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machine-learningtensorflowcomputer-visiondeep-learningtf-slim

TensorFlow: does tf.train.batch automatically load the next batch when the batch has finished training?


For instance, after I have created my operations, fed the batch data through the operation and run the operation, does tf.train.batch automatically feed in another batch of data to the session?

I ask this because tf.train.batch has an attribute of allow_smaller_final_batch which makes it possible for the final batch to be loaded as a size lesser than the indicated batch size. Does this mean even without a loop, the next batch could be automatically fed? From the tutorial codes I am rather confused. When I load a single batch, I get literally a single batch size of shape [batch_size, height, width, num_channels], but the documentation says it Creates batches of tensors in tensors. Also, when I read the tutorial code in the tf-slim walkthrough tutorial, where there is a function called load_batch, there are only 3 tensors returned: images, images_raw, labels. Where are 'batches' of data as explained in the documentation?

Thank you for your help.


Solution

  • ... does tf.train.batch automatically feeds in another batch of data to the session?

    No. Nothing happens automatically. You must call sess.run(...) again to load a new batch.

    Does this mean even without a loop, the next batch could be automatically fed?

    No. tf.train.batch(..) will always load batch_size tensors. If you have for example 100 images and a batch_size=30 then you will have 3*30 batches as in you can call sess.run(batch) three times before the input queue will start from the beginning (or stop if epoch=1). This means that you miss out 100-3*30=10 samples from training. In case you do not want to miss them you can do tf.train.batch(..., allow_smaller_final_batch=True) so now you will have 3x 30-sample-batches and 1x 10-sample-batch before the input queue will restart.

    Let me also elaborate with a code sample:

    queue = tf.train.string_input_producer(filenames,
            num_epochs=1) # only iterate through all samples in dataset once
    
    reader = tf.TFRecordReader() # or any reader you need
    _, example = reader.read(queue)
    
    image, label = your_conversion_fn(example)
    
    # batch will now load up to 100 image-label-pairs on sess.run(...)
    # most tf ops are tuned to work on batches
    # this is faster and also gives better result on e.g. gradient calculation
    batch = tf.train.batch([image, label], batch_size=100)
    
    with tf.Session() as sess:
        # "boilerplate" code
        sess.run([
            tf.local_variables_initializer(),
            tf.global_variables_initializer(),
        ])
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    
        try:
            # in most cases coord.should_stop() will return True
            # when there are no more samples to read
            # if num_epochs=0 then it will run for ever
            while not coord.should_stop():
                # will start reading, working data from input queue
                # and "fetch" the results of the computation graph
                # into raw_images and raw_labels
                raw_images, raw_labels = sess.run([images, labels])
        finally:
            coord.request_stop()
            coord.join(threads)