26.1 First  Deep Learning model - MNIST.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",
    "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",
    "- Author: Aymeric Damien,\n",
    "- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Overview\n",
    "![2 Layer Nural Network](http://cs231n.github.io/assets/nn1/neural_net2.jpeg)\n",
    "\n",
    "## MNIST Dataset Overview,\n",
    "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 \n",
    "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",
    "    ![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
    "    More info: http://yann.lecun.com/exdb/mnist/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\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": {},
   "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": {},
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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/tmpznpnfayb\n",
      "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpznpnfayb', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fef8527c748>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "# Build the Estimator\n",
    "model = tf.estimator.Estimator(model_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpznpnfayb/model.ckpt.\n",
      "INFO:tensorflow:loss = 2.587326, step = 1\n",
      "INFO:tensorflow:global_step/sec: 87.7596\n",
      "INFO:tensorflow:loss = 0.50272995, step = 101 (1.148 sec)\n",
      "INFO:tensorflow:global_step/sec: 90.5721\n",
      "INFO:tensorflow:loss = 0.325121, step = 201 (1.103 sec)\n",
      "INFO:tensorflow:global_step/sec: 89.4743\n",
      "INFO:tensorflow:loss = 0.37347275, step = 301 (1.117 sec)\n",
      "INFO:tensorflow:global_step/sec: 91.4957\n",
      "INFO:tensorflow:loss = 0.3612227, step = 401 (1.095 sec)\n",
      "INFO:tensorflow:global_step/sec: 84.2536\n",
      "INFO:tensorflow:loss = 0.3039285, step = 501 (1.186 sec)\n",
      "INFO:tensorflow:global_step/sec: 90.6761\n",
      "INFO:tensorflow:loss = 0.21526912, step = 601 (1.103 sec)\n",
      "INFO:tensorflow:global_step/sec: 90.475\n",
      "INFO:tensorflow:loss = 0.32824463, step = 701 (1.105 sec)\n",
      "INFO:tensorflow:global_step/sec: 89.4561\n",
      "INFO:tensorflow:loss = 0.4502485, step = 801 (1.117 sec)\n",
      "INFO:tensorflow:global_step/sec: 91.2325\n",
      "INFO:tensorflow:loss = 0.292844, step = 901 (1.097 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpznpnfayb/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.3994684.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.estimator.Estimator at 0x7fef8527c7b8>"
      ]
     },
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2018-03-07-13:28:12\n",
      "INFO:tensorflow:Restoring parameters from /tmp/tmpznpnfayb/model.ckpt-1000\n",
      "INFO:tensorflow:Finished evaluation at 2018-03-07-13:28:12\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9169, global_step = 1000, loss = 0.29070553\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9169, 'global_step': 1000, 'loss': 0.29070553}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/tmpznpnfayb/model.ckpt-1000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef709ed7f0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 7\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef730d28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef730950b8>"
      ]
     },
     "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 0x7fef739569b0>"
      ]
     },
     "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
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