cavis/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/weights/WeightInitVarScalingNormalFanAvg.java

/*******************************************************************************
 * Copyright (c) 2015-2019 Skymind, Inc.
 *
 * 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.
 *
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.deeplearning4j.nn.weights;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.impl.TruncatedNormalDistribution;
import org.nd4j.linalg.factory.Nd4j;

/**
 * Truncated aussian distribution with mean 0, variance 1.0/((fanIn + fanOut)/2)
 *
 * @author Adam Gibson
 */
@Data
@NoArgsConstructor
public class WeightInitVarScalingNormalFanAvg implements IWeightInit {

    private Double scale;

    public WeightInitVarScalingNormalFanAvg(Double scale){
        this.scale = scale;
    }

    @Override
    public INDArray init(double fanIn, double fanOut, long[] shape, char order, INDArray paramView) {
        double std;
        if(scale == null){
            std = Math.sqrt(2.0 / (fanIn + fanOut));
        } else {
            std = Math.sqrt(2.0 * scale / (fanIn + fanOut));
        }

        Nd4j.exec(new TruncatedNormalDistribution(paramView, 0.0, std));
        return paramView.reshape(order, shape);
    }
}