# tf.keras.metrics.Precision
|---|
| [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/confusion_metrics.py#L348-L507) |
Computes the precision of the predictions with respect to the labels.
Inherits From: [`Metric`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/Metric), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)
#### View aliases
**Main aliases**
[`tf.metrics.Precision`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Precision)
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
\`tf.compat.v1.keras.metrics.Precision\`
tf.keras.metrics.Precision(
thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
The metric creates two local variables, `true_positives` and
`false_positives` that are used to compute the precision. This value is
ultimately returned as `precision`, an idempotent operation that simply
divides `true_positives` by the sum of `true_positives` and
`false_positives`.
If `sample_weight` is `None`, weights default to 1.
Use `sample_weight` of 0 to mask values.
If `top_k` is set, we'll calculate precision as how often on average a class
among the top-k classes with the highest predicted values of a batch entry
is correct and can be found in the label for that entry.
If `class_id` is specified, we calculate precision by considering only the
entries in the batch for which `class_id` is above the threshold and/or in
the top-k highest predictions, and computing the fraction of them for which
`class_id` is indeed a correct label.
| ## Args ||
|---|---|
| `thresholds` | (Optional) A float value, or a Python list/tuple of float threshold values in \[0, 1\]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If used with a loss function that sets `from_logits=True` (i.e. no sigmoid applied to predictions), `thresholds` should be set to 0. One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate precision with `thresholds=0.5`. |
| `top_k` | (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating precision. |
| `class_id` | (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval `[0, num_classes)`, where `num_classes` is the last dimension of predictions. |
| `name` | (Optional) string name of the metric instance. |
| `dtype` | (Optional) data type of the metric result. |
#### Standalone usage:
m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.result().numpy()
0.6666667
m.reset_state()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0
# With top_k=2, it will calculate precision over y_true[:2]
# and y_pred[:2]
m = tf.keras.metrics.Precision(top_k=2)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.0
# With top_k=4, it will calculate precision over y_true[:4]
# and y_pred[:4]
m = tf.keras.metrics.Precision(top_k=4)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.5
Usage with `compile()` API:
model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.Precision()])
Usage with a loss with `from_logits=True`:
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.Precision(thresholds=0)])
## Methods
### `merge_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L288-L326)
merge_state(
metrics
)
Merges the state from one or more metrics.
This method can be used by distributed systems to merge the state
computed by different metric instances. Typically the state will be
stored in the form of the metric's weights. For example, a
tf.keras.metrics.Mean metric contains a list of two weight values: a
total and a count. If there were two instances of a
tf.keras.metrics.Accuracy that each independently aggregated partial
state for an overall accuracy calculation, these two metric's states
could be combined as follows:
m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75
| Args ||
|---|---|
| `metrics` | an iterable of metrics. The metrics must have compatible state. |
| Raises ||
|---|---|
| `ValueError` | If the provided iterable does not contain metrics matching the metric's required specifications. |
### `reset_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/confusion_metrics.py#L491-L498)
reset_state()
Resets all of the metric state variables.
This function is called between epochs/steps,
when a metric is evaluated during training.
### `result`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/confusion_metrics.py#L484-L489)
result()
Computes and returns the scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the
metric value using the state variables.
| Returns ||
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. ||
### `update_state`
[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/confusion_metrics.py#L455-L482)
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates true positive and false positive statistics.
| Args ||
|---|---|
| `y_true` | The ground truth values, with the same dimensions as `y_pred`. Will be cast to `bool`. |
| `y_pred` | The predicted values. Each element must be in the range `[0, 1]`. |
| `sample_weight` | Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`. |
| Returns ||
|---|---|
| Update op. ||