aesara.tensor.nnet.ctc – Connectionist Temporal Classification (CTC) loss

Note

Usage of connectionist temporal classification (CTC) loss Op, requires that the warp-ctc library is available. In case the warp-ctc library is not in your compiler’s library path, the config.ctc__root configuration option must be appropriately set to the directory containing the warp-ctc library files.

Note

This interface is the preferred interface.

Note

Unfortunately, Windows platforms are not yet supported by the underlying library.

aesara.tensor.nnet.ctc.ctc(activations, labels, input_lengths)[source]

Compute CTC loss function.

Notes

Using the loss function requires that the Baidu’s warp-ctc library be installed. If the warp-ctc library is not on the compiler’s default library path, the configuration variable config.ctc__root must be properly set.

Parameters:
  • activations – Three-dimensional tensor, which has a shape of (t, m, p), where t is the time index, m is the minibatch index, and p is the index over the probabilities of each symbol in the alphabet. The memory layout is assumed to be in C-order, which consists in the slowest to the fastest changing dimension, from left to right. In this case, p is the fastest changing dimension.
  • labels – A 2-D tensor of all the labels for the minibatch. In each row, there is a sequence of target labels. Negative values are assumed to be padding, and thus are ignored. Blank symbol is assumed to have index 0 in the alphabet.
  • input_lengths – A 1-D tensor with the number of time steps for each sequence in the minibatch.
Returns:

Cost of each example in the minibatch.
Return type:

1-D array
class aesara.tensor.nnet.ctc.ConnectionistTemporalClassification(compute_grad=True, openmp=None)[source]

CTC loss function wrapper.

Notes

Using the wrapper requires that Baidu’s warp-ctc library is installed. If the warp-ctc library is not on your compiler’s default library path, you must set the configuration variable config.ctc__root appropriately.

Parameters:compute_grad – If set to True, enables the computation of gradients of the CTC loss function.