protected void AdjustWeights(Layer lyr)
{
bool convtype = lyr.GetType().ToString() == "AI.ML.CNN.Layers.Convolution" ? true : false;
bool conctype = lyr.GetType().ToString() == "AI.ML.CNN.Layers.Connected" ? true : false;
switch (convtype)
{
case true:
CNN.Layers.Convolution convLyr = (CNN.Layers.Convolution)lyr;
double wv;
double ws, diff, gradient;
AI.ML.CNN.Layers.Convolution.Kernel krn;
double?[][][] weights;
for (int j = 0; j < convLyr.Filters.Count; j++)
{
krn = (AI.ML.CNN.Layers.Convolution.Kernel)((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels[0];
weights = krn.Weights;
wv = 0;
ws = 0;
wv = (beta1.Value * wv) + (1 - beta1.Value) * (weights[0][0][2].Value / batchSize.Value);
ws = (beta2.Value * ws) + (1 - beta2.Value) * System.Math.Pow((weights[0][0][2].Value / batchSize.Value), 2);;
gradient = learningRate.Value * (wv / System.Math.Sqrt(ws + 1e-7));
diff = weights[0][0][0].Value - gradient;
krn.Weights[0][0][0] = diff;
// j: indexing filters
for (int k = 1; k < ((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels.Length; k++)
{
//k: indexing kernels
krn = (AI.ML.CNN.Layers.Convolution.Kernel)((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels[k];
weights = krn.Weights;
for (int m = 0; m < weights.Length; m++)
{
for (int n = 0; n < weights[m].Length; n++)
{
wv = 0;
ws = 0;
wv = (beta1.Value * wv) + (1 - beta1.Value) * (weights[m][n][2].Value / batchSize.Value);
ws = (beta2.Value * ws) + (1 - beta2.Value) * System.Math.Pow((weights[m][n][2].Value / batchSize.Value), 2);
gradient = learningRate.Value * (wv / System.Math.Sqrt(ws + 1e-7));
diff = weights[m][n][0].Value - gradient;
krn.Weights[m][n][0] = diff;
}
}
}
}
break;
case false:
switch (conctype)
{
case true:
CNN.Layers.Connected connLyr = (CNN.Layers.Connected)lyr;
Neuron n; Synapse syn;
for (int j = 0; j < connLyr.Neurons.Length; j++)
{
n = connLyr.Neurons[j];
for (int k = 0; k < n.Synapse.Count; k++)
{
syn = n.Synapse[k];
wv = 0;
ws = 0;
wv = (beta1.Value * wv) + (1 - beta1.Value) * (syn.Weights[2].Value / batchSize.Value);
ws = (beta2.Value * ws) + (1 - beta2.Value) * System.Math.Pow((syn.Weights[2].Value / batchSize.Value), 2);
gradient = learningRate.Value * (wv / System.Math.Sqrt(ws + 1e-7));
diff = syn.W.Value - gradient;
syn.W = diff;
}
}
break;
case false:
break;
}
break;
default:
break;
}
}
protected void AdjustWeights()
{
for (int i = 1; i < Model.Layers.Length; i++)
{
Model.Unit lyr;
lyr = Model.Layers[i];
bool convtype = lyr.GetType().ToString() == "AI.ML.CNN.Layers.Convolution" ? true : false;
bool conctype = lyr.GetType().ToString() == "AI.ML.CNN.Layers.Connected" ? true : false;
switch (convtype)
{
case true:
CNN.Layers.Convolution convLyr = (CNN.Layers.Convolution)lyr;
AI.ML.CNN.Layers.Convolution.Kernel krn;
double?[][][] weights;
double?[][] wc;
for (int j = 0; j < convLyr.Filters.Count; j++)
{
krn = (AI.ML.CNN.Layers.Convolution.Kernel)((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels[0];
wc = krn.WeightCorrection;
krn.Weights[0][0][0] += learningRate.Value * wc[0][0];
//dW = (Momentum.Value * krn.Weights[0][0][2]) + (LearningRate.Value * wc[0][0]);
//krn.Weights[0][0][2] = dW;
//krn.Weights[0][0][0] += dW;
// j: indexing filters
for (int k = 1; k < ((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels.Length; k++)
{
//k: indexing kernels
krn = (AI.ML.CNN.Layers.Convolution.Kernel)((AI.ML.CNN.Layers.Convolution.Filter)convLyr.Filters[j]).Kernels[k];
weights = krn.Weights;
wc = krn.WeightCorrection;
for (int m = 0; m < weights.Length; m++)
{
for (int n = 0; n < weights[m].Length; n++)
{
//dW = (Momentum.Value * krn.Weights[m][n][2]) + (LearningRate.Value * wc[m][n]);
//krn.Weights[m][n][2] = dW;
krn.Weights[m][n][0] += learningRate.Value * wc[m][n];
}
}
}
}
break;
case false:
switch (conctype)
{
case true:
CNN.Layers.Connected connLyr = (CNN.Layers.Connected)lyr;
Neuron n; Synapse syn;
for (int j = 0; j < connLyr.Neurons.Length; j++)
{
n = connLyr.Neurons[j];
for (int k = 0; k < n.Synapse.Count; k++)
{
syn = n.Synapse[k];
syn.W -= learningRate * syn.dW;
}
}
break;
case false:
break;
}
break;
default:
break;
}
}
}
