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.