org.deeplearning4j.nn.conf.constraint.UnitNormConstraint Maven / Gradle / Ivy

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/*
 *  ******************************************************************************
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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.
 *  *
 *  *  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
 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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 *  * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.conf.constraint;

import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Broadcast;

import java.util.Collections;
import java.util.Set;

@Data
@EqualsAndHashCode(callSuper = true)
public class UnitNormConstraint extends BaseConstraint {

    private UnitNormConstraint(){
        //No arg for json ser/de
    }

    /**
     * Apply to weights but not biases by default
     *
     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
     *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
     *                       parameters which have order [depthOut, depthIn, kH, kW]
     */
    public UnitNormConstraint(int... dimensions){
        this(Collections.emptySet(), dimensions);
    }


    /**
     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
     *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
     *                       parameters which have order [depthOut, depthIn, kH, kW]
     */
    public UnitNormConstraint(Set paramNames, int... dimensions){
        super(paramNames, dimensions);
    }

    @Override
    public void apply(INDArray param) {
        INDArray norm2 = param.norm2(dimensions);
        Broadcast.div(param, norm2, param, getBroadcastDims(dimensions, param.rank()) );
    }

    @Override
    public UnitNormConstraint clone() {
        return new UnitNormConstraint( params, dimensions);
    }
}