2019-06-06 15:21:15 +03:00

40 lines
1.6 KiB
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.EqualsAndHashCode;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
/**
* As per Glorot and Bengio 2010: Uniform distribution U(-s,s) with s = sqrt(6/(fanIn + fanOut))
*
* @author Adam Gibson
*/
@EqualsAndHashCode
public class WeightInitXavierUniform implements IWeightInit {
@Override
public INDArray init(double fanIn, double fanOut, long[] shape, char order, INDArray paramView) {
//As per Glorot and Bengio 2010: Uniform distribution U(-s,s) with s = sqrt(6/(fanIn + fanOut))
//Eq 16: http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
double s = Math.sqrt(6.0) / Math.sqrt(fanIn + fanOut);
Nd4j.rand(paramView, Nd4j.getDistributions().createUniform(-s, s));
return paramView.reshape(order, shape);
}
}