强化学习优化unet训练的学习率和优化器选择,pytorch详细代码
时间: 2023-09-24 17:13:41 浏览: 194
在进行强化学习优化Unet训练时,选择合适的学习率和优化器对模型的训练效果有很大影响。下面是使用PyTorch实现的代码示例,展示如何选择优化器和学习率:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from dataset import MyDataset
from model import Unet
# 定义超参数
batch_size = 4
epochs = 20
lr = 0.001
gamma = 0.1
# 定义数据预处理
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
)
# 加载数据集
train_dataset = MyDataset('train', transform)
val_dataset = MyDataset('val', transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
# 定义模型和优化器
model = Unet()
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=gamma, patience=5, verbose=True)
# 定义损失函数
criterion = nn.BCEWithLogitsLoss()
# 训练模型
for epoch in range(epochs):
train_loss = 0.0
val_loss = 0.0
model.train()
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item() * images.size(0)
train_loss /= len(train_loader.dataset)
model.eval()
with torch.no_grad():
for images, labels in val_loader:
outputs = model(images)
loss = criterion(outputs, labels)
val_loss += loss.item() * images.size(0)
val_loss /= len(val_loader.dataset)
scheduler.step(val_loss)
print('Epoch: {} Train Loss: {:.4f} Val Loss: {:.4f}'.format(epoch+1, train_loss, val_loss))
```
在上面的代码中,我们使用了Adam优化器和ReduceLROnPlateau学习率调整策略。ReduceLROnPlateau会在验证集上监测模型的性能,并根据验证集上的表现动态调整学习率。具体来说,如果连续5个epoch中模型在验证集上的表现没有提升,则将学习率乘以gamma。
如果遇到模型训练过程中出现梯度爆炸或梯度弥散的情况,可以尝试使用梯度裁剪或权重衰减等方法进行优化。
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