org.nd4j.linalg.api.ops.random.impl.DropOut Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 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
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* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.random.impl;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.BaseRandomOp;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
/**
* DropOut implementation as Op
*
* @author [email protected]
*/
@NoArgsConstructor
public class DropOut extends BaseRandomOp {
private double p;
public DropOut(SameDiff sameDiff, SDVariable input, double p) {
super(sameDiff, input);
this.p = p;
//https://github.com/deeplearning4j/deeplearning4j/issues/5650
throw new UnsupportedOperationException("Dropout SameDiff support disabled pending backprop support");
}
public DropOut(@NonNull INDArray x, double p) {
this(x, x, p, x.lengthLong());
}
public DropOut(@NonNull INDArray x, @NonNull INDArray z, double p) {
this(x, z, p, x.lengthLong());
}
public DropOut(@NonNull INDArray x, @NonNull INDArray z, double p, long n) {
this.p = p;
init(x, null, z, n);
}
@Override
public Map propertiesForFunction() {
Map ret = new LinkedHashMap<>();
ret.put("p",p);
return ret;
}
@Override
public int opNum() {
return 1;
}
@Override
public String opName() {
return "dropout";
}
@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
super.init(x, y, z, n);
this.extraArgs = new Object[] {p, (double) n};
}
@Override
public String onnxName() {
return "Dropout";
}
@Override
public String tensorflowName() {
return opName();
}
@Override
public Type opType() {
return Type.RANDOM ;
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Not supported"); //We should only use *inverted* dropout with samediff
}
}