学习率pytorch
时间: 2023-10-30 17:59:25 浏览: 70
在PyTorch中,学习率是模型训练中的一个重要参数。合理调整学习率可以使优化器快速收敛。一般来说,在训练初期给予较大的学习率,随着训练的进行,学习率逐渐减小。为了方便学习率的调整,PyTorch提供了六种学习率调整方法。其中包括:
1. StepLR:这个方法会根据设定的步长和衰减因子来调整学习率。在每个步长的倍数处,学习率会乘以衰减因子。
2. Warmup:这是一种学习率预热的方法,可以在训练开始时先使用一个较小的学习率,然后再修改为预先设置的学习率进行训练。
3. CosineAnnealingLR:这个方法可以根据余弦函数的形状来调整学习率。学习率会在一个周期内逐渐降低,然后再重新开始一个新的周期。
4. ReduceLROnPlateau:这个方法可以根据模型在验证集上的表现来动态调整学习率。当模型的性能停止改善时,学习率会减小。
5. CyclicLR:这个方法会在指定的范围内不断调整学习率,形成一个周期性的模式。
6. OneCycleLR:这个方法会在一个训练循环中动态调整学习率,使得学习率在训练初期快速增加,然后逐渐减小。
这些学习率调整方法可以根据具体问题和需求选择使用。通过合理调整学习率,可以帮助模型更好地收敛并提升训练效果。
相关问题
动态调整学习率pytorch
可以使用 PyTorch 中的 torch.optim.lr_scheduler 模块来动态调整学习率。该模块提供了多种学习率调度器,如 StepLR、MultiStepLR、ExponentialLR 等。这些调度器可以根据训练的 epoch 数或者训练过程中的某些指标来动态地调整学习率。具体的使用方法可以参考 PyTorch 的官方文档。
使用强化学习优化unet训练的学习率pytorch代码
以下是使用强化学习优化UNet训练的学习率的PyTorch代码,包括深度强化学习部分和UNet训练部分:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import gym
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Actor(nn.Module):
def __init__(self, state_dim, action_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(state_dim, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, action_dim)
def forward(self, state):
x = torch.relu(self.fc1(state))
x = torch.relu(self.fc2(x))
action_prob = torch.softmax(self.fc3(x), dim=-1)
return action_prob
class Critic(nn.Module):
def __init__(self, state_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, state):
x = torch.relu(self.fc1(state))
x = torch.relu(self.fc2(x))
value = self.fc3(x)
return value
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
# define UNet layers
def forward(self, x):
# perform UNet forward pass
return out
# define hyperparameters
state_dim = 10
action_dim = 1
gamma = 0.99
eps = np.finfo(np.float32).eps.item()
actor_lr = 0.001
critic_lr = 0.001
num_episodes = 1000
batch_size = 32
# create actor, critic, and UNet models
actor = Actor(state_dim, action_dim).to(device)
critic = Critic(state_dim).to(device)
unet = UNet().to(device)
# define optimizer for actor and critic
actor_optimizer = optim.Adam(actor.parameters(), lr=actor_lr)
critic_optimizer = optim.Adam(critic.parameters(), lr=critic_lr)
# define environment
env = gym.make('CartPole-v0')
# start training
for i_episode in range(num_episodes):
state = env.reset()
done = False
total_reward = 0
while not done:
# perform UNet forward pass on state to get learning rate
lr = unet(torch.from_numpy(state).float().to(device)).item()
# sample action from actor
action_prob = actor(torch.from_numpy(state).float().to(device))
action_dist = torch.distributions.Categorical(action_prob)
action = action_dist.sample()
# perform action and observe next state and reward
next_state, reward, done, _ = env.step(action.item())
# calculate TD error and update critic
value = critic(torch.from_numpy(state).float().to(device))
next_value = critic(torch.from_numpy(next_state).float().to(device))
td_error = reward + gamma * next_value.item() * (1 - int(done)) - value.item()
critic_loss = td_error**2
critic_optimizer.zero_grad()
critic_loss.backward()
critic_optimizer.step()
# calculate actor loss and update actor
advantage = td_error.detach()
actor_loss = -action_dist.log_prob(action) * advantage
actor_optimizer.zero_grad()
actor_loss.backward()
actor_optimizer.step()
# perform UNet backward pass to update weights
unet_optimizer = optim.Adam(unet.parameters(), lr=lr)
unet_loss = critic_loss
unet_optimizer.zero_grad()
unet_loss.backward()
unet_optimizer.step()
# update state, total reward, and time step
state = next_state
total_reward += reward
# print episode statistics
print("Episode {}: Total Reward = {}".format(i_episode+1, total_reward))
```
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