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deeplearningzerotoall
GitHub Repository: deeplearningzerotoall/PyTorch
 Path: blob/master/CNN/lab-09-1-xor.ipynb
618 views
Kernel: Python 3
# Lab 9 XOR
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# for reproducibility
torch.manual_seed(777)
if device == 'cuda':
    torch.cuda.manual_seed_all(777)

X = torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]).to(device)
Y = torch.FloatTensor([[0], [1], [1], [0]]).to(device)

# nn layers
linear = torch.nn.Linear(2, 1, bias=True)
sigmoid = torch.nn.Sigmoid()

# model
model = torch.nn.Sequential(linear, sigmoid).to(device)

# define cost/loss & optimizer
criterion = torch.nn.BCELoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=1)

for step in range(10001):
    optimizer.zero_grad()
    hypothesis = model(X)

    # cost/loss function
    cost = criterion(hypothesis, Y)
    cost.backward()
    optimizer.step()

    if step % 100 == 0:
        print(step, cost.item())
0 0.7273974418640137 100 0.6931475400924683 200 0.6931471824645996 300 0.6931471824645996 400 0.6931471824645996 500 0.6931471824645996 600 0.6931471824645996 700 0.6931471824645996 800 0.6931471824645996 900 0.6931471824645996 1000 0.6931471824645996 1100 0.6931471824645996 1200 0.6931471824645996 1300 0.6931471824645996 1400 0.6931471824645996 1500 0.6931471824645996 1600 0.6931471824645996 1700 0.6931471824645996 1800 0.6931471824645996 1900 0.6931471824645996 2000 0.6931471824645996 2100 0.6931471824645996 2200 0.6931471824645996 2300 0.6931471824645996 2400 0.6931471824645996 2500 0.6931471824645996 2600 0.6931471824645996 2700 0.6931471824645996 2800 0.6931471824645996 2900 0.6931471824645996 3000 0.6931471824645996 3100 0.6931471824645996 3200 0.6931471824645996 3300 0.6931471824645996 3400 0.6931471824645996 3500 0.6931471824645996 3600 0.6931471824645996 3700 0.6931471824645996 3800 0.6931471824645996 3900 0.6931471824645996 4000 0.6931471824645996 4100 0.6931471824645996 4200 0.6931471824645996 4300 0.6931471824645996 4400 0.6931471824645996 4500 0.6931471824645996 4600 0.6931471824645996 4700 0.6931471824645996 4800 0.6931471824645996 4900 0.6931471824645996 5000 0.6931471824645996 5100 0.6931471824645996 5200 0.6931471824645996 5300 0.6931471824645996 5400 0.6931471824645996 5500 0.6931471824645996 5600 0.6931471824645996 5700 0.6931471824645996 5800 0.6931471824645996 5900 0.6931471824645996 6000 0.6931471824645996 6100 0.6931471824645996 6200 0.6931471824645996 6300 0.6931471824645996 6400 0.6931471824645996 6500 0.6931471824645996 6600 0.6931471824645996 6700 0.6931471824645996 6800 0.6931471824645996 6900 0.6931471824645996 7000 0.6931471824645996 7100 0.6931471824645996 7200 0.6931471824645996 7300 0.6931471824645996 7400 0.6931471824645996 7500 0.6931471824645996 7600 0.6931471824645996 7700 0.6931471824645996 7800 0.6931471824645996 7900 0.6931471824645996 8000 0.6931471824645996 8100 0.6931471824645996 8200 0.6931471824645996 8300 0.6931471824645996 8400 0.6931471824645996 8500 0.6931471824645996 8600 0.6931471824645996 8700 0.6931471824645996 8800 0.6931471824645996 8900 0.6931471824645996 9000 0.6931471824645996 9100 0.6931471824645996 9200 0.6931471824645996 9300 0.6931471824645996 9400 0.6931471824645996 9500 0.6931471824645996 9600 0.6931471824645996 9700 0.6931471824645996 9800 0.6931471824645996 9900 0.6931471824645996 10000 0.6931471824645996 
# Accuracy computation
# True if hypothesis>0.5 else False
with torch.no_grad():
    predicted = (model(X) > 0.5).float()
    accuracy = (predicted == Y).float().mean()
    print('\nHypothesis: ', hypothesis.detach().cpu().numpy(), '\nCorrect: ', predicted.detach().cpu().numpy(), '\nAccuracy: ', accuracy.item())
Hypothesis: [[0.5] [0.5] [0.5] [0.5]] Correct: [[0.] [0.] [0.] [0.]] Accuracy: 0.5