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主要思路：
对于唐诗生成来说，我们定义一个"S" 和 "E"作为开始和结束。
示例的唐诗大概有40000多首，
首先数据预处理，将唐诗加载到内存，生成对应的word2idx、idx2word、以及唐诗按顺序的字序列。
运行结果：
代码部分： Dataset_Dataloader.py
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoaderdef deal_tangshi():with open("tangshis.txt", "r", encoding="utf-8") as fr:lines = fr.read().strip().split("\n")tangshis = []for line in lines:splits = line.split(":")if len(splits) != 2:continuetangshis.append("S" + splits[1] + "E")word2idx = {"S": 0, "E": 1}word2idx_count = 2tangshi_ids = []for tangshi in tangshis:for word in tangshi:if word not in word2idx:word2idx[word] = word2idx_countword2idx_count += 1idx2word = {idx: w for w, idx in word2idx.items()}for tangshi in tangshis:tangshi_ids.extend([word2idx[w] for w in tangshi])return word2idx, idx2word, tangshis, word2idx_count, tangshi_idsword2idx, idx2word, tangshis, word2idx_count, tangshi_ids = deal_tangshi()class TangShiDataset(Dataset):def __init__(self, tangshi_ids, num_chars):# 语料数据self.tangshi_ids = tangshi_ids# 语料长度self.num_chars = num_chars# 词的数量self.word_count = len(self.tangshi_ids)# 句子数量self.number = self.word_count // self.num_charsdef __len__(self):return self.numberdef __getitem__(self, idx):# 修正索引值到: [0, self.word_count - 1]start = min(max(idx, 0), self.word_count - self.num_chars - 2)x = self.tangshi_ids[start: start + self.num_chars]y = self.tangshi_ids[start + 1: start + 1 + self.num_chars]return torch.tensor(x), torch.tensor(y)def __test_Dataset():dataset = TangShiDataset(tangshi_ids, 8)x, y = dataset[0]print(x, y)if __name__ == '__main__':# deal_tangshi()__test_Dataset()
TangShiModel.py：唐诗的模型
import torch
import torch.nn as nn
from Dataset_Dataloader import *
import torch.nn.functional as Fclass TangShiRNN(nn.Module):def __init__(self, vocab_size):super().__init__()# 初始化词嵌入层self.ebd = nn.Embedding(vocab_size, 128)# 循环网络层self.rnn = nn.RNN(128, 128, 1)# 输出层self.out = nn.Linear(128, vocab_size)def forward(self, inputs, hidden):embed = self.ebd(inputs)# 正则化层embed = F.dropout(embed, p=0.2)output, hidden = self.rnn(embed.transpose(0, 1), hidden)# 正则化层embed = F.dropout(output, p=0.2)output = self.out(output.squeeze())return output, hiddendef init_hidden(self):return torch.zeros(1, 64, 128)main.py:
import timeimport torchfrom Dataset_Dataloader import *
from TangShiModel import *
import torch.optim as optim
from tqdm import tqdmdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")def train():dataset = TangShiDataset(tangshi_ids, 128)epochs = 100model = TangShiRNN(word2idx_count).to(device)criterion = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(), lr=1e-3)for idx in range(epochs):dataloader = DataLoader(dataset, batch_size=64, shuffle=True, drop_last=True)start_time = time.time()total_loss = 0total_num = 0total_correct = 0total_correct_num = 0hidden = model.init_hidden()for x, y in tqdm(dataloader):x = x.to(device)y = y.to(device)# 隐藏状态hidden = model.init_hidden()hidden = hidden.to(device)# 模型计算output, hidden = model(x, hidden)# print(output.shape)# print(y.shape)# 计算损失loss = criterion(output.permute(1, 2, 0), y)# 梯度清零optimizer.zero_grad()# 反向传播loss.backward()# 参数更新optimizer.step()total_loss += loss.sum().item()total_num += len(y)total_correct_num += y.shape[0] * y.shape[1]# print(output.shape)total_correct += (torch.argmax(output.permute(1, 0, 2), dim=-1) == y).sum().item()print("epoch : %d average_loss : %.3f average_correct : %.3f use_time : %ds" %(idx + 1, total_loss / total_num, total_correct / total_correct_num, time.time() - start_time))torch.save(model.state_dict(), f"./modules/tangshi_module_{idx + 1}.bin")if __name__ == '__main__':train()
predict.py：
import torch
import torch.nn as nn
from Dataset_Dataloader import *
from TangShiModel import *device = torch.device("cuda" if torch.cuda.is_available() else "cpu")def predict():model = TangShiRNN(word2idx_count)model.load_state_dict(torch.load("./modules/tangshi_module_100.bin", map_location=torch.device('cpu')))model.eval()hidden = torch.zeros(1, 1, 128)start_word = input("输入第一个字:")flag = Nonetangshi_strs = []while True:if not flag:outputs, hidden = model(torch.tensor([[word2idx["S"]]], dtype=torch.long), hidden)tangshi_strs.append("S")flag = Trueelse:tangshi_strs.append(start_word)outputs, hidden = model(torch.tensor([[word2idx[start_word]]], dtype=torch.long), hidden)top_i = torch.argmax(outputs, dim=-1)if top_i.item() == word2idx["E"]:breakprint(top_i)start_word = idx2word[top_i.item()]print(tangshi_strs)if __name__ == '__main__':predict()
完整代码如下：
https://github.com/STZZ-1992/tangshi-generator.git
https://github.com/STZZ-1992/tangshi-generator.git