org.nd4j.linalg.api.ops.impl.shape.ApplyGradientDescent Maven / Gradle / Ivy
/*-
*
* * Copyright 2015 Skymind,Inc.
* *
* * 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 org.nd4j.linalg.api.ops.impl.shape;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
/**
* Reshape function
*
* @author Adam Gibson
*/
@Slf4j
public class ApplyGradientDescent extends DynamicCustomOp {
public ApplyGradientDescent() {
}
@Override
public String opName() {
return "applygradientdescent";
}
@Override
public String onnxName() {
return "ApplyGradientDescent";
}
@Override
public String tensorflowName() {
return "ApplyGradientDescent";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
/*
strided slice typically takes 4 tensor arguments:
0) input, it's shape determines number of elements in other arguments
1) begin indices
2) end indices
3) strides
*/
/* val inputBegin = tNode.getInputs().get(1);
val inputEnd = tNode.getInputs().get(2);
val inputStrides = tNode.getInputs().get(3);
val iArgs = new ArrayList();
// bit masks for this slice
val bm = nodeDef.getAttrOrThrow("begin_mask");
val xm = nodeDef.getAttrOrThrow("ellipsis_mask");
val em = nodeDef.getAttrOrThrow("end_mask");
val nm = nodeDef.getAttrOrThrow("new_axis_mask");
val sm = nodeDef.getAttrOrThrow("shrink_axis_mask");
iArgs.add((int) bm.getI());
iArgs.add((int) xm.getI());
iArgs.add((int) em.getI());
iArgs.add((int) nm.getI());
iArgs.add((int) sm.getI());
if (inputBegin.getNode() < 0 && inputEnd.getNode() < 0 && inputStrides.getNode() < 0) {
// order matters, hehe
val strides = graph.getVariableSpace().getVariable(tNode.getInputs().remove(3));
val end = graph.getVariableSpace().getVariable(tNode.getInputs().remove(2));
val begin = graph.getVariableSpace().getVariable(tNode.getInputs().remove(1));
for (int e = 0; e < begin.getArray().length(); e++)
iArgs.add((int) begin.getArray().getInt(e));
for (int e = 0; e < end.getArray().length(); e++)
iArgs.add((int) end.getArray().getInt(e));
for (int e = 0; e < strides.getArray().length(); e++)
iArgs.add((int) strides.getArray().getInt(e));
} else {
// do nothing
}
val bits = Ints.toArray(iArgs);*/
}
@Override
public List doDiff(List i_v) {
SDVariable ret = this.outputVariables()[0];
return Arrays.asList(ret);
}
}