Table Of Contents
Table Of Contents


class mxnet.gluon.loss.CTCLoss(layout='NTC', label_layout='NT', weight=None, **kwargs)[source]

Connectionist Temporal Classification Loss.

  • layout (str, default 'NTC') – Layout of prediction tensor. ‘N’, ‘T’, ‘C’ stands for batch size, sequence length, and alphabet_size respectively.
  • label_layout (str, default 'NT') – Layout of the labels. ‘N’, ‘T’ stands for batch size, and sequence length respectively.
  • weight (float or None) – Global scalar weight for loss.
  • pred: unnormalized prediction tensor (before softmax). Its shape depends on layout. If layout is ‘TNC’, pred should have shape (sequence_length, batch_size, alphabet_size). Note that in the last dimension, index alphabet_size-1 is reserved for internal use as blank label. So alphabet_size is one plus the actual alphabet size.
  • label: zero-based label tensor. Its shape depends on label_layout. If label_layout is ‘TN’, label should have shape (label_sequence_length, batch_size).
  • pred_lengths: optional (default None), used for specifying the length of each entry when different pred entries in the same batch have different lengths. pred_lengths should have shape (batch_size,).
  • label_lengths: optional (default None), used for specifying the length of each entry when different label entries in the same batch have different lengths. label_lengths should have shape (batch_size,).
  • loss: output loss has shape (batch_size,).

Example: suppose the vocabulary is [a, b, c], and in one batch we have three sequences ‘ba’, ‘cbb’, and ‘abac’. We can index the labels as {‘a’: 0, ‘b’: 1, ‘c’: 2, blank: 3}. Then alphabet_size should be 4, where label 3 is reserved for internal use by CTCLoss. We then need to pad each sequence with -1 to make a rectangular label tensor:

[[1, 0, -1, -1],
 [2, 1,  1, -1],
 [0, 1,  0,  2]]


Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

__init__(layout='NTC', label_layout='NT', weight=None, **kwargs)[source]

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


__init__([layout, label_layout, weight]) Initialize self.
apply(fn) Applies fn recursively to every child block as well as self.
cast(dtype) Cast this Block to use another data type.
collect_params([select]) 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[, …]) Overrides to construct symbolic graph for this Block.
hybridize([active]) Activates or deactivates HybridBlock s recursively.
infer_shape(*args) Infers shape of Parameters from inputs.
infer_type(*args) 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.
name_scope() Returns a name space object managing a child Block and parameter names.
register_child(block[, name]) Registers block as a child of self.
register_forward_hook(hook) Registers a forward hook on the block.
register_forward_pre_hook(hook) Registers a forward pre-hook on the block.
save_parameters(filename) Save parameters to file.
save_params(filename) [Deprecated] Please use save_parameters.
summary(*inputs) Print the summary of the model’s output and parameters.


name Name of this Block, without ‘_’ in the end.
params Returns this Block’s parameter dictionary (does not include its children’s parameters).
prefix Prefix of this Block.