96fad3497a4373cc289f9e7c4272d4722a22f381,src/mjsynth.py,,bucketed_input_pipeline,#,29

Before Change

        iterator = dataset.make_one_shot_iterator()
        image, width, label, length, text, filename, get_input = iterator.get_next()

        label = tf.deserialize_many_sparse(label, tf.int64) // post-batching...
        label = tf.cast(label, tf.int32) // for ctc_loss
        
    return image, width, label, length, text, filename

def threaded_input_pipeline(base_dir,file_patterns,
                            num_threads=4,
                            batch_size=32,

After Change

        dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(batch_size, 
                                                                   count=num_epoch))

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

    return dataset

def threaded_input_pipeline(base_dir,file_patterns,

Instances