Deeplearning4j - 使用nd4j导入tensorflow模型

傷城~ 2022-05-26 00:05 205阅读 0赞

# Deeplearning4j - 使用nd4j导入tensorflow模型 #

在dl4j-example里面新增了模型导入的例子，这里简单的说一下。

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在dl4j新版本的特性介绍：[https://github.com/deeplearning4j/deeplearning4j-docs/blob/releasenotes\_100a/releasenotes.md][https_github.com_deeplearning4j_deeplearning4j-docs_blob_releasenotes_100a_releasenotes.md] 中，对于nd4j的模型导入进行了特别强调。

ND4J: New Features
    
    Technology preview of tensorflow import added (supports 1.4.0 and up)

其中一项就是对于tf模型的导入提供了功能预览版本，所支持的tf版本为1.4版本及其以上。

并于最近增加了导入tensorflow模型的示例代码，导入模型为 MINST 手写数字分类模型。从代码注释上面来看，因为是预览版本，目前只支持cpu运行，还不支持gpu的加速。

![资源文件列表.png-23.3kB][.png-23.3kB]

并且提供了如上的文件，用于本次示例的测试。

1.  freeze\_model\_after.py 和 generate\_model.py 是生成模型的 python 文件。
2.  frozen\_model.pb 为tensorflow的模型文件
3.  input\_\*.csv 和 input\_\*.shape为配套的特征数据文件。csv文件存放的是特征数据，一个特征一行；shape文件保存的是输入模型时的形状。
4.  prediction 文件同理，为预测的标签数据文件。

**注：** 使用该示例的时候，最好 IDE 已经安装了的相对的 lombok 插件。

# 资源文件夹 #

//Python code for this can be found in resources/import/tensorflow under generate_model.py and freeze_model_after.py
    //Input node/Placeholder in this graph is names "input"
    //Output node/op in this graph is names "output"
    public final static String BASE_DIR = "import/tensorflow";

首先定义了一个根目录用于寻找对应的文件。

接下来在主函数中获取模型文件的绝对路径

final String FROZEN_MLP = new ClassPathResource(BASE_DIR + "/frozen_model.pb").getFile().getPath();

# 读取tf中的占位符输入 #

//Load placeholder inputs and corresponding predictions generated from tensorflow Map<String, INDArray> inputsPredictions = readPlaceholdersAndPredictions();

这里面所用`readPlaceholdersAndPredictions`方法的全部代码如下：

//A simple helper function to load the inputs and corresponding outputs generated from tensorflow
    //Two cases: {input_a,prediction_a} and {input_b,prediction_b}
    protected static Map<String, INDArray> readPlaceholdersAndPredictions() throws IOException {
        String[] toReadList = {
       "input_a", "input_b", "prediction_a", "prediction_b"};
        Map<String, INDArray> arraysFromPython = new HashMap<>();
        for (int i = 0; i < toReadList.length; i++) {
            String varShapePath = new ClassPathResource(BASE_DIR + "/" + toReadList[i] + ".shape").getFile().getPath();
            String varValuePath = new ClassPathResource(BASE_DIR + "/" + toReadList[i] + ".csv").getFile().getPath();
            int[] varShape = Nd4j.readNumpy(varShapePath, ",").data().asInt();
            float[] varContents = Nd4j.readNumpy(varValuePath).data().asFloat();
            arraysFromPython.put(toReadList[i], Nd4j.create(varContents).reshape(varShape));
        }
        return arraysFromPython;
    }

这段代码不难理解，就是把前缀为`toReadList`数组内容中的数据成对读取出来，并且转换成为INDArray对象，并且返回回去。

# 模型读取 #

//Load the graph into samediff
    val graph = TFGraphMapper.getInstance().importGraph(new File(FROZEN_MLP));

这里面的 val 并非java 10提供的变量自动推断，而是 lombok 所提供的功能。

# 数据关联 #

//libnd4j executor
    //running with input_a array expecting to get prediction_a
    graph.associateArrayWithVariable(inputsPredictions.get("input_a"), graph.variableMap().get("input"));

