import torch
import torch.nn as nn
import torch.optim as optim
 
# 假设我们已经有了预处理好的古诗数据集
# vocab_size：词汇表大小
# embed_dim：词嵌入维度
# hidden_dim：LSTM隐藏层维度
# num_layers：LSTM层数
# num_epochs：训练轮数
 
vocab_size = 10000
embed_dim = 256
hidden_dim = 512
num_layers = 2
num_epochs = 10
 
# 定义LSTM模型
class PoetryLSTM(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, num_layers):
        super(PoetryLSTM, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.lstm = nn.LSTM(embed_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)
 
    def forward(self, x):
        h0 = torch.zeros(num_layers, x.size(0), hidden_dim).to(device)
        c0 = torch.zeros(num_layers, x.size(0), hidden_dim).to(device)
       
        x = self.embedding(x)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out
 
# 初始化模型、损失函数和优化器
model = PoetryLSTM(vocab_size, embed_dim, hidden_dim, num_layers).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
 
# 训练模型
for epoch in range(num_epochs):
    # 训练代码...
    pass
 
# 使用模型生成诗句
def generate_poem(model, start_words, num_generate=10):
    # 生成代码...
    pass
 
# 示例：生成藏头诗
start_words = ['春', '江', '水', '暖', '鸭', '先', '知']
generated_poem = generate_poem(model, start_words)
print(generated_poem)