// Additional pointers for using TensorFlow & CustomVision together
// Python: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/label_image.py
// C++: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc
// Java: https://github.com/Azure-Samples/cognitive-services-android-customvision-sample/blob/master/app/src/main/java/demo/tensorflow/org/customvision_sample/MSCognitiveServicesClassifier.java
private static TFGraph ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output,
TFDataType destinationDataType = TFDataType.Float)
{
const int W = 224;
const int H = 224;
const float Scale = 1;
// Depending on your CustomVision.ai Domain - set appropriate Mean Values (RGB)
// https://github.com/Azure-Samples/cognitive-services-android-customvision-sample for RGB values (in BGR order)
var bgrValues = new TFTensor(new float[]
{ 104.0f, 117.0f, 123.0f }); // General (Compact) & Landmark (Compact)
//var bgrValues = new TFTensor(0f); // Retail (Compact)
var graph = new TFGraph();
input = graph.Placeholder(TFDataType.String);
var caster = graph.Cast(graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float);
var dims_expander = graph.ExpandDims(caster, graph.Const(0, "batch"));
var resized = graph.ResizeBilinear(dims_expander, graph.Const(new int[] { H, W }, "size"));
var resized_mean = graph.Sub(resized, graph.Const(bgrValues, "mean"));
var normalised = graph.Div(resized_mean, graph.Const(Scale));
output = normalised;
return(graph);
}
/// <summary>
/// Create learning rate time decay operation.
/// </summary>
protected void CreateDecayOps(float decay, TFOutput initialLearningRate)
{
if (decay > 0)
{
var _decay = _graph.Const(decay, "Decay");
var one = _graph.Const(1f);
_updateOps.Add(_graph.AssignVariableOp(LearningRate,
_graph.Mul(initialLearningRate,
_graph.Div(one,
_graph.Add(one,
_graph.Mul(_decay,
_graph.Cast(Iterations.Read, _decay.OutputType)
)
)
)
)));
}
}
/// <summary>
/// Create learning rate time decay operation.
/// </summary>
protected TFOutput CreateDecayOps(float decay, TFOutput initialLearningRate)
{
if (decay > 0)
{
var _decay = _graph.Const(decay, "Decay");
var one = _graph.Const(1f);
return
(_graph.Mul(initialLearningRate,
_graph.Div(one,
_graph.Add(one,
_graph.Mul(_decay,
_graph.Cast(_graph.Sub(Iterations.ReadAfter(_graph.CurrentDependencies), _graph.Const(1L)), _decay.OutputType)
)
)
), operName: "learningrate"
));
}
else
{
return(initialLearningRate);
}
}