NVIDIA_Challenge | Coding challenge implementation for my Spring | Machine Learning library

Here are answers to your questions.

1. You cannot give epochs directly to slim.learning.train. Instead, you give the number of batches as the argument. It is called number_of_steps. It is used to set an operation called should_stop_op on line 709. I assume you know how to convert number of epochs to batches.

2. I don't think the shuffle_batch function will repeat images because internally it uses the RandomShuffleQueue. According to this answer, the RandomShuffleQueue enqueues elements using a background thread as:

   • While size(queue) < capacity:
     • Add an element to the queue

It dequeues elements as:

• While the number of elements dequeued < batch_size:
  • Wait until the size(queue) >= min_after_dequeue + 1 elements.
  • Select an element from the queue uniformly at random, remove it from the queue, and add it the output batch.

So in my opinion, there is very little chance that the elements would be repeated, because in the dequeuing operation, the chosen element is removed from the queue. So it is sampling without replacement.

Will a new queue be created for every epoch?

The tensors being inputted to tf.train.shuffle_batch are image and label which ultimately come from the filename_queue. If that queue is producing TFRecord filenames indefinitely, then I don't think a new queue will be created by shuffle_batch. You can also create a toy code like this to understand how shuffle_batch works.

Coming to the next point, how to train over the whole dataset? In your code, the following line gets the list of TFRecord filenames.