# Computes the cosine similarity between `y_true`

& `y_pred`

.

Source: `R/losses.R`

`loss_cosine_similarity.Rd`

Formula:

`loss <- -sum(l2_norm(y_true) * l2_norm(y_pred))`

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. 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.

## Usage

```
loss_cosine_similarity(
y_true,
y_pred,
axis = -1L,
...,
reduction = "sum_over_batch_size",
name = "cosine_similarity"
)
```

## Arguments

- y_true
Tensor of true targets.

- y_pred
Tensor of predicted targets.

- axis
The axis along which the cosine similarity is computed (the features axis). Defaults to

`-1`

.- ...
For forward/backward compatability.

- reduction
Type of reduction to apply to the loss. In almost all cases this should be

`"sum_over_batch_size"`

. Supported options are`"sum"`

,`"sum_over_batch_size"`

or`NULL`

.- name
Optional name for the loss instance.

## Examples

## See also

Other losses: `Loss()`

`loss_binary_crossentropy()`

`loss_binary_focal_crossentropy()`

`loss_categorical_crossentropy()`

`loss_categorical_focal_crossentropy()`

`loss_categorical_hinge()`

`loss_hinge()`

`loss_huber()`

`loss_kl_divergence()`

`loss_log_cosh()`

`loss_mean_absolute_error()`

`loss_mean_absolute_percentage_error()`

`loss_mean_squared_error()`

`loss_mean_squared_logarithmic_error()`

`loss_poisson()`

`loss_sparse_categorical_crossentropy()`

`loss_squared_hinge()`

`metric_binary_crossentropy()`

`metric_binary_focal_crossentropy()`

`metric_categorical_crossentropy()`

`metric_categorical_focal_crossentropy()`

`metric_categorical_hinge()`

`metric_hinge()`

`metric_huber()`

`metric_kl_divergence()`

`metric_log_cosh()`

`metric_mean_absolute_error()`

`metric_mean_absolute_percentage_error()`

`metric_mean_squared_error()`

`metric_mean_squared_logarithmic_error()`

`metric_poisson()`

`metric_sparse_categorical_crossentropy()`

`metric_squared_hinge()`