# chainer.functions.connectionist_temporal_classification¶

chainer.functions.connectionist_temporal_classification(x, t, blank_symbol, input_length=None, label_length=None, reduce='mean')[source]

Connectionist Temporal Classification loss function.

Connectionist Temporal Classification(CTC) [Graves2006] is a loss function of sequence labeling where the alignment between the inputs and target is unknown. See also [Graves2012]

The output is a variable whose value depends on the value of the option reduce. If it is 'no', it holds the samplewise loss values. If it is 'mean', it takes the mean of loss values.

Parameters: x (sequence of Variable) – RNN output at each time. x must be a list of Variable s. Each element of x, x[i] is a Variable representing output of RNN at time i. t (Variable) – Expected label sequence. blank_symbol (int) – Index of blank_symbol. This value must be non-negative. input_length (Variable) – Length of valid sequence for each of mini batch x (optional). If input_length is skipped, It regards that all of x is valid input. label_length (Variable) – Length of valid sequence for each of mini batch t (optional). If label_length is skipped, It regards that all of t is valid input. reduce (str) – Reduction option. Its value must be either 'mean' or 'no'. Otherwise, ValueError is raised. A variable holding a scalar value of the CTC loss. If reduce is 'no', the output variable holds array whose shape is (B,) where B is the number of samples. If it is 'mean', it holds a scalar. Variable

Note

You need to input x without applying to activation functions(e.g. softmax function), because this function applies softmax functions to x before calculating CTC loss to avoid numerical limitations. You also need to apply softmax function to forwarded values before you decode it.

Note

This function is differentiable only by x.

Note

This function supports (batch, sequence, 1-dimensional input)-data.

 [Graves2006] Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber, Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks