tf.keras.losses.sparse_categorical_crossentropy | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.sparse_categorical_crossentropy
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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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Last updated 2024-06-07 UTC.