org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell Maven / Gradle / Ivy
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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.weights.GRUWeights;
import java.util.Arrays;
import java.util.List;
public class GRUCell extends DynamicCustomOp {
@Getter
private GRUWeights weights;
public GRUCell() {
}
public GRUCell(SameDiff sameDiff, SDVariable x, SDVariable hLast, GRUWeights weights) {
super(null, sameDiff, weights.argsWithInputs(x, hLast));
this.weights = weights;
}
public GRUCell(INDArray x, INDArray hLast, GRUWeights gruWeights) {
super(null, null, gruWeights.argsWithInputs(x, hLast));
this.weights = gruWeights;
}
@Override
public String opName() {
return "gruCell";
}
@Override
public String onnxName() {
return "GRU";
}
@Override
public String tensorflowName() {
return "GRUBlockCell";
}
@Override
public String[] onnxNames() {
return super.onnxNames();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 6, "Expected exactly 6 inputs to GRUCell, got %s", inputDataTypes);
//4 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);
}
@Override
public List doDiff(List grads){
throw new UnsupportedOperationException("Not yet implemented");
}
}