code: #  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
  
  from tensorflow.contrib import learn
  
  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
  
  
  def cnn_model(x, y):
    """2 layer Convolutional network 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].
    y = tf.one_hot(y, 15, 1, 0)
    word_vectors = learn.ops.categorical_variable(x, n_classes=n_words,
        embedding_size=EMBEDDING_SIZE, name='words')
    word_vectors = tf.expand_dims(word_vectors, 3)
    with tf.variable_scope('CNN_Layer1'):
      # Apply Convolution filtering on input sequence.
      conv1 = tf.contrib.layers.convolution2d(word_vectors, N_FILTERS,
                                              FILTER_SHAPE1, padding='VALID')
      # Add a RELU for non linearity.
      conv1 = tf.nn.relu(conv1)
      # Max pooling across output of Convolution+Relu.
      pool1 = tf.nn.max_pool(conv1, ksize=[1, POOLING_WINDOW, 1, 1],
          strides=[1, POOLING_STRIDE, 1, 1], 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.contrib.layers.convolution2d(pool1, N_FILTERS,
                                              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.
    prediction, loss = learn.models.logistic_regression(pool2, y)
  
    train_op = tf.contrib.layers.optimize_loss(
        loss, tf.contrib.framework.get_global_step(),
        optimizer='Adam', learning_rate=0.01)
  
    return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op
  
  
  def main(unused_argv):
    global n_words
    # Prepare training and testing data
    dbpedia = 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 = 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 = learn.Estimator(model_fn=cnn_model)
  
    # Train and predict
    classifier.fit(x_train, y_train, steps=100)
    y_predicted = [
        p['class'] for p in classifier.predict(x_test, as_iterable=True)]
    score = metrics.accuracy_score(y_test, y_predicted)
    print('Accuracy: {0:f}'.format(score))
  
  
  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)
  
apis: pandas.Series tensorflow.reduce_max tensorflow.variable_scope tensorflow.one_hot tensorflow.contrib.learn.preprocessing.VocabularyProcessor tensorflow.contrib.learn.models.logistic_regression tensorflow.contrib.learn.datasets.load_dataset tensorflow.nn.max_pool tensorflow.contrib.learn.ops.categorical_variable tensorflow.transpose tensorflow.app.run tensorflow.expand_dims sklearn.metrics.accuracy_score tensorflow.contrib.layers.convolution2d pandas.DataFrame tensorflow.contrib.framework.get_global_step tensorflow.contrib.learn.Estimator tensorflow.argmax tensorflow.nn.relu