neural

tiles.py

@@ -13,7 +13,6 @@ import matplotlib.pyplot as plt
 import os
 import cv2
 from tqdm import tqdm
-import keras
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPooling2D
@@ -620,6 +619,10 @@ def learn_neural_network(X, y):
     model.add(Activation('relu'))
     model.add(MaxPooling2D(pool_size=(2, 2)))
 
+    model.add(Conv2D(64, (3, 3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
     model.add(Flatten())
 
     model.add(Dense(64))
@@ -631,7 +634,7 @@ def learn_neural_network(X, y):
                   optimizer='adam',
                   metrics=['accuracy'])
 
-    model.fit(X, y, batch_size=1, epochs=10, validation_batch_size=0.1)
+    model.fit(X, y, batch_size=32, epochs=10, validation_batch_size=0.1)
 
     return model
 
@@ -649,38 +652,15 @@ def predict(model,filepath):
 
 def result(prediction):
     if prediction[0][0] >= 0.5:
+        print(prediction)
         print(math.ceil(prediction[0][0]))
         print('No pepperoni')
     elif prediction[0][0] < 0.5:
+        print(prediction)
         print(math.floor(prediction[0][0]))
         print("Pepperoni")
 
 
-#####################neural network##############################
-
-DATADIR = "C:/Datasets/Ingridients"
-CATEGORIES = ["yes", "no"]
-IMG_SIZE = 90
-training_data = []
-
-create_training_data()
-X = []
-y = []
-
-for features, label in training_data:
-    X.append(features)
-    y.append(label)
-X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
-y = np.array(y)
-"""
-m = learn_neural_network(X, y)
-prediction = m.predict([prepare_img('p1.jpg')])
-print(prediction[0][0])
-result(prediction)
-"""
-
-#######################################################
-
 map = Map()
 waiter = Waiter([32, 32])
 
@@ -703,6 +683,7 @@ for table in tables:
 
 client = Client(generate_client())
 
+neural_prediction_in = False
 
 def main():
     direction = []
@@ -810,10 +791,16 @@ def main():
                 image = pygame.image.load(path)
             first_time = False
             display_img(display, image)
+            if neural_prediction_in is False:
                 prediction = predict(model, path)
                 result(prediction)
+                neural_prediction_in = True
+
         else:
             first_time = True
+            neural_prediction_in = False
+
+
 
         pygame.display.update()