Numpy to TFrecords: Is there a more simple way to handle batch inputs from tfrecords?
My question is about how to get batch inputs from multiple (or sharded) tfrecords. I’ve read the example https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410. The basic pipeline is, take the training set as as example, (1) first generate a series of tfrecords (e.g., train-000-of-005, train-001-of-005, …), (2) from these filenames, generate a list and fed them into the tf.train.string_input_producer to get a queue, (3) simultaneously generate a tf.RandomShuffleQueue to do other stuff, (4) using tf.train.batch_join to generate batch inputs.