notebooks/3_NeuralNetworks/neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Example\n",
    "\n",
    "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n",
    "\n",
    "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n",
    "\n",
    "- Author: Aymeric Damien\n",
    "- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Overview\n",
    "\n",
    "<img src=\"http://cs231n.github.io/assets/nn1/neural_net2.jpeg\" alt=\"nn\" style=\"width: 400px;\"/>\n",
    "\n",
    "## MNIST Dataset Overview\n",
    "\n",
    "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
    "\n",
    "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
    "\n",
    "More info: http://yann.lecun.com/exdb/mnist/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.1\n",
    "num_steps = 1000\n",
    "batch_size = 128\n",
    "display_step = 100\n",
    "\n",
    "# Network Parameters\n",
    "n_hidden_1 = 256 # 1st layer number of neurons\n",
    "n_hidden_2 = 256 # 2nd layer number of neurons\n",
    "num_input = 784 # MNIST data input (img shape: 28*28)\n",
    "num_classes = 10 # MNIST total classes (0-9 digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the input function for training\n",
    "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={'images': mnist.train.images}, y=mnist.train.labels,\n",
    "    batch_size=batch_size, num_epochs=None, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the neural network\n",
    "def neural_net(x_dict):\n",
    "    # TF Estimator input is a dict, in case of multiple inputs\n",
    "    x = x_dict['images']\n",
    "    # Hidden fully connected layer with 256 neurons\n",
    "    layer_1 = tf.layers.dense(x, n_hidden_1)\n",
    "    # Hidden fully connected layer with 256 neurons\n",
    "    layer_2 = tf.layers.dense(layer_1, n_hidden_2)\n",
    "    # Output fully connected layer with a neuron for each class\n",
    "    out_layer = tf.layers.dense(layer_2, num_classes)\n",
    "    return out_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the model function (following TF Estimator Template)\n",
    "def model_fn(features, labels, mode):\n",
    "    \n",
    "    # Build the neural network\n",
    "    logits = neural_net(features)\n",
    "    \n",
    "    # Predictions\n",
    "    pred_classes = tf.argmax(logits, axis=1)\n",
    "    pred_probas = tf.nn.softmax(logits)\n",
    "    \n",
    "    # If prediction mode, early return\n",
    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n",
    "        \n",
    "    # Define loss and optimizer\n",
    "    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "        logits=logits, labels=tf.cast(labels, dtype=tf.int32)))\n",
    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n",
    "    \n",
    "    # Evaluate the accuracy of the model\n",
    "    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n",
    "    \n",
    "    # TF Estimators requires to return a EstimatorSpec, that specify\n",
    "    # the different ops for training, evaluating, ...\n",
    "    estim_specs = tf.estimator.EstimatorSpec(\n",
    "      mode=mode,\n",
    "      predictions=pred_classes,\n",
    "      loss=loss_op,\n",
    "      train_op=train_op,\n",
    "      eval_metric_ops={'accuracy': acc_op})\n",
    "\n",
    "    return estim_specs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpu7vjLA\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpu7vjLA', '_save_summary_steps': 100}\n"
     ]
    }
   ],
   "source": [
    "# Build the Estimator\n",
    "model = tf.estimator.Estimator(model_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpu7vjLA/model.ckpt.\n",
      "INFO:tensorflow:loss = 2.44919, step = 1\n",
      "INFO:tensorflow:global_step/sec: 602.544\n",
