tensorflow - 使用 ml-engine 调整超参数返回状态：失败

问题描述

我正在尝试使用 ml 引擎调整我的模型超参数，但我不太确定它是否工作。

我没有在中指定algorithm标签HyperparameterSpec，根据文档，它应该默认为贝叶斯优化方法。我也没有设置maxFailedTrials，根据文档，如果第一个路径失败，应该结束所有路径。

这是我的配置

trainingInput:
  scaleTier: CUSTOM
  masterType: standard_gpu
  hyperparameters:
    goal: MAXIMIZE
    maxTrials: 8
    maxParallelTrials: 2
    hyperparameterMetricTag: test_accuracy
    params:
    - parameterName: dropout_rate
      type: DOUBLE
      minValue: 0.3
      maxValue: 0.7
      scaleType: UNIT_LINEAR_SCALE
    - parameterName: lr
      type: DOUBLE
      minValue: 0.0001
      maxValue: 0.0003
      scaleType: UNIT_LINEAR_SCALE

这是训练输出：

{
  "completedTrialCount": "8",
  "trials": [
    {
      "trialId": "1",
      "hyperparameters": {
        "lr": "0.00014959385395050048",
        "dropout_rate": "0.42217149734497067"
      },
      "startTime": "2019-10-07T09:40:02.143968039Z",
      "endTime": "2019-10-07T09:47:50Z",
      "state": "FAILED"
    },
    {
      "trialId": "2",
      "hyperparameters": {
        "dropout_rate": "0.62217149734497068",
        "lr": "0.00028292718728383382"
      },
      "startTime": "2019-10-07T09:40:02.144192681Z",
      "endTime": "2019-10-07T09:47:19Z",
      "state": "FAILED"
    },
    {
      "trialId": "3",
      "hyperparameters": {
        "lr": "0.00014846909046173097",
        "dropout_rate": "0.31717863082885739"
      },
      "startTime": "2019-10-07T09:48:09.266596472Z",
      "endTime": "2019-10-07T09:55:26Z",
      "state": "FAILED"
    },
    {
      "trialId": "4",
      "hyperparameters": {
        "lr": "0.00018741662502288819",
        "dropout_rate": "0.34178204536437984"
      },
      "startTime": "2019-10-07T09:48:10.761305330Z",
      "endTime": "2019-10-07T09:55:58Z",
      "state": "FAILED"
    },
    {
      "trialId": "5",
      "hyperparameters": {
        "dropout_rate": "0.6216828346252441",
        "lr": "0.00010192830562591553"
      },
      "startTime": "2019-10-07T09:56:15.904704865Z",
      "endTime": "2019-10-07T10:04:04Z",
      "state": "FAILED"
    },
    {
      "trialId": "6",
      "hyperparameters": {
        "dropout_rate": "0.42288427352905272",
        "lr": "0.000230206298828125"
      },
      "startTime": "2019-10-07T09:56:17.895067636Z",
      "endTime": "2019-10-07T10:04:05Z",
      "state": "FAILED"
    },
    {
      "trialId": "7",
      "hyperparameters": {
        "lr": "0.00019101441543291624",
        "dropout_rate": "0.36415641310447144"
      },
      "startTime": "2019-10-07T10:05:22.147233194Z",
      "endTime": "2019-10-07T10:13:09Z",
      "state": "FAILED"
    },
    {
      "trialId": "8",
      "hyperparameters": {
        "dropout_rate": "0.69955616224911532",
        "lr": "0.00029989311482522672"
      },
      "startTime": "2019-10-07T10:05:22.147396438Z",
      "endTime": "2019-10-07T10:13:30Z",
      "state": "FAILED"
    }
  ],
  "consumedMLUnits": 2.29,
  "isHyperparameterTuningJob": true,
  "hyperparameterMetricTag": "test_accuracy"
}

所有路径都运行，所以我相信它的搜索算法由于某种原因失败了。我无法找到更多信息，说明它为什么会返回这个或通过以另一种冗长运行的搜索算法中的任何日志。

对我来说，它似乎无法在 tensorflow 事件文件中找到指标，但我不明白为什么，因为名称完全相同，使用 tensorboard 打开事件文件我能够看到数据。也许对我不知道的日志结构有一些要求？

记录指标的代码：

from tensorflow.contrib.summary import summary as summary_ops

# in __init__
self.tf_board_writer = summary_ops.create_file_writer(self.save_path)
....

# During training
with self.tf_board_writer.as_default(), summary_ops.always_record_summaries():
    summary_ops.scalar(name=name, tensor=value, step=step)

一个小问题，如果来自 ml-engine 团队的任何问题在这里结束，现在 TF2 已经稳定并发布，你知道它什么时候可以在运行时环境中使用吗？

无论如何，希望有人可以帮助我:)

标签： tensorflowgoogle-cloud-mlhyperparameters

tensorflow - 使用 ml-engine 调整超参数返回状态：失败

问题描述

解决方案

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