tf.keras.metrics.RootMeanSquaredError

Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean, Metric

Used in the notebooks

Used in the tutorials

Formula:

loss = sqrt(mean((y_pred - y_true) ** 2))

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

Example:

Example:

m = keras.metrics.RootMeanSquaredError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result()
0.5
m.reset_state()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
               sample_weight=[1, 0])
m.result()
0.70710677

Usage with compile() API:

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

dtype

variables

Methods

add_variable

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add_weight

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from_config

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get_config

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Return the serializable config of the metric.

reset_state

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Reset 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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Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_result

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stateless_update_state

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update_state

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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. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true. Defaults to 1.

Returns
Update op.

__call__

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Call self as a function.