tf.compat.v1.metrics.precision_at

Computes precision@k of the predictions with respect to sparse labels.

tf.compat.v1.metrics.precision_at_top_k(
    labels, predictions_idx, k=None, class_id=None, weights=None,
    metrics_collections=None, updates_collections=None, name=None
)

Differs from sparse_precision_at_k in that predictions must be in the form of top k class indices, whereas sparse_precision_at_k expects logits. Refer to sparse_precision_at_k for more details.

Args
`labels`	`int64` `Tensor` or `SparseTensor` with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and `labels` has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. Values outside this range are ignored.
`predictions_idx`	Integer `Tensor` with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the top `k` predicted class indices. [D1, ... DN] must match `labels`.
`k`	Integer, k for @k metric. Only used for the default op name.
`class_id`	Integer class ID for which we want binary metrics. This should be in range [0, num_classes], where num_classes is the last dimension of `predictions`. If `class_id` is outside this range, the method returns NAN.
`weights`	`Tensor` whose rank is either 0, or n-1, where n is the rank of `labels`. If the latter, it must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `labels` dimension).
`metrics_collections`	An optional list of collections that values should be added to.
`updates_collections`	An optional list of collections that updates should be added to.
`name`	Name of new update operation, and namespace for other dependent ops.

Returns
`precision`	Scalar `float64` `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives`.
`update_op`	`Operation` that increments `true_positives` and `false_positives` variables appropriately, and whose value matches `precision`.

Raises
`ValueError`	If `weights` is not `None` and its shape doesn't match `predictions`, or if either `metrics_collections` or `updates_collections` are not a list or tuple.
`RuntimeError`	If eager execution is enabled.

TensorFlow

tf.compat / v1 / v1.metrics.precision_at_thresholds

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TensorFlow

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tf.compat / v1 / v1.metrics.precision_at_thresholds

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