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Meet Gabor Layer

Project description

GaborNet

PyPI-Status Build Status LICENSE DeepSource

Installation

GaborNet can be installed via pip from Python 3.7 and above:

pip install GaborNet

Getting started

import torch
import torch.nn as nn
from torch.nn import functional as F
from GaborNet import GaborConv2d

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


class GaborNN(nn.Module):
    def __init__(self):
        super(GaborNN, self).__init__()
        self.g0 = GaborConv2d(in_channels=1, out_channels=96, kernel_size=(11, 11))
        self.c1 = nn.Conv2d(96, 384, (3,3))
        self.fc1 = nn.Linear(384*3*3, 64)
        self.fc2 = nn.Linear(64, 2)

    def forward(self, x):
        x = F.leaky_relu(self.g0(x))
        x = nn.MaxPool2d()(x)
        x = F.leaky_relu(self.c1(x))
        x = nn.MaxPool2d()(x)
        x = x.view(-1, 384*3*3)
        x = F.leaky_relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = GaborNN().to(device)

Original research paper (preprint): https://arxiv.org/abs/1904.13204

This research on deep convolutional neural networks proposes a modified architecture that focuses on improving convergence and reducing training complexity. The filters in the first layer of network are constrained to fit the Gabor function. The parameters of Gabor functions are learnable and updated by standard backpropagation techniques. The proposed architecture was tested on several datasets and outperformed the common convolutional networks

Citation

Please use this bibtex if you want to cite this repository in your publications:

@misc{gabornet,
    author = {Alekseev, Andrey},
    title = {GaborNet: Gabor filters with learnable parameters in deep convolutional
               neural networks},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/iKintosh/GaborNet}},
}

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