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)看完上述内容，是不是对keras2.0如何将Merge层改为函数式有进一步的了解，如果还想学习更多内容，欢迎关注亿速云行业资讯频道。
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