public IGradientDescentOptimisation Create(IGradientDescentOptimisation prev, IMatrix template, IPropertySet propertySet)
{
var cache = propertySet.LinearAlgebraProvider.CreateZeroMatrix(template.RowCount, template.ColumnCount);
var cache2 = propertySet.LinearAlgebraProvider.CreateZeroMatrix(template.RowCount, template.ColumnCount);
return(new Adam(_decay, _decay2, cache, cache2, prev));
}
public override void ReadFrom(GraphFactory factory, BinaryReader reader)
{
var lap = factory?.LinearAlgebraProvider;
InputSize = reader.ReadInt32();
OutputSize = reader.ReadInt32();
// read the bias parameters
var bias = FloatVector.ReadFrom(reader);
if (Bias == null)
{
Bias = lap.CreateVector(bias);
}
else
{
Bias.Data = bias;
}
// read the weight parameters
var weight = FloatMatrix.ReadFrom(reader);
if (Weight == null)
{
Weight = lap.CreateMatrix(weight);
}
else
{
Weight.Data = weight;
}
if (_updater == null)
{
_updater = factory?.CreateWeightUpdater(Weight);
}
}
public Convolutional(
bool shouldBackpropagate,
IWeightInitialisation weightInitialisation,
Func <IMatrix, IGradientDescentOptimisation> updater,
int inputDepth,
int filterCount,
int padding,
int filterWidth,
int filterHeight,
int xStride,
int yStride,
string name = null) : base(name)
{
_shouldBackpropagate = shouldBackpropagate;
_padding = padding;
_filterWidth = filterWidth;
_filterHeight = filterHeight;
_xStride = xStride;
_yStride = yStride;
_inputDepth = inputDepth;
_bias = weightInitialisation.CreateBias(filterCount);
_filter = weightInitialisation.CreateWeight(_filterWidth * _filterHeight * _inputDepth, filterCount);
_updater = updater(_filter);
}
public FeedForward(int inputSize, int outputSize, IVector bias, IMatrix weight, IGradientDescentOptimisation updater, string name = null) : base(name)
{
_bias = bias;
_weight = weight;
_updater = updater;
_inputSize = inputSize;
_outputSize = outputSize;
}
public virtual IGradientDescentOptimisation Create(IGradientDescentOptimisation prev,
IMatrix template, IPropertySet propertySet)
{
var cache =
propertySet.LinearAlgebraProvider.CreateZeroMatrix(template.RowCount, template.ColumnCount);
return(new Momentum(_momentum, cache, prev));
}
public virtual void ReadFrom(GraphFactory factory, BinaryReader reader)
{
var rows = reader.ReadInt32();
var columns = reader.ReadInt32();
_cache = factory.LinearAlgebraProvider.CreateZeroMatrix(rows, columns);
_updater = factory.CreateGradientDescentOptimisation(reader);
}
public DropConnect(float dropOutPercentage, int inputSize, int outputSize, IVector bias,
IMatrix weight, bool stochastic, IGradientDescentOptimisation updater, string name = null) :
base(inputSize, outputSize, bias, weight, updater, name)
{
_dropOutPercentage = dropOutPercentage;
_probabilityToDrop = stochastic ? new Bernoulli(_dropOutPercentage)
: new Bernoulli(_dropOutPercentage, new Random(0));
}
public override void ReadFrom(GraphFactory factory, BinaryReader reader)
{
var lap = factory?.LinearAlgebraProvider;
_padding = reader.ReadInt32();
_filterWidth = reader.ReadInt32();
_filterHeight = reader.ReadInt32();
_xStride = reader.ReadInt32();
_yStride = reader.ReadInt32();
_inputDepth = reader.ReadInt32();
_shouldBackpropagate = reader.ReadBoolean();
// read the bias parameters
var bias = FloatVector.ReadFrom(reader);
if (_bias == null)
{
_bias = lap.CreateVector(bias);
}
else
{
_bias.Data = bias;
}
// read the weight parameters
var weight = FloatMatrix.ReadFrom(reader);
if (_filter == null)
{
_filter = lap.CreateMatrix(weight);
}
else
{
_filter.Data = weight;
}
// read the updater
if (_updater == null)
{
_updater = factory?.CreateWeightUpdater(_filter);
}
}
public NesterovMomentum(float momentum, IMatrix cache, IGradientDescentOptimisation updater) :
base(momentum, cache, updater) { }
public RmsProp(float decayRate, IMatrix cache, IGradientDescentOptimisation updater) : base(cache, updater)
{
_decayRate = decayRate;
}
internal static void Write(this BinaryWriter writer, IGradientDescentOptimisation optimisation)
{
writer.Write(optimisation.GetType().AssemblyQualifiedName);
optimisation.WriteTo(writer);
}
public Adam(float decay, float decay2, IMatrix cache, IMatrix cache2,
IGradientDescentOptimisation updater) : base(decay, cache, updater)
{
_decayRate2 = decay2;
_cache2 = cache2;
}
public IPropertySet Use(IGradientDescentOptimisation optimisation)
{
GradientDescent = optimisation; return(this);
}
public Momentum(float momentum, IMatrix cache, IGradientDescentOptimisation updater) : base(cache, updater)
{
_momentum = momentum;
}
public AdaGrad(IMatrix cache, IGradientDescentOptimisation updater)
{
_cache = cache;
_updater = updater;
}
