{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "11-v25jgKxMC"
   },
   "source": [
    "# CS 490/590 Tutorial 3: Backpropagation\n",
    "\n",
    "We've seen in lecture that a linear classifier is bound to produce errors if\n",
    "our data is not linearly separable. We can avoid this issue by using a more\n",
    "powerful classifier like a multi-layer perceptron (aka a neural network\n",
    "with fully-connected layers).\n",
    "\n",
    "In this tutorial, we examine a classification problem for which the data\n",
    "is not linearly separable. We will implement a 2-layer neural network and\n",
    "train it using gradient descent, computing gradients using the\n",
    "backpropagation algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "sstu454hKxMH"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import expit as sigmoid\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1d1yP1VdKxMI"
   },
   "source": [
    "## Data\n",
    "\n",
    "We will generate a toy data set, similar to the one that you saw in \n",
    "http://playground.tensorflow.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "JMKCRyQkKxMI",
    "outputId": "62304ffe-7838-4096-df9e-e4534bc9d0d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14286e4d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "def make_dataset(num_points):\n",
    "    radius = 5\n",
    "    data = []\n",
    "    labels = []\n",
    "    # Generate positive examples (labeled 1).\n",
    "    for i in range(num_points // 2):\n",
    "        r = np.random.uniform(0, radius*0.5)\n",
    "        angle = np.random.uniform(0, 2*math.pi)\n",
    "        x = r * math.sin(angle)\n",
    "        y = r * math.cos(angle)\n",
    "        data.append([x, y])\n",
    "        labels.append(1)\n",
    "        \n",
    "    # Generate negative examples (labeled 0).\n",
    "    for i in range(num_points // 2):\n",
    "        r = np.random.uniform(radius*0.7, radius)\n",
    "        angle = np.random.uniform(0, 2*math.pi)\n",
    "        x = r * math.sin(angle)\n",
    "        y = r * math.cos(angle)\n",
    "        data.append([x, y])\n",
    "        labels.append(0)\n",
    "        \n",
    "    data = np.asarray(data)\n",
    "    labels = np.asarray(labels)\n",
    "    return data, labels\n",
    "    \n",
    "num_data = 500\n",
    "data, labels = make_dataset(num_data)\n",
    "\n",
    "# Note: red indicates a label of 1, blue indicates a label of 0\n",
    "plt.scatter(data[:num_data//2, 0], data[:num_data//2, 1], color='red') \n",
    "plt.scatter(data[num_data//2:, 0], data[num_data//2:, 1], color='blue')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rFeKhFtMKxMJ"
   },
   "source": [
    "## Neural network definition\n",
    "\n",
    "We will try to classify this data by training a neural network. As a reminder, our goal is to take as input a two dimensional vector $\\mathbf{x} = [x_1, x_2]^T$ and output $\\text{Pr}(t = 1 | \\mathbf{x})$, where $t$ is the label of the datapoint $\\mathbf{x}$. \n",
    "\n",
    "We will use a neural network with one hidden layer which has three hidden units. The equations describing our neural network are below:\n",
    "\n",
    "$$\\mathbf{g} = \\mathbf{U} \\mathbf{x} + \\mathbf{b}$$\n",
    "$$\\mathbf{h} = \\tanh(\\mathbf{g})$$\n",
    "$$z = \\mathbf{W} \\mathbf{h} + c$$\n",
    "$$y = \\sigma(z)$$\n",
    "\n",
    "In the equations above, $\\mathbf{U} = \\begin{pmatrix} u_{11} & u_{12} \\\\ u_{21} & u_{22} \\\\  u_{31} & u_{32} \\end{pmatrix} \\in \\mathbb{R}^{3 \\times 2}, \\mathbf{b} = \\begin{pmatrix} b_1  \\\\ b_2 \\\\ b_3 \\end{pmatrix} \\in \\mathbb{R}^3, \\mathbf{W} = \\begin{pmatrix} w_{1} & w_{2} & w_{3} \\end{pmatrix} \\in \\mathbb{R}^{1 \\times 3}, c \\in \\mathbb{R}$ are the parameters of our neural network which we must learn. Notice we are writing $\\mathbf{W}$ as a matrix with one row.\n",
    "\n",
    "\n",
    "## Vectorizing the neural network\n",
    "\n",
    "We want our neural network to produce predictions for multiple points efficiently. We can do so by vectorizing over training examples. Let  $\\mathbf{X} = \\begin{pmatrix} x_{11} & x_{12} \\\\ \\vdots   & \\vdots  \\\\  x_{N1} & x_{N2}\n",
    "\\end{pmatrix}$ be a matrix containing $N$ datapoints in separate rows. Then we can vectorize by using:\n",
    "\n",
    "$$\\mathbf{G} = \\mathbf{X}\\mathbf{U}^T + \\mathbf{1}\\mathbf{b}^T$$\n",
    "$$\\mathbf{H} = \\tanh(\\mathbf{G})$$\n",
