tf.keras.metrics.SparseTopKCategoricalAccuracy

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

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

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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

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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.