本模型使用PyTorch实现，具体代码如下：

import torch import torch.nn as nn import torch.optim as optim import numpy as np

定义数据集

with open('pinyin.txt', 'r', encoding='utf-8') as f: data = f.read() chars = list(set(data)) char_to_int = {ch:i for i, ch in enumerate(chars)} int_to_char = {i:ch for i, ch in enumerate(chars)} data_size, vocab_size = len(data), len(chars) print('data has %d characters, %d unique.' % (data_size, vocab_size))

定义一次采样数据形状

seq_length = 50 batch_size = 64 num_batches = int((data_size - 1) / seq_length)

将数据划分为X和Y

X = np.zeros((num_batches, seq_length, vocab_size), dtype=np.float32) Y = np.zeros((num_batches, seq_length, vocab_size), dtype=np.float32) for i in range(num_batches): X_sequence = data[i*seq_length:(i+1)seq_length] X_sequence_int = [char_to_int[ch] for ch in X_sequence] input_sequence = np.zeros((seq_length, vocab_size), dtype=np.float32) for j in range(seq_length): input_sequence[j][X_sequence_int[j]] = 1.0 X[i] = input_sequence Y_sequence = data[iseq_length+1:(i+1)*seq_length+1] Y_sequence_int = [char_to_int[ch] for ch in Y_sequence] output_sequence = np.zeros((seq_length, vocab_size), dtype=np.float32) for j in range(seq_length): output_sequence[j][Y_sequence_int[j]] = 1.0 Y[i] = output_sequence

定义模型

class RNN(nn.Module): def init(self, input_size, hidden_size, output_size, num_layers, batch_size): super(RNN, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.batch_size = batch_size self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size)

def forward(self, x, h):
    out, h = self.rnn(x, h)
    out = out.contiguous().view(-1, self.hidden_size)
    out = self.fc(out)
    return out, h

def init_hidden(self):
    return torch.zeros(self.num_layers, self.batch_size, self.hidden_size)

定义训练函数

def train(model, criterion, optimizer, X, Y): model.train() hidden = model.init_hidden() for i in range(X.shape[0]): optimizer.zero_grad() inputs = torch.from_numpy(X[i]) targets = torch.from_numpy(Y[i]) inputs = inputs.view(1, inputs.shape[0], inputs.shape[1]) targets = targets.view(1, targets.shape[0], targets.shape[1]) hidden = hidden.detach() output, hidden = model(inputs, hidden) loss = criterion(output, targets.view(-1)) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), 5) optimizer.step()

定义预测函数

def predict(model, prefix, k): model.eval() hidden = model.init_hidden() input = torch.zeros(1, 1, vocab_size) for ch in prefix: idx = char_to_int[ch] input[0][0][idx] = 1.0 , hidden = model(input, hidden) input = input.view(1, 1, vocab_size) result = [] for i in range(k): output, hidden = model(input, hidden) output = nn.functional.softmax(output, dim=1) output = output.detach().numpy()[0] output_idx = np.random.choice(range(vocab_size), p=output.ravel()) result.append(int_to_char[output_idx]) input[0][0].zero() input[0][0][output_idx] = 1.0 return ''.join(result)

定义超参数

input_size = vocab_size hidden_size = 128 output_size = vocab_size num_layers = 2 learning_rate = 0.01 num_epochs = 200

定义模型、损失函数和优化器

model = RNN(input_size, hidden_size, output_size, num_layers, batch_size) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate)

训练模型

for epoch in range(num_epochs): train(model, criterion, optimizer, X, Y) if epoch % 10 == 0: prefix = 'ni' k = 10 print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item())) print('Sample:', predict(model, prefix, k)