def do_attention(self, x):
'''
add none or one of the following attention layers
:param x:
:param att_type:
:return:
'''
att_layer = None
if self.att_type == 'scaled_dot':
att_layer = scaled_dot_attention.ScaledDotProductAttention(
name='Attention')
x = att_layer(x)
# x = GlobalAveragePooling1D()(x)
x = GlobalMaxPooling1D()(x)
elif self.att_type == 'seq_self_attention':
att_layer = seq_self_attention.SeqSelfAttention(
attention_activation='sigmoid')
x = att_layer(x)
# x = GlobalAveragePooling1D()(x)
x = GlobalMaxPooling1D()(x)
elif self.att_type == 'seq_weighted_attention':
att_layer = seq_weighted_attention.SeqWeightedAttention()
x = att_layer(x)
# x = seq_weighted_attention.SeqWeightedAttention()(x)
elif self.att_type == 'attention_with_context':
att_layer = many_to_one_attention_with_context.AttentionWithContext(
)
x = att_layer(x)
return x, att_layer
def add_attention_layer(self, model):
'''
add the attention layer for the keras sequential modeling style
:param model:
:return:
'''
if self.att_type == 'scaled_dot':
model.add(scaled_dot_attention.ScaledDotProductAttention(name='Attention'))
model.add(GlobalAveragePooling1D())
elif self.att_type == 'seq_self_attention':
model.add(seq_self_attention.SeqSelfAttention(attention_activation='sigmoid'))
model.add(GlobalAveragePooling1D())
elif self.att_type == 'seq_weighted_attention':
model.add(seq_weighted_attention.SeqWeightedAttention())
elif self.att_type == 'attention_with_context':
model.add(many_to_one_attention_with_context.AttentionWithContext())