tf.keras.ops.sparse_categorical_crossentropy
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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.
Args |
---|
target | The target tensor representing the true class labels as integers. Its shape should match the shape of the output tensor except for the last dimension. |
output | The output tensor representing the predicted probabilities or logits. Its shape should match the shape of the target tensor except for the last dimension. |
from_logits | (optional) Whether output is a tensor of logits or probabilities. Set it to True if output represents logits; otherwise, set it to False if output represents probabilities. Defaults toFalse . |
axis | (optional) The axis along which the sparse categorical cross-entropy is computed. Defaults to -1 , which corresponds to the last dimension of the tensors. |
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)
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Last updated 2024-06-07 UTC.
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