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Batch normalization layer have 4 parameters. #1523
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Well, read the source code! It's straightforward. |
well I read it hours. But I cannot understood. (i'm not good at code.) |
Looks like gamma, beta, running mean and running std is the order (super.get_weights() returns the weights of self.params and then running mean and std are tacked on): https://github.com/fchollet/keras/blob/master/keras/layers/normalization.py#L61-L72 |
AvantiShri is right. And you can find the answer from this keras document |
Looking at the code : https://github.com/keras-team/keras/blob/master/keras/layers/normalization.py#L61-L72 I think it should be [gamma, beta, mean , variance] |
In case of Batch Normalization get_weights() will provide four additional parameter values for each hidden layers in neural network. Please refer link https://stackoverflow.com/questions/57087273 for more details. |
I'm wondering where is this line in source code: |
"Straight forward" is subjective & "Read the source code" is not a great reply. Well written and clear code documentation is expected for any usable codebase and what you are saying is that it's OK for many people to be wasting their hours sifting through the codebase.. |
the last one is variance! |
When I execute model.get_weights() , It seems each BN layer have 4 parameters serially
I believe there are gamma , inverse stdev , Gamma , beta are included.
But I cannot sure the order of the parameter.
what is the first parameter?
what is the second parameter?
what is the third parameter?
what is the fourth parameter?
It will be grateful for me to know.
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