def fit(cls, n_epoch=50, batch_size=96, image_size=(64, 64)):
""" Learn face images from databases.
use images at reception_robot.person.face_imgs
Returns:
"""
(X, Y), (X_test, Y_test) = Person.train_test_images_ids(
image_size=image_size)
print(X.shape, Y.shape, X_test.shape, Y_test.shape)
person_ids = np.sort(list(set(Y))).tolist()
print("person_ids: {}".format(person_ids))
person_num = len(person_ids)
print("label_num: {}".format(person_num))
X, Y = shuffle(X, Y)
Y = cls.to_categorical(Y, person_ids)
Y_test = cls.to_categorical(Y_test, person_ids)
face_recognizer_search_query = {
'n_epoch': n_epoch,
'batch_size': batch_size,
'image_size': image_size,
'person_ids': person_ids,
}
# face recognizer
face_recognizer = cls.objects(**face_recognizer_search_query).first()
if face_recognizer is None:
face_recognizer = cls(**face_recognizer_search_query,
person_num=person_num)
network = face_recognizer.generate_network()
# Train using classifier
run_id = 'face_cnn'
model = tflearn.DNN(network, tensorboard_verbose=3,
tensorboard_dir='tensorboard_log',
checkpoint_path='checkpoints/{}'.format(run_id))
model.fit(X, Y, n_epoch=n_epoch, shuffle=True,
validation_set=(X_test, Y_test),
show_metric=True, batch_size=batch_size, run_id=run_id)
import os
try:
os.makedirs('models')
except Exception as e:
print(e)
model.save(face_recognizer.generate_filename())
face_recognizer.save()
