tf.keras.metrics.SparseCategoricalAccuracy

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Calculates how often predictions matches integer labels.

tf.keras.metrics.SparseCategoricalAccuracy(
    name='sparse_categorical_accuracy', dtype=None
)

For example, if y_true is [[2], [1]] and y_pred is [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] then the categorical accuracy is 1/2 or .5. If the weights were specified as [0.7, 0.3] then the categorical accuracy would be .3. You can provide logits of classes as y_pred, since argmax of logits and probabilities are same.

This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true. This frequency is ultimately returned as sparse categorical accuracy: an idempotent operation that simply divides total by count.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Usage:

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

Usage with tf.keras API:

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

Args:

• fn: The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.
• **kwargs: The keyword arguments that are passed on to fn.

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.