org.nd4j.linalg.lossfunctions.LossUtil Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.nd4j.linalg.lossfunctions;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Arrays;
public class LossUtil {
/**
*
* @param to
* @param mask
* @return
*/
public static boolean isPerOutputMasking(INDArray to, INDArray mask) {
return !mask.isColumnVector() || Arrays.equals(to.shape(), mask.shape());
}
/**
*
* @param to
* @param mask
*/
public static void applyMask(INDArray to, INDArray mask) {
//Two possibilities exist: it's *per example* masking, or it's *per output* masking
//These cases have different mask shapes. Per example: column vector. Per output: same shape as score array
if (mask.isColumnVectorOrScalar()) {
to.muliColumnVector(mask.castTo(to.dataType()));
} else if (Arrays.equals(to.shape(), mask.shape())) {
to.muli(mask.castTo(to.dataType()));
} else {
throw new IllegalStateException("Invalid mask array: per-example masking should be a column vector, "
+ "per output masking arrays should be the same shape as the labels array. Mask shape: "
+ Arrays.toString(mask.shape()) + ", output shape: " + Arrays.toString(to.shape()));
}
}
}