": {
+        "id": "TfhAGy0TXEvV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First, let's investigate the token themselves.  The code below prints out the vocabulary size and shows 20 random tokens.  "
+      ],
+      "metadata": {
+        "id": "vsmO9ptzau3_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(1)\n",
+        "print(\"Number of tokens in dictionary = %d\"%(tokenizer.vocab_size))\n",
+        "for i in range(20):\n",
+        "  index = np.random.randint(tokenizer.vocab_size)\n",
+        "  print(\"Token: %d \"%(index)+tokenizer.decode(torch.tensor(index), skip_special_tokens=True))\n"
+      ],
+      "metadata": {
+        "id": "dmmBNS5GY_yk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Sampling\n",
+        "\n",
+        "Each time we run GPT2 it will take in a set of tokens, and return a probability over each of the possible next tokens.  The simplest thing we could do is to just draw a sample from this probability distribution each time."
+      ],
+      "metadata": {
+        "id": "MUM3kLEjbTso"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def sample_next_token(input_tokens, model, tokenizer):\n",
+        "  # Run model to get prediction over next output\n",
+        "  outputs = model(input_ids = input_tokens['input_ids'], attention_mask = input_tokens['attention_mask'])\n",
+        "  # Find prediction\n",
+        "  prob_over_tokens = F.softmax(outputs.logits, dim=-1).detach().numpy()[0,-1]\n",
+        "  # TODO Draw a random token according to the probabilities\n",
+        "  # next_token should be an array with an sole integer in it (as below)\n",
+        "  # Use:  https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html\n",
+        "  # Replace this line\n",
+        "  next_token = [5000]\n",
+        "\n",
+        "\n",
+        "  # Append token to sentence\n",
+        "  output_tokens = input_tokens\n",
+        "  output_tokens[\"input_ids\"] = torch.cat((output_tokens['input_ids'],torch.tensor([next_token])),dim=1)\n",
+        "  output_tokens['attention_mask'] = torch.cat((output_tokens['attention_mask'],torch.tensor([[1]])),dim=1)\n",
+        "  output_tokens['last_token_prob'] = prob_over_tokens[next_token]\n",
+        "\n",
+        "  return output_tokens"
+      ],
+      "metadata": {
+        "id": "TIyNgg0FkJKO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected output:\n",
+        "# \"The best thing about Bath is that they don't even change or shrink anymore.\"\n",
+        "\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = sample_next_token(input_tokens, model, tokenizer)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "BHs-IWaz9MNY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Modify the code below by changing the number of tokens generated and the initial sentence\n",
+        "# to get a feel for how well this works.  Since I didn't reset the seed, it will give a different\n",
+        "# answer every time that you run it.\n",
+        "\n",
+        "# TODO Experiment with changing this line:\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "# TODO Experiment with changing this line:\n",
+        "for i in range(10):\n",
+        "    input_tokens = sample_next_token(input_tokens, model, tokenizer)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "yN98_7WqbvIe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Greedy token selection\n",
+        "\n",
+        "You probably (correctly) got the impression that the text from pure sampling of the probability model can be kind of random.  How about if we choose most likely token at each step?\n"
+      ],
+      "metadata": {
+        "id": "7eHFLCeZcmmg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_best_next_token(input_tokens, model, tokenizer):\n",
+        "  # Run model to get prediction over next output\n",
+        "  outputs = model(input_ids = input_tokens['input_ids'], attention_mask = input_tokens['attention_mask'])\n",
+        "  # Find prediction\n",
+        "  prob_over_tokens = F.softmax(outputs.logits, dim=-1).detach().numpy()[0,-1]\n",
+        "\n",
+        "  # TODO -- find the token index with the maximum probability\n",
+        "  # It should be returns as a list (i.e., put squared brackets around it)\n",
+        "  # Use https://numpy.org/doc/stable/reference/generated/numpy.argmax.html\n",
+        "  # Replace this line\n",
+        "  next_token = [5000]\n",
+        "\n",
+        "\n",
+        "  # Append token to sentence\n",
+        "  output_tokens = input_tokens\n",
+        "  output_tokens[\"input_ids\"] = torch.cat((output_tokens['input_ids'],torch.tensor([next_token])),dim=1)\n",
+        "  output_tokens['attention_mask'] = torch.cat((output_tokens['attention_mask'],torch.tensor([[1]])),dim=1)\n",
+        "  output_tokens['last_token_prob'] = prob_over_tokens[next_token]\n",
+        "  return output_tokens"
+      ],
+      "metadata": {
+        "id": "OhRzynEjxpZF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected output:\n",
+        "# The best thing about Bath is that it's a place where you can go to\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_best_next_token(input_tokens, model, tokenizer)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "gKB1Mgndj-Hm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Modify the code below by changing the number of tokens generated and the initial sentence\n",
+        "# to get a feel for how well this works.\n",
+        "\n",
+        "# TODO Experiment with changing this line:\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "# TODO Experiment with changing this line:\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_best_next_token(input_tokens, model, tokenizer)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "L1YHKaYFfC0M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Top-K sampling\n",
+        "\n",
+        "You probably noticed that the greedy strategy produces quite realistic text, but it's kind of boring.  It produces generic answers.  Also, if this was a chatbot, then we wouldn't necessarily want it to produce the same answer to a question each time.  \n",
+        "\n",
+        "Top-K sampling is a compromise strategy that samples randomly from the top K most probable tokens.  We could just choose them with a uniform distribution, or (as here) we could sample them according to their original probabilities."
+      ],
+      "metadata": {
+        "id": "1ORFXYX_gBDT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_top_k_token(input_tokens, model, tokenizer, k=20):\n",
+        "  # Run model to get prediction over next output\n",
+        "  outputs = model(input_ids = input_tokens['input_ids'], attention_mask = input_tokens['attention_mask'])\n",
+        "  # Find prediction\n",
+        "  prob_over_tokens = F.softmax(outputs.logits, dim=-1).detach().numpy()[0,-1]\n",
+        "\n",
+        "  # Draw a sample from the top K most likely tokens.\n",
+        "  # Take copy of the probabilities and sort from largest to smallest (use np.sort)\n",
+        "  # TODO -- replace this line\n",
+        "  sorted_prob_over_tokens =  prob_over_tokens\n",
+        "\n",
+        "  # Find the probability at the k'th position\n",
+        "  # TODO -- replace this line\n",
+        "  kth_prob_value = 0.0\n",
+        "\n",
+        "  # Set all probabilities below this value to zero\n",
+        "  prob_over_tokens[prob_over_tokens<kth_prob_value] = 0\n",
+        "\n",
+        "  # Renormalize the probabilities so that they sum to one\n",
+        "  # TODO -- replace this line\n",
+        "  prob_over_tokens = prob_over_tokens\n",
+        "\n",
+        "\n",
+        "  # Draw random token\n",
+        "  next_token = np.random.choice(len(prob_over_tokens), 1, replace=False, p=prob_over_tokens)\n",
+        "\n",
+        "  # Append token to sentence\n",
+        "  output_tokens = input_tokens\n",
+        "  output_tokens[\"input_ids\"] = torch.cat((output_tokens['input_ids'],torch.tensor([next_token])),dim=1)\n",
+        "  output_tokens['attention_mask'] = torch.cat((output_tokens['attention_mask'],torch.tensor([[1]])),dim=1)\n",
+        "  output_tokens['last_token_prob'] = prob_over_tokens[next_token]\n",
+        "  return output_tokens"
+      ],
+      "metadata": {
+        "id": "7RFbn6c-0Z4v"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected output:\n",
+        "# The best thing about Bath is that you get to see all the beautiful faces of\n",
+        "\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_top_k_token(input_tokens, model, tokenizer, k=10)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "G3w1GVED4HYv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO\n",
+        "# Experiment with different values of k\n",
+        "# If you set it to a lower number (say 3) the text will be less random\n",
+        "# If you set it to a higher number (say 5000) the text will be more random\n",
+        "\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_top_k_token(input_tokens, model, tokenizer, k=10)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "RySu2bzqpW9E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Nucleus sampling\n",
+        "\n",
+        "Top-K sampling has the disadvantage that sometimes there are only a few plausible next tokens, and sometimes there are a lot.  How do we adapt to this situation?  One way is to sample from a fixed proportion of the probability mass.  That is we order the tokens in terms of probability and cut off the possibility of sampling when the cumulative sum is greater than a threshold.\n",
+        "\n",
+        "This way, we adapt the number of possible tokens that we can choose."
