Tensorflow, try and except do not handle exception

Question

Tensorflow, try and except do not handle exception

I am new to tensor flow and here I ran into an annoying problem.

I am making a program that downloads the "raw data" of an image tf.WholeFileReader.read(image_name_queue)from a tfrecord file and then decodes it with tf.image.decode_jpeg(raw_data, channels=3), and then passes it through a function that vectorizes it.

main code

logging.info('setting up folder')
create_image_data_folder()
save_configs()

logging.info('creating graph')
filename_queue = tf.train.string_input_producer([
                                             configs.TFRECORD_IMAGES_PATH],
                                             num_epochs=1)

image_tensor, name_tensor = read_and_decode(filename_queue)
image_batch_tensor, name_batch_tensor = tf.train.shuffle_batch(
                                        [image_tensor, name_tensor],
                                        configs.BATCH_SIZE,
                                        1000 + 3 * configs.BATCH_SIZE,
                                        min_after_dequeue=1000)
image_embedding_batch_tensor = configs.IMAGE_EMBEDDING_FUNCTION(image_batch_tensor)

init = tf.initialize_all_variables()
init_local = tf.initialize_local_variables()
logging.info('starting session')
with tf.Session().as_default() as sess:
    sess.run(init)
    sess.run(init_local)
    tf.train.start_queue_runners()

    logging.info('vectorizing')
    data_points = []
    for _ in tqdm(xrange(get_n_batches())):
        name_batch = sess.run(name_batch_tensor)
        image_embedding_batch = sess.run(image_embedding_batch_tensor)
        for vector, name in zip(list(image_embedding_batch), name_batch):
            data_points.append((vector, name))

logging.info('saving')
save_pkl_file(data_points, 'vectors.pkl')

read_and_decode function

def read_and_decode(tfrecord_file_queue):
    logging.debug('reading image and decodes it from queue')
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(tfrecord_file_queue)
    features = tf.parse_single_example(serialized_example,
        features={
            'image': tf.FixedLenFeature([], tf.string),
            'name': tf.FixedLenFeature([], tf.string)
        }
    )
    image = process_image_data(features['image'])

    return image, features['name']

The code works, but ultimately it encounters a bad, non-jpeg file, and an error occurs and the program stops working .

Error

InvalidArgumentError (see above for traceback): Invalid JPEG data, size 556663

I want to skip these "mistakes." I tried to surround the code with tryand except.

new code

for _ in tqdm(xrange(get_n_batches())):
    try:
        name_batch = sess.run(name_batch_tensor)
        image_embedding_batch = sess.run(image_embedding_batch_tensor)
        for vector, name in zip(list(image_embedding_batch), name_batch):
            data_points.append((vector, name))
    except Exception as e:
        logging.warning('error occured: {}'.format(e))