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Computes the crossentropy loss between the labels and predictions.
Inherits From: Loss
tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=losses_utils.ReductionV2.AUTO,
name='sparse_categorical_crossentropy'
)
Use this crossentropy loss function when there are two or more label classes.
We expect labels to be provided as integers. If you want to provide labels
using one-hot
representation, please use CategoricalCrossentropy
loss.
There should be # classes
floating point values per feature for y_pred
and a single floating point value per feature for y_true
.
In the snippet below, there is a single floating point value per example for
y_true
and # classes
floating pointing values per example for y_pred
.
The shape of y_true
is [batch_size]
and the shape of y_pred
is
[batch_size, num_classes]
.
Standalone usage:
y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
# Using 'auto'/'sum_over_batch_size' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy()
scce(y_true, y_pred).numpy()
1.177
# Calling with 'sample_weight'.
scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
# Using 'sum' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy(
reduction=tf.keras.losses.Reduction.SUM)
scce(y_true, y_pred).numpy()
2.354
# Using 'none' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy(
reduction=tf.keras.losses.Reduction.NONE)
scce(y_true, y_pred).numpy()
array([0.0513, 2.303], dtype=float32)
Usage with the compile()
API:
model.compile(optimizer='sgd',
loss=tf.keras.losses.SparseCategoricalCrossentropy())
Args | |
---|---|
from_logits
|
Whether y_pred is expected to be a logits tensor. By
default, we assume that y_pred encodes a probability distribution.
|
reduction
|
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 this custom training tutorial for
more details.
|
name
|
Optional name for the instance. Defaults to 'sparse_categorical_crossentropy'. |
Methods
from_config
@classmethod
from_config( config )
Instantiates a Loss
from its config (output of get_config()
).
Args | |
---|---|
config
|
Output of get_config() .
|
Returns | |
---|---|
A Loss instance.
|
get_config
get_config()
Returns the config dictionary for a Loss
instance.
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss
instance.
Args | |
---|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN] , except
sparse loss functions such as sparse categorical crossentropy where
shape = [batch_size, d0, .. dN-1]
|
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.
|