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Matt Pitkin
Matt Pitkin
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I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

 

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

typo
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simon
simon
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Pytorch LigtningLightning logs separately for train, validation and test datasets

 

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

Pytorch Ligtning logs separately for train, validation and test datasets

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

Pytorch Lightning logs separately for train, validation and test datasets

 

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.

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Apurva
Apurva
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Pytorch Ligtning logs separately for train, validation and test datasets

I am trying to log the loss and auc for all 3 of my datasets - train, validation and test. The datamodule defines the 3 loaders and I finally invoke the model as:

trainer.fit(model,datamodule)
trainer.test(model,datamodule)

This ends up creating 2 different log files - 1 with train and validation metrics, and 1 with test metrics. Is there a way to collect these together? Context is that I am using the mlflow logger and would like to see all 3 sets of metrics on the same chart, but because of this behavior, it posts 2 different sets of metrics with the same run id, creating 2 different graphs.