Keras输出每一层网络大小

示例代码：

model = Model(inputs=self.inpt, outputs=self.net)
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

print("[INFO] Method 1...")
model.summary()

print("[INFO] Method 2...")
for i in range(len(model.layers)):
    print(model.get_layer(index=i).output)

print("[INFO] Method 3...")
for layer in model.layers:
	print(layer.output_shape)

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2019/5/20
# @Author  : Chen

from keras.models import Model
from keras.layers import Dense, Flatten, Input
from keras.layers import Conv2D

class Example:
    def __init__(self):
        self.inpt = Input(shape=(224, 224, 3))
        self.net = self.build_network()

    def build_network(self):
        inpt = self.inpt
        x = Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu')(inpt)
        ...
        x = Flatten()(x)
        x = Dense(1000)(x)
        return x

    def get_layer(self):
        model = Model(inputs=self.inpt, outputs=self.net)
        model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

        print("[INFO] Method 1...")
        model.summary()

        print("[INFO] Method 2...")
        for i in range(len(model.layers)):
            print(model.get_layer(index=i).output)

        print("[INFO] Method 3...")
        for layer in model.layers:
            print(layer.output_shape)

if __name__ == '__main__':
    ex = Example()
    ex.get_layer()

输出结果：

[INFO] Method 1...
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         (None, 224, 224, 3)       0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 224, 224, 64)      1792
_________________________________________________________________
flatten_1 (Flatten)          (None, 3211264)           0
_________________________________________________________________
dense_1 (Dense)              (None, 1000)              -108370229
=================================================================
Total params: -1,083,700,504
Trainable params: -1,083,700,504
Non-trainable params: 0
_________________________________________________________________
[INFO] Method 2...
Tensor("input_1:0", shape=(?, 224, 224, 3), dtype=float32)
Tensor("conv2d_1/Relu:0", shape=(?, 224, 224, 64), dtype=float32)
Tensor("flatten_1/Reshape:0", shape=(?, ?), dtype=float32)
Tensor("dense_1/BiasAdd:0", shape=(?, 1000), dtype=float32)
[INFO] Method 3...
(None, 224, 224, 3)
(None, 224, 224, 64)
(None, 3211264)
(None, 1000)