tf.keras.metrics.R2Score

Computes R2 score.

Inherits From: Metric

tf.keras.metrics.R2Score(
    class_aggregation='uniform_average',
    num_regressors=0,
    name='r2_score',
    dtype=None
)

Formula:

sum_squares_residuals = sum((y_true - y_pred) ** 2)
sum_squares = sum((y_true - mean(y_true)) ** 2)
R2 = 1 - sum_squares_residuals / sum_squares

This is also called the coefficient of determination.

It indicates how close the fitted regression line is to ground-truth data.

  • The highest score possible is 1.0. It indicates that the predictors perfectly accounts for variation in the target.
  • A score of 0.0 indicates that the predictors do not account for variation in the target.
  • It can also be negative if the model is worse than random.

This metric can also compute the "Adjusted R2" score.

class_aggregation Specifies how to aggregate scores corresponding to different output classes (or target dimensions), i.e. different dimensions on the last axis of the predictions. Equivalent to multioutput argument in Scikit-Learn. Should be one of None (no aggregation), "uniform_average", "variance_weighted_average".
num_regressors Number of independent regressors used ("Adjusted R2" score). 0 is the standard R2 score. Defaults to 0.
name Optional. string name of the metric instance.
dtype Optional. data type of the metric result.

Example:

y_true = np.array([[1], [4], [3]], dtype=np.float32)
y_pred = np.array([[2], [4], [4]], dtype=np.float32)
metric = keras.metrics.R2Score()
metric.update_state(y_true, y_pred)
result = metric.result()
result
0.57142854

dtype

variables

Methods

add_variable

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add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

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add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset all of the metric state variables.

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

result

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result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

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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__

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__call__(
    *args, **kwargs
)

Call self as a function.