This: -improved printing of results -fixed a bug where picking tomato fertilizer caused crash -added trained model_500_hidden.pth trained with 500 hidden layers

Tractor.py

@@ -15,6 +15,7 @@ condition=Condition.Condition()
 drzewo=Drzewo.Drzewo()
 
 format_string = "{:<25}{:<25}{:<25}{:<10}{:<10}{:<10}{:<25}{:<15}{:<20}{:<10}{:<15}"
+format_string_nn="{:<10}{:<20}{:<20}{:<15}{:<20}"
 
 
 tab = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
@@ -193,6 +194,8 @@ class Tractor:
         print("podlanych slotów: ", str(counter))
 
     def snake_move_predict_plant(self, pole, model):
+        headers=['Coords','Real plant','Predicted plant','Result','Fertilizer']
+        print(format_string_nn.format(*headers))
         initPos = (self.slot.x_axis, self.slot.y_axis)
         count = 0
         for i in range(initPos[1], dCon.NUM_Y):
@@ -204,10 +207,7 @@ class Tractor:
                     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])
-                    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")
-
+                    print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
                     if self.slot.label != predictedLabel:
                         self.slot.mark_visited()
                         count += 1
@@ -220,7 +220,7 @@ class Tractor:
                 self.turn_left()
                 self.move_forward(pole, False)
                 self.turn_left()
-        print(f"źle nawiezionych roślin: {count}")
+        print(f"Dobrze nawiezionych roślin: {20*12-count}, źle nawiezionych roślin: {count}")
 
     def snake_move(self,pole,x,y):
         next_slot_coordinates=(x,y)

neuralnetwork.py

@@ -10,7 +10,7 @@ import random
 
 imageSize = (128, 128)
 labels = ['carrot','corn', 'potato', 'tomato'] # musi być w kolejności alfabetycznej
-fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat'}
+fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat', labels[3]:'obornik kurzy'}
 
 torch.manual_seed(42)
 
@@ -59,7 +59,7 @@ def accuracy(model, dataset):
     return correct.float() / len(dataset)
 
 def getModel():
-    hidden_size = 300
+    hidden_size = 500
     model = nn.Sequential(
         nn.Linear(imageSize[0] * imageSize[1] * 3, hidden_size),
         nn.ReLU(),