Keras 如何編寫並行 model，用於多類預測

Question

我有以下 model，其中 keep_features=900 左右，y 是類的 one-hot 編碼。 我正在尋找下面的架構（keras 是否可能，符號的想法是什么樣的，特別是並行部分和連接）

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'])

Answer 1

通過此處的“多輸入和多輸出模型”一章，您可以為所需的 model 制作類似的東西：

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'])

這導致：