MNIST.ipynb

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   "source": [
    "import tensorflow as tf"
   ]
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   "outputs": [
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     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "from tensorflow.examples.tutorials import mnist\n",
    "mnist = mnist.input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "# Declare variables\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Define model\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "source": [
    "# Define the cost function\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "cross_entropy = -tf.reduce_sum(y_ * tf.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Define the optimization function\n",
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# Initialize session\n",
    "init = tf.initialize_all_variables()\n",
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run the training\n",
    "for _ in xrange(1000):\n",
    "    xs, ys = mnist.train.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={x: xs, y_: ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9144\n"
     ]
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   ],
   "source": [
    "# Compare results with test results\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})"
   ]
  }
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