codes/ipython/3-neural_networks/multi-layer-perceptron/code/train_mlp.ipynb · luguodong/TensorFlow-Course

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train_mlp.ipynb 13.85 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "######################################\n",
    "######### Necessary Flags ############\n",
    "######################################\n",
    "\n",
    "num_classes = 10\n",
    "batch_size = 128\n",
    "num_epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##########################################\n",
    "######## Learning rate flags #############\n",
    "##########################################\n",
    "initial_learning_rate = 0.001\n",
    "learning_rate_decay_factor = 0.95\n",
    "num_epochs_per_decay = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#########################################\n",
    "########## status flags #################\n",
    "#########################################\n",
    "is_training = False\n",
    "fine_tuning = False\n",
    "online_test = True\n",
    "allow_soft_placement = True\n",
    "log_device_placement = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "##########################################\n",
    "####### Load and Organize Data ###########\n",
    "##########################################\n",
    "'''\n",
    "In this part the input must be prepared.\n",
    "\n",
    "   1 - The MNIST data will be downloaded.\n",
    "   2 - The images and labels for both training and testing will be extracted.\n",
    "   3 - The prepared data format(?,784) is different by the appropriate image shape(?,28,28,1) which needs\n",
    "        to be fed to the CNN architecture. So it needs to be reshaped.\n",
    "\n",
    "'''\n",
    "\n",
    "# Download and get MNIST dataset(available in tensorflow.contrib.learn.python.learn.datasets.mnist)\n",
    "# It checks and download MNIST if it's not already downloaded then extract it.\n",
    "# The 'reshape' is True by default to extract feature vectors but we set it to false to we get the original images.\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", reshape=True, one_hot=True)\n",
    "train_data = mnist.train.images\n",
    "train_label = mnist.train.labels\n",
    "test_data = mnist.test.images\n",
    "test_label = mnist.test.labels\n",
    "\n",
    "# # The 'input.provide_data' is provided to organize any custom dataset which has specific characteristics.\n",
    "# data = input.provide_data(mnist)\n",
    "\n",
    "# Dimentionality of train\n",
    "dimensionality_train = train_data.shape\n",
    "\n",
    "# Dimensions\n",
    "num_train_samples = dimensionality_train[0]\n",
    "num_features = dimensionality_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1, Train Loss=0.248\n",
      "Test Accuracy= 0.8565\n",
      "Epoch #2, Train Loss=0.242\n",
      "Test Accuracy= 0.8610\n",
      "Epoch #3, Train Loss=0.240\n",
      "Test Accuracy= 0.8676\n",
      "Epoch #4, Train Loss=0.236\n",
      "Test Accuracy= 0.8737\n",
      "Epoch #5, Train Loss=0.235\n",
      "Test Accuracy= 0.8740\n",
      "Epoch #6, Train Loss=0.234\n",
      "Test Accuracy= 0.8753\n",
      "Epoch #7, Train Loss=0.234\n",
      "Test Accuracy= 0.8756\n",
      "Epoch #8, Train Loss=0.244\n",
      "Test Accuracy= 0.8766\n",
      "Epoch #9, Train Loss=0.234\n",
      "Test Accuracy= 0.8765\n",
      "Epoch #10, Train Loss=0.236\n",
      "Test Accuracy= 0.8786\n",
      "Final Test Accuracy is 0.88\n"
     ]
    }
   ],
   "source": [
    "#######################################\n",
    "########## Defining Graph ############\n",
    "#######################################\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    ###################################\n",
    "    ########### Parameters ############\n",
    "    ###################################\n",
    "\n",
    "    # global step\n",
    "    global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n",
    "\n",
    "    # learning rate policy\n",
    "    decay_steps = int(num_train_samples / batch_size *\n",
    "                      num_epochs_per_decay)\n",
    "    learning_rate = tf.train.exponential_decay(initial_learning_rate,\n",
    "                                               global_step,\n",
    "                                               decay_steps,\n",
    "                                               learning_rate_decay_factor,\n",
    "                                               staircase=True,\n",
    "                                               name='exponential_decay_learning_rate')\n",
    "\n",
    "    ###############################################\n",
    "    ########### Defining place holders ############\n",
    "    ###############################################\n",
    "    image_place = tf.placeholder(tf.float32, shape=([None, num_features]), name='image')\n",
    "    label_place = tf.placeholder(tf.float32, shape=([None, num_classes]), name='gt')\n",
    "    dropout_param = tf.placeholder(tf.float32)\n",
    "\n",
    "    ##################################################\n",
    "    ########### Model + Loss + Accuracy ##############\n",
    "    ##################################################\n",
    "\n",
    "    # MODEL(MPL with two hidden layer)\n",
    "\n",
    "    # LAYER-1\n",
    "    net = tf.contrib.layers.fully_connected(inputs=image_place, num_outputs=250, scope='fc-1')\n",
    "\n",
    "    # LAYER-2\n",
    "    net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=250, scope='fc-2')\n",
    "\n",
    "    # SOFTMAX\n",
    "    logits_pre_softmax = tf.contrib.layers.fully_connected(inputs=net, num_outputs=num_classes, scope='fc-3')\n",
    "\n",
    "    # Define loss\n",
    "    softmax_loss = tf.reduce_mean(\n",
    "        tf.nn.softmax_cross_entropy_with_logits(logits=logits_pre_softmax, labels=label_place))\n",
    "\n",
    "    # Accuracy\n",
    "    accuracy = tf.reduce_mean(\n",
    "        tf.cast(tf.equal(tf.argmax(logits_pre_softmax, 1), tf.argmax(label_place, 1)), tf.float32))\n",
    "\n",
    "    #############################################\n",
    "    ########### training operation ##############\n",
    "    #############################################\n",
