PyTorch实现Logistic Regression (mini-batch，多维特征处理）_logistic regression 特征处理

1. Logistic Regression

import torch
import torch.nn.functional as F  # functional下有很多函数包

x_data = torch.Tensor([[1.0], [2.0], [3.0]])  # 3*1 Tensor
y_data = torch.Tensor([[0], [0], [1]])  # 3*1 Tensor


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 = F.sigmoid(self.linear(x))
        return y_pred


model = LogisticRegressionModel()

critetion = torch.nn.BCELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    y_pred = model(x_data)
    loss = critetion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
   
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