"comment":"\nAn operator integrating the open-source\n[warp-ctc](https://github.com/baidu-research/warp-ctc) library, which is used in\n[Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin](\nhttps://arxiv.org/pdf/1512.02595v1.pdf),\nto compute Connectionist Temporal Classification (CTC) loss.\nIt can be aliased as softmax with ctc, since a native softmax activation is\ninterated to the warp-ctc library, to to normlize values for each row of the\ninput tensor.\n\nMore detail of CTC loss can be found by refering to\n[Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with\nRecurrent Neural Networks](\nhttp://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf).\n",
"inputs":[
{
"name":"Logits",
"comment":"(LodTensor, default: LoDTensor<float>), the unscaled probabilities of variable-length sequences, which is a 2-D Tensor with LoD information. It's shape is [Lp, num_classes + 1], where Lp is the sum of all input sequences' length and num_classes is the true number of classes (not including the blank label).",
"duplicable":0,
"intermediate":0
},{
"name":"Label",
"comment":"(LodTensor, default: LoDTensor<int>), the ground truth of variable-length sequence, which is a 2-D Tensor with LoD information. It is of the shape [Lg, 1], where Lg is th sum of all labels' length.",
"duplicable":0,
"intermediate":0
}],
"outputs":[
{
"name":"WarpCTCGrad",
"comment":"(Tensor, default: Tensor<float>), a temporary output Tensor to store the gradients of warp-ctc, which is computed with loss together in one call. It is a 3-D Tensor of the shape [max_sequence_length, batch_size, num_classes + 1].",
"duplicable":0,
"intermediate":1
},{
"name":"Loss",
"comment":"(Tensor, default: Tensor<float>), the Connectionist Temporal Classification (CTC) loss, which is a 2-D Tensor of the shape [batch_size, 1]",
"duplicable":0,
"intermediate":0
}],
"attrs":[
{
"name":"blank",
"type":"int",
"comment":"(int, default: 0), the blank label of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1).",
"generated":0
},{
"name":"norm_by_times",
"type":"bool",
"comment":"(bool, default: false), whether to normalize the gradients by the number of time-step, which is also the sequence's length.",
"generated":0
}]
},{
},{
"type":"cos_sim",
"type":"cos_sim",
"comment":"\nCosine Similarity Operator.\n\n$Out = X^T * Y / (\\sqrt{X^T * X} * \\sqrt{Y^T * Y})$\n\nThe input X and Y must have the same shape, except that the 1st dimension\nof input Y could be just 1 (different from input X), which will be\nbroadcasted to match the shape of input X before computing their cosine\nsimilarity.\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n",
"comment":"\nCosine Similarity Operator.\n\n$Out = X^T * Y / (\\sqrt{X^T * X} * \\sqrt{Y^T * Y})$\n\nThe input X and Y must have the same shape, except that the 1st dimension\nof input Y could be just 1 (different from input X), which will be\nbroadcasted to match the shape of input X before computing their cosine\nsimilarity.\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n",
