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
|
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
@classmethod
from_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.