本地词向量文件加载到情感分类模型中的pytorch代码

加载本地词向量文件到情感分类模型中的PyTorch代码可以按照以下步骤进行：

1. 导入必要的库

import torch
import torch.nn as nn
import numpy as np

2. 定义情感分类模型

class SentimentClassifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, embeddings):
        super(SentimentClassifier, self).__init__()

        self.embedding_dim = embeddings.shape[1]
        self.embedding = nn.Embedding(input_dim, self.embedding_dim)
        self.embedding.weight.data.copy_(torch.from_numpy(embeddings))
        self.embedding.weight.requires_grad = False

        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(self.embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, text, text_lengths):
        embedded = self.embedding(text)
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths.cpu(), batch_first=True)
        packed_output, (hidden, cell) = self.lstm(packed_embedded)
        output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output, batch_first=True)
        hidden = self.fc(torch.mean(output, dim=1))
        return hidden

3. 加载本地词向量文件

def load_embeddings(embedding_file):
    with open(embedding_file, 'r') as f:
        embeddings = {}
        for line in f:
            values = line.strip().split()
            word = values[0]
            vector = np.asarray(values[1:], dtype='float32')
            embeddings[word] = vector
    return embeddings

4. 准备数据并创建模型实例

# 准备数据
vocab_size = len(vocab)
hidden_dim = 256
output_dim = 2
embeddings_file = 'word_embeddings.txt'
embeddings = load_embeddings(embeddings_file)

# 创建模型实例
model = SentimentClassifier(vocab_size, hidden_dim, output_dim, embeddings)

其中，`vocab` 是一个词汇表，可以使用 PyTorch 中的 `torchtext` 库来创建。`word_embeddings.txt` 是包含每个单词的向量表示的本地文件。

5. 训练和评估模型

使用 `torch.optim` 库中的优化器来训练模型，并使用 `torch.utils.data` 库中的数据加载器来加载数据。最后，使用测试集来评估模型的性能。

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 定义训练循环
def train(model, iterator, optimizer, criterion):
    model.train()
    for batch in iterator:
        text, text_lengths = batch.text
        optimizer.zero_grad()
        predictions = model(text, text_lengths).squeeze(1)
        loss = criterion(predictions, batch.label)
        loss.backward()
        optimizer.step()

# 定义评估函数
def evaluate(model, iterator, criterion):
    model.eval()
    total_loss = 0
    total_correct = 0
    with torch.no_grad():
        for batch in iterator:
            text, text_lengths = batch.text
            predictions = model(text, text_lengths).squeeze(1)
            loss = criterion(predictions, batch.label)
            total_loss += loss.item()
            total_correct += (predictions.argmax(1) == batch.label).sum().item()
    return total_loss / len(iterator), total_correct / len(iterator.dataset)

# 训练模型
N_EPOCHS = 10
for epoch in range(N_EPOCHS):
    train(model, train_iterator, optimizer, criterion)
    train_loss, train_acc = evaluate(model, train_iterator, criterion)
    test_loss, test_acc = evaluate(model, test_iterator, criterion)
    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}% | Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')