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# tf.keras.losses.CategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a `one_hot` representation. If you want to provide labels as integers, please use `SparseCategoricalCrossentropy` loss. There should be `# classes` floating point values per feature.

In the snippet below, there is `# classes` floating pointing values per example. The shape of both `y_pred` and `y_true` are `[batch_size, num_classes]`.

#### Usage:

````y_true = [[0, 1, 0], [0, 0, 1]]`
`y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]`
`# Using 'auto'/'sum_over_batch_size' reduction type.`
`cce = tf.keras.losses.CategoricalCrossentropy()`
`cce(y_true, y_pred).numpy()`
`1.177`
```
````# Calling with 'sample_weight'.`
`cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()`
`0.814`
```
````# Using 'sum' reduction type.`
`cce = tf.keras.losses.CategoricalCrossentropy(`
`    reduction=tf.keras.losses.Reduction.SUM)`
`cce(y_true, y_pred).numpy()`
`2.354`
```
````# Using 'none' reduction type.`
`cce = tf.keras.losses.CategoricalCrossentropy(`
`    reduction=tf.keras.losses.Reduction.NONE)`
`cce(y_true, y_pred).numpy()`
`array([0.0513, 2.303], dtype=float32)`
```

Usage with the `compile` API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.CategoricalCrossentropy())
``````

`from_logits` Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. Note - Using from_logits=True is more numerically stable.
`label_smoothing` Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`"
`reduction` (Optional) 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 op. Defaults to 'categorical_crossentropy'.

## 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]`
`sample_weight` Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises
`ValueError` If the shape of `sample_weight` is invalid.

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