org.nd4j.linalg.api.ops.impl.scalar.PRelu Maven / Gradle / Ivy
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* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
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*/
package org.nd4j.linalg.api.ops.impl.scalar;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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.api.ops.impl.transforms.gradient.PReluBp;
import org.nd4j.shade.guava.primitives.Ints;
/**
* Parameterized ReLU op
*/
@NoArgsConstructor
public class PRelu extends DynamicCustomOp {
@Getter
protected int[] sharedAxes;
public PRelu(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable alpha, @NonNull int... sharedAxes) {
super(sameDiff, new SDVariable[]{x, alpha});
this.sharedAxes = sharedAxes;
addIArgument(sharedAxes);
}
public PRelu(@NonNull INDArray x, @NonNull INDArray alpha, @NonNull int... sharedAxes) {
this(x, null, alpha, sharedAxes);
}
public PRelu(@NonNull INDArray x, INDArray z, @NonNull INDArray alpha, @NonNull int... sharedAxes) {
super(new INDArray[]{x, alpha}, new INDArray[]{z});
this.sharedAxes = sharedAxes;
addIArgument(sharedAxes);
}
@Override
public String opName() {
return "prelu";
}
@Override
public String onnxName() { throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public void configureFromArguments() {
this.sharedAxes = Ints.toArray(iArguments);
}
@Override
public void setPropertiesForFunction(Map properties) {
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
Preconditions
.checkArgument(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes, got %s", dataTypes);
Preconditions.checkArgument(dataTypes.get(0).isFPType() && dataTypes.get(1).isFPType(), "Input datatypes must be floating point, got %s", dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
@Override
public List doDiff(List i_v) {
return new PReluBp(sameDiff, arg(0), arg(1), i_v.get(0), sharedAxes).outputs();
}
}