如何为每个时期提取展平层输出？

Question

model = Sequential()
model.add(Conv2D(50, (5,5), activation='relu', input_shape =(5,5,1), kernel_initializer='he_normal'))

model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.summary()

# compile the model
model.compile(loss='binary_crossentropy', optimizer= 'adam', metrics=['accuracy'])
model_checkpoint=ModelCheckpoint(r'C:\Users\globo\Desktop\Test_CNN\Results\Kernel5x5\Weights'+'\\'+test+'\model_test{epoch:02d}.h5',save_freq=1,save_weights_only=True)

# fit the model
history = model.fit(X_train, Y_train, epochs=10, batch_size=32, verbose=1, callbacks=[model_checkpoint], shuffle=True, validation_split=0.5)

我已经使用“ ModelCheckpoint”为每个时期提取权重，但是如何提取每个时期的平整层输出并保存它们呢？

Answer 1

使用顺序模型执行此操作根本不可行。您应该使用功能性API

inp = Input((5,5,1))
x = Conv2D(50, (5,5), activation='relu', kernel_initializer='he_normal')(inp)
xflatten = Flatten()(x)
out = Dense(1, activation='sigmoid')(xflatten)

main_model = Model(inp, out) # this works same as your model
flatten_model = Model(inp, xflatten) # and this only outputs the flatten layer and is not necessary to compile it because we won't train it, it just shows the output of a layer

main_model.compile(loss='binary_crossentropy', optimizer= 'adam', metrics=['accuracy'])
history = main_model.fit(X_train, Y_train, epochs=10, batch_size=32, verbose=1, callbacks=[model_checkpoint], shuffle=True, validation_split=0.5)

查看拼合图层的输出：

flatten_model.predict(X)