layers.py 文件源码

def misconception_with_bypass(input,
                              window_size,
                              stride,
                              depth,
                              is_training,
                              scope=None):
    with tf.name_scope(scope):
        with slim.arg_scope(
            [slim.conv2d],
                padding='SAME',
                activation_fn=tf.nn.relu,
                normalizer_fn=slim.batch_norm,
                normalizer_params={'is_training': is_training}):
            residual = misconception_layer(input, window_size, stride, depth,
                                           is_training, scope)

            if stride > 1:
                input = slim.avg_pool2d(
                    input, [1, stride], stride=[1, stride], padding='SAME')

            input = zero_pad_features(input, depth)

            return input + residual