Google I / O가 5 월 18 ~ 20 일에 돌아옵니다! 공간을 예약하고 일정을 짜세요

# tf.keras.metrics.Accuracy

Calculates how often predictions equal labels.

Inherits From: `Mean`, `Metric`, `Layer`, `Module`

### Used in the notebooks

Used in the guide Used in the tutorials

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 `binary 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.

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

#### Standalone usage:

````m = tf.keras.metrics.Accuracy()`
`m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])`
`m.result().numpy()`
`0.75`
```
````m.reset_state()`
`m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],`
`               sample_weight=[1, 1, 0, 0])`
`m.result().numpy()`
`0.5`
```

Usage with `compile()` API:

``````model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.Accuracy()])
``````

## Methods

### `reset_state`

View source

Resets all of the metric state variables.

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

### `result`

View source

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`

View source

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.

[{ "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" }]