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Paper link

My Blog Post link

Commands for replicating experiment results

All the experiments were done using Kaggle notebooks with GPU enabled.. This environment had Tensorflow 2.6 and other required libraries pre-installed .

MNIST experiments

For training CNN model: python train_mnist_example.py --backbone=cnn

For training Residual model: python train_mnist_example.py --backbone=residual

For training PDE model: python train_mnist_example.py --backbone=pde

CIFAR-10 experiments

For original Resnet32 training: python train_cifar.py --net='resnet'

For original PDENet training: python train_cifar.py --net='pdenet'

For PDENet training with advection disabled: python train_cifar.py --net='pdenet' --disable_advection True

For PDENet training with constant Dxy (Dx=1, Dy=1): python train_cifar.py --epochs 5 --net='pdenet' --constant_Dxy True

MNIST Confusion matrix comparison

Confusion matrices for CNN, Residual and PDE Models. It can be noted that the PDE model has lowest confusion b/w 3 & 5 among the three models. [Open in new tab to see the enlarged image.]

About the code

  • train_cifar.py: This file can be used for training different networks on CIFAR-10 dataset. The training & evaluation loop and default training settings are defined in this file.
  • train_mnist_example.py: Can be used to train cnn, residual cnn & PDENet depending on the user provided arguments.
  • mnist_model.py: Toy models used for MNIST experiment are defined here.
  • utils.py: Plotting, logging, metric calculators and other miscellaneous functions are defined in this file.
  • resnet.py: Resnet32 model is defined here. Depending on user settings, different types of diffusion and anisotropic blocks are invoked here.
  • global_layer.py: Contains implementation of Diffusion-Advection layer as proposed in the paper.
  • loader.py: Functions for MNIST & CIFAR data loading and augmentation are defined here.
  • viz_pde.py: This script can be used to visualize Dx, Dy and advection terms using trained model checkpoint weights.

Directory Structure of logsfolder

The expt_logs folder contains detailed logs for various experiments.
The directory structure is as follows:

--- expt_logs
    --- <expt_name>
        --- logs
            --- <model_name>
                --- images (contains confusion matrix/activation plots)
                --- train (contains tensorboard logs)

The logs folder also contains settings_log.txt file. This file contains the training/testing loss and accuracy.\