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# tf.keras.metrics.MeanTensor

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

Inherits From: `Metric`

`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.

#### Usage:

``````m = tf.keras.metrics.MeanTensor()
m.update_state([0, 1, 2, 3])
m.update_state([4, 5, 6, 7])
print('Result: ', m.result().numpy())  # Result: [2, 3, 4, 5]
m.update_state([12, 10, 8, 6], sample_weights= [0, 0.2, 0.5, 1])
print('Result: ', m.result().numpy())  # Result: [2, 3.636, 4.8, 5.333]
``````

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

`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":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]