Design doc: Batch Normalization Operator #3748
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| use_global_est = False, | ||
| epsilon = 1e-6, | ||
| momentum = 0.99): | ||
| mean_cache = scope.new_var(name = 'estimated_mean', trainable = False) |
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There might be more than one batch_norm_op in the same topology, make sure variables like mean_cache and others have unique names?
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make sure variables such as mean_cache invisible to other operators, or may be two operators write it.
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- Yes. we need to make sure
mean_cachehas the unique name. mean_cacheis defined insidedef batch_norm_layer, could this make sure it's invisible for other operators?
paddle/operators/batch_norm_op.md
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| # ... | ||
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| `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables. How to organize them is related with Python API design, so I leave it here for further discussion. |
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Currently, different Block shares the same operator objects. Did this require that different blocks has their own operator objects?
Add some details here?
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We do need two distinct batch_norm_op. And it seems really require blocks hold their own operator objects... We shall have more discussion on it.
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This design is temporarily placed on hold for it's strongly related to Python API design. |
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Related: #3684
Here may be easier to read.
Some part of
batch_norm_opdesign is strongly related with Python API and needs future discussions.