Authors: Manny Ko, Ujjawal K. Panchal, Héctor Andrade Loarca, Andres Mendez-Vazquez
Architecture
CoShNet is a fully complex hybrid neural network. We use the CoShREM (now call SymFD) http://www.math.uni-bremen.de/cda/software.html
signal transform to produce a stable embedding. The network operates entirely in
Architecture Brief
- Input is any
$32\times32 \in \mathbb{R}$ image. - Input is CoShREM transformed to produce a
$32\times32\times20 \in \mathbb{C}$ output. - CoShREM output is convolved with the
$2$ cplx-conv layers.Each cplx-conv layer is composted of :=
$\mathbb{C}$ -Conv +$\mathbb{C}$ -ReLU +$\mathbb{C}$ -AvgPool2d. - The response is flattened and passed through
$2$ cplx-linear layers.Each cplx-linear layer is composted of :=
$\mathbb{C}$ -linear layer +$\mathbb{C}$ -ReLU. - The
$\mathbb{R}$ ,$\mathbb{I}$ components are stacked together (see shape) and passed through$1$ final$\mathbb{R}$ -linear layer.
Python 3.8.x and newer are supported:
Automated Setup
- Create a virtualenv at the root of the repo:
python -m venv venv4coshnet - Activate venv4coshnet:
- Windows:
venv4coshnet\Scripts\activate - Linux/MacOS:
source venv4coshnet/bin/activate
- Windows:
- Run setup.py:
Manual Setup
- Please follow:
setup.txt
Contents List
-
code/: Contains all code essential to run experiments in this repo. -
libs/: Contains all custom-made and collected libs and modules we use for our experiments. (Installed automatically in setup.txt) -
data/: Folder where datasets are present. Created automatically when running for first time. -
setup.txt: Steps for setting up repo. -
requirements.txt: requirements file. -
changelog.md: all changes relevant to releases, branch prs, or any other general notes needed for maintenance.
Running Instructions
cd code/. Following are the possible cases:
- Running our models: run:
python test_fashion.py --helpto see several arguments you are allowed to tune. (Default run (10k20E) gets 89.2% onRTX 2080 Super). The default will use the 10k test set of Fashion to train for 20 epochs, and the 60k training set to test. - Running resnet(18|50): run:
python test_resnet.py --helpto see several arguments you are allowed to set. (Default run (RN18, 10k20E) gets 88.3% onRTX 2080 Super).
| Model | Epochs | Parameters | Size Ratio | Top-1 Accuracy (60k) | Top-1 Accuracy (10k) |
|---|---|---|---|---|---|
| ResNet-18 | 100 | 11.18M | 229 | 91.8% | 88.3% |
| ResNet-50 | 100 | 23.53M | 481 | 90.7% | 87.8% |
| CoShNet(base) | 20 | 1.37M | 28 | 92.2% | 89.2% |
| CoShNet (tiny) | 20 | 49.99K | 1 | 91.6% | 88.0% |
Note: 60k = train on train-set (60k observations), test on test-set (10k observations). 10k = vice-versa. K or k = 1000, M = Million.
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