Keras Self-Attention
Attention mechanism for processing sequential data that considers the context for each timestamp.
Install
pip install keras-self-attention
Usage
Basic
By default, the attention layer uses additive attention and considers the whole context while calculating the relevance. The following code creates an attention layer that follows the equations in the first section (attention_activation is the activation function of 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()
Local Attention
The global context may be too broad for one piece of data. The parameter attention_width controls the width of the local context:
from keras_self_attention import SeqSelfAttention
SeqSelfAttention(
attention_width=15,
attention_activation='sigmoid',
name='Attention',
)
Multiplicative Attention
You can use multiplicative attention by setting attention_type:
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',
)
Regularizer
To use the regularizer, set attention_regularizer_weight to a positive number:
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)
Load the Model
Make sure to add SeqSelfAttention to custom objects:
import keras
keras.models.load_model(model_path, custom_objects=SeqSelfAttention.get_custom_objects())
History Only
Set history_only to True when only historical data could be used:
SeqSelfAttention(
attention_width=3,
history_only=True,
name='Attention',
)
Multi-Head
Please refer to keras-multi-head.
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Metadata
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