合并具有不同输入形状的不同型号的输出

Question

我是Keras的新手。我正在尝试合并Keras中三个预训练模型的输出层。每个模型都有两个独立的输入，但尺寸不同，密集层输出。

model1 = MyModel1() #returns keras.engine.training.Model 
    model2 = MyModel2() #returns keras.engine.training.Model 
    model3 = MyModel3() #returns keras.engine.training.Model 

    x = merge([model1(model1.input), 
       model2(model2.input), 
       model3(model3.input)], 
       mode='concat', concat_axis=1) 

    # add some trainable layers here... 

    # and a final softmax layer 
    x = Dense(2, activation='softmax')(x) 

    return Model(input=[model1.input, 
         model2.input, 
         model3.input], 
       output=x) 

由于型号？.input返回张量列表，这是行不通的。我尝试了不同的事情，似乎没有任何工作。有这个问题的简单解决方案吗？

编辑： 来自indraforyou的适应性工作解决方案适用于各种型号的多个输入。

from keras.models import Model 
    from keras.layers import Input, Dense, merge 


    def MyModel1(): 
     inp1 = Input(batch_shape=(None,32,)) 
     inp2 = Input(batch_shape=(None,32)) 
     x = Dense(8)(inp1) 
     y = Dense(8)(inp2) 
     merged = merge([x, y], mode='concat', concat_axis=-1) 
     out = Dense(8)(merged) 
     return Model(input=[inp1,inp2], output=out) 

    def MyModel2(): 
     inp1 = Input(batch_shape=(None,10,)) 
     inp2 = Input(batch_shape=(None,10,)) 
     x = Dense(4)(inp1) 
     y = Dense(4)(inp2) 
     merged = merge([x, y], mode='concat', concat_axis=-1) 
     out = Dense(4)(merged) 
     return Model(input=[inp1,inp2], output=out) 

    def MyModel3(): 
     inp1 = Input(batch_shape=(None,12,)) 
     inp2 = Input(batch_shape=(None,12,)) 
     x = Dense(6)(inp1) 
     y = Dense(6)(inp1) 
     merged = merge([x, y], mode='concat', concat_axis=-1) 
     out = Dense(6)(merged) 
     return Model(input=[inp1,inp2], output=out) 

    model1 = MyModel1() 
    model2 = MyModel2() 
    model3 = MyModel3() 

    x = merge([model1.output, 
       model2.output, 
       model3.output], 
       mode='concat', concat_axis=-1) 

    x = Dense(2, activation='softmax')(x) 

    merged = Model(input=[model1.input[0], model1.input[1], 
          model2.input[0], model2.input[1], 
          model3.input[0], model3.input[1]], 
        output=x) 

    merged.summary() 

Answer 1

的模型对象不是可调用的函数。这应该可以解决这个问题：

x = merge([model1.output, 
      model2.output, 
      model3.output], 
      mode='concat', concat_axis=1) 

更新工作代码

from keras.models import Model 
from keras.layers import Input, Dense, merge 


def MyModel1(): 
    inp = Input(batch_shape=(None,32,)) 
    out = Dense(8)(inp) 
    return Model(input=inp, output=out) 

def MyModel2(): 
    inp = Input(batch_shape=(None,10,)) 
    out = Dense(4)(inp) 
    return Model(input=inp, output=out) 

def MyModel3(): 
    inp = Input(batch_shape=(None,12,)) 
    out = Dense(6)(inp) 
    return Model(input=inp, output=out) 

model1 = MyModel1() 
model2 = MyModel2() 
model3 = MyModel3() 

x = merge([model1.output, 
      model2.output, 
      model3.output], 
      mode='concat', concat_axis=1) 

x = Dense(2, activation='softmax')(x) 

merged = Model(input=[model1.input, 
        model2.input, 
        model3.input], 
      output=x) 

merged.summary()