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TensorFlow 1 version
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View source on GitHub
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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'
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
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 | |
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from_logits
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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.
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reduction
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(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 this custom training tutorial
for more details.
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name
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Optional name for the op. Defaults to 'sparse_categorical_crossentropy'. |
Methods
from_config
@classmethodfrom_config( config )
Instantiates a Loss from its config (output of get_config()).
| Args | |
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config
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Output of get_config().
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| Returns | |
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A Loss instance.
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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 | |
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y_true
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Ground truth values. shape = [batch_size, d0, .. dN], except
sparse loss functions such as sparse categorical crossentropy where
shape = [batch_size, d0, .. dN-1]
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y_pred
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The predicted values. shape = [batch_size, d0, .. dN]
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sample_weight
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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.)
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| Returns | |
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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.)
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| Raises | |
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ValueError
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If the shape of sample_weight is invalid.
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