    "$$\\mathbf{z} =  \\mathbf{H}\\mathbf{W}^T + \\mathbf{1}c$$\n",
    "$$\\mathbf{y} = \\sigma(\\mathbf{z})$$\n",
    "\n",
    "$\\mathbf{G}$, for example, will store each of the three hidden unit values for each datapoint in each corresponding row.\n",
    "\n",
    "We can rewrite in scalar form as:\n",
    "$$g_{ij} = u_{j1} x_{i1} + u_{j2} x_{i2} + b_j$$\n",
    "$$h_{ij} = \\tanh(g_{ij})$$\n",
    "$$z_{i} = w_1 h_{i1} + w_2 h_{i2} + w_{3} h_{i3} + c$$\n",
    "$$y_i = \\sigma(z_i)$$\n",
    "Here, $i$ indexes data points and $j$ indexes hidden units, so $i \\in \\{1, \\dots, N\\}$ and $j \\in \\{1, 2, 3\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "YhMqCIG4KxMJ"
   },
   "outputs": [],
   "source": [
    "# First, initialize our neural network parameters.\n",
    "params = {}\n",
    "params['U'] = np.random.randn(3, 2)\n",
    "params['b'] = np.zeros(3)\n",
    "params['W'] = np.random.randn(3)\n",
    "params['c'] = 0\n",
    "\n",
    "# Notice we make use of numpy's broadcasting when adding the bias b.\n",
    "def forward(X, params):    \n",
    "    G = np.dot(X, params['U'].T)  + params['b']\n",
    "    H = np.tanh(G)\n",
    "    z = np.dot(H, params['W'].T) + params['c']\n",
    "    y = sigmoid(z)\n",
    "    \n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bok6coAyKxMK"
   },
   "source": [
    "## Visualize the network's predictions\n",
    "\n",
    "Let's visualize the predictions of our untrained network. As we can see, the network does not succeed at classifying the points without training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "dbgN0noAKxMK",
    "outputId": "921dec94-9477-4379-b421-4832159b8dcf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kk/8hxq9wzj401gpxvrv787r2600000gq/T/ipykernel_52618/3106566481.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  plt.pcolormesh(X1, X2, Y, cmap=plt.cm.get_cmap('YlGn'))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x142942dd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points = 200\n",
    "x1s = np.linspace(-6.0, 6.0, num_points)\n",
    "x2s = np.linspace(-6.0, 6.0, num_points)\n",
    "\n",
    "points = np.transpose([np.tile(x1s, len(x2s)), np.repeat(x2s, len(x1s))])\n",
    "Y = forward(points, params).reshape(num_points, num_points)\n",
    "X1, X2 = np.meshgrid(x1s, x2s)\n",
    "\n",
    "plt.pcolormesh(X1, X2, Y, cmap=plt.cm.get_cmap('YlGn'))\n",
    "plt.colorbar()\n",
    "plt.scatter(data[:num_data//2, 0], data[:num_data//2, 1], color='red') \n",
    "plt.scatter(data[num_data//2:, 0], data[num_data//2:, 1], color='blue') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EkVDg0TXKxMK"
   },
   "source": [
    "## Loss function\n",
    "\n",
    "We will use the same cross entropy loss function as in logistic regression. This loss function is:\n",
    "\n",
    "$$\\mathcal{L}_{CE}(y, t) = -t \\log(y) - (1 - t)\\log(1 - y)$$\n",
    "\n",
    "Here $y = Pr(t = 1|\\mathbf{x})$ and $t$ is the true label.\n",
    "\n",
    "Remember that computing the derivative of this loss function $\\frac{d L}{dy}$ can become numerically unstable. Instead, we combine the logistic function and the cross entropy loss into a single function called logistic cross-entropy:\n",
    "\n",
    "$$\\mathcal{L}_{LCE}(z, t) = t \\log(1 + \\exp(-z)) + (1 -t) \\log(1 + \\exp(z))$$\n",
    "\n",
    "See Lecture 4 Notes for review on this. \n",
    "\n",
    "Our cost function is the sum over multiple examples of the loss function, normalized by the number of examples:\n",
    "\n",
    "$$\\mathcal{E}(\\mathbf{z}, \\mathbf{t}) = \\frac{1}{N} \\left[\\sum_{i=1}^N \\mathcal{L}(z_i, t_i)\\right]$$\n",
    "\n",
    "## Derive backpropagation equations\n",
    "\n",
    "We now derive the backpropagation equations in scalar form and then vectorize on the board.\n",
    "\n",
    "\n",
    "## Implement backpropagation equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "jXpMNvkqKxMK"
   },
   "outputs": [],
   "source": [
    "def backprop(X, t, params):\n",
    "    N = X.shape[0]\n",
    "    \n",
    "    # Perform forwards computation.\n",
    "    G = np.dot(X, params['U'].T)  + params['b']\n",
    "    H = np.tanh(G)\n",
    "    z = np.dot(H, params['W'].T) + params['c']\n",
    "    y = sigmoid(z)\n",
    "    loss = (1./N) * np.sum(-t * np.log(y) - (1 - t) * np.log(1 - y))\n",
    "    \n",
    "    # Perform backwards computation.\n",
    "    E_bar = 1\n",
