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# tf.keras.metrics.MeanAbsoluteError

Computes the mean absolute error between the labels and predictions.

### Used in the notebooks

Used in the guide Used in the tutorials

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

#### Standalone usage:

````m = tf.keras.metrics.MeanAbsoluteError()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])`
`m.result().numpy()`
`0.25`
```
````m.reset_state()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],`
`               sample_weight=[1, 0])`
`m.result().numpy()`
`0.5`
```

Usage with `compile()` API:

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

## Methods

### `merge_state`

View source

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

````m1 = tf.keras.metrics.Accuracy()`
`_ = m1.update_state([[1], [2]], [[0], [2]])`
```
````m2 = tf.keras.metrics.Accuracy()`
`_ = m2.update_state([[3], [4]], [[3], [4]])`
```
````m2.merge_state([m1])`
`m2.result().numpy()`
`0.75`
```

Args
`metrics` an iterable of metrics. The metrics must have compatible state.

Raises
`ValueError` If the provided iterable does not contain metrics matching the metric's required specifications.

### `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.

For sparse categorical metrics, the shapes of `y_true` and `y_pred` are different.

Args
`y_true` Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`.
`y_pred` The predicted probability 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.