Revert "Clarify -1 is multiplied on cosine similarity"
This reverts commit 02ed30383bac9e36b557c15cebe1dd702f889302.
diff --git a/tensorflow/python/keras/losses.py b/tensorflow/python/keras/losses.py
index 3942a71..84639a9 100644
--- a/tensorflow/python/keras/losses.py
+++ b/tensorflow/python/keras/losses.py
@@ -1922,16 +1922,16 @@
])
@dispatch.add_dispatch_support
def cosine_similarity(y_true, y_pred, axis=-1):
- """Computes the negative cosine similarity between labels and predictions.
+ """Computes the cosine similarity between labels and predictions.
- Note that it is a number between -1 and 1. As -1 is multiplied,
- values closer to -1 indicate greater similarity and 0 indicates
- orthogonality, when it is a negative number between -1 and 0.
- The values closer to 1 indicate greater dissimilarity. This makes
- it usable as a loss function in a setting where you try to maximize
- the proximity between predictions and targets. If either `y_true`
- or `y_pred` is a zero vector, cosine similarity will be 0 regardless
- of the proximity between predictions and targets.
+ Note that it is a number between -1 and 1. When it is a negative number
+ between -1 and 0, 0 indicates orthogonality and values closer to -1
+ indicate greater similarity. The values closer to 1 indicate greater
+ dissimilarity. This makes it usable as a loss function in a setting
+ where you try to maximize the proximity between predictions and
+ targets. If either `y_true` or `y_pred` is a zero vector, cosine
+ similarity will be 0 regardless of the proximity between predictions
+ and targets.
`loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`
@@ -1949,7 +1949,7 @@
axis: Axis along which to determine similarity.
Returns:
- Negative Cosine similarity tensor.
+ Cosine similarity tensor.
"""
y_true = nn.l2_normalize(y_true, axis=axis)
y_pred = nn.l2_normalize(y_pred, axis=axis)
@@ -1958,16 +1958,15 @@
@keras_export('keras.losses.CosineSimilarity')
class CosineSimilarity(LossFunctionWrapper):
- """Computes the negative cosine similarity between labels and predictions.
+ """Computes the cosine similarity between labels and predictions.
- Note that it is a number between -1 and 1. As -1 is multiplied,
- values closer to -1 indicate greater similarity and 0 indicates
- orthogonality, when it is a negative number between -1 and 0.
- The values closer to 1 indicate greater dissimilarity. This makes
- it usable as a loss function in a setting where you try to maximize
- the proximity between predictions and targets. If either `y_true`
- or `y_pred` is a zero vector, cosine similarity will be 0 regardless
- of the proximity between predictions and targets.
+ Note that it is a number between -1 and 1. When it is a negative number
+ between -1 and 0, 0 indicates orthogonality and values closer to -1
+ indicate greater similarity. The values closer to 1 indicate greater
+ dissimilarity. This makes it usable as a loss function in a setting
+ where you try to maximize the proximity between predictions and targets.
+ If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0
+ regardless of the proximity between predictions and targets.
`loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`