      "INFO:tensorflow:loss = 0.344767, step = 101 (0.167 sec)\n",
      "INFO:tensorflow:global_step/sec: 618.839\n",
      "INFO:tensorflow:loss = 0.277633, step = 201 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 626.418\n",
      "INFO:tensorflow:loss = 0.407796, step = 301 (0.160 sec)\n",
      "INFO:tensorflow:global_step/sec: 624.765\n",
      "INFO:tensorflow:loss = 0.376889, step = 401 (0.160 sec)\n",
      "INFO:tensorflow:global_step/sec: 624.091\n",
      "INFO:tensorflow:loss = 0.319697, step = 501 (0.160 sec)\n",
      "INFO:tensorflow:global_step/sec: 616.907\n",
      "INFO:tensorflow:loss = 0.39049, step = 601 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 623.371\n",
      "INFO:tensorflow:loss = 0.336831, step = 701 (0.161 sec)\n",
      "INFO:tensorflow:global_step/sec: 617.429\n",
      "INFO:tensorflow:loss = 0.312776, step = 801 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 620.825\n",
      "INFO:tensorflow:loss = 0.312817, step = 901 (0.161 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpu7vjLA/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.24931.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.estimator.Estimator at 0x7f21ac597690>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Model\n",
    "model.train(input_fn, steps=num_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2017-08-21-13:57:02\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n",
      "INFO:tensorflow:Finished evaluation at 2017-08-21-13:57:02\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9189, global_step = 1000, loss = 0.286567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.91890001, 'global_step': 1000, 'loss': 0.28656715}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the Model\n",
    "# Define the input function for evaluating\n",
    "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={'images': mnist.test.images}, y=mnist.test.labels,\n",
    "    batch_size=batch_size, shuffle=False)\n",
    "# Use the Estimator 'evaluate' method\n",
    "model.evaluate(input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21a418dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 7\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21a34d5f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADCRJREFUeJzt3X/oXfV9x/Hne1n6h2n/MKvGYMV0RaclYjK+iGCYHdXi\nRND8I1UYkcnSPxqwsD8m7o8JYyCydgz/KKQ0NJXOZkSDWqdtJ8N0MKpRM383OvmWJsREUahVpDN5\n74/viXzV7z33m3vPvecm7+cDLt9zz+eee94c8srn/LrnE5mJpHr+oO8CJPXD8EtFGX6pKMMvFWX4\npaIMv1SU4ZeKMvxSUYZfKuoPp7myiPB2QmnCMjOW87mxev6IuCYifhURr0XE7eN8l6TpilHv7Y+I\nFcAB4GrgIPAUcFNmvtSyjD2/NGHT6PkvA17LzNcz8/fAj4Hrx/g+SVM0TvjPBX6z6P3BZt7HRMTW\niNgXEfvGWJekjk38hF9mbge2g7v90iwZp+c/BJy36P0XmnmSTgHjhP8p4IKI+GJEfAb4OvBQN2VJ\nmrSRd/sz88OI2Ab8FFgB7MjMFzurTNJEjXypb6SVecwvTdxUbvKRdOoy/FJRhl8qyvBLRRl+qSjD\nLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UZfqmo\nqQ7RrXouvPDCgW2vvPJK67K33XZba/s999wzUk1aYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0WN\ndZ0/IuaBd4FjwIeZOddFUTp9bNy4cWDb8ePHW5c9ePBg1+VokS5u8vnzzHyrg++RNEXu9ktFjRv+\nBH4WEU9HxNYuCpI0HePu9m/KzEMRcTbw84h4JTP3Lv5A85+C/zFIM2asnj8zDzV/jwJ7gMuW+Mz2\nzJzzZKA0W0YOf0SsiojPnZgGvga80FVhkiZrnN3+NcCeiDjxPf+amY91UpWkiRs5/Jn5OnBph7Xo\nNLRhw4aBbe+9917rsnv27Om6HC3ipT6pKMMvFWX4paIMv1SU4ZeKMvxSUT66W2NZv359a/u2bdsG\ntt17771dl6OTYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0V5nV9jueiii1rbV61aNbBt165dXZej\nk2DPLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFRWZOb2UR01uZpuLJJ59sbT/rrLMGtg17FsCwR3tr\naZkZy/mcPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX09/wRsQO4DjiameubeauBXcA6YB64MTPf\nmVyZ6su6deta2+fm5lrbDxw4MLDN6/j9Wk7P/wPgmk/Mux14PDMvAB5v3ks6hQwNf2buBd7+xOzr\ngZ3N9E7gho7rkjRhox7zr8nMw830G8CajuqRNCVjP8MvM7Ptnv2I2ApsHXc9kro1as9/JCLWAjR/\njw76YGZuz8y5zGw/MyRpqkYN/0PAlmZ6C/BgN+VImpah4Y+I+4D/Bv4kIg5GxK