tensorflow1_ipynb/basic-ml/perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.1\n",
      "IPython 6.0.0\n",
      "\n",
      "tensorflow 1.2.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- (Classic) Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of the classic Perceptron by Frank Rosenblatt for binary classification (here: 0/1 class labels)."
   ]
  },
  {
   "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",
    "import numpy as np\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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b8cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "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.xlabel('feature 1')\n",
    "plt.ylabel('feature 2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters:\n",
      "\n",
      "Weights:\n",
      " [[ 2.02931881]\n",
      " [ 0.5932976 ]]\n",
      "Bias: [[-1.]]\n",
      "\n",
      "Number of training errors: 0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "\n",
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Architecture\n",
    "n_features = X.shape[1]\n",
    "\n",
    "\n",
    "##########################\n",
    "### GRAPH DEFINITION\n",
    "##########################\n",
    "\n",
    "g = tf.Graph()\n",
    "with g.as_default():\n",
    "   \n",
    "    # Graph Inputs\n",
    "    features = tf.placeholder(dtype=tf.float32, \n",
    "                              shape=[None, n_features], name='features')\n",
    "    targets = tf.placeholder(dtype=tf.float32, \n",
    "                             shape=[None, 1], name='targets')\n",
    "\n",
    "    # Model Parameters\n",
    "    weights = tf.Variable(tf.zeros(shape=[n_features, 1], \n",
    "                          dtype=tf.float32), name='weights')\n",
    "    bias = tf.Variable([[0.]], dtype=tf.float32, name='bias')\n",
    "    \n",
    "\n",
    "    \n",
    "    # Forward Pass\n",
    "    linear = tf.add(tf.matmul(features, weights), bias, name='linear')\n",
    "    ones = tf.ones(shape=tf.shape(linear)) \n",
    "    zeros = tf.zeros(shape=tf.shape(linear))\n",
    "    prediction = tf.where(condition=tf.less(linear, 0.),\n",
    "                          x=zeros, \n",
    "                          y=ones, \n",
    "                          name='prediction')\n",
    "    \n",
    "    # Backward Pass\n",
    "    errors = targets - prediction\n",
    "    weight_update = tf.assign_add(weights, \n",
    "                                  tf.reshape(errors * features, (n_features, 1)),\n",
    "                                  name='weight_update')\n",
    "    bias_update = tf.assign_add(bias, errors,\n",
    "                                name='bias_update')\n",
    "    \n",
    "    train = tf.group(weight_update, bias_update, name='train')\n",
    "    \n",
    "    saver = tf.train.Saver(name='saver')\n",
    "\n",
    "    \n",
    "    \n",
    "##########################\n",
    "### TRAINING & EVALUATION\n",
    "##########################\n",
    "\n",
    "with tf.Session(graph=g) as sess:\n",
    "    \n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    for epoch in range(5):\n",
    "        for example, target in zip(X_train, y_train):\n",
    "            feed_dict = {'features:0': example.reshape(-1, n_features),\n",
    "                         'targets:0': target.reshape(-1, 1)}\n",
    "            _ = sess.run(['train'], feed_dict=feed_dict)\n",
    "\n",
    "\n",
    "    w, b = sess.run(['weights:0', 'bias:0'])    \n",
    "    print('Model parameters:\\n')\n",
    "    print('Weights:\\n', w)\n",
    "    print('Bias:', b)\n",
    "\n",
    "    saver.save(sess, save_path='perceptron')\n",
    "    \n",
    "    pred = sess.run('prediction:0', feed_dict={features: X_train})\n",
    "    errors = np.sum(pred.reshape(-1) != y_train)\n",
    "    print('\\nNumber of training errors:', errors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c4v7haw1JroasZJxyVVd+89bac0ZeGgxZum/U6sqU5rHkZWcw7PUvGTErn8WT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\nDy8v+wqX087tl3UxOyyfSU6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147adcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "### 2D Decision Boundary\n",
    "##########################\n",
    "\n",
    "x_min = -2\n",
    "y_min = ( -(w[0] * x_min) / w[1]\n",
    "          -(b[0] / w[1]) )\n",
    "\n",
    "x_max = 2\n",
    "y_max = ( -(w[0] * x_max) / w[1]\n",
    "          -(b[0] / 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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}