org.nd4j.linalg.api.ops.compat.CompatSparseToDense Maven / Gradle / Ivy
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* Copyright (c) 2015-2019 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.
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* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.compat;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
/**
* This is a wrapper for SparseToDense op that impelements corresponding TF operation
*
* @author [email protected]
*/
public class CompatSparseToDense extends DynamicCustomOp {
public CompatSparseToDense() {
//
}
public CompatSparseToDense(INDArray indices, INDArray shape, INDArray values) {
Preconditions.checkArgument(shape.isZ() && indices.isZ(), "Shape & indices arrays must have one integer data types");
inputArguments.add(indices);
inputArguments.add(shape);
inputArguments.add(values);
}
public CompatSparseToDense(INDArray indices, INDArray shape, INDArray values, INDArray defaultVaule) {
this(indices, shape, values);
Preconditions.checkArgument(defaultVaule.dataType() == values.dataType(), "Values array must have the same data type as defaultValue array");
inputArguments.add(defaultVaule);
}
@Override
public String opName() {
return "compat_sparse_to_dense";
}
}