《PyTorch深度学习实践》第六节：逻辑斯蒂回归

import torch

'''数据'''
x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[0], [0], [1]])

'''建立模型'''
class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = torch.sigmoid(self.linear(x))
        return y_pred

model = LogisticRegressionModel()

'''计算损失函数和优化器'''
criterion = torch.nn.BCELoss(size_average = False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)


'''training cycle'''
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item(), 'w=', model.linear.weight.item(), 'b=', model.linear.bias.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print('w=', model.linear.weight.item())
print('b=', model.linear.bias.item())

x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred=', y_test.data)