mxnet.gluon.loss.CTCLoss¶

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

Connectionist Temporal Classification Loss.

Parameters

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.

Inputs:

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,).

Outputs:

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]]

References

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.

Methods

`__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.

Attributes

`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`.