protected Vector[] ConvertDataToSequence(NeuralNetworkData data)
{
CheckConditionOnException(data.DataType != NeuralNetworkDataType.SequenceOfVectors, "Recurrent neural network implements the 'sequence to sequence' approach, " +
"so the input shape must contain only a sequence of vectors. For example: " +
"nt.Run(new NeuralNetworkData(new Vector[] { new [] { 0.0, 0.3 }, new [] { 0.4, 0.7 } }))");
return(data.Shape.Values.First());
}
public override NeuralNetworkResult Run(NeuralNetworkData inputData)
{
var input = ConvertDataToVector(inputData);
var output = Run(input);
return(new NeuralNetworkResult(output));
}
public override NeuralNetworkResult Run(NeuralNetworkData inputData)
{
var inputs = ConvertDataToSequence(inputData);
var outputs = Run(inputs);
return(new NeuralNetworkResult(outputs));
}
public override NeuralNetworkLearnResult Learn(NeuralNetworkData inputData, NeuralNetworkData idealData)
{
var input = ConvertDataToVector(inputData);
var ideal = ConvertDataToVector(idealData);
var(output, error) = Learn(input, ideal);
return(new NeuralNetworkLearnResult(output, error));
}
public override NeuralNetworkLearnResult Learn(NeuralNetworkData inputData, NeuralNetworkData idealData)
{
var inputs = ConvertDataToSequence(inputData);
var ideals = ConvertDataToSequence(idealData);
var(outputs, errors) = Learn(inputs, ideals);
return(new NeuralNetworkLearnResult(outputs, errors));
}
public override NeuralNetworkLearnResult Learn(NeuralNetworkData inputData)
{
throw new NotImplementedException();
}
protected Vector ConvertDataToVector(NeuralNetworkData data)
{
CheckConditionOnException(data.DataType != NeuralNetworkDataType.Vector, "Perceptron implements the 'one to one' approach, " +
"so the input shape must contain only a single vector. For example: nt.Run(new NeuralNetworkData(new [] { 0.0, 0.3 }))");
return(data.Shape.Values.First().First().Copy());
}
public abstract NeuralNetworkLearnResult Learn(NeuralNetworkData inputData);
public abstract NeuralNetworkResult Run(NeuralNetworkData inputData);
