 View source on GitHub |
Computes root mean squared error metric between y_true and y_pred.
tf.keras.metrics.RootMeanSquaredError( name='root_mean_squared_error', dtype=None ) Used in the notebooks
| Used in the tutorials |
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Formula:
loss = sqrt(mean((y_pred - y_true) ** 2)) Args | |
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name | (Optional) string name of the metric instance. |
dtype | (Optional) data type of the metric result. |
Example:
Example:
m = keras.metrics.RootMeanSquaredError()m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])m.result()0.5
m.reset_state()m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],sample_weight=[1, 0])m.result()0.70710677
Usage with compile() API:
model.compile( optimizer='sgd', loss='mse', metrics=[keras.metrics.RootMeanSquaredError()]) Attributes | |
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dtype | |
variables | |
Methods
add_variable
add_variable( shape, initializer, dtype=None, aggregation='sum', name=None ) add_weight
add_weight( shape=(), initializer=None, dtype=None, name=None ) from_config
@classmethodfrom_config( config )
get_config
get_config() Return the serializable config of the metric.
reset_state
reset_state() Reset all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result() Compute the current metric value.
| Returns | |
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. |
stateless_reset_state
stateless_reset_state() stateless_result
stateless_result( metric_variables ) stateless_update_state
stateless_update_state( metric_variables, *args, **kwargs ) 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. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true. Defaults to 1. |
| Returns | |
|---|---|
| Update op. |
__call__
__call__( *args, **kwargs ) Call self as a function.