def build(self, input_shape):
self.input_spec = [InputSpec(dtype=K.floatx(),
shape=(None, input_shape[0][1], input_shape[0][2])),
InputSpec(dtype=K.floatx(),
shape=(None, input_shape[1][1], input_shape[1][2])),
InputSpec(dtype=K.floatx(),
shape=(None, input_shape[2][1], input_shape[2][2]))]
self.W_h = self.init((self.nb_feature, input_shape[0][2], self.output_dim),
name='{}_W_h'.format(self.name))
self.W_y = self.init((self.nb_feature, input_shape[1][2], self.output_dim),
name='{}_W_y'.format(self.name))
self.W_c = self.init((self.nb_feature, input_shape[2][2], self.output_dim),
name='{}_W_c'.format(self.name))
trainable = [self.W_h, self.W_y, self.W_c]
if self.bias:
self.b = K.zeros((self.nb_feature, self.output_dim),
name='{}_b'.format(self.name))
self.trainable_weights = trainable + [self.b]
else:
self.trainable_weights = trainable
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