58 lines
1.2 KiB
C++
58 lines
1.2 KiB
C++
#pragma once
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#include "core/omp_config.h"
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#include "utils/vector.h"
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#include "utils/matrix.h"
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#include "utils/matcast.h"
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#include "numerics/clip.h"
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#include "numerics/log.h"
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#include "numerics/sub.h"
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#include "Loss.h"
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namespace neural_networks{
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template <typename T>
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struct Loss_MeanAbsoluteError : Loss<T> {
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utils::Matrix<T> dinputs;
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utils::Matrix<T> y_true;
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utils::Vector<T> sample_losses;
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utils::Vector<T> forward(const utils::Matrix<T>& y_pred, const utils::Matrix<T>& y_true) override{
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// Calculate loss
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sample_losses = numerics::mean_rowwise(numerics::abs(numerics::sub(y_true, y_pred)));
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// Return losses
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return sample_losses;
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}
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void backward(const utils::Matrix<T>& dvalues, const utils::Matrix<T>& y_true) override{
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// Number of samples
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const T samples = static_cast<T> (dvalues.rows());
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// Number of outputs in every sample
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const T outputs = static_cast<T> (dvalues.cols());
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// Gradient values
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dinputs = numerics::div(numerics::sign(numerics::sub(y_true, dvalues)), outputs);
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// Normalise gradient
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dinputs = numerics::div(dinputs, samples);
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}
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};
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} // end namespace neural_networks
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