tf.keras.metrics.SparseTopKCategoricalAccuracy

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Computes how often integer targets are in the top K predictions.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.SparseTopKCategoricalAccuracy, `tf.compat.v2.keras.metrics.SparseTopKCategoricalAccuracy`, `tf.compat.v2.metrics.SparseTopKCategoricalAccuracy`

tf.keras.metrics.SparseTopKCategoricalAccuracy(
    k=5, name='sparse_top_k_categorical_accuracy', dtype=None
)

Usage:

m = tf.keras.metrics.SparseTopKCategoricalAccuracy()
m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
print('Final result: ', m.result().numpy())  # Final result: 1.0

Usage with tf.keras API:

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

Args
k	(Optional) Number of top elements to look at for computing accuracy. Defaults to 5.
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

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result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

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.