合并多个CNN

Question

我试图对模型中的多个输入执行Conv1D。所以我有15个大小为1x1500的输入，每个输入都是一系列层的输入。所以我有15个卷积模型，我想在完全连接的层之前合并。我已经在函数中定义了卷积模型，但是我不知道如何调用函数，然后将它们合并。

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 model


models = {}
for i in range(15):
    models[i] = defineModel(64,2,0.75,(64,1))

我已经成功地连接了4种模式如下：

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个层执行此操作，因为单独编写15个层并不有效？

Answer 1

我认为最好的方法是在任何地方使用functional：

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_input


models = []
inputs = []
for i in range(15):
    model, input = defineModel(64,2,0.75,(64,1))
    models.append( model )
    inputs.append( input )

这样就可以很容易地恢复子模型的输入和输出列表，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)

通常情况下，这些操作不是瓶颈。所有这些都不应在训练或推理过程中产生重大影响。

Answer 2

当然，正如@GabrielM所建议的，使用functional是最好的方法，但是如果您不想修改define_model函数，也可以这样做：

models = []
inputs = []
outputs = []
for i in range(15):
    model = defineModel(64,2,0.75,(64,1))
    models.append(model)
    inputs.append(model.input)
    outputs.append(model.output)


merged = Concatenate()(outputs) # this should be output tensors and not models

# the rest is the same ...

model = Model(inputs=inputs, outputs=merged)