TF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", shape=[None, 784])\n",
    "y_ = tf.placeholder(\"float\", shape=[None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = tf.nn.softmax(tf.matmul(x, W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_ * tf.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(1000):\n",
    "  batch = mnist.train.next_batch(50)\n",
    "  train_step.run(feed_dict={ x: batch[0], y_: batch[1] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9092\n"
     ]
    }
   ],
   "source": [
    "print accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def weight_variable(shape):\n",
    "  initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "  return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bias_variable(shape):\n",
    "  initial = tf.constant(0.1, shape=shape)\n",
    "  return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W):\n",
    "  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def max_pool_2x2(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "b_conv1 = bias_variable([32])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_image = tf.reshape(x, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W_fc1 = weight_variable([7 * 7 * 64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keep_prob = tf.placeholder(\"float\")\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "\n",
    "y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "step 0, training accuracy 0.14\n",
      "step 100, training accuracy 0.88\n",
      "step 200, training accuracy 0.94\n",
      "step 300, training accuracy 0.86\n",
      "step 400, training accuracy 0.82\n",
      "step 500, training accuracy 0.9\n",
      "step 600, training accuracy 0.94\n",
      "step 700, training accuracy 0.92\n",
      "step 800, training accuracy 0.94\n",
      "step 900, training accuracy 0.98\n",
      "step 1000, training accuracy 0.98\n",
      "step 1100, training accuracy 0.94\n",
      "step 1200, training accuracy 1\n",
      "step 1300, training accuracy 1\n",
      "step 1400, training accuracy 0.94\n",
      "step 1500, training accuracy 0.96\n",
      "step 1600, training accuracy 0.94\n",
      "step 1700, training accuracy 0.9\n",
      "step 1800, training accuracy 0.98\n",
      "step 1900, training accuracy 1\n",
      "step 2000, training accuracy 0.96\n",
      "step 2100, training accuracy 0.96\n",
      "step 2200, training accuracy 1\n",
      "step 2300, training accuracy 1\n",
      "step 2400, training accuracy 1\n",
      "step 2500, training accuracy 0.96\n",
      "step 2600, training accuracy 0.98\n",
      "step 2700, training accuracy 0.98\n",
      "step 2800, training accuracy 0.94\n",
      "step 2900, training accuracy 1\n",
      "step 3000, training accuracy 0.98\n",
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      "step 4000, training accuracy 0.98\n",
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      "step 4300, training accuracy 0.98\n",
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      "step 15200, training accuracy 0.98\n",
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      "step 15700, training accuracy 0.98\n",
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      "step 17000, training accuracy 0.98\n",
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      "step 18500, training accuracy 1\n",
      "step 18600, training accuracy 1\n",
      "step 18700, training accuracy 1\n",
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      "step 18900, training accuracy 1\n",
      "step 19000, training accuracy 1\n",
      "step 19100, training accuracy 0.98\n",
      "step 19200, training accuracy 1\n",
      "step 19300, training accuracy 1\n",
      "step 19400, training accuracy 1\n",
      "step 19500, training accuracy 1\n",
      "step 19600, training accuracy 1\n",
      "step 19700, training accuracy 1\n",
      "step 19800, training accuracy 1\n",
      "step 19900, training accuracy 1\n",
      "test accuracy 0.9912\n"
     ]
    }
   ],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.initialize_all_variables())\n",
    "\n",
    "for i in range(20000):\n",
    "  batch = mnist.train.next_batch(50)\n",
    "  if i%100 == 0:\n",
    "    train_accuracy = accuracy.eval(feed_dict={ x:b atch[0], y_: batch[1], keep_prob: 1.0 })\n",
    "    print \"step %d, training accuracy %g\" % (i, train_accuracy)\n",
    "  train_step.run(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 0.5 })\n",
    "\n",
    "print \"test accuracy %g\" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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