4_convolutions.ipynb

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    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 4\n",
    "------------\n",
    "\n",
    "Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n",
    "\n",
    "The goal of this assignment is make the neural network convolutional."
   ]
  },
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   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
  },
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     "text": [
      "Training set (200000, 28, 28) (200000,)\n",
      "Validation set (10000, 28, 28) (10000,)\n",
      "Test set (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "  save = pickle.load(f)\n",
    "  train_dataset = save['train_dataset']\n",
    "  train_labels = save['train_labels']\n",
    "  valid_dataset = save['valid_dataset']\n",
    "  valid_labels = save['valid_labels']\n",
    "  test_dataset = save['test_dataset']\n",
    "  test_labels = save['test_labels']\n",
    "  del save  # hint to help gc free up memory\n",
    "  print('Training set', train_dataset.shape, train_labels.shape)\n",
    "  print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "  print('Test set', test_dataset.shape, test_labels.shape)"
   ]
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  {
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   "source": [
    "Reformat into a TensorFlow-friendly shape:\n",
    "- convolutions need the image data formatted as a cube (width by height by #channels)\n",
    "- labels as float 1-hot encodings."
   ]
  },
  {
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     "text": [
      "Training set (200000, 28, 28, 1) (200000, 10)\n",
      "Validation set (10000, 28, 28, 1) (10000, 10)\n",
      "Test set (10000, 28, 28, 1) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "image_size = 28\n",
    "num_labels = 10\n",
    "num_channels = 1 # grayscale\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "  dataset = dataset.reshape(\n",
    "    (-1, image_size, image_size, num_channels)).astype(np.float32)\n",
    "  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
    "  return dataset, labels\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
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   "source": [
    "def accuracy(predictions, labels):\n",
    "  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
    "          / predictions.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes."
   ]
  },
  {
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   "execution_count": 5,
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   "source": [
    "batch_size = 16\n",
    "patch_size = 5\n",
    "depth = 16\n",
    "num_hidden = 64\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  tf_train_dataset = tf.placeholder(\n",
    "    tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "  # Variables.\n",
    "  layer1_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, num_channels, depth], stddev=0.1))\n",
    "  layer1_biases = tf.Variable(tf.zeros([depth]))\n",
    "  layer2_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, depth, depth], stddev=0.1))\n",
    "  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n",
    "  layer3_weights = tf.Variable(tf.truncated_normal(\n",
    "      [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n",
    "  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n",
    "  layer4_weights = tf.Variable(tf.truncated_normal(\n",
    "      [num_hidden, num_labels], stddev=0.1))\n",
    "  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
    "  \n",
    "  # Model.\n",
    "  def model(data):\n",
    "    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer1_biases)\n",
    "    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer2_biases)\n",
    "    shape = hidden.get_shape().as_list()\n",
    "    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n",
    "    return tf.matmul(hidden, layer4_weights) + layer4_biases\n",
    "  \n",
    "  # Training computation.\n",
    "  logits = model(tf_train_dataset)\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "    \n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "  \n",
    "  # Predictions for the training, validation, and test data.\n",
    "  train_prediction = tf.nn.softmax(logits)\n",
    "  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n",
    "  test_prediction = tf.nn.softmax(model(tf_test_dataset))"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 3.019285\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 10.0%\n",
      "Minibatch loss at step 50: 1.696954\n",
      "Minibatch accuracy: 37.5%\n",
      "Validation accuracy: 48.7%\n",
      "Minibatch loss at step 100: 0.560466\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 70.9%\n",
      "Minibatch loss at step 150: 0.484849\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 75.0%\n",
      "Minibatch loss at step 200: 0.544467\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 76.3%\n",
      "Minibatch loss at step 250: 1.307465\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 74.1%\n",
      "Minibatch loss at step 300: 0.816255\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.7%\n",
      "Minibatch loss at step 350: 0.935395\n",
      "Minibatch accuracy: 62.5%\n",
      "Validation accuracy: 79.3%\n",
      "Minibatch loss at step 400: 1.279940\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 450: 0.590867\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 80.2%\n",
      "Minibatch loss at step 500: 0.484792\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.5%\n",
      "Minibatch loss at step 550: 0.119280\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 600: 0.376400\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.4%\n",
      "Minibatch loss at step 650: 0.338570\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 82.1%\n",
      "Minibatch loss at step 700: 0.700716\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 81.2%\n",
      "Minibatch loss at step 750: 0.282587\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 82.9%\n",
      "Minibatch loss at step 800: 0.931289\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 850: 0.661143\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 900: 0.436847\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.4%\n",
      "Minibatch loss at step 950: 0.118114\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 1000: 0.242386\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 82.8%\n",
      "Test accuracy: 89.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  for step in range(num_steps):\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "    _, l, predictions = session.run(\n",