这段代码是将从文件中读取出来的 `input_a` INDArray关联模型的数据。

# 模型预测 #

val executioner = new NativeGraphExecutioner();
    val results = executioner.executeGraph(graph); //returns an array of the outputs
    INDArray libnd4jPred = ((INDArray[]) results)[0];
    System.out.println("LIBND4J exec prediction for input_a:\n" + libnd4jPred);

模型预测，并且获取模型的结果输出。并且将其打印到控制台上。

# 结果判断 #

if (libnd4jPred.equals(inputsPredictions.get("prediction_a"))) {
        //this is true and therefore predictions are equal
        System.out.println("Predictions are equal to tensorflow");
    } else {
        throw new RuntimeException("Predictions don't match!");
    }

用于判断结果预测，和所给的标签是否相同

# 使用不同的API用于预测 input\_b 的值 #

//Now to run with the samediff executor, with input_b array expecting to get prediction_b
    val graphSD = TFGraphMapper.getInstance().importGraph(new File(FROZEN_MLP)); //Reimport graph here, necessary for the 1.0 alpha release
    graphSD.associateArrayWithVariable(inputsPredictions.get("input_b"), graph.variableMap().get("input"));
    INDArray samediffPred = graphSD.execAndEndResult();
    System.out.println("SameDiff exec prediction for input_b:\n" + samediffPred);
    if (samediffPred.equals(inputsPredictions.get("prediction_b"))) {
        //this is true and therefore predictions are equal
        System.out.println("Predictions are equal to tensorflow");
    }

# 对模型进行新增op #

//add to graph to demonstrate pytorch like capability
    System.out.println("Adding new op to graph..");
    SDVariable linspaceConstant = graphSD.var("linspace", Nd4j.linspace(1, 10, 10));
    SDVariable totalOutput = graphSD.getVariable("output").add(linspaceConstant);
    INDArray totalOutputArr = totalOutput.eval();
    System.out.println(totalOutputArr);

这个代码的意思就是对原有模型添加新的操作。

1.  首先使用`graphSD.var("linspace", Nd4j.linspace(1, 10, 10))`获取\[1,2,3 … 10\]，10个整数的向量
2.  `graphSD.getVariable("output").add(linspaceConstant);`将这个向量加入到模型的输出中。

# 整体输出 #

LIBND4J exec prediction for input_a:
    [[ 0, 0, 0, 0, 0, 0, 0, 1.0000, 0, 0]]
    Predictions are equal to tensorflow
    22:39:16,498 WARN  ~ No input found for Add and op name mmul
    22:39:16,498 WARN  ~ No input found for Add_1 and op name mmul
    SameDiff exec prediction for input_b:
    [[ 0, 0, 1.0000, 0, 0, 0, 0, 0, 0, 0]]
    Predictions are equal to tensorflow
    Adding new op to graph..
    [[ 1.0000, 2.0000, 4.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000, 10.0000]]

在最后我们可以看到，因为增加了新的op操作， 模型的原本输出`[[ 0, 0, 1.0000, 0, 0, 0, 0, 0, 0, 0]]` 加上了`[1,2,3 ... 10]`就会变成对应的`[[ 1.0000, 2.0000, 4.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000, 10.0000]]`。

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更多文档可以查看 [https://github.com/sjsdfg/deeplearning4j-issues][https_github.com_sjsdfg_deeplearning4j-issues]。  
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代码地址已经放在github上面，自行下载即可： [https://github.com/sjsdfg/dl4j-tutorials][https_github.com_sjsdfg_dl4j-tutorials]

[https_github.com_deeplearning4j_deeplearning4j-docs_blob_releasenotes_100a_releasenotes.md]: https://github.com/deeplearning4j/deeplearning4j-docs/blob/releasenotes_100a/releasenotes.md
[.png-23.3kB]: /images/20220526/084b870ad1dd4cfb8f3d75157eddb0c8.png
[https_github.com_sjsdfg_deeplearning4j-issues]: https://github.com/sjsdfg/deeplearning4j-issues
[https_github.com_sjsdfg_dl4j-tutorials]: https://github.com/sjsdfg/dl4j-tutorials