def _test_valid(self, weights, values):
static_op = weights_broadcast_ops.assert_broadcastable(
weights=weights, values=values)
weights_placeholder = array_ops.placeholder(dtypes_lib.float32)
values_placeholder = array_ops.placeholder(dtypes_lib.float32)
dynamic_op = weights_broadcast_ops.assert_broadcastable(
weights=weights_placeholder, values=values_placeholder)
with self.cached_session():
static_op.run()
dynamic_op.run(feed_dict={
weights_placeholder: weights,
values_placeholder: values,
})
def _test_valid(self, weights, values):
static_op = weights_broadcast_ops.assert_broadcastable(
weights=weights, values=values)
weights_placeholder = array_ops.placeholder(dtypes_lib.float32)
values_placeholder = array_ops.placeholder(dtypes_lib.float32)
dynamic_op = weights_broadcast_ops.assert_broadcastable(
weights=weights_placeholder, values=values_placeholder)
with self.test_session():
static_op.run()
dynamic_op.run(feed_dict={
weights_placeholder: weights,
values_placeholder: values,
})
def _test_invalid(self, weights, values):
error_msg = 'weights can not be broadcast to values'
with self.assertRaisesRegex(ValueError, error_msg):
weights_broadcast_ops.assert_broadcastable(weights=weights, values=values)
weights_placeholder = array_ops.placeholder(dtypes_lib.float32)
values_placeholder = array_ops.placeholder(dtypes_lib.float32)
dynamic_op = weights_broadcast_ops.assert_broadcastable(
weights=weights_placeholder, values=values_placeholder)
with self.cached_session():
with self.assertRaisesRegex(errors_impl.OpError, error_msg):
dynamic_op.run(feed_dict={
weights_placeholder: weights,
values_placeholder: values,
})
def _test_invalid(self, weights, values):
error_msg = 'weights can not be broadcast to values'
with self.assertRaisesRegexp(ValueError, error_msg):
weights_broadcast_ops.assert_broadcastable(weights=weights, values=values)
weights_placeholder = array_ops.placeholder(dtypes_lib.float32)
values_placeholder = array_ops.placeholder(dtypes_lib.float32)
dynamic_op = weights_broadcast_ops.assert_broadcastable(
weights=weights_placeholder, values=values_placeholder)
with self.test_session():
with self.assertRaisesRegexp(errors_impl.OpError, error_msg):
dynamic_op.run(feed_dict={
weights_placeholder: weights,
values_placeholder: values,
})
def compute_weighted_loss(losses,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
reduction: Type of reduction to apply to loss.
Returns:
Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
`NONE`, this has the same shape as `losses`; otherwise, it is scalar.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
Note:
When calculating the gradient of a weighted loss contributions from
both `losses` and `weights` are considered. If your `weights` depend
on some model parameters but you do not want this to affect the loss
gradient, you need to apply @{tf.stop_gradient} to `weights` before
passing them to `compute_weighted_loss`.
"""
Reduction.validate(reduction)
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
with ops.control_dependencies(
(weights_broadcast_ops.assert_broadcastable(weights, losses), )):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.to_float(losses)
weights = math_ops.to_float(weights)
weighted_losses = math_ops.multiply(losses, weights)
if reduction == Reduction.NONE:
loss = weighted_losses
else:
loss = math_ops.reduce_sum(weighted_losses)
if reduction == Reduction.MEAN:
loss = _safe_mean(
loss,
math_ops.reduce_sum(
array_ops.ones_like(losses) * weights))
elif (reduction == Reduction.SUM_BY_NONZERO_WEIGHTS
or reduction == Reduction.SUM_OVER_NONZERO_WEIGHTS):
loss = _safe_mean(loss, _num_present(losses, weights))
elif reduction == Reduction.SUM_OVER_BATCH_SIZE:
loss = _safe_mean(loss, _num_elements(losses))
# Convert the result back to the input type.
loss = math_ops.cast(loss, input_dtype)
util.add_loss(loss, loss_collection)
return loss
def compute_weighted_loss(
losses, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
Returns:
A scalar `Tensor` that returns the weighted loss.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
"""
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
with ops.control_dependencies((
weights_broadcast_ops.assert_broadcastable(weights, losses),)):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.to_float(losses)
weights = math_ops.to_float(weights)
total_loss = _scale_losses(losses, weights)
num_present = _num_present(losses, weights)
mean_loss = _safe_mean(total_loss, num_present)
# Convert the result back to the input type.
mean_loss = math_ops.cast(mean_loss, input_dtype)
util.add_loss(mean_loss, loss_collection)
return mean_loss
def compute_weighted_loss(
losses, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
reduction: Type of reduction to apply to loss.
