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Validation loss is neither increasing or decreasing

原文 2020-11-29 05:38:30 5 1 python/ pytorch/ unity3d-unet

Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. But for my case, training loss still goes down but validation loss stays at same level. Hence validation accuracy also stays at same level but training accuracy goes up. I am trying to reconstruct a 2D image from a 3D volume using UNet. Same is the behavior when I am trying to reconstruct 3D volume from 2D image but at higher loss and lower accuracy. Can someone explain the curve that why validation loss is not going down from the point of overfitting?

1 answers

The trends show that your model is overfitting. Ways to overcome overfitting include:

Use data augmentation
Use more data
Use Dropout
Use regularization
Try slowing down your learning rate!

loss is neither increasing nor decreasing in siamese network

Training acc decreasing, validation - increasing. Training loss, validation loss decreasing

Loss function increasing instead of decreasing

Validation accuracy and Validation Loss starts decreasing rapidly and then starts increasing rapidly with CNN and Batch normalization

Validation loss increasing

Validation accuracy increasing but validation loss is also increasing

Validation loss hitting minimum then increasing

Accuracy of 1.0 while Training Loss and Validation Loss still decreasing

Increasing validation loss from the very beginning

avoid x is neither increasing nor decreasing : {}.".format(x) when calculate auc score

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Related Question loss is neither increasing nor decreasing in siamese network Training acc decreasing, validation - increasing. Training loss, validation loss decreasing Loss function increasing instead of decreasing Validation accuracy and Validation Loss starts decreasing rapidly and then starts increasing rapidly with CNN and Batch normalization Validation loss increasing Validation accuracy increasing but validation loss is also increasing Validation loss hitting minimum then increasing Accuracy of 1.0 while Training Loss and Validation Loss still decreasing Increasing validation loss from the very beginning avoid x is neither increasing nor decreasing : {}.".format(x) when calculate auc score

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Validation loss is neither increasing or decreasing

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1 answers

solution1 1 2020-11-29 13:50:53

solution1
1 2020-11-29 13:50:53