如何使用keras进行XOR [英] How to use keras for XOR

问题描述

我想通过代码xor来练习keras，但结果不正确，其次是我的代码，谢谢大家的帮助.

I want to practice keras by code a xor, but the result is not right, the followed is my code, thanks for everybody to help me.

from keras.models import Sequential
from keras.layers.core import Dense,Activation
from keras.optimizers import SGD
import numpy as np

model = Sequential()# two layers
model.add(Dense(input_dim=2,output_dim=4,init="glorot_uniform"))
model.add(Activation("sigmoid"))
model.add(Dense(input_dim=4,output_dim=1,init="glorot_uniform"))
model.add(Activation("sigmoid"))
sgd = SGD(l2=0.0,lr=0.05, decay=1e-6, momentum=0.11, nesterov=True)
model.compile(loss='mean_absolute_error', optimizer=sgd)
print "begin to train"
list1 = [1,1]
label1 = [0]
list2 = [1,0]
label2 = [1]
list3 = [0,0]
label3 = [0]
list4 = [0,1]
label4 = [1] 
train_data = np.array((list1,list2,list3,list4)) #four samples for epoch = 1000
label = np.array((label1,label2,label3,label4))

model.fit(train_data,label,nb_epoch = 1000,batch_size = 4,verbose = 1,shuffle=True,show_accuracy = True)
list_test = [0,1]
test = np.array((list_test,list1))
classes = model.predict(test)
print classes

输出

[[ 0.31851079] [ 0.34130159]] [[ 0.49635666] [0.51274764]] 

推荐答案

如果我将代码中的纪元数增加到50000，它确实会收敛到正确的答案，只需要一点时间:)

If I increase the number of epochs in your code to 50000 it does often converge to the right answer for me, just takes a little while :)

尽管如此，它的确经常卡住.如果将损失函数更改为"mean_squared_error"，则收敛性能会更好.

It does often get stuck, though. I get better convergence properties if I change your loss function to 'mean_squared_error', which is a smoother function.

如果使用Adam或RMSProp优化器，收敛速度会更快.我的最终编译行有效:

I get still faster convergence if I use the Adam or RMSProp optimizers. My final compile line, which works:

model.compile(loss='mse', optimizer='adam')
...
model.fit(train_data, label, nb_epoch = 10000,batch_size = 4,verbose = 1,shuffle=True,show_accuracy = True)

这篇关于如何使用keras进行XOR的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！