tf.keras.metrics.RootMeanSquaredError

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Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean, Metric, Layer, Module

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Main aliases

tf.metrics.RootMeanSquaredError

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.RootMeanSquaredError

tf.keras.metrics.RootMeanSquaredError(
    name='root_mean_squared_error', dtype=None
)

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_state()
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

`merge_state`

View source

merge_state(
    metrics
)

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`

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reset_state()

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`.

Returns
Update op.