+      ],
+      "metadata": {
+        "id": "fOHak_QJfU-2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_nucleus_sampling_token(input_tokens, model, tokenizer, thresh=0.25):\n",
+        "  # Run model to get prediction over next output\n",
+        "  outputs = model(input_ids = input_tokens['input_ids'], attention_mask = input_tokens['attention_mask'])\n",
+        "  # Find prediction\n",
+        "  prob_over_tokens = F.softmax(outputs.logits, dim=-1).detach().numpy()[0,-1]\n",
+        "\n",
+        "  # Find the most likely tokens that make up the first (thresh) of the probability\n",
+        "  # TODO -- sort the probabilities in decreasing order\n",
+        "  # Replace this line\n",
+        "  sorted_probs_decreasing = prob_over_tokens\n",
+        "  # TODO -- compute the cumulative sum of these probabilities\n",
+        "  # Replace this line\n",
+        "  cum_sum_probs = sorted_probs_decreasing\n",
+        "\n",
+        "\n",
+        "\n",
+        "  # Find index where that the cumulative sum is greater than the threshold\n",
+        "  thresh_index = np.argmax(cum_sum_probs>thresh)\n",
+        "  print(\"Choosing from %d tokens\"%(thresh_index))\n",
+        "  # TODO:  Find the probability value to threshold\n",
+        "  # Replace this line:\n",
+        "  thresh_prob = 0.5\n",
+        "\n",
+        "\n",
+        "\n",
+        "  # Set any probabilities less than this to zero\n",
+        "  prob_over_tokens[prob_over_tokens<thresh_prob] = 0\n",
+        "  # Renormalize\n",
+        "  prob_over_tokens = prob_over_tokens / np.sum(prob_over_tokens)\n",
+        "  # Draw random token\n",
+        "  next_token = np.random.choice(len(prob_over_tokens), 1, replace=False, p=prob_over_tokens)\n",
+        "\n",
+        "  # Append token to sentence\n",
+        "  output_tokens = input_tokens\n",
+        "  output_tokens[\"input_ids\"] = torch.cat((output_tokens['input_ids'],torch.tensor([next_token])),dim=1)\n",
+        "  output_tokens['attention_mask'] = torch.cat((output_tokens['attention_mask'],torch.tensor([[1]])),dim=1)\n",
+        "  output_tokens['last_token_prob'] = prob_over_tokens[next_token]\n",
+        "  return output_tokens"
+      ],
+      "metadata": {
+        "id": "PtxS4kNDyUcm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected output:\n",
+        "# The best thing about Bath is that it's not a city that has been around\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_nucleus_sampling_token(input_tokens, model, tokenizer, thresh = 0.2)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))\n"
+      ],
+      "metadata": {
+        "id": "K2Vk1Ly40S6c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- experiment with setting the threshold probability to larger or smaller values\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_nucleus_sampling_token(input_tokens, model, tokenizer, thresh = 0.2)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "eQNNHe14wDvC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Beam search\n",
+        "\n",
+        "All of the methods we've seen so far choose the tokens one by one.  But this isn't necessarily sensible.  Even greedily choosing the best token doesn't necessarily retrieve the sequence with the highest probability.  It might be that the most likely token only has very unlikely tokens following it.\n",
+        "\n",
+        "Beam search maintains $K$ hypotheses about the best possible continuation.  It starts with the top $K$ continuations.  Then for each of those, it finds the top K continuations, giving $K^2$ hypotheses.  Then it retains just the top $K$ of these so that the number of hypotheses stays the same."
+      ],
+      "metadata": {
+        "id": "WMMNeLixwlgM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This routine returns the k'th most likely next token.\n",
+        "# If k =0 then it returns the most likely token, if k=1 it returns the next most likely and so on\n",
+        "# We will need this for beam search\n",
+        "def get_kth_most_likely_token(input_tokens, model, tokenizer, k):\n",
+        "  # Run model to get prediction over next output\n",
+        "  outputs = model(input_ids = input_tokens['input_ids'], attention_mask = input_tokens['attention_mask'])\n",
+        "  # Find prediction\n",
+        "  prob_over_tokens = F.softmax(outputs.logits, dim=-1).detach().numpy()[0,-1]\n",
+        "\n",
+        "  # Find the k'th most likely token\n",
+        "  # TODO Sort the probabilities from largest to smallest\n",
+        "  # Replace this line:\n",
+        "  sorted_prob_over_tokens = prob_over_tokens\n",
+        "  # TODO Find the k'th sorted probability\n",
+        "  # Replace this line\n",
+        "  kth_prob_value = prob_over_tokens[0]\n",
+        "\n",
+        "\n",
+        "\n",
+        "  # Find position of this token.\n",
+        "  next_token = np.where(prob_over_tokens == kth_prob_value)[0]\n",
+        "\n",
+        "  # Append token to sentence\n",
+        "  output_tokens = input_tokens\n",
+        "  output_tokens[\"input_ids\"] = torch.cat((output_tokens['input_ids'],torch.tensor([next_token])),dim=1)\n",
+        "  output_tokens['attention_mask'] = torch.cat((output_tokens['attention_mask'],torch.tensor([[1]])),dim=1)\n",
+        "  output_tokens['last_token_prob'] = prob_over_tokens[next_token]\n",
+        "  output_tokens['log_prob'] = output_tokens['log_prob'] + np.log(prob_over_tokens[next_token])\n",
+        "  return output_tokens"
+      ],
+      "metadata": {
+        "id": "sAI2bClXCe2F"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We can test this code and see that if we choose the 2nd most likely (K=1) token each time\n",
+        "# then we get much better generation results than if we choose the 2001st most likely token\n",
+        "\n",
+        "# Expected output:\n",
+        "# The best thing about Bath is the way you get the most bang outta the\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "input_tokens['log_prob'] = 0.0\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_kth_most_likely_token(input_tokens, model, tokenizer, k=1)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))\n",
+        "\n",
+        "# Expected output:\n",
+        "# The best thing about Bath is mixed profits partnerships» buy generic+ Honda throttlecont\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "input_tokens['log_prob'] = 0.0\n",
+        "for i in range(10):\n",
+        "    input_tokens = get_kth_most_likely_token(input_tokens, model, tokenizer, k=2000)\n",
+        "    print(tokenizer.decode(input_tokens[\"input_ids\"][0], skip_special_tokens=True))\n",
+        "\n",
+        "# TODO -- play around with different values of K"
+      ],
+      "metadata": {
+        "id": "6kSc0WrTELMd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Print out each beam plus the log probability\n",
+        "def print_beams(beams):\n",
+        "  for index,beam in enumerate(beams):\n",
+        "    print(\"Beam %d, Prob %3.3f: \"%(index,beam['log_prob'])+tokenizer.decode(beam[\"input_ids\"][0], skip_special_tokens=True))\n",
+        "  print('---')\n",
+        "\n",
+        "\n",
+        "# TODO:  Read this code carefully!\n",
+        "def do_beam_search(input_tokens_in, model, tokenizer, n_beam=5, beam_length=10):\n",
+        "  # Store beams in a list\n",
+        "  input_tokens['log_prob'] = 0.0\n",
+        "\n",
+        "  # Initialize with n_beam most likely continuations\n",
+        "  beams = [None] * n_beam\n",
+        "  for c_k in range(n_beam):\n",
+        "    beams[c_k] = dict(input_tokens_in)\n",
+        "    beams[c_k] = get_kth_most_likely_token(beams[c_k], model, tokenizer, c_k)\n",
+        "\n",
+        "  print_beams(beams)\n",
+        "\n",
+        "  # For each token in the sequence we will add\n",
+        "  for c_pos in range(beam_length-1):\n",
+        "    # Now for each beam, we continue it in the most likely ways, making n_beam*n_beam type hypotheses\n",
+        "    beams_all = [None] * (n_beam*n_beam)\n",
+        "    log_probs_all = np.zeros(n_beam*n_beam)\n",
+        "    # For each current hypothesis\n",
+        "    for c_beam in range(n_beam):\n",
+        "      # For each continuation\n",
+        "      for c_k in range(n_beam):\n",
+        "        # Store the continuation and the probability\n",
+        "        beams_all[c_beam * n_beam + c_k] = dict(get_kth_most_likely_token(beams[c_beam], model, tokenizer, c_k))\n",
+        "        log_probs_all[c_beam * n_beam + c_k] = beams_all[c_beam * n_beam + c_k]['log_prob']\n",
+        "\n",
+        "    # Keep the best n_beams sequences with the highest probabilities\n",
+        "    sorted_index = np.argsort(np.array(log_probs_all)*-1)\n",
+        "    for c_k in range(n_beam):\n",
+        "      beams[c_k] = dict(beams_all[sorted_index[c_k]])\n",
+        "\n",
+        "    # Print the beams\n",
+        "    print_beams(beams)\n",
+        "\n",
+        "  return beams[0]"
+      ],
+      "metadata": {
+        "id": "Y4hFfwPFFxka"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected output:\n",
+        "# The best thing about Bath is that it's a place where you don't have to\n",
+        "\n",
+        "set_seed(0)\n",
+        "input_txt = \"The best thing about Bath is\"\n",
+        "input_tokens = tokenizer(input_txt, return_tensors='pt')\n",
+        "\n",
+        "# Now let's call the beam search\n",
+        "# It takes a while as it has to run the model multiple times to add a token\n",
+        "n_beams = 5\n",
+        "best_beam = do_beam_search(input_tokens,model,tokenizer)\n",
+        "print(\"Beam search result:\")\n",
+        "print(tokenizer.decode(best_beam[\"input_ids\"][0], skip_special_tokens=True))\n",