    "      [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    if (step % 50 == 0):\n",
    "      print('Minibatch loss at step %d: %f' % (step, l))\n",
    "      print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))\n",
    "      print('Validation accuracy: %.1f%%' % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KedKkn4EutIK"
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   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides by a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "patch_size = 5\n",
    "depth = 16\n",
    "num_hidden = 64\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  tf_train_dataset = tf.placeholder(\n",
    "    tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "  # Variables.\n",
    "  layer1_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, num_channels, depth], stddev=0.1))\n",
    "  layer1_biases = tf.Variable(tf.zeros([depth]))\n",
    "  layer2_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, depth, depth], stddev=0.1))\n",
    "  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n",
    "  layer3_weights = tf.Variable(tf.truncated_normal(\n",
    "      [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n",
    "  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n",
    "  layer4_weights = tf.Variable(tf.truncated_normal(\n",
    "      [num_hidden, num_labels], stddev=0.1))\n",
    "  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
    "  \n",
    "  # Model.\n",
    "  def model(data):\n",
    "    conv = tf.nn.conv2d(data, layer1_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer1_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer2_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    shape = hidden.get_shape().as_list()\n",
    "    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n",
    "    return tf.matmul(hidden, layer4_weights) + layer4_biases\n",
    "  \n",
    "  # Training computation.\n",
    "  logits = model(tf_train_dataset)\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "    \n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "  \n",
    "  # Predictions for the training, validation, and test data.\n",
    "  train_prediction = tf.nn.softmax(logits)\n",
    "  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n",
    "  test_prediction = tf.nn.softmax(model(tf_test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 3.544411\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 10.0%\n",
      "Minibatch loss at step 50: 2.319788\n",
      "Minibatch accuracy: 6.2%\n",
      "Validation accuracy: 13.3%\n",
      "Minibatch loss at step 100: 1.728831\n",
      "Minibatch accuracy: 37.5%\n",
      "Validation accuracy: 41.5%\n",
      "Minibatch loss at step 150: 0.961047\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 67.4%\n",
      "Minibatch loss at step 200: 0.659611\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 75.0%\n",
      "Minibatch loss at step 250: 1.476114\n",
      "Minibatch accuracy: 56.2%\n",
      "Validation accuracy: 73.0%\n",
      "Minibatch loss at step 300: 0.773400\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 78.4%\n",
      "Minibatch loss at step 350: 1.036982\n",
      "Minibatch accuracy: 56.2%\n",
      "Validation accuracy: 78.6%\n",
      "Minibatch loss at step 400: 1.131140\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 450: 0.746564\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.9%\n",
      "Minibatch loss at step 500: 0.417154\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 80.4%\n",
      "Minibatch loss at step 550: 0.137856\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 82.1%\n",
      "Minibatch loss at step 600: 0.523593\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 650: 0.413809\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.3%\n",
      "Minibatch loss at step 700: 0.649267\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 750: 0.316358\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.1%\n",
      "Minibatch loss at step 800: 0.827050\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 850: 0.825358\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 900: 0.570712\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 950: 0.151696\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 1000: 0.219050\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 83.2%\n",
      "Test accuracy: 90.0%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  for step in range(num_steps):\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "    _, l, predictions = session.run(\n",
    "      [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    if (step % 50 == 0):\n",
    "      print('Minibatch loss at step %d: %f' % (step, l))\n",
    "      print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))\n",
    "      print('Validation accuracy: %.1f%%' % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "klf21gpbAgb-"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "patch_size = 5\n",
    "depth = 32\n",
    "num_hidden = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  tf_train_dataset = tf.placeholder(\n",
    "    tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "\n",
    "  keep_prob = tf.placeholder(tf.float32)\n",
    "  \n",
    "  # Variables.\n",
    "  layer1_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, num_channels, depth], stddev=0.1))\n",
    "  layer1_biases = tf.Variable(tf.zeros([depth]))\n",
    "  layer2_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, depth, depth], stddev=0.1))\n",
    "  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n",
    "  layer3_weights = tf.Variable(tf.truncated_normal(\n",
    "      [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n",
    "  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n",
    "  layer4_weights = tf.Variable(tf.truncated_normal(\n",
    "      [num_hidden, num_labels], stddev=0.1))\n",
    "  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
    "  \n",
    "  # Model.\n",
    "  def model(data):\n",
    "    conv = tf.nn.conv2d(data, layer1_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer1_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer2_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    shape = hidden.get_shape().as_list()\n",
    "    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n",
    "    hidden = tf.nn.dropout(hidden, keep_prob)\n",
    "    return tf.matmul(hidden, layer4_weights) + layer4_biases\n",
    "  \n",
    "  # Training computation.\n",
    "  logits = model(tf_train_dataset)\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "    \n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "  \n",
    "  # Predictions for the training, validation, and test data.\n",
