TensorFlow train batches for multiple epochs?

Question

I don't understand how to run the result of tf.train.batch for multiple epochs. It runs out once of course and I don't know how to restart it.

• Maybe I can repeat it using tile , which is complicated but described in full here .
• If I can redraw a batch each time that would be fine -- I would need batch_size random integers between 0 and num_examples. (My examples all sit in local RAM). I haven't found an easy way to get these random draws at once.
• Ideally there is a reshuffle too when the batch is repeated, but it makes more sense to me to run an epoch then reshuffle, etc., instead of join the training space to itself num_epochs size, then shuffle.

I think this is confusing because I'm not really building an input pipeline since my input fits in memory, but yet I still need to be building out batching, shuffling and multiple epochs which possibly requires more knowledge of input pipeline.

Answer 1

tf.train.batch simply groups upstream samples into batches, and nothing more. It is meant to be used at the end of an input pipeline. Data and epochs are dealt with upstream.

For example, if your training data fits into a tensor, you could use tf.train.slice_input_producer to produce samples. This function has arguments for shuffling and epochs.