View source on GitHub |
Computes the crossentropy metric between the labels and predictions.
Inherits From: MeanMetricWrapper
, Mean
, Metric
tf.keras.metrics.CategoricalCrossentropy(
name='categorical_crossentropy',
dtype=None,
from_logits=False,
label_smoothing=0,
axis=-1
)
This is the crossentropy metric class to be used when there are multiple
label classes (2 or more). It assumes that labels are one-hot encoded,
e.g., when labels values are [2, 0, 1]
, then
y_true
is [[0, 0, 1], [1, 0, 0], [0, 1, 0]]
.
Example:
Example:
# EPSILON = 1e-7, y = y_true, y` = y_pred
# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
# y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
# xent = -sum(y * log(y'), axis = -1)
# = -((log 0.95), (log 0.1))
# = [0.051, 2.302]
# Reduced xent = (0.051 + 2.302) / 2
m = keras.metrics.CategoricalCrossentropy()
m.update_state([[0, 1, 0], [0, 0, 1]],
[[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
m.result()
1.1769392
m.reset_state()
m.update_state([[0, 1, 0], [0, 0, 1]],
[[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.CategoricalCrossentropy()])
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.