- Errorneous instantiation in notebook and text. `WeatherPredictionModel` instead of `WeatherPredictionModelBatchNorm`. See https://learn.mimer-ai.eu/deep-learning-intro/3-monitor-the-model/#batchnorm-the-standard-scaler-for-deep-learning - `accumulate` is not the best name for storing targets for inference. `store_targets` maybe? - Arrange order of function parameters in a nicer way and make them consistent across episodes. See `fit` and `fit_with tensorboard`: ```py def fit(model, train_dl, loss_fn, optimizer, val_dl): ... def fit_with_tensorboard(model, train_loader, val_loader, loss_fn, optimizer): ... ```
WeatherPredictionModelinstead ofWeatherPredictionModelBatchNorm. See https://learn.mimer-ai.eu/deep-learning-intro/3-monitor-the-model/#batchnorm-the-standard-scaler-for-deep-learningaccumulateis not the best name for storing targets for inference.store_targetsmaybe?fitandfit_with tensorboard: