Public Member Functions | |
template<typename ParameterContainer > | |
RMSprop (ParameterContainer &¶meters, const RMSpropOptions &options) | |
void | step () override |
Adapted from https://github.com/pytorch/pytorch/blob/master/torch/optim/rmsprop.py. | |
void | save (serialize::OutputArchive &archive) const override |
Serializes the optimizer state into the given archive . | |
void | load (serialize::InputArchive &archive) override |
Deserializes the optimizer state from the given archive . | |
Public Member Functions inherited from torch::optim::detail::OptimizerBase | |
OptimizerBase (std::vector< Tensor > parameters) | |
Constructs the Optimizer from a vector of parameters. | |
void | add_parameters (const std::vector< Tensor > ¶meters) |
Adds the given vector of parameters to the optimizer's parameter list. | |
virtual void | zero_grad () |
Zeros out the gradients of all parameters. | |
const std::vector< Tensor > & | parameters () const noexcept |
Provides a const reference to the parameters this optimizer holds. | |
std::vector< Tensor > & | parameters () noexcept |
Provides a reference to the parameters this optimizer holds. | |
size_t | size () const noexcept |
Returns the number of parameters referenced by the optimizer. | |
Data Fields | |
RMSpropOptions | options |
std::vector< Tensor > | square_average_buffers |
std::vector< Tensor > | momentum_buffers |
std::vector< Tensor > | grad_average_buffers |
Additional Inherited Members | |
Protected Member Functions inherited from torch::optim::detail::OptimizerBase | |
template<typename T > | |
T & | buffer_at (std::vector< T > &buffers, size_t index) |
Accesses a buffer at the given index. More... | |
Tensor & | buffer_at (std::vector< Tensor > &buffers, size_t index) |
Accesses a buffer at the given index, converts it to the type of the parameter at the corresponding index (a no-op if they match). More... | |
Protected Attributes inherited from torch::optim::detail::OptimizerBase | |
std::vector< Tensor > | parameters_ |
The parameters this optimizer optimizes. | |