modules.py 文件源码

def prenet(inputs, num_units=None, dropout_rate=0, is_training=True, scope="prenet", reuse=None):
    '''Prenet for Encoder and Decoder.
    Args:
      inputs: A 3D tensor of shape [N, T, hp.embed_size].
      num_units" A list of two integers.
      is_training: A boolean.
      scope: Optional scope for `variable_scope`.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.

    Returns:
      A 3D tensor of shape [N, T, num_units/2].
    '''
    if num_units is None:
        num_units = [inputs.get_shape()[-1], inputs.get_shape()[-1]]

    with tf.variable_scope(scope, reuse=reuse):
        outputs = tf.layers.dense(inputs, units=num_units[0], activation=tf.nn.relu, name="dense1")
        outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=is_training, name="dropout1")
        outputs = tf.layers.dense(outputs, units=num_units[1], activation=tf.nn.relu, name="dense2")
        outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=is_training, name="dropout2")

    return outputs  # (N, T, num_units[1])