139 lines
5.2 KiB
Java
139 lines
5.2 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.eval;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.datasets.iterator.ExistingDataSetIterator;
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import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.junit.Test;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import java.util.Collections;
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import static org.junit.Assert.assertEquals;
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import static org.nd4j.linalg.indexing.NDArrayIndex.all;
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import static org.nd4j.linalg.indexing.NDArrayIndex.interval;
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/**
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* @author Alex Black
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*/
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public class RegressionEvalTest extends BaseDL4JTest {
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@Test
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public void testRegressionEvalMethods() {
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//Basic sanity check
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.ZERO).list()
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.layer(0, new OutputLayer.Builder().activation(Activation.TANH)
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.lossFunction(LossFunctions.LossFunction.MSE).nIn(10).nOut(5).build())
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.build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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INDArray f = Nd4j.zeros(4, 10);
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INDArray l = Nd4j.ones(4, 5);
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DataSet ds = new DataSet(f, l);
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DataSetIterator iter = new ExistingDataSetIterator(Collections.singletonList(ds));
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RegressionEvaluation re = net.evaluateRegression(iter);
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for (int i = 0; i < 5; i++) {
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assertEquals(1.0, re.meanSquaredError(i), 1e-6);
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assertEquals(1.0, re.meanAbsoluteError(i), 1e-6);
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}
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ComputationGraphConfiguration graphConf =
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new NeuralNetConfiguration.Builder().weightInit(WeightInit.ZERO).graphBuilder()
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.addInputs("in").addLayer("0", new OutputLayer.Builder()
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.lossFunction(LossFunctions.LossFunction.MSE)
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.activation(Activation.TANH).nIn(10).nOut(5).build(), "in")
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.setOutputs("0").build();
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ComputationGraph cg = new ComputationGraph(graphConf);
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cg.init();
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RegressionEvaluation re2 = cg.evaluateRegression(iter);
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for (int i = 0; i < 5; i++) {
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assertEquals(1.0, re2.meanSquaredError(i), 1e-6);
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assertEquals(1.0, re2.meanAbsoluteError(i), 1e-6);
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}
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}
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@Test
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public void testRegressionEvalPerOutputMasking() {
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INDArray l = Nd4j.create(new double[][] {{1, 2, 3}, {10, 20, 30}, {-5, -10, -20}});
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INDArray predictions = Nd4j.zeros(l.shape());
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INDArray mask = Nd4j.create(new double[][] {{0, 1, 1}, {1, 1, 0}, {0, 1, 0}});
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RegressionEvaluation re = new RegressionEvaluation();
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re.eval(l, predictions, mask);
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double[] mse = new double[] {(10 * 10) / 1.0, (2 * 2 + 20 * 20 + 10 * 10) / 3, (3 * 3) / 1.0};
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double[] mae = new double[] {10.0, (2 + 20 + 10) / 3.0, 3.0};
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double[] rmse = new double[] {10.0, Math.sqrt((2 * 2 + 20 * 20 + 10 * 10) / 3.0), 3.0};
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for (int i = 0; i < 3; i++) {
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assertEquals(mse[i], re.meanSquaredError(i), 1e-6);
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assertEquals(mae[i], re.meanAbsoluteError(i), 1e-6);
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assertEquals(rmse[i], re.rootMeanSquaredError(i), 1e-6);
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}
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}
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@Test
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public void testRegressionEvalTimeSeriesSplit(){
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INDArray out1 = Nd4j.rand(new int[]{3, 5, 20});
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INDArray outSub1 = out1.get(all(), all(), interval(0,10));
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INDArray outSub2 = out1.get(all(), all(), interval(10, 20));
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INDArray label1 = Nd4j.rand(new int[]{3, 5, 20});
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INDArray labelSub1 = label1.get(all(), all(), interval(0,10));
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INDArray labelSub2 = label1.get(all(), all(), interval(10, 20));
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RegressionEvaluation e1 = new RegressionEvaluation();
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RegressionEvaluation e2 = new RegressionEvaluation();
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e1.eval(label1, out1);
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e2.eval(labelSub1, outSub1);
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e2.eval(labelSub2, outSub2);
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assertEquals(e1, e2);
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}
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}
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