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.
  """Example of Estimator for CNN-based text classification with DBpedia data."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  
  import numpy as np
  import pandas
  from sklearn import metrics
  import tensorflow as tf
  
  FLAGS = None
  
  MAX_DOCUMENT_LENGTH = 100
  EMBEDDING_SIZE = 20
  N_FILTERS = 10
  WINDOW_SIZE = 20
  FILTER_SHAPE1 = [WINDOW_SIZE, EMBEDDING_SIZE]
  FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS]
  POOLING_WINDOW = 4
  POOLING_STRIDE = 2
  n_words = 0
  MAX_LABEL = 15
  WORDS_FEATURE = 'words'  # Name of the input words feature.
  
  
  def cnn_model(features, labels, mode):
    """2 layer ConvNet to predict from sequence of words to a class."""
    # Convert indexes of words into embeddings.
    # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
    # maps word indexes of the sequence into [batch_size, sequence_length,
    # EMBEDDING_SIZE].
    word_vectors = tf.contrib.layers.embed_sequence(
        features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
    word_vectors = tf.expand_dims(word_vectors, 3)
    with tf.variable_scope('CNN_Layer1'):
      # Apply Convolution filtering on input sequence.
      conv1 = tf.layers.conv2d(
          word_vectors,
          filters=N_FILTERS,
          kernel_size=FILTER_SHAPE1,
          padding='VALID',
          # Add a ReLU for non linearity.
          activation=tf.nn.relu)
      # Max pooling across output of Convolution+Relu.
      pool1 = tf.layers.max_pooling2d(
          conv1,
          pool_size=POOLING_WINDOW,
          strides=POOLING_STRIDE,
          padding='SAME')
      # Transpose matrix so that n_filters from convolution becomes width.
      pool1 = tf.transpose(pool1, [0, 1, 3, 2])
    with tf.variable_scope('CNN_Layer2'):
      # Second level of convolution filtering.
      conv2 = tf.layers.conv2d(
          pool1,
          filters=N_FILTERS,
          kernel_size=FILTER_SHAPE2,
          padding='VALID')
      # Max across each filter to get useful features for classification.
      pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
  
    # Apply regular WX + B and classification.
    logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)
  
    predicted_classes = tf.argmax(logits, 1)
    if mode == tf.estimator.ModeKeys.PREDICT:
      return tf.estimator.EstimatorSpec(
          mode=mode,
          predictions={
              'class': predicted_classes,
              'prob': tf.nn.softmax(logits)
          })
  
    onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0)
    loss = tf.losses.softmax_cross_entropy(
        onehot_labels=onehot_labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
      optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
      train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
      return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
  
    eval_metric_ops = {
        'accuracy': tf.metrics.accuracy(
            labels=labels, predictions=predicted_classes)
    }
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
  
  
  def main(unused_argv):
    global n_words
    # Prepare training and testing data
    dbpedia = tf.contrib.learn.datasets.load_dataset(
        'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
    x_train = pandas.DataFrame(dbpedia.train.data)[1]
    y_train = pandas.Series(dbpedia.train.target)
    x_test = pandas.DataFrame(dbpedia.test.data)[1]
    y_test = pandas.Series(dbpedia.test.target)
  
    # Process vocabulary
    vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(
        MAX_DOCUMENT_LENGTH)
    x_train = np.array(list(vocab_processor.fit_transform(x_train)))
    x_test = np.array(list(vocab_processor.transform(x_test)))
    n_words = len(vocab_processor.vocabulary_)
    print('Total words: %d' % n_words)
  
    # Build model
    classifier = tf.estimator.Estimator(model_fn=cnn_model)
  
    # Train.
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={WORDS_FEATURE: x_train},
        y=y_train,
        batch_size=len(x_train),
        num_epochs=None,
        shuffle=True)
    classifier.train(input_fn=train_input_fn, steps=100)
  
    # Predict.
    test_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={WORDS_FEATURE: x_test},
        y=y_test,
        num_epochs=1,
        shuffle=False)
    predictions = classifier.predict(input_fn=test_input_fn)
    y_predicted = np.array(list(p['class'] for p in predictions))
    y_predicted = y_predicted.reshape(np.array(y_test).shape)
  
    # Score with sklearn.
    score = metrics.accuracy_score(y_test, y_predicted)
    print('Accuracy (sklearn): {0:f}'.format(score))
  
    # Score with tensorflow.
    scores = classifier.evaluate(input_fn=test_input_fn)
    print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
  
  
  if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--test_with_fake_data',
        default=False,
        help='Test the example code with fake data.',
        action='store_true')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  