wassname
/ jaccard_coef_loss.py
Last active
January 30, 2024 15:45
jaccard_coef_loss for keras. This loss is usefull when you have unbalanced classes within a sample such as segmenting each pixel of an image. For example you are trying to predict if each pixel is cat, dog, or background. You may have 80% background, 10% dog, and 10% cat. Should a model that predicts 100% background be 80% right, or 30%? Categor…
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| from keras import backend as K | |
| def jaccard_distance_loss(y_true, y_pred, smooth=100): | |
| """ | |
| Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) | |
| = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|)) | |
| The jaccard distance loss is usefull for unbalanced datasets. This has been | |
| shifted so it converges on 0 and is smoothed to avoid exploding or disapearing | |
| gradient. |
ravnoor
/ Keras.ipynb
Created
April 24, 2017 02:38
— forked from prhbrt/Keras.ipynb
V-Net in Keras and tensorflow
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| """ | |
| Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss. | |
| It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy | |
| """ | |
| # define custom loss and metric functions | |
| from keras import backend as K | |
| def dice_coef(y_true, y_pred, smooth=1): |