Update utils.py

utils.py

@@ -8,36 +8,34 @@ from PIL import Image
 
 def gen_labels():
     train = 'Dataset/Train'
-    train_generator = ImageDataGenerator(rescale = 1/255)
+    train_generator = ImageDataGenerator(rescale=1/255)
 
     train_generator = train_generator.flow_from_directory(train,
-                                                        target_size = (300,300),
-                                                        batch_size = 32,
-                                                        class_mode = 'sparse')
-    labels = (train_generator.class_indices)
-    labels = dict((v,k) for k,v in labels.items())
+                                                          target_size=(256, 256),
+                                                          batch_size=32,
+                                                          class_mode='sparse')
+    labels = train_generator.class_indices
+    labels = dict((v, k) for k, v in labels.items())
 
     return labels
 
 def preprocess(image):
     image = np.array(image.resize((256, 256), Image.LANCZOS))
-    image = np.array(image, dtype='uint8')
-    image = np.array(image) / 255.0
-
+    image = image.astype('float32') / 255.0
     return image
 
 def model_arc():
     model = Sequential()
 
     # Convolution blocks
-    model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(256, 256, 3), activation='relu'))
-    model.add(MaxPooling2D(pool_size=2)) 
+    model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=(256, 256, 3), activation='relu'))
+    model.add(MaxPooling2D(pool_size=2))
 
-    model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
-    model.add(MaxPooling2D(pool_size=2)) 
+    model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
+    model.add(MaxPooling2D(pool_size=2))
 
-    model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))
-    model.add(MaxPooling2D(pool_size=2)) 
+    model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))
+    model.add(MaxPooling2D(pool_size=2))
 
     # Classification layers
     model.add(Flatten())