public double[] test(int[,] inputImage)
{
double[,] input = AdaptResolution(inputImage, originalResolution, inputResolution);
int inputRes = inputResolution; // inputRes = 32
/*Debug.Log(inputRes);
* string inp = "";
* for (int i = 0; i < inputRes; i++)
* {
* for (int j = 0; j < inputRes; j++)
* {
* Debug.Log(i + " " + j + " " + input[i, j]);
* inp += input[i, j] + " ";
* }
* inp += "End\n";
* }
* Debug.Log(inp);*/
ConvolutionalLayer layer1 = new ConvolutionalLayer(input, inputRes, 1, 6, convFiltersResolution, FirstConvolutionLayerFilters, initialization);
inputRes = inputRes - convFiltersResolution + 1; // inputRes = 28
MaxpoolLayer layer2 = new MaxpoolLayer(layer1.Convolution(), inputRes, 6, poolFieldResolution);
inputRes /= 2; // inputRes = 14
ConvolutionalLayer layer3 = new ConvolutionalLayer(layer2.Subsample(), inputRes, 6, 16, convFiltersResolution, SecondConvolutionLayerFilters, initialization);
inputRes = inputRes - convFiltersResolution + 1; // inputRes = 10
MaxpoolLayer layer4 = new MaxpoolLayer(layer3.Convolution(), inputRes, 16, poolFieldResolution);
inputRes /= 2; // inputRes = 5
InputLayer layer5 = new InputLayer(layer4.Subsample(), inputRes, 72, InputLayerFilters, initialization);
FullyConnectedLayer layer6 = new FullyConnectedLayer(layer5.Flatten(), 72, 50, FullyConnectedLayerWeights, initialization);
FullyConnectedLayer final = new FullyConnectedLayer(layer6.Calculate(), 50, 6, OutputLayerWeights, initialization);
//final.Calculate();
double[] output = final.Calculate();
SetGoal(output);
double[] loss = LossFunction(output, goal);
double[] results = GetResults(output);
Debug.Log("Output: " + output[0] + ", " + output[1] + ", " + output[2]);
Debug.Log("Loss: " + loss[0] + ", " + loss[1] + ", " + loss[2]);
Debug.Log("Rezultat: " + results[0].ToString("#.000") + "%, " + results[1].ToString("#.000") + "%, " + results[2].ToString("#.000") + "%");
if (training)
{
double[] dx = final.Backpropagation(loss, LearningRate);
dx = layer6.Backpropagation(dx, LearningRate);
double[,,] convdx = layer5.Backpropagation(dx, LearningRate);
convdx = layer4.Backpropagation(convdx);
convdx = layer3.Backpropagation(convdx, LearningRate);
convdx = layer2.Backpropagation(convdx);
convdx = layer1.Backpropagation(convdx, LearningRate);
}
return(output);
}