Returns:
Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
`NONE`, this has the same shape as `losses`; otherwise, it is scalar.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
Note:
When calculating the gradient of a weighted loss contributions from
both `losses` and `weights` are considered. If your `weights` depend
on some model parameters but you do not want this to affect the loss
gradient, you need to apply @{tf.stop_gradient} to `weights` before
passing them to `compute_weighted_loss`.
"""
Reduction.validate(reduction)
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
with ops.control_dependencies((
weights_broadcast_ops.assert_broadcastable(weights, losses),)):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.to_float(losses)
weights = math_ops.to_float(weights)
weighted_losses = math_ops.multiply(losses, weights)
if reduction == Reduction.NONE:
loss = weighted_losses
else:
loss = math_ops.reduce_sum(weighted_losses)
if reduction == Reduction.MEAN:
loss = _safe_mean(
loss,
math_ops.reduce_sum(array_ops.ones_like(losses) * weights))
elif (reduction == Reduction.SUM_BY_NONZERO_WEIGHTS or
reduction == Reduction.SUM_OVER_NONZERO_WEIGHTS):
loss = _safe_mean(loss, _num_present(losses, weights))
elif reduction == Reduction.SUM_OVER_BATCH_SIZE:
loss = _safe_mean(loss, _num_elements(losses))
# Convert the result back to the input type.
loss = math_ops.cast(loss, input_dtype)
util.add_loss(loss, loss_collection)
return loss
def compute_weighted_loss(losses,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES):
with tf.name_scope(scope, "weighted_loss", (losses, weights)):
with tf.control_dependencies(
(weights_broadcast_ops.assert_broadcastable(weights, losses), )):
losses = tf.convert_to_tensor(losses)
input_type = losses.dtype
losses = tf.to_float(losses)
weights = tf.to_float(weights)
weighted_losses = tf.multiply(losses, weights)
loss = weighted_losses
loss = tf.cast(loss, input_type)
util.add_loss(loss, loss_collection)
return loss
def compute_weighted_loss(losses,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.WEIGHTED_SUM_BY_NONZERO_WEIGHTS):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
reduction: Type of reduction to apply to loss.
Returns:
A scalar `Tensor` that returns the weighted loss.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
"""
Reduction.validate(reduction)
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
with ops.control_dependencies(
(weights_broadcast_ops.assert_broadcastable(weights, losses), )):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.to_float(losses)
weights = math_ops.to_float(weights)
weighted_losses = math_ops.multiply(losses, weights)
loss = math_ops.reduce_sum(weighted_losses)
if reduction == Reduction.WEIGHTED_SUM_BY_NONZERO_WEIGHTS:
loss = _safe_mean(loss, _num_present(losses, weights))
# Convert the result back to the input type.
loss = math_ops.cast(loss, input_dtype)
util.add_loss(loss, loss_collection)
return loss
def mean_pairwise_squared_error(labels,
predictions,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES):
"""Adds a pairwise-errors-squared loss to the training procedure.
Unlike `mean_squared_error`, which is a measure of the differences between
corresponding elements of `predictions` and `labels`,
`mean_pairwise_squared_error` is a measure of the differences between pairs of
corresponding elements of `predictions` and `labels`.
For example, if `labels`=[a, b, c] and `predictions`=[x, y, z], there are
three pairs of differences are summed to compute the loss:
loss = [ ((a-b) - (x-y)).^2 + ((a-c) - (x-z)).^2 + ((b-c) - (y-z)).^2 ] / 3
Note that since the inputs are of shape `[batch_size, d0, ... dN]`, the
corresponding pairs are computed within each batch sample but not across
samples within a batch. For example, if `predictions` represents a batch of
16 grayscale images of dimension [batch_size, 100, 200], then the set of pairs
is drawn from each image, but not across images.
`weights` acts as a coefficient for the loss. If a scalar is provided, then
the loss is simply scaled by the given value. If `weights` is a tensor of size
`[batch_size]`, then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector.
Args:
labels: The ground truth output tensor, whose shape must match the shape of
`predictions`.
predictions: The predicted outputs, a tensor of size
`[batch_size, d0, .. dN]` where N+1 is the total number of dimensions in
`predictions`.
weights: Coefficients for the loss a scalar, a tensor of shape
`[batch_size]` or a tensor whose shape matches `predictions`.
scope: The scope for the operations performed in computing the loss.
loss_collection: collection to which the loss will be added.
Returns:
A scalar `Tensor` that returns the weighted loss.
Raises:
ValueError: If the shape of `predictions` doesn't match that of `labels` or
if the shape of `weights` is invalid. Also if `labels` or `predictions`
is None.
@compatibility(eager)
The `loss_collection` argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a `tf.keras.Model`.
