public void MLP_Shape()
{
TensorCachingAllocator tca = new TensorCachingAllocator();
var shape = new MultiLayerPerception.Shape {
inputSize = 2,
outputSize = 3,
hiddenSize = 5
};
MultiLayerPerception mlp = new MultiLayerPerception(shape);
IWorker worker = WorkerFactory.CreateWorker(mlp.model, WorkerFactory.Device.GPU);
Tensor input = tca.Alloc(new TensorShape(1, 1, 1, shape.inputSize));
for (int i = 0; i < shape.inputSize; i++)
{
input[i] = i;
}
IWorker ex = worker.Execute(input);
ex.FlushSchedule(true);
Tensor output = ex.PeekOutput();
for (int i = 0; i < shape.outputSize; i++)
{
Debug.Log($"output[{i}] = {output[i]}");
}
tca.Dispose();
ex.Dispose();
worker.Dispose();
Debug.Assert(true);
}
public void TestWeightSections()
{
var shp = new MultiLayerPerception.Shape {
hiddenSize = 2, inputSize = 2, outputSize = 2
};
MultiLayerPerception mlp = new MultiLayerPerception(shp);
Debug.Log(string.Join(",", mlp.GetWeightSections()));
Debug.Assert(true);
}
public void MLP_Calc()
{
TensorCachingAllocator tca = new TensorCachingAllocator();
var shape = new MultiLayerPerception.Shape {
inputSize = 2,
outputSize = 3,
hiddenSize = 2
};
MultiLayerPerception mlp = new MultiLayerPerception(shape);
int layerCnt = 0;
foreach (Layer layer in mlp.model.layers)
{
layerCnt++;
for (int iWB = 0; iWB < layer.weights.Length; iWB++)
{
layer.weights[iWB] = iWB * layerCnt;
}
if (layer.datasets.Length == 2)
{
Debug.Log($"" +
$"{layer.name} " +
$"({layer.weights.Length}: W{layer.datasets[0].length} + B{layer.datasets[1].length}): " +
$"<{string.Join(", ", layer.weights)}>");
}
}
string HiddenLayer = MultiLayerPerception.LayerNames.Hidden;
IWorker worker = WorkerFactory.CreateWorker(mlp.model, new string[] { HiddenLayer }, WorkerFactory.Device.GPU);
Tensor inTensor = tca.Alloc(new TensorShape(1, 1, 1, shape.inputSize));
for (int i = 0; i < shape.inputSize; i++)
{
inTensor[i] = i;
Debug.Log($"input[{i}] = {inTensor[i]}");
}
IWorker ex = worker.Execute(inTensor);
ex.FlushSchedule(true);
Tensor hTensor = ex.PeekOutput(HiddenLayer);
Debug.Assert(hTensor.length == shape.hiddenSize);
for (int i = 0; i < hTensor.length; i++)
{
Debug.Log($"hidden1[{i}] = {hTensor[i]}");
}
Tensor output = ex.PeekOutput();
Debug.Assert(output.length == shape.outputSize);
for (int i = 0; i < output.length; i++)
{
Debug.Log($"output[{i}] = {output[i]}");
}
for (int iHNode = 0; iHNode < shape.hiddenSize; iHNode++)
{
string str = "";
float sum = 0;
for (int iINode = 0; iINode < shape.inputSize; iINode++)
{
float w = mlp.GetWeight(HiddenLayer, iINode, iHNode);
str += $"{w} * {inTensor[iINode]} + ";
sum += w * inTensor[iINode];
}
float b = mlp.GetBias(HiddenLayer, iHNode);
str += $"{b}";
sum += b;
str += $"= {hTensor[iHNode]} ({sum})";
Debug.Assert(Mathf.Approximately(sum, hTensor[iHNode]));
Debug.Log(str);
}
tca.Dispose();
ex.Dispose();
worker.Dispose();
Debug.Assert(true);
}
