protected void CanOptimizeRosenbrock(OptimizerBase <float> optimizer)
{
var weight = new NeuroWeight <float>(Matrix <float> .Build.Dense(2, 1));
//_output.WriteLine($"Rosenbrock: {Rosenbrock(weight.Weight)}");
//_output.WriteLine(weight.Weight.ToMatrixString());
var watch = new Stopwatch();
watch.Start();
for (int i = 0; i < 10000; i++)
{
RosenbrockGrad(weight.Weight, weight.Gradient);
optimizer.Optimize(weight);
//_output.WriteLine($"Rosenbrock: {Rosenbrock(weight.Weight)}");
//_output.WriteLine(weight.Weight.ToMatrixString());
}
watch.Stop();
double result = Rosenbrock(weight.Weight);
result.ShouldBeLessThanOrEqualTo(6e-5);
_output.WriteLine($"Rosenbrock: {result}");
_output.WriteLine($"Optimized in {watch.Elapsed}");
}
private GruLayer(GruLayer <T> other) : base(other)
{
_wxh = other._wxh.Clone();
_wxr = other._wxr.Clone();
_wxz = other._wxz.Clone();
_whh = other._whh.Clone();
_whr = other._whr.Clone();
_whz = other._whz.Clone();
_bxh = other._bxh.Clone();
_bxr = other._bxr.Clone();
_bxz = other._bxz.Clone();
_bhh = other._bhh.Clone();
_bhr = other._bhr.Clone();
_bhz = other._bhz.Clone();
_lastH = other._lastH.CloneMatrix();
_hiddenOnes = other._hiddenOnes.CloneMatrix();
Inputs = other.Inputs.Clone();
Outputs = other.Outputs.Clone();
_hPropVals = other._hPropVals.Clone();
_hNewVals = other._hNewVals.Clone();
_rVals = other._rVals.Clone();
_zVals = other._zVals.Clone();
_hSize = other._hSize;
RegisterWeights();
}
public GruLayer(int xSize, int hSize,
IMatrixInitializer <T> linearWeightInitializer,
IMatrixInitializer <T> hiddenWeightInitializer,
IMatrixInitializer <T> biasInitializer)
{
_hSize = hSize;
_wxh = new NeuroWeight <T>(linearWeightInitializer.CreateMatrix(hSize, xSize));
_wxr = new NeuroWeight <T>(linearWeightInitializer.CreateMatrix(hSize, xSize));
_wxz = new NeuroWeight <T>(linearWeightInitializer.CreateMatrix(hSize, xSize));
_whh = new NeuroWeight <T>(hiddenWeightInitializer.CreateMatrix(hSize, hSize));
_whr = new NeuroWeight <T>(hiddenWeightInitializer.CreateMatrix(hSize, hSize));
_whz = new NeuroWeight <T>(hiddenWeightInitializer.CreateMatrix(hSize, hSize));
_bxh = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
_bxr = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
_bxz = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
_bhh = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
_bhr = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
_bhz = new NeuroWeight <T>(biasInitializer.CreateMatrix(hSize, 1));
ResetOptimizer();
ErrorFunction = new MeanSquareError <T>();
RegisterWeights();
}
private LinearLayer(LinearLayer <T> other) : base(other)
{
_weights = other._weights.Clone();
_bias = other._bias.Clone();
RegisterWeights(_bias, _weights);
}
public override void AdagradUpdate(Float learningRate, NeuroWeight <Float> weight)
{
using (var ptrs = new MatrixPointersBag <Float>(weight.Weight, weight.Cache2, weight.Gradient))
{
AdagradUpdate(learningRate, ptrs[0], ptrs[1], ptrs[2], weight.Weight.Length());
}
}
public override void AdamUpdate(float learningRate, float b1, float b2, NeuroWeight <Float> weight)
{
using (var ptrs = new MatrixPointersBag <Float>(weight.Weight, weight.Cache1, weight.Cache2, weight.Gradient))
{
AdamUpdate(learningRate, b1, b2, weight.Timestep, ptrs[0], ptrs[1], ptrs[2], ptrs[3], weight.Weight.Length());
}
}
public void RMSPropValuesAreEqual(float learningRate, float decayRate, float weightDecay, float momentum)
{
var local = new NeuroWeight <float>(MatrixFactory.RandomMatrix <float>(10, 10, 1e-2f));
var remote = local.Clone();
for (int i = 0; i < 100; i++)
{
var grad = MatrixFactory.RandomMatrix <float>(10, 10, 1.0f);
grad.CopyTo(local.Gradient);
grad.CopyTo(remote.Gradient);
MathProvider.GravesRmsPropUpdate(weightDecay, learningRate, decayRate, momentum, local);
using (var ptrs = new MatrixPointersBag <float>(true, remote.Weight, remote.Gradient, remote.Cache1, remote.Cache2, remote.CacheM))
{
Interface.TestRMSPropUpdate(ptrs.Definitions[0], ptrs.Definitions[1], ptrs.Definitions[2], ptrs.Definitions[3], ptrs.Definitions[4],
learningRate, decayRate, momentum, weightDecay);
}
local.Weight.ShouldMatrixEqualWithinError(remote.Weight);
local.Cache1.ShouldMatrixEqualWithinError(remote.Cache1);
local.Cache2.ShouldMatrixEqualWithinError(remote.Cache2);
local.CacheM.ShouldMatrixEqualWithinError(remote.CacheM);
