Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.nd4j.linalg.api.ops.impl.layers.Linear 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.layers;
import lombok.Builder;
import lombok.NoArgsConstructor;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.VariableType;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseModule;
import org.nd4j.linalg.api.ops.Module;
import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.weightinit.WeightInitScheme;
import org.nd4j.weightinit.impl.ZeroInitScheme;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
/**
* Linear:
* a * bT
*
* @author Adam Gibson
*/
@NoArgsConstructor
public class Linear extends BaseModule {
private DifferentialFunction forward;
private int nIn,nOut;
private WeightInitScheme weightInitScheme,biasWeightInitScheme;
@Builder(builderMethodName = "execBuilder")
public Linear(int nIn,
int nOut,
WeightInitScheme weightInitScheme,
WeightInitScheme biasWeightInitScheme) {
super(null,
getParams(nIn,nOut,weightInitScheme,biasWeightInitScheme),
new INDArray[]{},
new ArrayList(), new ArrayList(), new ArrayList());
this.weightInitScheme = weightInitScheme;
this.biasWeightInitScheme = biasWeightInitScheme;
this.nIn = nIn;
this.nOut = nOut;
}
@Builder(builderMethodName = "sameDiffBuilder")
public Linear(SameDiff sameDiff,
int nIn,
int nOut,
WeightInitScheme weightInitScheme,
WeightInitScheme biasWeightInitScheme) {
super(null, sameDiff, null, false, new ArrayList());
this.weightInitScheme = weightInitScheme;
this.biasWeightInitScheme = biasWeightInitScheme;
this.nIn = nIn;
this.nOut = nOut;
}
@Override
public String opName() {
return "linear";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
}
@Override
public List doDiff(List f1) {
execSameDiff();
return forward.doDiff(f1);
}
@Override
public List calculateOutputShape() {
List ret = new ArrayList<>();
ret.add(LongShapeDescriptor.fromShape(Shape.getMatrixMultiplyShape(inputArguments()[0].shape(),new long[]{nOut,nIn}), inputArguments()[1].dataType()));
ret.add(LongShapeDescriptor.fromShape(Shape.getMatrixMultiplyShape(inputArguments()[0].shape(),inputArguments()[1].transpose().shape()), inputArguments()[1].dataType()));
if(biasWeightInitScheme != null) {
ret.add(LongShapeDescriptor.fromShape(new long[]{nOut,1}, inputArguments()[1].dataType()));
}
return ret;
}
@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 exec(INDArray... inputs) {
val inputArguments = inputArguments();
if(inputArguments == null || inputArguments.length < 1) {
throw new IllegalStateException("No arguments found.");
}
INDArray weights = inputArguments[0];
INDArray right = inputArguments[1];
val outputArguments = outputArguments();
if(outputArguments == null || outputArguments.length < 1) {
if(inputArguments.length == 1)
addOutputArgument(inputs[0].mmul(weights.transpose()));
else
addOutputArgument(inputs[0].mmul(weights.transpose()).addiColumnVector(right));
}
else {
inputs[0].mmul(weights.transpose(),outputArguments[0]);
}
}
@Override
public void execSameDiff(SDVariable... input) {
val args = args();
if(args == null || args.length == 0) {
throw new IllegalStateException("No arguments found");
}
if(forward == null) {
//bias needs to be added yet
if(args.length > 1)
forward = f().add(new Mmul(sameDiff, input[0],args()[0],
MMulTranspose.builder()
.transposeA(false)
.transposeB(true)
.build()).outputVariables()[0],args()[1]);
else {
forward = new Mmul(sameDiff, input[0],args()[0],
MMulTranspose.builder().transposeA(false).transposeB(true).build());
}
this.outputVariables = forward.outputVariables();
}
}
private static INDArray[] getParams(int nIn,
int nOut,
WeightInitScheme paramsScheme,
WeightInitScheme biasInitScheme) {
if(biasInitScheme != null) {
return new INDArray[] {paramsScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,nIn}),biasInitScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,1})};
}
else {
return new INDArray[] {paramsScheme.create(Nd4j.defaultFloatingPointType(), new long[]{nOut,nIn})};
}
}
}
