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org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer 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.deeplearning4j.nn.layers.samediff;
import lombok.Getter;
import lombok.Setter;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.AbstractLayer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.*;
public class SameDiffOutputLayer extends AbstractLayer
implements IOutputLayer {
public static final String INPUT_KEY = "input";
public static final String LABELS_KEY = "labels";
protected SameDiff sameDiff;
protected SDVariable outputVar;
protected String outputKey;
@Getter @Setter
protected INDArray labels;
protected INDArray params;
protected INDArray gradients;
protected Map paramTable;
protected Map gradTable;
public SameDiffOutputLayer(NeuralNetConfiguration conf, DataType dataType){
super(conf, dataType);
}
@Override
public Layer clone() {
throw new UnsupportedOperationException();
}
@Override
public boolean isPretrainLayer() {
return false;
}
@Override
public void clearNoiseWeightParams() {
//TODO - properly support weight noise...
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
return activateHelper(true, workspaceMgr);
}
private INDArray activateHelper(boolean activations, LayerWorkspaceMgr workspaceMgr){
assertInputSet(false);
//Check where the output occors. If it's a simple loss layer (no params) this could
// just be the input!
if(activations && INPUT_KEY.equals(layerConf().activationsVertexName())){
return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input);
}
if(sameDiff == null){
doInit();
}
//TODO optimize
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
sameDiff.associateArrayWithVariable(input.dup(), sameDiff.getVariable(INPUT_KEY));
if(layerConf().labelsRequired() && labels != null) {
sameDiff.associateArrayWithVariable(labels.dup(), sameDiff.getVariable(LABELS_KEY));
}
for(String s : paramTable.keySet() ) {
sameDiff.associateArrayWithVariable(paramTable.get(s), s);
}
INDArray score = sameDiff.execAndEndResult();
if(activations) {
INDArray result = sameDiff.getArrForVarName(layerConf().activationsVertexName());
Preconditions.checkNotNull(result, "Activations (result) array for variable \"%s\" was " +
"null - error during execution or this variable (as defined by method activationsVertexName()) " +
"does not exist", layerConf().activationsVertexName());
return workspaceMgr.dup(ArrayType.ACTIVATIONS, result);
} else {
return score;
}
}
}
@Override
public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
Preconditions.checkState(!layerConf().labelsRequired() || labels != null, "Cannot execute backprop: Labels are not set. " +
"If labels are not required for this SameDiff output layer, override SameDiffOutputLayer.labelsRequired()" +
" to return false instead");
Gradient g = new DefaultGradient();
INDArray dLdIn;
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()){
INDArray castInput = input.castTo(Nd4j.defaultFloatingPointType());
if(castInput.isAttached())
castInput = castInput.dup();
sameDiff.associateArrayWithVariable(castInput, sameDiff.getVariable(INPUT_KEY));
if(layerConf().labelsRequired()) {
INDArray castLabels = labels.castTo(Nd4j.defaultFloatingPointType());
if(castLabels.isAttached())
castLabels = castLabels.dup();
sameDiff.associateArrayWithVariable(castLabels, sameDiff.getVariable(LABELS_KEY));
}
for(String s : paramTable.keySet() ){
//TODO this should only be necessary, in theory, once!
sameDiff.associateArrayWithVariable(paramTable.get(s), s);
}
sameDiff.execBackwards(Collections.emptyMap());
for(String s : paramTable.keySet() ){
INDArray sdGrad = sameDiff.grad(s).getArr();
INDArray dl4jGrad = gradTable.get(s);
dl4jGrad.assign(sdGrad); //TODO OPTIMIZE THIS
g.gradientForVariable().put(s, dl4jGrad);
}
dLdIn = sameDiff.grad(INPUT_KEY).getArr();
}
return new Pair<>(g, workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, dLdIn)); //TODO OPTIMIZE THIS
}
/**Returns the parameters of the neural network as a flattened row vector
* @return the parameters of the neural network
*/
@Override
public INDArray params() {
return params;
}
@Override
public INDArray getParam(String param) {
return paramTable.get(param);
}
@Override
public long numParams(){
return params == null ? 0 : (int)params.length();
}
@Override
public void setParam(String key, INDArray val) {
if(!paramTable.containsKey(key)){
throw new IllegalArgumentException("Cannot set parameter, invalid/unknown parameter key: " + key);
}
INDArray current = paramTable.get(key);
if(!Arrays.equals(current.shape(), val.shape())){
throw new IllegalArgumentException("Cannot set parameter \"" + key + "\", invalid shape: parameter array has shape "
+ Arrays.toString(current.shape()) + ", trying to set parameter of shape " + Arrays.toString(val.shape()));
}
}
@Override
public void setParams(INDArray params) {
if (params != null) {
throw new UnsupportedOperationException("Not supported");
}
}
protected void setParams(INDArray params, char order) {
setParams(params);
}
@Override
public void setParamsViewArray(INDArray params) {
this.params = params;
}
@Override
public INDArray getGradientsViewArray() {
return gradients;
}
@Override
public void setBackpropGradientsViewArray(INDArray gradients) {
this.gradients = gradients;
this.gradTable = layerConf().initializer().getGradientsFromFlattened(conf(), gradients);
}
@Override
public void setParamTable(Map paramTable) {
if(this.paramTable == null){
this.paramTable = paramTable;
} else {
for (Map.Entry e : paramTable.entrySet()) {
setParam(e.getKey(), e.getValue());
}
}
}
@Override
public Map paramTable() {
return paramTable(false);
}
@Override
public Map paramTable(boolean backpropParamsOnly) {
return paramTable;
}
protected void doInit(){
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer bl = layerConf();
sameDiff = SameDiff.create();
Map p = paramTable();
val inputShape = input.shape().clone();
SDVariable inputVar = sameDiff.var(INPUT_KEY, dataType, inputShape);
SDVariable labelVar = null;
if(layerConf().labelsRequired()){
long[] labelShape = labels == null ? new long[]{1} : labels.shape().clone();
labelVar = sameDiff.var(LABELS_KEY, dataType, labelShape);
}
Map paramShapes = layerConf().getLayerParams().getParamShapes();
Map params = new LinkedHashMap<>();
for (String s : paramShapes.keySet()) {
val ps = paramShapes.get(s);
SDVariable v = sameDiff.var(s, ps);
params.put(s, v);
}
SDVariable layerOutput = bl.defineLayer(sameDiff, inputVar, labelVar, params);
Preconditions.checkNotNull(layerOutput, "Invalid output: layer output is null");
outputVar = layerOutput;
for (Map.Entry e : p.entrySet()) {
sameDiff.associateArrayWithVariable(e.getValue(), sameDiff.getVariable(e.getKey()));
}
this.outputKey = layerOutput.getVarName();
}
}
@Override
public boolean needsLabels() {
return layerConf().labelsRequired();
}
@Override
public double computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr) {
return (activateHelper(false, workspaceMgr).getDouble(0) + fullNetRegTerm) / input.size(0);
}
@Override
public INDArray computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public double f1Score(DataSet data) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public double f1Score(INDArray examples, INDArray labels) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public int numLabels() {
return 0;
}
@Override
public void fit(DataSetIterator iter) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public int[] predict(INDArray examples) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public List predict(DataSet dataSet) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public void fit(INDArray examples, INDArray labels) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public void fit(DataSet data) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public void fit(INDArray examples, int[] labels) {
throw new UnsupportedOperationException("Not supported");
}
}
