machine-learning - 合并具有不同输入形状的不同模型的输出

Question

我是 Keras 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，以及一个密集层输出。

    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 model?.input 返回张量列表，因此这不起作用。我尝试了不同的方法，但似乎没有任何效果。有没有简单的方法可以解决这个问题？

编辑:针对每个模型中的多个输入，调整了 indraforyou 的工作解决方案。

    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

模型对象不是可调用函数。这应该可以解决问题:

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()