local.Gradient.ShouldMatrixEqualWithinError(remote.Gradient);
}
}
public GruLayer(BinaryReader reader) : base(reader)
{
_bxr = NeuroWeight <T> .Load(reader.BaseStream);
_bxz = NeuroWeight <T> .Load(reader.BaseStream);
_bxh = NeuroWeight <T> .Load(reader.BaseStream);
_bhr = NeuroWeight <T> .Load(reader.BaseStream);
_bhz = NeuroWeight <T> .Load(reader.BaseStream);
_bhh = NeuroWeight <T> .Load(reader.BaseStream);
_wxr = NeuroWeight <T> .Load(reader.BaseStream);
_wxz = NeuroWeight <T> .Load(reader.BaseStream);
_wxh = NeuroWeight <T> .Load(reader.BaseStream);
_whr = NeuroWeight <T> .Load(reader.BaseStream);
_whz = NeuroWeight <T> .Load(reader.BaseStream);
_whh = NeuroWeight <T> .Load(reader.BaseStream);
_lastH = MatrixFactory.Load <T>(reader.BaseStream);
_hiddenOnes = Matrix <T> .Build.Dense(_hSize, _lastH.ColumnCount, Matrix <T> .One);
RegisterWeights();
}
public LinearLayer(BinaryReader reader) : base(reader)
{
_bias = NeuroWeight <T> .Load(reader.BaseStream);
_weights = NeuroWeight <T> .Load(reader.BaseStream);
RegisterWeights(_bias, _weights);
}
public LinearLayer(int xSize, int ySize, IMatrixInitializer <T> matrixInitializer)
{
_weights = new NeuroWeight <T>(matrixInitializer.CreateMatrix(ySize, xSize));
_bias = new NeuroWeight <T>(matrixInitializer.CreateMatrix(ySize, 1));
ErrorFunction = new MeanSquareError <T>();
RegisterWeights(_bias, _weights);
}
public void CanTransferWeightThroughNeuroWeightRowMajor()
{
var weight = MatrixFactory.RandomMatrix <float>(2, 3, 5.0f);
var local = new NeuroWeight <float>(weight);
var remote = local.Clone();
MutateMatrixRowMajor(local.Weight);
MutateMatrixRowMajor(local.Gradient);
MutateMatrixRowMajor(local.Cache1);
MutateMatrixRowMajor(local.Cache2);
MutateMatrixRowMajor(local.CacheM);
using (var ptrs = new WeightDefinitionBag <float>(true, remote))
{
GpuInterface.Testing.TestComplexWeightTransferRowMajor(ptrs.Definitions[0]);
}
local.Weight.ShouldMatrixEqualWithinError(remote.Weight);
local.Gradient.ShouldMatrixEqualWithinError(remote.Gradient);
local.Cache1.ShouldMatrixEqualWithinError(remote.Cache1);
local.Cache2.ShouldMatrixEqualWithinError(remote.Cache2);
local.CacheM.ShouldMatrixEqualWithinError(remote.CacheM);
}
/// <summary> /// Performs Adam update on a weight. /// </summary> /// <remarks>See https://arxiv.org/pdf/1412.6980.pdf </remarks> /// <param name="learningRate">Learning rate.</param> /// <param name="b1">Decay rate of first order MAV.</param> /// <param name="b2">Decay rate of second order MAV.</param> /// <param name="weight">Weight.</param> public abstract void AdamUpdate(float learningRate, float b1, float b2, NeuroWeight <T> weight);
public override void Optimize(NeuroWeight <T> weight)
{
MathProvider.GravesRmsPropUpdate(_weightDecay, LearningRate, _decayRate, _momentum, weight);
}
/// <summary> /// Performs Graves' version of RMSProp update on a weight. /// </summary> /// <remarks>See http://arxiv.org/pdf/1308.0850v5.pdf, page 23</remarks> /// <param name="weightDecay">Weight decay rate.</param> /// <param name="learningRate">Learning rate.</param> /// <param name="decayRate">Decay rate.</param> /// <param name="momentum">Momentum.</param> /// <param name="weight">Weight.</param> public abstract void GravesRmsPropUpdate(float weightDecay, float learningRate, float decayRate, float momentum, NeuroWeight <T> weight);
public override void GravesRmsPropUpdate(float weightDecay, float learningRate, float decayRate, float momentum, NeuroWeight <Float> weight)
{
using (var ptrs = new MatrixPointersBag <Float>(weight.Weight, weight.Cache1, weight.Cache2, weight.CacheM, weight.Gradient))
{
GravesRMSPropUpdate(weightDecay, learningRate, decayRate, momentum, ptrs[0], ptrs[1], ptrs[2], ptrs[3], ptrs[4], weight.Weight.Length());
}
}
public abstract void Optimize(NeuroWeight <T> weight);
/// <summary> /// Performs Adagrad update on a weight. /// </summary> /// <param name="learningRate">Learning rate.</param> /// <param name="weight">Weight.</param> public abstract void AdagradUpdate(T learningRate, NeuroWeight <T> weight);
public override void Optimize(NeuroWeight <T> weight)
{
MathProvider.AdagradUpdate(MathProvider.Scalar(LearningRate), weight);
}
public override void Optimize(NeuroWeight <T> weight)
{
weight.Timestep++;
MathProvider.AdamUpdate(LearningRate, _b1, _b2, weight);
}
