Principle:Tensorflow Tfjs Model Evaluation And Deployment

Metadata

Field	Value
Principle Name	Tensorflow Tfjs Model Evaluation And Deployment
Library	TensorFlow.js
Domains	Transfer_Learning, Deployment
Type	Principle
Implemented By	Implementation:Tensorflow_Tfjs_LayersModel_Evaluate_And_Save_For_Transfer
Source	TensorFlow.js
Last Updated	2026-02-10 00:00 GMT

Overview

Model Evaluation and Deployment is the final stage of the transfer learning pipeline. After fine-tuning, the model must be validated on held-out test data to verify that it generalizes to unseen examples from the target domain, and then serialized (saved) for production use. This stage ensures the model meets performance requirements before deployment and produces a portable artifact that can be loaded for inference in browsers or Node.js environments.

Description

Evaluation and deployment serve complementary purposes in the transfer learning lifecycle:

Evaluation measures the fine-tuned model's performance on data it has never seen during training. This is critical for transfer learning because:

• The model may have overfit to the small target dataset, achieving high training accuracy but poor generalization.
• The pretrained features may not transfer well to the target domain, resulting in poor test performance despite apparent training success.
• The task head may be miscalibrated -- producing confident but incorrect predictions.

Deployment serializes the complete model (frozen base layers + trained task head) into a portable format that can be loaded and used for inference in production environments.

Evaluation Methodology

The evaluation must use a held-out test set that was not used during training or validation. The test set should be:

• Representative of the target domain's real-world data distribution.
• Completely separated from training and validation data (no data leakage).
• Large enough to provide statistically meaningful metrics.

Key Evaluation Metrics

Task Type	Metric	Description
Classification	Accuracy	Fraction of correct predictions
Classification	Precision	Fraction of positive predictions that are correct
Classification	Recall	Fraction of actual positives that are correctly identified
Classification	F1 Score	Harmonic mean of precision and recall
Classification	Cross-entropy loss	Information-theoretic measure of prediction quality
Regression	Mean Squared Error (MSE)	Average squared difference between predictions and targets
Regression	Mean Absolute Error (MAE)	Average absolute difference between predictions and targets
Regression	R-squared	Proportion of variance in targets explained by predictions

Theoretical Basis

Generalization in Transfer Learning

The fundamental question that evaluation answers is: Does the fine-tuned model generalize beyond its training data? Transfer learning models face a specific generalization challenge:

• The base model was trained on the source domain (e.g., ImageNet) and may encode biases specific to that domain.
• The task head was trained on a small target dataset and may overfit.
• The combination of frozen base features and a trained head may not capture the target domain's full complexity.

Test set evaluation provides an unbiased estimate of the model's expected performance on new, unseen data from the target domain.

Model Serialization

Deployment requires saving the model in a format that preserves:

1. Architecture -- The complete model topology, including both the pretrained base and the trained task head.
2. Weights -- All weight values, both frozen (base) and trained (head).
3. Optimizer state -- Optionally, the optimizer's internal state for potential continued training.

The saved model must be self-contained: loading it should produce a model identical to the one that was saved, without requiring the original base model or any external dependencies.

Deployment Considerations

Consideration	Description
Model size	Transfer learning models can be large (e.g., MobileNet base + head). Consider model quantization or pruning for constrained environments.
Inference latency	The full base model runs during inference. For real-time applications, choose a lightweight base (e.g., MobileNet over ResNet).
Storage format	TensorFlow.js supports multiple storage backends: IndexedDB (browser), localStorage (browser, small models), file:// (Node.js), HTTP (remote serving).
Input preprocessing	The deployment environment must apply the same preprocessing (resize, normalize) as was used during training.

Usage

Model evaluation and deployment are used at the conclusion of every transfer learning project:

• Evaluate the model on the test set to obtain final performance metrics.
• Compare metrics against baselines (random classifier, source model without fine-tuning, etc.).
• Save the model to the appropriate storage backend for the deployment target.
• Load the saved model in the production environment for inference.

Evaluation Best Practices

1. Never tune hyperparameters on the test set. Use the validation set for hyperparameter selection and the test set only for final reporting.
2. Report multiple metrics. Accuracy alone can be misleading, especially for imbalanced datasets.
3. Evaluate on stratified data. Ensure the test set has a representative distribution of classes.
4. Dispose of tensors. Evaluation produces scalar tensors that must be read and then disposed to prevent memory leaks.

Deployment Best Practices

1. Save the complete model. The saved artifact should include both the base and head, not just the head weights.
2. Version your models. Include version information in the save path for rollback capability.
3. Test the saved model. Load the saved model and verify it produces the same outputs as the original.

Related Pages

• Principle:Tensorflow_Tfjs_Fine_Tuning -- Training the model that will be evaluated and deployed
• Principle:Tensorflow_Tfjs_Base_Model_Loading -- Loading models (the inverse of saving)
• Implementation:Tensorflow_Tfjs_LayersModel_Evaluate_And_Save_For_Transfer -- TensorFlow.js implementation of evaluation and saving