基于Java语言实现的深度学习框架，方便Java开发者学习.zip

/******************************************************************************* * Copyright (c) 2015-2018 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.linalg; import lombok.extern.slf4j.Slf4j; import lombok.val; import lombok.var; import org.apache.commons.io.FilenameUtils; import org.apache.commons.math3.stat.descriptive.rank.Percentile; import org.apache.commons.math3.util.FastMath; import org.junit.*; import org.junit.runner.RunWith; import org.junit.runners.Parameterized; import org.nd4j.imports.TFGraphs.NodeReader; import org.nd4j.linalg.api.blas.params.GemmParams; import org.nd4j.linalg.api.blas.params.MMulTranspose; import org.nd4j.linalg.api.buffer.DataBuffer; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.environment.Nd4jEnvironment; import org.nd4j.linalg.api.iter.INDArrayIterator; import org.nd4j.linalg.api.iter.NdIndexIterator; import org.nd4j.linalg.api.memory.conf.WorkspaceConfiguration; import org.nd4j.linalg.api.memory.enums.LearningPolicy; import org.nd4j.linalg.api.memory.enums.SpillPolicy; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.BroadcastOp; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.Op; import org.nd4j.linalg.api.ops.custom.Flatten; import org.nd4j.linalg.api.ops.executioner.GridExecutioner; import org.nd4j.linalg.api.ops.executioner.OpExecutioner; import org.nd4j.linalg.api.ops.impl.broadcast.*; import org.nd4j.linalg.api.ops.impl.broadcast.bool.BroadcastEqualTo; import org.nd4j.linalg.api.ops.impl.broadcast.bool.BroadcastGreaterThan; import org.nd4j.linalg.api.ops.impl.broadcast.bool.BroadcastGreaterThanOrEqual; import org.nd4j.linalg.api.ops.impl.broadcast.bool.BroadcastLessThan; import org.nd4j.linalg.api.ops.impl.indexaccum.IAMax; import org.nd4j.linalg.api.ops.impl.indexaccum.IAMin; import org.nd4j.linalg.api.ops.impl.indexaccum.IMax; import org.nd4j.linalg.api.ops.impl.indexaccum.IMin; import org.nd4j.linalg.api.ops.impl.layers.convolution.Im2col; import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig; import org.nd4j.linalg.api.ops.impl.reduce.Mmul; import org.nd4j.linalg.api.ops.impl.reduce.bool.All; import org.nd4j.linalg.api.ops.impl.reduce.custom.LogSumExp; import org.nd4j.linalg.api.ops.impl.reduce.floating.Norm1; import org.nd4j.linalg.api.ops.impl.reduce.floating.Norm2; import org.nd4j.linalg.api.ops.impl.reduce.same.Sum; import org.nd4j.linalg.api.ops.impl.reduce3.*; import org.nd4j.linalg.api.ops.impl.scalar.LeakyReLU; import org.nd4j.linalg.api.ops.impl.scalar.ReplaceNans; import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate; import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax; import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform; import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet; import org.nd4j.linalg.api.ops.impl.transforms.comparison.Eps; import org.nd4j.linalg.api.ops.impl.transforms.pairwise.BinaryRelativeError; import org.nd4j.linalg.api.ops.impl.transforms.pairwise.Set; import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.Axpy; import org.nd4j.linalg.api.ops.impl.transforms.same.OldReverse; import org.nd4j.linalg.api.ops.impl.transforms.same.Sign; import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh; import org.nd4j.linalg.api.ops.impl.transforms.strict.OldSoftMax; import org.nd4j.linalg.api.ops.impl.transforms.strict.SoftMaxDerivative; import org.nd4j.linalg.api.ops.impl.transforms.strict.Tanh; import org.nd4j.linalg.api.shape.Shape; import org.nd4j.linalg.checkutil.NDArrayCreationUtil; import org.nd4j.linalg.exception.ND4JIllegalStateException; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4jBackend; import org.nd4j.linalg.indexing.BooleanIndexing; import org.nd4j.linalg.indexing.INDArrayIndex; import org.nd4j.linalg.indexing.NDArrayIndex; import org.nd4j.linalg.indexing.SpecifiedIndex; import org.nd4j.linalg.indexing.conditions.Conditions; import org.nd4j.linalg.io.ClassPathResource; import org.nd4j.linalg.ops.transforms.Transforms; import org.nd4j.linalg.primitives.Pair; import org.nd4j.linalg.profiler.OpProfiler; import org.nd4j.linalg.profiler.ProfilerConfig; import org.nd4j.linalg.util.ArrayUtil; import org.nd4j.linalg.util.MathUtils; import java.io.*; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.nio.file.Files; import java.nio.file.Paths; import java.util.*; import static org.junit.Assert.*; import static org.junit.Assert.assertArrayEquals; /** * NDArrayTests * * @author Adam Gibson */ @Slf4j @RunWith(Parameterized.class) public class Nd4jTestsC extends BaseNd4jTest { DataType initialType; public Nd4jTestsC(Nd4jBackend backend) { super(backend); this.initialType = Nd4j.dataType(); } @Before public void before() throws Exception { super.before(); Nd4j.setDataType(DataType.DOUBLE); Nd4j.getRandom().setSeed(123); Nd4j.getExecutioner().enableDebugMode(false); Nd4j.getExecutioner().enableVerboseMode(false); } @After public void after() throws Exception { super.after(); Nd4j.setDataType(initialType); } @Test public void testArangeNegative() { INDArray arr = Nd4j.arange(-2,2); INDArray assertion = Nd4j.create(new double[]{-2, -1, 0, 1}); assertEquals(assertion,arr); } @Test public void testTri() { INDArray assertion = Nd4j.create(new double[][]{ {1,1,1,0,0}, {1,1,1,1,0}, {1,1,1,1,1} }); INDArray tri = Nd4j.tri(3,5,2); assertEquals(assertion,tri); } @Test public void testTriu() { INDArray input = Nd4j.linspace(1,12,12, DataType.DOUBLE).reshape(4,3); int k = -1; INDArray test = Nd4j.triu(input,k); INDArray create = Nd4j.create(new double[][]{ {1,2,3}, {4,5,6}, {0,8,9}, {0,0,12} }); assertEquals(test,create); } @Test public void testDiag() { INDArray diag = Nd4j.diag(Nd4j.linspace(1,4,4, DataType.DOUBLE).reshape(4,1)); assertArrayEquals(new long[] {4,4},diag.shape()); } @Test public void testGetRowEdgeCase() { INDArray orig = Nd4j.linspace(1,300,300, DataType.DOUBLE).reshape('c', 100, 3); INDArray col = orig.getColumn(0).reshape(100, 1); for( int i = 0; i < 100; i++) { INDArray row = col.getRow(i); INDArray rowDup = row.dup(); double d = orig.getDouble(i, 0); double d2 = col.getDouble(i); double dRowDup = rowDup.getDouble(0); double dRow = row.getDouble(0); String s = String.valueOf(i); assertEquals(s, d, d2, 0.0); assertEquals(s, d, dRowDup, 0.0); //Fails assertEquals(s, d, dRow, 0.0); //Fails } } @Test public void testNd4jEnvironment() { System.out.println(Nd4j.getExecutioner().getEnvironmentInformation()); int manualNumCores = Integer.parseInt(Nd4j.getExecutioner().getEnvironmentInformation() .get(Nd4jEnvironment.CPU_CORES_KEY).toString()); assertEquals(Runtime.getRuntime().availableProcessors(), manualNumCores); assertEquals(Runtime.getR