model.load_state_dict(torch.load("mnist_mobile.pt")) model.eval()

这段代码是用 PyTorch 加载预训练的模型 "mnist_mobile.pt" 并将其设置为评估模式。具体来说，"load_state_dict" 方法将模型的状态加载到内存中，"model.eval()" 方法将模型设置为评估模式，这会关闭一些训练时使用的特性，如 dropout 和 batch normalization，以便在测试过程中获得更稳定的结果。

相关问题

构建一个全连接网络，网络中只允许存在Linear层(层数不得超过5)，可以调节的超参数为learning_rate和epoch(eopch不得超过50)。使用FashionMNIST数据集训练并测试网络的精度，将最优(在测试集上的精度)的模型保存为model.pth文件，

构建一个全连接神经网络（也称为多层感知机，MLP）仅包含线性层的情况，我们可以按照以下步骤进行：

1. 导入必要的库：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor

2. 定义网络结构：

class LinearNet(nn.Module):
    def __init__(self, input_dim, num_classes=10, max_layers=5):
        super(LinearNet, self).__init__()
        layers = [nn.Linear(input_dim, hidden_size) for _ in range(max_layers - 1)]
        layers.append(nn.Linear(hidden_size, num_classes)) # 最后一层是输出层
        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        return self.layers(x)

这里假设隐层大小`hidden_size`未指定，可以根据需要设置。

3. 设置超参数：

input_dim = 784 # FashionMNIST每张图片的像素数
num_classes = 10
max_epochs = 50
learning_rate = 0.001

4. 加载和预处理数据：

train_dataset = FashionMNIST(root='./data', train=True, transform=ToTensor(), download=True)
test_dataset = FashionMNIST(root='./data', train=False, transform=ToTensor())

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

5. 训练网络：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = LinearNet(input_dim).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)

best_accuracy = 0
for epoch in range(max_epochs):
    net.train()
    running_loss = 0.0
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = net(inputs.to(device))
        loss = criterion(outputs, labels.to(device))
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    net.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for inputs, labels in test_loader:
            outputs = net(inputs.to(device))
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels.to(device)).sum().item()

    accuracy = 100 * correct / total
    print(f'Epoch {epoch+1}/{max_epochs}, Loss: {running_loss / len(train_loader):.4f}, Accuracy: {accuracy:.2f}%')

    if accuracy > best_accuracy:
        best_accuracy = accuracy
        torch.save(net.state_dict(), 'model.pth')

在这个过程中，网络会在每个epoch结束后在测试集上计算精度，并检查是否达到最佳性能。如果找到更好的模型，就将其权重保存到'model.pth'文件中。

请用Anaconda3 写python代码： 设计自定义的ResNet 数据：MINST <导入必要的PyTorch包 1.使用Dataset与Dataloader加载数据 - 首次加载数据使用直接下载的方式，数据存放至./data文件夹 - 本次实验的提交文件中不必包含MINST数据文件 2.自定义ResidualBlock类 - 使用两层卷积层 - 每一个卷积层保持输入和输出的通道数、宽高一致（输入通道作为ResidualBlock初始化的一个参数由外部传入） - 使用3x3的卷积核 3.定义前面给出的模型结构并实例化网络模型、交叉熵损失、SGD优化器 4.定义单次训练/测试的函数训练阶段每300个batch打印一下损失 5.设置训练周期为10次，打印每个周期中训练过程中的模型损失及测试过程中的准确率 6.定义一个函数get_n_params，来计算上述模型中的参数个数 ```def get_n_params(model)``` Hint: 调研model.parameters()的使用 7.保存训练好的模型到文件resnet.pt

好的，以下是代码实现：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset

# 定义自定义的ResidualBlock类
class ResidualBlock(nn.Module):
    def __init__(self, in_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += identity
        out = self.relu(out)
        return out

# 定义自定义的ResNet模型
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = nn.Sequential(
            ResidualBlock(64),
            ResidualBlock(64)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ResidualBlock(128),
            ResidualBlock(128)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ResidualBlock(256),
            ResidualBlock(256)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            ResidualBlock(512),
            ResidualBlock(512)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义自定义的MINST数据集
class CustomMNISTDataset(Dataset):
    def __init__(self, train=True, transform=None):
        self.train = train
        self.transform = transform
        if self.train:
            self.data, self.labels = torch.utils.data.Subset(torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor()), list(range(0, 50000)))
        else:
            self.data, self.labels = torch.utils.data.Subset(torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor()), list(range(50000, 60000)))

    def __getitem__(self, index):
        img, target = self.data[index], self.labels[index]
        if self.transform is not None:
            img = self.transform(img)
        return img, target

    def __len__(self):
        return len(self.data)

# 定义数据预处理方式
transform = transforms.Compose([
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载训练集和测试集
train_dataset = CustomMNISTDataset(train=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_dataset = CustomMNISTDataset(train=False, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

# 创建ResNet模型
model = ResNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 定义单次训练函数
def train_one_epoch(model, train_loader, criterion, optimizer, epoch):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 300 == 299:    # 每300个batch打印一下损失
            print('[Epoch %d, Batch %d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 300))
            running_loss = 0.0

# 定义测试函数
def test(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    return accuracy

# 训练模型
for epoch in range(10):
    train_one_epoch(model, train_loader, criterion, optimizer, epoch)
    test_accuracy = test(model, test_loader)
    print('[Epoch %d] test accuracy: %.2f%%' % (epoch + 1, test_accuracy))

# 定义函数计算模型参数个数
def get_n_params(model):
    return sum([p.numel() for p in model.parameters()])

# 打印模型参数个数
print('Number of parameters: %d' % get_n_params(model))

# 保存模型
torch.save(model.state_dict(), 'resnet.pt')

注意：这段代码在第一次运行时会直接下载MINST数据集，存放至./data文件夹下。如果之后再次运行需要手动删除./data文件夹下的所有文件。同时，为了减小训练集大小，代码中只使用前50000个样本作为训练集，后10000个样本作为测试集。如果需要使用完整的训练集，可以将`self.data, self.labels`的赋值语句改为`torchvision.datasets.MNIST(root='./data', train=self.train, download=True, transform=transforms.ToTensor())`。