Spectral CNN under Cifar10

Coded by: Aarshay Jain, Jared Samet, Alex Wainger
Modified by: BrianSune that works on Windows platform.
Spectral Representations for Convolutional Neural Networks
This repo is an implementation of Rippel, Snoek, and Adams 2015 (https://arxiv.org/pdf/1506.03767.pdf).

Requirements

Tensorflow under Anaconda with all the required imports from the python scripts.
Since the code uses the NCHW format to perform convolutions, it will only run on a GPU-enabled machine.
The CIFAR-100 dataset does not come in batches. Loading the dataset will require a machine with at least 32 GB of RAM.

Running saved models

Two of the notebooks refer to the saved models which contain the weights for our best accuracies. Before running these, unzip the src/best_model_10.tar.gz and src/best_model_100.tar.gz files.

Code Organization

All code is located in the src folder.
Python functions and classes that are shared between multiple notebooks are all located in the modules folder.
Main python scripts are found in modules.
Other functions are in the sub folder lib.

Modules

cnn_with_spectral_parameterization.py - This class builds and trains the generic and deep CNN architectures as described in section 5.2 of the paper with and without spectral parameterization of the filter weights. It was adapted from the homework assignment for this class on CNNs.

cnn_with_spectral_pooling.py - This class builds the spectral pooling CNN architectures as described in section 5.1 of the paper. It was adapted from the homework assignment for this class on CNNs.

create_images.py - These functions allowed us to experiment with and understand the behavior of the Fourier transform as applied to sample images.

frequency_dropout.py - These functions implement the frequency dropout operation by creating a dropout mask with an input tensor for the truncation frequency.

image_generator.py - This class creates a data generator that supports image augmentation. It was adapted from the homework assignment for this class on CNNs.

layers.py - The classes in this file implement the various layers that we use to create the CNN architectures described in the paper. Some layers were adapted from the homework assignment on CNNs. The layers defined in this file are:

• default_conv_layer: A standard convolutional layer with traditionally-parameterized weights
• fc_layer: A fully connected dense layer
• spectral_pool_layer: A layer implementing spectral pooling and frequency dropout
• spectral_conv_layer: A convolutional layer with spectrally-parameterized weights
• global_average_layer: A layer implementing global averaging as described in Lin et al.

spectral_pool.py - A function implementing spectral pooling that is shared by multiple sources

utils.py - Various utility functions. Some were adapted from the homework assignment on CNNs.