commit b2798b24cc24b31d2b9b15e711963d3ec2a44f38 Author: wangbing Date: Fri Apr 18 17:14:08 2025 +0800 上传备份 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..13e058c --- /dev/null +++ b/.gitignore @@ -0,0 +1,21 @@ +target/ +pom.xml.tag +pom.xml.releaseBackup +pom.xml.versionsBackup +pom.xml.next +release.properties +/.idea +*.iml +/.settings +/bin +/gen +/build +/gradle +/classes +.classpath +.project +*.gradle +gradlew +local.properties +node_modules/ +data/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..dc06b18 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +## 深度学习 \ No newline at end of file diff --git a/pom.xml b/pom.xml new file mode 100644 index 0000000..9c2f0ef --- /dev/null +++ b/pom.xml @@ -0,0 +1,91 @@ + + + 4.0.0 + xyz.wbsite + starter-dl4j + 0.0.1-SNAPSHOT + jar + + + 17 + + 1.0.0-beta2 + + + + + + central + Central Repository + default + https://maven.aliyun.com/repository/public + + + + + + central + Central Repository + https://maven.aliyun.com/repository/public + default + + + + + + org.apache.maven.plugins + maven-compiler-plugin + + 17 + 17 + + + + + + + + + cn.hutool + hutool-all + 5.8.24 + + + + + org.deeplearning4j + deeplearning4j-core + 1.0.0-M2.1 + + + + org.nd4j + nd4j-native + 1.0.0-M2.1 + + + org.nd4j + nd4j-native-platform + 1.0.0-beta7 + + + org.deeplearning4j + arbiter-ui + 1.0.0-beta7 + + + + + ch.qos.logback + logback-classic + 1.2.11 + + + org.slf4j + slf4j-api + 1.7.32 + + + \ No newline at end of file diff --git a/src/main/java/xyz/wbsite/ai/Dl4j_Example.java b/src/main/java/xyz/wbsite/ai/Dl4j_Example.java new file mode 100644 index 0000000..6b23a19 --- /dev/null +++ b/src/main/java/xyz/wbsite/ai/Dl4j_Example.java @@ -0,0 +1,15 @@ +package xyz.wbsite.ai; + +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.Nd4j; + +public class Dl4j_Example { + + public static void main(String[] args) { + // https://blog.csdn.net/m290345792/article/details/147009026 + + // 测试 + INDArray matrix = Nd4j.create(new float[]{1,2,3,4}, new int[]{2,2}); + System.out.println("矩阵求和: " + matrix.sum()); + } +} diff --git a/src/main/java/xyz/wbsite/ai/Dl4j_LeNetMNIST.java b/src/main/java/xyz/wbsite/ai/Dl4j_LeNetMNIST.java new file mode 100644 index 0000000..0bc216c --- /dev/null +++ b/src/main/java/xyz/wbsite/ai/Dl4j_LeNetMNIST.java @@ -0,0 +1,158 @@ +package xyz.wbsite.ai; + +import org.apache.commons.io.FilenameUtils; +import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; +import org.deeplearning4j.nn.conf.MultiLayerConfiguration; +import org.deeplearning4j.nn.conf.NeuralNetConfiguration; +import org.deeplearning4j.nn.conf.inputs.InputType; +import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; +import org.deeplearning4j.nn.conf.layers.DenseLayer; +import org.deeplearning4j.nn.conf.layers.OutputLayer; +import org.deeplearning4j.nn.conf.layers.PoolingType; +import org.deeplearning4j.nn.conf.layers.SubsamplingLayer; +import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; +import org.deeplearning4j.nn.weights.WeightInit; +import org.deeplearning4j.optimize.api.InvocationType; +import org.deeplearning4j.optimize.listeners.EvaluativeListener; +import org.deeplearning4j.optimize.listeners.ScoreIterationListener; +import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; +import org.nd4j.linalg.learning.config.Adam; +import org.nd4j.linalg.lossfunctions.LossFunctions; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.io.File; + +/** + * 构建手写数字识别模型 + *

+ *

+ * 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. + * See the NOTICE file distributed with this work for additional + * information regarding copyright ownership. + *

+ * 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 + *

+ * *本计划和随附材料可在 + * Apache许可证2.0版的条款，可在 + * https://www.apache.org/licenses/LICENSE-2.0. + * 有关更多信息，请参阅随此作品分发的通知文件 + * 关于版权所有权的信息。 + *

+ * 除非适用法律要求或书面同意，否则软件 + * 根据许可证分发的内容是按“原样”分发的，没有 + * 任何明示或暗示的保证或条件。请参阅 + * 特定语言的许可证管理权限和限制 + * 根据许可证。 + *