@end_compatibility
"""
if labels is None:
raise ValueError("labels must not be None.")
if predictions is None:
raise ValueError("predictions must not be None.")
with ops.name_scope(scope, "mean_pairwise_squared_error",
(predictions, labels, weights)) as scope:
weights = math_ops.to_float(weights)
labels = math_ops.to_float(labels)
with ops.control_dependencies(
(weights_broadcast_ops.assert_broadcastable(weights, labels), )):
predictions = math_ops.to_float(predictions)
predictions.get_shape().assert_is_compatible_with(
labels.get_shape())
diffs = math_ops.subtract(predictions, labels)
axis = math_ops.range(1, array_ops.rank(diffs))
sum_squares_diff_per_batch = math_ops.reduce_sum(
math_ops.square(diffs), axis=axis, keepdims=True)
num_present_per_batch = _num_present(diffs,
weights,
per_batch=True)
term1 = 2.0 * math_ops.div_no_nan(
sum_squares_diff_per_batch,
math_ops.maximum(num_present_per_batch - 1, 0),
name="value")
sum_diff = math_ops.reduce_sum(diffs, axis=axis, keepdims=True)
term2 = 2.0 * math_ops.div_no_nan(
math_ops.square(sum_diff),
math_ops.maximum(
math_ops.multiply(num_present_per_batch,
num_present_per_batch - 1), 0),
name="value")
weighted_losses = math_ops.multiply(term1 - term2, weights)
loss = math_ops.reduce_sum(weighted_losses)
mean_loss = array_ops.where(
math_ops.reduce_sum(num_present_per_batch) > 0,
loss,
array_ops.zeros_like(loss),
name="value")
util.add_loss(mean_loss, loss_collection)
return mean_loss
def mean_pairwise_squared_error(
labels, predictions, weights=1.0, scope=None,
loss_collection=ops.GraphKeys.LOSSES):
"""Adds a pairwise-errors-squared loss to the training procedure.
Unlike `mean_squared_error`, which is a measure of the differences between
corresponding elements of `predictions` and `labels`,
`mean_pairwise_squared_error` is a measure of the differences between pairs of
corresponding elements of `predictions` and `labels`.
For example, if `labels`=[a, b, c] and `predictions`=[x, y, z], there are
three pairs of differences are summed to compute the loss:
loss = [ ((a-b) - (x-y)).^2 + ((a-c) - (x-z)).^2 + ((b-c) - (y-z)).^2 ] / 3
Note that since the inputs are of shape `[batch_size, d0, ... dN]`, the
corresponding pairs are computed within each batch sample but not across
samples within a batch. For example, if `predictions` represents a batch of
16 grayscale images of dimension [batch_size, 100, 200], then the set of pairs
is drawn from each image, but not across images.
`weights` acts as a coefficient for the loss. If a scalar is provided, then
the loss is simply scaled by the given value. If `weights` is a tensor of size
[batch_size], then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector.
Args:
labels: The ground truth output tensor, whose shape must match the shape of
`predictions`.
predictions: The predicted outputs, a tensor of size
`[batch_size, d0, .. dN]` where N+1 is the total number of dimensions in
`predictions`.
weights: Coefficients for the loss a scalar, a tensor of shape
`[batch_size]` or a tensor whose shape matches `predictions`.
scope: The scope for the operations performed in computing the loss.
loss_collection: collection to which the loss will be added.
Returns:
A scalar `Tensor` that returns the weighted loss.
Raises:
ValueError: If the shape of `predictions` doesn't match that of `labels` or
if the shape of `weights` is invalid. Also if `labels` or `predictions`
is None.
"""
if labels is None:
raise ValueError("labels must not be None.")
if predictions is None:
raise ValueError("predictions must not be None.")
with ops.name_scope(scope, "mean_pairwise_squared_error",
(predictions, labels, weights)) as scope:
weights = math_ops.to_float(weights)
labels = math_ops.to_float(labels)
with ops.control_dependencies((
weights_broadcast_ops.assert_broadcastable(weights, labels),)):
predictions = math_ops.to_float(predictions)
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
diffs = math_ops.subtract(predictions, labels)
reduction_indices = math_ops.range(1, array_ops.rank(diffs))
sum_squares_diff_per_batch = math_ops.reduce_sum(
math_ops.square(diffs), reduction_indices=reduction_indices,
keep_dims=True)
num_present_per_batch = _num_present(diffs, weights, per_batch=True)
term1 = 2.0 * _safe_div(sum_squares_diff_per_batch,
num_present_per_batch-1)
sum_diff = math_ops.reduce_sum(
diffs, reduction_indices=reduction_indices, keep_dims=True)
term2 = 2.0 * _safe_div(
math_ops.square(sum_diff),
math_ops.multiply(num_present_per_batch, num_present_per_batch-1))
weighted_losses = math_ops.multiply(term1 - term2, weights)
loss = math_ops.reduce_sum(weighted_losses)
mean_loss = array_ops.where(
math_ops.reduce_sum(num_present_per_batch) > 0,
loss,
array_ops.zeros_like(loss),
name="value")
util.add_loss(mean_loss, loss_collection)
return mean_loss
def compute_weighted_loss(losses,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
reduction: Type of reduction to apply to loss.
