keras-gemini

A Python package that combines the power of Keras with Gemini for natural language-driven neural network building.

Built With

• Python
• Keras
• Gemini API
• NLTK

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Python 3.x

Installation

pip install keras-gemini

Usage

from keras_gemini import prompt_to_keras

model = prompt_to_keras("Build a 3-layer sequential model")
if model:
    model.summary()

Run the Examples

To run these examples, users simply need to navigate to the examples/ directory and run any of the scripts. For example:

python examples/build_basic_model.py

Features

• Natural Language Model Building: Build Keras sequential models by simply describing the desired architecture in natural language.

For example: Build a 3-layer sequential model

• Automatic Model Compilation: The package automatically compiles the generated Keras model with default settings:

• optimizer='adam'

• loss='sparse_categorical_crossentropy'

• metrics=['accuracy']

• Seamless Integration with Gemini: The KerasGemini() integrates directly into your Gemini chatbot flow, allowing for natural conversational model building.

Upcoming Features (Roadmap)

• Support for More Layer Types: Add support for a wider range of Keras layers (Convolutional, Recurrent, etc.) to enable building diverse network architectures.

• Customizable Layer Parameters: Allow users to specify layer parameters (activation functions, number of units, etc.) through natural language prompts.

• Advanced NLP for Model Understanding: Implement more robust natural language processing techniques to better extract user intent and complex model specifications.

• Model Training and Evaluation: Provide functionality to train and evaluate the generated Keras models directly within the Gemini conversation.

• Model Persistence: Allow users to save and load their custom-built models for later use.

• Interactive Model Building: Enable users to iteratively refine their models by adding or removing layers, modifying parameters, and getting feedback in real-time.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See License for more information.