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這篇文章主要講解了keras2.0如何將Merge層改為函數(shù)式,內(nèi)容清晰明了,對(duì)此有興趣的小伙伴可以學(xué)習(xí)一下,相信大家閱讀完之后會(huì)有幫助。
不能再向以前一樣使用
model.add(Merge([Model1,Model2]))
必須使用函數(shù)式
out = Concatenate()([model1.output, model2.output])
補(bǔ)充知識(shí):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)
看完上述內(nèi)容,是不是對(duì)keras2.0如何將Merge層改為函數(shù)式有進(jìn)一步的了解,如果還想學(xué)習(xí)更多內(nèi)容,歡迎關(guān)注億速云行業(yè)資訊頻道。
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