+        "\n",
+        "# You should see that the best answer is not the same as the greedy solution we found above\n"
+      ],
+      "metadata": {
+        "id": "0YWKwZmz4NXb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can read about more decoding strategies in this blog (which uses a recursive neural network, not a transformer, but the principles are the same).\n",
+        "\n",
+        "https://www.borealisai.com/research-blogs/tutorial-6-neural-natural-language-generation-decoding-algorithms/\n",
+        "\n",
+        "You can also look at other possible language models via hugging face:\n",
+        "\n",
+        "https://huggingface.co/docs/transformers/v4.25.1/en/model_summary#decoders-or-autoregressive-models\n"
+      ],
+      "metadata": {
+        "id": "-SXpjZPYsMhv"
+      }
+    }
+  ]
+}

Notebooks/Chap13/13_1_Graph_Representation.ipynb

@@ -0,0 +1,159 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMuzP1/oqTRTw4Xs/R4J/M3",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 13.1: Graph representation**\n",
+        "\n",
+        "This notebook investigates representing graphs with matrices as illustrated in figure 13.4 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import networkx as nx"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Routine to draw graph structure\n",
+        "def draw_graph_structure(adjacency_matrix):\n",
+        "\n",
+        "  G = nx.Graph()\n",
+        "  n_node = adjacency_matrix.shape[0]\n",
+        "  for i in range(n_node):\n",
+        "    for j in range(i):\n",
+        "      if adjacency_matrix[i,j]:\n",
+        "          G.add_edge(i,j)\n",
+        "\n",
+        "  nx.draw(G, nx.spring_layout(G, seed = 0), with_labels=True)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "O1QMxC7X4vh9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a graph\n",
+        "# Note that the nodes are labelled from 0 rather than 1 as in the book\n",
+        "A = np.array([[0,1,0,1,0,0,0,0],\n",
+        "     [1,0,1,1,1,0,0,0],\n",
+        "     [0,1,0,0,1,0,0,0],\n",
+        "     [1,1,0,0,1,0,0,0],\n",
+        "     [0,1,1,1,0,1,0,1],\n",
+        "     [0,0,0,0,1,0,1,1],\n",
+        "     [0,0,0,0,0,1,0,0],\n",
+        "     [0,0,0,0,1,1,0,0]]);\n",
+        "print(A)\n",
+        "draw_graph_structure(A)"
+      ],
+      "metadata": {
+        "id": "TIrihEw-7DRV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- find algorithmically how many walks of length three are between nodes 3 and 7\n",
+        "# Replace this line\n",
+        "print(\"Number of  walks between nodes three and seven = ???\")"
+      ],
+      "metadata": {
+        "id": "PzvfUpkV4zCj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- find algorithmically what the minimum path distance between nodes 0 and 6 is\n",
+        "# (i.e. what is the first walk length with non-zero count between 0 and 6)\n",
+        "# Replace this line\n",
+        "print(\"Minimum distance = ???\")\n",
+        "\n",
+        "\n",
+        "# What is the worst case complexity of your method?"
+      ],
+      "metadata": {
+        "id": "MhhJr6CgCRb5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's represent node 0 as a vector\n",
+        "x = np.array([[1],[0],[0],[0],[0],[0],[0],[0]]);\n",
+        "print(x)"
+      ],
+      "metadata": {
+        "id": "lCQjXlatABGZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO: Find algorithmically how many paths of length 3 are there between node 0 and every other node\n",
+        "# Replace this line\n",
+        "print(np.zeros_like(x))"
+      ],
+      "metadata": {
+        "id": "nizLdZgLDzL4"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap13/13_2_Graph_Classification.ipynb

@@ -0,0 +1,243 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 13.2: Graph classification**\n",
+        "\n",
+        "This notebook investigates representing graphs with matrices as illustrated in figure 13.4 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import networkx as nx"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's build a model that maps a chemical structure to a binary decision.  This model might be used to predict whether a chemical is liquid at room temperature or not.  We'll start by drawing the chemical structure."
+      ],
+      "metadata": {
+        "id": "UNleESc7k5uB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a graph that represents the chemical structure of ethanol and draw it\n",
+        "# Each node is labelled with the node number and the element (carbon, hydrogen, oxygen)\n",
+        "G = nx.Graph()\n",
+        "G.add_edge('0:H','2:C')\n",
+        "G.add_edge('1:H','2:C')\n",
+        "G.add_edge('3:H','2:C')\n",
+        "G.add_edge('2:C','5:C')\n",
+        "G.add_edge('4:H','5:C')\n",
+        "G.add_edge('6:H','5:C')\n",
+        "G.add_edge('7:O','5:C')\n",
+        "G.add_edge('8:H','7:O')\n",
+        "nx.draw(G, nx.spring_layout(G, seed = 0), with_labels=True, node_size=600)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "TIrihEw-7DRV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define adjacency matrix\n",
+        "# TODO -- Define the adjacency matrix for this chemical\n",
+        "# Replace this line\n",
+        "A = np.zeros((9,9)) ;\n",
+        "\n",
+        "\n",
+        "print(A)\n",
+        "\n",
+        "# TODO -- Define node matrix\n",
+        "# There will be 9 nodes and 118 possible chemical elements\n",
+        "# so we'll define a 118x9 matrix.  Each column represents one\n",
+        "# node and is a one-hot vector (i.e. all zeros, except a single one at the\n",
+        "# chemical number of the element).\n",
+        "# Chemical numbers:  Hydrogen-->1, Carbon-->6,  Oxygen-->8\n",
+        "# Since the indices start at 0, we'll set element 0 to 1 for hydrogen, element 5\n",
+        "# to one for carbon, and element 7 to one for oxygen\n",
+        "# Replace this line:\n",
+        "X = np.zeros((118,9))\n",
+        "\n",
+        "\n",
+        "# Print the top 15 rows of the data matrix\n",
+        "print(X[0:15,:])"
+      ],
+      "metadata": {
+        "id": "gKBD5JsPfrkA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define a network with four layers that maps this graph to a binary value, using the formulation in equation 13.11."
+      ],
+      "metadata": {
+        "id": "40FLjNIcpHa9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We'll need these helper functions\n",
+        "\n",
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "# Define the logistic sigmoid function\n",
+        "def sigmoid(x):\n",
+        "  return 1.0/(1.0+np.exp(-x))"
+      ],
+      "metadata": {
+        "id": "52IFREpepHE4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Our network will have K=3 hidden layers, and will use a dimension of D=200.\n",
+        "K = 3; D = 200\n",
+        "# Set seed so we always get the same random numbers\n",
+        "np.random.seed(1)\n",
+        "# Let's initialize the parameter matrices randomly with He initialization\n",
+        "Omega0 = np.random.normal(size=(D, 118)) * 2.0 / D\n",
+        "beta0 = np.random.normal(size=(D,1)) * 2.0 / D\n",
+        "Omega1 = np.random.normal(size=(D, D)) * 2.0 / D\n",
+        "beta1 = np.random.normal(size=(D,1)) * 2.0 / D\n",
+        "Omega2 = np.random.normal(size=(D, D)) * 2.0 / D\n",
+        "beta2 = np.random.normal(size=(D,1)) * 2.0 / D\n",
+        "omega3 = np.random.normal(size=(1, D))\n",
+        "beta3 = np.random.normal(size=(1,1))"
+      ],
+      "metadata": {
+        "id": "ag0YdEgnpApK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def graph_neural_network(A,X, Omega0, beta0, Omega1, beta1, Omega2, beta2, omega3, beta3):\n",
+        "  # Define this network according to equation 13.11 from the book\n",
+        "  # Replace this line\n",
+        "  f = np.ones((1,1))\n",
+        "\n",
+        "  return f;"
+      ],
+      "metadata": {
+        "id": "RQuTMc2WrsU3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this network\n",
+        "f = graph_neural_network(A,X, Omega0, beta0, Omega1, beta1, Omega2, beta2, omega3, beta3)\n",
+        "print(\"Your value is %3f: \"%(f[0,0]), \"True value of f: 0.310843\")"
+      ],
+      "metadata": {
+        "id": "X7gYgOu6uIAt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's check that permuting the indices of the graph doesn't change\n",
+        "# the output of the network\n",
+        "# Define a permutation matrix\n",
+        "P = np.array([[0,1,0,0,0,0,0,0,0],\n",
+        "              [0,0,0,0,1,0,0,0,0],\n",
+        "              [0,0,0,0,0,1,0,0,0],\n",
+        "              [0,0,0,0,0,0,0,0,1],\n",
+        "              [1,0,0,0,0,0,0,0,0],\n",
+        "              [0,0,1,0,0,0,0,0,0],\n",
+        "              [0,0,0,1,0,0,0,0,0],\n",
+        "              [0,0,0,0,0,0,0,1,0],\n",
+        "              [0,0,0,0,0,0,1,0,0]]);\n",
+        "\n",
+        "# TODO -- Use this matrix to permute the adjacency matrix A and node matrix X\n",
+        "# Replace these lines\n",
+        "A_permuted = np.copy(A)\n",
+        "X_permuted = np.copy(X)\n",
+        "\n",
+        "f = graph_neural_network(A_permuted,X_permuted, Omega0, beta0, Omega1, beta1, Omega2, beta2, omega3, beta3)\n",
+        "print(\"Your value is %3f: \"%(f[0,0]), \"True value of f: 0.310843\")"
+      ],
+      "metadata": {
+        "id": "F0zc3U_UuR5K"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO -- encode the adjacency matrix and node matrix for propanol and run the network again.  Show that the network still runs even though the size of the input graph is different.\n",
+        "\n",
+        "Propanol structure can be found [here](https://upload.wikimedia.org/wikipedia/commons/b/b8/Propanol_flat_structure.png)."
