text: #  Copyright 2015-present The Scikit Flow 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 is an example of using convolutional networks over characters
  for DBpedia dataset to predict class from description of an entity.
  
  This model is similar to one described in this paper:
     "Character-level Convolutional Networks for Text Classification"
     http://arxiv.org/abs/1509.01626
  
  and is somewhat alternative to the Lua code from here:
     https://github.com/zhangxiangxiao/Crepe
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from sklearn import metrics
  import pandas
  
  import tensorflow as tf
  from tensorflow.contrib import learn
  
  FLAGS = tf.app.flags.FLAGS
  tf.app.flags.DEFINE_bool('test_with_fake_data', False,
                           'Test the example code with fake data.')
  
  MAX_DOCUMENT_LENGTH = 100
  N_FILTERS = 10
  FILTER_SHAPE1 = [20, 256]
  FILTER_SHAPE2 = [20, N_FILTERS]
  POOLING_WINDOW = 4
  POOLING_STRIDE = 2
  
  
  def char_cnn_model(x, y):
    """Character level convolutional neural network model to predict classes."""
    y = tf.one_hot(y, 15, 1, 0)
    byte_list = tf.reshape(learn.ops.one_hot_matrix(x, 256),
                           [-1, MAX_DOCUMENT_LENGTH, 256, 1])
    with tf.variable_scope('CNN_Layer1'):
      # Apply Convolution filtering on input sequence.
      conv1 = learn.ops.conv2d(byte_list, 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 = learn.ops.conv2d(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):
    # Prepare training and testing data
    dbpedia = learn.datasets.load_dataset(
        'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data, size='large')
    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
    char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
    x_train = np.array(list(char_processor.fit_transform(x_train)))
    x_test = np.array(list(char_processor.transform(x_test)))
  
    # Build model
    classifier = learn.Estimator(model_fn=char_cnn_model)
  
    # Train and predict
    classifier.fit(x_train, y_train, steps=100)
    y_predicted = classifier.predict(x_test)
    score = metrics.accuracy_score(y_test, y_predicted['class'])
    print('Accuracy: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  