Créer son propre générateur de données
L'API ImageDataGenerator de Keras peut-être limitée dans certaines situations nécessitant la création d'un générateur de données personalisé, c'est l'objet de ce nouveau tutoriel.
Nous avons besoin d'une fonction qui permet de lire les données
def load_image(path, size):
"""custom function to load image"""
image = Image.open(path)
image = image.convert('RGB')
image = image.resize(size)
image = np.array(image)*(1./255)
return image
class DataGenerator(tf.keras.utils.Sequence):
"""Custom generator"""
def __init__(self, x_set, y_set, batch_size=32, target_size=(150, 150)):
self.x, self.y = x_set, y_set
self.batch_size = batch_size
self.target_size = target_size
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size:(idx + 1) *
self.batch_size]
batch_y = self.y[idx * self.batch_size:(idx + 1) *
self.batch_size]
return np.array([
load_image(file_name, target_size)
for file_name in batch_x]), np.array(batch_y)
Appeler la classe
train_ds = DataGenerator(train_images, train_labels, batch_size=BATCH_SIZE, target_size=(WIDTH, HEIGHT))
val_ds = DataGenerator(val_images, val_labels, batch_size=BATCH_SIZE, target_size=(WIDTH, HEIGHT))
Code complet
def load_image(path, size):
"""custom function to load image"""
image = Image.open(path)
image = image.convert('RGB')
image = image.resize(size)
image = np.array(image)*(1./255)
return image
class DataGenerator(tf.keras.utils.Sequence):
"""Custom generator"""
def __init__(self, x_set, y_set, batch_size=32, target_size=(150, 150)):
self.x, self.y = x_set, y_set
self.batch_size = batch_size
self.target_size = target_size
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size:(idx + 1) *
self.batch_size]
batch_y = self.y[idx * self.batch_size:(idx + 1) *
self.batch_size]
return np.array([
load_image(file_name, target_size)
for file_name in batch_x]), np.array(batch_y)