    "    z_bar = (1./N) * (y - t)\n",
    "    W_bar = np.dot(H.T, z_bar)\n",
    "    c_bar = np.dot(z_bar, np.ones(N))\n",
    "    H_bar = np.outer(z_bar, params['W'].T)\n",
    "    G_bar = H_bar * (1 - np.tanh(G)**2)\n",
    "    U_bar = np.dot(G_bar.T, X)\n",
    "    b_bar = np.dot(G_bar.T, np.ones(N))\n",
    "    \n",
    "    # Wrap our gradients in a dictionary.\n",
    "    grads = {}\n",
    "    grads['U'] = U_bar\n",
    "    grads['b'] = b_bar\n",
    "    grads['W'] = W_bar\n",
    "    grads['c'] = c_bar\n",
    "    \n",
    "    return grads, loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dtMidr6GKxML"
   },
   "source": [
    "## Training the network\n",
    "\n",
    "We can train our network parameters using gradient descent once we have computed derivatives using the backpropagation algorithm. Recall that the gradient descent update rule for a given parameter $p$ and a learning rate $\\alpha$ is:\n",
    "\n",
    "$$p \\gets p - \\alpha * \\frac{\\partial \\mathcal{E}}{\\partial p}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "syHQMyQ3KxML",
    "outputId": "c401afdc-b551-413e-8bb8-31ca376f9129"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step   0 | Loss 0.75\n",
      "Step  50 | Loss 0.43\n",
      "Step 100 | Loss 0.35\n",
      "Step 150 | Loss 0.16\n",
      "Step 200 | Loss 0.11\n",
      "Step 250 | Loss 0.09\n",
      "Step 300 | Loss 0.07\n",
      "Step 350 | Loss 0.06\n",
      "Step 400 | Loss 0.06\n",
      "Step 450 | Loss 0.05\n",
      "Step 500 | Loss 0.05\n",
      "Step 550 | Loss 0.05\n",
      "Step 600 | Loss 0.04\n",
      "Step 650 | Loss 0.04\n",
      "Step 700 | Loss 0.04\n",
      "Step 750 | Loss 0.04\n",
      "Step 800 | Loss 0.04\n",
      "Step 850 | Loss 0.04\n",
      "Step 900 | Loss 0.04\n",
      "Step 950 | Loss 0.03\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1000\n",
    "alpha = 1\n",
    "for step in range(num_steps):        \n",
    "    grads, loss = backprop(data, labels, params)\n",
    "    for k in params:\n",
    "        params[k] -= alpha * grads[k]\n",
    "\n",
    "    # Print loss every so often.\n",
    "    if step % 50 == 0:\n",
    "        print(\"Step {:3d} | Loss {:3.2f}\".format(step, loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k2_vCbzoKxML"
   },
   "source": [
    "## Visualizing the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "5WkazS7bKxMM",
    "outputId": "89d27232-ca79-4c86-9a91-4f612db84b55"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kk/8hxq9wzj401gpxvrv787r2600000gq/T/ipykernel_52618/3106566481.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  plt.pcolormesh(X1, X2, Y, cmap=plt.cm.get_cmap('YlGn'))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x142a24a10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points = 200\n",
    "x1s = np.linspace(-6.0, 6.0, num_points)\n",
    "x2s = np.linspace(-6.0, 6.0, num_points)\n",
    "\n",
    "points = np.transpose([np.tile(x1s, len(x2s)), np.repeat(x2s, len(x1s))])\n",
    "Y = forward(points, params).reshape(num_points, num_points)\n",
    "X1, X2 = np.meshgrid(x1s, x2s)\n",
    "\n",
    "plt.pcolormesh(X1, X2, Y, cmap=plt.cm.get_cmap('YlGn'))\n",
    "plt.colorbar()\n",
    "plt.scatter(data[:num_data//2, 0], data[:num_data//2, 1], color='red') \n",
    "plt.scatter(data[num_data//2:, 0], data[num_data//2:, 1], color='blue') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kBbISwTXKxMM"
   },
   "source": [
    "## Looking forward: Automatic differentiation\n",
    "\n",
    "You probably noticed that manually deriving the backpropagation equations is\n",
    "slow and error prone. It becomes even easier to make an error when implementing\n",
    "in code. Luckily, we almost never have to derive the backwards equations by hand.\n",
    "Instead, we make use of automatic differentation software packaged in libraries\n",
    "such as PyTorch to compute derivatives for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W5tZLqUKKxMM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torch\n",
      "  Obtaining dependency information for torch from https://files.pythonhosted.org/packages/96/4e/970cd3e13ad95aed81102272f0678d8cc48101880b8be5bae8aad22e7f3b/torch-2.2.0-cp311-none-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading torch-2.2.0-cp311-none-macosx_11_0_arm64.whl.metadata (25 kB)\n",
      "Collecting torchvision\n",
