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dz做的网站容易收录吗,抖音代运营电销话术,商丘seo博客,微分销手机网站制作使用PyTorch构建多轮对话系统的示例代码。这个示例项目包括一个简单的Seq2Seq模型用于对话生成#xff0c;并使用GRU作为RNN的变体。以下是代码的主要部分#xff0c;包括数据预处理、模型定义和训练循环。 数据预处理 首先#xff0c;准备数据并进行预处理。这部分代码假…使用PyTorch构建多轮对话系统的示例代码。这个示例项目包括一个简单的Seq2Seq模型用于对话生成并使用GRU作为RNN的变体。以下是代码的主要部分包括数据预处理、模型定义和训练循环。 数据预处理 首先准备数据并进行预处理。这部分代码假定你有一个对话数据集格式为成对的问答句子。 import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset import numpy as np import random# 假设数据集是一个成对的问答列表 pairs [[Hi, how are you?, Im good, thank you! How about you?],[What is your name?, My name is Chatbot.],# 添加更多对话数据 ]# 简单的词汇表和索引映射 word2index {PAD: 0, SOS: 1, EOS: 2, UNK: 3} index2word {0: PAD, 1: SOS, 2: EOS, 3: UNK} vocab_size len(word2index)def tokenize(sentence):return sentence.lower().split()def build_vocab(pairs):global word2index, index2word, vocab_sizefor pair in pairs:for sentence in pair:for word in tokenize(sentence):if word not in word2index:word2index[word] vocab_sizeindex2word[vocab_size] wordvocab_size 1def sentence_to_tensor(sentence):tokens tokenize(sentence)indices [word2index.get(word, word2index[UNK]) for word in tokens]return torch.tensor(indices [word2index[EOS]], dtypetorch.long)build_vocab(pairs)数据集和数据加载 定义一个Dataset类和DataLoader来加载数据。 class ChatDataset(Dataset):def init(self, pairs):self.pairs pairsdef len(self):return len(self.pairs)def getitem(self, idx):input_tensor sentence_to_tensor(self.pairs[idx][0])target_tensor sentence_to_tensor(self.pairs[idx][1])return input_tensor, target_tensordef collate_fn(batch):inputs, targets zip(*batch)input_lengths [len(seq) for seq in inputs]target_lengths [len(seq) for seq in targets]inputs nn.utils.rnn.pad_sequence(inputs, padding_valueword2index[PAD])targets nn.utils.rnn.pad_sequence(targets, padding_valueword2index[PAD])return inputs, targets, input_lengths, target_lengthsdataset ChatDataset(pairs) dataloader DataLoader(dataset, batch_size2, collate_fncollate_fn, shuffleTrue)模型定义 定义一个简单的Seq2Seq模型包括编码器和解码器。 class Encoder(nn.Module):def init(self, input_size, hidden_size, num_layers1):super(Encoder, self).init()self.embedding nn.Embedding(input_size, hidden_size)self.gru nn.GRU(hidden_size, hidden_size, num_layers)def forward(self, input_seq, input_lengths, hiddenNone):embedded self.embedding(input_seq)packed nn.utils.rnn.pack_padded_sequence(embedded, input_lengths, enforce_sortedFalse)outputs, hidden self.gru(packed, hidden)outputs, _ nn.utils.rnn.pad_packed_sequence(outputs)return outputs, hiddenclass Decoder(nn.Module):def init(self, output_size, hidden_size, num_layers1):super(Decoder, self).init()self.embedding nn.Embedding(output_size, hidden_size)self.gru nn.GRU(hidden_size, hidden_size, num_layers)self.out nn.Linear(hidden_size, output_size)self.softmax nn.LogSoftmax(dim1)def forward(self, input_step, hidden, encoder_outputs):embedded self.embedding(input_step)gru_output, hidden self.gru(embedded, hidden)output self.softmax(self.out(gru_output.squeeze(0)))return output, hiddenclass Seq2Seq(nn.Module):def init(self, encoder, decoder, device):super(Seq2Seq, self).init()self.encoder encoderself.decoder decoderself.device devicedef forward(self, input_tensor, target_tensor, input_lengths, target_lengths, teacher_forcing_ratio0.5):batch_size input_tensor.size(1)max_target_len max(target_lengths)vocab_size self.decoder.out.out_featuresoutputs torch.zeros(max_target_len, batch_size, vocab_size).to(self.device)encoder_outputs, encoder_hidden self.encoder(input_tensor, input_lengths)decoder_input torch.tensor([[word2index[SOS]] * batch_size]).to(self.device)decoder_hidden encoder_hiddenfor t in range(max_target_len):decoder_output, decoder_hidden self.decoder(decoder_input, decoder_hidden, encoder_outputs)outputs[t] decoder_outputtop1 decoder_output.argmax(1)decoder_input target_tensor[t].unsqueeze(0) if random.random() teacher_forcing_ratio else top1.unsqueeze(0)return outputsdevice torch.device(cuda if torch.cuda.is_available() else cpu) encoder Encoder(vocab_size, hidden_size256).to(device) decoder Decoder(vocab_size, hidden_size256).to(device) model Seq2Seq(encoder, decoder, device).to(device)训练循环 定义训练循环并进行模型训练。 def train(model, dataloader, num_epochs, learning_rate0.001):criterion nn.CrossEntropyLoss(ignore_indexword2index[PAD])optimizer optim.Adam(model.parameters(), lrlearning_rate)for epoch in range(num_epochs):model.train()total_loss 0for inputs, targets, input_lengths, target_lengths in dataloader:inputs, targets inputs.to(device), targets.to(device)optimizer.zero_grad()outputs model(inputs, targets, input_lengths, target_lengths)loss criterion(outputs.view(-1, vocab_size), targets.view(-1))loss.backward()optimizer.step()total_loss loss.item()print(fEpoch [{epoch1}/{num_epochs}], Loss: {total_loss/len(dataloader)})train(model, dataloader, num_epochs10)测试与推理 定义一个简单的推理函数来进行对话生成。 def evaluate(model, sentence, max_length10):model.eval()with torch.no_grad():input_tensor sentence_to_tensor(sentence).unsqueeze(1).to(device)input_length [input_tensor.size(0)]encoder_outputs, encoder_hidden model.encoder(input_tensor, input_length)decoder_input torch.tensor([[word2index[SOS]]]).to(device)decoder_hidden encoder_hiddendecoded_words []for _ in range(max_length):decoder_output, decoder_hidden model.decoder(decoder_input, decoder_hidden, encoder_outputs)top1 decoder_output.argmax(1).item()if top1 word2index[EOS]:breakelse:decoded_words.append(index2word[top1])decoder_input torch.tensor([[top1]]).to(device)return .join(decoded_words)print(evaluate(model, Hi, how are you?))总结 这只是一个简单的示例用于展示如何使用PyTorch构建一个基本的多轮对话系统。实际应用中可能需要更多的数据预处理、更复杂的模型如Transformer、更细致的训练策略和优化技术以及更丰富的对话数据集。希望这个示例对你有所帮助