content: #  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  """This showcases how simple it is to build image classification networks.
  
  It follows description from this TensorFlow tutorial:
      https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#deep-mnist-for-experts
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from sklearn import metrics
  import tensorflow as tf
  
  layers = tf.contrib.layers
  learn = tf.contrib.learn
  
  
  def max_pool_2x2(tensor_in):
    return tf.nn.max_pool(
        tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  
  
  def conv_model(feature, target, mode):
    """2-layer convolution model."""
    # Convert the target to a one-hot tensor of shape (batch_size, 10) and
    # with a on-value of 1 for each one-hot vector of length 10.
    target = tf.one_hot(tf.cast(target, tf.int32), 10, 1, 0)
  
    # Reshape feature to 4d tensor with 2nd and 3rd dimensions being
    # image width and height final dimension being the number of color channels.
    feature = tf.reshape(feature, [-1, 28, 28, 1])
  
    # First conv layer will compute 32 features for each 5x5 patch
    with tf.variable_scope('conv_layer1'):
      h_conv1 = layers.convolution2d(
          feature, 32, kernel_size=[5, 5], activation_fn=tf.nn.relu)
      h_pool1 = max_pool_2x2(h_conv1)
  
    # Second conv layer will compute 64 features for each 5x5 patch.
    with tf.variable_scope('conv_layer2'):
      h_conv2 = layers.convolution2d(
          h_pool1, 64, kernel_size=[5, 5], activation_fn=tf.nn.relu)
      h_pool2 = max_pool_2x2(h_conv2)
      # reshape tensor into a batch of vectors
      h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
  
    # Densely connected layer with 1024 neurons.
    h_fc1 = layers.dropout(
        layers.fully_connected(
            h_pool2_flat, 1024, activation_fn=tf.nn.relu),
        keep_prob=0.5,
        is_training=mode == tf.contrib.learn.ModeKeys.TRAIN)
  
    # Compute logits (1 per class) and compute loss.
    logits = layers.fully_connected(h_fc1, 10, activation_fn=None)
    loss = tf.losses.softmax_cross_entropy(target, logits)
  
    # Create a tensor for training op.
    train_op = layers.optimize_loss(
        loss,
        tf.contrib.framework.get_global_step(),
        optimizer='SGD',
        learning_rate=0.001)
  
    return tf.argmax(logits, 1), loss, train_op
  
  
  def main(unused_args):
    ### Download and load MNIST dataset.
    mnist = learn.datasets.load_dataset('mnist')
  
    ### Linear classifier.
    feature_columns = learn.infer_real_valued_columns_from_input(
        mnist.train.images)
    classifier = learn.LinearClassifier(
        feature_columns=feature_columns, n_classes=10)
    classifier.fit(mnist.train.images,
                   mnist.train.labels.astype(np.int32),
                   batch_size=100,
                   steps=1000)
    score = metrics.accuracy_score(mnist.test.labels,
                                   list(classifier.predict(mnist.test.images)))
    print('Accuracy: {0:f}'.format(score))
  
    ### Convolutional network
    classifier = learn.Estimator(model_fn=conv_model)
    classifier.fit(mnist.train.images,
                   mnist.train.labels,
                   batch_size=100,
                   steps=20000)
    score = metrics.accuracy_score(mnist.test.labels,
                                   list(classifier.predict(mnist.test.images)))
    print('Accuracy: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  