A super mini but powerful deep learning framework written in pure c language

Select one of the following experiments

• image painting
• mnist
• cifar10

Or try some funny examples:

• A Simple Example To Learn Deconvolution And Locally Connected Deconvolution
• Training An Artificial Neural Network To Sketch From Nature
• The "Hello World" Example Of Audio Recognition Using Raw Waveform Data As Input
• Audio Recognition Using Spectrogram As Input
• Sensitivity Analysis(Feature Importance Analysis) Using Artificial Neural Network
• Colorizing Black And White Photos - A Funny Example For Deep Learning Beginners

• Learn without writing any code
• Super easy
• Super mini
• Better performance over a variety of tasks

• Convolution Layer
  • Grouped Convolution Layer
  • Dilated (Grouped) Convolution Layer
  • Locally Connected (Grouped) Convolution Layer
  • Locally Connected Dilated (Grouped) Convolution Layer
• Deconvolution Layer
  • Grouped Deconvolution Layer
  • Dilated (Grouped) Deconvolution Layer
  • Locally Connected (Grouped) Deconvolution Layer
  • Locally Connected Dilated (Grouped) Deconvolution Layer
• Pooling Layer
  • Max Pooling
  • Abs Max Pooling
  • Average Pooling
  • Random Pooling
• Merge Layer
• Dropout Layer
• Shortcut Layer

Let's take the famous mnist dataset as example

# model.txt
[
    {Shape: [28, 28, 1]},
    {Shape: [14, 14, 2], Kernel: [6, 6], Pad: [2, 2]},
    {Shape: [7, 7, 4], Kernel: [6, 6], Pad: [2, 2]},
    {Shape: [1, 1, 10], Kernel: [7, 7]},
]

# conf.txt
{
    Load: 0,
    
    RateInit: 0.02,

    EpochTrain: 8,
    EpochDecay: 0.2,
    EpochLog: 0.2,

    InputType: 'pointwise',
    LossType: 'max',
    Regularization: 2,

    Image: {
      Draw: true,
      Src: 'layer 0',
    },

    FreqTest: 2,
}

# niy conf.txt

You will get a test accuracy around 98.5% after a few minutes

There are four kinds of file

1. Model
   　Stores model information
2. Conf
   　Stores configuration information
3. Sample
   　Stores sample data
4. Para
   　Stores biases, weights, loss, rate...

Niy uses CON to define model and conf files

• Support GPU
• Support Intel MKL
• Support RNN/LSTM
• Support Deep Reinforcement Learning

Apache License 2.0