38 lines
1.4 KiB
Java
38 lines
1.4 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.nn.weights;
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import lombok.EqualsAndHashCode;
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import org.apache.commons.math3.util.FastMath;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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/**
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* : He et al. (2015), "Delving Deep into Rectifiers". Normal distribution with variance 2.0/nIn
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*
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* @author Adam Gibson
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*/
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@EqualsAndHashCode
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public class WeightInitRelu implements IWeightInit {
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@Override
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public INDArray init(double fanIn, double fanOut, long[] shape, char order, INDArray paramView) {
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Nd4j.randn(paramView).muli(FastMath.sqrt(2.0 / fanIn)); //N(0, 2/nIn)
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return paramView.reshape(order, shape);
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}
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}
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