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Merge pull request #188 from yrahal/main

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Fix more Chap09 tiny typos
This commit is contained in:
udlbook
2024-05-16 16:01:49 -04:00
committed by GitHub
parent dd0eaeb781 0233131b07
commit 5c7a03172a
3 changed files with 15 additions and 15 deletions
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14
Notebooks/Chap09/9_3_Ensembling.ipynb
View File

@@ -52,7 +52,7 @@
"# import libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# Define seed so get same results each time\n",
"# Define seed to get same results each time\n",
"np.random.seed(1)"
]
},
@@ -80,7 +80,7 @@
" 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 functoin and adding noise\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",
@@ -96,7 +96,7 @@
{
"cell_type": "code",
"source": [
"# Draw the fitted function, together win uncertainty used to generate points\n",
"# 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",
@@ -137,7 +137,7 @@
"n_data = 15\n",
"x_data,y_data = generate_data(n_data, sigma_func)\n",
"\n",
"# Plot the functinon, data and uncertainty\n",
"# Plot the function, data and uncertainty\n",
"plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
],
"metadata": {
@@ -216,7 +216,7 @@
"# 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 grange\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",
@@ -297,7 +297,7 @@
{
"cell_type": "code",
"source": [
"# Plot the median of the results\n",
"# 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",
@@ -325,4 +325,4 @@
}
}
]
}
}

8
Notebooks/Chap09/9_4_Bayesian_Approach.ipynb
View File

@@ -36,7 +36,7 @@
"# import libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# Define seed so get same results each time\n",
"# Define seed to get same results each time\n",
"np.random.seed(1)"
]
},
@@ -85,7 +85,7 @@
},
"outputs": [],
"source": [
"# Draw the fitted function, together win uncertainty used to generate points\n",
"# 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",
@@ -220,7 +220,7 @@
" &\\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 already a normal distribution, the constant of proportionality must be one and we can write\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",
@@ -423,4 +423,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}

8
Notebooks/Chap09/9_5_Augmentation.ipynb
View File

@@ -95,7 +95,7 @@
"D_k = 200 # Hidden dimensions\n",
"D_o = 10 # Output dimensions\n",
"\n",
"# Define a model with two hidden layers of size 100\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",
@@ -186,7 +186,7 @@
"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.set_title('Train Error %3.2f, Test Error %3.2f'%(errors_train[-1],errors_test[-1]))\n",
"ax.legend()\n",
"plt.show()"
],
@@ -233,7 +233,7 @@
"cell_type": "code",
"source": [
"n_data_orig = data['x'].shape[0]\n",
"# We'll double the amount o fdata\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",
@@ -343,4 +343,4 @@
}
}
]
}
}