tf.compat.v1.metrics.accuracy

Calculates how often predictions matches labels.

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

Migrate to TF2

tf.compat.v1.metrics.accuracy is not compatible with eager execution and tf.function. Please use tf.keras.metrics.Accuracy instead for TF2 migration. After instantiating a tf.keras.metrics.Accuracy object, you can first call the update_state() method to record the prediction/labels, and then call the result() method to get the accuracy eagerly. You can also attach it to a Keras model when calling the compile method. Please refer to this guide for more details.

Structural Mapping to Native TF2

Before:

accuracy, update_op = tf.compat.v1.metrics.accuracy(
  labels=labels,
  predictions=predictions,
  weights=weights,
  metrics_collections=metrics_collections,
  update_collections=update_collections,
  name=name)

After:

 m = tf.keras.metrics.Accuracy(
   name=name,
   dtype=None)

 m.update_state(
 y_true=labels,
 y_pred=predictions,
 sample_weight=weights)

 accuracy = m.result()

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
`label`	`y_true`	In `update_state()` method
`predictions`	`y_true`	In `update_state()` method
`weights`	`sample_weight`	In `update_state()` method
`metrics_collections`	Not supported	Metrics should be tracked : explicitly or with Keras APIs, for example, add_metric, instead of via collections
`updates_collections`	Not supported	-
`name`	`name`	In constructor

Before & After Usage Example

Before:

g = tf.Graph()
with g.as_default():
  logits = [1, 2, 3]
  labels = [0, 2, 3]
  acc, acc_op = tf.compat.v1.metrics.accuracy(logits, labels)
  global_init = tf.compat.v1.global_variables_initializer()
  local_init = tf.compat.v1.local_variables_initializer()
sess = tf.compat.v1.Session(graph=g)
sess.run([global_init, local_init])
print(sess.run([acc, acc_op]))
[0.0, 0.66667]

After:

m = tf.keras.metrics.Accuracy()
m.update_state([1, 2, 3], [0, 2, 3])
m.result().numpy()
0.66667

# Used within Keras model
model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.Accuracy()])

Description

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.