fchollet 5.4-可视化-what-convnets-学习输入_13:0同时输入和获取错误

此错误由以下原因引起:

layer_outputs = [layer.output for layer in model.layers[:8]]

它表示第一层(输入层)同时被输入和获取。

这里有一个解决方法:

import keras.backend as K
layer_outputs = [K.identity(layer.output) for layer in model.layers[:8]]

编辑: 完整示例,代码改编自: Mask_RCNN - run_graph

import numpy as np
import keras.backend as K
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Flatten

model = Sequential()
ip = Input(shape=(512,512,3,))
fl = Flatten()(ip)
d1 = Dense(20, activation='relu')(fl)
d2 = Dense(3, activation='softmax')(d1)

model = Model(ip, d2)
model.compile('adam', 'categorical_crossentropy')
model.summary()

layer_outputs = [K.identity(layer.output) for layer in model.layers]
#layer_outputs = [layer.output for layer in model.layers] #fails
kf = K.function([ip], layer_outputs)
activations = kf([np.random.random((1,512,512,3))])
print(activations)