epoch的loss和acc如何绘制

epoch是指训练模型时的迭代次数。loss和acc是模型训练的两个重要指标。loss（损失）反映模型的预测结果与真实标签之间的差异程度，它的值越小说明模型的预测越接近实际值；而acc（准确率）则是指模型的预测结果与真实标签相符的比例，它的值越高则说明模型的预测效果越好。通过绘制loss和acc的变化曲线，可以直观地了解模型在训练过程中的表现情况，帮助我们优化训练过程和提高模型预测效果。

相关问题

cifar100图像分类实验训练集和测试集loss和acc曲线绘制源码

以下是使用PyTorch绘制cifar100图像分类实验训练集和测试集loss和acc曲线的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# 定义超参数
batch_size = 128
lr = 0.1
momentum = 0.9
weight_decay = 1e-4
epochs = 50

# 加载数据集
train_transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
])

test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
])

train_set = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=train_transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2)

test_set = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=test_transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=2)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc = nn.Linear(256 * 8 * 8, 100)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = x.view(-1, 256 * 8 * 8)
        x = self.fc(x)
        return x

# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)

# 训练模型
train_loss_list = []
train_acc_list = []
test_loss_list = []
test_acc_list = []

for epoch in range(epochs):
    train_loss = 0
    train_acc = 0
    net.train()
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()
    train_loss /= len(train_loader.dataset)
    train_acc /= len(train_loader.dataset)
    train_loss_list.append(train_loss)
    train_acc_list.append(train_acc)

    test_loss = 0
    test_acc = 0
    net.eval()
    with torch.no_grad():
        for inputs, labels in test_loader:
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            test_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            test_acc += (predicted == labels).sum().item()
        test_loss /= len(test_loader.dataset)
        test_acc /= len(test_loader.dataset)
        test_loss_list.append(test_loss)
        test_acc_list.append(test_acc)

    print('Epoch [%d/%d], Train Loss: %.4f, Train Acc: %.4f, Test Loss: %.4f, Test Acc: %.4f'
          % (epoch+1, epochs, train_loss, train_acc, test_loss, test_acc))

# 绘制loss和acc曲线
plt.plot(range(epochs), train_loss_list, label='train')
plt.plot(range(epochs), test_loss_list, label='test')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.plot(range(epochs), train_acc_list, label='train')
plt.plot(range(epochs), test_acc_list, label='test')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

运行该代码，即可绘制出cifar100图像分类实验训练集和测试集loss和acc曲线。

pytorch画acc和loss曲线图

可以使用 matplotlib 库在 PyTorch 中绘制 acc 和 loss 曲线图。首先需要在训练过程中记录 acc 和 loss 的数值，然后使用 matplotlib 的 plot 函数绘制曲线图即可。代码示例如下：

import matplotlib.pyplot as plt

# 在训练过程中记录 acc 和 loss
train_acc = []
train_loss = []
# ...
train_acc.append(acc)
train_loss.append(loss)

# 绘制 acc 曲线图
plt.plot(train_acc)
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()

# 绘制 loss 曲线图
plt.plot(train_loss)
plt.title('Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

请确保在训练过程中记录 acc 和 loss，并在绘图时使用相应的数值。