Watch keynotes, product sessions, workshops, and more from Google I/O See playlist

tf.keras.losses.cosine_similarity

View source on GitHub

Computes the cosine similarity between labels and predictions.

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)

y_true Tensor of true targets.
y_pred Tensor of predicted targets.
axis Axis along which to determine similarity.

Cosine similarity tensor.