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tf.keras.metrics.SparseTopKCategoricalAccuracy

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Class SparseTopKCategoricalAccuracy

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

Aliases:

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

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()])

__init__

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__init__(
    k=5,
    name='sparse_top_k_categorical_accuracy',
    dtype=None
)

Creates a SparseTopKCategoricalAccuracy instance.

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.

__new__

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__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

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.