Function cbhg

modules.py:304–324

(input, num_banks, hidden_units, num_highway_blocks, norm_type='bn', is_training=True, scope="cbhg")

def cbhg(input, num_banks, hidden_units, num_highway_blocks, norm_type='bn', is_training=True, scope="cbhg"):
    with tf.variable_scope(scope):
        out = conv1d_banks(input,
                           K=num_banks,
                           num_units=hidden_units,
                           norm_type=norm_type,
                           is_training=is_training)  # (N, T, K * E / 2)

        out = tf.layers.max_pooling1d(out, 2, 1, padding="same")  # (N, T, K * E / 2)

        out = conv1d(out, hidden_units, 3, scope="conv1d_1")  # (N, T, E/2)
        out = normalize(out, type=norm_type, is_training=is_training, activation_fn=tf.nn.relu)
        out = conv1d(out, hidden_units, 3, scope="conv1d_2")  # (N, T, E/2)
        out += input  # (N, T, E/2) # residual connections

        for i in range(num_highway_blocks):
            out = highwaynet(out, num_units=hidden_units,
                             scope='highwaynet_{}'.format(i))  # (N, T, E/2)

        out = gru(out, hidden_units, True)  # (N, T, E)
    return out