Computes categorical cross-entropy loss between target and output tensor.

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.

Usage

op_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1L)

Arguments

target: The target tensor representing the true categorical labels. 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 to FALSE.
axis: (optional) The axis along which the categorical cross-entropy is computed. Defaults to -1, which corresponds to the last dimension of the tensors.

Value

Integer tensor: The computed categorical cross-entropy loss between target and output.

Examples

target <- op_array(rbind(c(1, 0, 0),
                        c(0, 1, 0),
                        c(0, 0, 1)))
output <- op_array(rbind(c(0.9, 0.05, 0.05),
                        c(0.1, 0.8, 0.1),
                        c(0.2, 0.3, 0.5)))
op_categorical_crossentropy(target, output)

## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)

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Computes categorical cross-entropy loss between target and output tensor.

Usage

Arguments

Value

Examples

See also