Cross-entropy loss for classification tasks

The cross-entropy operation computes the cross-entropy loss between network predictions and target values for single-label and multi-label classification tasks.

This function computes the cross-entropy loss between predictions and targets stored as
`dlarray`

data. If
you want to calculate the cross-entropy loss within a `layerGraph`

object
or `Layer`

array for use
with `trainNetwork`

, use the following layer:

computes the categorical cross-entropy loss between the predictions `dlY`

= crossentropy(`dlX`

,`targets`

)`dlX`

and the target values `targets`

for single-label classification tasks. The
input `dlX`

is a formatted `dlarray`

with dimension
labels. The output `dlY`

is an unformatted scalar
`dlarray`

with no dimension labels.

specifies options using one or more name-value pair arguments in addition to the input
arguments in previous syntaxes. For example,
`dlY`

= crossentropy(___,`Name,Value`

)`'TargetCategories','independent'`

computes the cross-entropy loss for a
multi-label classification task.

`dlarray`

| `dlfeval`

| `dlgradient`

| `mse`

| `softmax`