The Connectionist Temporal Classification loss.

## Usage

```
nnf_ctc_loss(
log_probs,
targets,
input_lengths,
target_lengths,
blank = 0,
reduction = c("mean", "sum", "none"),
zero_infinity = FALSE
)
```

## Arguments

- log_probs
\((T, N, C)\) where C = number of characters in alphabet including blank, T = input length, and N = batch size. The logarithmized probabilities of the outputs (e.g. obtained with nnf_log_softmax).

- targets
\((N, S)\) or

`(sum(target_lengths))`

. Targets cannot be blank. In the second form, the targets are assumed to be concatenated.- input_lengths
\((N)\). Lengths of the inputs (must each be \(\leq T\))

- target_lengths
\((N)\). Lengths of the targets

- blank
(int, optional) Blank label. Default \(0\).

- reduction
(string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'mean'

- zero_infinity
(bool, optional) Whether to zero infinite losses and the associated gradients. Default:

`FALSE`

Infinite losses mainly occur when the inputs are too short to be aligned to the targets.