CTC (Connectionist Temporal Classification) loss.

## Arguments

- 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"`

or`NULL`

.- name
String, name for the object

## See also

Other losses: `Loss()`

`loss_binary_crossentropy()`

`loss_binary_focal_crossentropy()`

`loss_categorical_crossentropy()`

`loss_categorical_focal_crossentropy()`

`loss_categorical_hinge()`

`loss_cosine_similarity()`

`loss_dice()`

`loss_hinge()`

`loss_huber()`

`loss_kl_divergence()`

`loss_log_cosh()`

`loss_mean_absolute_error()`

`loss_mean_absolute_percentage_error()`

`loss_mean_squared_error()`

`loss_mean_squared_logarithmic_error()`

`loss_poisson()`

`loss_sparse_categorical_crossentropy()`

`loss_squared_hinge()`

`loss_tversky()`

`metric_binary_crossentropy()`

`metric_binary_focal_crossentropy()`

`metric_categorical_crossentropy()`

`metric_categorical_focal_crossentropy()`

`metric_categorical_hinge()`

`metric_hinge()`

`metric_huber()`

`metric_kl_divergence()`

`metric_log_cosh()`

`metric_mean_absolute_error()`

`metric_mean_absolute_percentage_error()`

`metric_mean_squared_error()`

`metric_mean_squared_logarithmic_error()`

`metric_poisson()`

`metric_sparse_categorical_crossentropy()`

`metric_squared_hinge()`