Save the date! Google I/O returns May 18-20 Register now


TensorFlow 1 version View source on GitHub

Computes root mean squared error metric between y_true and y_pred.

Inherits From: Mean


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()])

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



View source

Resets all of the metric state variables.

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


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.


View source

Accumulates root mean squared error statistics.

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.

Update op.