tf.keras.losses.sparse_categorical_crossentropy

Computes the sparse categorical crossentropy loss.

tf.keras.losses.sparse_categorical_crossentropy(
    y_true, y_pred, from_logits=False, ignore_class=None, axis=-1
)

Used in the notebooks

Used in the guideUsed in the tutorials

y_trueGround truth values.
y_predThe predicted values.
from_logitsWhether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
ignore_classOptional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (ignore_class=None), all classes are considered.
axisDefaults to -1. The dimension along which the entropy is computed.

Sparse categorical crossentropy loss value.

Examples:

y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
loss = keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss
array([0.0513, 2.303], dtype=float32)