Computes the crossentropy loss between the labels and predictions.
tf.keras.losses.CategoricalCrossentropy(
from_logits=False, label_smoothing=0, reduction=losses_utils.ReductionV2.AUTO,
name='categorical_crossentropy'
)
Use this crossentropy loss function when there are two or more label classes.
We expect labels to be provided in a one_hot
representation. If you want to
provide labels as integers, please use SparseCategoricalCrossentropy
loss.
There should be # classes
floating point values per feature.
In the snippet below, there is # classes
floating pointing values per
example. The shape of both y_pred
and y_true
are
[batch_size, num_classes]
.
Usage:
cce = tf.keras.losses.CategoricalCrossentropy()
loss = cce(
[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]],
[[.9, .05, .05], [.05, .89, .06], [.05, .01, .94]])
print('Loss: ', loss.numpy()) # Loss: 0.0945
Usage with the compile
API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.CategoricalCrossentropy())
Args |
from_logits
|
Whether y_pred is expected to be a logits tensor. By default,
we assume that y_pred encodes a probability distribution.
Note: Using from_logits=True may be more numerically stable.
|
label_smoothing
|
Float in [0, 1]. When > 0, label values are smoothed,
meaning the confidence on label values are relaxed. e.g.
label_smoothing=0.2 means that we will use a value of 0.1 for label
0 and 0.9 for label 1 "
|
reduction
|
(Optional) Type of tf.keras.losses.Reduction to apply to loss.
Default value is AUTO . AUTO indicates that the reduction option will
be determined by the usage context. For almost all cases this defaults to
SUM_OVER_BATCH_SIZE .
When used with tf.distribute.Strategy , outside of built-in training
loops such as tf.keras compile and fit , using AUTO or
SUM_OVER_BATCH_SIZE will raise an error. Please see
https://www.tensorflow.org/tutorials/distribute/custom_training
for more details on this.
|
name
|
Optional name for the op.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates a Loss
from its config (output of get_config()
).
Args |
config
|
Output of get_config() .
|
get_config
View source
get_config()
__call__
View source
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss
instance.
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 loss. If a scalar is provided, then the loss is
simply scaled by the given value. If sample_weight is a tensor of size
[batch_size] , then the total loss 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 loss element of y_pred is scaled
by the corresponding value of sample_weight . (Note ondN-1 : all loss
functions reduce by 1 dimension, usually axis=-1.)
|
Returns |
Weighted loss float Tensor . If reduction is NONE , this has
shape [batch_size, d0, .. dN-1] ; otherwise, it is scalar. (Note dN-1
because all loss functions reduce by 1 dimension, usually axis=-1.)
|
Raises |
ValueError
|
If the shape of sample_weight is invalid.
|