> ## Documentation Index > Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt > Use this file to discover all available pages before exploring further. # Modify the configure_optimizers() behavior of NeuralForecast models > Tutorial on how to achieve a full control of the > `configure_optimizers()` behavior of NeuralForecast models NeuralForecast models allow us to customize the default optimizer and learning rate scheduler behaviors via `optimizer`, `optimizer_kwargs`, `lr_scheduler`, `lr_scheduler_kwargs`. However this is not sufficient to support the use of [ReduceLROnPlateau](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html), for instance, as it requires the specification of `monitor` parameter. This tutorial provides an example of how to support the use of `ReduceLROnPlateau`. ## Load libraries ```python theme={null} import numpy as np import pandas as pd import torch import matplotlib.pyplot as plt from neuralforecast import NeuralForecast from neuralforecast.models import NHITS from neuralforecast.utils import AirPassengersPanel from utilsforecast.plotting import plot_series ``` ```text theme={null} /root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm 2025-02-25 15:57:21,708 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. 2025-02-25 15:57:21,760 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. ``` ## Data We use the AirPassengers dataset for the demonstration of conformal prediction. ```python theme={null} AirPassengersPanel_train = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) AirPassengersPanel_test = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) AirPassengersPanel_test['y'] = np.nan AirPassengersPanel_test['y_[lag12]'] = np.nan ``` ## Model training We now train a NHITS model on the above dataset. We consider two different predictions: 1. Training using the default `configure_optimizers()`. 2. Training by overwriting the `configure_optimizers()` of the subclass of NHITS model. ```python theme={null} horizon = 12 input_size = 24 class CustomNHITS(NHITS): def configure_optimizers(self): optimizer = torch.optim.Adadelta(params=self.parameters(), rho=0.75) scheduler=torch.optim.lr_scheduler.ReduceLROnPlateau( optimizer=optimizer, mode='min',factor=0.5, patience=2, ) scheduler_config = { 'scheduler': scheduler, 'interval': 'step', 'frequency': 1, 'monitor': 'train_loss', 'strict': True, 'name': None, } return {'optimizer': optimizer, 'lr_scheduler': scheduler_config} models = [ NHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-default-scheduler'), CustomNHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-ReduceLROnPlateau-scheduler'), ] nf = NeuralForecast(models=models, freq='ME') nf.fit(AirPassengersPanel_train) preds = nf.predict(futr_df=AirPassengersPanel_test) ``` ```text theme={null} Seed set to 1 Seed set to 1 GPU available: False, used: False TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs | Name | Type | Params | Mode ------------------------------------------------------- 0 | loss | MAE | 0 | train 1 | padder_train | ConstantPad1d | 0 | train 2 | scaler | TemporalNorm | 0 | train 3 | blocks | ModuleList | 2.4 M | train ------------------------------------------------------- 2.4 M Trainable params 0 Non-trainable params 2.4 M Total params 9.751 Total estimated model params size (MB) 34 Modules in train mode 0 Modules in eval mode `Trainer.fit` stopped: `max_steps=100` reached. GPU available: False, used: False TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs | Name | Type | Params | Mode ------------------------------------------------------- 0 | loss | MAE | 0 | train 1 | padder_train | ConstantPad1d | 0 | train 2 | scaler | TemporalNorm | 0 | train 3 | blocks | ModuleList | 2.4 M | train ------------------------------------------------------- 2.4 M Trainable params 0 Non-trainable params 2.4 M Total params 9.751 Total estimated model params size (MB) 34 Modules in train mode 0 Modules in eval mode `Trainer.fit` stopped: `max_steps=100` reached. GPU available: False, used: False TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs GPU available: False, used: False TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs ``` ```text theme={null} Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 2.50it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20]Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 2.49it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20] Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 2.78it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10]Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 2.77it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10] Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 33.39it/s] Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 246.29it/s] ``` ```python theme={null} plot_series(AirPassengersPanel_train, preds) ``` We can clearly notice the prediction outputs are different due to the change in `configure_optimizers()`.