mnist.ipynb

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   "source": [
    "## TensorFlow model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set directories\n",
    "Set directories for training data and model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = \"/home/jovyan/training_data/MNIST_data/\"\n",
    "model_output = \"/home/jovyan/models/mnist\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /home/jovyan/training_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting /home/jovyan/training_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting /home/jovyan/training_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /home/jovyan/training_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "0.9176\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(training_data, one_hot = True)\n",
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "os.chdir(model_output)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    x = tf.placeholder(tf.float32, [None,784], name=\"x\")\n",
    "\n",
    "    W = tf.Variable(tf.zeros([784,10]))\n",
    "    b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "    y = tf.nn.softmax(tf.matmul(x,W) + b, name=\"y\")\n",
    "\n",
    "    y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "\n",
    "    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
    "\n",
    "    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "    init = tf.initialize_all_variables()\n",
    "\n",
    "    sess = tf.Session()\n",
    "    sess.run(init)\n",
    "\n",
    "    for i in range(1000):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    print(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_:mnist.test.labels}))\n",
    "\n",
    "    saver = tf.train.Saver()\n",
    "\n",
    "    saver.save(sess, \"./deep_mnist_model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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