如何在keras中使用lambda层？

Question

我想要定义lambda层以将特征与叉积组合，然后合并这些模型，就像图。 ，我该怎么办？

测试model_1，得到128个尺寸形成密集，使用pywt得到两个64维特征（cA,cD），然后返回CA *的cD //我当然希望两个模型结合起来，但第一次尝试model_1。

from keras.models import Sequential,Model 
from keras.layers import Input,Convolution2D,MaxPooling2D 
from keras.layers.core import Dense,Dropout,Activation,Flatten,Lambda 
import pywt 

def myFunc(x): 
    (cA, cD) = pywt.dwt(x, 'db1') 
# x=x*x 
    return cA*cD 

batch_size=32 
nb_classes=3 
nb_epoch=20 
img_rows,img_cols=200,200 
img_channels=1 
nb_filters=32 
nb_pool=2 
nb_conv=3 

inputs=Input(shape=(1,img_rows,img_cols)) 
x=Convolution2D(nb_filters,nb_conv,nb_conv,border_mode='valid', 
        input_shape=(1,img_rows,img_cols),activation='relu')(inputs) 
x=Convolution2D(nb_filters,nb_conv,nb_conv,activation='relu')(x) 
x=MaxPooling2D(pool_size=(nb_pool,nb_pool))(x) 
x=Dropout(0.25)(x) 
x=Flatten()(x) 
y=Dense(128,activation='relu')(x) 
cross=Lambda(myFunc,output_shape=(64,))(y) 
predictions=Dense(nb_classes,activation='softmax')(cross) 
model = Model(input=inputs, output=predictions) 
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy']) 

model.fit(X_train,Y_train,batch_size=batch_size,nb_epoch=nb_epoch, 
      verbose=1,validation_data=(X_test,Y_test)) 

对不起，我可以问一个关于张量的问题吗？

import tensorflow as tf 
W1 = tf.Variable(np.array([[1,2],[3,4]])) 
init = tf.global_variables_initializer() 
sess = tf.Session() 
sess.run(init) 
array = W1.eval(sess) 
print (array) 

没错！然而，

from keras import backend as K 
import numpy as np 
kvar=K.variable(np.array([[1,2],[3,4]])) 
K.eval(kvar) 
print(kvar) 

我<CudaNdarrayType(float32, matrix)>和kvar.eval()我b'CudaNdarray([[ 1. 2.]\n [ 3. 4.]])'。我使用keras，所以如何使用keras获得类似tensorflow的数组？

Answer 1

我会probaly复制致密层。而不是有128个单位的2层，有64个单位的4层。结果是一样的，但你将能够更好地执行交叉产品。现在

from keras.models import Model 

#create dense layers and store their output tensors, they use the output of models 1 and to as input  
d1 = Dense(64, ....)(Model_1.output) 
d2 = Dense(64, ....)(Model_1.output) 
d3 = Dense(64, ....)(Model_2.output) 
d4 = Dense(64, ....)(Model_2.output) 

cross1 = Lambda(myFunc, output_shape=....)([d1,d4]) 
cross2 = Lambda(myFunc, output_shape=....)([d2,d3]) 

#I don't really know what kind of "merge" you want, so I used concatenate, there are Add, Multiply and others.... 
output = Concatenate()([cross1,cross2]) 
    #use the "axis" attribute of the concatenate layer to define better which axis will be doubled due to the concatenation  

model = Model([Model_1.input,Model_2.input], output) 

，为lambda函数：

import keras.backend as K 

def myFunc(x): 
    return x[0] * x[1]