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# tf.keras.metrics.CategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). Here we assume that labels are given as a `one_hot` representation. eg., When labels values are [2, 0, 1], `y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.
`from_logits` (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
`label_smoothing` (Optional) 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`"

#### Standalone usage:

````# EPSILON = 1e-7, y = y_true, y` = y_pred`
`# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)`
`# y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]`
`# xent = -sum(y * log(y'), axis = -1)`
`#      = -((log 0.95), (log 0.1))`
`#      = [0.051, 2.302]`
`# Reduced xent = (0.051 + 2.302) / 2`
`m = tf.keras.metrics.CategoricalCrossentropy()`
`m.update_state([[0, 1, 0], [0, 0, 1]],`
`               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])`
`m.result().numpy()`
`1.1769392`
```
````m.reset_states()`
`m.update_state([[0, 1, 0], [0, 0, 1]],`
`               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],`
`               sample_weight=tf.constant([0.3, 0.7]))`
`m.result().numpy()`
`1.6271976`
```

Usage with `compile()` API:

``````model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.CategoricalCrossentropy()])
``````

## Methods

### `reset_states`

View source

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

### `result`

View source

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

### `update_state`

View source

Accumulates metric statistics.

`y_true` and `y_pred` should have the same shape.

Args
`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`.
`sample_weight` Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric 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 metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]