theano.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 prefered interface. It will be moved automatically to the GPU.
Note
Unfortunately, Windows platforms are not yet supported by the underlying library.
-
theano.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
theano.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.