https://github.com/csukuangfj/opencnn
An Open Convolutional Neural Network Framework in C++ From Scratch
https://github.com/csukuangfj/opencnn
cnn deep-learning neural-networks-from-scratch opencnn
Last synced: about 1 month ago
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An Open Convolutional Neural Network Framework in C++ From Scratch
- Host: GitHub
- URL: https://github.com/csukuangfj/opencnn
- Owner: csukuangfj
- License: gpl-3.0
- Created: 2018-11-25T04:56:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-13T07:03:07.000Z (about 4 years ago)
- Last Synced: 2024-12-13T07:03:56.400Z (6 months ago)
- Topics: cnn, deep-learning, neural-networks-from-scratch, opencnn
- Language: C++
- Homepage:
- Size: 703 KB
- Stars: 59
- Watchers: 5
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: COPYING
Awesome Lists containing this project
README
[](https://travis-ci.com/csukuangfj/OpenCNN)
*I am trying to port to [Bazel][10] and adding more documentation. It may break some
of the existing functionalities; but it should be stable in three weeks*.
[Documentation][9]
# OpenCNN
OpenCNN is a convolutional neural network framework implemented
with C++11 from scratch.
## Table of contents
- [Features](#features)
- [Supported Layers](#supported-layers)
- [Build](#build)
- [Example with MNIST](#example-with-mnist)
- [Usage](#usage)
- [TODO](#todo)
- [License](#License)
## Features
- Easy to understand
* Simply implemented and a good source for learning CNN
- Easy to extend
* Well defined interface for adding new layer types
- Few dependencies
* Depends only on [protobuf][1], [glog][2] and [gflags][3]
- Fully tested
* Every layer is covered by unit test with [googletest][4]
* [autodiff][5] (in forward mode) is implemented to verify the correctness of forward/backward propagation
- Pure C++
* If you are a big fan of C++
- Runs on CPU
* No GPU is needed.
* 95.21% accuracy on MNIST test dataset in 5000 iterations with a batch size of 16
## Supported Layers
- convolutional
- batch normalization
- ReLU
- leaky ReLU
- max pooling
- full connected
- dropout
- softmax
- cross entropy loss (i.e., negative log loss)
- softmax with cross entropy loss
- L2 loss
## Build
### Install Dependencies on Linux (Ubuntu)
```sh
sudo apt-get install libprotobuf-dev protobuf-compiler libgflags-dev libgoogle-glog-dev
```
### Install Dependencies on Mac OS X
```
brew install gflags glog protobuf
```
### Compile From Source
```sh
git clone https://github.com/csukuangfj/OpenCNN.git
cd OpenCNN
mkdir build
cd build
cmake ..
make
```
### Run Unit Test
```sh
cd OpenCNN/build
./gtest
```
It should pass all the test cases on your system.
## Example with MNIST
We use the following network architecture for MNIST:
| Layers | Description |
|-----------------------|----------------------------------|
| Input | dim: 1x28x28 |
| Convolution-1 | num_output: 32, kernel_size: 3x3 |
| Batch normalization-1 | |
| ReLU-1 | |
| Convolution-2 | num_output: 32, kernel_size: 3x3 |
| Batch normalization-2 | |
| ReLU-2 | |
| Max pooling-1 | win_size: 2x2, stride: 2x2 |
| Convolution-3 | num_output:64, kernel_size: 3x3 |
| Batch normalization-3 | |
| ReLU-3 | |
| Convolution-4 | num_output: 64, kernel_size: 3x3 |
| Batch normalization-4 | |
| ReLU-4 | |
| Max pooling-2 | win_size: 2x2, stride: 2x2 |
| Full connected-1 | num_output: 512 |
| Batch normalization-5 | |
| ReLU-5 | |
| Dropout-1 | keep_prob: 0.8 |
| Full connected-2 | num_output: 10 |
| Softmax with log loss | |
During the training a batch size of 16 is used and the accuracy
reaches 95.21% after 5000 iterations. The results for training loss and
test accuracy are plotted in the following figure:
![training-loss-test-accuracy-versus-iterations][6]
A pretrained model taken after 20000 iterations achieving an accuracy
of 96.74% is provided in [OpenCNN-Models][8].
## Usage
Please refer to [examples/mnist][7] for how to use OpenCNN.
More tutorials will be provided later.
## TODO
- [ ] Add advanced optimizers
- [ ] Add more layer types
- [ ] Make code run faster
- [ ] Tutorials and documentation
## License
[10]: https://bazel.build/
[9]: https://csukuangfj.github.io/doc/OpenCNN/
[8]: https://github.com/csukuangfj/OpenCNN-Models/tree/master/mnist
[7]: https://github.com/csukuangfj/OpenCNN/tree/master/examples/mnist
[6]: /examples/mnist/loss-accuracy-iter.png
[5]: https://en.wikipedia.org/wiki/Automatic_differentiation
[4]: https://github.com/abseil/googletest
[3]: https://github.com/gflags/gflags
[2]: https://github.com/google/glog
[1]: https://github.com/protocolbuffers/protobuf