org.deeplearning4j.nn.layers.RepeatVector Maven / Gradle / Ivy
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package org.deeplearning4j.nn.layers;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import java.util.Arrays;
public class RepeatVector extends AbstractLayer {
public RepeatVector(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
}
@Override
public double calcRegularizationScore(boolean backpropParamsOnly){
return 0;
}
@Override
public Type type() {
return Type.UPSAMPLING;
}
@Override
public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
if(epsilon.dataType() != dataType){
epsilon = epsilon.castTo(dataType);
}
INDArray outEpsilon;
try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)){
if (layerConf().getDataFormat() == RNNFormat.NCW) {
outEpsilon = epsilon.sum(2);
}else{
outEpsilon = epsilon.sum(1);
}
}
Gradient gradient = new DefaultGradient();
return new Pair<>(gradient, outEpsilon);
}
protected int getN(){
return layerConf().getN();
}
protected INDArray preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
applyDropOutIfNecessary(training, workspaceMgr);
if (input.rank() != 2) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to RepeatVector with shape " + Arrays.toString(input.shape())
+ ". Expected rank 2 array with shape [minibatchSize, size]. "
+ layerId());
}
if (preOutput != null && forBackprop) {
return preOutput;
}
long miniBatch = input.size(0);
long size = input.size(1);
if (getDataFormat() == RNNFormat.NCW) {
INDArray output = input.reshape(miniBatch, size, 1).castTo(dataType);
try (MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATIONS)) {
return output.repeat(2, (long) getN());
}
}
else{
INDArray output = input.reshape(miniBatch, 1, size).castTo(dataType);
try (MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATIONS)) {
return output.repeat(1, (long) getN());
}
}
}
public RNNFormat getDataFormat(){
return layerConf().getDataFormat();
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
if (cacheMode == null)
cacheMode = CacheMode.NONE;
INDArray z = preOutput(training, false, workspaceMgr);
if (training && cacheMode != CacheMode.NONE && workspaceMgr.hasConfiguration(ArrayType.FF_CACHE)
&& workspaceMgr.isWorkspaceOpen(ArrayType.FF_CACHE)) {
try (MemoryWorkspace wsB = workspaceMgr.notifyScopeBorrowed(ArrayType.FF_CACHE)) {
preOutput = z.unsafeDuplication();
}
}
return z;
}
@Override
public boolean isPretrainLayer() {
return false;
}
@Override
public void clearNoiseWeightParams() {
//No op
}
@Override
public Gradient gradient() {
throw new UnsupportedOperationException("Not supported - no parameters");
}
@Override
public void fit() {
}
@Override
public long numParams() {
return 0;
}
@Override
public void fit(INDArray input, LayerWorkspaceMgr workspaceMgr) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public double score() {
return 0;
}
@Override
public void update(INDArray gradient, String paramType) {
}
@Override
public INDArray params() {
return null;
}
@Override
public INDArray getParam(String param) {
return params();
}
@Override
public void setParams(INDArray params) {
}
}