public NeuralNetAction Think(IPercept percept)
{
var data = percept.ToDoubleArray();
if (data.Length != _model.GetInputDimension().c)
{
throw new ArgumentOutOfRangeException(nameof(ArgumentOutOfRangeException),
$"Percept count should be {_model.GetInputDimension().c}, but is {data.Length}, check if the " +
$"input layer in DefaultLayer is set to the right value (currently: {_layers[0]})");
}
var input = new Data2D(1, 1, _layers[0], 1);
input.SetData(data);
var result = (Data2D)_model.ExecuteNetwork(input);
return(new NeuralNetAction(result.ToDoubleArray(_layers)));
}
public static void SetWeights(this SequentialModel sequentialModel, int[] layers, double[] weights)
{
var dim = sequentialModel.GetInputDimension();
if (dim.c != layers[0])
{
throw new ArgumentOutOfRangeException(nameof(ArgumentOutOfRangeException),
"Size of input layer should be" +
$"{sequentialModel.GetInputDimension().c} but is {layers[0]}.");
}
var data = new List <IData>();
var count = 0;
for (var i = 0; i < layers.Length - 1; i++)
{
// create weight list
data.Add(new Data2D(1, 1, layers[i], layers[i + 1]));
data.Add(new DataArray(layers[i + 1]));
data.Add(new DataArray(0));
// assign weights to weight list
for (var j = 0; j < layers[i]; j++)
{
for (var k = 0; k < layers[i + 1]; k++)
{
((Data2D)data[i * 3])[0, 0, j, k] = weights[count++];
}
}
// assign biases to weight list
for (var j = 0; j < layers[i + 1]; j++)
{
((DataArray)data[i * 3 + 1])[j] = weights[count++];
}
}
sequentialModel.SetWeights(data);
}
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());
}