Awesome

Cat's Eye

Neural network library written in C and Javascript

Features

• Lightweight and minimalistic:
  • Header only
  • Just include catseye.h and write your model in c. There is nothing to install.
  • Small dependency & simple implementation
• Fast: [under construction]
  • OpenCL support (GPGPU)
  • OpenGL support (GPGPU)
  • SSE, AVX support (But gcc and clang support SIMD...)
  • OpenMP support
  • Support half precision floats (16bit)
• Supported networks:
  • Activation functions
    • sigmoid
    • softmax
    • tanh, scaled tanh (1.7519 * tanh(2/3x))
    • ReLU, Leaky ReLU, ELU, RReLU
    • abs
    • identity
  • Loss functions
    • cross-entropy, mean-squared-error
  • Optimization algorithms
    • SGD (stochastic gradient descent) with/without L2 normalization
    • Momentum SGD
    • AdaGrad
    • RMSProp
    • Adam
  • Layer types
    • linear (mlp)
    • convolution
    • convolution 1d
    • deconvolution
    • Sub-Pixel Convolution (Pixel Shuffler)
    • max pooling
    • average pooling
    • global average pooling (GAP)
    • batch normalization
    • concat
    • shortcut
• Loader formats:
  • PNG
  • cifar [https://www.cs.toronto.edu/~kriz/cifar.html]
  • MNIST
  • Data Augmentation (noise, zoom, translation)

Usage

Just include header files in your project.

for more information, see example/

$ dnf install ghostscript ocl-icd-devel
$ cd example
$ make
$ ./sin

Demo

• Recognizing handwritten digits by MNIST training (example/mnist_train.c)

  • http://yui0.github.io/catseye/example/html/mnist.html

• Recognizing pictures (example/cifar10_train.c)

  • http://yui0.github.io/catseye/example/html/cifar10.html

• Neural Network 'paints' an image (example/paint.c)

  

  

  

• Function approximation (example/sin.c)

  

• Convolution Autoencoder (example/mnist_cnn_autoencoder.c,example/cifar_autoencoder.c)

  

• DCGAN (example/mnist_lsgan.c)

  • epoch 1900

  

• Autoencoder (example/mnist_autoencoder.c,example/mnist_autoencoder2.c)

  

• VanillaGAN

  

• Convolutional Neural Networks (example/mnist_cnn_train.c)

  • tanh, 7x7, 32ch, 99.2%

  

Question

• Neural Network Always Produces Same/Similar Outputs for Any Input
  • Scale down the problem to manageable size.
  • Make sure you have enough hidden units.
  • Change the activation function and its parameters.
  • Change learning algorithm parameters.

Refrences

• Documents

  • Neural Networks and Deep Learning [http://nnadl-ja.github.io/nnadl_site_ja/chap1.html]

  • Explain easy backpropagation in the universe [https://www.yukisako.xyz/entry/backpropagation]

  • Optimization algorithm with super easy explanation [https://qiita.com/omiita/items/1735c1d048fe5f611f80]

  • Basic parts of calculation graph used for backpropagation method, etc. [https://qiita.com/t-tkd3a/items/031c0a4dbf25fd2866a3]

  • Automatic differentiation [https://tech-lab.sios.jp/archives/21072]

  • Machine learning [http://hokuts.com/category/%E3%83%97%E3%83%AD%E3%82%B0%E3%83%A9%E3%83%A0/%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92/]

  • tiny-cnn [https://github.com/nyanp/tiny-cnn/wiki/%E5%AE%9F%E8%A3%85%E3%83%8E%E3%83%BC%E3%83%88]

  • CS231n Convolutional Neural Networks for Visual Recognition [http://cs231n.github.io/neural-networks-3/#anneal]

  • SVM [http://d.hatena.ne.jp/echizen_tm/20110627/1309188711]

  • Autoencoder

    • Summary of research on VAE [https://www.hiro877.com/entry/vae-research]
    • Hello Autoencoder [https://kiyukuta.github.io/2013/08/20/hello_autoencoder.html]
    • Autoencoder [https://pc.atsuhiro-me.net/entry/2015/08/18/003402]
    • Autoencoder [https://www.slideshare.net/at_grandpa/chapter5-50042838]

  • Convolutional Neural Networks [http://blog.yusugomori.com/post/129688163130/%E6%95%B0%E5%BC%8F%E3%81%A7%E6%9B%B8%E3%81%8D%E4%B8%8B%E3%81%99-convolutional-neural-networks-cnn]

  • Backpropagation [http://postd.cc/2015-08-backprop/]

  • Perceptron [http://tkengo.github.io/blog/2015/08/21/visual-perceptron/]

• Programing
  • Multilayer perceptron [http://kivantium.hateblo.jp/entry/2014/12/22/004640]
  • Weather example [http://arakilab.media.eng.hokudai.ac.jp/~t_ogawa/wiki/index.php?LibSVM]
  • Recognizing handwritten digits [http://aidiary.hatenablog.com/entry/20140201/1391218771]
  • Recognizing handwritten digits on Web [http://d.hatena.ne.jp/sugyan/20151124/1448292129]
  • Image generator by Denoising Autoencoder [http://joisino.hatenablog.com/entry/2015/09/09/224157]
  • Neural Network 'paints' an image [http://cs.stanford.edu/people/karpathy/convnetjs/demo/image_regression.html]
• Data
  • UTKFace - Large Scale Face Dataset
  • Caltech 101