src/core/layer/normalization/WeightNormalization.java
protected void defineProcedure() throws MatrixException, DynamicParamException, NeuralNetworkException {
        if (getNextLayer().getWeightsMap().isEmpty()) throw new NeuralNetworkException("Unable initialize weight normalization. Next layer does not contain any weights.");

        procedures = new HashMap<>();
        HashSet<Matrix> nextLayerNormalizedWeights = getNextLayer().getNormalizedWeights();
        for (Matrix weight : nextLayerNormalizedWeights) {
            input = weight;
            Procedure procedure = new ProcedureFactory().getProcedure(this, null, getConstantMatrices(), getStopGradients(), false);
            procedures.put(weight, procedure);
        }
    }
/**
     * Defines layer procedure for forward and backward calculation (automatic gradient) by applying procedure factory.<br>
     *
     * @throws MatrixException throws exception if matrix operation fails.
     * @throws DynamicParamException throws exception if parameter (params) setting fails.
     * @throws NeuralNetworkException thrown if initialization of layer fails.
     */
Defines layer procedure for forward and backward calculation (automatic gradient) by applying procedure factory.

@throws MatrixException throws exception if matrix operation fails.
@throws DynamicParamException throws exception if parameter (params) setting fails.
@throws NeuralNetworkException thrown if initialization of layer fails.
Defines layer procedure for forward and backward calculation ( automatic gradient ) by applying procedure factory . @throws MatrixException throws exception if matrix operation fails . @throws DynamicParamException throws exception if parameter ( params ) setting fails . @throws NeuralNetworkException thrown if initialization of layer fails .
protected void defineProcedure() throws MatrixException, DynamicParamException, NeuralNetworkException {
        if (getNextLayer().getWeightsMap().isEmpty()) throw new NeuralNetworkException("Unable initialize weight normalization. Next layer does not contain any weights.");
        procedures = new HashMap<>();
        HashSet<Matrix> nextLayerNormalizedWeights = getNextLayer().getNormalizedWeights();
        for (Matrix weight : nextLayerNormalizedWeights) {
            input = weight;
            Procedure procedure = new ProcedureFactory().getProcedure(this, null, getConstantMatrices(), getStopGradients(), false);
            procedures.put(weight, procedure);
        }
    }
protected void defineProcedure ( ) throws MatrixException , DynamicParamException , NeuralNetworkException { if ( getNextLayer ( ) . getWeightsMap ( ) . isEmpty ( ) ) throw new NeuralNetworkException ( "Unable initialize weight normalization. Next layer does not contain any weights." ) ; procedures = new HashMap < > ( ) ; HashSet < Matrix > nextLayerNormalizedWeights = getNextLayer ( ) . getNormalizedWeights ( ) ; for ( Matrix weight : nextLayerNormalizedWeights ) { input = weight ; Procedure procedure = new ProcedureFactory ( ) . getProcedure ( this , null , getConstantMatrices ( ) , getStopGradients ( ) , false ) ; procedures . put ( weight , procedure ) ; } }
Defines layer procedure for forward and backward calculation (automatic gradient) by applying procedure factory.<br>
Defines layer procedure for forward and backward calculation ( automatic gradient ) by applying procedure factory . <br >