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I embarked on this project over the weekend, inspired by the remarkable code shared by Amir Hossein Fouladi. My goal was to adapt it for command line application, showcasing a typical example of my collaboration with data scientists. I've refined and enhanced it significantly, yet the foundational code is a testament to Amir's original work. My sincere thanks to Amir!

This code exemplifies a CGAN deep learning model using PyTorch, short for Conditional Generative Adversarial Net. CGAN networks determine the class of generated images. The architecture comprises Multi-Layer Perceptron (MLP) networks for both generator and discriminator. Trained on the MNIST dataset, the model can generate images representing numbers 0 to 9 based on specified labels.

This project aims to showcase the transformation of a Jupyter notebook into an enjoyable commandline utility. Enhancements include:

Features

• MNIST/EMNIST: Build on MNIST or EMNIST (numeric (10 classes) or alpha numeric (62 classes))
• Command Line Utility Integration: A seamless command line interface for easy access and control.
• Model Persistence: Functionality to save models for future use.
• Checkpoint Resumption: Ability to load models and resume from checkpoints.
• Organized Model Directories: Each model has its own dedicated directory.
• Epoch Image Saving: Saves images after each epoch for visual progress tracking.
• Training Progress Logging: Detailed logging of epochs and training metrics.
• Configurable Runs: Use configuration files to document and customize each run.
• Progress Bar: A visual progress bar to monitor process status.
• Configurable Learning Rates: Configurable learning rates for both the generator and discriminator to optimize training.
• Multiple Optimizers: Integration of different optimizers to enhance model performance (ongoing development).
• CLI Ascii Art: Snazzy TAAG Ascii art for an enhanced command line interface experience.
• GIF Generation: Handy tool for creating GIFs to visualize training progression.
• FONT Writer: use the generated fonr to write text onto an image aker

Thoughts

After about 100 iterations, loss just loops for both models.. so I dont see a need for 800 iterations. I would love to get this working better, please give feedback.

Install

pipenv install

Use

pipenv shell
usage: __main__.py [-h] [--model {MNIST,EMNIST}] [--output-dir OUTPUT_DIR] [--workers WORKERS] [--batch BATCH]
                   [--save-checkpoints] [--checkpoint-interval CHECKPOINT_INTERVAL] [--gen_model-path GEN_MODEL_PATH]
                   [--dis_model-path DIS_MODEL_PATH] [--epochs EPOCHS] [--optimizer {adam,rmsprop,sgd}]
                   [--gen-lr GEN_LR] [--dis-lr DIS_LR] [--config CONFIG]

Train a Conditional GAN on MNIST or EMNST data.

options:
  -h, --help            show this help message and exit
  --model {MNIST,EMNIST}
                        Dataset to use
  --output-dir OUTPUT_DIR
                        Directory to save all output_data
  --workers WORKERS     The number of threads for dataloaders
  --batch BATCH         The batch size for the dataloader
  --save-checkpoints    Flag to save model checkpoints.
  --checkpoint-interval CHECKPOINT_INTERVAL
                        Interval (in epochs) between saving model checkpoints.
  --gen_model-path GEN_MODEL_PATH
                        Path to the pre-trained generator model to load.
  --dis_model-path DIS_MODEL_PATH
                        Path to the pre-trained discriminator model to load.
  --epochs EPOCHS       Number of epochs to train the model.
  --optimizer {adam,rmsprop,sgd}
                        Optimizer for training: adam, rmsprop, or sgd.
  --gen-lr GEN_LR       Learning rate for the generator.
  --dis-lr DIS_LR       Learning rate for the discriminator.
  --config CONFIG       Path to a YAML configuration file.


python  -m cganMNIST --epochs=200 --optimizer=adam

Make the gif

There is a gif maker script in the root of the project.

./ gif.sh ./output/20240311-144708/images.txt

Timelapse

Created from EMNIST trained model

Sample training data (not from the model)

Cli action

reference

The original source code for the Jupyter notebook

• Author: Amir Hossein Fouladi
• Original Repository
• MNIST Data
• EMNIST Data

Thanks for all the project that I've used to get me here

• alive_progress