README.zh-CN.md

Keras自注意力

[中文|English]

Attention mechanism for processing sequential data that considers the context for each timestamp.

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安装

pip install keras-self-attention

使用

基本

默认情况下，注意力层使用加性注意力机制，并使用全部上下文进行计算。下面的代码根据页首的公式创建了一个注意力层（attention_activation是注意力权重e_{t, t'}）：

import keras
from keras_self_attention import SeqSelfAttention


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=10000,
                                 output_dim=300,
                                 mask_zero=True))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128,
                                                       return_sequences=True)))
model.add(SeqSelfAttention(attention_activation='sigmoid'))
model.add(keras.layers.Dense(units=5))
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['categorical_accuracy'],
)
model.summary()

局部注意力

参数attention_width控制着局部注意力的宽度：

from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_activation='sigmoid',
    name='Attention',
)

乘性注意力

用attention_type来改变注意力机制的计算方法：

from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
    attention_activation=None,
    kernel_regularizer=keras.regularizers.l2(1e-6),
    use_attention_bias=False,
    name='Attention',
)

正则化

通过将attention_regularizer_weight设置为一个正数来使用正则化：

import keras
from keras_self_attention import SeqSelfAttention

inputs = keras.layers.Input(shape=(None,))
embd = keras.layers.Embedding(input_dim=32,
                              output_dim=16,
                              mask_zero=True)(inputs)
lstm = keras.layers.Bidirectional(keras.layers.LSTM(units=16,
                                                    return_sequences=True))(embd)
att = SeqSelfAttention(attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
                       kernel_regularizer=keras.regularizers.l2(1e-4),
                       bias_regularizer=keras.regularizers.l1(1e-4),
                       attention_regularizer_weight=1e-4,
                       name='Attention')(lstm)
dense = keras.layers.Dense(units=5, name='Dense')(att)
model = keras.models.Model(inputs=inputs, outputs=[dense])
model.compile(
    optimizer='adam',
    loss={'Dense': 'sparse_categorical_crossentropy'},
    metrics={'Dense': 'categorical_accuracy'},
)
model.summary(line_length=100)

加载模型

Make sure to add SeqSelfAttention to custom objects:

import keras

keras.models.load_model(model_path, custom_objects=SeqSelfAttention.get_custom_objects())

只使用历史进行计算

对于decoder等场景，为了保持输出固定只能使用上文的信息：

SeqSelfAttention(
    attention_width=3,
    history_only=True,
    name='Attention',
)

多头注意力

参考keras-multi-head。