public NeuralLayer(MathOperationManager mathManager, int neuronCount, int batchSize, NeuronActivationType activation, bool isInputLayer = false, bool isOutputLayer = false)
{
this.mathManager = mathManager;
this.IsInputLayer = isInputLayer;
this.IsOutputLayer = isOutputLayer;
this.NeuralLink = null;
this.LayerIn = null;
this.LayerOut = null;
this.Activation = activation;
this.Data = mathManager.CreateMatrix(neuronCount, batchSize);
this.Bias = mathManager.CreateMatrix(ArrayUtilities.GetArrayWithRandomValues(neuronCount), neuronCount);
this.ErrorGradient = mathManager.CreateMatrix(neuronCount, batchSize);
}
public NeuralLayer(MathOperationManager mathManager, float[] biasData, int neuronCount, int batchSize, NeuronActivationType activation, bool isInputLayer = false, bool isOutputLayer = false)
{
this.mathManager = mathManager;
this.IsInputLayer = isInputLayer;
this.IsOutputLayer = isOutputLayer;
this.NeuralLink = null;
this.LayerIn = null;
this.LayerOut = null;
this.Activation = activation;
this.Data = mathManager.CreateMatrix(neuronCount, batchSize);
this.Bias = mathManager.CreateMatrix(biasData, neuronCount);
this.ErrorGradient = mathManager.CreateMatrix(neuronCount, batchSize);
}
/// <summary>
/// The neuron constructor
/// </summary>
/// <param name="_inputPorts">An array of input ports to the neuron</param>
/// <param name="_numOutputIntBits">The number of integer bits to use on the output</param>
/// <param name="_numOutputFracBits">The number of fractional bits to be used on the output</param>
/// <param name="_numWeightIntBits">The number of integer bits used to represent the weights</param>
/// <param name="_activationType">The thresholding function used to compute the output of the neuron</param>
/// <param name="_name">The name of the neuron</param>
public AsyncNeuron(Port[] _inputPorts, int _numOutputIntBits, int _numOutputFracBits, int _numWeightIntBits, NeuronActivationType _activationType, string _name)
{
/*Copy arguments*/
this.neuronInputs = _inputPorts;
this.numOutputFracBits = _numOutputFracBits;
this.numOutputIntBits = _numOutputIntBits;
this.activationType = _activationType;
this.name = _name;
/*Initialize weight signals*/
this.weightSignals = new Signal[this.neuronInputs.Length];
int weightTopVal = _numWeightIntBits; //NOT -1 => signed
int weightBottomVal = this.neuronInputs[0].bottom;
for (int i = 0; i < this.neuronInputs.Length; i++)
{
this.weightSignals[i] = new Signal(String.Format("w_{0}", i), Utilities.VHDLDataType.SIGNED_FIXED_POINT, null, weightTopVal, weightBottomVal);
}
/*Initialize product signals*/
this.productSignals = new Signal[this.neuronInputs.Length];
int productTopVal = _numWeightIntBits + this.neuronInputs[0].top + 1;
int productBottomVal = 2 * (weightBottomVal);
for (int i = 0; i < this.neuronInputs.Length; i++)
{
this.productSignals[i] = new Signal(String.Format("p_{0}", i), Utilities.VHDLDataType.SIGNED_FIXED_POINT, null, productTopVal, productBottomVal);
}
/*Initialize sum signal*/
int sumTopVal = productTopVal + this.neuronInputs.Length;
int sumBottomVal = productBottomVal;
this.sum = new Signal("sum", Utilities.VHDLDataType.SIGNED_FIXED_POINT, null, sumTopVal, sumBottomVal);
/*Initialize thresholded sum signal*/
int threshTopVal = this.numOutputIntBits; //NOT -1 => signed
int threshBottomVal = this.numOutputFracBits;
this.thresholdedSum = new Signal("thresh_sum", Utilities.VHDLDataType.SIGNED_FIXED_POINT, null, threshTopVal, -1 * threshBottomVal);
/*Initialize loading signals*/
int numOffsetBits = Utilities.getNumUnsignedBits(this.weightSignals.Length);
this.loadClkPort = new Port(Port.portDirection.IN, "loadClk", Utilities.VHDLDataType.STD_LOGIC, 0, 0);
this.finalLoadPort = new Port(Port.portDirection.OUT, "finalLoad", Utilities.VHDLDataType.STD_LOGIC, 0, 0);
this.loadEnablePort = new Port(Port.portDirection.IN, "loadEnable", Utilities.VHDLDataType.STD_LOGIC, 0, 0);
this.loadOffsetPort = new Port(Port.portDirection.IN, "loadOff", Utilities.VHDLDataType.UNSIGNED, numOffsetBits - 1, 0);
this.loadValuePort = new Port(Port.portDirection.IN, "loadVal", Utilities.VHDLDataType.SIGNED_FIXED_POINT, weightTopVal, weightBottomVal);
/*Initialize output*/
this.neuronOutput = new Port(Port.portDirection.OUT, "neuron_out", Utilities.VHDLDataType.SIGNED_FIXED_POINT, this.numOutputIntBits, -1 * this.numOutputFracBits); //NOT -1 => signed
/*Initialize attached entities*/
if (this.activationType == NeuronActivationType.SIGMOID_POLY_APPROX)
{
this.sigmoid_poly = new Sigmoid_PolyApprox(this.sum, Math.Abs(this.thresholdedSum.top) + 1, Math.Abs(this.thresholdedSum.bottom), String.Format("sigmoid_poly_{0}_{1}", this.numOutputIntBits, this.numOutputFracBits));
}
else
{
/*Linear node has no dependent entities!*/
}
return;
}