tensorflow1_ipynb/mlp/mlp-batchnorm.ipynb

{
 "cells": [
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   "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 -- Multilayer Perceptron with Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "\n",
    "##########################\n",
    "### DATASET\n",
    "##########################\n",
    "\n",
    "mnist = input_data.read_data_sets(\"./\", one_hot=True)\n",
    "\n",
    "\n",
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Hyperparameters\n",
    "learning_rate = 0.1\n",
    "training_epochs = 10\n",
    "batch_size = 64\n",
    "\n",
    "# Architecture\n",
    "n_hidden_1 = 128\n",
    "n_hidden_2 = 256\n",
    "n_input = 784\n",
    "n_classes = 10\n",
    "\n",
    "# Other\n",
    "random_seed = 123\n",
    "\n",
    "\n",
    "##########################\n",
    "### GRAPH DEFINITION\n",
    "##########################\n",
    "\n",
    "g = tf.Graph()\n",
    "with g.as_default():\n",
    "    \n",
    "    tf.set_random_seed(random_seed)\n",
    "    \n",
    "    # Batchnorm settings\n",
    "    training_phase = tf.placeholder(tf.bool, None, name='training_phase')\n",
    "\n",
    "    # Input data\n",
    "    tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')\n",
    "    tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n",
    "\n",
    "    # Multilayer perceptron\n",
    "    layer_1 = tf.layers.dense(tf_x, n_hidden_1, \n",
    "                              activation=None, # Batchnorm comes before nonlinear activation\n",
    "                              use_bias=False, # Note that no bias unit is used in batchnorm\n",
    "                              kernel_initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "    \n",
    "    layer_1 = tf.layers.batch_normalization(layer_1, training=training_phase)\n",
    "    layer_1 = tf.nn.relu(layer_1)\n",
    "    \n",
    "    layer_2 = tf.layers.dense(layer_1, n_hidden_2, \n",
    "                              activation=None,\n",
    "                              use_bias=False,\n",
    "                              kernel_initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "    layer_2 = tf.layers.batch_normalization(layer_2, training=training_phase)\n",
    "    layer_2 = tf.nn.relu(layer_2)\n",
    "    \n",
    "    out_layer = tf.layers.dense(layer_2, n_classes, activation=None, name='logits')\n",
    "\n",
    "    # Loss and optimizer\n",
    "    loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_layer, labels=tf_y)\n",
    "    cost = tf.reduce_mean(loss, name='cost')\n",
    "    \n",
    "    # control dependency to ensure that batchnorm parameters are also updated\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "        train = optimizer.minimize(cost, name='train')\n",
    "\n",
    "    # Prediction\n",
    "    correct_prediction = tf.equal(tf.argmax(tf_y, 1), tf.argmax(out_layer, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001 | AvgCost: 0.280 | Train/Valid ACC: 0.962/0.960\n",
      "Epoch: 002 | AvgCost: 0.131 | Train/Valid ACC: 0.978/0.972\n",
      "Epoch: 003 | AvgCost: 0.095 | Train/Valid ACC: 0.984/0.973\n",
      "Epoch: 004 | AvgCost: 0.074 | Train/Valid ACC: 0.988/0.976\n",
      "Epoch: 005 | AvgCost: 0.059 | Train/Valid ACC: 0.992/0.980\n",
      "Epoch: 006 | AvgCost: 0.049 | Train/Valid ACC: 0.995/0.980\n",
      "Epoch: 007 | AvgCost: 0.039 | Train/Valid ACC: 0.996/0.979\n",
      "Epoch: 008 | AvgCost: 0.033 | Train/Valid ACC: 0.997/0.981\n",
      "Epoch: 009 | AvgCost: 0.030 | Train/Valid ACC: 0.997/0.977\n",
      "Epoch: 010 | AvgCost: 0.024 | Train/Valid ACC: 0.998/0.979\n",
      "Test ACC: 0.977\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "##########################\n",
    "### TRAINING & EVALUATION\n",
    "##########################\n",
    "    \n",
    "with tf.Session(graph=g) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    np.random.seed(random_seed) # random seed for mnist iterator\n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost = 0.\n",
    "        total_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            _, c = sess.run(['train', 'cost:0'], feed_dict={'features:0': batch_x,\n",
    "                                                            'targets:0': batch_y,\n",
    "                                                            'training_phase:0': True})\n",
    "            avg_cost += c\n",
    "        \n",
    "        train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,\n",
    "                                                      'targets:0': mnist.train.labels,\n",
    "                                                      'training_phase:0': False})\n",
    "        valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,\n",
    "                                                      'targets:0': mnist.validation.labels,\n",
    "                                                      'training_phase:0': False})  \n",
    "        \n",
    "        print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)), end=\"\")\n",
    "        print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n",
    "        \n",
    "    test_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.test.images,\n",
    "                                                 'targets:0': mnist.test.labels,\n",
    "                                                 'training_phase:0': False})\n",
    "    print('Test ACC: %.3f' % test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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