+      ],
+      "metadata": {
+        "id": "l44vHi50zGqY"
+      }
+    }
+  ]
+}

Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb

@@ -0,0 +1,314 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNXqwmC4yEc1mGv9/74b0jY",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 13.3: Neighborhood sampling**\n",
+        "\n",
+        "This notebook investigates neighborhood sampling of graphs as in figure 13.10 from the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import networkx as nx"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's construct the graph from figure 13.10, which has 23 nodes."
+      ],
+      "metadata": {
+        "id": "UNleESc7k5uB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define adjacency matrix\n",
+        "A = np.array([[0,1,1,1,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [1,0,1,0,0, 0,0,0,1,1, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [1,1,0,1,0, 0,0,0,0,1, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [1,0,1,0,1, 0,1,1,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,0,0,1,0, 1,0,1,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,0,0,0,1, 0,0,1,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,0,0,1,0, 0,0,1,0,1, 1,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,0,0,1,1, 1,1,0,0,0, 1,0,0,1,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,1,0,0,0, 0,0,0,0,1, 0,0,0,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,1,1,0,0, 0,1,0,1,0, 0,1,1,0,0, 0,1,0,0,0, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,1,1,0,0, 0,0,1,0,0, 0,0,0,0,0, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,1, 0,0,0,0,1, 1,1,0,0,0, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,1, 1,0,0,1,0, 0,1,1,0,0, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,1,0,0, 0,0,1,0,0, 0,0,1,1,0, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,1,0,0,0, 1,0,0,0,1, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,1,0,0,1, 0,1,0,0,1, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,1, 0,1,1,0,0, 1,0,1,0,1, 0,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,1,1,0, 0,1,0,1,0, 1,1,1],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,0,1,0, 0,0,1,0,0, 0,0,1],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,1, 1,1,0,0,0, 1,0,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,1,0,1, 0,1,0],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,1,0,0, 1,0,1],\n",
+        "              [0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,0, 0,0,1,1,0, 0,1,0]]);\n",
+        "print(A)"
+      ],
+      "metadata": {
+        "id": "fHgH5hdG_W1h"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Routine to draw graph structure, highlighting original node (brown in fig 13.10)\n",
+        "# and neighborhood nodes (orange in figure 13.10)\n",
+        "def draw_graph_structure(adjacency_matrix, original_node, neighborhood_nodes=None):\n",
+        "\n",
+        "  G = nx.Graph()\n",
+        "  n_node = adjacency_matrix.shape[0]\n",
+        "  for i in range(n_node):\n",
+        "    for j in range(i):\n",
+        "      if adjacency_matrix[i,j]:\n",
+        "          G.add_edge(i,j)\n",
+        "\n",
+        "  color_map = []\n",
+        "\n",
+        "  for node in G:\n",
+        "    if original_node[node]:\n",
+        "      color_map.append('brown')\n",
+        "    else:\n",
+        "      if neighborhood_nodes[node]:\n",
+        "        color_map.append('orange')\n",
+        "      else:\n",
+        "        color_map.append('white')\n",
+        "\n",
+        "  nx.draw(G, nx.spring_layout(G, seed = 7), with_labels=True,node_color=color_map)\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "TIrihEw-7DRV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_nodes = A.shape[0]\n",
+        "\n",
+        "# Define a single output layer node\n",
+        "output_layer_nodes=np.zeros((n_nodes,1)); output_layer_nodes[16]=1\n",
+        "# Define the neighboring nodes to draw (none)\n",
+        "neighbor_nodes = np.zeros((n_nodes,1))\n",
+        "print(\"Output layer:\")\n",
+        "draw_graph_structure(A, output_layer_nodes, neighbor_nodes)"
+      ],
+      "metadata": {
+        "id": "gKBD5JsPfrkA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's imagine that we want to form a batch for a node labelling task that consists of just node 16 in the output layer (highlighted).   The network consists of the input, hidden layer 1, hidden layer2, and the output layer."
+      ],
+      "metadata": {
+        "id": "JaH3g_-O-0no"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Find the nodes in hidden layer 2 that connect to node 16 in the output layer\n",
+        "# using the adjacency matrix\n",
+        "# Replace this line:\n",
+        "hidden_layer2_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "print(\"Hidden layer 2:\")\n",
+        "draw_graph_structure(A, output_layer_nodes, hidden_layer2_nodes)"
+      ],
+      "metadata": {
+        "id": "9oSiuP3B3HNS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO - Find the nodes in hidden layer 1 that connect that connect to node 16 in the output layer\n",
+        "# using the adjacency matrix\n",
+        "# Replace this line:\n",
+        "hidden_layer1_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "print(\"Hidden layer 1:\")\n",
+        "draw_graph_structure(A, output_layer_nodes, hidden_layer1_nodes)"
+      ],
+      "metadata": {
+        "id": "zZFxw3m1_wWr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Find the nodes in the input layer that connect to node 16 in the output layer\n",
+        "# using the adjacency matrix\n",
+        "# Replace this line:\n",
+        "input_layer_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "print(\"Input layer:\")\n",
+        "draw_graph_structure(A, output_layer_nodes, input_layer_nodes)"
+      ],
+      "metadata": {
+        "id": "EL3N8BXyCu0F"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This is bad news.  This is a fairly sparsely connected graph (i.e. adjacency matrix is mostly zeros) and there are only two hidden layers.  Nonetheless, we have to involve almost all the nodes in the graph to compute the loss at this output.\n",
+        "\n",
+        "To resolve this problem, we'll use neighborhood sampling.  We'll start again with a single node in the output layer."
+      ],
+      "metadata": {
+        "id": "CE0WqytvC7zr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Output layer:\")\n",
+        "draw_graph_structure(A, output_layer_nodes, neighbor_nodes)"
+      ],
+      "metadata": {
+        "id": "59WNys3KC5y6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define umber of neighbors to sample\n",
+        "n_sample = 3"
+      ],
+      "metadata": {
+        "id": "uCoJwpcTNFdI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Find the nodes in hidden layer 2 that connect to node 16 in the output layer\n",
+        "# using the adjacency matrix.  Then sample n_sample of these nodes randomly without\n",
+        "# replacement.\n",
+        "\n",
+        "# Replace this line:\n",
+        "hidden_layer2_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "draw_graph_structure(A, output_layer_nodes, hidden_layer2_nodes)"
+      ],
+      "metadata": {
+        "id": "_WEop6lYGNhJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Find the nodes in hidden layer 1 that connect to the nodes in hidden layer 2\n",
+        "# using the adjacency matrix.  Then sample n_sample of these nodes randomly without\n",
+        "# replacement.  Make sure not to sample nodes that were already included in hidden layer 2 our the output layer.\n",
+        "# The nodes at hidden layer 1 are the union of these nodes and the nodes in hidden layer 2\n",
+        "\n",
+        "# Replace this line:\n",
+        "hidden_layer1_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "draw_graph_structure(A, output_layer_nodes, hidden_layer1_nodes)\n"
+      ],
+      "metadata": {
+        "id": "k90qW_LDLpNk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Find the nodes in the input layer that connect to the nodes in hidden layer 1\n",
+        "# using the adjacency matrix.  Then sample n_sample of these nodes randomly without\n",
+        "# replacement.  Make sure not to sample nodes that were already included in hidden layer 1,2, or the output layer.\n",
+        "# The nodes at the input layer are the union of these nodes and the nodes in hidden layers 1 and 2\n",
+        "\n",
+        "# Replace this line:\n",
+        "input_layer_nodes = np.zeros((n_nodes,1));\n",
+        "\n",
+        "draw_graph_structure(A, output_layer_nodes, input_layer_nodes)"
+      ],
+      "metadata": {
+        "id": "NDEYUty_O3Zr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "If you did this correctly, there should be 9 yellow nodes in the figure.  The \"receptive field\" of node 16 in the output layer increases much more slowly as we move back through the layers of the network."
