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Trainer Agnostic Deep Learning
/*
* Copyright (c) 2016, Peter Abeles. All Rights Reserved.
*
* This file is part of DeepBoof
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
package deepboof.impl.backward.standard;
import deepboof.backward.DActivationReLU;
import deepboof.impl.forward.standard.ActivationReLU_F64;
import deepboof.tensors.Tensor_F64;
import java.util.List;
/**
* Implementation of {@link DActivationReLU} for {@link Tensor_F64}
*
* @author Peter Abeles
*/
public class DActivationReLU_F64 extends ElementWiseDFunction implements DActivationReLU{
@Override
public void _forward(Tensor_F64 input, Tensor_F64 output) {
ActivationReLU_F64._relu_forwards(input, output);
}
@Override
protected void _backwards(Tensor_F64 input,
Tensor_F64 dout,
Tensor_F64 gradientInput, List gradientParameters) {
int length = gradientInput.length();
int indexIn = input.startIndex;
int indexDIn = gradientInput.startIndex;
int indexDOut = dout.startIndex;
for (int i = 0; i < length; i++) {
double value = input.d[indexIn+i];
if( value <= 0 )
gradientInput.d[indexDIn+i] = 0;
else
gradientInput.d[indexDIn+i] = dout.d[indexDOut+i];
}
}
@Override
public Class getTensorType() {
return Tensor_F64.class;
}
}
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