问复杂模型中的自定义预测(Functional API Keras)

EN

## define left model
left = Input((33))
xl = Dense(512)(left)
left_model = Model(left, xl)

## define right model
right = Input((10))
xr = Dense(64)(right)
right_model = Model(right, xr)

## define final shared model
concat_inp = Input((576))
x = BatchNormalization()(concat_inp)
out = Dense(1)(x)
combi_model = Model(concat_inp, out)

## combine left and right model
concat = Concatenate()([left_model.output, right_model.output])
## combine branches with final shared model
combi = combi_model(concat)

full_model = Model([left_model.input, right_model.input], combi)

# full_model.fit(...)

## replace left branch in fitted model
fake_left_input = Input((512))

## combine fake left branch with right fitted branch 
new_concat = Concatenate()([fake_left_input, right_model.output])
## combine branches with final shared model
new_combi = combi_model(new_concat)

new_full_model = Model([fake_left_input, right_model.input], new_combi)
new_full_model.summary()

X_right_test = np.random.uniform(0,1, (20,10))
X_left_test = np.zeros((len(X_right_test),512)) 
new_full_model([X_left_test, X_right_test])