+      ],
+      "metadata": {
+        "id": "vu4eJURmVkc5"
+      }
+    }
+  ]
+}

Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb

@@ -0,0 +1,213 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyO/wJ4N9w01f04mmrs/ZSHY",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 13.4: Graph attention networks**\n",
+        "\n",
+        "This notebook builds a graph attention mechanism from scratch, as discussed in section 13.8.6 of the book and illustrated in figure 13.12c\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The self-attention mechanism maps $N$ inputs $\\mathbf{x}_{n}\\in\\mathbb{R}^{D}$ and returns $N$ outputs $\\mathbf{x}'_{n}\\in \\mathbb{R}^{D}$.  \n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "9OJkkoNqCVK2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so we get the same random numbers\n",
+        "np.random.seed(1)\n",
+        "# Number of nodes in the graph\n",
+        "N = 8\n",
+        "# Number of dimensions of each input\n",
+        "D = 4\n",
+        "\n",
+        "# Define a graph\n",
+        "A = np.array([[0,1,0,1,0,0,0,0],\n",
+        "              [1,0,1,1,1,0,0,0],\n",
+        "              [0,1,0,0,1,0,0,0],\n",
+        "              [1,1,0,0,1,0,0,0],\n",
+        "              [0,1,1,1,0,1,0,1],\n",
+        "              [0,0,0,0,1,0,1,1],\n",
+        "              [0,0,0,0,0,1,0,0],\n",
+        "              [0,0,0,0,1,1,0,0]]);\n",
+        "print(A)\n",
+        "\n",
+        "# Let's also define some random data\n",
+        "X = np.random.normal(size=(D,N))"
+      ],
+      "metadata": {
+        "id": "oAygJwLiCSri"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll also need the weights and biases for the keys, queries, and values (equations 12.2 and 12.4)"
+      ],
+      "metadata": {
+        "id": "W2iHFbtKMaDp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Choose random values for the parameters\n",
+        "omega = np.random.normal(size=(D,D))\n",
+        "beta = np.random.normal(size=(D,1))\n",
+        "phi = np.random.normal(size=(1,2*D))"
+      ],
+      "metadata": {
+        "id": "79TSK7oLMobe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll need a softmax operation that operates on the columns of the matrix and a ReLU function as well"
+      ],
+      "metadata": {
+        "id": "iYPf6c4MhCgq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define softmax operation that works independently on each column\n",
+        "def softmax_cols(data_in):\n",
+        "  # Exponentiate all of the values\n",
+        "  exp_values = np.exp(data_in) ;\n",
+        "  # Sum over columns\n",
+        "  denom = np.sum(exp_values, axis = 0);\n",
+        "  # Replicate denominator to N rows\n",
+        "  denom = np.matmul(np.ones((data_in.shape[0],1)), denom[np.newaxis,:])\n",
+        "  # Compute softmax\n",
+        "  softmax = exp_values / denom\n",
+        "  # return the answer\n",
+        "  return softmax\n",
+        "\n",
+        "\n",
+        "# Define the Rectified Linear Unit (ReLU) function\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n"
+      ],
+      "metadata": {
+        "id": "obaQBdUAMXXv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " # Now let's compute self attention in matrix form\n",
+        "def graph_attention(X,omega, beta, phi, A):\n",
+        "\n",
+        "  # TODO -- Write this function (see figure 13.12c)\n",
+        "  # 1. Compute X_prime\n",
+        "  # 2. Compute S\n",
+        "  # 3. To apply the mask, set S to a very large negative number (e.g. -1e20) everywhere where A+I is zero\n",
+        "  # 4. Run the softmax function to compute the attention values\n",
+        "  # 5. Postmultiply X' by the attention values\n",
+        "  # 6. Apply the ReLU function\n",
+        "  # Replace this line:\n",
+        "  output = np.ones_like(X) ;\n",
+        "\n",
+        "  return output;"
+      ],
+      "metadata": {
+        "id": "gb2WvQ3SiH8r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test out the graph attention mechanism\n",
+        "np.set_printoptions(precision=3)\n",
+        "output = graph_attention(X, omega, beta, phi, A);\n",
+        "print(\"Correct answer is:\")\n",
+        "print(\"[[0.    0.028 0.37  0.    0.97  0.    0.    0.698]\")\n",
+        "print(\" [0.    0.    0.    0.    1.184 0.    2.654 0.  ]\")\n",
+        "print(\" [1.13  0.564 0.    1.298 0.268 0.    0.    0.779]\")\n",
+        "print(\" [0.825 0.    0.    1.175 0.    0.    0.    0.  ]]]\")\n",
+        "\n",
+        "\n",
+        "print(\"Your answer is:\")\n",
+        "print(output)"
+      ],
+      "metadata": {
+        "id": "d4p6HyHXmDh5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO -- Try to construct a dot-product self-attention mechanism as in practical 12.1 that respects the geometry of the graph and has zero attention between non-neighboring nodes by combining figures 13.12a and 13.12b.\n"
+      ],
+      "metadata": {
+        "id": "QDEkIrcgrql-"
+      }
+    }
+  ]
+}

Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb

@@ -0,0 +1,418 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 15.1: GAN Toy example**\n",
+        "\n",
+        "This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get a batch of real data.  Our goal is to make data that looks like this.\n",
+        "def get_real_data_batch(n_sample):\n",
+        "  np.random.seed(0)\n",
+        "  x_true = np.random.normal(size=(1,n_sample)) + 7.5\n",
+        "  return x_true"
+      ],
+      "metadata": {
+        "id": "y_OkVWmam4Qx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define our generator.  This takes a standard normally-distributed latent variable $z$ and adds a scalar $\\theta$ to this, where $\\theta$ is the single parameter of this generative model according to:\n",
+        "\n",
+        "\\begin{equation}\n",
+        "x_i = z_i + \\theta.\n",
+        "\\end{equation}\n",
+        "\n",
+        "Obviously this model can generate the family of Gaussian distributions with unit variance, but different means."
