tiny-dnn based on libtorch

header only, deep learning framework with no dependencies other than libtorch

Directory structure

This project aims to be a wrapper for libtorch to make tiny-dnn compatible with GPU. tiny-dnn really great, but unfortunately it can not be calculated on a GPU. At first glance, this header-only framework aims to be used as written in tiny-dnn.

Include path settings

Libtorch_path/include
Libtorch_path/include/torch/csrc/api/include
cpp_torch_path
cpp_torch_path/include

Library path setting

Libtorch_path/lib
cpp_torch_path

Minimum include file

#include "cpp_torch.h"

progress
tiny_dnn

tiny_dnn::progress_display disp(train_images.size());

cpp_torch

cpp_torch::progress_display disp(train_images.size());

cpp_torch::progress_display disp(train_images.size());

data set download
What you can do is still limited.

config.h

options	description	default
USE_WINDOWS		ON
USE_COLOR_CONSOLE		ON
USE_ZLIB		ON
USE_IMAGE_UTIL		ON
USE_OPENCV_UTIL	OpenCV >= 2.3	OFF	ex. C:\dev\opencv-3.4.0

example

MNIS
CIFAR10
DCGAN

Latest topic

C# applicatin example

C++ only super_resolution (train & test)
60epochs
It's still being verified
ESPCN(Efficient SubPixel Convolutional Neural Network)
SRCNN(Super-Resolution Convolutional Neural Network)

C++ only DCGAN(Deep Convolutional Generative Adversarial Network) (train & test) reference
It was not possible with tiny-dnn, but it became possible with cpp_torch(libtorch).

Requirements

visual studio 2015,2017

libtorch Please adapt the version of cuda to your environment

tiny-dnn

BSD 3-Clause License Copyright (c) 2013, Taiga Nomi