tf.keras.metrics.BinaryAccuracy
Stay organized with collections
Save and categorize content based on your preferences.
Calculates how often predictions match binary labels.
Inherits From: MeanMetricWrapper, Mean, Metric
tf.keras.metrics.BinaryAccuracy(
name='binary_accuracy', dtype=None, threshold=0.5
)
Used in the notebooks
This metric creates two local variables, total and count that are used
to compute the frequency with which y_pred matches y_true. This
frequency is ultimately returned as binary accuracy: an idempotent
operation that simply divides total by count.
If sample_weight is None, weights default to 1.
Use sample_weight of 0 to mask values.
Args |
|---|
name
|
(Optional) string name of the metric instance.
|
dtype
|
(Optional) data type of the metric result.
|
threshold
|
(Optional) Float representing the threshold for deciding
whether prediction values are 1 or 0.
|
Example:
m = keras.metrics.BinaryAccuracy()
m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
m.result()
0.75
m.reset_state()
m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
sample_weight=[1, 0, 0, 1])
m.result()
0.5
Usage with compile() API:
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=[keras.metrics.BinaryAccuracy()])
Attributes |
|---|
dtype
|
|
variables
|
|
Methods
add_variable
View source
add_variable(
shape, initializer, dtype=None, aggregation='sum', name=None
)
add_weight
View source
add_weight(
shape=(), initializer=None, dtype=None, name=None
)
from_config
View source
@classmethod
from_config(
config
)
get_config
View source
get_config()
Return the serializable config of the metric.
reset_state
View source
reset_state()
Reset all of the metric state variables.
This function is called between epochs/steps,
when a metric is evaluated during training.
result
View source
result()
Compute the current metric value.
| Returns |
|---|
|
A scalar tensor, or a dictionary of scalar tensors.
|
stateless_reset_state
View source
stateless_reset_state()
stateless_result
View source
stateless_result(
metric_variables
)
stateless_update_state
View source
stateless_update_state(
metric_variables, *args, **kwargs
)
update_state
View source
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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2024-06-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.metrics.BinaryAccuracy\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/accuracy_metrics.py#L78-L137) |\n\nCalculates how often predictions match binary labels.\n\nInherits From: [`MeanMetricWrapper`](../../../tf/keras/metrics/MeanMetricWrapper), [`Mean`](../../../tf/keras/metrics/Mean), [`Metric`](../../../tf/keras/Metric) \n\n tf.keras.metrics.BinaryAccuracy(\n name='binary_accuracy', dtype=None, threshold=0.5\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Transfer learning and fine-tuning](https://www.tensorflow.org/tutorials/images/transfer_learning) - [Classification on imbalanced data](https://www.tensorflow.org/tutorials/structured_data/imbalanced_data) - [TensorFlow Constrained Optimization Example Using CelebA Dataset](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study) - [TFX Keras Component Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/components_keras) |\n\nThis metric creates two local variables, `total` and `count` that are used\nto compute the frequency with which `y_pred` matches `y_true`. This\nfrequency is ultimately returned as `binary accuracy`: an idempotent\noperation that simply divides `total` by `count`.\n\nIf `sample_weight` is `None`, weights default to 1.\nUse `sample_weight` of 0 to mask values.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------|------------------------------------------------------------------------------------------------|\n| `name` | (Optional) string name of the metric instance. |\n| `dtype` | (Optional) data type of the metric result. |\n| `threshold` | (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. |\n\n\u003cbr /\u003e\n\n#### Example:\n\n m = keras.metrics.BinaryAccuracy()\n m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])\n m.result()\n 0.75\n\n m.reset_state()\n m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],\n sample_weight=[1, 0, 0, 1])\n m.result()\n 0.5\n\nUsage with `compile()` API: \n\n model.compile(optimizer='sgd',\n loss='binary_crossentropy',\n metrics=[keras.metrics.BinaryAccuracy()])\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------|---------------|\n| `dtype` | \u003cbr /\u003e \u003cbr /\u003e |\n| `variables` | \u003cbr /\u003e \u003cbr /\u003e |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `add_variable`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L186-L202) \n\n add_variable(\n shape, initializer, dtype=None, aggregation='sum', name=None\n )\n\n### `add_weight`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L204-L208) \n\n add_weight(\n shape=(), initializer=None, dtype=None, name=None\n )\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L215-L219) \n\n @classmethod\n from_config(\n config\n )\n\n### `get_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/accuracy_metrics.py#L132-L137) \n\n get_config()\n\nReturn the serializable config of the metric.\n\n### `reset_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L150-L152) \n\n reset_state()\n\nReset all of the metric state variables.\n\nThis function is called between epochs/steps,\nwhen a metric is evaluated during training.\n\n### `result`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L154-L157) \n\n result()\n\nCompute the current metric value.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A scalar tensor, or a dictionary of scalar tensors. ||\n\n\u003cbr /\u003e\n\n### `stateless_reset_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L164-L177) \n\n stateless_reset_state()\n\n### `stateless_result`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L148-L162) \n\n stateless_result(\n metric_variables\n )\n\n### `stateless_update_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L115-L138) \n\n stateless_update_state(\n metric_variables, *args, **kwargs\n )\n\n### `update_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L200-L207) \n\n update_state(\n y_true, y_pred, sample_weight=None\n )\n\nAccumulate statistics for the metric.\n\n### `__call__`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L217-L220) \n\n __call__(\n *args, **kwargs\n )\n\nCall self as a function."]]
View source on GitHub