L03_perceptron/code/perceptron-numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STAT 479: Deep Learning (Spring 2019)  \n",
    "Instructor: Sebastian Raschka (sraschka@wisc.edu)  \n",
    "Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/  \n",
    "GitHub repository: https://github.com/rasbt/stat479-deep-learning-ss19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.5\n",
      "IPython 6.5.0\n",
      "\n",
      "numpy 1.15.4\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L03: Perceptrons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of the classic Perceptron by Frank Rosenblatt for binary classification (here: 0/1 class labels)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing a toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class label counts: [50 50]\n",
      "X.shape: (100, 2)\n",
      "y.shape: (100,)\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### DATASET\n",
    "##########################\n",
    "\n",
    "data = np.genfromtxt('data/perceptron_toydata.txt', delimiter='\\t')\n",
    "X, y = data[:, :2], data[:, 2]\n",
    "y = y.astype(np.int)\n",
    "\n",
    "print('Class label counts:', np.bincount(y))\n",
    "print('X.shape:', X.shape)\n",
    "print('y.shape:', y.shape)\n",
    "\n",
    "# Shuffling & train/test split\n",
    "shuffle_idx = np.arange(y.shape[0])\n",
    "shuffle_rng = np.random.RandomState(123)\n",
    "shuffle_rng.shuffle(shuffle_idx)\n",
    "X, y = X[shuffle_idx], y[shuffle_idx]\n",
    "\n",
    "X_train, X_test = X[shuffle_idx[:70]], X[shuffle_idx[70:]]\n",
    "y_train, y_test = y[shuffle_idx[:70]], y[shuffle_idx[70:]]\n",
    "\n",
    "# Normalize (mean zero, unit variance)\n",
    "mu, sigma = X_train.mean(axis=0), X_train.std(axis=0)\n",
    "X_train = (X_train - mu) / sigma\n",
    "X_test = (X_test - mu) / sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], label='class 0', marker='o')\n",
    "plt.scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], label='class 1', marker='s')\n",
    "plt.title('Training set')\n",
    "plt.xlabel('feature 1')\n",
    "plt.ylabel('feature 2')\n",
    "plt.xlim([-3, 3])\n",
    "plt.ylim([-3, 3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test[y_test==0, 0], X_test[y_test==0, 1], label='class 0', marker='o')\n",
    "plt.scatter(X_test[y_test==1, 0], X_test[y_test==1, 1], label='class 1', marker='s')\n",
    "plt.title('Test set')\n",
    "plt.xlabel('feature 1')\n",
    "plt.ylabel('feature 2')\n",
    "plt.xlim([-3, 3])\n",
    "plt.ylim([-3, 3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Perceptron model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Perceptron():\n",
    "    def __init__(self, num_features):\n",
    "        self.num_features = num_features\n",
    "        self.weights = np.zeros((num_features, 1), dtype=np.float)\n",
    "        self.bias = np.zeros(1, dtype=np.float)\n",
    "\n",
    "    def forward(self, x):\n",
    "        linear = np.dot(x, self.weights) + self.bias\n",
    "        predictions = np.where(linear > 0., 1, 0)\n",
    "        return predictions\n",
    "        \n",
    "    def backward(self, x, y):  \n",
    "        predictions = self.forward(x)\n",
    "        errors = y - predictions\n",
    "        return errors\n",
    "        \n",
    "    def train(self, x, y, epochs):\n",
    "        for e in range(epochs):\n",
    "            \n",
    "            for i in range(y.shape[0]):\n",
    "                errors = self.backward(x[i].reshape(1, self.num_features), y[i]).reshape(-1)\n",
    "                self.weights += (errors * x[i]).reshape(self.num_features, 1)\n",
    "                self.bias += errors\n",
    "                \n",
    "    def evaluate(self, x, y):\n",
    "        predictions = self.forward(x).reshape(-1)\n",
    "        accuracy = np.sum(predictions == y) / y.shape[0]\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters:\n",
      "\n",
      "\n",
      "  Weights: [[1.27340847]\n",
      " [1.34642288]]\n",
      "\n",
      "  Bias: [-1.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ppn = Perceptron(num_features=2)\n",
    "\n",
    "ppn.train(X_train, y_train, epochs=5)\n",
    "\n",
    "print('Model parameters:\\n\\n')\n",
    "print('  Weights: %s\\n' % ppn.weights)\n",
    "print('  Bias: %s\\n' % ppn.bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 93.33%\n"
     ]
    }
   ],
   "source": [
    "test_acc = ppn.evaluate(X_test, y_test)\n",
    "print('Test set accuracy: %.2f%%' % (test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "### 2D Decision Boundary\n",
    "##########################\n",
    "\n",
    "w, b = ppn.weights, ppn.bias\n",
    "\n",
    "x_min = -2\n",
    "y_min = ( (-(w[0] * x_min) - b[0]) \n",
    "          / w[1] )\n",
    "\n",
    "x_max = 2\n",
    "y_max = ( (-(w[0] * x_max) - b[0]) \n",
    "          / w[1] )\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, sharex=True, figsize=(7, 3))\n",
    "\n",
    "ax[0].plot([x_min, x_max], [y_min, y_max])\n",
    "ax[1].plot([x_min, x_max], [y_min, y_max])\n",
    "\n",
    "ax[0].scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], label='class 0', marker='o')\n",
    "ax[0].scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], label='class 1', marker='s')\n",
    "\n",
    "ax[1].scatter(X_test[y_test==0, 0], X_test[y_test==0, 1], label='class 0', marker='o')\n",
    "ax[1].scatter(X_test[y_test==1, 0], X_test[y_test==1, 1], label='class 1', marker='s')\n",
    "\n",
    "ax[1].legend(loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}