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Adagrad.h
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Adagrad.h
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// @(#)root/tmva/tmva/dnn:$Id$
// Author: Ravi Kiran S
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : TAdagrad *
* *
* *
* Description: *
* Adagrad Optimizer Class *
* *
* Authors (alphabetical): *
* Ravi Kiran S <sravikiran0606@gmail.com> - CERN, Switzerland *
* *
* Copyright (c) 2005-2018: *
* CERN, Switzerland *
* U. of Victoria, Canada *
* MPI-K Heidelberg, Germany *
* U. of Bonn, Germany *
* *
* Redistribution and use in source and binary forms, with or without *
* modification, are permitted according to the terms listed in LICENSE *
* (see tmva/doc/LICENSE) *
**********************************************************************************/
#ifndef TMVA_DNN_ADAGRAD
#define TMVA_DNN_ADAGRAD
#include "TMatrix.h"
#include "TMVA/DNN/Optimizer.h"
#include "TMVA/DNN/Functions.h"
#include <vector>
namespace TMVA {
namespace DNN {
/** \class TAdagrad
* Adagrad Optimizer class
*
* This class represents the Adagrad Optimizer.
*/
template <typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>,
typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>
class TAdagrad : public VOptimizer<Architecture_t, Layer_t, DeepNet_t> {
public:
using Matrix_t = typename Architecture_t::Matrix_t;
using Scalar_t = typename Architecture_t::Scalar_t;
protected:
Scalar_t fEpsilon; ///< The Smoothing term used to avoid division by zero.
std::vector<std::vector<Matrix_t>>
fPastSquaredWeightGradients; ///< The sum of the square of the past weight gradients associated with the deep net.
std::vector<std::vector<Matrix_t>>
fPastSquaredBiasGradients; ///< The sum of the square of the past bias gradients associated with the deep net.
std::vector<std::vector<Matrix_t>>
fWorkWeightTensor; ///< working tensor used to keep a temporary copy of weights or weight gradients
std::vector<std::vector<Matrix_t>>
fWorkBiasTensor; ///< working tensor used to keep a temporary copy of bias or bias gradients
/*! Update the weights, given the current weight gradients. */
void UpdateWeights(size_t layerIndex, std::vector<Matrix_t> &weights, const std::vector<Matrix_t> &weightGradients);
/*! Update the biases, given the current bias gradients. */
void UpdateBiases(size_t layerIndex, std::vector<Matrix_t> &biases, const std::vector<Matrix_t> &biasGradients);
public:
/*! Constructor. */
TAdagrad(DeepNet_t &deepNet, Scalar_t learningRate = 0.01, Scalar_t epsilon = 1e-8);
/*! Destructor. */
~TAdagrad() = default;
/*! Getters */
Scalar_t GetEpsilon() const { return fEpsilon; }
std::vector<std::vector<Matrix_t>> &GetPastSquaredWeightGradients() { return fPastSquaredWeightGradients; }
std::vector<Matrix_t> &GetPastSquaredWeightGradientsAt(size_t i) { return fPastSquaredWeightGradients[i]; }
std::vector<std::vector<Matrix_t>> &GetPastSquaredBiasGradients() { return fPastSquaredBiasGradients; }
std::vector<Matrix_t> &GetPastSquaredBiasGradientsAt(size_t i) { return fPastSquaredBiasGradients[i]; }
};
//
//
// The Adagrad Optimizer Class - Implementation
//_________________________________________________________________________________________________
template <typename Architecture_t, typename Layer_t, typename DeepNet_t>
TAdagrad<Architecture_t, Layer_t, DeepNet_t>::TAdagrad(DeepNet_t &deepNet, Scalar_t learningRate, Scalar_t epsilon)
: VOptimizer<Architecture_t, Layer_t, DeepNet_t>(learningRate, deepNet), fEpsilon(epsilon)
{
std::vector<Layer_t *> &layers = deepNet.GetLayers();
const size_t layersNSlices = layers.size();
fPastSquaredWeightGradients.resize(layersNSlices);
fPastSquaredBiasGradients.resize(layersNSlices);
fWorkWeightTensor.resize(layersNSlices);
fWorkBiasTensor.resize(layersNSlices);
for (size_t i = 0; i < layersNSlices; i++) {
const size_t weightsNSlices = (layers[i]->GetWeights()).size();
