给我一串可运行的神经网络的情感分析代码

好的，这是一段使用PyTorch框架编写的简单情感分析神经网络代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.legacy import data
from torchtext.legacy import datasets

# 设置随机数种子，以便复现结果
SEED = 1234

torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True

# 定义字段
TEXT = data.Field(tokenize = 'spacy', tokenizer_language='en_core_web_sm')
LABEL = data.LabelField(dtype = torch.float)

# 加载IMDb数据集
train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)

# 构建词汇表
MAX_VOCAB_SIZE = 25000

TEXT.build_vocab(train_data, 
                 max_size = MAX_VOCAB_SIZE, 
                 vectors = "glove.6B.100d", 
                 unk_init = torch.Tensor.normal_)

LABEL.build_vocab(train_data)

# 构建迭代器
BATCH_SIZE = 64

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

train_iterator, test_iterator = data.BucketIterator.splits(
    (train_data, test_data), 
    batch_size = BATCH_SIZE, 
    device = device)

# 定义模型
class SentimentNet(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, 
                           hidden_dim, 
                           num_layers=n_layers, 
                           bidirectional=bidirectional, 
                           dropout=dropout)
        self.fc = nn.Linear(hidden_dim * 2, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, text):
        embedded = self.dropout(self.embedding(text))
        output, (hidden, cell) = self.rnn(embedded)
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))
        return self.fc(hidden.squeeze(0))

# 实例化模型
VOCAB_SIZE = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5

model = SentimentNet(VOCAB_SIZE, 
                     EMBEDDING_DIM, 
                     HIDDEN_DIM, 
                     OUTPUT_DIM, 
                     N_LAYERS, 
                     BIDIRECTIONAL, 
                     DROPOUT)

# 加载预训练词向量
pretrained_embeddings = TEXT.vocab.vectors

model.embedding.weight.data.copy_(pretrained_embeddings)

# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()

# 将模型移至GPU
model = model.to(device)
criterion = criterion.to(device)

# 训练模型
def train(model, iterator, optimizer, criterion):
    epoch_loss = 0
    epoch_acc = 0

    model.train()

    for batch in iterator:
        optimizer.zero_grad()
        
        predictions = model(batch.text).squeeze(1)
        
        loss = criterion(predictions, batch.label)
        
        rounded_preds = torch.round(torch.sigmoid(predictions))
        correct = (rounded_preds == batch.label).float()
        acc = correct.sum() / len(correct)
        
        loss.backward()
        
        optimizer.step()
        
        epoch_loss += loss.item()
        epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 测试模型
def evaluate(model, iterator, criterion):
    epoch_loss = 0
    epoch_acc = 0

    model.eval()

    with torch.no_grad():
        for batch in iterator:
            predictions = model(batch.text).squeeze(1)
            
            loss = criterion(predictions, batch.label)
            
            rounded_preds = torch.round(torch.sigmoid(predictions))
            correct = (rounded_preds == batch.label).float()
            acc = correct.sum() / len(correct)

            epoch_loss += loss.item()
            epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 训练模型
N_EPOCHS = 5

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):

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

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut1-model.pt')

    print(f'Epoch: {epoch+1:02}')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')

# 加载训练好的模型
model.load_state_dict(torch.load('tut1-model.pt'))

# 预测新数据
def predict_sentiment(model, sentence):
    model.eval()
    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]
    tensor = torch.LongTensor(indexed).to(device)
    tensor = tensor.unsqueeze(1)
    prediction = torch.sigmoid(model(tensor))
    return prediction.item()

这段代码使用了LSTM神经网络模型对IMDB电影评论进行情感分析，可以输出评论的正面情感概率。请注意，这里使用了PyTorch框架和torchtext库来处理数据集和构建模型，还使用了spaCy库来进行英文分词，需要提前安装以上库。