tf.keras.metrics.SparseCategoricalAccuracy

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Calculates how often predictions matches integer labels.

Inherits From: Mean, Metric, Layer, Module

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Main aliases

tf.metrics.SparseCategoricalAccuracy

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.SparseCategoricalAccuracy

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

acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1))

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.

Args
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.

Standalone usage:

m = tf.keras.metrics.SparseCategoricalAccuracy()
m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
m.result().numpy()
0.5

m.reset_states()
m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [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.SparseCategoricalAccuracy()])

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	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.