CTC (Connectionist Temporal Classification) loss.

Usage

loss_ctc(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "ctc",
  dtype = NULL
)

y_true: A tensor of shape (batch_size, target_max_length) containing the true labels in integer format. 0 always represents the blank/mask index and should not be used for classes.
y_pred: A tensor of shape (batch_size, output_max_length, num_classes) containing logits (the output of your model). They should not be normalized via softmax.
...: For forward/backward compatability.
reduction: Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or NULL. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and NULL perform no aggregation. Defaults to "sum_over_batch_size".
name: Optional name for the loss instance.
dtype: The dtype of the loss's computations. Defaults to NULL, which means using config_floatx(). config_floatx() is a "float32" unless set to different value (via config_set_floatx()). If a keras$DTypePolicy is provided, then the compute_dtype will be utilized.

CTC loss value.