def build_model(self, n_features, encoding_w=None, encoding_b=None):
        """Create the computational graph for the reconstruction task.

        :param n_features: Number of features
        :param encoding_w: list of weights for the encoding layers.
        :param encoding_b: list of biases for the encoding layers.
        :return: self
        """
        self._create_placeholders(n_features, n_features)

        if encoding_w and encoding_b:
            self.encoding_w_ = encoding_w
            self.encoding_b_ = encoding_b
        else:
            self._create_variables(n_features)

        self._create_encoding_layers()
        self._create_decoding_layers()

        variables = []
        variables.extend(self.encoding_w_)
        variables.extend(self.encoding_b_)
        regterm = Layers.regularization(variables, self.regtype, self.regcoef)

        self.cost = self.loss.compile(
            self.reconstruction, self.input_labels, regterm=regterm)
        self.train_step = self.trainer.compile(self.cost)
def build_model ( self , n_features , encoding_w = None , encoding_b = None ) : self . _create_placeholders ( n_features , n_features ) if encoding_w and encoding_b : self . encoding_w_ = encoding_w self . encoding_b_ = encoding_b else : self . _create_variables ( n_features ) self . _create_encoding_layers ( ) self . _create_decoding_layers ( ) variables = [ ] variables . extend ( self . encoding_w_ ) variables . extend ( self . encoding_b_ ) regterm = Layers . regularization ( variables , self . regtype , self . regcoef ) self . cost = self . loss . compile ( self . reconstruction , self . input_labels , regterm = regterm ) self . train_step = self . trainer . compile ( self . cost )
Create the computational graph for the reconstruction task.

        :param n_features: Number of features
        :param encoding_w: list of weights for the encoding layers.
        :param encoding_b: list of biases for the encoding layers.
        :return: self