我有以下模型,其中 keep_features=900 左右,y 是类的 one-hot 编码。我正在寻找下面的架构(这对于 keras 来说是可能的,符号的想法会是什么样子,特别是并行部分和串联)
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'])
最佳答案
随章节“多输入多输出模型”here您可以为您想要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'])
关于python - Keras 如何编写并行模型,用于多类预测,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/58730615/