Artificog Machine Learning Framework

Artificog is a simple Machine Learning Framework consists of implementation of various algorithms including Deep Learning methods.

Building

In order to build the code you can use cmake ,

$ mkdir build 
$ cd build 
$ cmake ..
$ make

Usage

JSON File

First you should have a JSON file which stores the topology and parameters of the network.

{
  "Network": {
    "Train_Data" : "../Data/mnist_train.art",
    "Test_Data"  : "../Data/mnist_test.art",
    "GPU" : true,
    "Epochs" : 2,
    "Learning_Rate": 0.004,
    "Layers": [
      {
        "Type": "FC",
        "Neurons_Count": 784,
        "Function": "tanh"
      },
      {
        "Type": "FC",
        "Neurons_Count": 470,
        "Function": "tanh"
      },
      {
        "Type": "FC",
        "Neurons_Count": 10,
        "Function": "softmax"
      }
    ],
    "Labels":[0,1,2,3,4,5,6,7,8,9]
  }
}

We need to specify the path for train data and test data using Train_Data and Test_Data fields.

Data Format

The data format that is used by Artificog is similar to LIBSVM input format.

MNIST Example

In order to convert MNIST standard data format to Artificog compatible data format you can use the script Examples/MNIST/convert.sh. It stores compatible dataset into Data/ directory and now you can run ./artificog inside build direectory (or ./artificog ../Network.json)and see how it learns to classify MNIST dataset.

For More information see : http://artificog.com