Performance issues in your project (by P3)

[DLPerf]

Hello! I've found a performance issue in your project: batch() should be called before map(), which could make your program more efficient. Here is the tensorflow document to support it.

Detailed description is listed below:

• sparse_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
• sparse_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
• dense_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
• dense_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_csv_function)(here).
• dense_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
• dense_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_csv_function)(here).

Besides, you need to check the function called in map()(e.g., parse_csv_function called in map()) whether to be affected or not to make the changed code work properly. For example, if parse_csv_function needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z).

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

[DLPerf]

Hello, I'm looking forward to your reply~