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 recurrent neural networks over characters for DBpedia dataset.
  
  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 argparse
  import sys
  
  import numpy as np
  import pandas
  import tensorflow as tf
  
  FLAGS = None
  
  MAX_DOCUMENT_LENGTH = 100
  HIDDEN_SIZE = 20
  MAX_LABEL = 15
  CHARS_FEATURE = 'chars'  # Name of the input character feature.
  
  
  def char_rnn_model(features, labels, mode):
    """Character level recurrent neural network model to predict classes."""
    byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
    byte_list = tf.unstack(byte_vectors, axis=1)
  
    cell = tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE)
    _, encoding = tf.nn.static_rnn(cell, byte_list, dtype=tf.float32)
  
    logits = tf.layers.dense(encoding, 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):
    # 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
    char_processor = tf.contrib.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 = tf.estimator.Estimator(model_fn=char_rnn_model)
  
    # Train.
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={CHARS_FEATURE: x_train},
        y=y_train,
        batch_size=128,
        num_epochs=None,
        shuffle=True)
    classifier.train(input_fn=train_input_fn, steps=100)
  
    # Eval.
    test_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={CHARS_FEATURE: x_test},
        y=y_test,
        num_epochs=1,
        shuffle=False)
  
    scores = classifier.evaluate(input_fn=test_input_fn)
    print('Accuracy: {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)
  