tf.keras.losses.cosine_similarity

Computes the cosine similarity between labels and predictions.

View aliases

Main aliases

tf.losses.cosine_similarity

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v1.keras.losses.cosine`, `tf.compat.v1.keras.losses.cosine_proximity`, `tf.compat.v1.keras.losses.cosine_similarity`, `tf.compat.v1.keras.metrics.cosine`, `tf.compat.v1.keras.metrics.cosine_proximity`

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.