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

Question

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

Answer 1

... 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)

Answer 2

You need to call sess.run and pass the batch to it everytime when you want to load the next batch. See the code below.

img = [0,1,2,3,4,5,6,7,8]
lbl = [0,1,2,3,4,5,6,7,8]
images = tf.convert_to_tensor(img)
labels = tf.convert_to_tensor(lbl)
input_queue = tf.train.slice_input_producer([images,labels])
sliced_img = input_queue[0]
sliced_lbl = input_queue[1]

img_batch, lbl_batch = tf.train.batch([sliced_img,sliced_lbl], batch_size=3)
with tf.Session() as sess:
    coord   = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for i in range(0,3): #batch size
        image_batch,label_batch = sess.run([img_batch,lbl_batch ])
        print(image_batch, label_batch)

    coord.request_stop()
    coord.join(threads)

the answer would be something like this:

[4,1,8] [4,1,8]

[2,3,7] [2,3,7]

[2,6,8] [2,6,8]