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 or 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

Used in the notebooks

Used in the guide Used in the tutorials

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.

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.

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.

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.