def build_model(self, norm=True, act='relu'):
    'Build DCE using the initialized attributes\n\n        Args:\n            norm: boolean, wheher to add a normalization layer at the begining\n                  of the autoencoder\n            act: string, keras activation function name for autoencoder\n        '
    autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)
    autoencoder.build_model(norm=norm)
    embeding = autoencoder.model.get_layer(name='embedding_layer').output
    clustering = KMeansLayer(self.n_clusters, alpha=self.alpha, name='clustering')(embeding)
    self.model = Model(inputs=autoencoder.model.input, outputs=[clustering, autoencoder.model.output])
    return
Build DCE using the initialized attributes

Args:
    norm: boolean, wheher to add a normalization layer at the begining
          of the autoencoder
    act: string, keras activation function name for autoencoder
def build_model(self, norm=True, act='relu'):
    'Build DCE using the initialized attributes\n\n        Args:\n            norm: boolean, wheher to add a normalization layer at the begining\n                  of the autoencoder\n            act: string, keras activation function name for autoencoder\n        '
    autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)
    autoencoder.build_model(norm=norm)
    embeding = autoencoder.model.get_layer(name='embedding_layer').output
    clustering = KMeansLayer(self.n_clusters, alpha=self.alpha, name='clustering')(embeding)
    self.model = Model(inputs=autoencoder.model.input, outputs=[clustering, autoencoder.model.output])
    return