public override Volume Forward(Volume input, bool isTraining = false)
{
this.InputActivation = input;
var output = input.Clone();
var length = input.Weights.Length;
if (isTraining)
{
// do dropout
for (var i = 0; i < length; i++)
{
if (Random.NextDouble() < this.DropProb.Value)
{
output.Weights[i] = 0;
this.dropped[i] = true;
} // drop!
else
{
this.dropped[i] = false;
}
}
}
else
{
// scale the activations during prediction
for (var i = 0; i < length; i++)
{
output.Weights[i] *= this.DropProb.Value;
}
}
this.OutputActivation = output;
return this.OutputActivation; // dummy identity function for now
}
public override Volume Forward(Volume input, bool isTraining = false)
{
this.InputActivation = input;
var output = input.Clone();
var length = input.Weights.Length;
if (isTraining)
{
// do dropout
for (var i = 0; i < length; i++)
{
if (Random.NextDouble() < this.DropProb.Value)
{
output.Weights[i] = 0;
this.dropped[i] = true;
} // drop!
else
{
this.dropped[i] = false;
}
}
}
else
{
// scale the activations during prediction
for (var i = 0; i < length; i++)
{
output.Weights[i] *= this.DropProb.Value;
}
}
this.OutputActivation = output;
return(this.OutputActivation); // dummy identity function for now
}
public override Volume Forward(Volume input, bool isTraining = false)
{
this.InputActivation = input;
var output = input.Clone();
var length = input.Weights.Length;
double[] outputWeights = output.Weights;
#if PARALLEL
Parallel.For(0, length, i =>
#else
for (var i = 0; i < length; i++)
#endif
{
if (outputWeights[i] < 0)
{
outputWeights[i] = 0; // threshold at 0
}
}
public override Volume Forward(Volume input, bool isTraining = false)
{
this.InputActivation = input;
var output = input.Clone();
var length = input.Weights.Length;
double[] outputWeights = output.Weights;
#if PARALLEL
Parallel.For(0, length, i =>
#else
for (var i = 0; i < length; i++)
#endif
{
if (outputWeights[i] < 0)
{
outputWeights[i] = 0; // threshold at 0
}
}
#if PARALLEL
);
#endif
this.OutputActivation = output;
return this.OutputActivation;
}