mnist.py.ipynb

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     "data": {
      "text/plain": [
       "'A deep MNIST classifier using convolutional layers.\\nSee extensive documentation at\\nhttps://www.tensorflow.org/get_started/mnist/pros\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# ==============================================================================\n",
    "\n",
    "\"\"\"A deep MNIST classifier using convolutional layers.\n",
    "See extensive documentation at\n",
    "https://www.tensorflow.org/get_started/mnist/pros\n",
    "\"\"\"\n",
    "# Disable linter warnings to maintain consistency with tutorial.\n",
    "# pylint: disable=invalid-name\n",
    "# pylint: disable=g-bad-import-order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 - Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "import tempfile\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 - Define Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deepnn(x):\n",
    "  \"\"\"deepnn builds the graph for a deep net for classifying digits.\n",
    "  Args:\n",
    "    x: an input tensor with the dimensions (N_examples, 784), where 784 is the\n",
    "    number of pixels in a standard MNIST image.\n",
    "  Returns:\n",
    "    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values\n",
    "    equal to the logits of classifying the digit into one of 10 classes (the\n",
    "    digits 0-9). keep_prob is a scalar placeholder for the probability of\n",
    "    dropout.\n",
    "  \"\"\"\n",
    "  # Reshape to use within a convolutional neural net.\n",
    "  # Last dimension is for \"features\" - there is only one here, since images are\n",
    "  # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.\n",
    "  with tf.name_scope('reshape'):\n",
    "    x_image = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "  # First convolutional layer - maps one grayscale image to 32 feature maps.\n",
    "  with tf.name_scope('conv1'):\n",
    "    W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "    b_conv1 = bias_variable([32])\n",
    "    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "\n",
    "  # Pooling layer - downsamples by 2X.\n",
    "  with tf.name_scope('pool1'):\n",
    "    h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "  # Second convolutional layer -- maps 32 feature maps to 64.\n",
    "  with tf.name_scope('conv2'):\n",
    "    W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "    b_conv2 = bias_variable([64])\n",
    "    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "\n",
    "  # Second pooling layer.\n",
    "  with tf.name_scope('pool2'):\n",
    "    h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image\n",
    "  # is down to 7x7x64 feature maps -- maps this to 1024 features.\n",
    "  with tf.name_scope('fc1'):\n",
    "    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)\n",
    "\n",
    "  # Dropout - controls the complexity of the model, prevents co-adaptation of\n",
    "  # features.\n",
    "  with tf.name_scope('dropout'):\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "  # Map the 1024 features to 10 classes, one for each digit\n",
    "  with tf.name_scope('fc2'):\n",
    "    W_fc2 = weight_variable([1024, 10])\n",
    "    b_fc2 = bias_variable([10])\n",
    "\n",
    "    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n",
    "  return y_conv, keep_prob\n",
    "\n",
    "\n",
    "# Helpers\n",
    "def conv2d(x, W):\n",
    "  \"\"\"conv2d returns a 2d convolution layer with full stride.\"\"\"\n",
    "  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "  \"\"\"max_pool_2x2 downsamples a feature map by 2X.\"\"\"\n",
    "  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n",
    "                        strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "\n",
    "def weight_variable(shape):\n",
    "  \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n",
    "  initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "\n",
    "def bias_variable(shape):\n",
    "  \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n",
    "  initial = tf.constant(0.1, shape=shape)\n",
    "  return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 - Write the main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main(_):\n",
    "  # Import data\n",
    "  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n",
    "\n",
    "  # Create the model\n",
    "  x = tf.placeholder(tf.float32, [None, 784])\n",
    "\n",
    "  # Define loss and optimizer\n",
    "  y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "  # Build the graph for the deep net\n",
    "  y_conv, keep_prob = deepnn(x)\n",
    "\n",
    "  with tf.name_scope('loss'):\n",
    "    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,\n",
    "                                                            logits=y_conv)\n",
    "  cross_entropy = tf.reduce_mean(cross_entropy)\n",
    "\n",
    "  with tf.name_scope('adam_optimizer'):\n",
    "    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "  with tf.name_scope('accuracy'):\n",
    "    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "    correct_prediction = tf.cast(correct_prediction, tf.float32)\n",
    "  accuracy = tf.reduce_mean(correct_prediction)\n",
    "\n",
    "  graph_location = tempfile.mkdtemp()\n",
    "  print('Saving graph to: %s' % graph_location)\n",
    "  train_writer = tf.summary.FileWriter(graph_location)\n",
    "  train_writer.add_graph(tf.get_default_graph())\n",
    "\n",
    "  with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\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={\n",
    "            x: batch[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={\n",
    "        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - Execute the main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz\n",
      "Saving graph to: /var/folders/mb/g0lw4yvd66796p88qw87s7zw0000gn/T/tmpbcOYlM\n",
      "step 0, training accuracy 0.12\n",
      "step 100, training accuracy 0.78\n",
      "step 200, training accuracy 0.88\n",
      "step 300, training accuracy 0.94\n",
      "step 400, training accuracy 1\n",
      "step 500, training accuracy 0.96\n",
      "step 600, training accuracy 0.98\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-512b05a56313>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m                       help='Directory for storing input data')\n\u001b[1;32m      6\u001b[0m   \u001b[0mFLAGS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munparsed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_known_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m   \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0munparsed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/platform/app.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(main, argv)\u001b[0m\n\u001b[1;32m     46\u001b[0m   \u001b[0;31m# Call the main function, passing through any arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m   \u001b[0;31m# to the final program.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m   \u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mflags_passthrough\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-d0d17b9d7b7b>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(_)\u001b[0m\n\u001b[1;32m     38\u001b[0m             x: batch[0], y_: batch[1], keep_prob: 1.0})\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'step %d, training accuracy %g'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m       \u001b[0mtrain_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     print('test accuracy %g' % accuracy.eval(feed_dict={\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m   1573\u001b[0m         \u001b[0mnone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0msession\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1574\u001b[0m     \"\"\"\n\u001b[0;32m-> 1575\u001b[0;31m     \u001b[0m_run_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36m_run_using_default_session\u001b[0;34m(operation, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   3814\u001b[0m                        \u001b[0;34m\"the operation's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3815\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 3816\u001b[0;31m   \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/luna/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "  parser = argparse.ArgumentParser()\n",
    "  parser.add_argument('--data_dir', type=str,\n",
    "                      default='/tmp/tensorflow/mnist/input_data',\n",
    "                      help='Directory for storing input data')\n",
    "  FLAGS, unparsed = parser.parse_known_args()\n",
    "  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 - Visualize the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the command in the terminal\n",
    "\n",
    "```tensorboard --logdir=/var/folders/mb/g0lw4yvd66796p88qw87s7zw0000gn/T/tmpbcOYlM```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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