This toolkit provides visualization methods for debugging tf.keras models, targeting deep learning practitioners and researchers. It enables understanding model behavior through techniques like activation maximization and various saliency maps, aiding in model interpretation and debugging.

How It Works

The library offers a flexible API for generating visualizations by accepting a tf.keras.Model and optional model_modifier classes. These modifiers can alter the model, for instance, by replacing the final layer's activation to linear. Users specify visualization targets using Score classes and can enhance the process with input modifiers (e.g., Jitter, Rotate2D) and regularizers (e.g., TotalVariation2D, Norm). The library supports N-dimensional image inputs, batch processing, and models with multiple inputs/outputs.

Quick Start & Requirements

• Install via pip: $ pip install tf-keras-vis
• Requirements: Python 3.7+, TensorFlow 2.0+.
• Documentation: https://keisen.github.io/tf-keras-vis-docs/

Highlighted Details

• Supports Activation Maximization, Class Activation Maps (GradCAM, GradCAM++, ScoreCAM, Faster-ScoreCAM, LayerCAM), and Saliency Maps (Vanilla Saliency, SmoothGrad).
• Handles N-dim image inputs, batch processing, and complex model architectures (multiple inputs/outputs, mixed precision).
• Offers customizable input modifiers and regularizers for fine-tuning visualizations.
• Includes callbacks like Progress for monitoring visualization generation.

Maintenance & Community

The project appears to be actively maintained, with recent updates and a clear roadmap for future features like Deep Dream and Style Transfer.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

Activation Maximization may produce blurry images with InceptionV3. GradCAM and GradCAM++ may encounter issues with cascading models, recommending FasterScoreCAM as an alternative. Channel-first models and data are not supported.