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package xyz.wbsite.ai;
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import org.apache.commons.io.FilenameUtils;
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import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
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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.inputs.InputType;
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import org.deeplearning4j.nn.conf.layers.*;
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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.deeplearning4j.optimize.api.InvocationType;
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import org.deeplearning4j.optimize.listeners.EvaluativeListener;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.learning.config.Adam;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import java.io.File;
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/* *****************************************************************************
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*
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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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* *本计划和随附材料可在
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* Apache许可证2.0版的条款，可在
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* https://www.apache.org/licenses/LICENSE-2.0.
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* 有关更多信息，请参阅随此作品分发的通知文件
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* 关于版权所有权的信息。
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*
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* 除非适用法律要求或书面同意，否则软件
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* 根据许可证分发的内容是按“原样”分发的，没有
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* 任何明示或暗示的保证或条件。请参阅
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* 特定语言的许可证管理权限和限制
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* 根据许可证。
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*
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*SPDX许可证标识符：Apache-2.0
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******************************************************************************/
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public class LeNetMNIST {
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private static final Logger log = LoggerFactory.getLogger(LeNetMNIST.class);
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public static void main(String[] args) throws Exception {
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int nChannels = 1; // Number of input channels 输入通道数量
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int outputNum = 10; // The number of possible outcomes 可能结果的数量
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int batchSize = 64; // Test batch size 试验批量
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int nEpochs = 1; // Number of training epochs 训练周期数
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int seed = 123; //
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/*
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* Create an iterator using the batch size for one iteration
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* 使用一次迭代的批大小创建迭代器
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*/
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log.info("Load data....");
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DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
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DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345);
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/*
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* Construct the neural network
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* 构建神经网络
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*/
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log.info("Build model....");
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.seed(seed)
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.l2(0.0005)
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.weightInit(WeightInit.XAVIER)
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.updater(new Adam(1e-3))
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.list()
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.layer(new ConvolutionLayer.Builder(5, 5)
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//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
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//nIn和nOut指定深度。n这里是nChannel，nOut是要应用的过滤器数量
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.nIn(nChannels)
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.stride(1, 1)
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.nOut(20)
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.activation(Activation.IDENTITY)
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.build())
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.layer(new SubsamplingLayer.Builder(PoolingType.MAX)
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.kernelSize(2, 2)
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.stride(2, 2)
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.build())
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.layer(new ConvolutionLayer.Builder(5, 5)
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//Note that nIn need not be specified in later layers
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//请注意，不需要在后面的层中指定nIn
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.stride(1, 1)
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.nOut(50)
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.activation(Activation.IDENTITY)
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.build())
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.layer(new SubsamplingLayer.Builder(PoolingType.MAX)
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.kernelSize(2, 2)
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.stride(2, 2)
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.build())
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.layer(new DenseLayer.Builder().activation(Activation.RELU)
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.nOut(500).build())
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.layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.nOut(outputNum)
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.activation(Activation.SOFTMAX)
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.build())
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.setInputType(InputType.convolutionalFlat(28, 28, 1)) //See note below
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.build();
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/*
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* Regarding the .setInputType(InputType.convolutionalFlat(28,28,1)) line: This does a few things.
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* (a) It adds preprocessors, which handle things like the transition between the convolutional/subsampling layers
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* and the dense layer
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* (b) Does some additional configuration validation
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* (c) Where necessary, sets the nIn (number of input neurons, or input depth in the case of CNNs) values for each
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* layer based on the size of the previous layer (but it won't override values manually set by the user)
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* InputTypes can be used with other layer types too (RNNs, MLPs etc) not just CNNs.
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* For normal images (when using ImageRecordReader) use InputType.convolutional(height,width,depth).
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* MNIST record reader is a special case, that outputs 28x28 pixel grayscale (nChannels=1) images, in a "flattened"
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* row vector format (i.e., 1x784 vectors), hence the "convolutionalFlat" input type used here.
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*
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* 关于.setInputType（InputType.convolutionalFlat（28,28,1））行：这做了一些事情。
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* （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
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* 致密层
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* （b） 是否进行了一些额外的配置验证
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* （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
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* 基于上一层大小的层（但它不会覆盖用户手动设置的值）
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* InputTypes也可以与其他层类型（RNN、MLP等）一起使用，而不仅仅是CNN。
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* 对于普通图像（使用ImageRecordReader时），请使用InputType.convolutional（高度、宽度、深度）。
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* 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
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* 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。
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*/
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MultiLayerNetwork model = new MultiLayerNetwork(conf);
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model.init();
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log.info("Train model...");
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model.setListeners(new ScoreIterationListener(10), new EvaluativeListener(mnistTest, 1, InvocationType.EPOCH_END)); //Print score every 10 iterations and evaluate on test set every epoch
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model.fit(mnistTrain, nEpochs);
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String path = FilenameUtils.concat(System.getProperty("java.io.tmpdir"), "lenetmnist.zip");
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log.info("Saving model to tmp folder: " + path);
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model.save(new File(path), true);
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log.info("****************Example finished********************");
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}
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}
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