Getting Started

conda create --name keras243 --file requirements.txt
conda activate keras243
cd custom-keras-callbacks/src
python3 [dataset].py; [dataset] = {mnist, cifar10, cifar100}

Experiment logs

For visualizing Tensorboard plots

cd custom-keras-callbacks
tensorboard --logdir ./logs/[dataset]/tensorboard; [dataset] = {mnist, cifar10, cifar100}

For past run logs

cat ./logs/[dataset]/terminal/run.txt; [dataset] = {mnist, cifar10, cifar100}

List of Callbacks per training script

src/mnist.py:
- CyclicLR - taken from this repo.
- Tensorboard, ModelCheckpoint, EarlyStopping - basic usage of built-in Keras Callbacks
src/cifar10.py:
- LearningRateScheduler (built-in) for epoch-based learning rate scheduling
- EarlyStop_ModelChkpt - a custom callback combining the functionalities of ModelCheckpoint & EarlyStopping. Can also monitor custom metrics defined in other callbacks.
src/cifar100.py:
- SGDRScheduler - modified from the implementation here.
- clf_metrics - a custom callback for calculating and monitoring global classification metrics like F1 score, precision etc. on validation set. As mentioned in this Keras issue, these were previously part of built-in Keras metrics, but were removed in later versions due to misleading batch-wise calculations.