U = torch.cuda.FloatTensor(shape).uniform_()

这是一个 Lua 代码的片段，用于生成一个指定形状的随机浮点型张量。
其中，torch.cuda.FloatTensor(shape) 是构造函数，用于生成指定形状的张量；
uniform_() 是方法，用于在张量上进行均匀分布随机初始化。
由于是在 GPU 上生成张量，故使用了torch.cuda模块。

相关问题

torch.nn.transformer进行文本分类

可以使用torch.nn.transformer来进行文本分类，具体流程如下：

1. 准备数据集，将训练数据和测试数据转化为tensor格式。

2. 构建Transformer模型，可以使用PyTorch提供的预训练模型，也可以自行构建模型。

3. 定义损失函数，常用的有交叉熵损失函数。

4. 定义优化器，常用的有Adam优化器。

5. 进行模型训练，使用训练数据对模型进行训练，并在测试数据上进行测试。

6. 对模型进行评估，可以使用准确率、F1分数等指标进行评估。

下面是一个简单的代码示例，用于实现基于Transformer的文本分类：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import IMDB
from torchtext.data import Field, LabelField, BucketIterator

# 将数据集转换为tensor格式
TEXT = Field(tokenize='spacy')
LABEL = LabelField(dtype=torch.float)

train_data, test_data = IMDB.splits(TEXT, LABEL)

TEXT.build_vocab(train_data, max_size=25000)
LABEL.build_vocab(train_data)

train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data), batch_size=64, device=torch.device('cuda'))

# 定义Transformer模型
class TransformerModel(nn.Module):

    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
        super(TransformerModel, self).__init__()
        from torch.nn import TransformerEncoder, TransformerEncoderLayer
        self.model_type = 'Transformer'
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        self.decoder = nn.Linear(ninp, 1)

        self.init_weights()

    def generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src, src_mask):
        src = self.encoder(src) * math.sqrt(self.ninp)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, src_mask)
        output = output.mean(dim=0)
        output = self.decoder(output)
        return output.squeeze()

# 定义损失函数和优化器
criterion = nn.BCEWithLogitsLoss()
model = TransformerModel(len(TEXT.vocab), 512, 8, 2048, 6, dropout=0.5).to(device)
optimizer = optim.Adam(model.parameters(), lr=0.0005)

# 进行模型训练
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0
    for batch in iterator:
        optimizer.zero_grad()
        src = batch.text
        trg = batch.label
        src_mask = model.generate_square_subsequent_mask(src.shape[0]).to(device)
        output = model(src, src_mask)
        loss = criterion(output, trg)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 在测试数据上进行测试
def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for batch in iterator:
            src = batch.text
            trg = batch.label
            src_mask = model.generate_square_subsequent_mask(src.shape[0]).to(device)
            output = model(src, src_mask)
            loss = criterion(output, trg)
            epoch_loss += loss.item()
    return epoch_loss / len(iterator)

N_EPOCHS = 10
best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):

    train_loss = train(model, train_iterator, optimizer, criterion)
    valid_loss = evaluate(model, test_iterator, criterion)

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut6-model.pt')

    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Val. Loss: {valid_loss:.3f}')

该示例中使用了IMDB数据集来进行文本分类，使用了PyTorch提供的Transformer模型，并使用Adam优化器进行模型训练。在进行模型训练时，使用了交叉熵损失函数来计算损失。最后，通过在测试数据上进行测试，评估了模型的性能。

连续动作空间的a3c代码及pytorch实现

代码如下：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import gym
import random
import time

# Hyperparameters
learning_rate = 0.0001
gamma = 0.98
n_episode = 3000
max_step = 500
epsilon = 0.1

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Replay buffer
class ReplayBuffer():
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = []

    def push(self, transition):
        if len(self.buffer) == self.buffer_size:
            self.buffer.pop(0)
        self.buffer.append(transition)

    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)

    def __len__(self):
        return len(self.buffer)

# Actor-Critic network
class ActorCritic(nn.Module):
    def __init__(self, n_state, n_action):
        super(ActorCritic, self).__init__()
        self.fc1 = nn.Linear(n_state, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.actor = nn.Linear(64, n_action)
        self.critic = nn.Linear(64, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        policy = F.softmax(self.actor(x), dim=1)
        value = self.critic(x)
        return policy, value

# A3C agent
class A3C():
    def __init__(self, n_state, n_action, buffer_size):
        self.n_state = n_state
        self.n_action = n_action
        self.buffer = ReplayBuffer(buffer_size)
        self.actor_critic = ActorCritic(n_state, n_action).to(device)
        self.optimizer = optim.Adam(self.actor_critic.parameters(), lr=learning_rate)

    def select_action(self, state):
        state = torch.FloatTensor(state).unsqueeze(0).to(device)
        policy, _ = self.actor_critic(state)
        action = torch.multinomial(policy, 1).item()
        return action

    def train(self):
        transitions = self.buffer.sample(batch_size)
        batch_state, batch_action, batch_reward, batch_next_state, batch_done = zip(*transitions)
        batch_state = torch.FloatTensor(batch_state).to(device)
        batch_action = torch.LongTensor(batch_action).unsqueeze(1).to(device)
        batch_reward = torch.FloatTensor(batch_reward).unsqueeze(1).to(device)
        batch_next_state = torch.FloatTensor(batch_next_state).to(device)
        batch_done = torch.FloatTensor(batch_done).unsqueeze(1).to(device)

        _, batch_value = self.actor_critic(batch_state)
        _, batch_next_value = self.actor_critic(batch_next_state)

        td_target = batch_reward + gamma * batch_next_value * (1 - batch_done)
        td_error = td_target - batch_value

        # Actor loss
        log_policy, _ = self.actor_critic(batch_state)
        log_policy = torch.log(log_policy.gather(1, batch_action))
        actor_loss = (-log_policy * td_error.detach()).mean()

        # Critic loss
        critic_loss = F.smooth_l1_loss(batch_value, td_target.detach())

        # Total loss
        loss = actor_loss + critic_loss

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def run(self):
        env = gym.make('ContinuousMountainCar-v0')
        for episode in range(n_episode):
            state = env.reset().astype(np.float32)
            episode_reward = 0
            for step in range(max_step):
                if np.random.rand() < epsilon:
                    action = np.random.uniform(-1, 1, (self.n_action,))
                else:
                    action = self.select_action(state)
                next_state, reward, done, _ = env.step(action)
                next_state = next_state.astype(np.float32)
                self.buffer.push((state, action, reward, next_state, done))
                episode_reward += reward
                state = next_state
                if len(self.buffer) > batch_size:
                    self.train()
                if done:
                    break
            print('Episode: {}, Reward: {}'.format(episode, episode_reward))
        env.close()

# Main
if __name__ == '__main__':
    env = gym.make('ContinuousMountainCar-v0')
    n_state = env.observation_space.shape[0]
    n_action = env.action_space.shape[0]
    buffer_size = 10000
    batch_size = 128
    agent = A3C(n_state, n_action, buffer_size)
    agent.run()