package org.deeplearning4j.nn.conf.layers;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;

import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

@Data @NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class RnnOutputLayer extends BaseOutputLayer {

	private RnnOutputLayer(Builder builder){
		super(builder);
	}
	
	@NoArgsConstructor
    public static class Builder extends BaseOutputLayer.Builder<Builder> {
        protected LossFunction lossFunction = LossFunction.MCXENT;

    	public Builder(LossFunction lossFunction) {
            this.lossFunction = lossFunction;
        }
        
        @Override
        @SuppressWarnings("unchecked")
        public RnnOutputLayer build() {
            return new RnnOutputLayer(this);
        }
    }
}

@@ -426,30 +426,9 @@
 %0A%09%7D%0A
-%09%0A%09@NoArgsConstructor
 %0A
+
     
@@ -498,16 +498,17 @@
 lder%3E %7B%0A
+%0A
         
@@ -512,30 +512,42 @@
    p
-rotected LossFunction 
+ublic Builder()%7B%0A            this.
 loss
@@ -577,16 +577,26 @@
 .MCXENT;
+%0A        %7D
 %0A%0A    %09p

Fix default loss function for RnnOutputLayer