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"
     ]
    }
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   "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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   "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"
     ]
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   "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((-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",
    "\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)) / predictions.shape[0])"
   ]
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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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     "text": [
      "\n",
      "conv (16, 14, 14, 16)\n",
      "\n",
      "conv (10000, 14, 14, 16)\n",
      "\n",
      "conv (10000, 14, 14, 16)\n"
     ]
    }
   ],
   "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(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([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([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([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([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",
    "        print(\"\\nconv\", conv.get_shape())\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(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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     "text": [
      "Initialized\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 0: 3.346263\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 10.0%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 50: 1.953195\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 47.1%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 100: 1.460067\n",
      "Minibatch accuracy: 31.2%\n",
      "Validation accuracy: 58.9%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 150: 0.513020\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 72.5%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 200: 0.779486\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 77.4%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 250: 1.097104\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 77.0%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 300: 0.379356\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 78.8%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 350: 0.638192\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 77.5%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 400: 0.339531\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 79.8%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 450: 1.013226\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.5%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 500: 0.787993\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 80.8%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 550: 0.819254\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 80.1%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 600: 0.300040\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 81.7%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 650: 0.851882\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 81.8%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 700: 0.804604\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 82.4%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 750: 0.045154\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 82.1%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 800: 0.783087\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 82.4%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 850: 0.775888\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 81.6%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 900: 0.588856\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 82.8%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 950: 0.479270\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 82.3%\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
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      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Tensor(\"Softmax:0\", shape=(16, 10), dtype=float32)\n",
      "Minibatch loss at step 1000: 0.376642\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.1%\n",
      "Test accuracy: 89.2%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    \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",
    "        \n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    \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(valid_prediction.eval(), valid_labels))\n",
    "\n",
    "    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KedKkn4EutIK"
   },
   "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(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([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([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([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([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",
    "        # Pool layer added instead of stride 2\n",
    "        pool = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        hidden = tf.nn.relu(pool + layer1_biases)\n",
    "        \n",
    "        conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "        # Pool layer added instead of stride 2\n",
    "        pool = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        hidden = tf.nn.relu(pool + layer2_biases)\n",
    "        \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(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": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 37
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 63292,
     "status": "ok",
     "timestamp": 1446658966251,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "noKFb2UovVFR",
    "outputId": "28941338-2ef9-4088-8bd1-44295661e628"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 3.022775\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 10.1%\n",
      "Minibatch loss at step 50: 1.681814\n",
      "Minibatch accuracy: 31.2%\n",
      "Validation accuracy: 47.1%\n",
      "Minibatch loss at step 100: 1.037581\n",
      "Minibatch accuracy: 62.5%\n",
      "Validation accuracy: 58.1%\n",
      "Minibatch loss at step 150: 0.489374\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 75.1%\n",
      "Minibatch loss at step 200: 1.026366\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 77.5%\n",
      "Minibatch loss at step 250: 1.136887\n",
      "Minibatch accuracy: 62.5%\n",
      "Validation accuracy: 76.9%\n",
      "Minibatch loss at step 300: 0.424850\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.2%\n",
      "Minibatch loss at step 350: 0.572246\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 77.6%\n",
      "Minibatch loss at step 400: 0.184428\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 450: 0.644994\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 500: 0.609201\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 550: 0.886887\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 81.5%\n",
      "Minibatch loss at step 600: 0.350613\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 650: 0.755634\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 700: 0.792527\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 750: 0.047734\n",
      "Minibatch accuracy: 100.0%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 800: 0.497673\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 850: 0.860808\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 82.8%\n",
      "Minibatch loss at step 900: 0.736407\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 950: 0.567053\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 1000: 0.341567\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 84.3%\n",
      "Test accuracy: 90.6%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    \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([optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    \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(valid_prediction.eval(), valid_labels))\n",
    "\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": null,
   "metadata": {
    "collapsed": false
   },
   "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(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([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([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([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([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",
    "        relu = tf.nn.relu(tf.nn.bias_add(conv, layer1_biases))\n",
    "        pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        \n",
    "        conv = tf.nn.conv2d(pool, layer2_weights, [1, 1, 1, 1], padding='SAME')\n",
    "        relu = tf.nn.relu(tf.nn.bias_add(conv, layer2_biases))\n",
    "        pool = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        \n",
    "        shape = pool.get_shape().as_list()\n",
    "        reshape = tf.reshape(pool, [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(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": null,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 37
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 63292,
     "status": "ok",
     "timestamp": 1446658966251,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "noKFb2UovVFR",
    "outputId": "28941338-2ef9-4088-8bd1-44295661e628"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 2.554732\n",
      "Minibatch accuracy: 6.2%\n",
      "Validation accuracy: 16.3%\n",
      "Minibatch loss at step 50: 1.215185\n",
      "Minibatch accuracy: 62.5%\n",
      "Validation accuracy: 73.8%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    \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([optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    \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(valid_prediction.eval(), valid_labels))\n",
    "\n",
    "    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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