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# tf.keras.metrics.SparseCategoricalAccuracy

## Class `SparseCategoricalAccuracy`

Calculates how often predictions matches integer labels.

### Aliases:

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

For example, if `y_true` is [[2], [1]] 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 `sparse categorical accuracy`: an idempotent operation that simply divides `total` by `count`.

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

#### Usage:

``````m = tf.keras.metrics.SparseCategoricalAccuracy()
m.update_state([[2], [1]], [[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.SparseCategoricalAccuracy()])
``````

## `__init__`

View source

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

## 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()
``````

### `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.