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Bigbatch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions. More...
Public Member Functions  
BigBatchSGD (const size_t batchSize=1000, const double stepSize=0.01, const double batchDelta=0.1, const size_t maxIterations=100000, const double tolerance=1e5, const bool shuffle=true)  
Construct the BigBatchSGD optimizer with the given function and parameters. More...  
double  BatchDelta () const 
Get the batch delta. More...  
double &  BatchDelta () 
Modify the batch delta. More...  
size_t  BatchSize () const 
Get the batch size. More...  
size_t &  BatchSize () 
Modify the batch size. More...  
size_t  MaxIterations () const 
Get the maximum number of iterations (0 indicates no limit). More...  
size_t &  MaxIterations () 
Modify the maximum number of iterations (0 indicates no limit). More...  
template < typename DecomposableFunctionType >  
double  Optimize (DecomposableFunctionType &function, arma::mat &iterate) 
Optimize the given function using bigbatch SGD. More...  
bool  Shuffle () const 
Get whether or not the individual functions are shuffled. More...  
bool &  Shuffle () 
Modify whether or not the individual functions are shuffled. More...  
double  StepSize () const 
Get the step size. More...  
double &  StepSize () 
Modify the step size. More...  
double  Tolerance () const 
Get the tolerance for termination. More...  
double &  Tolerance () 
Modify the tolerance for termination. More...  
UpdatePolicyType  UpdatePolicy () const 
Get the update policy. More...  
UpdatePolicyType &  UpdatePolicy () 
Modify the update policy. More...  
Detailed Description
template<typenameUpdatePolicyType=AdaptiveStepsize>
class mlpack::optimization::BigBatchSGD< UpdatePolicyType >
Bigbatch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions.
That is, suppose we have
and our task is to minimize . Bigbatch SGD iterates over batches of functions for some batch size , producing the following update scheme:
where is a parameter which specifies the step size. Each bigbatch is passed through either sequentially or randomly. The algorithm continues until reaches the maximum number of iterations—or when a full sequence of updates through each of the bigbatches produces an improvement within a certain tolerance .
The parameter is specified by the tolerance parameter tot he constructor, as is the maximum number of iterations specified by the maxIterations parameter.
This class is useful for datadependent functions whose objective function can be expressed as a sum of objective functions operating on an individual point. Then, bigbatch SGD considers the gradient of the objective function operation on an individual bigbatch of points in its update of .
For more information, please refer to:
For bigbatch SGD to work, a DecomposableFunctionType template parameter is required. This class must implement the following function:
size_t NumFunctions(); double Evaluate(const arma::mat& coordinates, const size_t i); void Gradient(const arma::mat& coordinates, const size_t i, arma::mat& gradient);
NumFunctions() should return the number of functions, and in the other two functions, the parameter i refers to which individual function (or gradient) is being evaluated. So, for the case of a datadependent function, such as NCA (see mlpack::nca::NCA), NumFunctions() should return the number of points in the dataset, and Evaluate(coordinates, 0) will evaluate the objective function on the first point in the dataset (presumably, the dataset is held internally in the DecomposableFunctionType).
 Template Parameters

UpdatePolicyType Update policy used during the iterative update process. By default the AdaptiveStepsize update policy is used.
Definition at line 93 of file bigbatch_sgd.hpp.
Constructor & Destructor Documentation
◆ BigBatchSGD()
BigBatchSGD  (  const size_t  batchSize = 1000 , 
const double  stepSize = 0.01 , 

const double  batchDelta = 0.1 , 

const size_t  maxIterations = 100000 , 

const double  tolerance = 1e5 , 

const bool  shuffle = true 

) 
Construct the BigBatchSGD optimizer with the given function and parameters.
The defaults here are not necessarily good for the given problem, so it is suggested that the values used be tailored for the task at hand. The maximum number of iterations refers to the maximum number of batches that are processed.
 Parameters

batchSize Initial batch size. stepSize Step size for each iteration. batchDelta Factor for the batch update step. maxIterations Maximum number of iterations allowed (0 means no limit). tolerance Maximum absolute tolerance to terminate algorithm. shuffle If true, the batch order is shuffled; otherwise, each batch is visited in linear order.
Member Function Documentation
◆ BatchDelta() [1/2]

inline 
Get the batch delta.
Definition at line 143 of file bigbatch_sgd.hpp.
◆ BatchDelta() [2/2]

inline 
Modify the batch delta.
Definition at line 145 of file bigbatch_sgd.hpp.
◆ BatchSize() [1/2]

inline 
Get the batch size.
Definition at line 133 of file bigbatch_sgd.hpp.
◆ BatchSize() [2/2]

inline 
Modify the batch size.
Definition at line 135 of file bigbatch_sgd.hpp.
◆ MaxIterations() [1/2]

inline 
Get the maximum number of iterations (0 indicates no limit).
Definition at line 148 of file bigbatch_sgd.hpp.
◆ MaxIterations() [2/2]

inline 
Modify the maximum number of iterations (0 indicates no limit).
Definition at line 150 of file bigbatch_sgd.hpp.
◆ Optimize()
double Optimize  (  DecomposableFunctionType &  function, 
arma::mat &  iterate  
) 
Optimize the given function using bigbatch SGD.
The given starting point will be modified to store the finishing point of the algorithm, and the final objective value is returned.
 Template Parameters

DecomposableFunctionType Type of the function to be optimized.
 Parameters

function Function to optimize. iterate Starting point (will be modified).
 Returns
 Objective value of the final point.
◆ Shuffle() [1/2]

inline 
Get whether or not the individual functions are shuffled.
Definition at line 158 of file bigbatch_sgd.hpp.
◆ Shuffle() [2/2]

inline 
Modify whether or not the individual functions are shuffled.
Definition at line 160 of file bigbatch_sgd.hpp.
◆ StepSize() [1/2]

inline 
Get the step size.
Definition at line 138 of file bigbatch_sgd.hpp.
◆ StepSize() [2/2]

inline 
Modify the step size.
Definition at line 140 of file bigbatch_sgd.hpp.
◆ Tolerance() [1/2]

inline 
Get the tolerance for termination.
Definition at line 153 of file bigbatch_sgd.hpp.
◆ Tolerance() [2/2]

inline 
Modify the tolerance for termination.
Definition at line 155 of file bigbatch_sgd.hpp.
◆ UpdatePolicy() [1/2]

inline 
Get the update policy.
Definition at line 163 of file bigbatch_sgd.hpp.
◆ UpdatePolicy() [2/2]

inline 
Modify the update policy.
Definition at line 165 of file bigbatch_sgd.hpp.
The documentation for this class was generated from the following file:
 /var/www/www.mlpack.org/mlpackgit/src/mlpack/core/optimizers/bigbatch_sgd/bigbatch_sgd.hpp
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