      "  Obtaining dependency information for torchvision from https://files.pythonhosted.org/packages/3e/4f/ad5c2a7d2783649c8ea691441a9f285accae922a1625e21603c45e3ddff4/torchvision-0.17.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading torchvision-0.17.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (6.6 kB)\n",
      "Requirement already satisfied: filelock in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torch) (3.9.0)\n",
      "Collecting typing-extensions>=4.8.0 (from torch)\n",
      "  Obtaining dependency information for typing-extensions>=4.8.0 from https://files.pythonhosted.org/packages/b7/f4/6a90020cd2d93349b442bfcb657d0dc91eee65491600b2cb1d388bc98e6b/typing_extensions-4.9.0-py3-none-any.whl.metadata\n",
      "  Downloading typing_extensions-4.9.0-py3-none-any.whl.metadata (3.0 kB)\n",
      "Requirement already satisfied: sympy in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torch) (1.11.1)\n",
      "Requirement already satisfied: networkx in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torch) (3.1)\n",
      "Requirement already satisfied: jinja2 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torch) (3.1.2)\n",
      "Requirement already satisfied: fsspec in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torch) (2023.4.0)\n",
      "Requirement already satisfied: numpy in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torchvision) (1.24.3)\n",
      "Requirement already satisfied: requests in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torchvision) (2.31.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from torchvision) (10.0.1)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from jinja2->torch) (2.1.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from requests->torchvision) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from requests->torchvision) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from requests->torchvision) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from requests->torchvision) (2023.11.17)\n",
      "Requirement already satisfied: mpmath>=0.19 in /Users/egultep/anaconda3/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
      "Downloading torch-2.2.0-cp311-none-macosx_11_0_arm64.whl (59.4 MB)\n",
      "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/59.4 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:09\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install torch torchvision\n",
    "\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "class PyTorchModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(PyTorchModel, self).__init__()\n",
    "        self.layer1 = nn.Linear(2, 3)\n",
    "        self.layer2 = nn.Linear(3, 1)\n",
    "    def forward(self, X):\n",
    "        h = torch.tanh(self.layer1(X))\n",
    "        return self.layer2(h)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9jwIaHncKxMM",
    "outputId": "254669aa-df26-4aa1-d9bb-73fe80d819d3"
   },
   "outputs": [],
   "source": [
    "data_tensor = torch.Tensor(data)\n",
    "labels_tensor = torch.Tensor(labels).float()\n",
    "labels_tensor = labels_tensor.reshape([500, 1]) # same shape as `y` below\n",
    "model = PyTorchModel()\n",
    "\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=1)\n",
    "\n",
    "num_steps = 1000\n",
    "for step in range(num_steps):        \n",
    "    y = model(data_tensor) \n",
    "    loss = criterion(y, labels_tensor)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Print loss every so often.\n",
    "    if step % 50 == 0:\n",
    "        print(\"Step {:3d} | Loss {:3.2f}\".format(step, float(loss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "jORSq9RhKxMM",
    "outputId": "e87be159-68b5-4470-b1fa-2c2b15f576a4"
   },
   "outputs": [],
   "source": [
    "num_points = 200\n",
    "x1s = np.linspace(-6.0, 6.0, num_points)\n",
    "x2s = np.linspace(-6.0, 6.0, num_points)\n",
    "\n",
    "points = np.transpose([np.tile(x1s, len(x2s)), np.repeat(x2s, len(x1s))])\n",
    "Y = torch.sigmoid(model(torch.Tensor(points)))\n",
    "Y = Y.detach().numpy() # convert to numpy\n",
    "Y = Y.reshape(num_points, num_points)\n",
    "X1, X2 = np.meshgrid(x1s, x2s)\n",
    "\n",
    "plt.pcolormesh(X1, X2, Y, cmap=plt.cm.get_cmap('YlGn'))\n",
    "plt.colorbar()\n",
    "plt.scatter(data[:num_data//2, 0], data[:num_data//2, 1], color='red') \n",
    "plt.scatter(data[num_data//2:, 0], data[num_data//2:, 1], color='blue') "
   ]
  }
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