Pytorch提供torchvision包，这个包提供MNIST数据集、CIFAR数据集实现代码：

#train=Ture表示取训练集数据，train=False表示取测试集数据，若第一次使用就有download=Ture下载数据，下载过了download=False
train_set = torchvision.datasets.MNIST(root='/Users/l/PycharmProjects/pythonProjectTest/mr_liu',train=True,download=True)  
test_set = torchvision.datasets.MNIST(root='/Users/l/PycharmProjects/pythonProjectTest/mr_liu',train=False,download=True)  
  
train_set = torchvision.datasets.CIFAR10(root='/Users/l/PycharmProjects/pythonProjectTest/mr_liu',train=True,download=True)  
test_set = torchvision.datasets.CIFAR10(root='/Users/l/PycharmProjects/pythonProjectTest/mr_liu',train=False,download=True)

Sigmoid函数：有极限、单调增、饱和函数。其中最典型的有logistic函数，所以有时把logistic函数称为Sigmoid函数。

Logistic回归模型代码

import torch  
import torch.nn.functional as F  
import matplotlib.pyplot as plt  
  
#prepare dataset  
x_data = torch.Tensor([[1.0], [2.0], [3.0]])  
y_data = torch.Tensor([[0], [0], [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 = F.sigmoid(self.linear(x))  
    return y_pred  
  
  
model = LogisticRegressionModel()  
  
# construct loss and optimizer  
# 默认情况下，loss会基于element平均，如果size_average=False的话，loss会被累加。  
criterion = torch.nn.BCELoss(size_average=False)#ruduction = 'sum'  
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  
  
# training cycle forward, backward, update  
num_epochs = 1000  
losses = []  
for epoch in range(num_epochs):  
    y_pred = model(x_data)  
    loss = criterion(y_pred, y_data)  
    print(epoch, loss.item())  
    losses.append(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([[4.0]])  
y_test = model(x_test)  
print('y_pred = ', y_test.data)  
  
plt.plot(range(num_epochs), losses)  
plt.ylabel('Loss')  
plt.xlabel('epoch')  
plt.show()

P6 logistic回归模型

Logistic回归模型代码