tf.keras.metrics.RootMeanSquaredError

Computes root mean squared error metric between y_true and y_pred.

Main aliases

tf.metrics.RootMeanSquaredError

Compat aliases for migration

See Migration guide for more details.

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

m = tf.keras.metrics.RootMeanSquaredError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result().numpy()
0.5

m.reset_states()
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

reset_states()

Resets all of the metric state variables.

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

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(
    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.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/metrics/RootMeanSquaredError