    "  train_prediction = tf.nn.softmax(model(tf_train_dataset))\n",
    "  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n",
    "  test_prediction = tf.nn.softmax(model(tf_test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 7.471056\n",
      "Minibatch accuracy: 21.9%\n",
      "Validation accuracy: 13.5%\n",
      "Minibatch loss at step 100: 1.258710\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 72.0%\n",
      "Minibatch loss at step 200: 1.742871\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 76.6%\n",
      "Minibatch loss at step 300: 0.582550\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 400: 0.818601\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 500: 0.961522\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 600: 0.524206\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 700: 1.034620\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 800: 0.515909\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 82.9%\n",
      "Minibatch loss at step 900: 0.788503\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 1000: 0.523067\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 1100: 0.822703\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 1200: 0.777548\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 84.7%\n",
      "Minibatch loss at step 1300: 0.614630\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 1400: 0.201674\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 85.1%\n",
      "Minibatch loss at step 1500: 0.369967\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 85.1%\n",
      "Minibatch loss at step 1600: 0.400780\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 1700: 0.604550\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 85.5%\n",
      "Minibatch loss at step 1800: 0.253865\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 85.6%\n",
      "Minibatch loss at step 1900: 0.375961\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 85.7%\n",
      "Minibatch loss at step 2000: 0.848185\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 86.0%\n",
      "Minibatch loss at step 2100: 0.381451\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 86.3%\n",
      "Minibatch loss at step 2200: 0.524803\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 86.1%\n",
      "Minibatch loss at step 2300: 0.504026\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.3%\n",
      "Minibatch loss at step 2400: 0.704605\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 86.4%\n",
      "Minibatch loss at step 2500: 0.952914\n",
      "Minibatch accuracy: 71.9%\n",
      "Validation accuracy: 86.5%\n",
      "Minibatch loss at step 2600: 0.809031\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 86.4%\n",
      "Minibatch loss at step 2700: 0.414440\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.0%\n",
      "Minibatch loss at step 2800: 0.356434\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 86.6%\n",
      "Minibatch loss at step 2900: 0.450767\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.9%\n",
      "Minibatch loss at step 3000: 0.304659\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 86.7%\n",
      "Minibatch loss at step 3100: 0.460453\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 86.8%\n",
      "Minibatch loss at step 3200: 0.436050\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 86.8%\n",
      "Minibatch loss at step 3300: 0.983125\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 86.7%\n",
      "Minibatch loss at step 3400: 0.524313\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 3500: 0.710544\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 87.3%\n",
      "Minibatch loss at step 3600: 0.310994\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.3%\n",
      "Minibatch loss at step 3700: 0.353601\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 3800: 0.919016\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 87.4%\n",
      "Minibatch loss at step 3900: 0.769191\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 4000: 0.568109\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.4%\n",
      "Test accuracy: 93.7%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 4001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  for step in range(num_steps):\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_prob: 0.5}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    if (step % 100 == 0):\n",
    "      print('Minibatch loss at step %d: %f' % (step, l))\n",
    "      feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_prob: 1.0}\n",
    "      print('Minibatch accuracy: %.1f%%' % accuracy(train_prediction.eval(feed_dict = feed_dict), batch_labels))\n",
    "      print('Validation accuracy: %.1f%%' % accuracy(valid_prediction.eval(feed_dict={keep_prob: 1.0}), valid_labels))\n",
    "  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(feed_dict={keep_prob: 1.0}), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minibatch loss at step 4000: 0.568109\n",
    "Minibatch accuracy: 90.6%\n",
    "Validation accuracy: 87.4%\n",
    "Test accuracy: 93.7%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "patch_size = 5\n",
    "depth = 32\n",
    "num_hidden = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  tf_train_dataset = tf.placeholder(\n",
    "    tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "\n",
    "  keep_prob = tf.placeholder(tf.float32)\n",
    "  \n",
    "  # Variables.\n",
    "  layer1_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, num_channels, depth], stddev=0.1))\n",
    "  layer1_biases = tf.Variable(tf.zeros([depth]))\n",
    "  layer2_weights = tf.Variable(tf.truncated_normal(\n",
    "      [patch_size, patch_size, depth, depth], stddev=0.1))\n",
    "  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n",
    "  layer3_weights = tf.Variable(tf.truncated_normal(\n",
    "      [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n",
    "  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n",
    "  layer4_weights = tf.Variable(tf.truncated_normal(\n",
    "      [num_hidden, num_labels], stddev=0.1))\n",
    "  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
    "  \n",
    "  # Model.\n",
    "  def model(data):\n",
    "    conv = tf.nn.conv2d(data, layer1_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer1_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "    hidden = tf.nn.relu(conv + layer2_biases)\n",
    "    hidden = tf.nn.max_pool(hidden, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    shape = hidden.get_shape().as_list()\n",
    "    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n",
    "    hidden = tf.nn.dropout(hidden, keep_prob)\n",
    "    return tf.matmul(hidden, layer4_weights) + layer4_biases\n",
    "  \n",
