Evaluation mode
Evaluation mode is a model-execution state that changes or disables training-specific behaviors for evaluation or inference.
Inside Deep Learning: Math, Algorithms, Models
A model setting used during inference or inspection that disables training-specific behavior such as dropout-like effects.
- How it is used
- Uses evaluation mode for held-out scoring, generation, pretrained detection, and inspection of predictions or attention.
- How deep it goes
- Explains the mode through PyTorch calls and workflows but does not detail the internal layers affected.
- Examples
- Demonstrates validation metrics, text sampling, Faster R-CNN predictions, translation outputs, and attention heatmaps.
- What you can do after
- Apply it via PyTorch to switch trained models into inference behavior before evaluating, generating, detecting, or inspecting outputs.
Read how Inside Deep Learning: Math, Algorithms, Models explains evaluation mode →
Designing Deep Learning Systems: A guide for software engineers
A model execution mode that disables training-specific behavior so a neural network can produce inference results.
- How it is used
- Runs trained neural networks for prediction inside model-serving workflows.
- How deep it goes
- Contrasts parameter-updating learning mode with closed-loop execution and applies the distinction to predictor-container design.
- Examples
- Uses predictor containers, single-model web applications, and face-swap applications with locally embedded models.
- What you can do after
- Operate/configure a predictor by executing its trained neural network without parameter updates.
Read how Designing Deep Learning Systems: A guide for software engineers explains evaluation mode →
Deep Learning with PyTorch, Second Edition
A model setting that switches layers such as dropout or batch normalization into inference behavior.
- How it is used
- Deploys trained models for inference and evaluates them on validation data without training-time layer behavior.
- How deep it goes
- Explains why dropout and batch normalization require inference behavior, and demonstrates the mode change without detailing their internals.
- Examples
- Demonstrates `vit.eval()` for ImageNet classification and `model.eval()` with `torch.no_grad()` for segmentation validation.
- What you can do after
- Apply it via PyTorch before running inference or validation, and restore training with `model.train()`.
Read how Deep Learning with PyTorch, Second Edition explains evaluation mode →
Related concepts
- dropoutconfigures · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
- Model Servingused by · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.
- Faster R-CNNused by · Faster R-CNN is an object-detection architecture that uses a learned region proposal network and detection heads to localize and classify objects in images.
- language modelconfigures · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- Gaussian processconfigures · A Gaussian process is a probability distribution over functions in which every finite collection of function values has a joint Gaussian distribution.
- epochcontrasts with · In machine learning, an epoch is one complete pass through the training data during model training.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.