def build_model(self, n_features, n_classes):
        """Create the computational graph.

        :param n_features: number of features
        :param n_classes: number of classes
        :return: self
        """
        self._create_placeholders(n_features, n_classes)
        self._create_variables(n_features, n_classes)

        self.mod_y = tf.nn.softmax(
            tf.add(tf.matmul(self.input_data, self.W_), self.b_))

        self.cost = self.loss.compile(self.mod_y, self.input_labels)
        self.train_step = tf.train.GradientDescentOptimizer(
            self.learning_rate).minimize(self.cost)
        self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
Create the computational graph.

        :param n_features: number of features
        :param n_classes: number of classes
        :return: self
        
yadlt/models/linear/logistic_regression.py