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:

• /ch10/cifar10_train.py: .batch(128)(here) should be called before .map(preprocess)(here).
• /ch10/cifar10_train.py: .batch(64)(here) should be called before .map(preprocess)(here).
• /ch10/resnet18_train.py: .batch(512)(here) should be called before .map(preprocess)(here).
• /ch10/resnet18_train.py: .batch(512)(here) should be called before .map(preprocess)(here).
• /ch15/train_scratch.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch15/train_scratch.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch15/train_scratch.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch15/pokemon.py: .batch(32)(here) should be called before .map(preprocess)(here).
• /ch15/train_transfer.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch15/train_transfer.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch15/train_transfer.py: .batch(batchsz)(here) should be called before .map(preprocess)(here).
• /ch08/nb.py: .batch(128)(here) should be called before .map(proprocess)(here).
• /ch08/nb.py: .batch(128)(here) should be called before .map(proprocess)(here).

Besides, you need to check the function called in map()(e.g., proprocess called in .map(proprocess)) whether to be affected or not to make the changed code work properly. For example, if proprocess 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.

Hello,I found a performance issue in ch10/cifar10_train.py ,
train_db = train_db.shuffle(1000).map was called without num_parallel_calls.
I think it will increase the efficiency of your program if you add this.

The same issues also exist in test_db = test_db.map ,
train_db = train_db.shuffle(1000).map,
test_db = test_db.map,
train_db = train_db.map ,test_db = test_db.shuffle(1000).batch(batchsz).map ,
db_train = db_train.shuffle(1000).map ,db_val = db_val.map ，db_test = db_test.map ，
db = db.shuffle(1000).map ，
db_train = db_train.shuffle(1000).map ，db_val = db_val.map ，db_test = db_test.map ，
train_db = train_db.shuffle(1000).map ，
val_db = val_db.shuffle(1000).map

Here is the documemtation of tensorflow to support this thing.

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