transformer-lstm代码

### Transformer 结合 LSTM 的模型实现 以下是基于 Python 和深度学习框架（如 TensorFlow 或 PyTorch）的 Transformer-LSTM 组合模型代码实现： #### 使用 TensorFlow/Keras 实现 Transformer-LSTM 模型 ```python import tensorflow as tf from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, MultiHeadAttention, LayerNormalization, Dropout from tensorflow.keras.models import Model def transformer_lstm_model(vocab_size, max_len, d_model=128, num_heads=8, ff_dim=512, lstm_units=64): inputs = Input(shape=(max_len,)) # 嵌入层 embedding_layer = Embedding(input_dim=vocab_size, output_dim=d_model)(inputs) # Positional Encoding (可选，视需求而定) pos_encoding = positional_encoding(max_len, d_model) x = embedding_layer + pos_encoding # 多头注意力机制 attention_output = MultiHeadAttention(num_heads=num_heads, key_dim=d_model)(x, x) attention_output = Dropout(0.1)(attention_output) out1 = LayerNormalization()(attention_output + x) # Feed Forward Network ffn_output = tf.keras.Sequential([ Dense(ff_dim, activation="relu"), Dense(d_model), Dropout(0.1) ])(out1) out2 = LayerNormalization()(ffn_output + out1) # 添加LSTM层 lstm_out = LSTM(lstm_units, return_sequences=False)(out2) # 输出层 outputs = Dense(vocab_size, activation='softmax')(lstm_out) model = Model(inputs=inputs, outputs=outputs) return model # 辅助函数：位置编码 def get_angles(pos, i, d_model): angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model)) return pos * angle_rates def positional_encoding(position, d_model): angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model) # 将 sin 应用于数组中的偶数索引处 angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2]) # 将 cos 应用于数组中的奇数索引处 angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2]) pos_encoding = angle_rads[np.newaxis, ...] return tf.cast(pos_encoding, dtype=tf.float32) model = transformer_lstm_model(vocab_size=10000, max_len=100) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) ``` 上述代码展示了如何通过 Keras 构建一个结合了多头自注意力机制和 LSTM 层的混合架构。此方法适用于序列预测任务或其他涉及时间依赖性的场景。 #### 使用 PyTorch 实现 Transformer-LSTM 模型 ```python import torch import torch.nn as nn class TransformerLSTMModel(nn.Module): def __init__(self, vocab_size, embed_size, num_heads, hidden_size, lstm_hidden_size, dropout=0.1): super(TransformerLSTMModel, self).__init__() self.embedding = nn.Embedding(vocab_size, embed_size) self.transformer_encoder = nn.TransformerEncoderLayer( d_model=embed_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout ) self.lstm = nn.LSTM(embed_size, lstm_hidden_size, batch_first=True) self.fc = nn.Linear(lstm_hidden_size, vocab_size) def forward(self, x): embedded = self.embedding(x) transformed = self.transformer_encoder(embedded.permute(1, 0, 2)).permute(1, 0, 2) lstm_out, _ = self.lstm(transformed) output = self.fc(lstm_out[:, -1, :]) # 取最后一个时间步的输出 return output vocab_size = 10000 embed_size = 128 num_heads = 8 hidden_size = 512 lstm_hidden_size = 64 model = TransformerLSTMModel(vocab_size, embed_size, num_heads, hidden_size, lstm_hidden_size) loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) ``` 以上代码实现了使用 PyTorch 创建的一个简单 Transformer-LSTM 混合网络结构。该模型能够利用 Transformer 提取特征的能力以及 LSTM 对于时间序列数据的记忆特性[^3]。 ### 注意事项 在实际应用中，可能需要调整超参数以适应具体的数据集和任务目标。此外，由于 Transformer 和 LSTM 都属于计算密集型组件，因此建议在 GPU 上运行此类模型以加速训练过程[^1]。