86 lines
3.5 KiB
Java
86 lines
3.5 KiB
Java
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/*******************************************************************************
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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.nn.conf;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
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import org.deeplearning4j.nn.api.OptimizationAlgorithm;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.params.DefaultParamInitializer;
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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.convolution.Convolution;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
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import static org.junit.Assert.assertArrayEquals;
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import static org.junit.Assert.assertFalse;
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/**
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* @author Jeffrey Tang.
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*/
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public class MultiNeuralNetConfLayerBuilderTest extends BaseDL4JTest {
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int numIn = 10;
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int numOut = 5;
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double drop = 0.3;
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Activation act = Activation.SOFTMAX;
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PoolingType poolType = PoolingType.MAX;
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int[] filterSize = new int[] {2, 2};
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int filterDepth = 6;
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int[] stride = new int[] {2, 2};
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int k = 1;
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Convolution.Type convType = Convolution.Type.FULL;
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LossFunction loss = LossFunction.MCXENT;
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WeightInit weight = WeightInit.XAVIER;
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double corrupt = 0.4;
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double sparsity = 0.3;
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@Test
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public void testNeuralNetConfigAPI() {
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LossFunction newLoss = LossFunction.SQUARED_LOSS;
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int newNumIn = numIn + 1;
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int newNumOut = numOut + 1;
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WeightInit newWeight = WeightInit.UNIFORM;
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double newDrop = 0.5;
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int[] newFS = new int[] {3, 3};
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int newFD = 7;
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int[] newStride = new int[] {3, 3};
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Convolution.Type newConvType = Convolution.Type.SAME;
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PoolingType newPoolType = PoolingType.AVG;
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double newCorrupt = 0.5;
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double newSparsity = 0.5;
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MultiLayerConfiguration multiConf1 =
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new NeuralNetConfiguration.Builder().list()
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.layer(0, new DenseLayer.Builder().nIn(newNumIn).nOut(newNumOut).activation(act)
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.build())
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.layer(1, new DenseLayer.Builder().nIn(newNumIn + 1).nOut(newNumOut + 1)
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.activation(act).build())
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.build();
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NeuralNetConfiguration firstLayer = multiConf1.getConf(0);
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NeuralNetConfiguration secondLayer = multiConf1.getConf(1);
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assertFalse(firstLayer.equals(secondLayer));
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}
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}
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