3AXcDVEfEqcFXz\nXtIpZOgxf2beNKDpqx3Xohl05ZVXjrX8m2++2VEl6pp3+ElFGX6pKMMvFWX4paIMv1SU4ZeKcohu\ntbrkkkvGWv7uu+/uqBJ1zZ5fKsrwS0UZfqkowy8VZfilogy/VJThl4pyiO7iLr/88tb2Rx55pLV9\nfn6+tf2KK64Y2PbBBx+0LqvROES3pFaGXyrK8EtFGX6pKMMvFWX4paIMv1SUv+cv7qqrrmptX716\ndWv7Y4891trutfzZZc8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UNvc4fETuA64Cjmbm+mXcn8NfA\nifGX78jMf59UkZqcSy+9tLV92PMedu/e3WU5mqLl9Pw/AK5ZYv4/Z+aG5mXwpVPM0PBn5l7g7SnU\nImmKxjnm3xYRz0XEjog4s7OKJE3FqOH/LvAlYANwGPj2oA9GxNaI2BcR+0Zcl6QJGCn8mXkkM49l\n5nHge8BlLZ/dnplzmTk3apGSujdS+CNi7aK3m4EXuilH0rQs51LffcBXgM9HxEHg74GvRMQGIIF5\n4BsTrFHSBPjc/tPcOeec09q+f//+1vZ33nmntf3iiy8+6Zo0WT63X1Irwy8VZfilogy/VJThl4oy\n/FJRPrr7NHfLLbe0tp999tmt7Y8++miH1WiW2PNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/N\nnX/++WMtP+wnvTp12fNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/NXXfddWMt//DDD3dUiWaN\nPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX0On9EnAf8EFgDJLA9M/8lIlYDu4B1wDxwY2b64+8e\nbNq0aWDbsCG6Vddyev4Pgb/JzC8DlwPfjIgvA7cDj2fmBcDjzXtJp4ih4c/Mw5n5TDP9LvAycC5w\nPbCz+dhO4IZJFSmpeyd1zB8R64CNwC+BNZl5uGl6g4XDAkmniGXf2x8RnwXuB76Vmb+NiI/aMjMj\nIgcstxXYOm6hkrq1rJ4/IlayEPwfZeYDzewjEbG2aV8LHF1q2czcnplzmTnXRcGSujE0/LHQxX8f\neDkzv7Oo6SFgSzO9BXiw+/IkTcpydvuvAP4SeD4i9jfz7gDuAv4tIm4Ffg3cOJkSNczmzZsHtq1Y\nsaJ12Weffba1fe/evSPVpNk3NPyZ+V9ADGj+arflSJoW7/CTijL8UlGGXyrK8EtFGX6pKMMvFeWj\nu08BZ5xxRmv7tddeO/J37969u7X92LFjI3+3Zps9v1SU4ZeKMvxSUYZfKsrwS0UZfqkowy8VFZlL\nPn1rMisb8KgvtVu5cmVr+xNPPDGw7ejRJR+w9JGbb765tf39999vbdfsycxBP8H/GHt+qSjDLxVl\n+KWiDL9UlOGXijL8UlGGXyrK6/zSacbr/JJaGX6pKMMvFWX4paIMv1SU4ZeKMvxSUUPDHxHnRcR/\nRsRLEfFiRNzWzL8zIg5FxP7mNfrD4yVN3dCbfCJiLbA2M5+JiM8BTwM3ADcCv8vMf1r2yrzJR5q4\n5d7kM3TEnsw8DBxupt+NiJeBc8crT1LfTuqYPyLWARuBXzaztkXEcxGxIyLOHLDM1ojYFxH7xqpU\nUqeWfW9/RHwWeAL4x8x8ICLWAG8BCfwDC4cGfzXkO9ztlyZsubv9ywp/RKwEfgL8NDO/s0T7OuAn\nmbl+yPcYfmnCOvthT0QE8H3g5cXBb04EnrAZeOFki5TUn+Wc7d8E/AJ4HjjezL4DuAnYwMJu/zzw\njebkYNt32fNLE9bpbn9XDL80ef6eX1Irwy8VZfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxVl\n+KWiDL9UlOGXijL8UlFDH+DZsbeAXy96//lm3iya1dpmtS6wtlF1Wdv5y/3gVH/P/6mVR+zLzLne\nCmgxq7XNal1gbaPqqzZ3+6WiDL9UVN/h397z+tvMam2zWhdY26h6qa3XY35J/em755fUk17CHxHX\nRMSvIuK1iLi9jxoGiYj5iHi+GXm41yHGmmHQjkbEC4vmrY6In0fEq83fJYdJ66m2mRi5uWVk6V63\n3ayNeD313f6IWAEcAK4GDgJPATdl5ktTLWSAiJgH5jKz92vCEfFnwO+AH54YDSki7gbezsy7mv84\nz8zMv52R2u7kJEdunlBtg0aWvoUet12XI153oY+e/zLgtcx8PTN/D/wYuL6HOmZeZu4F3v7E7OuB\nnc30Thb+8UzdgNpmQmYezsxnmul3gRMjS/e67Vrq6kUf4T8X+M2i9weZrSG/E/hZRDwdEVv7LmYJ\naxaNjPQGsKbPYpYwdOTmafrEyNIzs+1GGfG6a57w+7RNmfmnwF8A32x2b2dSLhyzzdLlmu8CX2Jh\nGLfDwLf7LKYZWfp+4FuZ+dvFbX1uuyXq6mW79RH+Q8B5i95/oZk3EzLzUPP3KLCHhcOUWXLkxCCp\nzd+jPdfzkcw8kpnHMvM48D163HbNyNL3Az/KzAea2b1vu6Xq6mu79RH+p4ALIuKLEfEZ4OvAQz3U\n8SkRsao5EUNErAK+xuyNPvwQsKWZ3gI82GMtHzMrIzcPGlmanrfdzI14nZlTfwHXsnDG/3+Bv+uj\nhgF1/THwP83rxb5rA+5jYTfw/1g4N3Ir8EfA48CrwH8Aq2eotntZGM35ORaCtran2jaxsEv/HLC/\neV3b97ZrqauX7eYdflJRnvCTijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1TU/wNRj+er2ohshAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21a343b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21a2365ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 0\n"
     ]
    }
   ],
   "source": [
    "# Predict single images\n",
    "n_images = 4\n",
    "# Get images from test set\n",
    "test_images = mnist.test.images[:n_images]\n",
    "# Prepare the input data\n",
    "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={'images': test_images}, shuffle=False)\n",
    "# Use the model to predict the images class\n",
    "preds = list(model.predict(input_fn))\n",
    "\n",
    "# Display\n",
    "for i in range(n_images):\n",
    "    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n",
    "    plt.show()\n",
    "    print(\"Model prediction:\", preds[i])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}