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…

jaccard_coef_loss.py

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.
    
    Ref: https://en.wikipedia.org/wiki/Jaccard_index
    
    @url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96
    @author: wassname
    """
    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


# Test and plot
y_pred = np.array([np.arange(-10, 10+0.1, 0.1)]).T
y_true = np.zeros(y_pred.shape)
name='jaccard_distance_loss'
try:
    loss = jaccard_distance_loss(
        K.variable(y_true),K.variable(y_pred)
    ).eval(session=K.get_session())
except Exception as e:
    print("error plotting", name ,e)
else:
    plt.title(name)
    plt.plot(y_pred,loss)
    plt.show()
    
# Test
# Test
print("TYPE                 |Almost_right |half right |all_wrong")
y_true = np.array([[0,0,1,0],[0,0,1,0],[0,0,1.,0.]])
y_pred = np.array([[0,0,0.9,0],[0,0,0.1,0],[1,1,0.1,1.]])

r = jaccard_distance_loss(
    K.variable(y_true),
    K.variable(y_pred),
).eval(session=K.get_session())
print('jaccard_distance_loss',r)
assert r[0]<r[1]
assert r[1]<r[2]

r = keras.losses.binary_crossentropy(
    K.variable(y_true),
    K.variable(y_pred),
).eval(session=K.get_session())
print('binary_crossentropy',r)
print('binary_crossentropy_scaled',r/r.max())
assert r[0]<r[1]
assert r[1]<r[2]

"""
TYPE                 |Almost_right |half right |all_wrong
jaccard_distance_loss [ 0.09900928  0.89108944  3.75000238]
binary_crossentropy [  0.02634021   0.57564634  12.53243446]
binary_crossentropy_scaled [ 0.00210176  0.04593252  1.        ]
"""