message: Move text_classification_character_rnn from .contrib utils to .core utils.
  Also removes sklearn comparison.
diff: @@ -30,7 +30,6 @@ import sys
  import numpy as np
  import pandas
  -from sklearn import metrics
  import tensorflow as tf
  FLAGS = None
  @@ -46,8 +45,8 @@ def char_rnn_model(features, labels, mode):
  byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
  byte_list = tf.unstack(byte_vectors, axis=1)
  - cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
  - _, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)
  + 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)
  @@ -98,28 +97,20 @@ def main(unused_argv):
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
  x={CHARS_FEATURE: x_train},
  y=y_train,
  - batch_size=len(x_train),
  + batch_size=128,
  num_epochs=None,
  shuffle=True)
  classifier.train(input_fn=train_input_fn, steps=100)
  - # Predict.
  + # Eval.
  test_input_fn = tf.estimator.inputs.numpy_input_fn(
  x={CHARS_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']))
  + print('Accuracy: {0:f}'.format(scores['accuracy']))
  if __name__ == '__main__':
  