40 lines
1.6 KiB
Java
40 lines
1.6 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.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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/**
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* As per Glorot and Bengio 2010: Uniform distribution U(-s,s) with s = sqrt(6/(fanIn + fanOut))
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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 WeightInitXavierUniform 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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//As per Glorot and Bengio 2010: Uniform distribution U(-s,s) with s = sqrt(6/(fanIn + fanOut))
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//Eq 16: http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
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double s = Math.sqrt(6.0) / Math.sqrt(fanIn + fanOut);
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Nd4j.rand(paramView, Nd4j.getDistributions().createUniform(-s, s));
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return paramView.reshape(order, shape);
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}
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}
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