'Create the computational graph.
: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, 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)
