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name: keras-vis description: "Routes keras-vis activation maximization, saliency and Grad-CAM, customization, and image utility workflows for legacy Keras/TensorFlow graph-mode models." disable-model-invocation: true metadata: disco-role: operating license: MIT

keras-vis

Use this skill for legacy keras-vis workflows that visualize or optimize what a model is doing: activation maximization, saliency maps, Grad-CAM, custom losses, optimizer control, and image/data-format helpers.

This release was verified against keras-vis 0.5.0 with standalone Keras 2.2.4 and TensorFlow 1.15.5 on a CPU graph-mode runtime. The package is intentionally routed through repo-skills-router in Researcher mode; read this file first, then jump to the sub-skill that matches the task.

Quick install and import check

If the environment is not ready, use the legacy package stack described in references/installation-and-compatibility.md. A known-good baseline is:

python -m pip install \
  "keras-vis==0.5.0" \
  "Keras==2.2.4" \
  "tensorflow==1.15.5" \
  "protobuf<=3.20.3" \
  "numpy==1.18.5" \
  "scipy==1.5.4" \
  "h5py==2.10.0" \
  "scikit-image==0.16.2" \
  "matplotlib==3.3.4" \
  "importlib-metadata" \
  Pillow imageio

Minimal import check:

python -c "import vis; from vis.visualization import visualize_saliency; print('vis imported')"

If that fails, read references/troubleshooting.md before changing workflow code.

Route map

Activation maximization

Use sub-skills/activation-maximization/SKILL.md when the task is to synthesize an input that maximizes a Dense unit, regression output, or convolutional filter. Typical trigger phrases include: activation maximization, feature visualization, class prototype, softmax-to-linear, Jitter, or animated optimization progress.

Saliency and Grad-CAM

Use sub-skills/saliency-and-cam/SKILL.md when the task is to explain an existing input with saliency maps, guided saliency, rectified saliency, Grad-CAM, or regression attention. Typical trigger phrases include: heatmap, input attribution, guided backprop, penultimate layer, or Grad-CAM.

Optimization building blocks

Use sub-skills/optimization-building-blocks/SKILL.md when the task needs custom losses, regularizers, Optimizer, input or gradient modifiers, backprop modifiers, or callbacks. Typical trigger phrases include: custom loss, wrt_tensor, LPNorm, TotalVariation, GifGenerator, or backend gradient override.

Image utilities

Use sub-skills/image-utilities/SKILL.md when the task is about image shapes, overlays, label lookup, loading, normalization, stitching, or optional Pillow/imageio diagnostics. Typical trigger phrases include: channels first/last, overlay two heatmaps, draw labels, lookup_imagenet_labels, or draw_text.

Shared checks

• Run scripts/check_keras_vis_env.py when you need a quick environment and import sanity check.
• Read references/repo-provenance.md before deciding whether this skill matches the current checkout or before refreshing it.
• Read references/installation-and-compatibility.md when package installation or backend compatibility is the blocker.
• Read references/troubleshooting.md when imports, protobuf, optional image packages, or legacy TensorFlow graph behavior fail.

What not to do here

• Do not route new activation or saliency workflows directly into the root router when a focused sub-skill exists.
• Do not send future agents to original repo notebooks, examples, or tests as runtime instructions; the sub-skills contain the distilled workflows instead.
• Do not replace a visualization workflow with a prose-only description when the bundled scripts or references cover it safely.
• Do not assume tensorflow.keras is the supported API surface for this release; the package was built around standalone Keras.

When the root router is enough

Use the root SKILL.md only when the user request spans multiple sub-skills or when you are still deciding which sub-skill owns the task family. Examples include:

• "I need keras-vis installed and want the right workflow for a model explanation task."
• "I have a heatmap request, but I also need to know whether I should use saliency or Grad-CAM."
• "I want to customize the optimizer path and then visualize the result."

If the task clearly matches one sub-skill, jump there immediately.