userguide.md

DSSTNE currently only supports Fully Connected layers and the network structure for training is defined through a config json representation.

Data Formats

DSSTNE Engine works with data only in NetCDF format.

If your data is not available in NetCDF format, DSSTNE includes a utility, generateNetCDF, that can be used to convert data from a simple (human-readable) form to NetCDF format.

Example

In the custom format supported by generateNetCDF, each line contains a sample. Each sample begins with a sample name followed by a TAB (\t) character, then a list of feature names associated with that sample. Feature names are delimited by a colon character (:).

In the following example, each sample is associated with three features:

Example1	Feature1:Feature2:Feature3
Example2	Feature5:Feature2:Feature4
Example3	Feature6:Feature7:Feature8

Note that sample and feature names should consist only of alpha-numeric characters, underscores and periods.

Indicators and Analog Values

In the example above, the features are all indicators - that is, they represent the presence of a given feature.

Our custom format also supports features with numeric, or analog, values. An analog value can be provided by appending a comma character (,) to a feature name, followed by an integer or floating point value.

Example1    Feature1,123
Example2    Feature2,234
Example3    Feature3,345:Feature4,456

Support for analog values in generateNetCDF is currently incomplete, but is coming soon.

Neural Network Layer Definition Language

The definitions for the Neural Network fed into DSSTNE is represented in a Json Format. All the supported feature can be found at LDL.txt. Sample one is given below

{
    "Version" : 0.8,
    "Name" : "2 Hidden Layer",
    "Kind" : "FeedForward",

    "ShuffleIndices" : false,


    "ScaledMarginalCrossEntropy" : {
        "oneTarget" : 1.0,
        "zeroTarget" : 0.0,
        "oneScale" : 1.0,
        "zeroScale" : 1.0
    },
    "Layers" : [
        { "Name" : "Input", "Kind" : "Input", "N" : "auto", "DataSet" : "input", "Sparse" : true },
        { "Name" : "Hidden1", "Kind" : "Hidden", "Type" : "FullyConnected", "Source" : "Input", "N" : 1024, "Activation" : "Relu", "Sparse" : false, "pDropout" : 0.5, "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01 } },
        { "Name" : "Hidden2", "Kind" : "Hidden", "Type" : "FullyConnected", "Source" : ["Hidden1"], "N" : 1024, "Activation" : "Relu", "Sparse" : false, "pDropout" : 0.5, "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01 } },  
        { "Name" : "Output", "Kind" : "Output", "Type" : "FullyConnected", "DataSet" : "output", "N" : "auto", "Activation" : "Sigmoid", "Sparse" : true , "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01, "Bias" : -10.2 }}
    ],

    "ErrorFunction" : "ScaledMarginalCrossEntropy"
}

Layers

"Layers" : [
        { "Name" : "Input", "Kind" : "Input", "N" : "auto", "DataSet" : "gl_input", "Sparse" : true },
        { "Name" : "Hidden1", "Kind" : "Hidden", "Type" : "FullyConnected", "Source" : "Input", "N" : 1024, "Activation" : "Relu", "Sparse" : false, "pDropout" : 0.5, "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01 } },
        { "Name" : "Hidden2", "Kind" : "Hidden", "Type" : "FullyConnected", "Source" : ["Hidden1"], "N" : 1024, "Activation" : "Relu", "Sparse" : false, "pDropout" : 0.5, "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01 } },  
        { "Name" : "Output", "Kind" : "Output", "Type" : "FullyConnected", "DataSet" : "gl_output", "N" : "auto", "Activation" : "Sigmoid", "Sparse" : true , "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01, "Bias" : -10.2 }}
    ],

Neural Network is represented in Layers in a configuration json. We only support Fully Connected layers and the layer can be of 3 different kinds

1. Input This is the input layer for the Neural Network and at least one Input layer is required for training. There should be a DataSet required for the input layer.

2. Hidden Hidden Layers are Layers which connect between layers. It Does require a DataSet but rather a Source. If Source is not mentioned then the previous Layer is taken as Source

3. Output Output Layer is the layer where the truths are compared against. At least one Output Layer is required and there should be a DataSet also for the Output Layer.

#Activation

{ "Name" : "Hidden2", "Kind" : "Hidden", ..............., "Activation" : "Relu", ................ }

Activation function for each layer is passed on as a parameter to the Layer Definition in the field Activation. The following Activation functions are supported:

• Sigmoid
• Tanh
• Linear
• RectifiedLinear (RELU)
• LeakyRectifiedLinear (LRELU)
• ExponentialLinear (ELU)
• ScaledExponentialLinear (SELU)
• SoftPlus
• SoftSign
• SoftMax
• ReluMax
• LinearMax

Size

{ "Name" : "Input", "Kind" : "Input", "N" : "auto", "DataSet" : "gl_input", "Sparse" : true }

{ "Name" : "Hidden1", "Kind" : "Hidden", "Type" : "FullyConnected", "Source" : "Input", "N" : 1024, "Activation" : ...}

Size of the Layer is added to the N field in Layer Definition and is represented as an Integer. For Input and Output layers auto is also supported so that the size is automatically figured from the dataset.

Initialization

{ "Name" : "Hidden1", ....... "WeightInit" : { "Scheme" : "Gaussian", "Scale" : 0.01 }............................ }

Weight Initialization between the Layers are defined by WeightInit filed in the Layer definition. The supported weight initializers are:

• Xavier
• CaffeXavier
• Gaussian
• Uniform
• UnitBall
• SELU
• Constant

Optimization

/ Set to default training mode Nesterov.
    TrainingMode mode=Nesterov;
    pNetwork->SetTrainingMode(mode)

Optimization for the Network is currently passed through the code. Currently supported optimizers are:

• SGD
• Momentum
• AdaGrad
• Nesterov
• RMSProp
• AdaDelta
• Adam