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* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
64 lines
2.7 KiB
Markdown
64 lines
2.7 KiB
Markdown
## CTCGreedyDecoder <a name="CTCGreedyDecoder"></a> {#openvino_docs_ops_sequence_CTCGreedyDecoder_1}
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**Versioned name**: *CTCGreedyDecoder-1*
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**Category**: Sequence processing
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**Short description**: *CTCGreedyDecoder* performs greedy decoding on the logits given in input (best path).
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**Detailed description**:
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This operation is similar [Reference](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder)
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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
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\f[
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p(C|X) = \prod_{t=1}^{T} p(c_{t}|X)
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\f]
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Sequences in the batch can have different length. The lengths of sequences are coded as values 1 and 0 in the second input tensor `sequence_mask`. Value `sequence_mask[j, i]` specifies whether there is a sequence symbol at index `i` in the sequence `i` in the batch of sequences. If there is no symbol at `j`-th position `sequence_mask[j, i] = 0`, and `sequence_mask[j, i] = 1` otherwise. Starting from `j = 0`, `sequence_mass[j, i]` are equal to 1 up to the particular index `j = last_sequence_symbol`, which is defined independently for each sequence `i`. For `j > last_sequence_symbol`, values in `sequence_mask[j, i]` are all zeros.
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**Attributes**
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* *merge_repeated*
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* **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC calculation.
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* **Range of values**: True or False
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* **Type**: `boolean`
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* **Default value**: True
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* **Required**: *no*
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**Inputs**
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* **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.
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* **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.
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**Output**
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* **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.
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**Example**
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```xml
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<layer ... type="CTCGreedyDecoder" ...>
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<input>
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<port id="0">
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<dim>20</dim>
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<dim>8</dim>
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<dim>128</dim>
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</port>
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<port id="1">
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<dim>20</dim>
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<dim>8</dim>
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</port>
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</input>
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<output>
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<port id="0">
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<dim>8</dim>
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<dim>20</dim>
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<dim>1</dim>
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<dim>1</dim>
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</port>
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</output>
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</layer>
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``` |