Sequential data

Presentation

Sequential data is mostly used for classification of dataset composed of several examples of different durations. For example chess games, different sounds,videos, etc.

Declaration

Data(problem typeOfProblem,
     std::vector<std::vector<std::vector<float>>>& trainingInputs,
     std::vector<std::vector<float>>& trainingLabels,
     std::vector<std::vector<std::vector<float>>>& testingInputs,
     std::vector<std::vector<float>>& testingLabels,
     nature::timeSeries);

Arguments

• trainingInputs: 3D vector of all the data inputs use to train the neural network. Each vector<float> represents an input for the neural network for one example at one moment.
• trainingLabels: 2D vector of all the expected outputs use to train the neural network. Each vector<float> represents the expected output by the neural network for the corresponding input.
• testingInputs: 3D vector of all the data inputs use to evaluate the neural network. Each vector<float> represents an input for the neural network for one example at one moment.
• testingLabels: 2D vector of all the expected outputs use to evaluate the neural network. Each vector<float> represents the expected output by the neural network for the corresponding input.
• typeOfTemporal: An enum corresponding to the temporal nature of problem associated with the data. There are 3 types of temporal nature nonTemporal, sequential and timeSeries.
• numberOfRecurrences: Size of sequence used for train neural network. Only used for timeSeries otherwise leave the value at 0.

The labels is a 2D vector and inputs is a 3D vector because each example is represented by a 2D vector of each vector of input at each time.