Fix regressions caused by a previous change

tutorials/rnn/translate/data_utils.py

@@ -239,8 +239,8 @@ def data_to_token_ids(data_path, target_path, vocabulary_path,
           counter += 1
           if counter % 100000 == 0:
             print("  tokenizing line %d" % counter)
-          token_ids = sentence_to_token_ids(line, vocab, tokenizer,
-                                            normalize_digits)
+          token_ids = sentence_to_token_ids(tf.compat.as_bytes(line), vocab,
+                                            tokenizer, normalize_digits)
           tokens_file.write(" ".join([str(tok) for tok in token_ids]) + "\n")
 
 

tutorials/rnn/translate/seq2seq_model.py

@@ -108,24 +108,29 @@ def sampled_loss(labels, inputs):
         local_b = tf.cast(b, tf.float32)
         local_inputs = tf.cast(inputs, tf.float32)
         return tf.cast(
-            tf.nn.sampled_softmax_loss(local_w_t, local_b, local_inputs, labels,
-                                       num_samples, self.target_vocab_size),
+            tf.nn.sampled_softmax_loss(
+                weights=local_w_t,
+                biases=local_b,
+                labels=labels,
+                inputs=local_inputs,
+                num_sampled=num_samples,
+                num_classes=self.target_vocab_size),
             dtype)
       softmax_loss_function = sampled_loss
 
     # Create the internal multi-layer cell for our RNN.
     def single_cell():
-      return tf.nn.rnn_cell.GRUCell(size)
+      return tf.contrib.rnn.GRUCell(size)
     if use_lstm:
       def single_cell():
-        return tf.nn.rnn_cell.BasicLSTMCell(size)
+        return tf.contrib.rnn.BasicLSTMCell(size)
     cell = single_cell()
     if num_layers > 1:
-      cell = tf.nn.rnn_cell.MultiRNNCell([single_cell() for _ in range(num_layers)])
+      cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(num_layers)])
 
     # The seq2seq function: we use embedding for the input and attention.
     def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
-      return tf.nn.seq2seq.embedding_attention_seq2seq(
+      return tf.contrib.legacy_seq2seq.embedding_attention_seq2seq(
           encoder_inputs,
           decoder_inputs,
           cell,
@@ -155,7 +160,7 @@ def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
 
     # Training outputs and losses.
     if forward_only:
-      self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
+      self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
           self.encoder_inputs, self.decoder_inputs, targets,
           self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
           softmax_loss_function=softmax_loss_function)
@@ -167,7 +172,7 @@ def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
               for output in self.outputs[b]
           ]
     else:
-      self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
+      self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
           self.encoder_inputs, self.decoder_inputs, targets,
           self.target_weights, buckets,
           lambda x, y: seq2seq_f(x, y, False),