public unsafe void Eval(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
float *x = ((float *)input.DataPointer);
float *y = ((float *)output.DataPointer);
var sumExp = 0.0f;
int i;
for (i = 0; i < neurons; i++)
{
y[i] = (float)Math.Exp(x[i]);
sumExp += y[i];
}
for (i = 0; i < neurons; i++)
{
y[i] /= sumExp;
}
}
public void EvalDerivate(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
throw new NotImplementedException();
}
public unsafe void EvalDerivate(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
float *x = (float *)input.DataPointer;
float *y = (float *)output.DataPointer;
Parallel.For(0, neurons, neuron =>
{
y[neuron] = x[neuron] * (1 - x[neuron]);
});
}
public unsafe void Eval(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
float *x = (float *)input.DataPointer;
float *y = (float *)output.DataPointer;
Parallel.For(0, neurons, i =>
{
y[i] = 1.0f / (1 + (float)Math.Exp(-x[i]));
});
}
public unsafe void EvalDerivate(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
for (int neuron = 0; neuron < neurons; neuron++)
{
float x = ((float *)input.DataPointer)[neuron];
((float *)output.DataPointer)[neuron] = x * (1 - x);
}
}
public unsafe void Eval(ComputeBuffer input, ComputeBuffer output, int neurons, int layer, FFNNet net)
{
float *x = (float *)input.DataPointer;
float *y = (float *)output.DataPointer;
for (int i = 0; i < neurons; i++)
{
y[i] = 1.0f / (1 + (float)Math.Exp(-x[i]));
}
}
public FFNNBackPropCPUTrainer(IFFNNetEvaluator evaluator)
{
this.net = evaluator.NeuralNet;
}
