Hybrid Models using Pytorch

This practice will focus on supervised hybrid methods from fuzzy autoencoders, passing through convolutional neural networks (CNN'S) for pattern recognition in high dimensions or patterns of high and low spatial frequencies and generating images with GAN's. All this concepts will be applied to the MNIST dataset, a widely known dataset in the field of Machine Learning composed by handwritten digits and commonly used for training various image processing systems.

Installation instructions 💻

To replicate the results follow this steps:

Clone the repository:

git clone https://github.com/IsitaRex/Hybrid-Models.git

Install the requirements list on your environment

pip install -r requirements.txt

Tasks 🔮

CNN (LeNet5):

The base code for this implementation can be found on this link

To train a CNN run:

python main.py  --task Hybrid-Models-CNN --epochs 10 --batch_size 32 --lr 0.001

GAN:

The base code for this implementation can be found on this link

To train a GAN run:

python main.py  --task "Hybrid-Models-GAN" --epochs 200 --lr 0.0001 --batch_size 512 --device 'cpu'

⚠️ DO NOT USE 'mps' AS DEVICE/ CUDA NOT TESTED -> RECOMMENDED TO USE 'cpu'

AUTOENCODER:

First, an autoencoder is trained

To train an autoencoder:

python main.py --epochs 15 --lr 0.0002 --task "Hybrid-Models-AUTOENCODER" --batch_size 512

Results

To keep track of the experiments Weights and Biases can be easily integrated to the code by adding the flag

--use_wandb True