sieć neuronowa, rozpoznawanie cyfry z obrazka oraz testowe cyfry

.idea/Sztuczna_Inteligencja_Gr16.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (Sztuczna_Inteligencja_Gr16)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (1213)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (Sztuczna_Inteligencja_Gr16)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (1213)" project-jdk-type="Python SDK" />
 </project>

main.py

@@ -8,7 +8,7 @@ from agent import *
 from okno import *
 from ramy_czyli_wiedza_agenta import *
 from rescue import *
-
+from neural_network import *
 # aby działalo w oknie + rozdzielczość ekranu
 # ctypes.windll.shcore.SetProcessDpiAwareness(1)
 
@@ -146,6 +146,7 @@ def main():
                 pygame.time.wait(1500)
             flaga1 = 0
             t = threading.Timer(5.0, zdarzenie_osoba).start()
+    recognition()
 
 try:
     main()

rescue.py

@@ -17,9 +17,9 @@ def drzewo_decyzyjne():
     clf = clf.fit(x, y)
     # print("Dokładność: ", metrics.accuracy_score(y_test, y_pred))
 
-    dot_data = tree.export_graphviz(clf, out_file=None, feature_names=kolumny_x, class_names=['0', '1'])
-    graph = pydotplus.graph_from_dot_data(dot_data)
-    graph.write_png('drzewo.png')
+  #  dot_data = tree.export_graphviz(clf, out_file=None, feature_names=kolumny_x, class_names=['0', '1'])
+   # graph = pydotplus.graph_from_dot_data(dot_data)
+    #graph.write_png('drzewo.png')
 
     return clf