VishnuEcoClim commited on
Commit
aee66d8
·
1 Parent(s): 2cc8d92

Update utils.py

Browse files
Files changed (1) hide show
  1. utils.py +13 -15
utils.py CHANGED
@@ -8,36 +8,34 @@ from PIL import Image
8
 
9
  def gen_labels():
10
  train = 'Dataset/Train'
11
- train_generator = ImageDataGenerator(rescale = 1/255)
12
 
13
  train_generator = train_generator.flow_from_directory(train,
14
- target_size = (300,300),
15
- batch_size = 32,
16
- class_mode = 'sparse')
17
- labels = (train_generator.class_indices)
18
- labels = dict((v,k) for k,v in labels.items())
19
 
20
  return labels
21
 
22
  def preprocess(image):
23
  image = np.array(image.resize((256, 256), Image.LANCZOS))
24
- image = np.array(image, dtype='uint8')
25
- image = np.array(image) / 255.0
26
-
27
  return image
28
 
29
  def model_arc():
30
  model = Sequential()
31
 
32
  # Convolution blocks
33
- model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(256, 256, 3), activation='relu'))
34
- model.add(MaxPooling2D(pool_size=2))
35
 
36
- model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
37
- model.add(MaxPooling2D(pool_size=2))
38
 
39
- model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))
40
- model.add(MaxPooling2D(pool_size=2))
41
 
42
  # Classification layers
43
  model.add(Flatten())
 
8
 
9
  def gen_labels():
10
  train = 'Dataset/Train'
11
+ train_generator = ImageDataGenerator(rescale=1/255)
12
 
13
  train_generator = train_generator.flow_from_directory(train,
14
+ target_size=(256, 256),
15
+ batch_size=32,
16
+ class_mode='sparse')
17
+ labels = train_generator.class_indices
18
+ labels = dict((v, k) for k, v in labels.items())
19
 
20
  return labels
21
 
22
  def preprocess(image):
23
  image = np.array(image.resize((256, 256), Image.LANCZOS))
24
+ image = image.astype('float32') / 255.0
 
 
25
  return image
26
 
27
  def model_arc():
28
  model = Sequential()
29
 
30
  # Convolution blocks
31
+ model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=(256, 256, 3), activation='relu'))
32
+ model.add(MaxPooling2D(pool_size=2))
33
 
34
+ model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
35
+ model.add(MaxPooling2D(pool_size=2))
36
 
37
+ model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))
38
+ model.add(MaxPooling2D(pool_size=2))
39
 
40
  # Classification layers
41
  model.add(Flatten())