def reset_model():
yolo = Yolo()
random_image = tf.convert_to_tensor(np.random.random(
(1, image_size, image_size, 3)),
dtype=np.float32)
_ = yolo(random_image)
yolo.save_weights("./weights/yolo")
def train():
yolo = Yolo()
yolo.load_weights("./weights/yolo")
opt = Adam(learning_rate=5e-5)
with open("../data/data_detect_local_train.json") as json_file:
data = json.load(json_file)
data_index = 0
while str(data_index) in data:
img = get_img("../pictures/pictures_detect_local_train/{}.png".format(
data_index))
true_labels, true_boxes, true_preds = get_localization_data(
data[str(data_index)])
def get_loss():
preds = yolo(img)
return calculate_loss(preds, true_labels, true_boxes, true_preds)
opt.minimize(get_loss, [yolo.trainable_weights])
if (data_index % 100 == 99):
yolo.save_weights("./weights/yolo")
data_index += 1
