python代码不用tensorflow

如果你不想使用TensorFlow，你可以选择PyTorch作为深度学习框架，同样可以实现LSTM-Transformer混合模型。以下是使用PyTorch的一个简单示例：

import torch
import torch.nn as nn

class CustomModel(nn.Module):
    def __init__(self, vocab_size, embedding_size, lstm_units, d_model, num_heads, n_transformer_layers):
        super(CustomModel, self).__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_size)
        self.lstm = nn.LSTM(embedding_size, lstm_units, bidirectional=True)
        self.transformer_encoder = nn.TransformerEncoderLayer(d_model, num_heads)
        self.transformer_encoder_stack = nn.TransformerEncoder(self.transformer_encoder, n_transformer_layers)
        self.dense = nn.Linear(lstm_units * 2 + d_model, output_size)  # 综合LSTM和Transformer的结果

    def forward(self, inputs):
        embedded = self.embedding(inputs)  # 转换为嵌入向量
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, lengths, enforce_sorted=False)  # 将填充后的序列打包
        packed_output, _ = self.lstm(packed_embedded)  # LSTMTokenEmbedder的前向传播
        packed_output, _ = nn.utils.rnn.pad_packed_sequence(packed_output)  # 解包并恢复原始形状
        sequence_output = packed_output[:, -1]  # 取最后一个时间步的输出
        transformer_output = self.transformer_encoder_stack(sequence_output)  # Transformer编码
        combined_output = torch.cat((sequence_output, transformer_output), dim=-1)  # 结合LSTM和Transformer结果
        final_output = self.dense(combined_output)  # 输出层
        return final_output

# 初始化模型参数
model = CustomModel(vocab_size, embedding_size, lstm_units, d_model, num_heads, n_transformer_layers)
# 加载数据、设置优化器等步骤
optimizer = torch.optim.Adam(model.parameters())
loss_fn = nn.CrossEntropyLoss()

# ...其他训练和预测部分

在这个代码里，我们定义了一个名为`CustomModel`的自定义网络，它包含嵌入、双向LSTM、Transformer编码器和全连接层。请注意，你需要将`lengths`替换为你的实际序列长度列表。