tf.keras.losses.CosineSimilarity

Computes the cosine similarity between labels and predictions.

Inherits From: Loss

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.]]
y_pred = [[1., 0.], [1., 1.]]
# Using 'auto'/'sum_over_batch_size' reduction