如何在Keras中使用Lambda层?

本文介绍了如何在Keras中使用Lambda层?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我想定义lambda层以将要素与叉积组合，然后合并这些模型，就像无花果一样. ，我该怎么办?

I want to define lambda layer to combine features with cross product, then merge those models,just like the fig. ,What should I do?

测试model_1，获得密度为128的尺寸，使用pywt获得两个64尺寸的特征(cA,cD)，然后返回cA * cD//当然，我想合并两个模型，但首先尝试使用model_1.

Test model_1, get 128 dimensions form dense, use pywt get two 64 dimensions feature(cA,cD), then return cA*cD //of course I want to combine two models ,but try model_1 first.

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) modelpile(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))

抱歉，我可以问一个关于张量的问题吗?

Sorry, can I ask a question about tensor?

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)

是的！但是，

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

我得到了<CudaNdarrayType(float32, matrix)>和kvar.eval()我得到了b'CudaNdarray([[ 1. 2.]\n [ 3. 4.]])'.我使用keras，那么如何使用keras获得类似tensorflow的数组?

I got <CudaNdarrayType(float32, matrix)> and kvar.eval() I got b'CudaNdarray([[ 1. 2.]\n [ 3. 4.]])'. I use keras, so how can I get array like tensorflow using keras?

推荐答案

我可能会复制密集层.代替具有2个128个单位的层，而具有4个64个单位的层.结果是相同的，但是您将能够更好地执行交叉乘积.

I would probaly duplicate the dense layers. Instead of having 2 layers with 128 units, have 4 layers with 64 units. The result is the same, but you will be able to perform the cross products better.

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)

现在，使用lambda函数:

Now, for the lambda function:

import keras.backend as K def myFunc(x): return x[0] * x[1]