tf.keras.losses.cosine_similarity

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 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.