如何从Keras.layers实现合并 [英] How to implement Merge from Keras.layers

问题描述

我一直在尝试合并以下顺序模型，但未能合并.有人可以指出我的错误，谢谢.

I have been trying to merge the following sequential models but haven't been able to. Could somebody please point out my mistake, thank you.

使用合并"时代码会编译，但会出现以下错误"TypeError:'模块'对象不可调用" 但是，即使在使用合并"时也无法编译

The code compiles while using"merge" but give the following error "TypeError: 'module' object is not callable" However it doesn't even compile while using "Merge"

我正在使用keras版本2.2.0和python 3.6

I am using keras version 2.2.0 and python 3.6

from keras.layers import merge
def linear_model_combined(optimizer='Adadelta'):    
    modela = Sequential()
    modela.add(Flatten(input_shape=(100, 34)))
    modela.add(Dense(1024))
    modela.add(Activation('relu'))
    modela.add(Dense(512))

    modelb = Sequential()
    modelb.add(Flatten(input_shape=(100, 34)))
    modelb.add(Dense(1024))
    modelb.add(Activation('relu'))
    modelb.add(Dense(512))

    model_combined = Sequential()

    model_combined.add(Merge([modela, modelb], mode='concat'))

    model_combined.add(Activation('relu'))
    model_combined.add(Dense(256))
    model_combined.add(Activation('relu'))

    model_combined.add(Dense(4))
    model_combined.add(Activation('softmax'))

    model_combined.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    return model_combined

推荐答案

合并不能与顺序模型一起使用.在顺序模型中，图层只能有一个输入和一个输出. 您必须使用功能性API ，类似这样.我假设您对modela和modelb使用相同的输入层，但是如果不是这种情况，则可以创建另一个Input()并将它们都作为模型的输入.

Merge cannot be used with a sequential model. In a sequential model, layers can only have one input and one output. You have to use the functional API, something like this. I assumed you use the same input layer for modela and modelb, but you could create another Input() if it is not the case and give both of them as input to the model.

def linear_model_combined(optimizer='Adadelta'):    

    # declare input
    inlayer =Input(shape=(100, 34))
    flatten = Flatten()(inlayer)

    modela = Dense(1024)(flatten)
    modela = Activation('relu')(modela)
    modela = Dense(512)(modela)

    modelb = Dense(1024)(flatten)
    modelb = Activation('relu')(modelb)
    modelb = Dense(512)(modelb)

    model_concat = concatenate([modela, modelb])


    model_concat = Activation('relu')(model_concat)
    model_concat = Dense(256)(model_concat)
    model_concat = Activation('relu')(model_concat)

    model_concat = Dense(4)(model_concat)
    model_concat = Activation('softmax')(model_concat)

    model_combined = Model(inputs=inlayer,outputs=model_concat)

    model_combined.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    return model_combined

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