tf.keras.losses.sparse_categorical_crossentropy

Computes the sparse categorical crossentropy loss.

View aliases

Main aliases

tf.keras.metrics.sparse_categorical_crossentropy

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 guide	Used in the tutorials
Use TPUs	Using DTensors with Keras Introduction to the TensorFlow Models NLP library

Args
`y_true`	Ground truth values.
`y_pred`	The predicted values.
`from_logits`	Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution.
`ignore_class`	Optional 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.
`axis`	Defaults to `-1`. The dimension along which the entropy is computed.

Returns
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)

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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/losses/sparse_categorical_crossentropy