# tf.keras.metrics.MeanTensor

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

Inherits From: `Metric`

``````tf.keras.metrics.MeanTensor(
name='mean_tensor', dtype=None
)
``````

`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]
``````

#### Args:

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

#### Attributes:

• `count`
• `total`

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

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

``````update_state(
values, sample_weight=None
)
``````

Accumulates statistics for computing the element-wise mean.

#### Args:

• `values`: Per-example value.
• `sample_weight`: Optional weighting of each example. Defaults to 1.

Update op.