Package org.deeplearning4j.nn.layers.samediff

Class SameDiffLayer

    • Method Detail

      • isPretrainLayer

        public boolean isPretrainLayer()
        Description copied from interface: Layer
        Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
        Returns:
        true if the layer can be pretrained (using fit(INDArray), false otherwise

      • clearNoiseWeightParams

        public void clearNoiseWeightParams()

      • activate

        public INDArray activate​(boolean training,
                         LayerWorkspaceMgr workspaceMgr)
        Description copied from interface: Layer
        Perform forward pass and return the activations array with the last set input
        Parameters:
        training - training or test mode
        workspaceMgr - Workspace manager
        Returns:
        the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

      • backpropGradient

        public Pair<Gradient,​INDArray> backpropGradient​(INDArray epsilon,
                                                      LayerWorkspaceMgr workspaceMgr)
        Description copied from interface: Layer
        Calculate the gradient relative to the error in the next layer
        Parameters:
        epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.
        workspaceMgr - Workspace manager
        Returns:
        Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

      • setParams

        public void setParams​(INDArray params)
        Description copied from interface: Model
        Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
        Specified by:
        setParams in interface Model
        Overrides:
        setParams in class AbstractLayer<AbstractSameDiffLayer>
        Parameters:
        params - the parameters for the model

      • setParamsViewArray

        public void setParamsViewArray​(INDArray params)
        Description copied from interface: Model
        Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
        Specified by:
        setParamsViewArray in interface Model
        Overrides:
        setParamsViewArray in class AbstractLayer<AbstractSameDiffLayer>
        Parameters:
        params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array

      • setBackpropGradientsViewArray

        public void setBackpropGradientsViewArray​(INDArray gradients)
        Description copied from interface: Model
        Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
        Specified by:
        setBackpropGradientsViewArray in interface Model
        Overrides:
        setBackpropGradientsViewArray in class AbstractLayer<AbstractSameDiffLayer>
        Parameters:
        gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array

      • paramTable

        public Map<String,​INDArray> paramTable​(boolean backpropParamsOnly)
        Description copied from interface: Model
        Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
        Specified by:
        paramTable in interface Model
        Specified by:
        paramTable in interface Trainable
        Overrides:
        paramTable in class AbstractLayer<AbstractSameDiffLayer>
        Parameters:
        backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())
        Returns:
        Parameter table

      • doInit

        protected void doInit()

      • feedForwardMaskArray

        public Pair<INDArray,​MaskState> feedForwardMaskArray​(INDArray maskArray,
                                                           MaskState currentMaskState,
                                                           int minibatchSize)
        Description copied from interface: Layer
        Feed forward the input mask array, setting in the layer as appropriate. This allows different layers to handle masks differently - for example, bidirectional RNNs and normal RNNs operate differently with masks (the former sets activations to 0 outside of the data present region (and keeps the mask active for future layers like dense layers), whereas normal RNNs don't zero out the activations/errors )instead relying on backpropagated error arrays to handle the variable length case.
        This is also used for example for networks that contain global pooling layers, arbitrary preprocessors, etc.
        Specified by:
        feedForwardMaskArray in interface Layer
        Overrides:
        feedForwardMaskArray in class AbstractLayer<AbstractSameDiffLayer>
        Parameters:
        maskArray - Mask array to set
        currentMaskState - Current state of the mask - see MaskState
        minibatchSize - Current minibatch size. Needs to be known as it cannot always be inferred from the activations array due to reshaping (such as a DenseLayer within a recurrent neural network)
        Returns:
        New mask array after this layer, along with the new mask state.