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 TiedFeedForward(IFeedForward layer, IWeightInitialisation weightInit, string name = null)
: base(name)
{
_layer = layer;
_layerId = layer.Id;
_bias = weightInit.CreateBias(layer.InputSize);
}
internal WeightInitialisationProvider(ILinearAlgebraProvider lap)
{
Ones = new Constant(lap, 0f, 1f);
Zeroes = new Constant(lap, 0f, 0f);
Gaussian = new Gaussian(lap);
Xavier = new Xavier(lap);
Identity = new Identity(lap, 1f);
Identity01 = new Identity(lap, 0.1f);
}
public TiedLayer(ILinearAlgebraProvider lap, INeuralNetworkLayer layer, IWeightInitialisation weightInit)
{
_inputSize = layer.OutputSize;
_outputSize = layer.InputSize;
_layer = layer;
_weight = layer.Weight;
_bias = lap.Create(_outputSize, x => weightInit.GetBias());
_weightTranspose = _weight.Transpose();
}
public BatchNormalisation(GraphFactory graph, int inputSize, IWeightInitialisation weightInit, float momentum = 0.9f, string name = null) : base(name)
{
_inputSize = inputSize;
_momentum = momentum;
var lap = graph.LinearAlgebraProvider;
using (var matrix = weightInit.CreateWeight(1, _inputSize)) {
_Create(graph,
gamma: lap.CreateVector(Enumerable.Repeat(1f, _inputSize)),//matrix.Row(0),
beta: lap.CreateVector(Enumerable.Repeat(0f, _inputSize)),
mean: lap.CreateVector(Enumerable.Repeat(0f, _inputSize)),
variance: lap.CreateVector(Enumerable.Repeat(1f, _inputSize))
);
}
}
public IPropertySet Use(IWeightInitialisation weightInit)
{
WeightInitialisation = weightInit; return(this);
}
public Standard(ILinearAlgebraProvider lap, int inputSize, int outputSize, LayerDescriptor init, IActivationFunction activation, IWeightInitialisation weightInit)
{
_descriptor = init;
_activation = activation;
// initialise weights and bias
_bias = lap.Create(outputSize, x => weightInit.GetBias());
_weight = lap.Create(inputSize, outputSize, (x, y) => weightInit.GetWeight(inputSize, outputSize, x, y));
}
