TensorFlow 1 version View source on GitHub

Computes how often targets are in the top K predictions.


m = tf.keras.metrics.TopKCategoricalAccuracy()
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: 1.0

Usage with tf.keras API:

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

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.



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Resets all of the metric state variables.

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


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Computes and returns the metric value tensor.

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


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Accumulates metric statistics.

y_true and y_pred should have the same shape.

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.

Update op.