def backprop_XOR():
(I,T)=genData.my_xor()
(M_I,N_I) = I.shape
(M_T,N_T) = T.shape
M_H = 4
(W_IH,W_HO,net_H,net_O,A_H,A_O,Delta_H,Delta_O,DeltaW_IH,DeltaW_HO) = ANN.allocate_feedForward_ANN(N_I,M_I,M_H,M_T,valRange=(-0.4,0.4))
uhs.learningRate.rate = 0.000001
trainError = bp.backProp(I,T,W_IH,W_HO,
net_H,net_O,A_H,A_O,Delta_H,
Delta_O,DeltaW_IH,DeltaW_HO,
(afs.sigmoid,afs.sigmoid_prime),
(afs.sigmoid,afs.sigmoid_prime),
(efs.sumSquaredError,efs.sumSquaredError_prime),
maxEpochs=100000,epsilon=10**-8,
updateHooks=(uhs.learningRate,uhs.momentum))
