d90f5580d932b801ea3a9715d0dee00fc58ab55a,research/deep_speech/deep_speech_model.py,,_rnn_layer,#Any#Any#Any#Any#Any#Any#Any#,57

Before Change

    tensor output for the current layer.
  
  if is_batch_norm:
    input_data = tf.keras.layers.BatchNormalization(
        momentum=_MOMENTUM, epsilon=_EPSILON)(input_data)
  rnn_layer = rnn_cell(
      rnn_hidden_size, activation=rnn_activation, return_sequences=True,
      name="rnn_{}".format(layer_id))
  if is_bidirectional:

After Change

  bw_cell = rnn_cell(num_units=rnn_hidden_size,
                     name="rnn_bw_{}".format(layer_id))

  if is_bidirectional:
    outputs, _ = tf.nn.bidirectional_dynamic_rnn(
        cell_fw=fw_cell, cell_bw=bw_cell, inputs=inputs, dtype=tf.float32,
        swap_memory=True)
    rnn_outputs = tf.concat(outputs, -1)
  else:
    rnn_outputs = tf.nn.dynamic_rnn(
        fw_cell, inputs, dtype=tf.float32, swap_memory=True)

  return rnn_outputs


class DeepSpeech2(object):

Instances