network_helpers.py 文件源码

def spatial_batch_norm(input_layer, name='spatial_batch_norm'):
    """
    Batch-normalizes the layer as in http://arxiv.org/abs/1502.03167
    This is important since it allows the different scales to talk to each other when they get joined.
    """
    mean, variance = tf.nn.moments(input_layer, [0, 1, 2])
    variance_epsilon = 0.01  # TODO: Check what this value should be
    inv = tf.rsqrt(variance + variance_epsilon)
    num_channels = input_layer.get_shape().as_list()[3]  # TODO: Clean this up
    scale = tf.Variable(tf.random_uniform([num_channels]), name='scale')  # TODO: How should these initialize?
    offset = tf.Variable(tf.random_uniform([num_channels]), name='offset')
    return_val = tf.sub(tf.mul(tf.mul(scale, inv), tf.sub(input_layer, mean)), offset, name=name)
    return return_val