我正在尝试对模型中的多个输入执行Conv1D操作。我有15个输入，每个输入的大小为1×1500，每个输入都进入一系列层。因此，我有15个卷积模型，我希望在全连接层之前将它们合并。我已经在一个函数中定义了卷积模型，但我不知道如何调用这个函数并将它们合并。

def defineModel(nkernels, nstrides, dropout, input_shape):    model = Sequential()    model.add(Conv1D(nkernels, nstrides, activation='relu', input_shape=input_shape))    model.add(Conv1D(nkernels*2, nstrides, activation='relu'))    model.add(BatchNormalization())    model.add(MaxPooling1D(nstrides))    model.add(Dropout(dropout))    return modelmodels = {}for i in range(15):    models[i] = defineModel(64,2,0.75,(64,1))

我已经成功地连接了4个模型，如下所示：

merged = Concatenate()([ model1.output, model2.output, model3.output, model4.output])merged = Dense(512, activation='relu')(merged)merged = Dropout(0.75)(merged)merged = Dense(1024, activation='relu')(merged)merged = Dropout(0.75)(merged)merged = Dense(40, activation='softmax')(merged)model = Model(inputs=[model1.input, model2.input, model3.input, model4.input], outputs=merged)

如何在for循环中对15个层进行操作，因为单独编写15个层并不高效？

回答：

我认为最好的方法是处处使用函数式API：

def defineModel(nkernels, nstrides, dropout, input_shape):    l_input = Input( shape=input_shape )    model = Conv1D(nkernels, nstrides, activation='relu')(l_input)    model = Conv1D(nkernels*2, nstrides, activation='relu')(model)    model = BatchNormalization()(model)    model = MaxPooling1D(nstrides)(model)    model = Dropout(dropout)(model)    return model, l_inputmodels = []inputs = []for i in range(15):    model, input = defineModel(64,2,0.75,(64,1))    models.append( model )    inputs.append( input )

然后很容易恢复子模型的输入列表和输出，并将它们合并

merged = Concatenate()(models)merged = Dense(512, activation='relu')(merged)merged = Dropout(0.75)(merged)merged = Dense(1024, activation='relu')(merged)merged = Dropout(0.75)(merged)merged = Dense(40, activation='softmax')(merged)model = Model(inputs=inputs, outputs=merged)

通常，这些操作不是瓶颈。在训练或推理过程中，这些操作不会有重大影响