/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyConvolutionLayerView(m_network, this, 0xFFFFDDDD);
* }*/
public MyConvolutionLayer(MyAbstractFeedForwardNode network, uint featuresCount, uint kernelWidth, uint kernelHeight, uint xStride = 1, uint yStride = 1,
uint[][] featureInputs = null,
float[] initialWeight = null, float[] initialBias = null)
: base(network)
{
if (featureInputs == null)
{
// Full connection
m_output.Nb = featuresCount;
}
else
{
// Selective connection
m_output.Nb = featureInputs.Length;
FeatureInputs = featureInputs;
}
m_weight.Width = kernelWidth;
m_weight.Height = kernelHeight;
XStride = xStride;
YStride = yStride;
m_initialWeight = initialWeight;
m_initialBias = initialBias;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyWeightView(m_network, this, 0xFFD8BD99);
* }*/
public MyLinearLayer(MyAbstractFeedForwardNode network,
float[] initialWeights = null, float[] initialBias = null)
: base(network)
{
m_initialWeight = initialWeights;
m_initialBias = initialBias;
}
public MyGradientBackPropAgent(MyAbstractFeedForwardNode network, int nGPU, MyMemoryBlock <float> labelInput)
: base(network)
{
m_updateWeightKernel = MyKernelFactory.Instance.Kernel(nGPU, @"XmlFeedForwardNet\UpdateWeightKernel");
DeltaProvider = new MyLabelDeltaProvider(m_network, nGPU);
DeltaProvider.LabelInput = labelInput;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyWeightView(m_network, this, 0xCCAACCCC);
* }*/
public MyMirrorNeuronLayer(MyAbstractFeedForwardNode network, MyNeuronLayer originalLayer, float[] initialWeights = null)
: base(network)
{
m_originalLayer = originalLayer;
m_initialWeights = initialWeights;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyWeightView(m_network, this);
* }*/
public MyAbstractWeightLayer(MyAbstractFeedForwardNode network)
: base(network)
{
m_weight.Depth = 1;
m_weightChange.Depth = 1;
m_bias.Depth = 1;
m_biasChange.Depth = 1;
}
public MyInputLayer(MyAbstractFeedForwardNode network, MyMemoryBlock <float> input, SizeT offset, SizeT nb, SizeT width, SizeT height, SizeT nbSamplesPerStep)
: base(network)
{
m_inputBlock = input;
m_inputOffset = offset;
m_output.Nb = nb;
m_output.Width = width;
m_output.Height = height;
m_nbSamplesPerStep = nbSamplesPerStep;
}
public MyNeuronLayer(MyAbstractFeedForwardNode network, uint neuronsCount,
float[] initialWeights = null, float[] initialBias = null)
: base(network)
{
m_neuronsCount = neuronsCount;
m_output.Nb = m_neuronsCount;
m_output.Width = 1;
m_output.Height = 1;
m_initialWeight = initialWeights;
m_initialBias = initialBias;
}
public MyNeuronCopyLayer(MyAbstractFeedForwardNode network, uint neuronsCount,
float[] initialWeights = null, float[] initialBias = null)
: base(network)
{
m_neuronsCount = neuronsCount;
m_output.Nb = m_neuronsCount;
m_output.Width = 1;
m_output.Height = 1;
m_initialWeight = null;
// init biases to 0
m_initialBias = new float[m_output.Count];
}
public MyRBMAgent(MyAbstractFeedForwardNode network, int nGPU, MyMemoryBlock <float> labelInput, uint learningDuration)
: base(network)
{
layers = new List <MyAbstractFBLayer>();
foreach (MyAbstractFBLayer l in network.Layers)
{
MyNeuronLayer nl = l as MyNeuronLayer;
if (nl != null)
{
layers.Add(nl);
}
MyNeuronCopyLayer ncl = l as MyNeuronCopyLayer;
if (ncl != null)
{
layers.Add(ncl);
}
}
totalSteps = (int)learningDuration * (layers.Count - 1);
}
public MyDeltaProvider(MyAbstractFeedForwardNode network)
{
m_network = network;
}
public MyActivationLayer(MyAbstractFeedForwardNode network, MyActivationFunction activationFunction = MyActivationFunction.NO_ACTIVATION)
: base(network)
{
ActivationFunction = activationFunction;
}
public MyBackPropAgent(MyAbstractFeedForwardNode network)
{
m_network = network;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyDeltaView(m_network, this);
* }*/
public MyAbstractFBLayer(MyAbstractFeedForwardNode network)
: base(network)
{
m_delta.Depth = 1;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyDeltaView(m_network, this, 0xFFFFFFDD);
* }*/
public MySoftmaxLayer(MyAbstractFeedForwardNode network)
: base(network)
{
}
public MyLabelDeltaProvider(MyAbstractFeedForwardNode network, int nGPU)
: base(network)
{
m_combineKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Common\CombineVectorsKernel", "CombineTwoVectorsKernel");
m_energyKernel = MyKernelFactory.Instance.Kernel(nGPU, @"XmlFeedForwardNet\EnergyKernel");
}
/*
* Observers not implemented
*
* public virtual MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyOutputView(m_network, this);
* }*/
public MyAbstractFLayer(MyAbstractFeedForwardNode network)
{
m_output.Depth = 1;
m_network = network;
m_extraSize = 0;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyDeltaView(m_network, this, 0xDDBBBBDD);
* }*/
public MyMirrorPoolLayer(MyAbstractFeedForwardNode network, MyPoolLayer originalLayer)
: base(network)
{
m_originalLayer = originalLayer;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyDeltaView(m_network, this, 0xFFDDDDFF);
* }*/
public MyPoolLayer(MyAbstractFeedForwardNode network, uint stride, MyPoolRule poolRule)
: base(network)
{
Stride = stride;
PoolRule = poolRule;
}
/*
* Observers not implemented
*
* public override MyOutputView CreateView()
* {
* throw new NotImplementedException();
* //return new MyWeightView(m_network, this, 0xDDDDBBBB);
* }*/
public MyMirrorConvolutionLayer(MyAbstractFeedForwardNode network, MyConvolutionLayer originalLayer, float[] initialWeights = null)
: base(network)
{
m_originalLayer = originalLayer;
}