tf.keras.metrics.RootMeanSquaredError

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Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.RootMeanSquaredError, `tf.compat.v2.keras.metrics.RootMeanSquaredError`, `tf.compat.v2.metrics.RootMeanSquaredError`

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

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()])

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

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.

Returns
Update op.