https://github.com/hanzopgp/deeplearninglib

Deep learning library from scratch (MLP/CNN/Auto-Encoder etc...). Contains some experiences on MNIST/CIPHAR datasets + report.
https://github.com/hanzopgp/deeplearninglib

auto-encoder convolutional-neural-networks deep-learning experiments library multi-layer-perceptron university

Last synced: 11 months ago
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Deep learning library from scratch (MLP/CNN/Auto-Encoder etc...). Contains some experiences on MNIST/CIPHAR datasets + report.

• Host: GitHub
• URL: https://github.com/hanzopgp/deeplearninglib
• Owner: hanzopgp
• License: mit
• Created: 2022-03-10T09:47:34.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-05-25T23:24:36.000Z (about 4 years ago)
• Last Synced: 2025-03-05T17:23:56.075Z (about 1 year ago)
• Topics: auto-encoder, convolutional-neural-networks, deep-learning, experiments, library, multi-layer-perceptron, university
• Language: Python
• Homepage:
• Size: 7.83 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# DeepLearningLib

## Presentation

Librairies for deep learning (MLP/CNN/Auto-Encoder etc...).

## Project architecture

DeepLearningLib/

      ├── nndiy/                   

      |    ├── __init__.py        (Contains the sequential object and global variables)

      |    ├── activation.py      (Contains the activation functions such as ReLU/Tanh...)

      |    ├── core.py            (Contains the abstract classes)

      |    ├── early_stopping.py  (Contains the early stopping objects)

      |    ├── layer.py           (Contains the layers object such as Linear/Dropout...)

      |    ├── loss.py            (Contains the loss objects such as MSE/BCE...)

      |    ├── optimizer.py       (Contains the optimizer objects such as SGD/ADAM...)

      |    └── utils.py           (Contains the utility functions such as min_max_scale/one_hot...)

      ├── cnn_demo.py             (Contains the demo for CNN)

      ├── experiences.py          (Contains the MLP/AE/CNN experiences)

      ├── mlp_unit_test.py        (Contains MLP unit tests on simple problems) 

      ├── report/                 (Folder containing the images and report)

      |    ├── img_report/

      |    └── report.pdf         

      ├── README.md		          

      └── LICENSE  



## Features implemented

- Linear/Convo1D/MaxPool1D/AvgPool1D/Flatten/Dropout layers

- GD/MGD/SGD/ADAM optimizers

- LearkyReLU/ReLU/Identity/Tanh/Sigmoid/Softmax activation functions

- MAE/MSE/RMSE/BCE/SBCE/CCE/SCCE/SCCESoftmax loss functions

- Uniform/Xavier initialization

- L1/L2 regularisation

- EarlyStopping callback

## Experiences

All those experiments were done on MNIST digits and fashion datasets :

- Multi layer perceptron image classification

- Autoencoder image reconstruction (with different latent space dimensions)

- Autoencoder removing noise (with different percentage of noise)

- Multi layer perceptron image classification with latent space representation (using different dimension)

- SGD/ADAM/Tanh/ReLU benchmarks on MNIST

- 1D CNN on MNIST