diff options
Diffstat (limited to 'tensorflow/python/ops/losses/losses_impl.py')
| -rw-r--r-- | tensorflow/python/ops/losses/losses_impl.py | 22 |
1 files changed, 11 insertions, 11 deletions
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 51fb4cbac8..806539747e 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -193,7 +193,7 @@ def compute_weighted_loss( gradient, you need to apply `tf.stop_gradient` to `weights` before passing them to `compute_weighted_loss`. - @compatbility(eager) + @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 @@ -266,7 +266,7 @@ def absolute_difference( `labels` or if the shape of `weights` is invalid or if `labels` or `predictions` is None. - @compatbility(eager) + @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 @@ -317,7 +317,7 @@ def cosine_distance( ValueError: If `predictions` shape doesn't match `labels` shape, or `axis`, `labels`, `predictions` or `weights` is `None`. - @compatbility(eager) + @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 @@ -369,7 +369,7 @@ def hinge_loss(labels, logits, weights=1.0, scope=None, ValueError: If the shapes of `logits` and `labels` don't match or if `labels` or `logits` is None. - @compatbility(eager) + @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 @@ -437,7 +437,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. - @compatbility(eager) + @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 @@ -503,7 +503,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. - @compatbility(eager) + @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 @@ -571,7 +571,7 @@ def mean_pairwise_squared_error( if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. - @compatbility(eager) + @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 @@ -654,7 +654,7 @@ def mean_squared_error( if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. - @compatbility(eager) + @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 @@ -711,7 +711,7 @@ def sigmoid_cross_entropy( `multi_class_labels` or if the shape of `weights` is invalid, or if `weights` is None. Also if `multi_class_labels` or `logits` is None. - @compatbility(eager) + @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 @@ -777,7 +777,7 @@ def softmax_cross_entropy( or if the shape of `weights` is invalid or if `weights` is None. Also if `onehot_labels` or `logits` is None. - @compatbility(eager) + @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 @@ -894,7 +894,7 @@ def sparse_softmax_cross_entropy( ValueError: If the shapes of `logits`, `labels`, and `weights` are incompatible, or if any of them are None. - @compatbility(eager) + @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 |