一个简单的古诗生成模型_古诗生成数据集

下面是一个使用Python语言和PyTorch库来创建一个简单的古诗生成模型的示例代码。这个模型使用了一个简单的RNN架构。请注意，这个示例是为了说明目的而简化的，实际应用中可能需要更复杂的模型和更多的调优。首先，确保您已经安装了PyTorch和TorchText库。如果没有安装，可以通过以下命令安装：

pip install torch torchtext
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import WikiText2
from torchtext.data import Field, LabelField, TabularDataset
from torchtext.data.metrics import accuracy

# 定义设备，用于确定是使用CPU还是GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 定义字段，用于处理文本和标签
TEXT = Field(tokenize='spacy', include_lengths=True)
LABEL = LabelField(dtype=torch.int64)

# 创建数据集
train_data, test_data = WikiText2.splits(TEXT, LABEL)

# 定义模型
class SimpleRNN(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(SimpleRNN, self).__init__()
        self.rnn = nn.RNN(input_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
         hidden = self.init_hidden()
         output, hidden = self.rnn(x, hidden)
         output = self.fc(output)
         return output

    def init_hidden(self):
         return torch.zeros(1, 1, self.hidden_dim).to(device)

# 设置模型参数
input_dim = 1000  # 假设词汇表大小为1000
hidden_dim = 128
output_dim = 1000  # 假设输出也是1000个词汇

# 实例化模型
model = SimpleRNN(input_dim, hidden_dim, output_dim)
model.to(device)

# 定义损失函数和优化器
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
model.train()  # 设置为训练模式
for epoch in range(10):  # 训练10个epochs
    for sentence, label in train_data:
        # 将数据转移到设备上
        sentence = sentence.to(device)
        label = label.to(device)

        # 初始化隐藏状态
        hidden = model.init_hidden()

        # 前向传播
        output = model(sentence)

        # 计算损失
        loss = loss_function(output, label)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f'Epoch {epoch+1}/{10} - Loss: {loss.item()}')

# 评估模型
model.eval()  # 设置为评估模式
with torch.no_grad():
    for sentence, label in test_data:
        sentence = sentence.to(device)
        label = label.to(device)

        hidden = model.init_hidden()
        output = model(sentence)

        # 计算准确率
        predicted = output.argmax(1)
        accuracy += (predicted == label).sum().item()

print(f'Accuracy: {accuracy/len(test_data)}')

# 生成古诗
def generate_sentence(model, seed_text, num_words):
    model.eval()
    with torch.no_grad():
        hidden = model.init_hidden()
        sentence = seed_text.split()
        for _ in range(num_words):
            if len(sentence) > 1:
                input_tensor = torch.tensor([[TEXT.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97