public override bool Read(GH_IReader reader)
{
var nn = new BinaryFormatter();
var f = new List <Owl.Learning.NeuronFunctions.NeuronFunctionBase>();
int fc = reader.GetInt32("FunctionCount");
TensorSet w = default;
TensorSet b = default;
for (int i = 0, loopTo = fc - 1; i <= loopTo; i += 1)
{
using (var mstr = new MemoryStream(reader.GetByteArray("Function_" + i)))
{
f.Add(nn.Deserialize(mstr));
}
}
using (var mstr = new MemoryStream(reader.GetByteArray("Biases")))
{
b = Owl.Core.IO.ReadTensors(mstr);
}
using (var mstr = new MemoryStream(reader.GetByteArray("Weigths")))
{
w = Owl.Core.IO.ReadTensors(mstr);
}
this.Value = new Owl.Learning.Networks.Network(w, b, f);
return(true);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
TensorSet w = new TensorSet();
TensorSet b = new TensorSet();
List <int> f = new List <int>();
if (!DA.GetData(0, ref w))
{
return;
}
if (!DA.GetData(1, ref b))
{
return;
}
if (!DA.GetDataList(2, f))
{
return;
}
List <NeuronFunctionBase> fs = new List <NeuronFunctionBase>();
foreach (int val in f)
{
switch (val)
{
case 0:
{
fs.Add(new Linear());
break;
}
case 1:
{
fs.Add(new Relu());
break;
}
case 2:
{
fs.Add(new Sigmoid());
break;
}
case 3:
{
fs.Add(new Tanh());
break;
}
}
}
Network nn = new Network(w, b, fs);
DA.SetData(0, new GH_OwlNetwork(nn));
}
protected override void SolveInstance(IGH_DataAccess DA)
{
GH_OwlNetwork nn = null /* TODO Change to default(_) if this is not a reference type */;
GH_OwlTensorSet ins = new GH_OwlTensorSet();
if (!DA.GetData(0, ref nn))
{
return;
}
if (!DA.GetData(1, ref ins))
{
return;
}
TensorSet outs = nn.Value.ComputeOptimized(ins.Value);
// For i As Integer = 0 To ins.Value.Count - 1 Step 1
// outs.Add(nn.Value.Compute(ins.Value(i)))
// Next
DA.SetData(0, outs);
}
public override bool CastFrom(object source)
{
var switchExpr = source.GetType();
switch (switchExpr)
{
case var @case when @case == typeof(Tensor):
{
this.Value = (Tensor)source;
return(true);
}
case var case1 when case1 == typeof(TensorSet):
{
TensorSet ts = (TensorSet)source;
if (ts.Count != 1)
{
return(false);
}
this.Value = ts[0];
return(true);
}
case var case2 when case2 == typeof(Point3d):
{
Point3d asp = source;
this.Value = new Tensor(new[] { asp.X, asp.Y, asp.Z });
return(true);
}
case var case3 when case3 == typeof(GH_Point):
{
GH_Point asp = source;
this.Value = new Tensor(new[] { asp.Value.X, asp.Value.Y, asp.Value.Z });
return(true);
}
case var case4 when case4 == typeof(GH_Vector):
{
GH_Vector asp = source;
this.Value = new Tensor(new[] { asp.Value.X, asp.Value.Y, asp.Value.Z });
return(true);
}
case var case5 when case5 == typeof(Vector3d):
{
Vector3d asp = source;
this.Value = new Tensor(new[] { asp.X, asp.Y, asp.Z });
return(true);
}
case var case6 when case6 == typeof(Color):
{
Color asp = (Color)source;
this.Value = new Tensor(new[] { Convert.ToDouble(asp.A), Convert.ToDouble(asp.R), Convert.ToDouble(asp.G), Convert.ToDouble(asp.B) });
return(true);
}
case var case7 when case7 == typeof(GH_Colour):
{
GH_Colour asp = source;
this.Value = new Tensor(new[] { Convert.ToDouble(asp.Value.A), Convert.ToDouble(asp.Value.R), Convert.ToDouble(asp.Value.G), Convert.ToDouble(asp.Value.B) });
return(true);
}
case var case8 when case8 == typeof(Line):
{
Line asp = source;
this.Value = new Tensor(new[] { asp.From.X, asp.From.Y, asp.From.Z, asp.To.X, asp.To.Y, asp.To.Z });
return(true);
}
case var case9 when case9 == typeof(GH_Line):
{
GH_Line thisv = source;
Line asp = thisv.Value;
this.Value = new Tensor(new[] { asp.From.X, asp.From.Y, asp.From.Z, asp.To.X, asp.To.Y, asp.To.Z });
return(true);
}
}
return(false);
}