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# tf.keras.metrics.sparse_categorical_accuracy

Calculates how often predictions match integer labels.

#### Standalone usage:

````y_true = [2, 1]`
`y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]`
`m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)`
`assert m.shape == (2,)`
`m.numpy()`
`array([0., 1.], dtype=float32)`
```

You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same.

`y_true` Integer ground truth values.
`y_pred` The prediction values.

Sparse categorical accuracy values.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"필요한 정보가 없음" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"너무 복잡함/단계 수가 너무 많음" },{ "type": "thumb-down", "id": "outOfDate", "label":"오래됨" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"기타" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"이해하기 쉬움" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"문제가 해결됨" },{ "type": "thumb-up", "id": "otherUp", "label":"기타" }]