jaccard distance loss

keras_contrib/losses/jaccard.py

@@ -0,0 +1,17 @@
+from keras import backend as K
+
+def jaccard_distance(y_true, y_pred, smooth=100):
+    """    
+    Jaccard distance is a usefull loss when you have unbalanced classes within a sample
+    because it gives all classes equal weight. 
+    
+    For example, you are trying to predict if each pixel is cat, dog, or background.
+    You have 80% background pixels, 10% dog, and 10% cat. If the model predicts 100% background
+    should it be be 80% right (as with categorical cross entropy) or 30% (with this loss)?
+     
+    The loss has been modified to have a smooth gradient as it converges on zero.
+    """
+    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
+    sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
+    jac = (intersection + smooth) / (sum_ - intersection + smooth)
+    return (1 - jac) * smooth