| |
| |
| ``mx.symbol.MakeLoss`` |
| ============================================ |
| |
| 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 = MakeLoss(cross_entropy) |
| |
| We will need to use ``MakeLoss`` 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``. |
| |
| In addition, we can give a scale to the loss by setting ``grad_scale``, |
| so that the gradient of the loss will be rescaled in the backpropagation. |
| |
| |
| .. note:: This operator should be used as a Symbol instead of NDArray. |
| |
| |
| |
| |
| Usage |
| ---------- |
| |
| .. code:: r |
| |
| mx.symbol.MakeLoss(...) |
| |
| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``data`` | NDArray-or-Symbol. | |
| | | | |
| | | Input array. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``grad.scale`` | float, optional, default=1. | |
| | | | |
| | | Gradient scale as a supplement to unary and binary | |
| | | operators | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``valid.thresh`` | float, optional, default=0. | |
| | | | |
| | | clip each element in the array to 0 when it is less than | |
| | | ``valid_thresh``. This is used when ``normalization`` is | |
| | | set to | |
| | | ``'valid'``. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``normalization`` | {'batch', 'null', 'valid'},optional, default='null'. | |
| | | | |
| | | If this is set to null, the output gradient will not be | |
| | | normalized. If this is set to batch, the output gradient | |
| | | will be divided by the batch size. If this is set to | |
| | | valid, the output gradient will be divided by the number | |
| | | of valid input | |
| | | elements. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``name`` | string, optional. | |
| | | | |
| | | Name of the resulting symbol. | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
| |
| ``out`` The result mx.symbol |
| |
| |
| Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/make_loss.cc#L71 |
| |
