Skip to main content

Meet Gabor Layer

Project description


PyPI-Status Build Status LICENSE DeepSource


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):

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


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

    author = {Alekseev, Andrey},
    title = {GaborNet: Gabor filters with learnable parameters in deep convolutional
               neural networks},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{}},

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gabornet, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size GaborNet-0.2.0-py3-none-any.whl (5.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size GaborNet-0.2.0.tar.gz (5.1 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page