# tf.keras.metrics.CategoricalCrossentropy

## Class `CategoricalCrossentropy`

Computes the crossentropy metric between the labels and predictions.

### Aliases:

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]].

#### Usage:

``````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]])

# 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

print('Final result: ', m.result().numpy())  # Final result: 1.176
``````

Usage with tf.keras API:

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

#### Args:

• `name`: (Optional) string name of the metric instance.
• `dtype`: (Optional) data type of the metric result.
• `from_logits`: (Optional ) 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. 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`"

## `__init__`

View source

``````__init__(
name='categorical_crossentropy',
dtype=None,
from_logits=False,
label_smoothing=0
)
``````

Creates a `MeanMetricWrapper` instance.

#### Args:

• `fn`: The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`.
• `name`: (Optional) string name of the metric instance.
• `dtype`: (Optional) data type of the metric result.
• `**kwargs`: The keyword arguments that are passed on to `fn`.

## `__new__`

View source

``````__new__(
cls,
*args,
**kwargs
)
``````

Create and return a new object. See help(type) for accurate signature.

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

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

``````update_state(
y_true,
y_pred,
sample_weight=None
)
``````

Accumulates metric statistics.

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

#### Args:

• `y_true`: The ground truth values.
• `y_pred`: The predicted values.
• `sample_weight`: Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Update op.