*（星号）应用于 TensorFlow 层时有什么作用？

Question

目前正在阅读 Inception-ResNet 的 Python 实现，以帮助用不同的语言（Deeplearning4j）构建模型。 这个实现是 Inception-ResNet-v1，我试图弄清楚它是如何实现 ResNet 风格的残差快捷方式的。

在下面的代码块是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 是介于 0 和 1 之间的double精度值。 up是一堆层，最后一层是 conv2d 层。

scale * up具体情况是什么？

Answer 1

up每一层都乘以scale的标量值。 然后net被重新定义为net + scale * up 。 所以net应该与up具有相同的尺寸。