加入attention的LSTM文本分类
时间: 2023-08-27 20:05:05 浏览: 38
模型
在LSTM模型中加入attention机制可以提高模型的性能,使模型更加关注输入序列中的关键信息。以下是一个加入attention的LSTM文本分类模型的示例:
1. 定义输入和嵌入层
```python
import tensorflow as tf
vocab_size = 10000
embedding_size = 128
sequence_length = 100
inputs = tf.keras.Input(shape=(sequence_length,))
x = tf.keras.layers.Embedding(vocab_size, embedding_size)(inputs)
```
2. 定义LSTM层
```python
lstm_units = 64
lstm = tf.keras.layers.LSTM(lstm_units, return_sequences=True)(x)
```
3. 定义attention机制
```python
from tensorflow.keras import backend as K
attention_units = 32
# 计算权重
attention = tf.keras.layers.Dense(attention_units, activation='tanh')(lstm)
attention = tf.keras.layers.Dense(1, activation='softmax')(attention)
attention = tf.keras.layers.Flatten()(attention)
attention = tf.keras.layers.RepeatVector(lstm_units)(attention)
attention = tf.keras.layers.Permute([2, 1])(attention)
# 应用权重
sent_representation = tf.keras.layers.Multiply()([lstm, attention])
sent_representation = tf.keras.layers.Lambda(lambda xin: K.sum(xin, axis=-2), output_shape=(lstm_units,))(sent_representation)
```
4. 定义输出层和模型
```python
num_classes = 2
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(sent_representation)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
```
5. 编译和训练模型
```python
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_x, train_y, validation_data=(val_x, val_y), epochs=10, batch_size=32)
```
可以根据实际情况调整模型结构和超参数,以获得更好的性能。