plt.title("Loss:{:.4f}".format(loss.item()))

这段代码的作用是设置一个标题，标题的内容是 "Loss: " 以及一个浮点数，这个浮点数是从一个 PyTorch 的 tensor 变量 loss 中提取出来的，然后使用 format 方法将其保留4位小数，并插入到标题字符串中。最终的标题字符串将会显示在通过 plt.show() 函数展示的图像上方。通常这种操作用于可视化训练过程中的损失函数值。

相关问题

从model模型中获取每一轮的accurac和loss训练数据，并绘制图形。 参考输出如下:

好的，可以使用PyTorch中的`nn.Module`来创建模型，并使用`nn.CrossEntropyLoss`作为损失函数。在训练过程中，我们可以使用PyTorch提供的`nn.utils`来计算每个epoch的平均loss和accuracy，并绘制图形。以下是一个示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# 定义模型
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 10)
    
    def forward(self, x):
        x = x.view(-1, 784)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 加载数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())
train_loader = data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 定义模型、损失函数、优化器
model = MyModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
epochs = 10
loss_list = []
acc_list = []
for epoch in range(epochs):
    running_loss = 0.0
    running_corrects = 0
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == labels.data)
    epoch_loss = running_loss / len(train_dataset)
    epoch_acc = running_corrects.double() / len(train_dataset)
    loss_list.append(epoch_loss)
    acc_list.append(epoch_acc)
    print('Epoch [{}/{}], Loss: {:.4f}, Acc: {:.4f}'.format(epoch+1, epochs, epoch_loss, epoch_acc))

# 绘制loss和accuracy图像
plt.plot(range(epochs), loss_list)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Training Loss')
plt.show()

plt.plot(range(epochs), acc_list)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Training Accuracy')
plt.show()

输出结果可能类似于：

Epoch [1/10], Loss: 1.3121, Acc: 0.6596
Epoch [2/10], Loss: 0.6635, Acc: 0.8314
Epoch [3/10], Loss: 0.5197, Acc: 0.8645
Epoch [4/10], Loss: 0.4566, Acc: 0.8807
Epoch [5/10], Loss: 0.4193, Acc: 0.8903
Epoch [6/10], Loss: 0.3947, Acc: 0.8964
Epoch [7/10], Loss: 0.3774, Acc: 0.9013
Epoch [8/10], Loss: 0.3637, Acc: 0.9049
Epoch [9/10], Loss: 0.3524, Acc: 0.9084
Epoch [10/10], Loss: 0.3429, Acc: 0.9112

并且会弹出两个图形窗口，分别显示训练loss和accuracy的变化趋势。

loss函数曲线对比曲线代码

以下是一个简单的示例代码，比较两个模型在训练过程中的loss曲线：

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

# 定义模型1
class Model1(nn.Module):
    def __init__(self):
        super(Model1, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义模型2
class Model2(nn.Module):
    def __init__(self):
        super(Model2, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义训练函数
def train(model, optimizer, criterion):
    losses = []
    for epoch in range(num_epochs):
        running_loss = 0.0
        for inputs, labels in data_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        epoch_loss = running_loss / len(data_loader)
        losses.append(epoch_loss)
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, epoch_loss))
    return losses

# 定义数据集和数据加载器
data = torch.randn(100, 10)
labels = torch.randn(100, 1)
dataset = torch.utils.data.TensorDataset(data, labels)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True)

# 定义超参数
num_epochs = 50
learning_rate = 0.1

# 初始化两个模型和优化器
model1 = Model1()
model2 = Model2()
optimizer1 = optim.SGD(model1.parameters(), lr=learning_rate)
optimizer2 = optim.SGD(model2.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

# 分别训练两个模型
losses1 = train(model1, optimizer1, criterion)
losses2 = train(model2, optimizer2, criterion)

# 绘制loss曲线
plt.plot(losses1, label='Model1')
plt.plot(losses2, label='Model2')
plt.legend()
plt.title('Training Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

运行以上代码，可以得到两个模型在训练过程中的loss曲线对比图。