使用keras2.0 将Merge层改为函数式
作者：Addmana
这篇文章主要介绍了使用keras2.0 将Merge层改为函数式，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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)
以上这篇使用keras2.0 将Merge层改为函数式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。