tf.keras.metrics.RootMeanSquaredError

Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean

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 tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile(
    'sgd',
    loss='mse',
    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()

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.