View source on GitHub |
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) / 2
m = 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
@classmethod
from_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.