Interactive Training using Python

Layer class (layer.py) has the following methods for an interactive training. For the basic usage of Python binding features, please refer to python.md.

ComputeFeature(self, *srclys)

  • This method creates and sets up singa::Layer and maintains its source layers, then call singa::Layer::ComputeFeature(...) for data transformation.
    • *srclys: (an arbtrary number of) source layers

ComputeGradient(self)

  • This method creates calls singa::Layer::ComputeGradient(...) for gradient computation.

GetParams(self)

  • This method calls singa::Layer::GetParam() to retrieve parameter values of the layer. Currently, it returns weight and bias. Each parameter is a 2D numpy array.

SetParams(self, *params)

  • This method sets parameter values of the layer.
    • *params: (an arbitrary number of) parameters, each of which is a 2D numpy array. Typically, it sets weight and bias, 2D numpy array.

Dummy class is a subclass of Layer, which is provided to fetch input data and/or label information. Specifically, it creates singa::DummyLayer.

Feed(self, shape, data, aux_data)

  • This method sets input data and/or auxiary data such as labels.
    • shape: the shape (width and height) of dataset
    • data: input dataset
    • aux_data: auxiary dataset (e.g., labels)

In addition, Dummy class has two subclasses named ImageInput and LabelInput.

  • ImageInput class will take three arguments as follows.

    __init__(self, height=None, width=None, nb_channel=1)

  • Both ImageInput and LabelInput classes have their own Feed method to call Feed of Dummy class.

    Feed(self, data)