Table Of Contents
Table Of Contents

mx.symbol.make_loss

Description

Make your own loss function in network construction.

This operator accepts a customized loss function symbol as a terminal loss and the symbol should be an operator with no backward dependency. The output of this function is the gradient of loss with respect to the input data.

For example, if you are a making a cross entropy loss function. Assume out is the predicted output and label is the true label, then the cross entropy can be defined as:

 cross_entropy = label * log(out) + (1 - label) * log(1 - out)
 loss = make_loss(cross_entropy)

 We will need to use ``make_loss`` when we are creating our own loss function or we want to
 combine multiple loss functions. Also we may want to stop some variables' gradients
 from backpropagation. See more detail in ``BlockGrad`` or ``stop_gradient``.

 The storage type of ``make_loss`` output depends upon the input storage type:

- make_loss(default) = default
- make_loss(row_sparse) = row_sparse

Usage

mx.symbol.make_loss(...)

Arguments

Argument

Description

data

NDArray-or-Symbol.

The input array.

name

string, optional.

Name of the resulting symbol.