    "  # Training computation.\n",
    "  logits = model(tf_train_dataset)\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "    \n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "  \n",
    "  # Predictions for the training, validation, and test data.\n",
    "  train_prediction = tf.nn.softmax(model(tf_train_dataset))\n",
    "  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n",
    "  test_prediction = tf.nn.softmax(model(tf_test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 6.855901\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 9.5%\n",
      "Minibatch loss at step 100: 1.111650\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 72.0%\n",
      "Minibatch loss at step 200: 1.180758\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 78.5%\n",
      "Minibatch loss at step 300: 0.562926\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 400: 1.097594\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 500: 0.980465\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 81.2%\n",
      "Minibatch loss at step 600: 0.416382\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 700: 0.955136\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 800: 0.465390\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 900: 0.699457\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 1000: 0.423237\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 1100: 0.668030\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 84.6%\n",
      "Minibatch loss at step 1200: 0.681667\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 1300: 0.719206\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 85.2%\n",
      "Minibatch loss at step 1400: 0.263790\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 1500: 0.184007\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 1600: 0.572847\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 1700: 0.747651\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 85.5%\n",
      "Minibatch loss at step 1800: 0.237824\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 85.3%\n",
      "Minibatch loss at step 1900: 0.572570\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 85.6%\n",
      "Minibatch loss at step 2000: 0.687761\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 85.8%\n",
      "Minibatch loss at step 2100: 0.287267\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.1%\n",
      "Minibatch loss at step 2200: 0.432221\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 86.4%\n",
      "Minibatch loss at step 2300: 0.266744\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 86.2%\n",
      "Minibatch loss at step 2400: 0.545862\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 86.7%\n",
      "Minibatch loss at step 2500: 0.916306\n",
      "Minibatch accuracy: 71.9%\n",
      "Validation accuracy: 86.5%\n",
      "Minibatch loss at step 2600: 0.902767\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 86.6%\n",
      "Minibatch loss at step 2700: 0.296241\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 86.3%\n",
      "Minibatch loss at step 2800: 0.319796\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 86.6%\n",
      "Minibatch loss at step 2900: 0.539690\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.7%\n",
      "Minibatch loss at step 3000: 0.300618\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 86.5%\n",
      "Minibatch loss at step 3100: 0.295451\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 86.6%\n",
      "Minibatch loss at step 3200: 0.441230\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.0%\n",
      "Minibatch loss at step 3300: 0.642021\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 86.9%\n",
      "Minibatch loss at step 3400: 0.608933\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.3%\n",
      "Minibatch loss at step 3500: 0.670840\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 87.2%\n",
      "Minibatch loss at step 3600: 0.285245\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.6%\n",
      "Minibatch loss at step 3700: 0.332851\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.0%\n",
      "Minibatch loss at step 3800: 0.783204\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 87.4%\n",
      "Minibatch loss at step 3900: 0.701321\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 87.7%\n",
      "Minibatch loss at step 4000: 0.576624\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 4100: 0.222399\n",
      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 87.7%\n",
      "Minibatch loss at step 4200: 0.619610\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 87.5%\n",
      "Minibatch loss at step 4300: 0.409732\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.7%\n",
      "Minibatch loss at step 4400: 0.413227\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.2%\n",
      "Minibatch loss at step 4500: 0.533394\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 87.7%\n",
      "Minibatch loss at step 4600: 0.581693\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.6%\n",
      "Minibatch loss at step 4700: 0.579785\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 87.9%\n",
      "Minibatch loss at step 4800: 0.535356\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.7%\n",
      "Minibatch loss at step 4900: 0.620034\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 87.9%\n",
      "Minibatch loss at step 5000: 0.258570\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 87.8%\n",
      "Minibatch loss at step 5100: 0.566832\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 88.4%\n",
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      "Minibatch accuracy: 96.9%\n",
      "Validation accuracy: 89.9%\n",
      "Test accuracy: 95.7%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 16001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  for step in range(num_steps):\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_prob: 0.5}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    if (step % 100 == 0):\n",
    "      print('Minibatch loss at step %d: %f' % (step, l))\n",
    "      feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_prob: 1.0}\n",
    "      print('Minibatch accuracy: %.1f%%' % accuracy(train_prediction.eval(feed_dict = feed_dict), batch_labels))\n",
    "      print('Validation accuracy: %.1f%%' % accuracy(valid_prediction.eval(feed_dict={keep_prob: 1.0}), valid_labels))\n",
    "  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(feed_dict={keep_prob: 1.0}), test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "4_convolutions.ipynb",
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   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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