tf.keras.metrics.SparseTopKCategoricalAccuracy

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

Main aliases

tf.metrics.SparseTopKCategoricalAccuracy

Compat aliases for migration

See Migration guide for more details.

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

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.

m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1)
m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
m.result().numpy()
0.5

m.reset_states()
m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
               sample_weight=[0.7, 0.3])
m.result().numpy()
0.3

Usage with compile() API:

model.compile(
  optimizer='sgd',
  loss='mse',
  metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()])

Methods

reset_states()

Resets all of the metric state variables.

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

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(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
`y_true`	Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred`	The predicted values. shape = `[batch_size, d0, .. dN]`.
`sample_weight`	Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.