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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.

1 Answer

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You should call again to load a new batch.

The tf.train.batch(..) will always load batch_size tensors. For example 100 images and a batch_size=30, then you will have 3*30 batches as in you can call three times before the input queue will start from the beginning (or stop if epoch=1).

This means that you miss out on 100-3*30=10 samples from training. In case you do not want to miss them you can do tf.train.batch so now you will have 3x 30-sample-batches and 1x 10-sample-batch before the input queue will restart.

For example:

queue = tf.train.string_input_producer(filenames,

        num_epochs=1) # only iterate through all samples in dataset once

reader = tf.TFRecordReader() 

example =

image, label = your_conversion_fn(example)

# batch will now load up to 100 image-label-pairs on

# 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[




    coord = tf.train.Coordinator()

    threads = tf.train.start_queue_runners(sess=sess, coord=coord)


# 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 =[images, labels])




Hope this answer helps you! For more, study the Tensorflow Tutorial.

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