# tf.keras.metrics.RootMeanSquaredError

## Class `RootMeanSquaredError`

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

Inherits From: `Mean`

### Aliases:

#### Usage:

``````m = tf.keras.metrics.RootMeanSquaredError()
m.update_state([2., 4., 6.], [1., 3., 2.])
print('Final result: ', m.result().numpy())  # Final result: 2.449
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.RootMeanSquaredError()])
``````

## `__init__`

View source

``````__init__(
name='root_mean_squared_error',
dtype=None
)
``````

Creates a `Mean` instance.

#### Args:

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

## `__new__`

View source

``````__new__(
cls,
*args,
**kwargs
)
``````

Create and return a new object. See help(type) for accurate signature.

## 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(
y_true,
y_pred,
sample_weight=None
)
``````

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`.

Update op.