pytorch绘制acc图像代码

pytorch是深度学习领域的一种神经网络编程框架，支持GPU加速，其灵活性和可扩展性广受欢迎。在深度学习任务中，我们经常需要绘制训练过程中的准确率（acc）变化曲线，以便更好地评估模型的性能和优化方向。下面介绍一种使用pytorch绘制acc图像的代码。

首先需要导入相关的pytorch和matplotlib库:

import torch
import matplotlib.pyplot as plt

然后定义一个函数用于训练模型，并返回每个epoch的acc值:

def train(model, optimizer, criterion, train_loader, device):
    every_epoch_acc = []
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        total = labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct = (predicted == labels).sum().item()
        acc = correct / total
        every_epoch_acc.append(acc)
    return every_epoch_acc

在主函数中，进行模型训练并获取每个epoch的acc值，然后根据这些acc值绘制图像：

def main():
    ...
    every_epoch_acc = []
    for epoch in range(num_epochs):
        train_acc = train(model, optimizer, criterion, train_loader, device)
        every_epoch_acc += train_acc
        
    plt.plot(every_epoch_acc)
    plt.title('Training Accuracy vs. Epoch')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.savefig('acc.png')
    plt.show()

此处省略了主函数中的其他部分，完整代码如下：

import torch
import matplotlib.pyplot as plt

def train(model, optimizer, criterion, train_loader, device):
    every_epoch_acc = []
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        total = labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct = (predicted == labels).sum().item()
        acc = correct / total
        every_epoch_acc.append(acc)
    return every_epoch_acc

def main():
    # 导入数据集和定义模型、优化器等
    ...
    every_epoch_acc = []
    for epoch in range(num_epochs):
        train_acc = train(model, optimizer, criterion, train_loader, device)
        every_epoch_acc += train_acc
        
    plt.plot(every_epoch_acc)
    plt.title('Training Accuracy vs. Epoch')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.savefig('acc.png')
    plt.show()

if __name__ == '__main__':
    main()

以上就是使用pytorch绘制acc图像的代码。