Pytorch学习记录-逻辑回归

Pytorch学习记录-逻辑回归

1. 引入必须库&设定超参数

一样的套路

import torch import torchvision import torchvision.transforms as transforms import torch.nn as nn # 超参数 input_size = 784 num_classes = 10 num_epochs = 5 batch_size = 100 learning_rate = 0.01 

2. 获取数据和加载数据

train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=False) 

3. 构建逻辑回归模型

这里有一个问题，为什么使用Linear之后没有用softmax？

答案就在损失函数，这里的损失函数使用的是CrossEntropyLoss()，多分类用的交叉熵损失函数，用这个 loss 前面不需要加 Softmax 层。

我重新写了一个Model类，但是使用MSELoss等损失函数都会报错

class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.linear = nn.Linear(input_size, num_classes) self.sigmoid = nn.Sigmoid() def forward(self, x): y_pred = self.sigmoid(self.linear(x)) return y_pred model = Model() criterion = nn.MSELoss() # model = nn.Linear(input_size, num_classes) # criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) 

4. 训练模型

total_step = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = images.reshape(-1, 28 * 28) outputs = model(images) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (i + 1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}] ,Loss:{:.5f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item())) 

5. 测试模型并保存模型

with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.reshape(-1, 28 * 28) outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) torch.save(model.state_dict(),'LogisticModel.ckpt')