+      ],
+      "metadata": {
+        "id": "RFpL0uCXoTpV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This is our generator -- takes the single parameter theta\n",
+        "# of the generative model and generates n samples\n",
+        "def generator(z, theta):\n",
+        "    x_gen = z + theta\n",
+        "    return x_gen"
+      ],
+      "metadata": {
+        "id": "OtLQvf3Enfyw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, we define our discriminator.  This is a simple logistic regression model (1D linear model passed through sigmoid) that returns the probability that the data is real"
+      ],
+      "metadata": {
+        "id": "Xrzd8aehYAYR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define our discriminative model\n",
+        "\n",
+        "# Logistic sigmoid, maps from [-infty,infty] to [0,1]\n",
+        "def sig(data_in):\n",
+        "  return  1.0 / (1.0+np.exp(-data_in))\n",
+        "\n",
+        "# Discriminator computes y\n",
+        "def discriminator(x, phi0, phi1):\n",
+        "  return sig(phi0 + phi1 * x)"
+      ],
+      "metadata": {
+        "id": "vHBgAFZMsnaC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draws a figure like Figure 15.1a\n",
+        "def draw_data_model(x_real, x_syn, phi0=None, phi1=None):\n",
+        "  fix, ax = plt.subplots();\n",
+        "\n",
+        "  for x in x_syn:\n",
+        "    ax.plot([x,x],[0,0.33],color='#f47a60')\n",
+        "  for x in x_real:\n",
+        "    ax.plot([x,x],[0,0.33],color='#7fe7dc')\n",
+        "\n",
+        "  if phi0 is not None:\n",
+        "    x_model = np.arange(0,10,0.01)\n",
+        "    y_model = discriminator(x_model, phi0, phi1)\n",
+        "    ax.plot(x_model, y_model,color='#dddddd')\n",
+        "  ax.set_xlim([0,10])\n",
+        "  ax.set_ylim([0,1])\n",
+        "\n",
+        "\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "V1FiDBhepcQJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get data batch\n",
+        "x_real = get_real_data_batch(10)\n",
+        "\n",
+        "# Initialize generator and synthesize a batch of examples\n",
+        "theta = 3.0\n",
+        "np.random.seed(1)\n",
+        "z = np.random.normal(size=(1,10))\n",
+        "x_syn = generator(z, theta)\n",
+        "\n",
+        "# Initialize discriminator model\n",
+        "phi0 = -2\n",
+        "phi1 = 1\n",
+        "\n",
+        "draw_data_model(x_real, x_syn, phi0, phi1)"
+      ],
+      "metadata": {
+        "id": "U8pFb497x36n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that the synthesized (orange) samples don't look much like the real (cyan) ones, and the initial model to discriminate them (gray line represents probability of being real) is pretty bad as well.\n",
+        "\n",
+        "Let's deal with the discriminator first.  Let's define the loss"
+      ],
+      "metadata": {
+        "id": "SNDV1G5PYhcQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Discriminator loss\n",
+        "def compute_discriminator_loss(x_real, x_syn, phi0, phi1):\n",
+        "\n",
+        "  # TODO -- compute the loss for the discriminator\n",
+        "  # Run the real data and the synthetic data through the discriminator\n",
+        "  # Then use the standard binary cross entropy loss with the y=1 for the real samples\n",
+        "  # and y=0 for the synthesized ones.\n",
+        "  # Replace this line\n",
+        "  loss = 0.0\n",
+        "\n",
+        "\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "Bc3VwCabYcfg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test the loss\n",
+        "loss = compute_discriminator_loss(x_real, x_syn, phi0, phi1)\n",
+        "print(\"True Loss = 13.814757170851447, Your loss=\", loss )"
+      ],
+      "metadata": {
+        "id": "MiqM3GXSbn0z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Gradient of loss (cheating, using finite differences)\n",
+        "def compute_discriminator_gradient(x_real, x_syn, phi0, phi1):\n",
+        "  delta = 0.0001;\n",
+        "  loss1 = compute_discriminator_loss(x_real, x_syn, phi0, phi1)\n",
+        "  loss2 = compute_discriminator_loss(x_real, x_syn, phi0+delta, phi1)\n",
+        "  loss3 = compute_discriminator_loss(x_real, x_syn, phi0, phi1+delta)\n",
+        "  dl_dphi0 = (loss2-loss1) / delta\n",
+        "  dl_dphi1 = (loss3-loss1) / delta\n",
+        "\n",
+        "  return dl_dphi0, dl_dphi1\n",
+        "\n",
+        "# This routine performs gradient descent with the discriminator\n",
+        "def update_discriminator(x_real, x_syn, n_iter, phi0, phi1):\n",
+        "\n",
+        "  # Define learning rate\n",
+        "  alpha = 0.01\n",
+        "\n",
+        "  # Get derivatives\n",
+        "  print(\"Initial discriminator loss = \", compute_discriminator_loss(x_real, x_syn, phi0, phi1))\n",
+        "  for iter in range(n_iter):\n",
+        "    # Get gradient\n",
+        "    dl_dphi0, dl_dphi1 = compute_discriminator_gradient(x_real, x_syn, phi0, phi1)\n",
+        "    # Take a gradient step downhill\n",
+        "    phi0 = phi0 - alpha * dl_dphi0 ;\n",
+        "    phi1 = phi1 - alpha * dl_dphi1 ;\n",
+        "\n",
+        "  print(\"Final Discriminator Loss= \", compute_discriminator_loss(x_real, x_syn, phi0, phi1))\n",
+        "\n",
+        "  return phi0, phi1"
+      ],
+      "metadata": {
+        "id": "zAxUPo3p0CIW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's update the discriminator (sigmoid curve)\n",
+        "n_iter = 100\n",
+        "print(\"Initial parameters (phi0,phi1)\", phi0, phi1)\n",
+        "phi0, phi1 = update_discriminator(x_real, x_syn, n_iter, phi0, phi1)\n",
+        "print(\"Final parameters (phi0,phi1)\", phi0, phi1)\n",
+        "draw_data_model(x_real, x_syn, phi0, phi1)"
+      ],
+      "metadata": {
+        "id": "FE_DeweeAbMc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's update the generator"
+      ],
+      "metadata": {
+        "id": "pRv9myh0d3Xm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_generator_loss(z, theta, phi0, phi1):\n",
+        "  # TODO -- Run the generator on the latent variables z with the parameters theta\n",
+        "  # to generate new data x_syn\n",
+        "  # Then run the discriminator on the new data to get the probability of being real\n",
+        "  # The loss is the total negative log probability of being synthesized (i.e. of not being real)\n",
+        "  # Replace this code\n",
+        "  loss = 1\n",
+        "\n",
+        "\n",
+        "  return loss"
+      ],
+      "metadata": {
+        "id": "5uiLrFBvJFAr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test generator loss to check you have it correct\n",
+        "loss = compute_generator_loss(z, theta, -2, 1)\n",
+        "print(\"True Loss = 13.78437035945412, Your loss=\", loss )"
+      ],
+      "metadata": {
+        "id": "cqnU3dGPd6NK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_generator_gradient(z, theta, phi0, phi1):\n",
+        "  delta = 0.0001\n",
+        "  loss1 = compute_generator_loss(z,theta, phi0, phi1) ;\n",
+        "  loss2 = compute_generator_loss(z,theta+delta, phi0, phi1) ;\n",
+        "  dl_dtheta = (loss2-loss1)/ delta\n",
+        "  return dl_dtheta\n",
+        "\n",
+        "def update_generator(z, theta, n_iter, phi0, phi1):\n",
+        "    # Define learning rate\n",
+        "    alpha = 0.02\n",
+        "\n",
+        "    # Get derivatives\n",
+        "    print(\"Initial generator loss = \", compute_generator_loss(z, theta, phi0, phi1))\n",
+        "    for iter in range(n_iter):\n",
+        "      # Get gradient\n",
+        "      dl_dtheta = compute_generator_gradient(z, theta, phi0, phi1)\n",
+        "      # Take a gradient step (uphill, since we are trying to make synthesized data less well classified by discriminator)\n",
+        "      theta = theta + alpha * dl_dtheta ;\n",
+        "\n",
+        "    print(\"Final generator loss = \", compute_generator_loss(z, theta, phi0, phi1))\n",
+        "    return theta\n"
+      ],
+      "metadata": {
+        "id": "P1Lqy922dqal"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_iter = 10\n",
+        "theta = 3.0\n",
+        "print(\"Theta before\", theta)\n",
+        "theta = update_generator(z, theta, n_iter, phi0, phi1)\n",
+        "print(\"Theta after\", theta)\n",
+        "\n",
+        "x_syn = generator(z,theta)\n",
+        "draw_data_model(x_real, x_syn, phi0, phi1)"
+      ],
+      "metadata": {
+        "id": "Q6kUkMO1P8V0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Now let's define a full GAN loop\n",
+        "\n",
+        "# Initialize the parameters\n",
+        "theta = 3\n",
+        "phi0 = -2\n",
+        "phi1 = 1\n",
+        "\n",
+        "# Number of iterations for updating generator and discriminator\n",
+        "n_iter_discrim = 300\n",
+        "n_iter_gen = 3\n",
+        "\n",
+        "print(\"Final parameters (phi0,phi1)\", phi0, phi1)\n",
+        "for c_gan_iter in range(5):\n",
+        "\n",
+        "  # Run generator to product synthesized data\n",
+        "  x_syn = generator(z, theta)\n",
+        "  draw_data_model(x_real, x_syn, phi0, phi1)\n",
+        "\n",
+        "  # Update the discriminator\n",
+        "  print(\"Updating discriminator\")\n",
+        "  phi0, phi1 = update_discriminator(x_real, x_syn, n_iter_discrim, phi0, phi1)\n",
+        "  draw_data_model(x_real, x_syn, phi0, phi1)\n",
+        "\n",
+        "  # Update the generator\n",
+        "  print(\"Updating generator\")\n",
+        "  theta = update_generator(z, theta, n_iter_gen, phi0, phi1)\n"
+      ],
+      "metadata": {
+        "id": "pcbdK2agTO-y"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see that the synthesized data (orange) is becoming closer to the true data (cyan).  However, this is extremely unstable -- as you will find if you mess around with the number of iterations of each optimization and the total iterations overall."
