class InferenceEngine::GRUCell¶

Overview¶

GRU Cell layer. More…

#include <ie_layers.h>

class GRUCell: public InferenceEngine::RNNCellBase
{
public:
};

Inherited Members¶

public:
    // typedefs

    typedef std::shared_ptr<CNNLayer> Ptr;

    // enums

    enum CellType;

    // fields

    std::string name;
    std::string type;
    Precision precision;
    std::vector<DataPtr> outData;
    std::vector<DataWeakPtr> insData;
    Ptr _fusedWith;
    UserValue userValue;
    std::string affinity;
    std::map<std::string, std::string> params;
    std::map<std::string, Blob::Ptr> blobs;
    Blob::Ptr _weights;
    Blob::Ptr _biases;
    CellType cellType = LSTM;
    int hidden_size = 0;
    float clip = 0.0f;
    std::vector<std::string> activations;
    std::vector<float> activation_alpha;
    std::vector<float> activation_beta;

    // methods

    std::shared_ptr<ngraph::Node> getNode() const;
    void fuse(Ptr& layer);
    virtual const DataPtr input() const;
    void parseParams();
    float GetParamAsFloat(const char \* param, float def) const;
    float GetParamAsFloat(const char \* param) const;
    std::vector<float> GetParamAsFloats(const char \* param, std::vector<float> def) const;
    std::vector<float> GetParamAsFloats(const char \* param) const;
    int GetParamAsInt(const char \* param, int def) const;
    int GetParamAsInt(const char \* param) const;
    std::vector<int> GetParamAsInts(const char \* param, std::vector<int> def) const;
    std::vector<int> GetParamAsInts(const char \* param) const;
    unsigned int GetParamAsUInt(const char \* param, unsigned int def) const;
    unsigned int GetParamAsUInt(const char \* param) const;
    size_t GetParamAsSizeT(const char \* param, size_t def) const;
    size_t GetParamAsSizeT(const char \* param) const;

    std::vector<unsigned int> GetParamAsUInts(
        const char \* param,
        std::vector<unsigned int> def
        ) const;

    std::vector<unsigned int> GetParamAsUInts(const char \* param) const;
    bool GetParamAsBool(const char \* param, bool def) const;
    bool GetParamAsBool(const char \* param) const;
    std::string GetParamAsString(const char \* param, const char \* def) const;
    bool CheckParamPresence(const char \* param) const;
    std::string GetParamAsString(const char \* param) const;
    std::string getBoolStrParamAsIntStr(const char \* param) const;

    std::vector<std::string> GetParamAsStrings(
        const char \* param,
        std::vector<std::string> def
        ) const;

    static float ie_parse_float(const std::string& str);
    static std::string ie_serialize_float(float value);
    CNNLayer(const LayerParams& prms);
    CNNLayer(const CNNLayer& other);
    WeightableLayer(const LayerParams& prms);

Detailed Documentation¶

GRU Cell layer.

Deprecated Migrate to IR v10 and work with ngraph::Function directly. The method will be removed in 2021.1

G - number of gates (=3) N - batch size S - state size (=hidden_size)

Inputs: [N,D] Xt - input data [N,S] Ht-1 - initial hidden state

Outputs: [N,S] Ht - out hidden state

Weights:

weights [G,S,D+S]
biases [G,S] NB! gates order is ZRH {update, reset, output}

activations is {_f, _g} default: {_f=sigm, _g=tanh}

Equations:

- matrix mult (.) - eltwise mult [,] - concatenation

zt = _f(Wz*[Ht-1, Xt] + Bz)

rt = _f(Wr*[Ht-1, Xt] + Br)
ht = _g(Wh*[rt (.) Ht-1, Xt] + Bh)
Ht = (1 - zt) (.) ht + zt (.) Ht-1