-
-
Notifications
You must be signed in to change notification settings - Fork 146
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Binary classification metrics throws error in v 1.0.0 #148
Comments
Known bug and will be fixed in next release. Workaround: Define metrics explicitly, like below: model_config = CategoryEmbeddingModelConfig(
task="classification",
layers="32-16", # Number of nodes in each layer
activation="LeakyReLU", # Activation between each layers
dropout=0.1,
initialization="kaiming",
learning_rate = 1e-3,
metrics=['accuracy', "f1_score"],
metrics_params=[dict(task="multiclass", num_classes=2), dict(task="multiclass", num_classes=2)]
) |
This is fixed in v1.0.1 in PyPi.
|
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
I run into a similar problem. I'm using the I'm running v1.0.1 just in case. |
|
Describe the bug
Binary Classification task with default settings throws an error in metric calculation.
To Reproduce
Steps to reproduce the behavior:
will throw below error:
Expected behavior
It should train with default Accuracy metric
Additional context
pytorch_tabular version 1.0.0
The text was updated successfully, but these errors were encountered: