Uses of Class
org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Packages that use BatchNormalization.Builder

Package	Description
org.deeplearning4j.nn.conf.layers

Uses of BatchNormalization.Builder in org.deeplearning4j.nn.conf.layers

Methods in org.deeplearning4j.nn.conf.layers that return BatchNormalization.Builder

Modifier and Type	Method	Description
BatchNormalization.Builder	BatchNormalization.Builder.beta(double beta)	Used only when 'true' is passed to lockGammaBeta(boolean).
BatchNormalization.Builder	BatchNormalization.Builder.constrainBeta(LayerConstraint... constraints)	Set constraints to be applied to the beta parameter of this batch normalisation layer.
BatchNormalization.Builder	BatchNormalization.Builder.constrainGamma(LayerConstraint... constraints)	Set constraints to be applied to the gamma parameter of this batch normalisation layer.
BatchNormalization.Builder	BatchNormalization.Builder.cudnnAllowFallback(boolean allowFallback)	Deprecated. Use helperAllowFallback(boolean)
BatchNormalization.Builder	BatchNormalization.Builder.dataFormat(CNN2DFormat format)	Set the input and output array data format.
BatchNormalization.Builder	BatchNormalization.Builder.decay(double decay)	At test time: we can use a global estimate of the mean and variance, calculated using a moving average of the batch means/variances.
BatchNormalization.Builder	BatchNormalization.Builder.eps(double eps)	Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in https://arxiv.org/pdf/1502.03167v3.pdf) to reduce/avoid underflow issues. Default: 1e-5
BatchNormalization.Builder	BatchNormalization.Builder.gamma(double gamma)	Used only when 'true' is passed to lockGammaBeta(boolean).
BatchNormalization.Builder	BatchNormalization.Builder.helperAllowFallback(boolean allowFallback)	When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
BatchNormalization.Builder	BatchNormalization.Builder.lockGammaBeta(boolean lockGammaBeta)	If set to true: lock the gamma and beta parameters to the values for each activation, specified by gamma(double) and beta(double).
BatchNormalization.Builder	BatchNormalization.Builder.minibatch(boolean minibatch)	If doing minibatch training or not.
BatchNormalization.Builder	BatchNormalization.Builder.useLogStd(boolean useLogStd)	How should the moving average of variance be stored? Two different parameterizations are supported.