An implementation of a gru network layer. More...
Public Member Functions | |
GRU () | |
Create the GRU object. More... | |
GRU (const size_t inSize, const size_t outSize, const size_t rho=std::numeric_limits< size_t >::max()) | |
Create the GRU layer object using the specified parameters. More... | |
~GRU () | |
Delete the GRU and the layers it holds. More... | |
template < typename eT > | |
void | Backward (const arma::Mat< eT > &&, arma::Mat< eT > &&gy, arma::Mat< eT > &&g) |
Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f. More... | |
OutputDataType const & | Delta () const |
Get the delta. More... | |
OutputDataType & | Delta () |
Modify the delta. More... | |
bool | Deterministic () const |
The value of the deterministic parameter. More... | |
bool & | Deterministic () |
Modify the value of the deterministic parameter. More... | |
template < typename eT > | |
void | Forward (arma::Mat< eT > &&input, arma::Mat< eT > &&output) |
Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More... | |
template < typename eT > | |
void | Gradient (arma::Mat< eT > &&input, arma::Mat< eT > &&, arma::Mat< eT > &&) |
OutputDataType const & | Gradient () const |
Get the gradient. More... | |
OutputDataType & | Gradient () |
Modify the gradient. More... | |
std::vector< LayerTypes<> > & | Model () |
Get the model modules. More... | |
OutputDataType const & | OutputParameter () const |
Get the output parameter. More... | |
OutputDataType & | OutputParameter () |
Modify the output parameter. More... | |
OutputDataType const & | Parameters () const |
Get the parameters. More... | |
OutputDataType & | Parameters () |
Modify the parameters. More... | |
void | ResetCell (const size_t size) |
size_t | Rho () const |
Get the maximum number of steps to backpropagate through time (BPTT). More... | |
size_t & | Rho () |
Modify the maximum number of steps to backpropagate through time (BPTT). More... | |
template < typename Archive > | |
void | serialize (Archive &ar, const unsigned int) |
Serialize the layer. More... | |
An implementation of a gru network layer.
This cell can be used in RNN networks.
InputDataType | Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube). |
OutputDataType | Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube). |
GRU | ( | const size_t | inSize, |
const size_t | outSize, | ||
const size_t | rho = std::numeric_limits< size_t >::max() |
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Create the GRU layer object using the specified parameters.
inSize | The number of input units. |
outSize | The number of output units. |
rho | Maximum number of steps to backpropagate through time (BPTT). |
void Backward | ( | const arma::Mat< eT > && | , |
arma::Mat< eT > && | gy, | ||
arma::Mat< eT > && | g | ||
) |
Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f.
Using the results from the feed forward pass.
input | The propagated input activation. |
gy | The backpropagated error. |
g | The calculated gradient. |
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void Forward | ( | arma::Mat< eT > && | input, |
arma::Mat< eT > && | output | ||
) |
Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.
input | Input data used for evaluating the specified function. |
output | Resulting output activation. |
void Gradient | ( | arma::Mat< eT > && | input, |
arma::Mat< eT > && | , | ||
arma::Mat< eT > && | |||
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Get the model modules.
Definition at line 154 of file gru.hpp.
References GRU< InputDataType, OutputDataType >::serialize().
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void ResetCell | ( | const size_t | size | ) |
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void serialize | ( | Archive & | ar, |
const unsigned | int | ||
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Serialize the layer.
Referenced by GRU< InputDataType, OutputDataType >::Model().