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name: neural-network-visualization description: "Use this operating sub-skill to create, adapt, and troubleshoot ManimML neural-network scenes: NeuralNetwork containers, feed-forward and convolutional layers, image/embedding/vector/math/triplet/paired-query layers, connective layers, forward-pass animations, dropout, residual/manual connections, insertion/removal animations, and small safe render scripts." disable-model-invocation: true metadata: disco-role: operating package: manim_ml repo-skill: manim-ml sub-skill: neural-network-visualization license: MIT

Neural-Network Visualization with ManimML

Use this sub-skill when

• The task is to draw or animate a neural-network architecture with manim_ml.neural_network.
• The requested scene involves NeuralNetwork, feed-forward layers, convolution/max-pooling/image layers, activation functions, embeddings, vector outputs, math-operation nodes, triplet or paired-query image inputs, VAE-like diagrams, dropout, forward-pass animations, or residual/skip connections.
• The user needs a small script that writes a standalone Manim scene without relying on repository assets.

Route decision-tree, MCMC, Gaussian/probability, and matplotlib/statistical workflows to the sibling statistical-visualization sub-skill. Route Manim Community installation, cairo/Pango/ffmpeg, or system-render failures to the root ManimML troubleshooting reference first, then return here for layer/API mistakes.

Assumptions and safe operating checks

ManimML scenes require Manim Community, not the original 3Blue1Brown Manim package. Before writing task-specific code, use a small import check in the user's active environment:

python - <<'PY'
import manim
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer
print("manim", getattr(manim, "__version__", "unknown"))
print(NeuralNetwork, FeedForwardLayer)
PY

For a no-assets starter script, prefer the bundled helper:

python sub-skills/neural-network-visualization/scripts/render_neural_network_example.py --help
python sub-skills/neural-network-visualization/scripts/render_neural_network_example.py --mode feed-forward --scene-file nn_example.py
manim -ql -s nn_example.py ManimMLNeuralNetworkExample

The helper writes a scene by default and renders only when explicitly asked with --render.

Reference map

• API reference: verified constructors, layer map, connective dispatch, animation calls, wrapper APIs, and known limitations.
• Workflows: copyable recipes for feed-forward, CNN/image-CNN/max-pool, residual connections, dropout, embeddings, triplet/paired-query layers, VAE-style diagrams, insertion/removal, and safe render commands.
• Troubleshooting: common exceptions and fixes for activation names, layouts, connection styles, image shapes, CNN dimensions, dropout, and render mistakes.
• Safe helper script: generates standalone tiny-scene examples for feed-forward, cnn, image-cnn, residual, dropout, embedding, triplet, paired-query, vector-math, and vae.

Core operating pattern

from manim import *
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer

class MyScene(Scene):
    def construct(self):
        nn = NeuralNetwork([
            FeedForwardLayer(3),
            FeedForwardLayer(5, activation_function="ReLU"),
            FeedForwardLayer(2),
        ])
        nn.move_to(ORIGIN)
        self.add(nn)
        self.play(nn.make_forward_pass_animation(run_time=3))

Use ThreeDScene when the network includes Convolutional2DLayer or MaxPooling2DLayer, because those layers are rendered as rotated 3D-style feature-map stacks.

Rules of thumb

1. Import most public neural-network classes directly from manim_ml.neural_network.
2. Use a list of layer objects for sequential networks and a dictionary of named layer objects when later manual connections should address layers by name.
3. Keep image examples self-contained by generating tiny PNGs or by converting a caller-supplied image with Pillow; do not reference repository-relative assets.
4. Use exact activation names: "ReLU" and "Sigmoid".
5. make_forward_pass_animation(layer_args=...) keys are layer/connective object instances, not layer names.
6. add_connection(...) supports the default connection style; choose arc_direction="straight", "up", "down", "left", or "right" for the visual route.
7. Treat full video rendering as optional and potentially slow. For quick checks, render a still with manim -ql -s.

Verification hooks for downstream Researcher tasks

• Minimal construction: build a NeuralNetwork([FeedForwardLayer(3), FeedForwardLayer(2)]), add it to a Scene, and confirm the scene imports.
• CNN construction: build a ThreeDScene with Convolutional2DLayer, MaxPooling2DLayer, and FeedForwardLayer; prefer a still render first.
• Asset-free image paths: use the bundled script's image-cnn, triplet, or paired-query modes to generate tiny fixtures, then render the produced scene file.