optimization_evaluate

    def evaluate(self, test_loader, batch_size=1, n_features=1):
        with torch.no_grad():
            predictions = []
            values = []
            for x_test, y_test in test_loader:
                x_test = x_test.view([batch_size, -1, n_features]).to(device)
                y_test = y_test.to(device)
                self.model.eval()
                yhat = self.model(x_test)
                predictions.append(yhat.to(device).detach().numpy())
                values.append(y_test.to(device).detach().numpy())

        return predictions, values