Update caller to move from tf.nn.rnn to (the identical) tf.contrib.rnn.static_rnn.

Change: 141658438

1 files changed, 12 insertions, 7 deletions

diff --git a/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py b/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
index c908fbc655..7d550771b8 100644
--- a/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
+++ b/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py
@@ -31,7 +31,7 @@ class Seq2SeqTest(tf.test.TestCase):
     with self.test_session() as sess:
       with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
-        _, enc_state = tf.nn.rnn(
+        _, enc_state = tf.contrib.rnn.static_rnn(
             tf.contrib.rnn.GRUCell(2), inp, dtype=tf.float32)
         dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
         cell = tf.contrib.rnn.OutputProjectionWrapper(
@@ -86,7 +86,7 @@ class Seq2SeqTest(tf.test.TestCase):
       with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
         cell = tf.contrib.rnn.BasicLSTMCell(2, state_is_tuple=True)
-        _, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+        _, enc_state = tf.contrib.rnn.static_rnn(cell, inp, dtype=tf.float32)
         dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
         dec, mem = tf.contrib.legacy_seq2seq.embedding_rnn_decoder(
             dec_inp, enc_state, cell, num_symbols=4, embedding_size=2)
@@ -230,7 +230,8 @@ class Seq2SeqTest(tf.test.TestCase):
       with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
         cell = tf.contrib.rnn.GRUCell(2)
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+        enc_outputs, enc_state = tf.contrib.rnn.static_rnn(
+            cell, inp, dtype=tf.float32)
         attn_states = tf.concat_v2(
             [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs], 1)
         dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
@@ -250,7 +251,8 @@ class Seq2SeqTest(tf.test.TestCase):
       with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
         cell = tf.contrib.rnn.GRUCell(2)
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+        enc_outputs, enc_state = tf.contrib.rnn.static_rnn(
+            cell, inp, dtype=tf.float32)
         attn_states = tf.concat_v2(
             [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs], 1)
         dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
@@ -312,7 +314,8 @@ class Seq2SeqTest(tf.test.TestCase):
         cell = tf.contrib.rnn.MultiRNNCell(cells=[cell] * 2,
                                            state_is_tuple=True)
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
-        enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+        enc_outputs, enc_state = tf.contrib.rnn.static_rnn(
+            cell, inp, dtype=tf.float32)
         attn_states = tf.concat_v2(
             [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs], 1)
         dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
@@ -339,7 +342,8 @@ class Seq2SeqTest(tf.test.TestCase):
           cell = tf.contrib.rnn.MultiRNNCell(cells=[cell] * 2,
                                              state_is_tuple=True)
           inp = tf.constant(0.5, shape=[2, 2, 2])
-          enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+          enc_outputs, enc_state = tf.contrib.rnn.static_rnn(
+              cell, inp, dtype=tf.float32)
           attn_states = tf.concat_v2(
               [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs],
               1)
@@ -364,7 +368,8 @@ class Seq2SeqTest(tf.test.TestCase):
       with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
         inp = [tf.constant(0.5, shape=[2, 2])] * 2
         cell = tf.contrib.rnn.GRUCell(2)
-        enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
+        enc_outputs, enc_state = tf.contrib.rnn.static_rnn(
+            cell, inp, dtype=tf.float32)
         attn_states = tf.concat_v2(
             [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs], 1)
         dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]