keras2.0如何将Merge层改为函数式

这篇文章主要讲解了keras2.0如何将Merge层改为函数式，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。

不能再向以前一样使用

model.add(Merge([Model1,Model2]))

必须使用函数式

out = Concatenate()([model1.output, model2.output])

补充知识：keras 新版接口修改

1.

# b = MaxPooling2D((3, 3), strides=(1, 1), border_mode='valid', dim_ordering='tf')(x)

b = MaxPooling2D((3, 3), strides=(1, 1), padding='valid', data_format="channels_last")(x)

2.

from keras.layers.merge import concatenate
# x = merge([a, b], mode='concat', concat_axis=-1)
x = concatenate([a, b], axis=-1)

3.

from keras.engine import merge
m = merge([init, x], mode='sum')
Equivalent Keras 2.0.2 code:

from keras.layers import add
m = add([init, x])

4.

 # x = Convolution2D(32 // nb_filters_reduction_factor, 3, 3, subsample=(1, 1), activation='relu',
 #     init='he_normal', border_mode='valid', dim_ordering='tf')(x)
 x = Conv2D(32 // nb_filters_reduction_factor, (3, 3), activation="relu", strides=(1, 1), padding="valid",
    data_format="channels_last",
    kernel_initializer="he_normal")(x)

1.

# b = MaxPooling2D((3, 3), strides=(1, 1), border_mode='valid', dim_ordering='tf')(x)
b = MaxPooling2D((3, 3), strides=(1, 1), padding='valid', data_format="channels_last")(x)

2.

from keras.layers.merge import concatenate
# x = merge([a, b], mode='concat', concat_axis=-1)
x = concatenate([a, b], axis=-1)

3.

from keras.engine import merge
m = merge([init, x], mode='sum')
Equivalent Keras 2.0.2 code:

from keras.layers import add
m = add([init, x])

4.

 # x = Convolution2D(32 // nb_filters_reduction_factor, 3, 3, subsample=(1, 1), activation='relu',
 #     init='he_normal', border_mode='valid', dim_ordering='tf')(x)
 x = Conv2D(32 // nb_filters_reduction_factor, (3, 3), activation="relu", strides=(1, 1), padding="valid",
    data_format="channels_last",
    kernel_initializer="he_normal")(x)

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