Help protect the Great Barrier Reef with TensorFlow on Kaggle

# tf.keras.losses.CategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Inherits From: `Loss`

### Used in the notebooks

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]`.

#### Standalone 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.compile(optimizer='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.
`label_smoothing` Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels.
`axis` The axis along which to compute crossentropy (the features axis). Defaults to -1.
`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 'categorical_crossentropy'.

View source