A python SDK for Deep Learning Backtrace
Project description
AryaXai-Backtrace
Backtrace module for Generating Explainability on Deep learning models using TensorFlow / Pytorch
Backtrace Module
Overview
The Backtrace Module is a powerful and patent-pending algorithm developed by AryaXAI for enhancing the explainability of AI models, particularly in the context of complex techniques like deep learning.
Features
-
Explainability: Gain deep insights into your AI models by using the Backtrace algorithm, providing multiple explanations for their decisions.
-
Consistency: Ensure consistent and accurate explanations across different scenarios and use cases.
-
Mission-Critical Support: Tailored for mission-critical AI use cases where transparency is paramount.
Installation
To integrate the Backtrace Module into your project, follow these simple steps:
pip install dl-backtrace
usage for Tensoflow based models
from dl_backtrace.tf_backtrace import Backtrace as B
from dl_backtrace.tf_backtrace import contrast as UC
from dl_backtrace.tf_backtrace import prop as UP
from dl_backtrace.tf_backtrace import activation_master
usage for Pytorch based models
from dl_backtrace.pytorch_backtrace import Backtrace as B
from dl_backtrace.pytorch_backtrace import contrast as UC
from dl_backtrace.pytorch_backtrace import prop as UP
from dl_backtrace.pytorch_backtrace import activation_master
Example Notebooks
Name | Link |
---|---|
Tensorflow Backtrace Tabular Dataset | Colab Link |
Tensorflow Backtrace Textual Dataset | Colab Link |
Tensorflow Backtrace Image Dataset | Colab Link |
Pytorch Backtrace Tabular Dataset | Colab Link |
Pytorch Backtrace Image Dataset | Colab Link |
For more detailed examples and use cases, check out our documentation.
Supported Layers and Future Work
- Dense (Fully Connected) Layer
- Convolutional Layer (Conv2D)
- Reshape Layer
- Flatten Layer
- Global Average Pooling 2D Layer
- Max Pooling 2D Layer
- Average Pooling 2D Layer
- Concatenate Layer
- Add Layer
- Long Short-Term Memory (LSTM) Layer
- Batch Normalisation Layer
- Dropout Layer
- Embedding Layer
- Other Custom Layers
Getting Started
If you are new to Backtrace, head over to our Getting Started Guide to quickly set up and use the module in your projects.
Contributing
We welcome contributions from the community. To contribute, please follow our Contribution Guidelines.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contact
For any inquiries or support, please contact AryaXAI Support.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for dl_backtrace-0.0.16-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8d919a6c05e0961380cbb36dba287202123c5397fb0238f3b658d83e93505921 |
|
MD5 | 6e71b95b1366cc9eeb8778cc84133ede |
|
BLAKE2b-256 | c9eb4eced13ad203fbc6f04fa5a2c5937344d4a555a835af9c549f401c06687f |