Debugging Keras nets by making them dream out loud

What it does

keras-vis is a Python toolkit for visualizing and debugging trained Keras neural networks. It implements activation maximization, saliency maps, and class activation maps to show which parts of an input image drive a model’s decisions. The library frames every visualization as an energy minimization problem, letting you compose weighted loss functions and regularizers—like total variation and Lp-norm—to generate or highlight inputs via gradient descent. It handles N-dimensional image inputs and works with both Theano and TensorFlow backends in either channel format.

The interesting bit

Instead of scattered one-off scripts, the toolkit unifies these techniques under a single optimization interface. You stack losses and regularizers into an Optimizer object that minimizes them jointly, and use callbacks like GifGenerator to animate the descent, turning a debugging session into a slow-motion replay of the model “deciding” what to see.

Key highlights

• Supports activation maximization, saliency maps, and class activation maps out of the box.
• Treats visualizations as composable energy-minimization problems with pluggable losses and regularizers.
• Handles N-dimensional image inputs and both channels_first and channels_last formats across Theano and TensorFlow.
• Includes input modifiers like Jitter and callbacks like GifGenerator to tweak and record the optimization process.
• Requires Keras 2.0 or higher.

Caveats

• Most documentation links are currently broken; the README explicitly redirects users to the examples/ directory for working samples.

Verdict

Worth a look if you are debugging Keras CNNs and prefer composing gradient-based visualizations from modular losses and regularizers. Not for you if you are outside the Keras 2.x/Theano-TensorFlow backend ecosystem or need fully maintained documentation.