Hi Team,
Below given is the XOR NN using PyTorch. Looks like the output giving by the max()[1] is wrong. Please review my output as POC.
import torch as th
from torch.autograd import Variable
epochs = 2000
lr = 1
XOR_X = [[0, 0], [0, 1], [1, 0], [1, 1]]
XOR_Y = [[0, 1], [1, 0], [1, 0], [0, 1]]
x_ = Variable(th.FloatTensor(XOR_X), requires_grad=False)
y_ = Variable(th.FloatTensor(XOR_Y), requires_grad=False)
w1 = Variable(th.randn(2, 3), requires_grad=True)
w2 = Variable(th.randn(3, 2), requires_grad=True)
b1 = Variable(th.zeros(3), requires_grad=True)
b2 = Variable(th.zeros(2), requires_grad=True)
def forward(x):
a2 = x.mm(w1)
# pytorch didn't have numpy like broadcasting when i wrote this script
# expand_as make the tensor as similar size as the other tensor
a2 = a2.add(b1.expand_as(a2))
h2 = a2.sigmoid()
a3 = h2.mm(w2)
a3 = a3.add(b2.expand_as(a3))
hyp = a3.sigmoid()
return hyp
for epoch in range(epochs):
hyp = forward(x_)
cost = y_ - hyp
cost = cost.pow(2).sum()
if epoch % 500 == 0:
print(cost.data[0])
cost.backward()
w1.data -= lr * w1.grad.data
w2.data -= lr * w2.grad.data
b1.data -= lr * b1.grad.data
b2.data -= lr * b2.grad.data
w1.grad.data.zero_()
w2.grad.data.zero_()
for x in XOR_X:
hyp = forward(Variable(th.FloatTensor([x])))
values, indices = hyp.max(0)
print('==========================\nX is: ', x)
print('==========================\n hyp is: ', hyp)
print('==========================\n indices from argmax: ', indices)
==========================
X is: [0, 0]hyp is: Variable containing:
0.0166 0.9810
[torch.FloatTensor of size 1x2]
==========================
indices from argmax: Variable containing:
0 0
[torch.LongTensor of size 1x2]