    "\n",
    "    # Define optimizer by its default values\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "\n",
    "    # 'train_op' is a operation that is run for gradient update on parameters.\n",
    "    # Each execution of 'train_op' is a training step.\n",
    "    # By passing 'global_step' to the optimizer, each time that the 'train_op' is run, Tensorflow\n",
    "    # update the 'global_step' and increment it by one!\n",
    "\n",
    "    # gradient update.\n",
    "    with tf.name_scope('train_scope'):\n",
    "        grads = optimizer.compute_gradients(softmax_loss)\n",
    "        train_op = optimizer.apply_gradients(grads, global_step=global_step)\n",
    "\n",
    "    ###############################################\n",
    "    ############ Define Sammaries #################\n",
    "    ###############################################\n",
    "\n",
    "    # Summaries for loss and accuracy\n",
    "    tf.summary.scalar(\"loss\", softmax_loss, collections=['train', 'test'])\n",
    "    tf.summary.scalar(\"accuracy\", accuracy, collections=['train', 'test'])\n",
    "    tf.summary.scalar(\"global_step\", global_step, collections=['train'])\n",
    "    tf.summary.scalar(\"learning_rate\", learning_rate, collections=['train'])\n",
    "\n",
    "    # Merge all summaries together.\n",
    "    summary_train_op = tf.summary.merge_all('train')\n",
    "    summary_test_op = tf.summary.merge_all('test')\n",
    "\n",
    "    ############################################\n",
    "    ############ Run the Session ###############\n",
    "    ############################################\n",
    "    session_conf = tf.ConfigProto(\n",
    "        allow_soft_placement=allow_soft_placement,\n",
    "        log_device_placement=log_device_placement)\n",
    "    sess = tf.Session(graph=graph, config=session_conf)\n",
    "\n",
    "    with sess.as_default():\n",
    "\n",
    "        # Initialize all variables\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\n",
    "        ###################################################################\n",
    "        ########## Run the training and loop over the batches #############\n",
    "        ###################################################################\n",
    "        for epoch in range(num_epochs):\n",
    "            total_batch_training = int(train_data.shape[0] / batch_size)\n",
    "\n",
    "            # go through the batches\n",
    "            for batch_num in range(total_batch_training):\n",
    "                #################################################\n",
    "                ########## Get the training batches #############\n",
    "                #################################################\n",
    "\n",
    "                start_idx = batch_num * batch_size\n",
    "                end_idx = (batch_num + 1) * batch_size\n",
    "\n",
    "                # Fit training using batch data\n",
    "                train_batch_data, train_batch_label = train_data[start_idx:end_idx], train_label[\n",
    "                                                                                     start_idx:end_idx]\n",
    "\n",
    "                ########################################\n",
    "                ########## Run the session #############\n",
    "                ########################################\n",
    "\n",
    "                # Run optimization op (backprop) and Calculate batch loss and accuracy\n",
    "                # When the tensor tensors['global_step'] is evaluated, it will be incremented by one.\n",
    "                batch_loss, _, training_step = sess.run(\n",
    "                    [softmax_loss, train_op, global_step],\n",
    "                    feed_dict={image_place: train_batch_data,\n",
    "                               label_place: train_batch_label,\n",
    "                               dropout_param: 0.5})\n",
    "\n",
    "\n",
    "                #################################################\n",
    "                ########## Plot the progressive bar #############\n",
    "                #################################################\n",
    "\n",
    "            print(\"Epoch #\" + str(epoch + 1) + \", Train Loss=\" + \\\n",
    "                  \"{:.3f}\".format(batch_loss))\n",
    "\n",
    "            #####################################################\n",
    "            ########## Evaluation on the test data #############\n",
    "            #####################################################\n",
    "\n",
    "            if online_test:\n",
    "                # WARNING: In this evaluation the whole test data is fed. In case the test data is huge this implementation\n",
    "                #          may lead to memory error. In presense of large testing samples, batch evaluation on testing is\n",
    "                #          recommended as in the training phase.\n",
    "                test_accuracy_epoch, test_summaries = sess.run(\n",
    "                    [accuracy, summary_test_op],\n",
    "                    feed_dict={image_place: test_data,\n",
    "                               label_place: test_label,\n",
    "                               dropout_param: 1.})\n",
    "                print(\"Test Accuracy= \" + \\\n",
    "                      \"{:.4f}\".format(test_accuracy_epoch))\n",
    "\n",
    "                ###########################################################\n",
    "                ########## Write the summaries for test phase #############\n",
    "                ###########################################################\n",
    "\n",
    "                # Returning the value of global_step if necessary\n",
    "                current_step = tf.train.global_step(sess, global_step)\n",
    "\n",
    "\n",
    "        # Evaluation of the model\n",
    "        total_test_accuracy = sess.run(accuracy, feed_dict={\n",
    "            image_place: test_data,\n",
    "            label_place: test_label,\n",
    "            dropout_param: 1.})\n",
    "\n",
    "        print(\"Final Test Accuracy is %.2f\" % total_test_accuracy)"
   ]
  },
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