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package tensorflow
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val batch_normalization :
?decay:float ->
[< `double | `float ] as 'a Node.t ->
update_moments:[ `always | `not_in_testing of [ `bool ] Node.t ] ->
dims:int ->
feature_count:int ->
'a Node.tbatch_normalization ?decay node ~update_moments ~dims ~feature_count takes as input a node which last dimension is assumed to be the feature dimension. dims has to be the number of dimensions for node excluding the feature dimension but including the batch dimension. feature_count is the number of features in the last dimension of node. If update_moments is `always or `not_in_testing false the batch mean and variance are computed and used to update the normalization variables.