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 |
|---|
Formula:
loss = sqrt(mean((y_pred - y_true) ** 2))
Args | |
|---|---|
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 | |
|---|---|
dtype
|
|
variables
|
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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.
View source on GitHub