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Tensorflow 2.0 Tutorials
Hello, I found a performance issue in the definition of test
, Project/NeuralMachineTranslation/model/test.py, tf.expand_dims(input=[1],axis=0)
will be called repeatedly during program execution, resulting in reduced efficiency. I think it should be created before the loop.
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
Hello! I've found a performance issue in /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:
parsed_dataset.batch(1)
(here) should be called before dataset.map(map_func=_parse_data)
(here).parsed_dataset.batch(2)
(here) should be called before dataset.map(map_func=_parse_data)
(here).parsed_dataset.batch(parameter.BATCH_SIZE)
(here) should be called before dataset.map(map_func=_parse_data)
(here)..batch(parameter.BATCH_SIZE)
(here) should be called before dataset.map(map_func=_parse_data)
(here).Besides, you need to check the function called in map()
(e.g., _parse_data
called in dataset.map(map_func=_parse_data)
) whether to be affected or not to make the changed code work properly. For example, if _parse_data
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 11.TFRecord/read_cifar10.py
,
dataset=dataset.map(map_func=_parse_data) 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 dataset=dataset.map(map_func=_parse_data) , parsed_dataset = dataset.map, parsed_dataset = dataset.map,parsed_dataset = dataset.map ,parsed_dataset = dataset.map and dataset=dataset.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.
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