TF Dataset API: Is the following sequence correct? map,cache,shuffle,batch,repeat,prefetch

Question

I am using this sequence to read images files from disk and feed into a TF Keras model.

  #Make dataset for training
    dataset_train = tf.data.Dataset.from_tensor_slices((file_ids_training,file_names_training))
    dataset_train = dataset_train.flat_map(lambda file_id,file_name: tf.data.Dataset.from_tensor_slices(
        tuple (tf.py_func(_get_data_for_dataset, [file_id,file_name], [tf.float32,tf.float32]))))
    dataset_train = dataset_train.cache()

    dataset_train= dataset_train.shuffle(buffer_size=train_buffer_size)
    dataset_train= dataset_train.batch(train_batch_size) #Make dataset, shuffle, and create batches
    dataset_train= dataset_train.repeat()
    dataset_train = dataset_train.prefetch(1)
    dataset_train_iterator = dataset_train.make_one_shot_iterator()
    get_train_batch = dataset_train_iterator.get_next()

I am having questions on whether this is the most optimal sequence. For e.g. Should repeat come after shuffle() and before batch()?, Should cache() come after batch?

Answer 1

The answer here Output differences when changing order of batch(), shuffle() and repeat() suggests repeat or shuffle before batching. The order I often use is (1) shuffle, (2) repeat, (3) map, (4) batch but it can vary based on your preferences. I use shuffle before repeat to avoid blurring epoch boundaries. I use map before batch because my mapping function applies to a single example (not to a batch of examples) but you can certainly write a map function that is vectorized and expects to see a batch as input.

Answer 2

I'd suggest using the following order

dataset
  .cache(filename='./data/cache/')
  .shuffle(BUFFER_SIZE)
  .repeat(Epoch)
  .map(func, num_parallel_calls=tf.data.AUTOTUNE)
  .filter(fltr)
  .batch(BATCH_SIZE)
  .prefetch(tf.data.AUTOTUNE)

in this way firstly to further speed up the training the processed data will be saved in binary format (done automatically by tf) by calling cache. The data will be saved in the cache file after, all the dataset is shuffled and repeated. After that just like @shivaraj said use map and filter function before batching the data. Lastly call the prefetch as said in tf documentation to prepare the data before hand while gpu is working on the previous batch.

Note:

Calling cache will take a lot of time on first call depending on the data size and memory available. But it speed up the training by at least 4 times, if you need to do multiple experiments while not making any change to dataset's inputs and outputs (labels). Also changing the order of calling cache will also effect the time it takes to create the cache files. I found this order to be the fastest, in every term and also doesn't raises any warnings.