Python Keras如何编写并行模型，用于多类预测

我有下面的模型，其中keep_features=900左右，y是类的一个热编码。我正在寻找下面的架构（keras是否可能，符号的想法是什么样子的，特别是平行部分和浓缩部分）

在“多输入和多输出模型”一章中，您可以为所需的模型制作如下内容：

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：

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您是否认为只有一个神经元的softmax总是产生恒定的1.0输出？我看到了这个问题，我想还有一个问题是，我如何才能让这些密集的softmax 1层为CostIV类输出一个置信值？请参阅“多输入和多输出模型”一章。它用例子解释了如何在Keras中建立一个多输入多输出模型谢谢你的帮助，太好了。请注意，任何在最后一层上绊倒的人都应该是乙状结肠或其他东西，以产生一些有意义的东西

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'])