+      ],
+      "metadata": {
+        "id": "loMx0TQUgBs7"
+      }
+    }
+  ]
+}

Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb

@@ -0,0 +1,246 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 15.2: Wasserstein Distance**\n",
+        "\n",
+        "This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import cm\n",
+        "from matplotlib.colors import ListedColormap\n",
+        "from scipy.optimize import linprog"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define two probability distributions\n",
+        "p = np.array([5, 3, 2, 1, 8, 7, 5, 9, 2, 1])\n",
+        "q = np.array([4, 10,1, 1, 4, 6, 3, 2, 0, 1])\n",
+        "p = p/np.sum(p);\n",
+        "q=  q/np.sum(q);\n",
+        "\n",
+        "# Draw those distributions\n",
+        "fig, ax =plt.subplots(2,1);\n",
+        "x = np.arange(0,p.size,1)\n",
+        "ax[0].bar(x,p, color=\"#cccccc\")\n",
+        "ax[0].set_ylim([0,0.35])\n",
+        "ax[0].set_ylabel(\"p(x=i)\")\n",
+        "\n",
+        "ax[1].bar(x,q,color=\"#f47a60\")\n",
+        "ax[1].set_ylim([0,0.35])\n",
+        "ax[1].set_ylabel(\"q(x=j)\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "ZIfQwhd-AV6L"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO Define the distance matrix from figure 15.8d\n",
+        "# Replace this line\n",
+        "dist_mat = np.zeros((10,10))\n",
+        "\n",
+        "# vectorize the distance matrix\n",
+        "c = dist_mat.flatten()"
+      ],
+      "metadata": {
+        "id": "EZSlZQzWBKTm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define pretty colormap\n",
+        "my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n",
+        "my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n",
+        "r = np.floor(my_colormap_vals_dec/(256*256))\n",
+        "g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n",
+        "b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n",
+        "my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n",
+        "\n",
+        "def draw_2D_heatmap(data, title, my_colormap):\n",
+        "  # Make grid of intercept/slope values to plot\n",
+        "  xv, yv = np.meshgrid(np.linspace(0, 1, 10), np.linspace(0, 1, 10))\n",
+        "  fig,ax = plt.subplots()\n",
+        "  fig.set_size_inches(4,4)\n",
+        "  plt.imshow(data, cmap=my_colormap)\n",
+        "  ax.set_title(title)\n",
+        "  ax.set_xlabel('$q$'); ax.set_ylabel('$p$')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "ABRANmp6F8iQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_2D_heatmap(dist_mat,r'Distance $|i-j|$', my_colormap)"
+      ],
+      "metadata": {
+        "id": "G0HFPBXyHT6V"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define b to be the verticalconcatenation of p and q\n",
+        "b = np.hstack((p,q))[np.newaxis].transpose()"
+      ],
+      "metadata": {
+        "id": "SfqeT3KlHWrt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO:  Now construct the matrix A that has the initial distribution constraints\n",
+        "# so that A @ TPFlat=b where TPFlat is the transport plan TP vectorized rows first so TPFlat = np.flatten(TP)\n",
+        "# Replace this line:\n",
+        "A = np.zeros((20,100))"
+      ],
+      "metadata": {
+        "id": "7KrybL96IuNW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we have all of the things we need.  The vectorized distance matrix $\\mathbf{c}$,  the constraint matrix $\\mathbf{A}$, the vectorized and concatenated original distribution $\\mathbf{b}$.  We can run the linear programming optimization."
+      ],
+      "metadata": {
+        "id": "zEuEtU33S8Ly"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We don't need the constraint that p>0 as this is the default\n",
+        "opt = linprog(c, A_eq=A, b_eq=b)\n",
+        "print(opt)"
+      ],
+      "metadata": {
+        "id": "wCfsOVbeSmF5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Extract the answer and display"
+      ],
+      "metadata": {
+        "id": "vpkkOOI2agyl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = np.array(opt.x).reshape(10,10)\n",
+        "draw_2D_heatmap(TP,r'Transport plan $\\mathbf{P}$', my_colormap)"
+      ],
+      "metadata": {
+        "id": "nZGfkrbRV_D0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Compute the Wasserstein distance\n"
+      ],
+      "metadata": {
+        "id": "ZEiRYRVgalsJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "was = np.sum(TP * dist_mat)\n",
+        "print(\"Your Wasserstein distance = \", was)\n",
+        "print(\"Correct answer =  0.15148578811369506\")"
+      ],
+      "metadata": {
+        "id": "yiQ_8j-Raq3c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "TODO -- Compute the\n",
+        "\n",
+        "*   Forward KL divergence $D_{KL}[p,q]$ between these distributions\n",
+        "*   Reverse KL divergence $D_{KL}[q,p]$ between these distributions\n",
+        "*  Jensen-Shannon divergence $D_{JS}[p,q]$ between these distributions\n",
+        "\n",
+        "What do you conclude?"
+      ],
+      "metadata": {
+        "id": "zf8yTusua71s"
+      }
+    }
+  ]
+}

Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb

@@ -0,0 +1,235 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMJLViYIpiivB2A7YIuZmzU",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 16.1: 1D normalizing flows**\n",
+        "\n",
+        "This notebook investigates a 1D normalizing flows example similar to that illustrated in figures 16.1 to 16.3 in the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First we start with a base probability density function"
+      ],
+      "metadata": {
+        "id": "IyVn-Gi-p7wf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the base pdf\n",
+        "def gauss_pdf(z, mu, sigma):\n",
+        "  pr_z = np.exp( -0.5 * (z-mu) * (z-mu) / (sigma * sigma))/(np.sqrt(2*3.1413) * sigma)\n",
+        "  return pr_z"
+      ],
+      "metadata": {
+        "id": "ZIfQwhd-AV6L"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "z = np.arange(-3,3,0.01)\n",
+        "pr_z = gauss_pdf(z, 0, 1)\n",
+        "\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(z, pr_z)\n",
+        "ax.set_xlim([-3,3])\n",
+        "ax.set_xlabel('$z$')\n",
+        "ax.set_ylabel('$Pr(z)$')\n",
+        "plt.show();"
+      ],
+      "metadata": {
+        "id": "gGh8RHmFp_Ls"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define a nonlinear function that maps from the latent space $z$ to the observed data $x$."
