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TensorFlow 1 version
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View source on GitHub
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Computes root mean squared error metric between y_true and y_pred.
Inherits From: Mean, Metric, Layer, Module
tf.keras.metrics.RootMeanSquaredError(
name='root_mean_squared_error', dtype=None
)
Used in the notebooks
| Used in the tutorials |
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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
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
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
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.
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| Returns | |
|---|---|
| Update op. |