我最近设置了一个天蓝色的机器学习实验,每天使用azure数据工厂重新训练,更新和执行示例文档
我的管道设置类似于下面
{
"name": "RetrainAndExecutePipeline",
"properties": {
"activities": [{
"type": "AzureMLBatchExecution",
"typeProperties": {
"webServiceOutputs": {
"Output-TrainedModel": "TrainedModel"
},
"webServiceInputs": {},
"globalParameters": {}
},
"outputs": [{
"name": "TrainedModel"
}
],
"policy": {
"timeout": "01:00:00",
"concurrency": 1,
"executionPriorityOrder": "NewestFirst",
"retry": 3
},
"scheduler": {
"frequency": "Day",
"interval": 1,
"offset": "22:00:00",
"style": "StartOfInterval"
},
"name": "Retrain ML Model",
"linkedServiceName": "TrainingService"
}],
"start": "2017-08-20T22:00:00Z",
"end": "9999-09-09T00:00:00Z",
"isPaused": false,
"hubName": "autdatafactoryml_hub",
"pipelineMode": "Scheduled"
}
}
以及下面的TrainedModel数据集
{
"name": "TrainedModel",
"properties": {
"published": false,
"type": "AzureBlob",
"linkedServiceName": "AzureStorageLinkedService",
"typeProperties": {
"fileName": "trainedModel.ilearner",
"folderPath": "trainingoutput",
"format": {
"type": "TextFormat"
}
},
"availability": {
"frequency": "Day",
"interval": 1,
"offset": "22:00:00",
"style": "StartOfInterval"
}
}
}
我注意到在训练完成后,从连接到“Train Model”节点的Web服务输出进入azure blob存储的输出是ilearner文件和两个随机命名的文件,没有扩展名,即使我没有' t指定它们 . 一个带内容的xml格式文件
<?xml version="1.0" encoding="utf-8"?>
<RuntimeInfo>
<Language>DotNet</Language>
<Version>4.5.0</Version>
</RuntimeInfo>
另一个是您在azure ML实验中可视化输出时可以看到的信息,格式为json,如下所示
{
"visualizationType": "learner",
"learner": {
"name": "LogisticRegressionClassifier",
"isTrained": true,
"settings": {
"records": [
...
],
"features": [
{
"name": "Setting",
"index": 0,
"elementType": "System.String",
"featureType": "String Feature"
},
{
"name": "Value",
"index": 1,
"elementType": "System.String",
"featureType": "String Feature"
}
],
"name": null,
"numberOfRows": 8,
"numberOfColumns": 2
},
"weights": {
"records": [
...
],
"features": [
{
"name": "Feature",
"index": 0,
"elementType": "System.String",
"featureType": "String Feature"
},
{
"name": "Weight",
"index": 1,
"elementType": "System.Double",
"featureType": "Numeric Feature"
}
],
"name": null,
"numberOfRows": 92,
"numberOfColumns": 2
}
}
}
这个json文件是我感兴趣的文件,因为我认为这是显示系数值的数据,我想跟踪个人系数值随着我更新训练模型的变化,我还没有能够找到捕获此输出的方法 .
我的问题是,有没有办法在使用azure数据工厂的天蓝ML实验中从单个Web服务输出中捕获多个输出?或者我有一个完全不同的方式来解决这个问题?
我感谢每个人的反馈,并提前感谢你
1 回答
在Azure ML Studio中,您可以通过附加多个Web服务输出模块来创建具有多个输出的Web服务 . 调用Web服务时,将以JSON格式返回这些模块的输出 . 例如,您还可以使用多个导出数据模块将多个结果写入Azure存储 .