+      ],
+      "metadata": {
+        "id": "wVXi5qIfrL9T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a function that maps from the base pdf over z to the observed space x\n",
+        "def f(z):\n",
+        "    x1 = 6/(1+np.exp(-(z-0.25)*1.5))-3\n",
+        "    x2 = z\n",
+        "    p = z * z/9\n",
+        "    x = (1-p) * x1 + p * x2\n",
+        "    return x\n",
+        "\n",
+        "# Compute gradient of that function using finite differences\n",
+        "def df_dz(z):\n",
+        "    return (f(z+0.0001)-f(z-0.0001))/0.0002"
+      ],
+      "metadata": {
+        "id": "shHdgZHjp52w"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = f(z)\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(z,x)\n",
+        "ax.set_xlim(-3,3)\n",
+        "ax.set_ylim(-3,3)\n",
+        "ax.set_xlabel('Latent variable, $z$')\n",
+        "ax.set_ylabel('Observed variable, $x$')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "sz7bnCLUq3Qs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's evaluate the density in the observed space using equation 16.1"
+      ],
+      "metadata": {
+        "id": "rmI0BbuQyXoc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO -- plot the density in the observed space\n",
+        "# Replace these line\n",
+        "x = np.ones_like(z)\n",
+        "pr_x = np.ones_like(pr_z)\n"
+      ],
+      "metadata": {
+        "id": "iPdiT_5gyNOD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the density in the observed space\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, pr_x)\n",
+        "ax.set_xlim([-3,3])\n",
+        "ax.set_ylim([0, 0.5])\n",
+        "ax.set_xlabel('$x$')\n",
+        "ax.set_ylabel('$Pr(x)$')\n",
+        "plt.show();"
+      ],
+      "metadata": {
+        "id": "Jlks8MW3zulA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's draw some samples from the new distribution (see section 16.1)"
+      ],
+      "metadata": {
+        "id": "1c5rO0HHz-FV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(1)\n",
+        "n_sample = 20\n",
+        "\n",
+        "# TODO -- Draw samples from the modeled density\n",
+        "# Replace this line\n",
+        "x_samples = np.ones((n_sample, 1))\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LIlTRfpZz2k_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Draw the samples\n",
+        "fig,ax = plt.subplots()\n",
+        "ax.plot(x, pr_x)\n",
+        "for x_sample in x_samples:\n",
+        "  ax.plot([x_sample, x_sample], [0,0.1], 'r-')\n",
+        "\n",
+        "ax.set_xlim([-3,3])\n",
+        "ax.set_ylim([0, 0.5])\n",
+        "ax.set_xlabel('$x$')\n",
+        "ax.set_ylabel('$Pr(x)$')\n",
+        "plt.show();"
+      ],
+      "metadata": {
+        "id": "JS__QPNv0vUA"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb

@@ -0,0 +1,307 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMe8jb5kLJqkNSE/AwExTpa",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Notebook 16.2: 1D autoregressive flows**\n",
+        "\n",
+        "This notebook investigates a 1D normalizing flows example similar to that illustrated in figure 16.7 in the book.\n",
+        "\n",
+        "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
+        "\n",
+        "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "OLComQyvCIJ7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First we'll define an invertible one dimensional function as in figure 16.5"
+      ],
+      "metadata": {
+        "id": "jTK456TUd2FV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# First let's make the 1D piecewise linear mapping as illustrated in figure 16.5\n",
+        "def g(h, phi):\n",
+        "  # TODO -- write this function (equation 16.12)\n",
+        "  # Note: If you have the first printing of the book, there is a mistake in equation 16.12\n",
+        "  # Check the errata for the correct equation (or figure it out yourself!)\n",
+        "  # Replace this line:\n",
+        "  h_prime = 1\n",
+        "\n",
+        "\n",
+        "  return h_prime"
+      ],
+      "metadata": {
+        "id": "zceww_9qFi00"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Let's test this out.  If you managed to vectorize the routine above, then good for you\n",
+        "# but I'll assume you didn't and so we'll use a loop\n",
+        "\n",
+        "# Define the parameters\n",
+        "phi = np.array([0.2, 0.1, 0.4, 0.05, 0.25])\n",
+        "\n",
+        "# Run the function on an array\n",
+        "h = np.arange(0,1,0.01)\n",
+        "h_prime = np.zeros_like(h)\n",
+        "for i in range(len(h)):\n",
+        "  h_prime[i] = g(h[i], phi)\n",
+        "\n",
+        "# Draw the function\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(h,h_prime, 'b-')\n",
+        "ax.set_xlim([0,1])\n",
+        "ax.set_ylim([0,1])\n",
+        "ax.set_xlabel('Input, $h$')\n",
+        "ax.set_ylabel('Output, $h^\\prime$')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "CLXhYl9ZIuRN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will also need the inverse of this function"
+      ],
+      "metadata": {
+        "id": "zOCMYC0leOyZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the inverse function\n",
+        "def g_inverse(h_prime, phi):\n",
+        "    # Lot's of ways to do this, but we'll just do it by bracketing\n",
+        "    h_low = 0\n",
+        "    h_mid = 0.5\n",
+        "    h_high = 0.999\n",
+        "\n",
+        "    thresh = 0.0001\n",
+        "    c_iter = 0\n",
+        "    while(c_iter < 20 and h_high - h_low > thresh):\n",
+        "        h_prime_low = g(h_low, phi)\n",
+        "        h_prime_mid = g(h_mid, phi)\n",
+        "        h_prime_high = g(h_high, phi)\n",
+        "        if h_prime_mid < h_prime:\n",
+        "          h_low = h_mid\n",
+        "        else:\n",
+        "          h_high = h_mid\n",
+        "\n",
+        "        h_mid = h_low+(h_high-h_low)/2\n",
+        "        c_iter+=1\n",
+        "\n",
+        "    return h_mid"
+      ],
+      "metadata": {
+        "id": "OIqFAgobeSM8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's define an autoregressive flow.  Let's switch to looking at figure 16.7.# We'll assume that our piecewise function will use five parameters phi1,phi2,phi3,phi4,phi5"
+      ],
+      "metadata": {
+        "id": "t8XPxipfd7hz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def ReLU(preactivation):\n",
+        "  activation = preactivation.clip(0.0)\n",
+        "  return activation\n",
+        "\n",
+        "def softmax(x):\n",
+        "  x = np.exp(x) ;\n",
+        "  x = x/ np.sum(x) ;\n",
+        "  return x\n",
+        "\n",
+        "# Return value of phi that doesn't depend on any of the inputs\n",
+        "def get_phi():\n",
+        "  return np.array([0.2, 0.1, 0.4, 0.05, 0.25])\n",
+        "\n",
+        "# Compute values of phi that depend on h1\n",
+        "def shallow_network_phi_h1(h1, n_hidden=10):\n",
+        "  # For neatness of code, we'll just define the parameters in the network definition itself\n",
+        "  n_input = 1\n",
+        "  np.random.seed(n_input)\n",
+        "  beta0 = np.random.normal(size=(n_hidden,1))\n",
+        "  Omega0 = np.random.normal(size=(n_hidden, n_input))\n",
+        "  beta1 = np.random.normal(size=(5,1))\n",
+        "  Omega1 = np.random.normal(size=(5, n_hidden))\n",
+        "  return softmax(beta1 + Omega1 @ ReLU(beta0 + Omega0 @ np.array([[h1]])))\n",
+        "\n",
+        "# Compute values of phi that depend on h1 and h2\n",
+        "def shallow_network_phi_h1h2(h1,h2,n_hidden=10):\n",
+        "  # For neatness of code, we'll just define the parameters in the network definition itself\n",
+        "  n_input = 2\n",
+        "  np.random.seed(n_input)\n",
+        "  beta0 = np.random.normal(size=(n_hidden,1))\n",
+        "  Omega0 = np.random.normal(size=(n_hidden, n_input))\n",
+        "  beta1 = np.random.normal(size=(5,1))\n",
+        "  Omega1 = np.random.normal(size=(5, n_hidden))\n",
+        "  return softmax(beta1 + Omega1 @ ReLU(beta0 + Omega0 @ np.array([[h1],[h2]])))\n",
+        "\n",
+        "# Compute values of phi that depend on h1, h2, and h3\n",
+        "def shallow_network_phi_h1h2h3(h1,h2,h3, n_hidden=10):\n",
+        "  # For neatness of code, we'll just define the parameters in the network definition itself\n",
+        "  n_input = 3\n",
+        "  np.random.seed(n_input)\n",
+        "  beta0 = np.random.normal(size=(n_hidden,1))\n",
+        "  Omega0 = np.random.normal(size=(n_hidden, n_input))\n",
+        "  beta1 = np.random.normal(size=(5,1))\n",
+        "  Omega1 = np.random.normal(size=(5, n_hidden))\n",
+        "  return softmax(beta1 + Omega1 @ ReLU(beta0 + Omega0 @ np.array([[h1],[h2],[h3]])))"
+      ],
+      "metadata": {
+        "id": "PnHGlZtcNEAI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The forward mapping as shown in figure 16.7 a"
+      ],
+      "metadata": {
+        "id": "8fXeG4V44GVH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def forward_mapping(h1,h2,h3,h4):\n",
+        "  #TODO implement the forward mapping\n",
+        "  #Replace this line:\n",
+        "  h_prime1 = 0 ; h_prime2=0; h_prime3=0; h_prime4 = 0\n",
+        "\n",
+        "  return h_prime1, h_prime2, h_prime3, h_prime4"
+      ],
+      "metadata": {
+        "id": "N1zjnIoX0TRP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The backward mapping as shown in figure 16.7b"
+      ],
+      "metadata": {
+        "id": "H8vQfFwI4L7r"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def backward_mapping(h1_prime,h2_prime,h3_prime,h4_prime):\n",
+        "  #TODO implement the backward mapping\n",
+        "  #Replace this line:\n",
+        "  h1=0; h2=0; h3=0; h4 = 0\n",
+        "\n",
+        "  return h1,h2,h3,h4"
+      ],
+      "metadata": {
+        "id": "HNcQTiVE4DMJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, let's make sure that the network really can be inverted"
+      ],
+      "metadata": {
+        "id": "W2IxFkuyZJyn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test the network to see if it does invert correctly\n",
+        "h1 = 0.22; h2 = 0.41; h3 = 0.83; h4 = 0.53\n",
+        "print(\"Original h values %3.3f,%3.3f,%3.3f,%3.3f\"%(h1,h2,h3,h4))\n",
+        "h1_prime, h2_prime, h3_prime, h4_prime = forward_mapping(h1,h2,h3,h4)\n",
+        "print(\"h_prime values %3.3f,%3.3f,%3.3f,%3.3f\"%(h1_prime,h2_prime,h3_prime,h4_prime))\n",
+        "h1,h2,h3,h4 =  backward_mapping(h1_prime,h2_prime,h3_prime,h4_prime)\n",
+        "print(\"Reconstructed h values %3.3f,%3.3f,%3.3f,%3.3f\"%(h1,h2,h3,h4))"
+      ],
+      "metadata": {
+        "id": "RT7qvEFp700I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "sDknSPMLZmzh"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}