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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * 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
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package org.deeplearning4j.nn.params;

import lombok.val;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;

public class EmbeddingLayerParamInitializer extends DefaultParamInitializer {

    private static final EmbeddingLayerParamInitializer INSTANCE = new EmbeddingLayerParamInitializer();

    public static EmbeddingLayerParamInitializer getInstance() {
        return INSTANCE;
    }



    protected INDArray createWeightMatrix(long nIn, long nOut, IWeightInit weightInit,
                                          INDArray weightParamView, boolean initializeParameters) {
        val shape = new long[] {nIn, nOut};

        if (initializeParameters) {
            INDArray ret = weightInit.init(1, //Fan in - note that fanIn=1 for embedding layer... if we used layer nIn (i.e., vocab size) the init would depend on vocab size (which doesn't make sense)
                    nOut, //Fan out
                    shape, IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, weightParamView);
            return ret;
        } else {
            return WeightInitUtil.reshapeWeights(shape, weightParamView);
        }
    }

}
