In Tensorflow, when use dataset.shuffle(1000), am I only using 1000 data from my whole dataset?

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When using the following code to train my network:

classifier = tf.estimator.Estimator(
    model_fn=my_neural_network_model, 
    model_dir=some_path_to_save_checkpoints,
    params={
        some_parameters
    }
)
classifier.train(input_fn=data_train_estimator, steps=step_num)

where data_train_estimator is defined as:

def data_train_estimator():
    dataset = tf.data.TextLineDataset(train_csv_file).map(_parse_csv_train)  
    dataset = dataset.batch(100)
    dataset = dataset.shuffle(1000)
    dataset = dataset.repeat()
    iterator = dataset.make_one_shot_iterator() 
    feature, label = iterator.get_next()
    return feature, label

How does dataset.shuffle(1000) actually work?

More specifically,

Let's say I have 20000 images, batch size = 100, shuffle buffer size = 1000, and I train the model for 5000 steps.

1. For every 1000 steps, am I using 10 batches(of size 100), each independently taken from the same 1000 images in the shuffle buffer?

2.1 Does the shuffle buffer work like a moving window?

2.2 Or, does it randomly pick 1000 out of the 5000 images (with or without replacement)?

3. In the whole 5000 steps, how many different states has the shuffle buffer been in?

tensorflow python
1 Answers

With shuffle_buffer=1000 you will keep a buffer in memory of 1000 points. When you need a data point during training, you will draw the point randomly from points 1-1000. After that there is only 999 points left in the buffer and point 1001 is added. The next point can then be drawn from the buffer.

To answer you in point form:

For every 1000 steps, am I using 10 batches(of size 100), each independently taken from the same 1000 images in the shuffle buffer?

No the image buffer will stay constant, but drawn images will be replaced with images not used before in that epoch.

Does the shuffle buffer work like a moving window? Or, does it randomly pick 1000 out of the 5000 images (with or without replacement)?

It draws without replacement and doesn't really work like a moving window, since drawn images are replaced dynamically.

In the whole 5000 steps, how many different states has the shuffle buffer been in?

Close to n_images * n_steps. So 25,000,000 in this case. There might be a few states that have been seen before by chance, but it is unlikely.

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