Formula:

`loss <- square(maximum(1 - y_true * y_pred, 0))`

`y_true`

values are expected to be -1 or 1. If binary (0 or 1) labels are
provided we will convert them to -1 or 1.

## Usage

```
loss_squared_hinge(
y_true,
y_pred,
...,
reduction = "sum_over_batch_size",
name = "squared_hinge"
)
```

## Arguments

- y_true
The ground truth values.

`y_true`

values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1 with shape =`[batch_size, d0, .. dN]`

.- y_pred
The predicted values with shape =

`[batch_size, d0, .. dN]`

.- ...
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

```
y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3))
y_pred <- random_uniform(c(2, 3))
loss <- loss_squared_hinge(y_true, y_pred)
```

## See also

Other losses: `Loss()`

`loss_binary_crossentropy()`

`loss_binary_focal_crossentropy()`

`loss_categorical_crossentropy()`

`loss_categorical_focal_crossentropy()`

`loss_categorical_hinge()`

`loss_cosine_similarity()`

`loss_ctc()`

`loss_dice()`

`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_tversky()`

`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()`