问题
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
Output
[[ 0.31851079] [ 0.34130159]] [[ 0.49635666] [0.51274764]]
回答1:
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 :)
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.
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)
回答2:
I used a single hidden layer with 4 hidden nodes, and it almost always converges to the right answer within 500 epochs. I used sigmoid activations.
来源：https://stackoverflow.com/questions/31556268/how-to-use-keras-for-xor