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# tf.keras.losses.SquaredHinge

Computes the squared hinge loss between `y_true` and `y_pred`.

Inherits From: `Loss`

`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.

#### Standalone usage:

````y_true = [[0., 1.], [0., 0.]]`
`y_pred = [[0.6, 0.4], [0.4, 0.6]]`
`# Using 'auto'/'sum_over_batch_size' reduction type.`
`h = tf.keras.losses.SquaredHinge()`
`h(y_true, y_pred).numpy()`
`1.86`
```
````# Calling with 'sample_weight'.`
`h(y_true, y_pred, sample_weight=[1, 0]).numpy()`
`0.73`
```
````# Using 'sum' reduction type.`
`h = tf.keras.losses.SquaredHinge(`
`    reduction=tf.keras.losses.Reduction.SUM)`
`h(y_true, y_pred).numpy()`
`3.72`
```
````# Using 'none' reduction type.`
`h = tf.keras.losses.SquaredHinge(`
`    reduction=tf.keras.losses.Reduction.NONE)`
`h(y_true, y_pred).numpy()`
`array([1.46, 2.26], dtype=float32)`
```

Usage with the `compile()` API:

``````model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())
``````

`reduction` Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training tutorial for more details.
`name` Optional name for the instance. Defaults to 'squared_hinge'.

## Methods

### `from_config`

View source

Instantiates a `Loss` from its config (output of `get_config()`).

Args
`config` Output of `get_config()`.

Returns
A `Loss` instance.

### `get_config`

View source

Returns the config dictionary for a `Loss` instance.

### `__call__`

View source

Invokes the `Loss` instance.

Args
`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]`
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`