神经架构搜索的深度强化学习的pytorch代码

神经架构搜索（NAS）是一种自动化的机器学习方法，它使用深度强化学习来学习神经网络的结构。以下是一个使用PyTorch实现深度强化学习的神经结构搜索的示例代码：

首先，我们需要定义一个搜索空间。这个搜索空间定义了我们想要搜索的神经网络结构。在这个示例中，我们将使用一个简单的搜索空间，它包含了一些卷积层和全连接层。

import random
import torch.nn as nn

class SearchSpace():
    def __init__(self):
        self.conv_layers = [
            nn.Conv2d(3, 32, 3, padding=1),
            nn.Conv2d(3, 64, 3, padding=1),
            nn.Conv2d(3, 128, 3, padding=1),
        ]
        self.fc_layers = [
            nn.Linear(128 * 8 * 8, 512),
            nn.Linear(128 * 8 * 8, 1024),
            nn.Linear(128 * 8 * 8, 2048),
        ]

    def random_conv_layer(self):
        return random.choice(self.conv_layers)

    def random_fc_layer(self):
        return random.choice(self.fc_layers)

    def random_layer(self):
        if random.random() < 0.5:
            return self.random_conv_layer()
        else:
            return self.random_fc_layer()

接下来，我们需要定义一个代理模型，它将作为我们在搜索过程中评估不同神经网络结构的模型。在这个示例中，我们将使用CIFAR-10数据集来评估每个神经网络结构的性能。

import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms

class ProxyModel():
    def __init__(self, search_space):
        self.search_space = search_space
        self.model = nn.Sequential(
            self.search_space.random_conv_layer(),
            nn.ReLU(),
            nn.MaxPool2d(2),
            self.search_space.random_layer(),
            nn.ReLU(),
            self.search_space.random_layer(),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            self.search_space.random_fc_layer(),
            nn.ReLU(),
            nn.Linear(512, 10),
        )
        self.optimizer = optim.SGD(self.model.parameters(), lr=0.1)
        self.criterion = nn.CrossEntropyLoss()

        transform = transforms.Compose([
            transforms.Resize(32),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ])
        train_set = datasets.CIFAR10(root='./data', train=True,
                                     download=True, transform=transform)
        self.train_loader = data.DataLoader(train_set, batch_size=64,
                                            shuffle=True, num_workers=2)

    def evaluate(self, architecture):
        self.model.load_state_dict(architecture)
        self.model.train()

        for i, (inputs, labels) in enumerate(self.train_loader, 0):
            self.optimizer.zero_grad()
            outputs = self.model(inputs)
            loss = self.criterion(outputs, labels)
            loss.backward()
            self.optimizer.step()
        return loss.item()

接下来，我们需要定义一个环境，它将接收来自代理模型的奖励并返回下一个状态。在这个示例中，我们将使用轮盘赌选择法来选择下一个神经网络结构。

import numpy as np

class Environment():
    def __init__(self, search_space, proxy_model):
        self.search_space = search_space
        self.proxy_model = proxy_model
        self.current_architecture = None

    def reset(self):
        self.current_architecture = {}
        self.current_architecture['conv1'] = self.search_space.random_conv_layer().state_dict()
        self.current_architecture['fc1'] = self.search_space.random_fc_layer().state_dict()
        self.current_architecture['fc2'] = self.search_space.random_fc_layer().state_dict()
        return self.current_architecture

    def step(self, action):
        if action == 0:
            self.current_architecture['conv1'] = self.search_space.random_conv_layer().state_dict()
        elif action == 1:
            self.current_architecture['fc1'] = self.search_space.random_fc_layer().state_dict()
        elif action == 2:
            self.current_architecture['fc2'] = self.search_space.random_fc_layer().state_dict()

        reward = self.proxy_model.evaluate(self.current_architecture)
        next_state = self.current_architecture
        done = False

        return next_state, reward, done

最后，我们需要定义一个智能体，它将使用深度强化学习来搜索最佳神经网络结构。在这个示例中，我们将使用深度Q学习算法。

import torch.nn.functional as F

class Agent():
    def __init__(self, search_space, proxy_model, env):
        self.search_space = search_space
        self.proxy_model = proxy_model
        self.env = env
        self.gamma = 0.9
        self.epsilon = 1.0
        self.epsilon_decay = 0.99
        self.epsilon_min = 0.01
        self.memory = []
        self.batch_size = 32
        self.model = nn.Sequential(
            nn.Linear(768, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 3),
        )
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.randint(3)
        else:
            state_tensor = torch.tensor([list(state['conv1'].values()) +
                                         list(state['fc1'].values()) +
                                         list(state['fc2'].values())])
            q_values = self.model(state_tensor.float())
            return torch.argmax(q_values).item()

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def replay(self):
        if len(self.memory) < self.batch_size:
            return

        batch = random.sample(self.memory, self.batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        state_tensors = torch.tensor([list(state['conv1'].values()) +
                                      list(state['fc1'].values()) +
                                      list(state['fc2'].values()) for state in states])
        action_tensors = torch.tensor(actions)
        reward_tensors = torch.tensor(rewards)
        next_state_tensors = torch.tensor([list(state['conv1'].values()) +
                                            list(state['fc1'].values()) +
                                            list(state['fc2'].values()) for state in next_states])
        done_tensors = torch.tensor(dones)

        q_values = self.model(state_tensors.float())
        next_q_values = self.model(next_state_tensors.float())
        max_next_q_values = torch.max(next_q_values, dim=1)[0]
        expected_q_values = reward_tensors + self.gamma * max_next_q_values * (1 - done_tensors)
        q_value = q_values.gather(1, action_tensors.unsqueeze(1)).squeeze()

        loss = F.smooth_l1_loss(q_value, expected_q_values.detach())
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def train(self, episodes=100):
        for episode in range(episodes):
            state = self.env.reset()
            done = False
            while not done:
                action = self.act(state)
                next_state, reward, done = self.env.step(action)
                self.remember(state, action, reward, next_state, done)
                state = next_state
                self.replay()

现在我们可以训练我们的智能体来搜索最佳神经网络结构：

search_space = SearchSpace()
proxy_model = ProxyModel(search_space)
env = Environment(search_space, proxy_model)
agent = Agent(search_space, proxy_model, env)
agent.train()

请注意，这只是一个简单的示例代码，实际上神经结构搜索可能需要更复杂的搜索空间和代理模型来获得更好的性能。