/// <summary>
/// Applies the gradients to the weights as a batch
/// </summary>
/// <param name="batchsize">The number of trials run per cycle</param>
/// <param name="clipparameter">What the max/min </param>
/// <param name="RMSDecay">How quickly the RMS gradients decay</param>
public override void Descend()
{
//Calculate gradients
WUpdates = new double[Length, InputLength];
BUpdates = new double[Length];
for (int i = 0; i < Length; i++)
{
for (int ii = 0; ii < InputLength; ii++)
{
//Normal gradient descent update
WUpdates[i, ii] = WeightGradient[i, ii] * (2d / NN.BatchSize);
//Root mean square propegation
if (NN.UseRMSProp)
{
WRMSGrad[i, ii] = (WRMSGrad[i, ii] * NN.RMSDecay) + ((1 - NN.RMSDecay) * (WUpdates[i, ii] * WUpdates[i, ii]));
WUpdates[i, ii] = (WUpdates[i, ii] / (Math.Sqrt(WRMSGrad[i, ii]) /* + NN.Infinitesimal*/));
}
WUpdates[i, ii] *= NN.LearningRate;
}
//Normal gradient descent update
BUpdates[i] = BiasGradient[i] * (2d / NN.BatchSize);
//Root mean square propegation
if (NN.UseRMSProp)
{
BRMSGrad[i] = (BRMSGrad[i] * NN.RMSDecay) + ((1 - NN.RMSDecay) * (BUpdates[i] * BUpdates[i]));
BUpdates[i] = (BUpdates[i] / (Math.Sqrt(BRMSGrad[i]) /* + NN.Infinitesimal*/));
}
BUpdates[i] *= NN.LearningRate;
}
//Gradient normalization
if (NN.NormGradients)
{
WUpdates = Maths.Scale(NN.LearningRate, Maths.Normalize(WUpdates));
BUpdates = Maths.Scale(NN.LearningRate, Maths.Normalize(BUpdates));
}
//Apply updates
for (int i = 0; i < Length; i++)
{
for (int ii = 0; ii < InputLength; ii++)
{
//Update weight and average
Weights[i, ii] -= WUpdates[i, ii];
AvgGradient -= WUpdates[i, ii];
//Weight clipping
if (NN.UseClipping)
{
if (Weights[i, ii] > NN.ClipParameter)
{
Weights[i, ii] = NN.ClipParameter;
}
if (Weights[i, ii] < -NN.ClipParameter)
{
Weights[i, ii] = -NN.ClipParameter;
}
}
}
Biases[i] -= BUpdates[i];
//Bias clipping
if (NN.UseClipping)
{
if (Biases[i] > NN.ClipParameter)
{
Biases[i] = NN.ClipParameter;
}
if (Biases[i] < -NN.ClipParameter)
{
Biases[i] = -NN.ClipParameter;
}
}
}
//Reset gradients
WeightGradient = new double[Length, InputLength];
BiasGradient = new double[Length];
}
/// <summary>
/// Calculates the dot product of the kernel and input matrix.
/// Matrices should be size [x, y] and [y], respectively, where x is the output size and y is the latent space's size
/// </summary>
/// <param name="inputs">The input matrix</param>
/// <param name="isoutput">Whether to use hyperbolic tangent on the output</param>
/// <returns></returns>
public override void Calculate(List <double[]> inputs, bool isoutput)
{
ZVals = new List <double[]>();
for (int b = 0; b < NN.BatchSize; b++)
{
ZVals.Add(Maths.Convert(DownOrUp ? Convolve(Weights, Pad(Maths.Convert(inputs[b]))) : FullConvolve(Weights, Pad(Maths.Convert(inputs[b])))));
}
//If normalizing, do so, but only if it won't return an all-zero matrix
if (NN.NormOutputs && ZVals[0].Length > 1)
{
ZVals = Maths.Normalize(ZVals);
}
//Use the specified type of activation function
if (ActivationFunction == 0)
{
Values = Maths.Tanh(ZVals); return;
}
if (ActivationFunction == 1)
{
Values = Maths.ReLu(ZVals); return;
}
Values = ZVals;
}
/// <summary>
/// Computes the error signal of the layer, also gradients if applicable
/// </summary>
/// <param name="input">Previous layer's values</param>
