Validation loss

Q: How deeply does Deep Learning with PyTorch, Second Edition cover validation loss?

Defines the metric, compares its trend with training loss, and demonstrates epoch-by-epoch computation without detailing loss-function internals.

As explained in Deep Learning with PyTorch, Second Edition

Loss computed on held-out validation data.

Validation loss measures how poorly a model performs on held-out data that it did not use for training. It shows whether improvements on training samples generalize to unseen samples. section 5.5.3

Compare validation loss with training loss to interpret learning:

If both losses decrease together, the model is learning and generalizing. section 5.5.3
If training loss decreases while validation loss increases, the model is overfitting. section 5.5.3
If neither loss decreases, the data may lack useful information or the model may have insufficient capacity. section 5.5.3

Evaluating each training epoch

Split the data into training and validation subsets, compute validation predictions with the current model parameters, and evaluate them with the same loss function. Print both values to monitor their trends.

How Deep Learning with PyTorch, Second Edition covers it

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Also explained in

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Deep Learning with R, Third EditionValidation loss is the loss value computed from a model's predictions on validation data.
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Deep Learning with Python, Third EditionThe loss value computed on validation data to estimate how well a model generalizes beyond the training set.
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LLMs in ProductionValidation loss is the loss computed on held-out validation examples to assess a model during development.
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Generative AI in ActionValidation loss is a metric measuring a model’s prediction error on examples held out for validation.

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Related concepts

objective functioncontrasts with · An objective function assigns values to candidate solutions, predictions, or parameter settings according to a criterion to be minimized or maximized.
generalizationmeasures · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
overfittingmeasures · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
evaluation datasetmeasures · An evaluation dataset is a collection of examples or test cases, often with expected or judged outcomes, designated to assess a model or system against defined criteria.
scaling lawmeasured by · A scaling law is an empirical relationship describing how a system’s properties change as its scale or resource levels vary.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.