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name: activation-maximization description: "Uses keras-vis activation maximization to synthesize inputs that maximize Dense outputs, regression outputs, and convolutional filters." disable-model-invocation: true metadata: disco-role: operating license: MIT

Activation Maximization

Use this sub-skill when a task asks for keras-vis activation maximization, feature visualization, class prototype synthesis, regression-output increase/decrease probes, or convolutional filter input synthesis.

Routing

• Use this sub-skill for vis.visualization.visualize_activation and visualize_activation_with_losses workflows that optimize an input tensor to maximize a layer unit or filter.
• Route saliency maps, guided saliency maps, and Grad-CAM to saliency-and-cam.
• Route custom loss authoring, optimizer internals, callbacks, input modifiers beyond activation-maximization usage, and advanced wrt_tensor design to optimization-building-blocks.
• Route image loading, overlay composition, stitched grids, labels, display, and file I/O to image-utilities.

Runtime assumptions

This skill targets the legacy keras-vis runtime: keras-vis 0.5.0 with standalone Keras 2.2.x and TensorFlow 1.x graph-mode backends. Use keras, not tensorflow.keras, in examples and scripts.

Read next

1. API reference for signatures, defaults, filter_indices, weights, modifiers, and return values.
2. Workflows for Dense classifier, regression, Conv filter, seeded refinement, Jitter, and GIF progress recipes.
3. Troubleshooting when imports, softmax outputs, regularizers, modifiers, or convergence fail.

Safe smoke check

From the generated keras-vis skill root, run the bundled smoke script without downloading data or models:

python sub-skills/activation-maximization/scripts/activation_smoke.py --help
python sub-skills/activation-maximization/scripts/activation_smoke.py --target dense --max-iter 3

Use the smoke script as an environment probe only; it creates a tiny untrained model and checks that activation maximization can execute.