tf.compat.v1.metrics

Calculates how often predictions matches labels.

tf.compat.v1.metrics.accuracy(
    labels, predictions, weights=None, metrics_collections=None,
    updates_collections=None, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate your TensorFlow 1 code to TensorFlow 2	Exploring the TF-Hub CORD-19 Swivel Embeddings

The accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as accuracy: an idempotent operation that simply divides total by count.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the accuracy. Internally, an is_correct operation computes a Tensor with elements 1.0 where the corresponding elements of predictions and labels match and 0.0 otherwise. Then update_op increments total with the reduced sum of the product of weights and is_correct, and it increments count with the reduced sum of weights.

If weights is None, weights default to 1. Use weights of 0 to mask values.

Args
`labels`	The ground truth values, a `Tensor` whose shape matches `predictions`.
`predictions`	The predicted values, a `Tensor` of any shape.
`weights`	Optional `Tensor` whose rank is either 0, or the same rank as `labels`, and 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 `accuracy` should be added to.
`updates_collections`	An optional list of collections that `update_op` should be added to.
`name`	An optional variable_scope name.

Returns
`accuracy`	A `Tensor` representing the accuracy, the value of `total` divided by `count`.
`update_op`	An operation that increments the `total` and `count` variables appropriately and whose value matches `accuracy`.

Raises
`ValueError`	If `predictions` and `labels` have mismatched shapes, or 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.

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tf.compat / v1 / v1.metrics.Overview

Used in the notebooks

Args

Returns

Raises