batch normalize about densenet-tensorflow

[taki0112]

If you look at this url, when you use tf.layers.batch_normalization, you have to define update_ops

However, there were the following issues (Only in my case... NOT all people)

1. There was a problem that the learning was not good(the accuracy was low).
2. I personally like simple code. In that case, I wrote the code in a way that I would not add it because I do not think it would be well understood by other users.
   It seemed like other users did not know something about control_dependencies or get_collection

So I used batch_norm in contrib
If you do updates_collections = None, the code is much simpler and easier to understand.
Also, the performance has been increased, and if you gave None, it's the same as doing control_dependencies above.

thank you

[ydnaandy123]

Oh wow! Many thanks for your detailed explanations!
You are so friendly~
I'm surprised that the performance will be affected!
Thanks for your sharing anyway.

BTW,
for the Global_Average_Pooling
I use tf.reduce_mean(input_tensor=x, axis=[1, 2], keep_dims=True)
What do you think~

[taki0112]

Oh good idea.
Also, using reduce_mean is global_average_pooling.