All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.nd4j.autodiff.loss.LossReduce Maven / Gradle / Ivy

There is a newer version: 1.0.0-M2.1
Show newest version
/*******************************************************************************
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.nd4j.autodiff.loss;

/**
 * The LossReduce enum specifies how (or if) the values of a loss function should be reduced to a single value.
 * See the javadoc comments on the individual enumeration constants for details.
 *
 * @author Alex Black
 */
public enum LossReduce {
    /**
     * No reduction. In most cases, output is the same shape as the predictions/labels.
* Weights (if any) are applied
* Example Input: 2d input array with mean squared error loss.
* Example Output: squaredDifference(predictions,labels), with same shape as input/labels
*/ NONE, /** * Weigted sum across all loss values, returning a scalar.
*/ SUM, /** * Weighted mean: sum(weights * perOutputLoss) / sum(weights) - gives a single scalar output
* Example: 2d input, mean squared error
* Output: squared_error_per_ex = weights * squaredDifference(predictions,labels)
* output = sum(squared_error_per_ex) / sum(weights)
*
* NOTE: if weights array is not provided, then weights default to 1.0 for all entries - and hence * MEAN_BY_WEIGHT is equivalent to MEAN_BY_NONZERO_WEIGHT_COUNT */ MEAN_BY_WEIGHT, /** * Weighted mean: sum(weights * perOutputLoss) / count(weights != 0)
* Example: 2d input, mean squared error loss.
* Output: squared_error_per_ex = weights * squaredDifference(predictions,labels)
* output = sum(squared_error_per_ex) / count(weights != 0)
* * NOTE: if weights array is not provided, then weights default to scalar 1.0 and hence MEAN_BY_NONZERO_WEIGHT_COUNT * is equivalent to MEAN_BY_WEIGHT */ MEAN_BY_NONZERO_WEIGHT_COUNT }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy