Tag: logistic-classification

model = BinaryClassifier()

# optimizer 설정
optimizer = optim.SGD(model.parameters(), lr=1)

nb_epochs = 100
for epoch in range(nb_epochs + 1):

    # H(x) 계산
    hypothesis = model(x_train)

    # cost 계산
    cost = F.binary_cross_entropy(hypothesis, y_train)

    # cost로 H(x) 개선
    optimizer.zero_grad()
    cost.backward()
    optimizer.step()
    
    # 20번마다 로그 출력
    if epoch % 10 == 0:
        prediction = hypothesis >= torch.FloatTensor([0.5])
        correct_prediction = prediction.float() == y_train
        accuracy = correct_prediction.sum().item() / len(correct_prediction)
        print('Epoch {:4d}/{} Cost: {:.6f} Accuracy {:2.2f}%'.format(
            epoch, nb_epochs, cost.item(), accuracy * 100,
        ))

'''output
Epoch    0/100 Cost: 0.704829 Accuracy 45.72%
Epoch   10/100 Cost: 0.572391 Accuracy 67.59%
Epoch   20/100 Cost: 0.539563 Accuracy 73.25%
Epoch   30/100 Cost: 0.520042 Accuracy 75.89%
Epoch   40/100 Cost: 0.507561 Accuracy 76.15%
Epoch   50/100 Cost: 0.499125 Accuracy 76.42%
Epoch   60/100 Cost: 0.493177 Accuracy 77.21%
Epoch   70/100 Cost: 0.488846 Accuracy 76.81%
Epoch   80/100 Cost: 0.485612 Accuracy 76.28%
Epoch   90/100 Cost: 0.483146 Accuracy 76.55%
Epoch  100/100 Cost: 0.481234 Accuracy 76.81%
'''