def weighted(y_true, y_pred, weights, mask=None):
# score_array has ndim >= 2
score_array = fn(y_true, y_pred)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
mask = K.cast(mask, K.floatX)
# mask should have the same shape as score_array
score_array *= mask
# the loss per batch should be proportional
# to the number of unmasked samples.
score_array /= K.mean(mask)
# reduce score_array to 1D
ndim = K.ndim(score_array)
for _ in range(ndim-1):
score_array = K.mean(score_array, axis=-1)
if weights is not None:
score_array *= weights
return K.mean(score_array)
def weighted(y_true, y_pred, weights, mask=None):
# score_array has ndim >= 2
score_array = fn(y_true, y_pred)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
mask = K.cast(mask, K.floatX)
# mask should have the same shape as score_array
score_array *= mask
# the loss per batch should be proportional
# to the number of unmasked samples.
score_array /= K.mean(mask)
# reduce score_array to 1D
ndim = K.ndim(score_array)
for _ in range(ndim - 1):
score_array = K.mean(score_array, axis=-1)
if weights is not None:
score_array *= weights
return K.mean(score_array)
def __call__(self, p):
p *= K.cast(p >= 0., K.floatX)
return p
def __call__(self, p):
p *= K.cast(p >= 0., K.floatX)
return p
