Package org.deeplearning4j.nn.conf.layers

Class BaseRecurrentLayer.Builder<T extends BaseRecurrentLayer.Builder<T>>

    • Field Detail

      • rnnDataFormat

        protected RNNFormat rnnDataFormat
        Set the format of data expected by the RNN. NCW = [miniBatchSize, size, timeSeriesLength], NWC = [miniBatchSize, timeSeriesLength, size]. Defaults to NCW.

      • recurrentConstraints

        protected List<LayerConstraint> recurrentConstraints
        Set constraints to be applied to the RNN recurrent weight parameters of this layer. Default: no constraints.
        Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.

      • inputWeightConstraints

        protected List<LayerConstraint> inputWeightConstraints
        Set constraints to be applied to the RNN input weight parameters of this layer. Default: no constraints.
        Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.

      • weightInitFnRecurrent

        protected IWeightInit weightInitFnRecurrent
        Set the weight initialization for the recurrent weights. Not that if this is not set explicitly, the same weight initialization as the layer input weights is also used for the recurrent weights.

    • Constructor Detail

      • Builder

        public Builder()

    • Method Detail

      • constrainRecurrent

        public T constrainRecurrent​(LayerConstraint... constraints)
        Set constraints to be applied to the RNN recurrent weight parameters of this layer. Default: no constraints.
        Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.
        Parameters:
        constraints - Constraints to apply to the recurrent weight parameters of this layer

      • constrainInputWeights

        public T constrainInputWeights​(LayerConstraint... constraints)
        Set constraints to be applied to the RNN input weight parameters of this layer. Default: no constraints.
        Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.
        Parameters:
        constraints - Constraints to apply to the input weight parameters of this layer

      • weightInitRecurrent

        public T weightInitRecurrent​(IWeightInit weightInit)
        Set the weight initialization for the recurrent weights. Not that if this is not set explicitly, the same weight initialization as the layer input weights is also used for the recurrent weights.
        Parameters:
        weightInit - Weight initialization for the recurrent weights only.

      • weightInitRecurrent

        public T weightInitRecurrent​(WeightInit weightInit)
        Set the weight initialization for the recurrent weights. Not that if this is not set explicitly, the same weight initialization as the layer input weights is also used for the recurrent weights.
        Parameters:
        weightInit - Weight initialization for the recurrent weights only.

      • weightInitRecurrent

        public T weightInitRecurrent​(Distribution dist)
        Set the weight initialization for the recurrent weights, based on the specified distribution. Not that if this is not set explicitly, the same weight initialization as the layer input weights is also used for the recurrent weights.
        Parameters:
        dist - Distribution to use for initializing the recurrent weights

      • dataFormat

        public T dataFormat​(RNNFormat rnnDataFormat)