View source on GitHub |
Computes the cosine similarity between labels and predictions.
tf.keras.losses.cosine_similarity(
y_true, y_pred, axis=-1
)
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))
Standalone usage:
y_true = [[0., 1.], [1., 1.], [1., 1.]]
y_pred = [[1., 0.], [1., 1.], [-1., -1.]]
loss = tf.keras.losses.cosine_similarity(y_true, y_pred, axis=1)
loss.numpy()
array([-0., -0.999, 0.999], dtype=float32)
Args | |
---|---|
y_true
|
Tensor of true targets. |
y_pred
|
Tensor of predicted targets. |
axis
|
Axis along which to determine similarity. |
Returns | |
---|---|
Cosine similarity tensor. |