解析这段代码import os import json import torch import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets from tqdm import tqdm from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print("using {} device.".format(device)) data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), "val": transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}

这代码定义了一个名为`main`的函数，该函数用于加载数据和模型，进行训练和验证。

首先，通过`torch.device`函数选择使用GPU或CPU进行计算。然后，定义了两个数据转换器，一个用于训练集，一个用于验证集。其中，训练集的数据转换器包括随机裁剪、随机水平翻转、转换为张量和归一化等操作；验证集的数据转换器包括调整大小、中心裁剪、转换为张量和归一化等操作。

接下来的代码会在`main`函数中继续执行。

相关问题

在这代码里面加入获取准确率和损失值并且绘制函数的代码import os import sys import json import torch import torch.nn as nn from torchvision import transforms, datasets, utils import matplotlib.pyplot as plt import numpy as np import torch.optim as optim from tqdm import tqdm from m

import os
import sys
import json
import torch
import torch.nn as nn
from torchvision import transforms, datasets, utils
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from tqdm import tqdm

def train(model, device, train_loader, optimizer, epoch):
model.train()
train_loss = 0
correct = 0
total = 0
progress_bar = tqdm(train_loader)

for batch_idx, (data, target) in enumerate(progress_bar):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = nn.CrossEntropyLoss()(output, target)
loss.backward()
optimizer.step()

train_loss += loss.item()
_, predicted = output.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()

progress_bar.set_description(
'Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item() / len(data)))

accuracy = 100. * correct / total
train_loss /= len(train_loader.dataset)

return accuracy, train_loss

def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
total = 0

with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += nn.CrossEntropyLoss()(output, target).item()
_, predicted = output.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / total

return accuracy, test_loss

def main():
# set up device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

# set up hyperparameters
batch_size = 64
epochs = 10
lr = 0.01

# set up data loaders
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = datasets.MNIST(
'../data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.MNIST(
'../data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=batch_size, shuffle=True)

# set up model
model = nn.Sequential(
nn.Conv2d(1, 10, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(10, 20, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Flatten(),
nn.Linear(320, 50),
nn.ReLU(),
nn.Linear(50, 10),
nn.LogSoftmax(dim=1)
).to(device)

# set up optimizer
optimizer = optim.SGD(model.parameters(), lr=lr)

# train and test the model
train_accuracies = []
train_losses = []
test_accuracies = []
test_losses = []

for epoch in range(1, epochs + 1):
train_accuracy, train_loss = train(
model, device, train_loader, optimizer, epoch)
test_accuracy, test_loss = test(model, device, test_loader)

train_accuracies.append(train_accuracy)
train_losses.append(train_loss)
test_accuracies.append(test_accuracy)
test_losses.append(test_loss)

print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))

# plot accuracy and loss curves
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(range(1, epochs + 1), train_accuracies, label='Train')
plt.plot(range(1, epochs + 1), test_accuracies, label='Test')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(range(1, epochs + 1), train_losses, label='Train')
plt.plot(range(1, epochs + 1), test_losses, label='Test')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

if __name__ == '__main__':
main()

import torch import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils. data import DataI oader 补全代码

下面是一个基本的 PyTorch MNIST 数据集训练代码示例，包括数据加载、模型定义、优化器和损失函数的定义以及训练过程。

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader

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

# 加载数据集
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

net = Net()

# 定义优化器和损失函数
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()

# 训练模型
def train(epoch):
    net.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = net(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def test():
    net.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = net(data)
            test_loss += criterion(output, target)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

for epoch in range(1, 5):
    train(epoch)
    test()

这是一个简单的卷积神经网络模型，用于对 MNIST 手写数字进行分类。训练过程中使用了交叉熵损失函数和随机梯度下降优化器。