Aktuell trainiere ich gerade ein neues Modell mit Tensorflow / Keras und ich habe mich gefragt, wozu eigentlich der Validation-Loss ermittelt wird? Also habe ich kurzerhand ChatGPT gefragt und folgende Antwort erhalten:
Validation loss and training loss are two key metrics used in machine learning models, particularly in the context of supervised learning. Here’s a breakdown of the differences between the two:
- Training loss, also known as the training error or empirical loss, is the measure of error or discrepancy between the predicted output of a model and the actual target output during the training phase.
- It is computed by evaluating the model’s performance on the training data that the model has been exposed to during the training process.
- The training loss is used to optimize the model’s parameters, such as adjusting the weights in a neural network, through techniques like gradient descent. The objective is to minimize the training loss, thereby improving the model’s ability to learn and make accurate predictions on the training data.
- Validation loss, also known as the validation error or holdout loss, is the measure of error or discrepancy between the predicted output of a model and the actual target output on a separate dataset called the validation set.
- The validation set is distinct from the training set and is used to assess the model’s generalization ability and performance on unseen data.
- The validation loss helps in monitoring the model’s performance during training and serves as an indicator of how well the model is learning without overfitting to the training data.
- Unlike the training loss, the validation loss is not used directly to update the model’s parameters. Instead, it is used for model selection and hyperparameter tuning to prevent overfitting. Models with low validation loss are typically preferred as they indicate good generalization.
In summary, the training loss measures the model’s performance on the training data, guiding the parameter updates during training. On the other hand, the validation loss assesses the model’s generalization by evaluating its performance on unseen data and helps in selecting the best model configuration.
Genau was ich vermutet habe. „… the validation loss is not used directly to update the model’s parameters.“ schade, dass man die Berechnung von val_loss nicht verhindern kann :-(.