tf.keras.losses.SparseCategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Inherits From: Loss

tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    ignore_class=None,
    reduction='sum_over_batch_size',
    axis=-1,
    name='sparse_categorical_crossentropy',
    dtype=None
)

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 num_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].

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 reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or None. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and None perform no aggregation. Defaults to "sum_over_batch_size".
axis The axis along which to compute crossentropy (the features axis). Defaults to -1.
name Optional name for the loss instance.
dtype The dtype of the loss's computations. Defaults to None, which means using keras.backend.floatx(). keras.backend.floatx() is a "float32" unless set to different value (via keras.backend.set_floatx()). If a keras.DTypePolicy is provided, then the compute_dtype will be utilized.

Examples:

y_true = np.array([1, 2])
y_pred = np.array([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
# Using 'auto'/'sum_over_batch_size' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy()
scce(y_true, y_pred)
1.177
 
# Calling with 'sample_weight'.
scce(y_true, y_pred, sample_weight=np.array([0.3, 0.7]))
0.814
 
# Using 'sum' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy(
    reduction="sum")
scce(y_true, y_pred)
2.354
 
# Using 'none' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy(
    reduction=None)
scce(y_true, y_pred)
array([0.0513, 2.303], dtype=float32)

Usage with the compile() API:

model.compile(optimizer='sgd',
              loss=keras.losses.SparseCategoricalCrossentropy())

dtype

Methods

call

View source

call(
    y_true, y_pred
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

__call__

View source

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.