public static int[] GetLayers(this SequentialModel sequentialModel)
{
var layers = new List <int>();
var l = sequentialModel.GetFieldValue <DefaultExecutor>("compiled").GetFieldValue <List <ILayer> >("layers");
var output = 0;
foreach (var layer in l)
{
if (!(layer is Dense2DLayer))
{
continue;
}
var denseSum = (layer as Dense2DLayer).GetLayerSummary();
layers.Add(denseSum.InputChannel);
output = denseSum.OutputChannel;
}
layers.Add(output);
return(layers.ToArray());
}
public static double[] GetWeights(this SequentialModel sequentialModel)
{
// reflection used because there is no other way to get the weights of a network in nnsharp
var weights = new List <double>();
var compiled = sequentialModel.GetFieldValue <DefaultExecutor>("compiled");
var layers = compiled.GetFieldValue <IList <ILayer> >("layers");
var dim = sequentialModel.GetInputDimension();
// set the initInput field otherwise the error: input is not Data2D (Dense2DLayer)
compiled.SetFieldValue <Data2D>("initInput", new Data2D(dim.h, dim.w, dim.c, dim.b));
foreach (var layer in layers)
{
if (layer is Dense2DLayer)
{
var t = (layer as Dense2DLayer).GetFieldValue <IData>("weights").GetFieldValue <double[, , , ]>("tensor")
.GetEnumerator();
while (t.MoveNext())
{
if (t.Current != null)
{
weights.Add((double)t.Current);
}
}
}
else if (layer is Bias2DLayer)
{
var a = layer.GetFieldValue <IData>("biases").GetFieldValue <double[]>("array");
weights.AddRange(a);
}
}
return(weights.ToArray());
}