def rtrl_batch_XOR(numSamples=500,seqLen=50,delay=2):
# generate data - a set of xor streams
I = np.empty([seqLen,numSamples,2])
T = np.empty([seqLen,numSamples,1])
for n in range(numSamples):
(tmpI,tmpT) = genData.my_xor_stream(seqLen,delay)
I[:,n,:] = tmpI
T[:,n,:] = tmpT
# init RNN
W=rtrl.allocate(2,1,5)
ANN.initWeights(W)
uhs.learningRate.rate = 0.000015
uhs.momentum.momentum = 0.1
uhs.momentum.paramUpdates_old = None
# learn
errorHist = rtrl.rtrl_batch(I,T,W,maxEpochs=3000,updateHooks=(uhs.learningRate,uhs.momentum))
# evaluate solution
net = np.empty((seqLen*1,numSamples,6))
A = np.zeros((seqLen*1+1,numSamples,6))
rtrl.batch_forward(I,W,net,A,afs.sigmoid,numCycles=1)
target = T[19:50,:,0] > 0.5
learned = A[20:51,:,0] > 0.5
result = np.logical_and(target,learned)
accuracy = float(np.sum(result)) / float(np.prod(result.shape))
print "Accuracy: " + str(accuracy)
# return activations
return (I,T,A)
def rtrl_XOR(numSamples=10000,delay=2):
(I,T) = genData.my_xor_stream(numSamples,delay)
