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org.nd4j.linalg.api.ops.impl.transforms.clip.ClipByValue Maven / Gradle / Ivy
/*******************************************************************************
* 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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.transforms.clip;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.dataset.DataSet;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class ClipByValue extends DynamicCustomOp {
private double clipValueMin;
private double clipValueMax;
public ClipByValue(INDArray[] inputs, INDArray[] outputs, double clipValueMin, double clipValueMax, boolean inPlace) {
super(null, inputs, outputs);
this.clipValueMin = clipValueMin;
this.clipValueMax = clipValueMax;
this.inplaceCall = inPlace;
addTArgument(clipValueMin, clipValueMax);
}
public ClipByValue() {
}
public ClipByValue(SameDiff sameDiff, SDVariable x, double clipValueMin, double clipValueMax, boolean inPlace) {
super(null, sameDiff, new SDVariable[]{x});
this.clipValueMin = clipValueMin;
this.clipValueMax = clipValueMax;
this.inplaceCall = inPlace;
addTArgument(clipValueMin, clipValueMax);
}
public ClipByValue(SameDiff sameDiff, SDVariable x, double clipValueMin, double clipValueMax) {
super(null, sameDiff, new SDVariable[]{x});
this.clipValueMin = clipValueMin;
this.clipValueMax = clipValueMax;
addTArgument(clipValueMin, clipValueMax);
}
@Override
public String opName() {
return "clipbyvalue";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public List doDiff(List grad) {
//dOut/dIn is 0 if clipped, 1 otherwise
SDVariable notClippedLower = f().gt(arg(), clipValueMin).castTo(arg().dataType());
SDVariable notClippedUpper = f().lt(arg(), clipValueMax).castTo(arg().dataType());
SDVariable ret = notClippedLower.mul(notClippedUpper).mul(grad.get(0));
return Collections.singletonList(ret);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
return inputDataTypes;
}
}