| |
| |
| ``mx.nd.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 |
| |
| |
| |
| |
| |
| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``data`` | NDArray-or-Symbol. | |
| | | | |
| | | The input array. | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
| |
| ``out`` The result mx.ndarray |
| |
| |
| Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/tensor/elemwise_unary_op_basic.cc#L360 |
| |
