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tf.keras.metrics.F1Score

Computes F-1 Score.

Inherits From: FBetaScore, Metric

tf.keras.metrics.F1Score(
    average=None, threshold=None, name='f1_score', dtype=None
)

Formula:

f1_score = 2 * (precision * recall) / (precision + recall)

This is the harmonic mean of precision and recall. Its output range is [0, 1]. It works for both multi-class and multi-label classification.

Args
`average`	Type of averaging to be performed on data. Acceptable values are `None`, `"micro"`, `"macro"` and `"weighted"`. Defaults to `None`. If `None`, no averaging is performed and `result()` will return the score for each class. If `"micro"`, compute metrics globally by counting the total true positives, false negatives and false positives. If `"macro"`, compute metrics for each label, and return their unweighted mean. This does not take label imbalance into account. If `"weighted"`, compute metrics for each label, and return their average weighted by support (the number of true instances for each label). This alters `"macro"` to account for label imbalance. It can result in an score that is not between precision and recall.
`threshold`	Elements of `y_pred` greater than `threshold` are converted to be 1, and the rest 0. If `threshold` is `None`, the argmax of `y_pred` is converted to 1, and the rest to 0.
`name`	Optional. String name of the metric instance.
`dtype`	Optional. Data type of the metric result.

Returns
F-1 Score: float.

Example:

metric = keras.metrics.F1Score(threshold=0.5)
y_true = np.array([[1, 1, 1],
                   [1, 0, 0],
                   [1, 1, 0]], np.int32)
y_pred = np.array([[0.2, 0.6, 0.7],
                   [0.2, 0.6, 0.6],
                   [0.6, 0.8, 0.0]], np.float32)
metric.update_state(y_true, y_pred)
result = metric.result()
array([0.5      , 0.8      , 0.6666667], dtype=float32)

Attributes
`dtype`
`variables`

Methods

`add_variable`

View source

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()

Returns 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
)

Accumulate statistics for the metric.

`call`

View source

__call__(
    *args, **kwargs
)

Call self as a function.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/metrics/F1Score