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

The binary cross-entropy loss is commonly used in binary classification tasks where each input sample belongs to one of the two classes. It measures the dissimilarity between the target and output probabilities or logits.

Usage

op_binary_crossentropy(target, output, from_logits = FALSE)

Arguments

target: The target tensor representing the true binary labels. Its shape should match the shape of the output tensor.
output: The output tensor representing the predicted probabilities or logits. Its shape should match the shape of the target tensor.
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.

Value

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

Examples

target <- op_array(c(0, 1, 1, 0))
output <- op_array(c(0.1, 0.9, 0.8, 0.2))
op_binary_crossentropy(target, output)

## tf.Tensor([0.10536055 0.10536055 0.22314353 0.22314353], shape=(4), dtype=float32)

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op_logsumexp()
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op_max()
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Computes binary cross-entropy loss between target and output tensor.

Usage

Arguments

Value

Examples

See also