Replace deprecated functions

im2txt/im2txt/ops/image_processing.py

@@ -128,6 +128,6 @@ def process_image(encoded_image,
   image_summary("final_image", image)
 
   # Rescale to [-1,1] instead of [0, 1]
-  image = tf.sub(image, 0.5)
-  image = tf.mul(image, 2.0)
+  image = tf.subtract(image, 0.5)
+  image = tf.multiply(image, 2.0)
   return image

im2txt/im2txt/ops/inputs.py

@@ -181,7 +181,7 @@ def batch_with_dynamic_pad(images_and_captions,
   enqueue_list = []
   for image, caption in images_and_captions:
     caption_length = tf.shape(caption)[0]
-    input_length = tf.expand_dims(tf.sub(caption_length, 1), 0)
+    input_length = tf.expand_dims(tf.subtract(caption_length, 1), 0)
 
     input_seq = tf.slice(caption, [0], input_length)
     target_seq = tf.slice(caption, [1], input_length)

im2txt/im2txt/show_and_tell_model.py

@@ -244,10 +244,10 @@ class ShowAndTellModel(object):
     # This LSTM cell has biases and outputs tanh(new_c) * sigmoid(o), but the
     # modified LSTM in the "Show and Tell" paper has no biases and outputs
     # new_c * sigmoid(o).
-    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(
+    lstm_cell = tf.contrib.rnn.BasicLSTMCell(
         num_units=self.config.num_lstm_units, state_is_tuple=True)
     if self.mode == "train":
-      lstm_cell = tf.nn.rnn_cell.DropoutWrapper(
+      lstm_cell = tf.contrib.rnn.DropoutWrapper(
           lstm_cell,
           input_keep_prob=self.config.lstm_dropout_keep_prob,
           output_keep_prob=self.config.lstm_dropout_keep_prob)
@@ -264,13 +264,13 @@ class ShowAndTellModel(object):
       if self.mode == "inference":
         # In inference mode, use concatenated states for convenient feeding and
         # fetching.
-        tf.concat(1, initial_state, name="initial_state")
+        tf.concat_v2(initial_state, 1, name="initial_state")
 
         # Placeholder for feeding a batch of concatenated states.
         state_feed = tf.placeholder(dtype=tf.float32,
                                     shape=[None, sum(lstm_cell.state_size)],
                                     name="state_feed")
-        state_tuple = tf.split(1, 2, state_feed)
+        state_tuple = tf.split(value=state_feed, num_or_size_splits=2, axis=1)
 
         # Run a single LSTM step.
         lstm_outputs, state_tuple = lstm_cell(
@@ -278,7 +278,7 @@ class ShowAndTellModel(object):
             state=state_tuple)
 
         # Concatentate the resulting state.
-        tf.concat(1, state_tuple, name="state")
+        tf.concat_v2(state_tuple, 1, name="state")
       else:
         # Run the batch of sequence embeddings through the LSTM.
         sequence_length = tf.reduce_sum(self.input_mask, 1)
@@ -307,8 +307,9 @@ class ShowAndTellModel(object):
       weights = tf.to_float(tf.reshape(self.input_mask, [-1]))
 
       # Compute losses.
-      losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, targets)
-      batch_loss = tf.div(tf.reduce_sum(tf.mul(losses, weights)),
+      losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=targets,
+                                                              logits=logits)
+      batch_loss = tf.div(tf.reduce_sum(tf.multiply(losses, weights)),
                           tf.reduce_sum(weights),
                           name="batch_loss")
       tf.losses.add_loss(batch_loss)