Upload utils.py

utils.py

@@ -0,0 +1,52 @@
+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')
+    image = np.array(image) / 255.0
+
+    return image
+
+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(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)) 
+
+    # Classification layers
+    model.add(Flatten())
+
+    model.add(Dense(64, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(Dense(32, activation='relu'))
+
+    model.add(Dropout(0.2))
+    model.add(Dense(6, activation='softmax'))
+
+    return model