App.py

@@ -21,7 +21,7 @@ if bfs3_flag or Astar or Astar2:
    Pole.stoneFlag = True
 TreeFlag=False
 nnFlag=True
-newModel=True
+newModel=False
 
 pygame.init()
 show_console=True
@@ -124,7 +124,7 @@ def init_demo(): #Demo purpose
                 if (newModel):
                     print_to_console("uczenie sieci neuronowej")
                     model = neuralnetwork.trainNewModel()
-                    neuralnetwork.saveModel(model, 'model2.pth')
+                    neuralnetwork.saveModel(model, 'model.pth')
                     print_to_console("sieć nuronowa nauczona")
                     print('model został wygenerowany')
                 else:

Tractor.py

@@ -202,7 +202,12 @@ class Tractor:
                         quit()
                 if self.slot.imagePath != None:
                     predictedLabel = nn.predictLabel(self.slot.imagePath, model)
-                    print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])
+
+                    # print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])
+                    if str(self.slot.label) != str(predictedLabel):
+                        print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis))
+                        print("real: ", str(self.slot.label), "\tpredicted: ", str(predictedLabel), "\n")
+
                     if self.slot.label != predictedLabel:
                         self.slot.mark_visited()
                         count += 1

neuralnetwork.py

@@ -15,6 +15,8 @@ fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'su
 torch.manual_seed(42)
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# device = torch.device("mps") if torch.backends.mps.is_available() else torch.device('cpu')
+# print(device)
 
 def getTransformation():
     transform=transforms.Compose([