96fad3497a4373cc289f9e7c4272d4722a22f381,src/mjsynth.py,,threaded_input_pipeline,#Any#Any#Any#Any#Any#Any#Any#,80

Before Change

                               length_threshold, 
                               training, data_tuples))

    iterator = dataset.make_one_shot_iterator()
    image, width, label, length, text, filename = iterator.get_next()
            
    with tf.device(batch_device): // Create batch queue

        image, width, label, length, text, filename  = tf.train.batch_join( 

After Change

    
    with tf.device(batch_device): // Create batch queue

        dataset = dataset.batch(batch_size)

        dataset = dataset.map(lambda image, 
                              width, label, 
                              length, text, 
                              filename: 
                              (image, width, 
                              tf.cast(tf.deserialize_many_sparse(label, tf.int64), tf.int32),
                              length, text, filename))

    return dataset

def _element_length_fn(image, width, label, length, text, filename, get_input):

Instances