executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
Explorer les Skills
Productivité · SKILL.md
layers-and-nets
layers-and-netsUse Sonnet built-in layers, normalization modules, initializers, metrics, and predefined nets with correct shapes and state.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: layers-and-nets description: "Use Sonnet built-in layers, normalization modules, initializers, metrics, and predefined nets with correct shapes and state." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0
Sonnet Layers and Nets
Use this sub-skill for Sonnet built-ins: Linear, Bias, convolutions, transpose/depthwise convolutions, embeddings, reshape/flatten, dropout, normalization, initializers, regularizers, metrics, moving averages, and snt.nets models such as MLP, Cifar10ConvNet, ResNet, and VQ-VAE quantizers.
Start here
- Read references/api-reference.md for grouped signatures and shape/state contracts.
- Read references/workflows.md for MLP, Conv2D+BatchNorm, ResNet, VQ-VAE, metrics, and regularizer recipes.
- Read references/troubleshooting.md for unknown dimensions, bias conflicts, normalization state, VQ-VAE errors, and dropout flag failures.
- Run scripts/layers_and_nets_smoke.py for a safe no-download shape/state check.
Boundaries
- Custom module design: ../module-authoring/SKILL.md.
- Training/optimizer loops: ../training-and-optimization/SKILL.md.
- Recurrent modules: ../sequence-and-rnn/SKILL.md.
- Serialization/distribution: ../serialization-and-distribution/SKILL.md.
Validation checklist
Known feature/channel dimensions are required on first call. with_bias=False forbids b_init. BatchNorm calls must pass is_training. VQ-VAE inputs must end with embedding_dim. snt.nets.MLP requires is_training only when dropout is enabled.