protected override VectorBatch _backPropagate(VectorBatch outputGradient)
{
if (outputGradient == null || outputGradient.Dimension != NumberOfOutputs)
{
throw new ArgumentException("outputgradient may not be null and must have dimension equal to the number of units.");
}
return(VectorBatch.ApplyFunction((x, y) => x * (1 - x) * y, _output, outputGradient));
}
protected override VectorBatch _run(VectorBatch inputbatch)
{
_input = inputbatch;
if (_neuralFunction != null)
{
_output = VectorBatch.ApplyFunction(x => _neuralFunction(x), _input);
}
else
{
_output = inputbatch;
}
return(_output);
}
protected override VectorBatch _run(VectorBatch inputbatch)
{
if (inputbatch == null || inputbatch.Dimension != NumberOfInputs)
{
throw new ArgumentException("input may not be null and must have dimension equal to the number of units.");
}
_input = inputbatch;
VectorBatch result = inputbatch.SubractVectorMaxima();
result = VectorBatch.ApplyFunction(x => Math.Exp(x), result);
result = result.DivideByComponentSums();
_output = result;
return(_output);
}
protected override VectorBatch _backPropagate(VectorBatch outputGradient)
{
if (outputGradient == null || outputGradient.Dimension != NumberOfOutputs)
{
throw new ArgumentException("outputgradient may not be null and must have dimension equal to NumberOfNeurons.");
}
if (_neuralFunctionDerivative == null)
{
return(outputGradient);
}
VectorBatch derivative = VectorBatch.ApplyFunction((x, y) => _neuralFunctionDerivative(x, y), _input, _output);
VectorBatch result = VectorBatch.ApplyFunction((x, y) => x * y, derivative, outputGradient);
return(result);
}