+ * SPDX许可证标识符：Apache-2.0 + ******************************************************************************/ +public class Dl4j_LeNetMNIST { + private static final Logger log = LoggerFactory.getLogger(Dl4j_LeNetMNIST.class); + + public static void main(String[] args) throws Exception { + int nChannels = 1; // Number of input channels 输入通道数量 + int outputNum = 10; // The number of possible outcomes 可能结果的数量 + int batchSize = 64; // Test batch size 试验批量 + int nEpochs = 1; // Number of training epochs 训练周期数 + int seed = 123; // + + /* + * Create an iterator using the batch size for one iteration + * 使用一次迭代的批大小创建迭代器 + */ + log.info("Load data...."); + DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345); + DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345); + + /* + * Construct the neural network + * 构建神经网络 + */ + log.info("Build model...."); + MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .seed(seed) + .l2(0.0005) + .weightInit(WeightInit.XAVIER) + .updater(new Adam(1e-3)) + .list() + .layer(new ConvolutionLayer.Builder(5, 5) + //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied + //nIn和nOut指定深度。n这里是nChannel，nOut是要应用的过滤器数量 + .nIn(nChannels) + .stride(1, 1) + .nOut(20) + .activation(Activation.IDENTITY) + .build()) + .layer(new SubsamplingLayer.Builder(PoolingType.MAX) + .kernelSize(2, 2) + .stride(2, 2) + .build()) + .layer(new ConvolutionLayer.Builder(5, 5) + //Note that nIn need not be specified in later layers + //请注意，不需要在后面的层中指定nIn + .stride(1, 1) + .nOut(50) + .activation(Activation.IDENTITY) + .build()) + .layer(new SubsamplingLayer.Builder(PoolingType.MAX) + .kernelSize(2, 2) + .stride(2, 2) + .build()) + .layer(new DenseLayer.Builder().activation(Activation.RELU) + .nOut(500).build()) + .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) + .nOut(outputNum) + .activation(Activation.SOFTMAX) + .build()) + .setInputType(InputType.convolutionalFlat(28, 28, 1)) //See note below + .build(); + + /* + * Regarding the .setInputType(InputType.convolutionalFlat(28,28,1)) line: This does a few things. + * (a) It adds preprocessors, which handle things like the transition between the convolutional/subsampling layers + * and the dense layer + * (b) Does some additional configuration validation + * (c) Where necessary, sets the nIn (number of input neurons, or input depth in the case of CNNs) values for each + * layer based on the size of the previous layer (but it won't override values manually set by the user) + * InputTypes can be used with other layer types too (RNNs, MLPs etc) not just CNNs. + * For normal images (when using ImageRecordReader) use InputType.convolutional(height,width,depth). + * MNIST record reader is a special case, that outputs 28x28 pixel grayscale (nChannels=1) images, in a "flattened" + * row vector format (i.e., 1x784 vectors), hence the "convolutionalFlat" input type used here. + * + * 关于.setInputType（InputType.convolutionalFlat（28,28,1））行：这做了一些事情。 + * （a） 它添加了预处理器，处理卷积/子采样层之间的转换等事情 + * 致密层 + * （b） 是否进行了一些额外的配置验证 + * （c） 必要时，为每个神经元设置nIn（输入神经元数量，或CNN情况下的输入深度）值 + * 基于上一层大小的层（但它不会覆盖用户手动设置的值） + * InputTypes也可以与其他层类型（RNN、MLP等）一起使用，而不仅仅是CNN。 + * 对于普通图像（使用ImageRecordReader时），请使用InputType.convolutional（高度、宽度、深度）。 + * MNIST记录读取器是一种特殊情况，它以“平坦”的方式输出28x28像素灰度（nCannels=1）图像 + * 行向量格式（即1x784向量），因此这里使用的是“卷积平面”输入类型。 + */ + + MultiLayerNetwork model = new MultiLayerNetwork(conf); + model.init(); + + log.info("Train model..."); + model.setListeners(new ScoreIterationListener(10), new EvaluativeListener(mnistTest, 1, InvocationType.EPOCH_END)); //Print score every 10 iterations and evaluate on test set every epoch + model.fit(mnistTrain, nEpochs); + + String path = FilenameUtils.concat(System.getProperty("java.io.tmpdir"), "lenetmnist.zip"); + + log.info("Saving model to tmp folder: " + path); + model.save(new File(path), true); + + log.info("****************Example finished********************"); + } +}