public override void DoDropout(Volume <double> result, bool isTraining, double dropProbability)
{
if (isTraining)
{
if (((NcwhVolumeStorage <double>) this.Storage).Dropped == null || ((NcwhVolumeStorage <double>) this.Storage).Dropped.Length != this.Shape.TotalLength)
{
((NcwhVolumeStorage <double>) this.Storage).Dropped = new bool[this.Shape.TotalLength];
}
}
if (isTraining)
{
// do dropout
this.Storage.Map((x, i) =>
{
var nextDouble = RandomUtilities.NextDouble();
if (nextDouble < dropProbability)
{
((NcwhVolumeStorage <double>) this.Storage).Dropped[i] = true;
return(0);
}
else
{
((NcwhVolumeStorage <double>) this.Storage).Dropped[i] = false;
return(x / (1 - dropProbability)); // a bit different than ConvNetJS here to match cudnn behaviour
}
}, result.Storage);
}
else
{
// scale the activations during prediction
this.Storage.Map(x => x, result.Storage);
}
}
public override void Dropout(float dropProbability, Volume <float> result)
{
if (((NcwhVolumeStorage <float>) this.Storage).Dropped == null || ((NcwhVolumeStorage <float>) this.Storage).Dropped.Length != this.Shape.TotalLength)
{
((NcwhVolumeStorage <float>) this.Storage).Dropped = new bool[this.Shape.TotalLength];
}
if (dropProbability > 0.0f)
{
// do dropout
this.Storage.Map((x, i) =>
{
var nextDouble = RandomUtilities.NextDouble();
if (nextDouble < dropProbability)
{
((NcwhVolumeStorage <float>) this.Storage).Dropped[i] = true;
return(0);
}
((NcwhVolumeStorage <float>) this.Storage).Dropped[i] = false;
return(x / (1 - dropProbability)); // Scale up so that magnitude remains constant accross training and testing
}, result.Storage);
}
else
{
this.Storage.Map(x => x, result.Storage);
}
}
protected override void Invoking(MessageReceivedEventArgs e, PlainText optionText, PlainText minValueText, PlainText maxValueText, ComplexMessage elements)
{
string option = optionText.Content.ToLower();
if (option == "number")
{
if (double.TryParse(minValueText, out double minValue) && double.TryParse(maxValueText, out double maxValue))
{
e.Reply(RandomUtilities.NextDouble(minValue, maxValue).ToString());
Handled = true;
}
}
else if (option == "numberint")
{
if (long.TryParse(minValueText, out long minValue) && long.TryParse(maxValueText, out long maxValue))
{
e.Reply(RandomUtilities.Next(minValue, maxValue).ToString());
Handled = true;
}
}
}
public override void DoDropout(Volume <float> result, bool isTraining, float dropProbability)
{
if (isTraining)
{
if (((NcwhVolumeStorage <float>) this.Storage).Dropped == null || ((NcwhVolumeStorage <float>) this.Storage).Dropped.Length != this.Shape.TotalLength)
{
((NcwhVolumeStorage <float>) this.Storage).Dropped = new bool[this.Shape.TotalLength];
}
}
var batchSize = this.Shape.DimensionCount > 1 ? this.Shape.GetDimension(-1) : 1;
for (var n = 0; n < batchSize; n++)
{
if (isTraining)
{
// do dropout
this.Storage.Map((x, i) =>
{
var nextDouble = RandomUtilities.NextDouble();
if (nextDouble < dropProbability)
{
((NcwhVolumeStorage <float>) this.Storage).Dropped[i] = true;
return(0);
}
((NcwhVolumeStorage <float>) this.Storage).Dropped[i] = false;
return(x / (1 - dropProbability)); // a bit different than ConvNetJS here to match cudnn behaviour
}, result.Storage);
}
else
{
// scale the activations during prediction
this.Storage.Map(x => x, result.Storage);
}
}
}
