【PyTorch深度学习】第五讲：逻辑斯蒂回归

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该博客介绍了如何使用PyTorch实现逻辑回归模型。通过添加激活函数避免线性回归问题，定义了一个简单的LogisticRegressionModel类，并使用SGD优化器进行训练。经过1000个epoch的训练，模型权重和偏置得到更新，能够对新输入进行预测。

1.原理

添加激活函数，避免线性回归的无效

2.代码实现

import torch

# import torch.nn.functional as F

# prepare dataset
x_data = torch.Tensor([[1.0], [2.0], [3.0], [4.0]])
y_data = torch.Tensor([[0], [0], [1], [1]])


# design model using class
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()


criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# training cycle forward, backward, update
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

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

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

x_test = torch.Tensor([[2.2]])
y_test = model(x_test)
print('y_test= ', y_test.data)

运行结果

0 0.8333730697631836
1 0.8216124773025513
2 0.8102343082427979
3 0.799232006072998
·············
·············
·············
997 0.3926210105419159
998 0.3925216794013977
999 0.3924223482608795
w =  0.8659290075302124
b =  -1.701526165008545
y_test=  tensor([[0.7102]])