47c71c0524c7804863f2f4bcffdef5a873d84a55,txtgen/modules/connectors/connectors.py,,_mlp_transform,#Any#Any#Any#,18

Before Change

    concat_input = tf.concat(flat_input, 1)

    // get output dimension
    iter_output_size = output_size if isinstance(output_size, (list, tuple)) else [output_size]
    shape_list = [tf.TensorShape(shape) for shape in iter_output_size]
    size_list = [0] * len(shape_list)
    for (i, shape) in enumerate(shape_list):
        size_list[i] = reduce(lambda x, y: x*y, [dim.value for dim in shape])
    sum_output_size = sum(size_list)

    fc_output = tf.contrib.layers.fully_connected(
        concat_input, sum_output_size, activation_fn=activation_fn)

    flat_output = tf.split(fc_output, size_list, axis=1)
    for (i, shape) in enumerate(shape_list):
        flat_output[i] = tf.reshape(flat_output[i], tf.TensorShape(batch_size).concatenate(shape))
    output = nest.pack_sequence_as(structure=output_size, flat_sequence=flat_output)

    return output

After Change

    // get output dimension
    flat_output_size = nest.flatten(output_size)
    if isinstance(flat_output_size[0], tf.TensorShape):
        size_list = [0] * len(flat_output_size)
        for (i, shape) in enumerate(flat_output_size):
            size_list[i] = reduce(lambda x, y: x*y,

Instances