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)