'Create the computational graph.
This graph is intented to be created for finetuning,
i.e. after unsupervised pretraining.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self'

def build_model(self, n_features, n_classes):

    self._create_placeholders(n_features, n_classes)
    self._create_variables(n_features)
    next_train = self._create_encoding_layers()
    (self.mod_y, _, _) = Layers.linear(next_train, n_classes)
    self.layer_nodes.append(self.mod_y)
    self.cost = self.loss.compile(self.mod_y, self.input_labels)
    self.train_step = self.trainer.compile(self.cost)
    self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
