a6691d3b19ea8d48ea71e917878bc0cf52aa1af7,mloop/neuralnet.py,NeuralNet,fit_neural_net,#NeuralNet#Any#Any#,615

Before Change

                    net.init()
                    net.fit(train_params, train_costs, self.initial_epochs)
                    this_cv_loss = net.cross_validation_loss(cv_params, cv_costs)
                    if this_cv_loss < best_cv_loss and this_cv_loss < 0.1 * orig_cv_loss:
                        best_cv_loss = this_cv_loss
                        self.log.debug("Switching to reg=" + str(r) + ", cv loss=" + str(best_cv_loss))
                        self.last_net_reg = r
                        self.net.destroy()
                        self.net = net

After Change

                // to a new one if it does better on the cross validation set
                // than the old one.
                previous_regularization = self.last_net_reg
                regularizations = [0.001, 0.01, 0.1, 1, 10]
                if previous_regularization not in regularizations:
                    regularizations.append(previous_regularization)
                cv_losses = []
                best_cv_loss = np.inf
                for r in regularizations:
                    net = self._make_net(r)

Instances