# tf.keras.metrics.CategoricalAccuracy

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## Class `CategoricalAccuracy`

Calculates how often predictions matches labels.

### Aliases:

• Class `tf.compat.v1.keras.metrics.CategoricalAccuracy`
• Class `tf.compat.v2.keras.metrics.CategoricalAccuracy`
• Class `tf.compat.v2.metrics.CategoricalAccuracy`
• Class `tf.metrics.CategoricalAccuracy`

For example, if `y_true` is [[0, 0, 1], [0, 1, 0]] and `y_pred` is [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] then the categorical accuracy is 1/2 or .5. If the weights were specified as [0.7, 0.3] then the categorical accuracy would be .3. You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same.

This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `categorical accuracy`: an idempotent operation that simply divides `total` by `count`.

`y_pred` and `y_true` should be passed in as vectors of probabilities, rather than as labels. If necessary, use `tf.one_hot` to expand `y_true` as a vector.

If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values.

#### Usage:

``````m = tf.keras.metrics.CategoricalAccuracy()
m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
print('Final result: ', m.result().numpy())  # Final result: 0.5
``````

Usage with tf.keras API:

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

## `__init__`

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``````__init__(
name='categorical_accuracy',
dtype=None
)
``````

Creates a `CategoricalAccuracy` instance.

#### Args:

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

## Methods

### `reset_states`

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

Resets all of the metric state variables.

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

### `result`

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``````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`

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``````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.