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# tf.keras.metrics.RootMeanSquaredError

Computes root mean squared error metric between `y_true` and `y_pred`.

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

### Used in the notebooks

Used in the tutorials

#### Standalone usage:

````m = tf.keras.metrics.RootMeanSquaredError()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])`
`m.result().numpy()`
`0.5`
```
````m.reset_states()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],`
`               sample_weight=[1, 0])`
`m.result().numpy()`
`0.70710677`
```

Usage with `compile()` API:

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

## 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 root mean squared error statistics.

Args
`y_true` The ground truth values.
`y_pred` The predicted values.
`sample_weight` Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Returns
Update op.

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