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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)
y_true: Tensor of true targets.
y_pred: Tensor of predicted targets.
axis: Axis along which to determine similarity.
Cosine similarity tensor.