public void RunFullTest(_Tensor[] initTensors)
{
if (isInit)
{
_Tensor[] layerResult = convLayers[0].ForwardPass(initTensors);
for (int i = 1; i < convLayers.Length; i++)
{
layerResult = convLayers[i].ForwardPass(layerResult);
}
flatten = NN_Utils.AppendToVector(layerResult);
layerResult = denseLayers[0].ForwardPass(new _Tensor[1] {
flatten
});
for (int k = 1; k < denseLayers.Length; k++)
{
layerResult = denseLayers[k].ForwardPass(layerResult);
}
layerResult[0].Shape();
layerResult[0].Print();
}
}
public _Tensor ForwardPass(_Tensor[] initTensors)
{
if (initTensors.Length == 0)
{
return(new _Tensor());
}
if (isInit)
{
_Tensor[] layerResult = convLayers[0].ForwardPass(initTensors);
for (int i = 1; i < convLayers.Length; i++)
{
layerResult = convLayers[i].ForwardPass(layerResult);
}
flatten = NN_Utils.AppendToVector(layerResult);
layerResult = denseLayers[0].ForwardPass(new _Tensor[1] {
flatten
});
for (int k = 1; k < denseLayers.Length; k++)
{
layerResult = denseLayers[k].ForwardPass(layerResult);
}
return(layerResult[0]);
}
else
{
InitializeNN(initTensors);
_Tensor[] layerResult = convLayers[0].ForwardPass(initTensors);
for (int i = 1; i < convLayers.Length; i++)
{
layerResult = convLayers[i].ForwardPass(layerResult);
}
flatten = NN_Utils.AppendToVector(layerResult);
layerResult = denseLayers[0].ForwardPass(new _Tensor[1] {
flatten
});
for (int k = 1; k < denseLayers.Length; k++)
{
layerResult = denseLayers[k].ForwardPass(layerResult);
}
return(layerResult[0]);
}
}
public void TestInitNNDENSE(_Tensor[] initTensors)
{
flatten = NN_Utils.AppendToVector(initTensors);
//Now, we start initializing the dense layers
denseLayers[0].InitLayer(new _Tensor[1] {
flatten
});
//We perform the same operations as above
//input_i = results_(i-1)
for (int i = 1; i < denseLayers.Length; i++)
{
denseLayers[i].InitLayer(denseLayers[i - 1].results);
}
isInit = true;
}
public void RunGEMMDenseTest(_Tensor[] initTensors)
{
//float startTime = Time.realtimeSinceStartup;
if (isInit)
{
flatten = NN_Utils.AppendToVector(initTensors);
_Tensor[] layerResult = gemmLayers[0].ForwardPass(new _Tensor[1] {
flatten
});
for (int k = 1; k < gemmLayers.Length; k++)
{
layerResult = gemmLayers[k].ForwardPass(layerResult);
}
}
//float finishTime = Time.realtimeSinceStartup;
//Debug.Log("GEMM Dense test time: " + (finishTime - startTime));
}
public void TestInitNN(_Tensor[] initTensors)
{
//Start initializing the first layer
convLayers[0].InitLayer(initTensors);
//The next layers are initialized with the results of the last layer, since it is the input of the i-th layer
//input_i = results_(i-1)
for (int i = 1; i < convLayers.Length; i++)
{
convLayers[i].InitLayer(convLayers[i - 1].results);
}
int lastConvIndex = convLayers.Length - 1;
//To start with the dense layers, we first need to flatten the 3D vector coming from the last conv layer.
flatten = NN_Utils.AppendToVector(convLayers[lastConvIndex].results);
//We first check if we have dense layers
if (denseLayers.Length == 0)
{
denseLayers = new DenseLayer[1] {
new DenseLayer()
};
denseLayers[0].activationFunction = new Softmax();
}
//Now, we start initializing the dense layers
denseLayers[0].InitLayer(new _Tensor[1] {
flatten
});
//We perform the same operations as above
//input_i = results_(i-1)
for (int i = 1; i < denseLayers.Length; i++)
{
denseLayers[i].InitLayer(denseLayers[i - 1].results);
}
isInit = true;
}
