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# tf.keras.metrics.MeanTensor

Computes the element-wise (weighted) mean of the given tensors.

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

`MeanTensor` returns a tensor with the same shape of the input tensors. The mean value is updated by keeping local variables `total` and `count`. The `total` tracks the sum of the weighted values, and `count` stores the sum of the weighted counts.

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

#### Standalone usage:

````m = tf.keras.metrics.MeanTensor()`
`m.update_state([0, 1, 2, 3])`
`m.update_state([4, 5, 6, 7])`
`m.result().numpy()`
`array([2., 3., 4., 5.], dtype=float32)`
```
````m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])`
`m.result().numpy()`
`array([2.       , 3.6363635, 4.8      , 5.3333335], dtype=float32)`
```

`count`

`total`

## Methods

### `reset_states`

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 statistics for computing the element-wise mean.

Args
`values` Per-example value.
`sample_weight` Optional weighting of each example. Defaults to 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" }]