在 Google AI Platform 超参数调整中更改 trialId
Change trialId in Google AI Platform hyperparameter tuning
我正在尝试按照本教程学习如何在 AI Platform 上调整超参数:https://cloud.google.com/blog/products/gcp/hyperparameter-tuning-on-google-cloud-platform-is-now-faster-and-smarter。
我的配置 yaml 文件如下所示:
trainingInput:
hyperparameters:
goal: MINIMIZE
hyperparameterMetricTag: loss
maxTrials: 4
maxParallelTrials: 2
params:
- parameterName: learning_rate
type: DISCRETE
discreteValues:
- 0.0005
- 0.001
- 0.0015
- 0.002
预期输出:
"completedTrialCount": "4",
"trials": [
{
"trialId": "3",
"hyperparameters": {
"learning_rate": "2e-03"
},
"finalMetric": {
"trainingStep": "123456",
"objectiveValue": 0.123456
},
},
有什么方法可以自定义 trialId
而不是默认数值(例如 1,2,3,4...)?
无法自定义 trialId
,因为它取决于超参数调整配置中的参数 maxTrials
。
maxTrials 只接受整数,因此分配给 trialId
的值将是从 1 到您定义的 maxTrials
.
的范围
也如 example in your post 中所述,其中设置了 maxTrials: 40
,它会产生一个 json,显示 trialId: 35
在 maxTrials
的范围内.
This indicates that 40 trials have been completed, and the best so far
is trial 35, which achieved an objective of 1.079 with the
hyperparameter values of nembeds=18 and nnsize=32.
示例输出:
我正在尝试按照本教程学习如何在 AI Platform 上调整超参数:https://cloud.google.com/blog/products/gcp/hyperparameter-tuning-on-google-cloud-platform-is-now-faster-and-smarter。
我的配置 yaml 文件如下所示:
trainingInput:
hyperparameters:
goal: MINIMIZE
hyperparameterMetricTag: loss
maxTrials: 4
maxParallelTrials: 2
params:
- parameterName: learning_rate
type: DISCRETE
discreteValues:
- 0.0005
- 0.001
- 0.0015
- 0.002
预期输出:
"completedTrialCount": "4",
"trials": [
{
"trialId": "3",
"hyperparameters": {
"learning_rate": "2e-03"
},
"finalMetric": {
"trainingStep": "123456",
"objectiveValue": 0.123456
},
},
有什么方法可以自定义 trialId
而不是默认数值(例如 1,2,3,4...)?
无法自定义 trialId
,因为它取决于超参数调整配置中的参数 maxTrials
。
maxTrials 只接受整数,因此分配给 trialId
的值将是从 1 到您定义的 maxTrials
.
也如 example in your post 中所述,其中设置了 maxTrials: 40
,它会产生一个 json,显示 trialId: 35
在 maxTrials
的范围内.
This indicates that 40 trials have been completed, and the best so far is trial 35, which achieved an objective of 1.079 with the hyperparameter values of nembeds=18 and nnsize=32.
示例输出: