def bias_initializer(shape, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units, ), *args, **kwargs),
initializers.Ones()((self.units, ), *args, **kwargs),
self.bias_initializer((self.units * 2, ), *args,
**kwargs),
])
def bias_initializer(_, *args, **kwargs):
return K.concatenate([self.bias_initializer(
(self.n_hidden,), *args, **kwargs),
initializers.Ones()((self.n_hidden,),
*args, **kwargs),
self.bias_initializer(
(self.n_hidden * 2,), *args,
**kwargs)])
def build(self, input_shape):
if len(input_shape) > 2:
raise ValueError("Input to attention layer hasn't been flattened")
self.input_dim = input_shape[-1]
self.kernel = self.add_weight(
shape=(self.input_dim, ),
initializer=initializers.Ones(),
name='kernel',
constraint=constraints.NonNeg()
#constraint=constraints.min_max_norm(min_value=0.0, max_value=1.0)
#constraint=constraints.UnitNorm(axis=self.axis)
)
self.input_spec = InputSpec(min_ndim=2, axes={-1: self.input_dim})
self.built = True
