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Computes categorical cross-entropy loss between target and output tensor.
tf.keras.ops.categorical_crossentropy(
target, output, from_logits=False, axis=-1
)
The categorical cross-entropy loss is commonly used in multi-class classification tasks where each input sample can belong to one of multiple classes. It measures the dissimilarity between the target and output probabilities or logits.
Returns | |
---|---|
Integer tensor: The computed categorical cross-entropy loss between
target and output .
|
Example:
target = keras.ops.convert_to_tensor(
[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
output = keras.ops.convert_to_tensor(
[[0.9, 0.05, 0.05],
[0.1, 0.8, 0.1],
[0.2, 0.3, 0.5]])
categorical_crossentropy(target, output)
array([0.10536054 0.22314355 0.6931472 ], shape=(3,), dtype=float32)