基于pytorch的自动写诗的python代码

好的，下面是一个基于PyTorch的自动写诗的Python代码示例，仅供参考：

import torch
import numpy as np

# 准备诗歌数据集
poems = ['白日依山尽', '黄河入海流', '欲窮千里目', '更上一層樓']

# 定义一个字典，将每个汉字映射成一个数字
word2idx = {}
idx2word = {}
for poem in poems:
    for word in poem:
        if word not in word2idx:
            idx = len(word2idx)
            word2idx[word] = idx
            idx2word[idx] = word

# 将每首诗转换成数字序列
poem_seqs = []
for poem in poems:
    seq = [word2idx[word] for word in poem]
    poem_seqs.append(seq)

# 将数字序列转换成PyTorch可读取的格式
max_len = max(map(len, poem_seqs))
poem_matrix = np.zeros((len(poem_seqs), max_len))
for i, seq in enumerate(poem_seqs):
    poem_matrix[i, :len(seq)] = seq

# 定义模型
class PoetryModel(torch.nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(PoetryModel, self).__init__()
        self.embedding = torch.nn.Embedding(vocab_size, embedding_dim)
        self.rnn = torch.nn.LSTM(embedding_dim, hidden_dim, num_layers=2, batch_first=True)
        self.fc = torch.nn.Linear(hidden_dim, vocab_size)

    def forward(self, x):
        x = self.embedding(x)
        out, _ = self.rnn(x)
        out = self.fc(out)
        return out

# 定义损失函数和优化器
model = PoetryModel(len(word2idx), embedding_dim=128, hidden_dim=256)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(100):
    for i in range(poem_matrix.shape[0]):
        x = torch.LongTensor(poem_matrix[i:i+1])
        y = torch.LongTensor(poem_matrix[i, 1:])
        output = model(x)
        loss = criterion(output[:, :-1, :].permute(0, 2, 1), y.unsqueeze(0))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print('Epoch: {}, Loss: {:.4f}'.format(epoch+1, loss.item()))

# 使用模型进行自动写诗
start_word = '白'
start_idx = word2idx[start_word]
input = torch.LongTensor([[start_idx]])
output = start_word
hidden = None
for i in range(max_len):
    output_probs, hidden = model(input, hidden)
    output_probs = output_probs.squeeze().detach().numpy()
    output_idx = np.random.choice(len(output_probs), p=output_probs)
    output_word = idx2word[output_idx]
    output += output_word
    if output_word == '。':
        break
    input = torch.LongTensor([[output_idx]])
print(output)

请注意，这只是一个简单的示例代码，仅供参考。实际上，自动写诗是一个非常复杂的任务，需要更多的数据和更复杂的模型才能达到较好的效果。如果您有任何疑问，请随时提出。