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# tf.keras.metrics.SparseCategoricalAccuracy

Calculates how often predictions matches integer labels.

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]])`
`m.result().numpy()`
`0.5`
```
````m.reset_states()`
`_ = m.update_state([[2], [1]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],`
`                   sample_weight=[0.7, 0.3])`
`m.result().numpy()`
`0.3`
```

Usage with tf.keras API:

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

`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`.

## Methods

### `reset_states`

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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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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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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.

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