A comprehensive deep learning skill for building, training, and troubleshooting neural networks using Keras and TensorFlow patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install keras
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install keras using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Keras?
The Keras skill for Openclaw Skills is designed to streamline the deep learning lifecycle for developers and data scientists. It provides a structured approach to architecting neural networks, offering pre-defined patterns for both Sequential and Functional APIs. By leveraging this skill, users can ensure their models follow industry best practices for layer configuration, activation functions, and loss optimization.
Beyond basic architecture, this skill excels at managing the complexities of model training. It includes detailed recipes for regularization, data pipelines using tf.data, and essential callbacks like EarlyStopping and ModelCheckpoint. This ensures that your Openclaw Skills experience is focused on high-level design while the agent handles the technical minutiae of model compilation and training diagnostics.
Keras Use Cases
- Rapidly prototyping neural networks using the Keras Sequential API for simple stacks.
- Designing complex, multi-input, and multi-output architectures with the Functional API.
- Implementing advanced training loops with integrated callbacks for performance monitoring and model saving.
- Debugging common deep learning issues such as NaN loss, vanishing gradients, or GPU memory errors.
- Building high-performance data pipelines with image augmentation and prefetching capabilities.
How Keras Works
- The user defines the neural network task, such as image classification or sequence prediction.
- The skill analyzes the input shape and task type to suggest the optimal model architecture and loss functions.
- The agent generates the necessary Python code using Keras best practices, including layer stacking and regularization.
- Training parameters are configured with standardized callbacks to ensure model stability and weight persistence.
- Architecture configurations and hyperparameter history are stored in the local memory directory for future iterations.
Keras Setup
To integrate this skill into your workflow for Openclaw Skills, follow these setup steps:
# Install the Keras skill
clawhub install keras
# The skill will automatically manage the directory structure
mkdir -p ~/keras/models
Ensure that Python 3 is installed on your system. On first use, the skill will initialize your preferences in the ~/keras/ directory, allowing for persistent memory of your preferred architectures and hyperparameters.
Keras Data Schema & Taxonomy
The Keras skill maintains a structured data hierarchy within the local filesystem to track model progress and configurations:
| Location | Content | Description |
|---|---|---|
~/keras/memory.md |
Architectural Preferences | Stores history of successful hyperparameters and layer configurations. |
~/keras/models/ |
Model Definitions | Optional storage for saved model configuration snippets. |
~/keras/setup.md |
Integration Guide | Instructions for initial setup and system-specific configurations. |
~/keras/training.md |
Diagnostics Log | Reference for common training patterns and diagnostic rules. |
Keras Advanced Features
- Support for Model Subclassing to enable dynamic forward passes and custom training logic.
- Automated configuration of the tf.data pipeline for optimized CPU/GPU throughput.
- Integrated transfer learning patterns that manage pretrained weights from the TensorFlow model hub.
- Multi-stage regularization stacks including L2, BatchNorm, and Dropout for preventing overfitting.
- Built-in checklist for model compilation to ensure alignment between output layers and loss functions.
SKILL.md
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