From 57151930ded4da2911e728cc399f75ad2041f97b Mon Sep 17 00:00:00 2001
From: udlbook <110402648+udlbook@users.noreply.github.com>
Date: Thu, 27 Nov 2025 15:50:21 -0500
Subject: [PATCH] Created using Colab
---
.../ShallowNN/LinearRegions_Answers.ipynb | 440 ++++++++++++++++++
1 file changed, 440 insertions(+)
create mode 100644 notebooks/ShallowNN/LinearRegions_Answers.ipynb
diff --git a/notebooks/ShallowNN/LinearRegions_Answers.ipynb b/notebooks/ShallowNN/LinearRegions_Answers.ipynb
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "authorship_tag": "ABX9TyOq8T2wOrCjh5hFtI51OCMX",
+ "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": [
+ "
"
+ ]
+ },
+ {
+ "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}
\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",
+ " # Tou can use math.comb() https://www.w3schools.com/python/ref_math_comb.asp\n",
+ " # Replace this code\n",
+ " N = 1;\n",
+ " # BEGIN_ANSWER\n",
+ " N= 0\n",
+ " for j in range (Di+1):\n",
+ " N = N+ math.comb(D, j)\n",
+ " # END_ANSWER\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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "AqSUfuJDigN9",
+ "outputId": "61be2ce7-7d38-4eab-8cae-1ec410702a3a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=2, D=3, Number of regions = 7, True value = 7\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Calculate the number of regions for 10D input (Di=2) 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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "krNKPV9gjCu-",
+ "outputId": "21b472e5-f0d2-4794-a155-d9d3a3faaf2e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=10, D=50, Number of regions = 13432735556, True value = 13432735556\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Calculate the number of regions for 5D input (Di=5) and 50 hidden units (D=50)\n",
+ "N = number_regions(5, 50)\n",
+ "print(f\"Di=5, D=40, Number of regions = {int(N)}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7g4j7OXaSniQ",
+ "outputId": "70e4e022-5485-41e6-fce6-e0536669ed8a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=5, D=40, Number of regions = 2369936\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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)}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OfgVQzHBfgI7",
+ "outputId": "19c72b90-ddfe-42ba-c725-16b4bde97c3f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=10, D=50, Number of regions = 13432735556\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": [
+ "# Show that calculation fails when $D_i < D$\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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uq5IeAZTkIMg",
+ "outputId": "908f3d89-585c-4f45-f88d-386be37f3ea2"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=10, D=8, Number of regions = 256, True value = 256\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Let's do the calculation properly when D"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "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",
+ " # BEGIN_ANSWER\n",
+ " N = D_i * D +D + D + 1\n",
+ " # END_ANSWER\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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VbhDmZ1gwkQj",
+ "outputId": "f1ee2adc-85b5-4d47-8e20-0ff6bb0b9bb2"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Di=10, D=8, Number of parameters = 97, True value = 97\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 536
+ },
+ "id": "AH4nA50Au8-a",
+ "outputId": "db638ee8-1f8b-4bce-ee35-93bcd4628f94"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Counting regions for 1 input dimensions\n",
+ "Counting regions for 5 input dimensions\n",
+ "Counting regions for 10 input dimensions\n",
+ "Counting regions for 50 input dimensions\n",
+ "Counting regions for 100 input dimensions\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "ycniN6aIxKyA"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file