nnlm代码解读链接

http://blog.youkuaiyun.com/a635661820/article/details/44730507
### NNLM模型 Python代码实现示例 下面展示了一个基于PyTorch框架的简单NNLM(神经网络语言模型)实现。此模型遵循Bengio等人提出的结构,采用三层全连接神经网络来预测下一个词的概率分布[^4]。 ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset class NNLMDataset(Dataset): def __init__(self, data, context_size=2): self.context_size = context_size words, contexts = zip(*[ (data[i], tuple(data[j] for j in range(i-self.context_size, i))) for i in range(context_size, len(data)) ]) self.words = list(words) self.contexts = list(contexts) def __len__(self): return len(self.words) def __getitem__(self, idx): return self.contexts[idx], self.words[idx] class NeuralNetworkLanguageModel(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim, context_size): super(NeuralNetworkLanguageModel, self).__init__() self.embeddings = nn.Embedding(vocab_size, embedding_dim) self.linear1 = nn.Linear(embedding_dim * context_size, hidden_dim) self.linear2 = nn.Linear(hidden_dim, vocab_size) def forward(self, inputs): embeds = self.embeddings(inputs).view((inputs.shape[0], -1)) out = torch.relu(self.linear1(embeds)) out = self.linear2(out) log_probs = torch.log_softmax(out, dim=-1) return log_probs def train_nnlm(model, dataset, num_epochs=5, learning_rate=0.01): criterion = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=learning_rate) dataloader = DataLoader(dataset, batch_size=8, shuffle=True) model.train() for epoch in range(num_epochs): total_loss = 0 for word_contexts, target_words in dataloader: model.zero_grad() log_probs = model(word_contexts) loss = criterion(log_probs, target_words) loss.backward() optimizer.step() total_loss += loss.item() print(f'Epoch {epoch}: Loss {total_loss / len(dataloader)}') if __name__ == "__main__": # 假设有一个简单的语料库用于训练 corpus = ["the", "quick", "brown", "fox", "jumps", "over", "lazy", "dog", "and", "then", "runs", "away"] unique_words = set(corpus) word_to_ix = {word: i for i, word in enumerate(unique_words)} ix_to_word = {i: word for word, i in word_to_ix.items()} encoded_corpus = [word_to_ix[word] for word in corpus] VOCAB_SIZE = len(unique_words) EMBEDDING_DIM = 10 CONTEXT_SIZE = 2 dataset = NNLMDataset(encoded_corpus, context_size=CONTEXT_SIZE) model = NeuralNetworkLanguageModel(VOCAB_SIZE, EMBEDDING_DIM, hidden_dim=128, context_size=CONTEXT_SIZE) train_nnlm(model, dataset) ``` 上述代码定义了`NeuralNetworkLanguageModel`类作为NNLM的核心组件,并实现了相应的训练函数`train_nnlm()`。为了简化说明过程中的复杂度,在这里构建了一个非常基础的数据集处理逻辑以及相对较小规模的实验环境来进行演示。
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