org.deeplearning4j.nn.params.CenterLossParamInitializer 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.params;


import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.Map;

/**
 * Initialize Center Loss params.
 *
 * @author Justin Long (@crockpotveggies)
 * @author Alex Black (@AlexDBlack)
 */
public class CenterLossParamInitializer extends DefaultParamInitializer {

    private static final CenterLossParamInitializer INSTANCE = new CenterLossParamInitializer();

    public static CenterLossParamInitializer getInstance() {
        return INSTANCE;
    }

    public final static String WEIGHT_KEY = DefaultParamInitializer.WEIGHT_KEY;
    public final static String BIAS_KEY = DefaultParamInitializer.BIAS_KEY;
    public final static String CENTER_KEY = "cL";

    @Override
    public int numParams(NeuralNetConfiguration conf) {
        org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf =
                        (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer();
        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut(); // also equal to numClasses
        return nIn * nOut + nOut + nIn * nOut; //weights + bias + embeddings
    }

    @Override
    public Map init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
        Map params = Collections.synchronizedMap(new LinkedHashMap());

        org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer layerConf =
                        (org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer) conf.getLayer();

        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut(); // also equal to numClasses

        int wEndOffset = nIn * nOut;
        int bEndOffset = wEndOffset + nOut;
        int cEndOffset = bEndOffset + nIn * nOut;

        INDArray weightView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, wEndOffset));
        INDArray biasView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(wEndOffset, bEndOffset));
        INDArray centerLossView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(bEndOffset, cEndOffset))
                        .reshape('c', nOut, nIn);

        params.put(WEIGHT_KEY, createWeightMatrix(conf, weightView, initializeParams));
        params.put(BIAS_KEY, createBias(conf, biasView, initializeParams));
        params.put(CENTER_KEY, createCenterLossMatrix(conf, centerLossView, initializeParams));
        conf.addVariable(WEIGHT_KEY);
        conf.addVariable(BIAS_KEY);
        conf.addVariable(CENTER_KEY);

        return params;
    }

    @Override
    public Map getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
        org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer layerConf =
                        (org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer) conf.getLayer();

        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut(); // also equal to numClasses

        int wEndOffset = nIn * nOut;
        int bEndOffset = wEndOffset + nOut;
        int cEndOffset = bEndOffset + nIn * nOut; // note: numClasses == nOut

        INDArray weightGradientView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, wEndOffset))
                        .reshape('f', nIn, nOut);
        INDArray biasView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(wEndOffset, bEndOffset)); //Already a row vector
        INDArray centerLossView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(bEndOffset, cEndOffset))
                        .reshape('c', nOut, nIn);

        Map out = new LinkedHashMap<>();
        out.put(WEIGHT_KEY, weightGradientView);
        out.put(BIAS_KEY, biasView);
        out.put(CENTER_KEY, centerLossView);

        return out;
    }


    protected INDArray createCenterLossMatrix(NeuralNetConfiguration conf, INDArray centerLossView,
                    boolean initializeParameters) {
        org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer layerConf =
                        (org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer) conf.getLayer();

        if (initializeParameters) {
            centerLossView.assign(0.0);
        }
        return centerLossView;
    }
}