def weighted(y_true, y_pred, weights, mask=None):
"""Wrapper function.
Arguments:
y_true: `y_true` argument of `fn`.
y_pred: `y_pred` argument of `fn`.
weights: Weights tensor.
mask: Mask tensor.
Returns:
Scalar tensor.
"""
# score_array has ndim >= 2
score_array = fn(y_true, y_pred)
if mask is not None:
mask = math_ops.cast(mask, y_pred.dtype)
# Update weights with mask.
if weights is None:
weights = mask
else:
# Update shape of weights if possible before adding mask.
# Update dimensions of weights to match with mask if possible.
mask, _, weights = metrics_module.squeeze_or_expand_dimensions(
mask, None, weights)
try:
# Broadcast weights if possible.
weights = weights_broadcast_ops.broadcast_weights(
weights, mask)
weights *= mask
except ValueError:
score_array *= mask
score_array /= K.mean(mask)
# TODO(psv): Handle case when mask and weight shapes are not
# compatible.
# Apply sample weighting.
if weights is not None:
# Update dimensions of weights to match with values if possible.
score_array, _, weights = metrics_module.squeeze_or_expand_dimensions(
score_array, None, weights)
try:
# Broadcast weights if possible.
weights = weights_broadcast_ops.broadcast_weights(
weights, score_array)
except ValueError:
# Reduce values to same ndim as weight array.
ndim = K.ndim(score_array)
weight_ndim = K.ndim(weights)
score_array = K.mean(score_array,
axis=list(range(weight_ndim, ndim)))
score_array = math_ops.multiply(score_array, weights)
score_array = math_ops.reduce_sum(score_array)
weights = math_ops.reduce_sum(weights)
score_array = metrics_module.safe_div(score_array, weights)
return K.mean(score_array)
def weighted(y_true, y_pred, weights, mask=None):
"""Wrapper function.
Arguments:
y_true: `y_true` argument of `fn`.
y_pred: `y_pred` argument of `fn`.
weights: Weights tensor.
mask: Mask tensor.
Returns:
Scalar tensor.
"""
# score_array has ndim >= 2
score_array = fn(y_true, y_pred)
if mask is not None:
mask = math_ops.cast(mask, y_pred.dtype)
# Update weights with mask.
if weights is None:
weights = mask
else:
# Update shape of weights if possible before adding mask.
# Update dimensions of weights to match with mask if possible.
mask, _, weights = metrics_module.squeeze_or_expand_dimensions(
mask, None, weights)
try:
# Broadcast weights if possible.
weights = weights_broadcast_ops.broadcast_weights(weights, mask)
weights *= mask
except ValueError:
score_array *= mask
score_array /= K.mean(mask)
# TODO(psv): Handle case when mask and weight shapes are not
# compatible.
# Apply sample weighting.
if weights is not None:
# Update dimensions of weights to match with values if possible.
score_array, _, weights = metrics_module.squeeze_or_expand_dimensions(
score_array, None, weights)
try:
# Broadcast weights if possible.
weights = weights_broadcast_ops.broadcast_weights(weights, score_array)
except ValueError:
# Reduce values to same ndim as weight array.
ndim = K.ndim(score_array)
weight_ndim = K.ndim(weights)
score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
score_array = math_ops.multiply(score_array, weights)
score_array = math_ops.reduce_sum(score_array)
weights = math_ops.reduce_sum(weights)
score_array = metrics_module.safe_div(score_array, weights)
return K.mean(score_array)
def weighted(y_true, y_pred, weights, mask=None):
"""Wrapper function.
Arguments:
y_true: `y_true` argument of `fn`.
y_pred: `y_pred` argument of `fn`.
weights: Weights tensor.
mask: Mask tensor.
Returns:
Scalar tensor.
"""
# 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 = math_ops.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)
# Apply sample weighting.
if weights is not None:
# Update dimensions of weights to match with values if possible.
score_array, _, weights = metrics_module.squeeze_or_expand_dimensions(
score_array, None, weights)
try:
# Broadcast weights if possible.
weights = weights_broadcast_ops.broadcast_weights(
weights, score_array)
except ValueError:
# Reduce values to same ndim as weight array.
ndim = K.ndim(score_array)
weight_ndim = K.ndim(weights)
score_array = K.mean(score_array,
axis=list(range(weight_ndim, ndim)))
score_array = math_ops.multiply(score_array, weights)
score_array = math_ops.reduce_sum(score_array)
weights = math_ops.reduce_sum(weights)
score_array = metrics_module.safe_div(score_array, weights)
return K.mean(score_array)