public IEnumerator Evaluate(CLayerType[] simpleLayers)
{
if (modelHandler)
{
modelHandler.ShowGPUs(true);
}
EvaluateRequest req = new EvaluateRequest {
};
bool flat = false;
for (int i = 0; i < simpleLayers.Length; i++)
{
CLayerType l = simpleLayers[i];
switch (l)
{
case CLayerType.Conv:
if (flat)
{
throw new System.ArgumentException("tried to add multidimensional layer after flatten");
}
req.Layers.Add(new Evaluator.Layer {
Convolution = new Evaluator.ConvolutionLayer {
Filters = 32
}
});
break;
case CLayerType.Full:
case CLayerType.Dense:
if (!flat)
{
req.Layers.Add(new Evaluator.Layer {
Flatten = new Evaluator.FlattenLayer {
}
});
flat = true;
}
req.Layers.Add(new Evaluator.Layer {
Dense = new Evaluator.DenseLayer {
Neurons = 128
}
});
break;
case CLayerType.Pool:
if (flat)
{
throw new System.ArgumentException("tried to add multidimensional layer after flatten");
}
req.Layers.Add(new Evaluator.Layer {
Maxpooling = new Evaluator.MaxpoolingLayer {
}
});
break;
case CLayerType.Dropout:
req.Layers.Add(new Evaluator.Layer {
Dropout = new Evaluator.DropoutLayer {
Dimension = 0.5f
}
});
break;
default:
throw new System.ArgumentException("unknown layer type: " + l);
}
}
if (!flat)
{
//req.Layers.Add(new Evaluator.Layer { Convolution = new Evaluator.ConvolutionLayer { Filters = 32 } });
req.Layers.Add(new Evaluator.Layer {
Flatten = new Evaluator.FlattenLayer {
}
});
}
bIsCalculating = true;
using (var call = client.EvaluateAsync(req)) {
//StartCoroutine(UpdateProgress());
while (!call.ResponseAsync.IsCompleted)
{
//accuracy = call.ResponseAsync.Result.Accuracy;
yield return(new WaitForSeconds(0.5f));
}
bIsCalculating = false;
accuracy = call.ResponseAsync.Result.Accuracy;
if (manager)
{
manager.CompleteComputation(accuracy);
}
yield return(new WaitForSeconds(0.5f));
if (modelHandler)
{
modelHandler.Reset();
}
}
}
public IEnumerator Evaluate(LayerType[] simpleLayers)
{
EvaluateRequest req = new EvaluateRequest {
};
bool flat = false;
for (int i = 0; i < simpleLayers.Length; i++)
{
LayerType l = simpleLayers[i];
switch (l)
{
case LayerType.Conv:
if (flat)
{
throw new System.ArgumentException("tried to add multidimensional layer after flatten");
}
req.Layers.Add(new Evaluator.Layer {
Convolution = new Evaluator.ConvolutionLayer {
Filters = 32
}
});
break;
case LayerType.Full:
case LayerType.Dense:
if (!flat)
{
req.Layers.Add(new Evaluator.Layer {
Flatten = new Evaluator.FlattenLayer {
}
});
flat = true;
}
req.Layers.Add(new Evaluator.Layer {
Dense = new Evaluator.DenseLayer {
Neurons = 128
}
});
break;
case LayerType.Pool:
if (flat)
{
throw new System.ArgumentException("tried to add multidimensional layer after flatten");
}
req.Layers.Add(new Evaluator.Layer {
Maxpooling = new Evaluator.MaxpoolingLayer {
}
});
break;
case LayerType.Dropout:
req.Layers.Add(new Evaluator.Layer {
Dropout = new Evaluator.DropoutLayer {
Dimension = 0.5f
}
});
break;
default:
throw new System.ArgumentException("unknown layer type: " + l);
}
}
if (!flat)
{
req.Layers.Add(new Evaluator.Layer {
Flatten = new Evaluator.FlattenLayer {
}
});
}
using (var call = client.EvaluateAsync(req)) {
while (!call.ResponseAsync.IsCompleted)
{
yield return(new WaitForSeconds(0.5f));
}
accuracy = call.ResponseAsync.Result.Accuracy;
if (manager)
{
manager.SetAcurracyText(accuracy);
}
}
}