# tf.keras.metrics.CategoricalCrossentropy
|---|
| [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/probabilistic_metrics.py#L168-L237) |
Computes the crossentropy metric between the labels and predictions.
Inherits From: [`MeanMetricWrapper`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/MeanMetricWrapper), [`Mean`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/Mean), [`Metric`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/Metric), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)
#### View aliases
**Main aliases**
[`tf.metrics.CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalCrossentropy)
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
\`tf.compat.v1.keras.metrics.CategoricalCrossentropy\`
tf.keras.metrics.CategoricalCrossentropy(
name='categorical_crossentropy',
dtype=None,
from_logits=False,
label_smoothing=0,
axis=-1
)
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` = \[\[0, 0, 1\], \[1, 0, 0\], \[0, 1, 0\]\].
| ## Args ||
|---|---|
| `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`" |
| `axis` | (Optional) Defaults to -1. The dimension along which entropy is computed. |
#### 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_state()
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
### `merge_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L288-L326)
merge_state(
metrics
)
Merges the state from one or more metrics.
This method can be used by distributed systems to merge the state
computed by different metric instances. Typically the state will be
stored in the form of the metric's weights. For example, a
tf.keras.metrics.Mean metric contains a list of two weight values: a
total and a count. If there were two instances of a
tf.keras.metrics.Accuracy that each independently aggregated partial
state for an overall accuracy calculation, these two metric's states
could be combined as follows:
m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75
| Args ||
|---|---|
| `metrics` | an iterable of metrics. The metrics must have compatible state. |
| Raises ||
|---|---|
| `ValueError` | If the provided iterable does not contain metrics matching the metric's required specifications. |
### `reset_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L249-L265)
reset_state()
Resets all of the metric state variables.
This function is called between epochs/steps,
when a metric is evaluated during training.
### `result`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L551-L563)
result()
Computes and returns the scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the
metric value using the state variables.
| Returns ||
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. ||
### `update_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L686-L728)
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates metric statistics.
For sparse categorical metrics, the shapes of `y_true` and `y_pred` are
different.
| Args ||
|---|---|
| `y_true` | Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`. |
| `y_pred` | The predicted probability 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. ||