Returns:
Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
`NONE`, this has the same shape as `losses`; otherwise, it is scalar.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
Note:
When calculating the gradient of a weighted loss contributions from
both `losses` and `weights` are considered. If your `weights` depend
on some model parameters but you do not want this to affect the loss
gradient, you need to apply `tf.stop_gradient` to `weights` before
passing them to `compute_weighted_loss`.
@compatibility(eager)
The `loss_collection` argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a `tf.keras.Model`.
@end_compatibility
"""
Reduction.validate(reduction)
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
# Save the `reduction` argument for loss normalization when distributing
# to multiple replicas.
# TODO(josh11b): Associate it with the returned op for more precision.
ops.get_default_graph()._last_loss_reduction = reduction # pylint: disable=protected-access
with ops.control_dependencies(
(weights_broadcast_ops.assert_broadcastable(weights, losses), )):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.cast(losses, dtype=dtypes.float32)
weights = math_ops.cast(weights, dtype=dtypes.float32)
weighted_losses = math_ops.multiply(losses, weights)
if reduction == Reduction.NONE:
loss = weighted_losses
else:
loss = math_ops.reduce_sum(weighted_losses)
if reduction == Reduction.MEAN:
loss = _safe_mean(
loss,
math_ops.reduce_sum(
array_ops.ones_like(losses) * weights))
elif (reduction == Reduction.SUM_BY_NONZERO_WEIGHTS
or reduction == Reduction.SUM_OVER_NONZERO_WEIGHTS):
loss = _safe_mean(loss, _num_present(losses, weights))
elif reduction == Reduction.SUM_OVER_BATCH_SIZE:
loss = _safe_mean(loss, _num_elements(losses))
# Convert the result back to the input type.
loss = math_ops.cast(loss, input_dtype)
util.add_loss(loss, loss_collection)
return loss
def compute_weighted_loss(
losses, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
"""Computes the weighted loss.
Args:
losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`losses`, and must be broadcastable to `losses` (i.e., all dimensions must
be either `1`, or the same as the corresponding `losses` dimension).
scope: the scope for the operations performed in computing the loss.
loss_collection: the loss will be added to these collections.
reduction: Type of reduction to apply to loss.
Returns:
Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
`NONE`, this has the same shape as `losses`; otherwise, it is scalar.
Raises:
ValueError: If `weights` is `None` or the shape is not compatible with
`losses`, or if the number of dimensions (rank) of either `losses` or
`weights` is missing.
Note:
When calculating the gradient of a weighted loss contributions from
both `losses` and `weights` are considered. If your `weights` depend
on some model parameters but you do not want this to affect the loss
gradient, you need to apply `tf.stop_gradient` to `weights` before
passing them to `compute_weighted_loss`.
@compatibility(eager)
The `loss_collection` argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a `tf.keras.Model`.
@end_compatibility
"""
Reduction.validate(reduction)
with ops.name_scope(scope, "weighted_loss", (losses, weights)):
# Save the `reduction` argument for loss normalization when distributing
# to multiple replicas. Used only for estimator + v1 optimizer flow.
ops.get_default_graph()._last_loss_reduction = reduction # pylint: disable=protected-access
with ops.control_dependencies((
weights_broadcast_ops.assert_broadcastable(weights, losses),)):
losses = ops.convert_to_tensor(losses)
input_dtype = losses.dtype
losses = math_ops.cast(losses, dtype=dtypes.float32)
weights = math_ops.cast(weights, dtype=dtypes.float32)
weighted_losses = math_ops.multiply(losses, weights)
if reduction == Reduction.NONE:
loss = weighted_losses
else:
loss = math_ops.reduce_sum(weighted_losses)
if reduction == Reduction.MEAN:
loss = _safe_mean(
loss, math_ops.reduce_sum(array_ops.ones_like(losses) * weights))
elif (reduction == Reduction.SUM_BY_NONZERO_WEIGHTS or
reduction == Reduction.SUM_OVER_NONZERO_WEIGHTS):
loss = _safe_mean(loss, _num_present(losses, weights))
elif reduction == Reduction.SUM_OVER_BATCH_SIZE:
loss = _safe_mean(loss, _num_elements(losses))
# Convert the result back to the input type.
loss = math_ops.cast(loss, input_dtype)
util.add_loss(loss, loss_collection)
return loss