op_binary_crossentropy: Computes binary cross-entropy loss between target and output...
In rstudio/keras: R Interface to 'Keras'

op_binary_crossentropy

R Documentation

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

Description

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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