010 - Deep Neural Networks.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./datasets/ud730/mnist/train-images-idx3-ubyte.gz\n",
      "Extracting ./datasets/ud730/mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting ./datasets/ud730/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./datasets/ud730/mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets('./datasets/ud730/mnist', one_hot=True, reshape=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Parameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 20\n",
    "batch_size = 128  # Decrease batch size if you don't have enough memory\n",
    "display_step = 1\n",
    "\n",
    "n_input = 784  # MNIST data input (img shape: 28*28)\n",
    "n_classes = 10  # MNIST total classes (0-9 digits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hidden Layer Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_hidden_layer = 256 # layer number of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weights and Biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = {\n",
    "    'hidden_layer': tf.Variable(tf.random_normal([n_input, n_hidden_layer])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_layer, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'hidden_layer': tf.Variable(tf.random_normal([n_hidden_layer])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", [None, 28, 28, 1])\n",
    "y = tf.placeholder(\"float\", [None, n_classes])\n",
    "\n",
    "x_flat = tf.reshape(x, [-1, n_input])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hidden layer with RELU activation\n",
    "layer_1 = tf.add(tf.matmul(x_flat, weights['hidden_layer']),biases['hidden_layer'])\n",
    "layer_1 = tf.nn.relu(layer_1)\n",
    "# Output layer with linear activation\n",
    "logits = tf.add(tf.matmul(layer_1, weights['out']), biases['out'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define loss and optimizer\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost= 37.288757324\n",
      "Epoch: 0002 cost= 28.099151611\n",
      "Epoch: 0003 cost= 21.492095947\n",
      "Epoch: 0004 cost= 15.777803421\n",
      "Epoch: 0005 cost= 13.104373932\n",
      "Epoch: 0006 cost= 16.443382263\n",
      "Epoch: 0007 cost= 12.179491997\n",
      "Epoch: 0008 cost= 11.330167770\n",
      "Epoch: 0009 cost= 8.374776840\n",
      "Epoch: 0010 cost= 10.616512299\n",
      "Epoch: 0011 cost= 8.541707993\n",
      "Epoch: 0012 cost= 5.481113434\n",
      "Epoch: 0013 cost= 5.806009293\n",
      "Epoch: 0014 cost= 8.566146851\n",
      "Epoch: 0015 cost= 10.841135025\n",
      "Epoch: 0016 cost= 8.520166397\n",
      "Epoch: 0017 cost= 6.733136177\n",
      "Epoch: 0018 cost= 4.299148560\n",
      "Epoch: 0019 cost= 5.420190334\n",
      "Epoch: 0020 cost= 8.197362900\n",
      "Optimization Finished!\n",
      "Accuracy: 0.839844\n"
     ]
    }
   ],
   "source": [
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "        total_batch = int(mnist.train.num_examples/batch_size)\n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            # Run optimization op (backprop) and cost op (to get loss value)\n",
    "            sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n",
    "        # Display logs per epoch step\n",
    "        if epoch % display_step == 0:\n",
    "            c = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n",
    "            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
    "                \"{:.9f}\".format(c))\n",
    "    print(\"Optimization Finished!\")\n",
    "\n",
    "    # Test model\n",
    "    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
    "    # Calculate accuracy\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "    # Decrease test_size if you don't have enough memory\n",
    "    test_size = 256\n",
    "    print(\"Accuracy:\", accuracy.eval({x: mnist.test.images[:test_size], y: mnist.test.labels[:test_size]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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