Table Of Contents
Table Of Contents


class mxnet.gluon.loss.LogisticLoss(weight=None, batch_axis=0, label_format='signed', **kwargs)[source]

Calculates the logistic loss (for binary losses only):

\[L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))\]

where pred is the classifier prediction and label is the target tensor containing values -1 or 1 (0 or 1 if label_format is binary). pred and label can have arbitrary shape as long as they have the same number of elements.

  • weight (float or None) – Global scalar weight for loss.

  • batch_axis (int, default 0) – The axis that represents mini-batch.

  • label_format (str, default 'signed') – Can be either ‘signed’ or ‘binary’. If the label_format is ‘signed’, all label values should be either -1 or 1. If the label_format is ‘binary’, all label values should be either 0 or 1.

  • Inputs

    • pred: prediction tensor with arbitrary shape.

    • label: truth tensor with values -1/1 (label_format is ‘signed’) or 0/1 (label_format is ‘binary’). Must have the same size as pred.

    • sample_weight: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1).

  • Outputs

    • loss: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out.

__init__(weight=None, batch_axis=0, label_format='signed', **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.


__init__([weight, batch_axis, label_format])

Initialize self.


Applies fn recursively to every child block as well as self.


Cast this Block to use another data type.


Returns a ParameterDict containing this Block and all of its children’s Parameters(default), also can returns the select ParameterDict which match some given regular expressions.

export(path[, epoch])

Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.

forward(x, *args)

Defines the forward computation.

hybrid_forward(F, pred, label[, sample_weight])

Overrides to construct symbolic graph for this Block.


Activates or deactivates HybridBlock s recursively.


Infers shape of Parameters from inputs.


Infers data type of Parameters from inputs.

initialize([init, ctx, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load_parameters(filename[, ctx, …])

Load parameters from file previously saved by save_parameters.

load_params(filename[, ctx, allow_missing, …])

[Deprecated] Please use load_parameters.


Returns a name space object managing a child Block and parameter names.

register_child(block[, name])

Registers block as a child of self.


Registers a forward hook on the block.


Registers a forward pre-hook on the block.


Save parameters to file.


[Deprecated] Please use save_parameters.


Print the summary of the model’s output and parameters.



Name of this Block, without ‘_’ in the end.


Returns this Block’s parameter dictionary (does not include its children’s parameters).


Prefix of this Block.