class paddle.nn. CTCLoss ( blank=0, reduction='mean' ) [source]

An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc) to compute Connectionist Temporal Classification (CTC) loss. It can be aliased as softmax with CTC, since a native softmax activation is interated to the Warp-CTC library to normalize values for each row of the input tensor.

  • blank (int, optional) – The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default is 0.

  • reduction (string, optional) – Indicate how to average the loss, the candicates are 'none' | 'mean' | 'sum'. If reduction is 'mean', the output loss will be divided by the label_lengths, and then return the mean of quotient; If reduction is 'sum', return the sum of loss; If reduction is 'none', no reduction will be applied. Default is 'mean'.


log_probs (Tensor): The unscaled probability sequence with padding, which is a 3-D Tensor. The tensor shape is [max_logit_length, batch_size, num_classes + 1], where max_logit_length is the longest length of input logit sequence. The data type should be float32 or float64. labels (Tensor): The ground truth sequence with padding, which must be a 3-D Tensor. The tensor shape is [batch_size, max_label_length], where max_label_length is the longest length of label sequence. The data type must be int32. input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64. label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64.


reduction is 'none', the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as log_probs.

Return type

Tensor, The Connectionist Temporal Classification (CTC) loss between log_probs and labels. If attr


# declarative mode
import numpy as np
import paddle

# length of the longest logit sequence
max_seq_length = 4
#length of the longest label sequence
max_label_length = 3
# number of logit sequences
batch_size = 2
# class num
class_num = 3

log_probs = np.array([[[4.17021990e-01, 7.20324516e-01, 1.14374816e-04],
                        [3.02332580e-01, 1.46755889e-01, 9.23385918e-02]],

                        [[1.86260208e-01, 3.45560730e-01, 3.96767467e-01],
                        [5.38816750e-01, 4.19194520e-01, 6.85219526e-01]],

                        [[2.04452246e-01, 8.78117442e-01, 2.73875929e-02],
                        [6.70467496e-01, 4.17304814e-01, 5.58689833e-01]],

                        [[1.40386939e-01, 1.98101491e-01, 8.00744593e-01],
                        [9.68261600e-01, 3.13424170e-01, 6.92322612e-01]],

                        [[8.76389146e-01, 8.94606650e-01, 8.50442126e-02],
                        [3.90547849e-02, 1.69830427e-01, 8.78142476e-01]]]).astype("float32")
labels = np.array([[1, 2, 2],
                [1, 2, 2]]).astype("int32")
input_lengths = np.array([5, 5]).astype("int64")
label_lengths = np.array([3, 3]).astype("int64")

log_probs = paddle.to_tensor(log_probs)
labels = paddle.to_tensor(labels)
input_lengths = paddle.to_tensor(input_lengths)
label_lengths = paddle.to_tensor(label_lengths)

loss = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels,
print(loss)  #[3.9179852 2.9076521]

loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels,
print(loss)  #[1.1376063]
forward ( log_probs, labels, input_lengths, label_lengths )

Defines the computation performed at every call. Should be overridden by all subclasses.

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments