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Computes sparse categorical cross-entropy loss.
tf.keras.ops.sparse_categorical_crossentropy(
target, output, from_logits=False, axis=-1
)
The sparse categorical cross-entropy loss is similar to categorical cross-entropy, but it is used when the target tensor contains integer class labels instead of one-hot encoded vectors. It measures the dissimilarity between the target and output probabilities or logits.
Returns | |
---|---|
Integer tensor: The computed sparse categorical cross-entropy
loss between target and output .
|
Example:
target = keras.ops.convert_to_tensor([0, 1, 2], dtype=int32)
output = keras.ops.convert_to_tensor(
[[0.9, 0.05, 0.05],
[0.1, 0.8, 0.1],
[0.2, 0.3, 0.5]])
sparse_categorical_crossentropy(target, output)
array([0.10536056 0.22314355 0.6931472 ], shape=(3,), dtype=float32)