Migrate text_classification.py from .contrib utils to .core.

Some usages are left untouched: datasets, VocabularyProcessor.  A tracking bug is filed for embed_sequence.

Tested by re-running and the loss numbers look similar to the ones before the change.

PiperOrigin-RevId: 172681096

1 files changed, 4 insertions, 2 deletions

diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py
index 26e6e086b3..ba89c532be 100644
--- a/tensorflow/examples/learn/text_classification.py
+++ b/tensorflow/examples/learn/text_classification.py
@@ -91,11 +91,11 @@ def rnn_model(features, labels, mode):
   word_list = tf.unstack(word_vectors, axis=1)
 
   # Create a Gated Recurrent Unit cell with hidden size of EMBEDDING_SIZE.
-  cell = tf.contrib.rnn.GRUCell(EMBEDDING_SIZE)
+  cell = tf.nn.rnn_cell.GRUCell(EMBEDDING_SIZE)
 
   # Create an unrolled Recurrent Neural Networks to length of
   # MAX_DOCUMENT_LENGTH and passes word_list as inputs for each unit.
-  _, encoding = tf.contrib.rnn.static_rnn(cell, word_list, dtype=tf.float32)
+  _, encoding = tf.nn.static_rnn(cell, word_list, dtype=tf.float32)
 
   # Given encoding of RNN, take encoding of last step (e.g hidden size of the
   # neural network of last step) and pass it as features for softmax
@@ -107,6 +107,8 @@ def rnn_model(features, labels, mode):
 
 def main(unused_argv):
   global n_words
+  tf.logging.set_verbosity(tf.logging.INFO)
+
   # Prepare training and testing data
   dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)