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Calculates how often predictions match one-hot labels.
Inherits From: MeanMetricWrapper, Mean, Metric
tf.keras.metrics.CategoricalAccuracy( name='categorical_accuracy', dtype=None ) 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 categorical accuracy: an idempotent operation that simply divides total by count.
y_pred and y_true should be passed in as vectors of probabilities, rather than as labels. If necessary, use ops.one_hot to expand y_true as a vector.
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. |
Example:
m = keras.metrics.CategoricalAccuracy()m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],[0.05, 0.95, 0]])m.result()0.5
m.reset_state()m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],[0.05, 0.95, 0]],sample_weight=[0.7, 0.3])m.result()0.3
Usage with compile() API:
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=[keras.metrics.CategoricalAccuracy()]) Attributes | |
|---|---|
dtype | |
variables | |
Methods
add_variable
add_variable( shape, initializer, dtype=None, aggregation='sum', name=None ) add_weight
add_weight( shape=(), initializer=None, dtype=None, name=None ) from_config
@classmethodfrom_config( config )
get_config
get_config() Return the serializable config of the metric.
reset_state
reset_state() Reset all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result() Compute the current metric value.
| Returns | |
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. |
stateless_reset_state
stateless_reset_state() stateless_result
stateless_result( metric_variables ) stateless_update_state
stateless_update_state( metric_variables, *args, **kwargs ) update_state
update_state( y_true, y_pred, sample_weight=None ) Accumulate statistics for the metric.
__call__
__call__( *args, **kwargs ) Call self as a function.