3311242ac4a0800a122dfb1e49ec577eb6bb5e22,research/deep_speech/deep_speech_model.py,,_conv_bn_layer,#Any#Any#Any#Any#Any#Any#Any#,61

Before Change

  inputs = tf.pad(
      inputs,
      [[0, 0], [padding[0], padding[0]], [padding[1], padding[1]], [0, 0]])
  inputs = tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
      padding="valid", use_bias=False, activation=tf.nn.relu6,
      name="cnn_{}".format(layer_id))
  return batch_norm(inputs, training)


def _rnn_layer(inputs, rnn_cell, rnn_hidden_size, layer_id, is_batch_norm,

After Change

  inputs = tf.pad(
      inputs,
      [[0, 0], [padding[0], padding[0]], [padding[1], padding[1]], [0, 0]])
  inputs = tf.keras.layers.Conv2D(
      filters=filters, kernel_size=kernel_size, strides=strides,
      padding="valid", use_bias=False, activation=tf.nn.relu6,
      name="cnn_{}".format(layer_id))(inputs)
  return batch_norm(inputs, training)


def _rnn_layer(inputs, rnn_cell, rnn_hidden_size, layer_id, is_batch_norm,

Instances