ef45b5da8d
* 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>
95 lines
2.7 KiB
Markdown
95 lines
2.7 KiB
Markdown
## RNNCell <a name="RNNCell"></a> {#openvino_docs_ops_sequence_RNNCell_3}
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**Versioned name**: *RNNCell-3*
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**Category**: Sequence processing
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**Short description**: *RNNCell* represents a single RNN cell that computes the output using the formula described in the [article](https://hackernoon.com/understanding-architecture-of-lstm-cell-from-scratch-with-code-8da40f0b71f4).
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**Attributes**
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* *hidden_size*
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* **Description**: *hidden_size* specifies hidden state size.
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* **Range of values**: a positive integer
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* **Type**: `int`
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* **Default value**: None
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* **Required**: *yes*
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* *activations*
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* **Description**: activation functions for gates
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* **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
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* **Type**: a list of strings
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* **Default value**: *sigmoid,tanh*
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* **Required**: *no*
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* *activations_alpha, activations_beta*
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* **Description**: *activations_alpha, activations_beta* functions attributes
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* **Range of values**: a list of floating-point numbers
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* **Type**: `float[]`
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* **Default value**: None
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* **Required**: *no*
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* *clip*
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* **Description**: *clip* specifies value for tensor clipping to be in *[-C, C]* before activations
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* **Range of values**: a positive floating-point number
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* **Type**: `float`
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* **Default value**: *infinity* that means that the clipping is not applied
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* **Required**: *no*
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**Inputs**
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* **1**: `X` - 2D tensor of type *T* `[batch_size, input_size]`, input data. Required.
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* **2**: `initial_hidden_state` - 2D tensor of type *T* `[batch_size, hidden_size]`. Required.
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* **3**: `W` - 2D tensor tensor of type *T* `[hidden_size, input_size]`, the weights for matrix multiplication. Required.
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* **4**: `R` - 2D tensor tensor of type *T* `[hidden_size, hidden_size]`, the recurrence weights for matrix multiplication. Required.
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* **5**: `B` 1D tensor tensor of type *T* `[hidden_size]`, the sum of biases (weights and recurrence weights). Required.
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**Outputs**
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* **1**: `Ho` - 2D tensor of type *T* `[batch_size, hidden_size]`, output hidden state.
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**Types**
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* *T*: any supported floating point type.
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**Example**
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```xml
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<layer ... type="RNNCell" ...>
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<data hidden_size="128"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>16</dim>
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</port>
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<port id="1">
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<dim>1</dim>
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<dim>128</dim>
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</port>
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<port id="2">
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<dim>128</dim>
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<dim>16</dim>
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</port>
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<port id="3">
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<dim>128</dim>
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<dim>128</dim>
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</port>
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<port id="4">
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<dim>128</dim>
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</port>
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</input>
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<output>
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<port id="5">
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<dim>1</dim>
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<dim>128</dim>
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</port>
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</output>
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</layer>
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``` |