org.deeplearning4j.nn.conf.layers.OutputLayer Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of deeplearning4j-nn Show documentation
There is a newer version: 1.0.0-M2.1
/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

package org.deeplearning4j.nn.conf.layers;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.exception.DL4JInvalidConfigException;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.util.LayerValidation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.util.Collection;
import java.util.Map;

/**
 * Output layer with different objective co-occurrences for different objectives.
 * This includes classification as well as regression
 *
 */
@Data @NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class OutputLayer extends BaseOutputLayer {

    protected OutputLayer(Builder builder) {
    	super(builder);
    }

    @Override
    public Layer instantiate(NeuralNetConfiguration conf, Collection iterationListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams) {
        LayerValidation.assertNInNOutSet("OutputLayer", getLayerName(), layerIndex, getNIn(), getNOut());

        org.deeplearning4j.nn.layers.OutputLayer ret
                = new org.deeplearning4j.nn.layers.OutputLayer(conf);
        ret.setListeners(iterationListeners);
        ret.setIndex(layerIndex);
        ret.setParamsViewArray(layerParamsView);
        Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
        ret.setParamTable(paramTable);
        ret.setConf(conf);
        return ret;
    }

    @Override
    public ParamInitializer initializer() {
        return DefaultParamInitializer.getInstance();
    }

    @NoArgsConstructor
    public static class Builder extends BaseOutputLayer.Builder {

        public Builder(LossFunction lossFunction) {
            super.lossFunction(lossFunction);
        }

        public Builder(ILossFunction lossFunction){
            this.lossFn = lossFunction;
        }
        
        @Override
        @SuppressWarnings("unchecked")
        public OutputLayer build() {
            return new OutputLayer(this);
        }
    }
}