利用torch.nn实现 softmax 回归在Fashion-MNIST数据集上进行训练和测试，并从loss，训 练集以及测试集上的准确率等多个角度对结果进行分析

好的，以下是使用torch.nn实现softmax回归在Fashion-MNIST数据集上进行训练和测试，并对结果进行分析的步骤：

首先，导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

然后，加载Fashion-MNIST数据集并进行预处理：

# 定义数据转换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载训练集和测试集
train_dataset = datasets.FashionMNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.FashionMNIST(root='./data', train=False, transform=transform, download=True)

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

接下来，定义softmax回归模型：

class SoftmaxRegression(nn.Module):
    def __init__(self, input_dim, num_classes):
        super(SoftmaxRegression, self).__init__()
        self.linear = nn.Linear(input_dim, num_classes)
        
    def forward(self, x):
        out = self.linear(x)
        return out

model = SoftmaxRegression(28 * 28, 10)

然后，定义损失函数和优化器：

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

接着，进行模型的训练：

num_epochs = 10

for epoch in range(num_epochs):
    total_loss = 0
    
    for images, labels in train_loader:
        images = images.view(-1, 28 * 28)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
    
    # 打印每个epoch的损失函数值
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, total_loss))

最后，对模型进行测试并分析结果：

# 在训练集上进行预测
correct_train = 0
total_train = 0

with torch.no_grad():
    for images, labels in train_loader:
        images = images.view(-1, 28 * 28)
        outputs = model(images)
        _, train_predicted = torch.max(outputs.data, 1)
        
        total_train += labels.size(0)
        correct_train += (train_predicted == labels).sum().item()

train_accuracy = correct_train / total_train

# 在测试集上进行预测
correct_test = 0
total_test = 0

with torch.no_grad():
    for images, labels in test_loader:
        images = images.view(-1, 28 * 28)
        outputs = model(images)
        _, test_predicted = torch.max(outputs.data, 1)
        
        total_test += labels.size(0)
        correct_test += (test_predicted == labels).sum().item()

test_accuracy = correct_test / total_test

print('Train Accuracy: {:.2f}%'.format(train_accuracy * 100))
print('Test Accuracy: {:.2f}%'.format(test_accuracy * 100))

通过以上步骤，我们可以使用torch.nn实现softmax回归模型在Fashion-MNIST数据集上进行训练和测试，并从loss、训练集以及测试集上的准确率等多个角度对结果进行分析。可以观察每个epoch的损失函数逐渐减小，同时计算训练集和测试集上的准确率来评估模型的性能。