tf.keras.metrics.RootMeanSquaredError

Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean

Used in the notebooks

Used in the tutorials

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_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

View source

Resets all of the metric state variables.

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

result

View source

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

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.