everlookneversee / diagtomodel

Implementations of Artificial Neural Networks Based on their Diagrams

Implementations of Artificial Neural Networks Based on their Diagrams

Authors

• Milad Sadeghi DM - EverlookNeverSee@GitHub
• List of all contributors to this repository

Implemented Models

• PLNet - Convolutional Neural Network with Parallel Layers
• MLANet - Convolutional Neural Network with Multiple Layer Additions
• LeNet-5 - Gradient-Based Learning Applied to Document Recognition
• AlexNet - ImageNet Classification with Deep Convolutional Neural Networks
• VGG-16 - Very Deep Convolutional Networks For Large Scale Image Recognition
• Inception-v1 - Going Deeper With Convolutions
• Xception - Deep Learning with Depthwise Separable Convolutions

License

This project licensed under the MIT License - see the LICENSE file for more details.

diagtomodel's Issues

[ENH] Inception-v1 Implementation

[ENH] diag2model

Using Tensorflow and Keras.

[FEAT] Implementation of inception-v4 NN

[FEAT] ResNet-50 Implementation

[FEAT] Implementation of Xception NN

[ENH] AlexNet Implementation

[ENH] Publish diag2model package

We are going to distribute and publish diag2model on pypi.

TODOs:

• Adding requirements.txt
• Adding setup.py
• Adding setup.cfg
• Adding MANIFEST.in
• Adding CHANGELOG.md
• Adding tests
• Adding docs directory
• generating documentation using sphinx
• publishing documentation on readthedocs

[ENH] diag2model: Parallel Layer CNN

Using Tensorflow and Keras.

[Invalid] Parameter rank unfilled

Discussed in https://github.com/EverLookNeverSee/diag2model/discussions/24

^{Originally posted by EverLookNeverSee November 1, 2021}
@prkhrsrvstv1

[ENH] Implementation of Lenet-5 Model

[ENH] VGG-16 Implementation

[FEAT] Inception-v3 Implementation