TensorFlow 2.0 Beta is available Learn more

tf.keras.metrics.CategoricalAccuracy

TensorFlow 2.0 version View source on GitHub

Class CategoricalAccuracy

Calculates how often predictions matches labels.

Aliases:

  • Class tf.compat.v1.keras.metrics.CategoricalAccuracy
  • Class tf.compat.v2.keras.metrics.CategoricalAccuracy
  • Class tf.compat.v2.metrics.CategoricalAccuracy

For example, if y_true is [[0, 0, 1], [0, 1, 0]] and y_pred is [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] then the categorical accuracy is 1/2 or .5. If the weights were specified as [0.7, 0.3] then the categorical accuracy would be .3. You can provide logits of classes as y_pred, since argmax of logits and probabilities are same.

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

y_pred and y_true should be passed in as vectors of probabilities, rather than as labels. If necessary, use tf.one_hot to expand y_true as a vector.

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

Usage:

m = tf.keras.metrics.CategoricalAccuracy()
m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
print('Final result: ', m.result().numpy())  # Final result: 0.5

Usage with tf.keras API:

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

__init__

View source

__init__(
    name='categorical_accuracy',
    dtype=None
)

Creates a CategoricalAccuracy instance.

Args:

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

update_state

View source

update_state(
    y_true,
    y_pred,
    sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args:

  • y_true: The ground truth values.
  • y_pred: The predicted values.
  • 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.

Returns:

Update op.