# tf.keras.metrics.Precision

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## Class `Precision`

Inherits From: `Metric`

### Aliases:

• Class `tf.compat.v1.keras.metrics.Precision`
• Class `tf.compat.v2.keras.metrics.Precision`
• Class `tf.compat.v2.metrics.Precision`
• Class `tf.metrics.Precision`

For example, if `y_true` is [0, 1, 1, 1] and `y_pred`免费成长做爱直播有哪些 is [1, 0, 1, 1] then the precision value is 2/(2+1) ie. 0.66. If the weights were specified as [0, 0, 1, 0] then the precision value would be 1.

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`免费成长做爱直播有哪些 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.

#### Usage:

``````m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
print('Final result: ', m.result().numpy())  # Final result: 0.66
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Precision()])
``````

## `__init__`

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``````__init__(
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None
)
``````

Creates a `Precision` instance.

#### 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`). 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.

## Methods

### `reset_states`

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``````reset_states()
``````

Resets 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()
``````

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`

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``````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`.

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