dl-backtrace 0.0.18

A python SDK for Deep Learning Backtrace

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

Tensoflow-Keras based models

from dl_backtrace.tf_backtrace import Backtrace as B

Pytorch based models

from dl_backtrace.pytorch_backtrace import Backtrace as B

Evalauting using Backtrace:

1. Step - 1: Initialize a Backtrace Object using your Model

backtrace = B(model=model)

2. Step - 2: Calculate layer-wise output using a data instance

layer_outputs = backtrace.predict(test_data[0])

3. Step - 3: Calculate layer-wise Relevance using Evaluation

relevance = backtrace.eval(layer_outputs,mode='default',scaler=1,thresholding=0.5,task="binary-classification")

Depending on Task we have several attributes for Relevance Calculation in Evalaution:

Attribute	Description	Values
mode	evaluation mode of algorithm	{ default, contrastive}
scaler	Total / Starting Relevance at the Last Layer	Integer ( Default: None, Preferred: 1)
thresholding	Thresholding Model Prediction in Segemntation Task to select Pixels predicting the actual class. (Only works in Segmentation Tasks)	Default:0.5
task	The task of the Model	{ binary-classification, multi-class classification, bbox-regression, binary-segmentation}
model-type	Type of the Model	{Encoder/ Encoder_Decoder}

Example Notebooks :

Tensorflow-Keras :

Name	Task	Link
Backtrace Loan Classification Tabular Dataset	Binary Classification	Colab Link
Backtrace Image FMNIST Dataset	Multi-Class Classification	Colab Link
Backtrace CUB Bounding Box Regression Image Dataset	Single Object Detection	Colab Link
Backtrace Next Word Generation Textual Dataset	Next Word Generation	Colab Link
Backtrace ImDB Sentiment Classification Textual Dataset	Sentiment Classification	Colab Link
Backtrace Binary Classification Textual Dataset	Binary Classification	Colab Link
Backtrace Multi-Class NewsGroup20 Classification Textual Dataset	Multi-Class Classification	Colab Link
Backtrace CVC-ClinicDB Colonoscopy Binary Segmentation	Organ Segmentation	Colab Link
Backtrace CamVid Road Car Binary Segmentation	Binary Segmentation	Colab Link
Backtrace Transformer Encoder for Sentiment Analysis	Binary Classification	Colab Link
Backtrace Transformer Encoder-Decoder Model for Neural Machine Translation	Neural Machine Translation	Colab Link
Backtrace Transformer Encoder-Decoder Model for Text Summarization	Text Summarization	Colab Link

Pytorch :

Name	Task	Link
Backtrace Tabular Dataset	Binary Classification	Colab Link
Backtrace Image Dataset	Multi-Class Classification	Colab Link

For more detailed examples and use cases, check out our documentation.

Supported Layers and Future Work :

Tensorflow-Keras:

• Dense (Fully Connected) Layer
• Convolutional Layer (Conv2D,Conv1D)
• Transpose Convolutional Layer (Conv2DTranspose,Conv1DTranspose)
• Reshape Layer
• Flatten Layer
• Global Max Pooling (2D & 1D) Layer
• Global Average Pooling (2D & 1D) Layer
• Max Pooling (2D & 1D) Layer
• Average Pooling (2D & 1D) Layer
• Concatenate Layer
• Add Layer
• Long Short-Term Memory (LSTM) Layer
• Dropout Layer
• Embedding Layer
• TextVectorization Layer
• Self-Attention Layer
• Cross-Attention Layer
• Feed-Forward Layer
• Pooler Layer
• Decoder LM (Language Model) Head
• Other Custom Layers

Pytorch :

(Note: Currently we only Support Binary and Multi-Class Classification in Pytorch, Segmentation and Single Object Detection will be supported in the next release.)

• Linear (Fully Connected) Layer
• Convolutional Layer (Conv2D)
• Reshape Layer
• Flatten Layer
• Global Average Pooling 2D Layer (AdaptiveAvgPool2d)
• Max Pooling 2D Layer (MaxPool2d)
• Average Pooling 2D Layer (AvgPool2d)
• Concatenate Layer
• Add Layer
• Long Short-Term Memory (LSTM) Layer
• Dropout Layer
• Embedding Layer
• EmbeddingBag Layer
• 1d Convolution Layer (Conv1d)
• 1d Pooling Layers (AvgPool1d,MaxPool1d,AdaptiveAvgPool1d,AdaptiveMaxPool1d)
• Transpose Convolution Layers (ConvTranspose2d,ConvTranspose1d)
• Global Max Pooling 2D Layer (AdaptiveMaxPool2d)
• 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.