stoney95

class AttentiveConv(Layer):

    def __init__(self, kernel_activation='tanh', filters=3, **kwargs):
        super(AttentiveConv, self).__init__(**kwargs)
        self.kernel_activation = activations.get(kernel_activation)
        if filters%2 == 0:
            self.filters = filters - 1
        else:
            self.filters = filters
        self.filters = filters

stoney95

from keras.layers import Lambda, Reshape, RepeatVector, Concatenate, Conv1D, Activation
from keras.layers import Layer
from keras import activations

class Attention(Layer):

    def __init__(self, kernel_activation='hard_sigmoid', before=False, **kwargs):
        super(Attention, self).__init__(**kwargs)
        self.kernel_activation = activations.get(kernel_activation)
        K.set_floatx('float32')