Follow the example in dataset/mnist.py and model/convnet_classifier.py for examples of how to define custom datasets and models.
pip install tensorflow numpy pillow matplotlib six
Currently the framework includes code for preprocessing mnist, cifar10, and cifar100 datasets.
To download and preprocess the mnist dataset run:
python -m dataset.mnist convert
Run the following to visualize an example:
python -m dataset.mnist visualize
In the above snippets you could replace mnist with cifar10 or cifar100 to preprocess the respective datasets.
To train an mnist classification model run:
python -m main --model=convnet_classifier --dataset=mnist
To visualize the training logs on Tensorboard run:
tensorboard --logdir=output