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Computes the cosine similarity between labels and predictions.
tf.keras.losses.cosine_similarity(
y_true, y_pred, axis=-1
)
Note that it is a negative quantity between -1 and 0, where 0 indicates orthogonality and values closer to -1 indicate greater similarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets.
loss = -sum(y_true * y_pred)
Args | |
|---|---|
y_true
|
Tensor of true targets. |
y_pred
|
Tensor of predicted targets. |
axis
|
Axis along which to determine similarity. |
Returns | |
|---|---|
| Cosine similarity tensor. |