小编Gar*_*ary的帖子

这是创建keras模型的两种方法，但是output shapes两种方法的汇总结果不同。显然，前者可以打印更多信息，并且可以更轻松地检查网络的正确性。

import tensorflow as tf
from tensorflow.keras import Input, layers, Model

class subclass(Model):
    def __init__(self):
        super(subclass, self).__init__()
        self.conv = layers.Conv2D(28, 3, strides=1)

    def call(self, x):
        return self.conv(x)


def func_api():
    x = Input(shape=(24, 24, 3))
    y = layers.Conv2D(28, 3, strides=1)(x)
    return Model(inputs=[x], outputs=[y])

if __name__ == '__main__':
    func = func_api()
    func.summary()

    sub = subclass()
    sub.build(input_shape=(None, 24, 24, 3))
    sub.summary()

输出？

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 24, 24, 3)         0         
_________________________________________________________________
conv2d (Conv2D) …