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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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.random.impl;
import lombok.NonNull;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.BaseRandomOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
/**
* Linspace/arange Op implementation, generates from..to distribution within Z
*
* @author [email protected]
*/
public class Linspace extends BaseRandomOp {
private double from;
private double to;
private double step;
private long length;
public Linspace() {
// no-op
}
public Linspace(double from, long length, double step, DataType dataType){
this(Nd4j.createUninitialized(dataType, new long[] {length}, Nd4j.order()), from, from, step);
}
public Linspace(double from, double to, long length, DataType dataType) {
this(Nd4j.createUninitialized(dataType, new long[] {length}, Nd4j.order()), from, to);
}
public Linspace(@NonNull INDArray z, double from, double to) {
super(null, null, z);
this.from = from;
this.to = to;
this.length = z.length();
double step = 0.0;
this.extraArgs = new Object[] {from, to, step};
}
public Linspace(@NonNull INDArray z, double from, double to, double step) {
super(null, null, z);
this.from = from;
this.to = to;
this.length = z.length();
this.step = step;
this.extraArgs = new Object[] {from, to, step};
}
public Linspace(SameDiff sd, double from, double to, long length){
super(sd, new long[]{length});
this.sameDiff = sd;
this.from = from;
this.to = to;
this.length = length;
double step = 0.0; //(to - from) / (length - 1);
this.extraArgs = new Object[] {from, to, step};
}
@Override
public int opNum() {
return 4;
}
@Override
public String opName(){
return "linspace_random";
}
@Override
public INDArray x(){
//Workaround/hack for: https://github.com/deeplearning4j/deeplearning4j/issues/6723
//If x or y is present, can't execute this op properly (wrong signature is used)
return null;
}
@Override
public INDArray y(){
//Workaround/hack for: https://github.com/deeplearning4j/deeplearning4j/issues/6723
//If x or y is present, can't execute this op properly (wrong signature is used)
return null;
}
@Override
public void setX(INDArray x){
//Workaround/hack for: https://github.com/deeplearning4j/deeplearning4j/issues/6723
//If x or y is present, can't execute this op properly (wrong signature is used)
this.x = null;
}
@Override
public void setY(INDArray y){
//Workaround for: https://github.com/deeplearning4j/deeplearning4j/issues/6723
//If x or y is present, can't execute this op properly (wrong signature is used)
this.y = null;
}
@Override
public List calculateOutputShape() {
return Collections.singletonList(LongShapeDescriptor.fromShape(new long[]{length}, DataType.FLOAT)); //TODO Don't hardcode float!
}
@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 List doDiff(List f1) {
//No inputs
return Collections.emptyList();
}
}