画出并保存每个epoch训练集和测试集的loss曲线的实现代码
时间: 2023-08-07 09:05:38 浏览: 49
这里提供一个基于PyTorch的示例代码,可以画出训练集和测试集的loss曲线,并保存为图片:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.functional.log_softmax(x, dim=1)
return output
# 加载数据集
train_dataset = MNIST(root='./data', train=True, transform=ToTensor(), download=True)
test_dataset = MNIST(root='./data', train=False, transform=ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 初始化模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
train_losses = []
test_losses = []
for epoch in range(10):
# 训练
train_loss = 0.0
model.train()
for i, data in enumerate(train_loader):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
train_loss /= len(train_loader)
train_losses.append(train_loss)
# 测试
test_loss = 0.0
model.eval()
with torch.no_grad():
for i, data in enumerate(test_loader):
inputs, labels = data
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item()
test_loss /= len(test_loader)
test_losses.append(test_loss)
# 打印当前epoch的训练集和测试集loss
print(f'Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}')
# 画出训练集和测试集的loss曲线,并保存为图片
plt.plot(train_losses, label='Train Loss')
plt.plot(test_losses, label='Test Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig('loss_curve.png')
plt.show()
```
在上面的代码中,我们使用了Matplotlib库画出训练集和测试集的loss曲线,并使用savefig()方法保存为图片。你可以根据自己的需求修改代码中的模型、数据集、超参数等。
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