/// <param name="output">Whether the layer is the output layer</param>
/// <param name="loss">The loss of the layer</param>
/// <param name="calcgradients">Whether or not to calculate gradients in the layer</param>
public void Backprop(List <double[]> inputs, Layer outputlayer, double loss, bool calcgradients)
{
//Reset errors
Errors = new List <double[]>();
//Calculate errors
if (outputlayer is null)
{
for (int j = 0; j < inputs.Count; j++)
{
Errors.Add(new double[Length]);
for (int i = 0; i < Length; i++)
{
//(i == loss ? 1d : 0d)
Errors[j][i] = 2d * (Values[j][i] - loss);
}
}
}
else
{
for (int i = 0; i < inputs.Count; i++)
{
Errors.Add(new double[outputlayer.InputLength]);
}
if (outputlayer is SumLayer)
{
//Errors with respect to the output of the convolution
//dl/do
for (int i = 0; i < outputlayer.ZVals.Count; i++)
{
for (int k = 0; k < outputlayer.Length; k++)
{
for (int j = 0; j < outputlayer.InputLength; j++)
{
Errors[i][j] += outputlayer.Errors[i][k];
}
}
}
}
//Apply tanhderriv, if applicable, to the output's zvals
var outputZVals = outputlayer.ZVals;
if (outputlayer.ActivationFunction == 0)
{
outputZVals = Maths.TanhDerriv(outputlayer.ZVals);
}
if (outputlayer.ActivationFunction == 1)
{
outputZVals = Maths.ReLuDerriv(outputlayer.ZVals);
}
if (outputlayer is FullyConnectedLayer)
{
var FCLOutput = outputlayer as FullyConnectedLayer;
for (int i = 0; i < outputlayer.ZVals.Count; i++)
{
for (int k = 0; k < FCLOutput.Length; k++)
{
for (int j = 0; j < FCLOutput.InputLength; j++)
{
Errors[i][j] += FCLOutput.Weights[k, j] * outputZVals[i][k] * FCLOutput.Errors[i][k];
}
}
}
}
if (outputlayer is ConvolutionLayer)
{
var CLOutput = outputlayer as ConvolutionLayer;
for (int i = 0; i < outputlayer.ZVals.Count; i++)
{
if ((outputlayer as ConvolutionLayer).DownOrUp)
{
Errors[i] = Maths.Convert(CLOutput.UnPad(CLOutput.FullConvolve(CLOutput.Weights, Maths.Convert(CLOutput.Errors[i]))));
}
else
{
Errors[i] = Maths.Convert(CLOutput.UnPad(CLOutput.Convolve(CLOutput.Weights, Maths.Convert(CLOutput.Errors[i]))));
}
}
//Errors = Maths.Convert(CLOutput.UnPad(CLOutput.FullConvolve(CLOutput.Weights, Maths.Convert(CLOutput.Errors))));
}
if (outputlayer is PoolingLayer)
{
var PLOutput = outputlayer as PoolingLayer;
for (int b = 0; b < NN.BatchSize; b++)
{
if (PLOutput.DownOrUp)
{
int iterator = 0;
var wets = Maths.Convert(PLOutput.Weights);
for (int i = 0; i < Length; i++)
{
if (wets[i] == 0)
{
continue;
}
Errors[b][i] = PLOutput.Errors[b][iterator];
iterator++;
}
}
else
{
//Sum the errors
double[,] outputerrors = Maths.Convert(PLOutput.Errors[b]);
int oel = outputerrors.GetLength(0);
int oew = outputerrors.GetLength(1);
double[,] errors = new double[oel / PLOutput.PoolSize, oew / PLOutput.PoolSize];
for (int i = 0; i < oel; i++)
{
for (int ii = 0; ii < oew; ii++)
{
errors[i / PLOutput.PoolSize, ii / PLOutput.PoolSize] += outputerrors[i, ii];
}
}
Errors[b] = Maths.Convert(errors);
}
}
}
}
//Normalize errors (if applicable)
if (NN.NormErrors && Errors[0].Length > 1)
{
Errors = Maths.Normalize(Errors);
}
if (calcgradients)
{
if (this is FullyConnectedLayer)
{
(this as FullyConnectedLayer).CalcGradients(inputs, outputlayer);
}
if (this is ConvolutionLayer)
{
(this as ConvolutionLayer).CalcGradients(inputs, outputlayer);
}
if (this is PoolingLayer)
{
return;
}
if (this is SumLayer)
{
return;
}
}
}