// weight and weight gradients tensors should have same
Architecture_t::CreateWeightTensors( fPastSquaredWeightGradients[i], layers[i]->GetWeights());
for (size_t j = 0; j < weightsNSlices; j++) {
initialize<Architecture_t>(fPastSquaredWeightGradients[i][j], EInitialization::kZero);
}
const size_t biasesNSlices = (layers[i]->GetBiases()).size();
Architecture_t::CreateWeightTensors( fPastSquaredBiasGradients[i], layers[i]->GetBiases());
for (size_t j = 0; j < biasesNSlices; j++) {
initialize<Architecture_t>(fPastSquaredBiasGradients[i][j], EInitialization::kZero);
}
Architecture_t::CreateWeightTensors(fWorkWeightTensor[i], layers[i]->GetWeights());
Architecture_t::CreateWeightTensors(fWorkBiasTensor[i], layers[i]->GetBiases());
}
}
//_________________________________________________________________________________________________
template <typename Architecture_t, typename Layer_t, typename DeepNet_t>
auto TAdagrad<Architecture_t, Layer_t, DeepNet_t>::UpdateWeights(size_t layerIndex, std::vector<Matrix_t> &weights,
const std::vector<Matrix_t> &weightGradients) -> void
{
auto ¤tLayerPastSquaredWeightGradients = this->GetPastSquaredWeightGradientsAt(layerIndex);
const size_t weightsNSlices = weights.size();
assert(currentLayerPastSquaredWeightGradients.size() == weightsNSlices);
for (size_t i = 0; i < weightsNSlices; i++) {
auto ¤tSquaredWeightGradients = fWorkWeightTensor[layerIndex][i];
// Vt = Vt-1 + currentSquaredWeightGradients
Architecture_t::Copy(currentSquaredWeightGradients, weightGradients[i]);
Architecture_t::SquareElementWise(currentSquaredWeightGradients);
Architecture_t::ScaleAdd(currentLayerPastSquaredWeightGradients[i], currentSquaredWeightGradients, 1.0);
// updating the weights.
// theta = theta - learningRate * currentWeightGradients / (sqrt(Vt + epsilon))
auto ¤tWeightUpdates = fWorkWeightTensor[layerIndex][i]; // reuse the work tensor for the weight updates now
Architecture_t::Copy(currentWeightUpdates, currentLayerPastSquaredWeightGradients[i]);
Architecture_t::ConstAdd(currentWeightUpdates, this->GetEpsilon());
Architecture_t::SqrtElementWise(currentWeightUpdates);
Architecture_t::ReciprocalElementWise(currentWeightUpdates);
Architecture_t::Hadamard(currentWeightUpdates, weightGradients[i]);
Architecture_t::ScaleAdd(weights[i], currentWeightUpdates, -this->GetLearningRate());
}
}
//_________________________________________________________________________________________________
template <typename Architecture_t, typename Layer_t, typename DeepNet_t>
auto TAdagrad<Architecture_t, Layer_t, DeepNet_t>::UpdateBiases(size_t layerIndex, std::vector<Matrix_t> &biases,
const std::vector<Matrix_t> &biasGradients) -> void
{
std::vector<Matrix_t> ¤tLayerPastSquaredBiasGradients = this->GetPastSquaredBiasGradientsAt(layerIndex);
const size_t biasesNSlices = biases.size();
assert(currentLayerPastSquaredBiasGradients.size() == biasesNSlices);
for (size_t i = 0; i < biasesNSlices; i++) {
// Vt = Vt-1 + currentSquaredBiasGradients
auto ¤tSquaredBiasGradients = fWorkBiasTensor[layerIndex][i];
Architecture_t::Copy(currentSquaredBiasGradients, biasGradients[i]);
Architecture_t::SquareElementWise(currentSquaredBiasGradients);
Architecture_t::ScaleAdd(currentLayerPastSquaredBiasGradients[i], currentSquaredBiasGradients, 1.0);
// updating the biases.
// theta = theta - learningRate * currentBiasGradients / (sqrt(Vt + epsilon))
auto ¤tBiasUpdates = fWorkBiasTensor[layerIndex][i];
Architecture_t::Copy(currentBiasUpdates, currentLayerPastSquaredBiasGradients[i]);
Architecture_t::ConstAdd(currentBiasUpdates, this->GetEpsilon());
Architecture_t::SqrtElementWise(currentBiasUpdates);
Architecture_t::ReciprocalElementWise(currentBiasUpdates);
Architecture_t::Hadamard(currentBiasUpdates, biasGradients[i]);
Architecture_t::ScaleAdd(biases[i], currentBiasUpdates, -this->GetLearningRate());
}
}
} // namespace DNN
} // namespace TMVA
#endif