org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
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.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class LSTMBlockCell extends DynamicCustomOp {
private LSTMConfiguration configuration;
@Getter
private LSTMWeights weights;
public LSTMBlockCell() {
}
public LSTMBlockCell(SameDiff sameDiff, SDVariable x, SDVariable cLast, SDVariable yLast, LSTMWeights weights, LSTMConfiguration configuration) {
super(null, sameDiff, weights.argsWithInputs(x, cLast, yLast));
this.configuration = configuration;
this.weights = weights;
addIArgument(configuration.iArgs(false));
addTArgument(configuration.tArgs());
}
public LSTMBlockCell(INDArray x, INDArray cLast, INDArray yLast, LSTMWeights lstmWeights, LSTMConfiguration lstmConfiguration) {
super(null, null, lstmWeights.argsWithInputs(x, cLast, yLast));
this.configuration = lstmConfiguration;
this.weights = lstmWeights;
addIArgument(configuration.iArgs(false));
addTArgument(configuration.tArgs());
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 8, "Expected exactly 8 inputs to LSTMBlockCell, got %s", inputDataTypes);
//7 outputs, all of same type as input
DataType dt = inputDataTypes.get(0);
Preconditions.checkState(dt.isFPType(), "Input type 0 must be a floating point type, got %s", dt);
return Arrays.asList(dt, dt, dt, dt, dt, dt, dt);
}
@Override
public List doDiff(List grads) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
configuration = LSTMConfiguration.builder()
.forgetBias(attributesForNode.get("forget_bias").getF())
.clippingCellValue(attributesForNode.get("cell_clip").getF())
.peepHole(attributesForNode.get("use_peephole").getB())
.build();
addIArgument(configuration.iArgs(false));
addTArgument(configuration.tArgs());
}
@Override
public String opName() {
return "lstmBlockCell";
}
@Override
public Map propertiesForFunction() {
if(configuration != null)
return configuration.toProperties(false);
return Collections.emptyMap();
}
@Override
public String tensorflowName() {
return "LSTMBlockCell";
}
}