tf.keras.metrics.BinaryAccuracy
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Calculates how often predictions matches binary labels.
tf.keras.metrics.BinaryAccuracy(
name='binary_accuracy', dtype=None, threshold=0.5
)
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.
Usage:
m = tf.keras.metrics.BinaryAccuracy()
_ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
m.result().numpy()
0.75
m.reset_states()
_ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
sample_weight=[1, 0, 0, 1])
m.result().numpy()
0.5
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()])
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.
|
Methods
reset_states
View source
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps,
when a metric is evaluated during training.
result
View source
result()
Computes and returns the metric value tensor.
Result computation is an idempotent operation that simply calculates the
metric value using the state variables.
update_state
View source
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates metric statistics.
y_true and y_pred should have the same shape.
| Args |
|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN].
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN].
|
sample_weight
|
Optional sample_weight acts as a
coefficient for the metric. If a scalar is provided, then the metric is
simply scaled by the given value. If sample_weight is a tensor of size
[batch_size], then the metric for each sample of the batch is rescaled
by the corresponding element in the sample_weight vector. If the shape
of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted
to this shape), then each metric element of y_pred is scaled by the
corresponding value of sample_weight. (Note on dN-1: all metric
functions reduce by 1 dimension, usually the last axis (-1)).
|
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Last updated 2020-10-01 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 2020-10-01 UTC."],[],[],null,["# tf.keras.metrics.BinaryAccuracy\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/metrics/BinaryAccuracy) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/metrics.py#L669-L711) |\n\nCalculates how often predictions matches binary labels.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.metrics.BinaryAccuracy`](/api_docs/python/tf/keras/metrics/BinaryAccuracy)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.metrics.BinaryAccuracy`](/api_docs/python/tf/keras/metrics/BinaryAccuracy)\n\n\u003cbr /\u003e\n\n tf.keras.metrics.BinaryAccuracy(\n name='binary_accuracy', dtype=None, threshold=0.5\n )\n\nThis metric creates two local variables, `total` and `count` that are used to\ncompute the frequency with which `y_pred` matches `y_true`. This frequency is\nultimately returned as `binary accuracy`: an idempotent operation that simply\ndivides `total` by `count`.\n\nIf `sample_weight` is `None`, weights default to 1.\nUse `sample_weight` of 0 to mask values.\n\n#### Usage:\n\n m = tf.keras.metrics.BinaryAccuracy()\n _ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])\n m.result().numpy()\n 0.75\n\n m.reset_states()\n _ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],\n sample_weight=[1, 0, 0, 1])\n m.result().numpy()\n 0.5\n\nUsage with tf.keras API: \n\n model = tf.keras.Model(inputs, outputs)\n model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()])\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\nMethods\n-------\n\n### `reset_states`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/metrics.py#L218-L224) \n\n reset_states()\n\nResets 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/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/metrics.py#L376-L386) \n\n result()\n\nComputes and returns the metric value tensor.\n\nResult computation is an idempotent operation that simply calculates the\nmetric value using the state variables.\n\n### `update_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/metrics.py#L574-L605) \n\n update_state(\n y_true, y_pred, sample_weight=None\n )\n\nAccumulates metric statistics.\n\n`y_true` and `y_pred` should have the same shape.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]`. |\n| `y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]`. |\n| `sample_weight` | Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| Update op. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub