47 lines
2.1 KiB
Java
47 lines
2.1 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.earlystopping.scorecalc;
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import org.deeplearning4j.nn.api.Model;
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import org.nd4j.shade.jackson.annotation.JsonInclude;
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import org.nd4j.shade.jackson.annotation.JsonSubTypes;
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import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
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import java.io.Serializable;
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/** ScoreCalculator interface is used to calculate a score for a neural network.
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* For example, the loss function, test set accuracy, F1, or some other (possibly custom) metric.
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* @param <T> Type of model. For example, {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork} or {@link org.deeplearning4j.nn.graph.ComputationGraph}
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*/
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@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
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@JsonInclude(JsonInclude.Include.NON_NULL)
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@JsonSubTypes(value = {
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@JsonSubTypes.Type(value = DataSetLossCalculator.class, name = "BestScoreEpochTerminationCondition"),
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@JsonSubTypes.Type(value = DataSetLossCalculatorCG.class, name = "MaxEpochsTerminationCondition"),
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})
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public interface ScoreCalculator<T extends Model> extends Serializable {
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/** Calculate the score for the given MultiLayerNetwork */
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double calculateScore(T network);
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/**
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* @return If true: the score should be minimized. If false: the score should be maximized.
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*/
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boolean minimizeScore();
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}
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