不能再向以前一樣使用
model.add(merge([model1,model2]))
必須使用函式式
out = concatenate()([model1.output, model2.output])
補充知識：keras 新版介面修改
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="channewww.cppcns.comls_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), activat'relu',
# init='he_normal', borde程式設計客棧r_mode='valid', dim_ordering='tf')(x)
x = conv2d(32 // nb_filters_reduction_factor, (3, 3), activation="relu", strides=(1, 1), padding="valid"程式設計客棧,
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 zjvbks= 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)
本文標題: 使用keras2.0 將merge層改為函式式
本文位址:
Keras使用使用動態LSTM RNN
padding def generate mtp 100,batch 50 最長時間步，詞向量長度為200，batch size 50 origin input np.random.random sample batch,np.random.randint mtp 2,mtp 200 時間長隨機從m...
Keras學習 1 使用keras建立序列模型
keras學習 1 使用keras建立序列模型 sequential model就是一些列layers的簡單堆疊。首先，我們建立乙個簡單的前向全連線網路。輸入維度784 from keras.models import sequential from keras.layers import dens...
使用 Keras 搭建 DNN
from keras.datasets import mnist import numpy as np x train,y train x test,y test mnist.load data print np.shape x train np.shape x test 60000,28,28 1...