From 58595fb0f09c5dcf1e202a0aade0213282848eeb Mon Sep 17 00:00:00 2001 From: Jozef Daniecki Date: Tue, 1 Jun 2021 12:58:45 +0200 Subject: [PATCH] CTCGreedyDecoder operation specification refactoring (#5885) * CTCGreedyDecoder spec refactored against explicit type indication. * Add backticks to output tensor description. --- docs/ops/sequence/CTCGreedyDecoder_1.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/ops/sequence/CTCGreedyDecoder_1.md b/docs/ops/sequence/CTCGreedyDecoder_1.md index 1104e1c096ef57..35414602cdef17 100644 --- a/docs/ops/sequence/CTCGreedyDecoder_1.md +++ b/docs/ops/sequence/CTCGreedyDecoder_1.md @@ -7,9 +7,6 @@ **Short description**: *CTCGreedyDecoder* performs greedy decoding on the logits given in input (best path). **Detailed description**: - -This operation is similar [Reference](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder) - Given an input sequence \f$X\f$ of length \f$T\f$, *CTCGreedyDecoder* assumes the probability of a length \f$T\f$ character sequence \f$C\f$ is given by \f[ p(C|X) = \prod_{t=1}^{T} p(c_{t}|X) @@ -24,20 +21,23 @@ Sequences in the batch can have different length. The lengths of sequences are c * *ctc_merge_repeated* * **Description**: *ctc_merge_repeated* is a flag for merging repeated labels during the CTC calculation. - * **Range of values**: true or false + * **Range of values**: `true` or `false` * **Type**: `boolean` - * **Default value**: true + * **Default value**: `true` * **Required**: *no* **Inputs** -* **1**: `data` - Input tensor with a batch of sequences. Type of elements is any supported floating point type. Shape of the tensor is `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. Required. +* **1**: `data` - input tensor with batch of sequences of type `T_F` and shape `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.** -* **2**: `sequence_mask` - 2D input floating point tensor with sequence masks for each sequence in the batch. Populated with values 0 and 1. Shape of this input is `[T, N]`. Required. +* **2**: `sequence_mask` - input tensor with sequence masks for each sequence in the batch of type `T_F` populated with values `0` and `1` and shape `[T, N]`. **Required.** **Output** -* **1**: Output tensor with shape `[N, T, 1, 1]` and integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with -1. Type of elements is floating point, but all values are integers. +* **1**: Output tensor of type `T_F` and shape `[N, T, 1, 1]` which is filled with integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with `-1`. + +**Types** +* *T_F*: any supported floating point type. **Example**