What does * (asterisk) do when applied to a TensorFlow layer?

Question

Currently reading a Python implementation of Inception-ResNet to assist in building a model in a different language (Deeplearning4j). This implementation is Inception-ResNet-v1 and I was trying to figure out how it implements the residual shortcuts in ResNet style.

In the following code block is net += scale * up .

# Inception-Renset-A
def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 35x35 resnet block."""
    with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
        with tf.variable_scope('Branch_0'):
            tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
        with tf.variable_scope('Branch_1'):
            tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
            tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
            tower_conv2_1 = slim.conv2d(tower_conv2_0, 32, 3, scope='Conv2d_0b_3x3')
            tower_conv2_2 = slim.conv2d(tower_conv2_1, 32, 3, scope='Conv2d_0c_3x3')
        mixed = tf.concat(3, [tower_conv, tower_conv1_1, tower_conv2_2])
        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                         activation_fn=None, scope='Conv2d_1x1')
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net

Scale is a double between 0 and 1. up is a stack of layers, the last being a conv2d layer.

What specifically is happening with scale * up ?

Answer 1

Each layer in up is being multiplied by the scalar value in scale . And then net is being redefined as net + scale * up . So net should have the same dimensions as up .