python zip 用代码举例子详细说明
时间: 2023-04-08 16:04:42 浏览: 58
当输入一个列表和一个元组时,zip 函数可以将它们打包成一个元组列表,其中每个元组包含来自每个列表的相应元素。以下是一个示例代码:
```
list1 = [1, 2, 3]
tuple1 = ('a', 'b', 'c')
zipped = zip(list1, tuple1)
print(list(zipped))
```
输出结果为:
```
[(1, 'a'), (2, 'b'), (3, 'c')]
```
在这个示例中,zip 函数将列表 list1 和元组 tuple1 打包成一个元组列表,并将其存储在变量 zipped 中。然后,我们使用 list 函数将 zipped 转换为列表,并将其打印出来。
相关问题
请举一个代码的例子详细说明
以下是一个简单的SAC算法代码实现,其中包含了reward scaling的实现:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class Actor(nn.Module):
def __init__(self, input_dim, output_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, output_dim)
def forward(self, state):
x = torch.relu(self.fc1(state))
x = torch.relu(self.fc2(x))
x = torch.tanh(self.fc3(x))
return x
class Critic(nn.Module):
def __init__(self, input_dim, output_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(input_dim + output_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, state, action):
x = torch.cat([state, action], 1)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
class SAC:
def __init__(self, state_dim, action_dim, gamma=0.99, alpha=0.2):
self.actor = Actor(state_dim, action_dim)
self.actor_target = Actor(state_dim, action_dim)
self.critic1 = Critic(state_dim, action_dim)
self.critic2 = Critic(state_dim, action_dim)
self.critic1_target = Critic(state_dim, action_dim)
self.critic2_target = Critic(state_dim, action_dim)
self.gamma = gamma
self.alpha = alpha
self.actor_optim = optim.Adam(self.actor.parameters(), lr=1e-3)
self.critic1_optim = optim.Adam(self.critic1.parameters(), lr=1e-3)
self.critic2_optim = optim.Adam(self.critic2.parameters(), lr=1e-3)
def select_action(self, state):
state = torch.tensor(state, dtype=torch.float32)
action = self.actor(state)
return action.detach().numpy()
def update(self, memory, batch_size):
state, action, reward, next_state, done = memory.sample(batch_size)
state = torch.tensor(state, dtype=torch.float32)
action = torch.tensor(action, dtype=torch.float32)
reward = torch.tensor(reward, dtype=torch.float32)
next_state = torch.tensor(next_state, dtype=torch.float32)
done = torch.tensor(done, dtype=torch.float32)
with torch.no_grad():
next_action = self.actor_target(next_state)
q1_next_target = self.critic1_target(next_state, next_action)
q2_next_target = self.critic2_target(next_state, next_action)
q_next_target = torch.min(q1_next_target, q2_next_target)
target = reward + (1 - done) * self.gamma * (q_next_target - self.alpha * torch.log(self.actor(next_state)))
q1 = self.critic1(state, action)
q2 = self.critic2(state, action)
critic1_loss = nn.functional.mse_loss(q1, target)
critic2_loss = nn.functional.mse_loss(q2, target)
self.critic1_optim.zero_grad()
critic1_loss.backward()
self.critic1_optim.step()
self.critic2_optim.zero_grad()
critic2_loss.backward()
self.critic2_optim.step()
if np.random.random() < 0.5:
actor_loss = -(self.critic1(state, self.actor(state)) - self.alpha * torch.log(self.actor(state))).mean()
else:
actor_loss = -(self.critic2(state, self.actor(state)) - self.alpha * torch.log(self.actor(state))).mean()
self.actor_optim.zero_grad()
actor_loss.backward()
self.actor_optim.step()
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(0.995 * target_param.data + 0.005 * param.data)
for param, target_param in zip(self.critic1.parameters(), self.critic1_target.parameters()):
target_param.data.copy_(0.995 * target_param.data + 0.005 * param.data)
for param, target_param in zip(self.critic2.parameters(), self.critic2_target.parameters()):
target_param.data.copy_(0.995 * target_param.data + 0.005 * param.data)
return critic1_loss.item(), critic2_loss.item(), actor_loss.item()
```
在这个SAC算法中,我们可以看到在update函数中,对reward进行了放缩。具体来说,在计算target值时,奖励值reward被乘上了一个(1 - done)的因子,同时除以了温度参数alpha的值,以控制策略的熵惩罚项大小。这个放缩的过程可以在以下代码中找到:
```
target = reward + (1 - done) * self.gamma * (q_next_target - self.alpha * torch.log(self.actor(next_state)))
```
其中,reward被乘上了(1 - done)的因子,除以了alpha的值,以控制策略的熵惩罚项大小。这样可以使得奖励值的范围变得更加合理,从而更好地适应神经网络模型的训练。
用python举个例子
当然,以下是使用 Python 进行文件压缩的示例代码:
```python
import zipfile
def zip_file(file_path, zip_path):
"""将文件 file_path 压缩为 zip_path"""
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
zipf.write(file_path)
# 示例:将 test.txt 压缩为 test.zip
zip_file('test.txt', 'test.zip')
```
首先,我们需要导入 `zipfile` 模块。然后,定义一个 `zip_file` 函数,接收两个参数:`file_path` 表示要压缩的文件路径,`zip_path` 表示压缩后的文件路径。
在函数内部,我们使用 `with` 语句打开一个 `ZipFile` 对象,并将其指定为写模式。`ZIP_DEFLATED` 表示使用默认压缩算法。
最后,我们调用 `write` 方法将文件添加到压缩文件中,并在最后关闭 `ZipFile` 对象。
在示例中,我们将 `test.txt` 文件压缩为 `test.zip` 文件。
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