我有以下模型，其中keep_features = 900左右，y是类的一键编码。我正在寻找下面的架构（keras可以做到这一点，表示法的想法是什么样的，特别是平行部分和隐喻）
model = Sequential()
model.add(Dense(keep_features,activation='relu'))
model.add(BatchNormalization())
model.add(Dense(256,activation='relu'))
model.add(BatchNormalization())
model.add(Dense(64,activation='relu'))
model.add(BatchNormalization())
model.add(Dense(3,activation='softmax'))
model.compile(loss=losses.categorical_crossentropy,optimizer='adam',metrics=['mae','acc'])
通过“多输入和多输出模型”一章here，您可以为所需的模型制作类似的东西：
K = tf.keras
input1 = K.layers.Input(keep_features_shape)
denseA1 = K.layers.Dense(256,activation='relu')(input1)
denseB1 = K.layers.Dense(256,activation='relu')(input1)
denseC1 = K.layers.Dense(256,activation='relu')(input1)
batchA1 = K.layers.BatchNormalization()(denseA1)
batchB1 = K.layers.BatchNormalization()(denseB1)
batchC1 = K.layers.BatchNormalization()(denseC1)
denseA2 = K.layers.Dense(64,activation='relu')(batchA1)
denseB2 = K.layers.Dense(64,activation='relu')(batchB1)
denseC2 = K.layers.Dense(64,activation='relu')(batchC1)
batchA2 = K.layers.BatchNormalization()(denseA2)
batchB2 = K.layers.BatchNormalization()(denseB2)
batchC2 = K.layers.BatchNormalization()(denseC2)
denseA3 = K.layers.Dense(32,activation='softmax')(batchA2) # individual layer
denseB3 = K.layers.Dense(16,activation='softmax')(batchB2) # individual layer
denseC3 = K.layers.Dense(8,activation='softmax')(batchC2) # individual layer
concat1 = K.layers.Concatenate(axis=-1)([denseA3,denseB3,denseC3])
model = K.Model(inputs=[input1],outputs=[concat1])
model.compile(loss = K.losses.categorical_crossentropy,optimizer='adam',metrics=['mae','acc'])
这导致：
