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Computes the crossentropy metric between the labels and predictions.
Inherits From: MeanMetricWrapper, Mean, Metric
tf.keras.metrics.SparseCategoricalCrossentropy( name='sparse_categorical_crossentropy', dtype=None, from_logits=False, axis=-1 ) Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
Use this crossentropy metric when there are two or more label classes. It expects labels to be provided as integers. If you want to provide labels that are one-hot encoded, please use the CategoricalCrossentropy metric instead.
There should be num_classes floating point values per feature for y_pred and a single floating point value per feature for y_true.
Example:
Example:
# y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]# logits = log(y_pred)# softmax = exp(logits) / sum(exp(logits), axis=-1)# softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]# xent = -sum(y * log(softmax), 1)# log(softmax) = [[-2.9957, -0.0513, -16.1181],# [-2.3026, -0.2231, -2.3026]]# y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]# xent = [0.0513, 2.3026]# Reduced xent = (0.0513 + 2.3026) / 2m = keras.metrics.SparseCategoricalCrossentropy()m.update_state([1, 2],[[0.05, 0.95, 0], [0.1, 0.8, 0.1]])m.result()1.1769392
m.reset_state()m.update_state([1, 2],[[0.05, 0.95, 0], [0.1, 0.8, 0.1]],sample_weight=np.array([0.3, 0.7]))m.result()1.6271976
Usage with compile() API:
model.compile( optimizer='sgd', loss='mse', metrics=[keras.metrics.SparseCategoricalCrossentropy()]) 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.