CODEWITHSUNDEEP
tensorflow
Albert
Albert

Reputation: 68160

tf.contrib.data.Dataset repeat with shuffle, notice epoch end, mixed epochs?

About the tf.contrib.data.Dataset (from TensorFlow 1.2, see here and here) usage: When I use repeat (for multiple epochs) together with shuffle (as read_batch_features does internally), how will I notice when some epochs ends, and what the current epoch is? Also, when the epoch ends, will the ShuffleDataset wait first to dequeue everything or will it already be filled with more data from the next epoch? In the last epoch, or if I don't use repeat, will the ShuffleDataset dequeue all remaining data, like tf.RandomShuffleQueue dequeueing does after close?

My current solution, which also gives me more control: I would not use repeat but go once over the data and use ShuffleDataset to get shuffling like RandomShuffleQueue, and then at some point I get OutOfRangeError and I know that I reached the end of the epoch. Then I reinitializable the iterator, like it is described here.

Upvotes: 5

Views: 6020

Answers (1)

mrry
mrry

Reputation: 126164

The behavior of Dataset.shuffle() depends on where in your pipeline it appears relative to the Dataset.repeat():

  • If you shuffle before the repeat, the sequence of outputs will first produce all records from epoch i, before any record from epoch i + 1.

  • If you shuffle after the repeat, the sequence of outputs may produce records from epoch i before or after epoch i + 1 (and, epoch i + k, with probability that increases with the buffer_size and decreases with k).

If you want to perform some computation between epochs, and avoid mixing data from different epochs, it is probably easiest to avoid repeat() and catch the OutOfRangeError at the end of each epoch.

There are some more interesting pipelines you could build to track the epoch number. For example, you could encode an epoch number as a component of each element:

dataset = (
    Dataset.range(None).flat_map(lambda epoch_num: 
        Dataset.zip(
            (Dataset.from_tensors(epoch_num).repeat(),  # Infinite repeat of `epoch_num`.
             ...,  # Definition of a Dataset over a single epoch.
            )
        )
    )
)

...where ... is the expression that defines a Dataset for a single epoch, and includes batching and shuffling.

Upvotes: 12

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