def WeightMask(target, mask_id=0, **kw):
del kw
if mask_id is None:
return np.ones_like(target)
return 1.0 - np.equal(target, mask_id).astype(np.float32)
def Accuracy(x, axis=-1, **kw):
del kw
prediction, target = x
predicted_class = np.argmax(prediction, axis=axis)
return np.equal(predicted_class, target)