Update utils.py

utils.py

@@ -1,24 +1,3 @@
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D
-from tensorflow.keras.losses import sparse_categorical_crossentropy, binary_crossentropy
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-import numpy as np
-from PIL import Image
-
-def gen_labels():
-    train = 'Dataset/Train'
-    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())
-
-    return labels
-
 def preprocess(image):
     image = np.array(image.resize((256, 256), Image.LANCZOS))
     image = np.array(image, dtype='uint8')
@@ -30,7 +9,7 @@ def model_arc():
     model = Sequential()
 
     # Convolution blocks
-    model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(300,300,3), activation='relu'))
+    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'))
@@ -49,4 +28,4 @@ def model_arc():
     model.add(Dropout(0.2))
     model.add(Dense(6, activation='softmax'))
 
-    return model
+    return model