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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.impl.reduce;
import lombok.NoArgsConstructor;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
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.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
@NoArgsConstructor
public class NormalizeMoments extends DynamicCustomOp {
private double shift = 0.0; // reporting for duty
public NormalizeMoments(SameDiff sameDiff, SDVariable counts, SDVariable means, SDVariable variances) {
this(sameDiff, counts, means, variances, 0.0);
}
public NormalizeMoments(SameDiff sameDiff, SDVariable counts, SDVariable means, SDVariable variances, double shift) {
super(null, sameDiff, new SDVariable[] {counts, means, variances}, false);
this.shift = shift;
addArgs();
}
public NormalizeMoments(INDArray counts, INDArray means, INDArray variances, double shift) {
super(null, new INDArray[]{counts, means, variances}, null);
this.shift = shift;
addArgs();
}
public NormalizeMoments(INDArray counts, INDArray ssSum, INDArray ssSqSum, INDArray outMean, INDArray outVar) {
super(null, new INDArray[]{counts, ssSum, ssSqSum}, new INDArray[]{outMean, outVar},
new ArrayList(), new ArrayList());
addArgs();
}
private void addArgs() {
addTArgument(shift);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public String opName() {
return "normalize_moments";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
//Count, mean_ss, variance_ss
if(inputDataTypes.get(1).isFPType())
return Arrays.asList(inputDataTypes.get(0), inputDataTypes.get(0));
return Arrays.asList(Nd4j.defaultFloatingPointType(), Nd4j.defaultFloatingPointType());
}
}