def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
logits = ops.convert_to_tensor(logits)
processed_labels = self._process_labels(labels)
processed_labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint:disable=protected-access
labels=processed_labels, logits=logits,
expected_labels_dimension=self.logits_dimension)
if self._loss_fn:
unweighted_loss = head_lib._call_loss_fn( # pylint:disable=protected-access
loss_fn=self._loss_fn, labels=processed_labels, logits=logits,
features=features, expected_loss_dim=1)
else:
unweighted_loss = losses.sigmoid_cross_entropy(
multi_class_labels=processed_labels, logits=logits,
reduction=losses.Reduction.NONE)
# Averages loss over classes.
unweighted_loss = math_ops.reduce_mean(
unweighted_loss, axis=-1, keepdims=True)
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features, weight_column=self._weight_column, logits=logits)
training_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=self._loss_reduction)
return head_lib.LossSpec(
training_loss=training_loss,
unreduced_loss=unweighted_loss,
weights=weights,
processed_labels=processed_labels)
def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
logits = ops.convert_to_tensor(logits)
processed_labels = self._process_labels(labels)
processed_labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint:disable=protected-access
labels=processed_labels,
logits=logits,
expected_labels_dimension=self.logits_dimension)
if self._loss_fn:
unweighted_loss = head_lib._call_loss_fn( # pylint:disable=protected-access
loss_fn=self._loss_fn,
labels=processed_labels,
logits=logits,
features=features,
expected_loss_dim=1)
else:
unweighted_loss = losses.sigmoid_cross_entropy(
multi_class_labels=processed_labels,
logits=logits,
reduction=losses.Reduction.NONE)
# Averages loss over classes.
unweighted_loss = math_ops.reduce_mean(unweighted_loss,
axis=-1,
keep_dims=True)
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features,
weight_column=self._weight_column,
logits=logits)
training_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=self._loss_reduction)
return head_lib.LossSpec(training_loss=training_loss,
unreduced_loss=unweighted_loss,
weights=weights,
processed_labels=processed_labels)
def _grad_and_hess_for_logloss(logits, labels):
"""A closed form gradient and hessian for logistic loss."""
# TODO(youngheek): add weights handling.
predictions = math_ops.reciprocal(math_ops.exp(-logits) + 1.0)
normalizer = math_ops.reciprocal(
math_ops.cast(array_ops.size(predictions), dtypes.float32))
labels = math_ops.cast(labels, dtypes.float32)
labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint: disable=protected-access
labels, logits, head.logits_dimension)
gradients = (predictions - labels) * normalizer
hessians = predictions * (1.0 - predictions) * normalizer
return gradients, hessians
def _grad_and_hess_for_logloss(logits, labels):
"""A closed form gradient and hessian for logistic loss."""
# TODO(youngheek): add weights handling.
predictions = math_ops.reciprocal(math_ops.exp(-logits) + 1.0)
normalizer = math_ops.reciprocal(
math_ops.cast(array_ops.size(predictions), dtypes.float32))
labels = math_ops.cast(labels, dtypes.float32)
labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint: disable=protected-access
labels, logits, head.logits_dimension)
gradients = (predictions - labels) * normalizer
hessians = predictions * (1.0 - predictions) * normalizer
return gradients, hessians
def create_loss2(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
print(labels)
labels = head_lib._check_dense_labels_match_logits_and_reshape(
labels=labels, logits=logits, expected_labels_dimension=1)
labels = math_ops.to_float(labels)
unweighted_loss = losses.mean_squared_error(
labels=labels,
predictions=logits,
reduction=losses.Reduction.NONE)
training_loss = losses.compute_weighted_loss(
unweighted_loss, reduction=self._loss_reduction)
return LossSpec(
training_loss=training_loss,
unreduced_loss=unweighted_loss,
processed_labels=labels)
