tf.keras.metrics.RootMeanSquaredError

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Class `RootMeanSquaredError`

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

Inherits From: `Mean`

Aliases:

• Class `tf.compat.v1.keras.metrics.RootMeanSquaredError`
• Class `tf.compat.v2.keras.metrics.RootMeanSquaredError`
• Class `tf.compat.v2.metrics.RootMeanSquaredError`
• Class `tf.metrics.RootMeanSquaredError`

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__`

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``````__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.

Methods

`reset_states`

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

Resets all of the metric state variables.

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

`result`

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``````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`

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``````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.