BUG: typo with LSTM cell

network.py

@@ -32,7 +32,7 @@ def init_inference(self, config):
  stride = layer['stride']
  bn = layer.get('enable_batch_norm', None)
  ln = layer.get('enable_layer_norm', None)
- 
+
  if bn is not None or ln is not None:
  acts = tf.contrib.layers.convolution2d(acts, num_outputs=num_filters,
  kernel_size=[filter_size, 1],
@@ -43,16 +43,16 @@ def init_inference(self, config):
 
  if bn == True:
  logger.debug("Adding Batch Norm Layer")
- acts = tf.contrib.layers.batch_norm(acts, decay=0.9, center=True, 
- scale=True, epsilon=1e-8, 
- activation_fn=tf.nn.relu, 
+ acts = tf.contrib.layers.batch_norm(acts, decay=0.9, center=True,
+ scale=True, epsilon=1e-8,
+ activation_fn=tf.nn.relu,
  is_training=True)
- 
+
  elif ln == True:
  logger.debug("Adding Layer Norm Layer")
- acts = tf.contrib.layers.layer_norm(acts, center=True, 
- scale=True, 
- activation_fn=tf.nn.relu) 
+ acts = tf.contrib.layers.layer_norm(acts, center=True,
+ scale=True,
+ activation_fn=tf.nn.relu)
  else:
  assert True, "Batch or Layer norm must be specified as True"
  else:
@@ -190,7 +190,7 @@ def _rnn(acts, input_dim, cell_type, scope=None):
  cell = tf.contrib.rnn.GRUCell(input_dim)
  elif cell_type == 'lstm':
  logger.info("Adding cell type " + cell_type + " to rnn")
- cell = tf.contrib.LSTMCell(input_dim)
+ cell = tf.contrib.rnn.LSTMCell(input_dim)
  else:
  msg = "Invalid cell type {}".format(cell_type)
  raise ValueError(msg)