This is a class for the sparse autoencoder objective function. More...
Public Member Functions | |
| SparseAutoencoderFunction (const arma::mat &data, const size_t visibleSize, const size_t hiddenSize, const double lambda=0.0001, const double beta=3, const double rho=0.01) | |
| Construct the sparse autoencoder objective function with the given parameters. More... | |
| void | Beta (const double b) |
| Sets the KL divergence parameter. More... | |
| double | Beta () const |
| Gets the KL divergence parameter. More... | |
| double | Evaluate (const arma::mat ¶meters) const |
| Evaluates the objective function of the sparse autoencoder model using the given parameters. More... | |
| const arma::mat & | GetInitialPoint () const |
| Return the initial point for the optimization. More... | |
| void | Gradient (const arma::mat ¶meters, arma::mat &gradient) const |
| Evaluates the gradient values of the objective function given the current set of parameters. More... | |
| void | HiddenSize (const size_t hidden) |
| Sets size of the hidden layer. More... | |
| size_t | HiddenSize () const |
| Gets the size of the hidden layer. More... | |
| const arma::mat | InitializeWeights () |
| Initializes the parameters of the model to suitable values. More... | |
| void | Lambda (const double l) |
| Sets the L2-regularization parameter. More... | |
| double | Lambda () const |
| Gets the L2-regularization parameter. More... | |
| void | Rho (const double r) |
| Sets the sparsity parameter. More... | |
| double | Rho () const |
| Gets the sparsity parameter. More... | |
| void | Sigmoid (const arma::mat &x, arma::mat &output) const |
| Returns the elementwise sigmoid of the passed matrix, where the sigmoid function of a real number 'x' is [1 / (1 + exp(-x))]. More... | |
| void | VisibleSize (const size_t visible) |
| Sets size of the visible layer. More... | |
| size_t | VisibleSize () const |
| Gets size of the visible layer. More... | |
Detailed Description
This is a class for the sparse autoencoder objective function.
It can be used to create learning models like self-taught learning, stacked autoencoders, conditional random fields (CRFs), and so forth.
Definition at line 26 of file sparse_autoencoder_function.hpp.
Constructor & Destructor Documentation
◆ SparseAutoencoderFunction()
| SparseAutoencoderFunction | ( | const arma::mat & | data, |
| const size_t | visibleSize, | ||
| const size_t | hiddenSize, | ||
| const double | lambda = 0.0001, |
||
| const double | beta = 3, |
||
| const double | rho = 0.01 |
||
| ) |
Construct the sparse autoencoder objective function with the given parameters.
- Parameters
-
data The data matrix. visibleSize Size of input vector expected at the visible layer. hiddenSize Size of input vector expected at the hidden layer. lambda L2-regularization parameter. beta KL divergence parameter. rho Sparsity parameter.
Member Function Documentation
◆ Beta() [1/2]
|
inline |
Sets the KL divergence parameter.
Definition at line 125 of file sparse_autoencoder_function.hpp.
◆ Beta() [2/2]
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inline |
Gets the KL divergence parameter.
Definition at line 131 of file sparse_autoencoder_function.hpp.
◆ Evaluate()
| double Evaluate | ( | const arma::mat & | parameters | ) | const |
Evaluates the objective function of the sparse autoencoder model using the given parameters.
The cost function has terms for the reconstruction error, regularization cost and the sparsity cost. The objective function takes a low value when the model is able to reconstruct the data well using weights which are low in value and when the average activations of neurons in the hidden layers agrees well with the sparsity parameter 'rho'.
- Parameters
-
parameters Current values of the model parameters.
◆ GetInitialPoint()
|
inline |
Return the initial point for the optimization.
Definition at line 86 of file sparse_autoencoder_function.hpp.
◆ Gradient()
| void Gradient | ( | const arma::mat & | parameters, |
| arma::mat & | gradient | ||
| ) | const |
Evaluates the gradient values of the objective function given the current set of parameters.
The function performs a feedforward pass and computes the error in reconstructing the data points. It then uses the backpropagation algorithm to compute the gradient values.
- Parameters
-
parameters Current values of the model parameters. gradient Matrix where gradient values will be stored.
◆ HiddenSize() [1/2]
|
inline |
Sets size of the hidden layer.
Definition at line 101 of file sparse_autoencoder_function.hpp.
◆ HiddenSize() [2/2]
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inline |
Gets the size of the hidden layer.
Definition at line 107 of file sparse_autoencoder_function.hpp.
◆ InitializeWeights()
| const arma::mat InitializeWeights | ( | ) |
Initializes the parameters of the model to suitable values.
◆ Lambda() [1/2]
|
inline |
Sets the L2-regularization parameter.
Definition at line 113 of file sparse_autoencoder_function.hpp.
◆ Lambda() [2/2]
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inline |
Gets the L2-regularization parameter.
Definition at line 119 of file sparse_autoencoder_function.hpp.
◆ Rho() [1/2]
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inline |
Sets the sparsity parameter.
Definition at line 137 of file sparse_autoencoder_function.hpp.
◆ Rho() [2/2]
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inline |
Gets the sparsity parameter.
Definition at line 143 of file sparse_autoencoder_function.hpp.
◆ Sigmoid()
|
inline |
Returns the elementwise sigmoid of the passed matrix, where the sigmoid function of a real number 'x' is [1 / (1 + exp(-x))].
- Parameters
-
x Matrix of real values for which we require the sigmoid activation. output Output matrix.
Definition at line 80 of file sparse_autoencoder_function.hpp.
◆ VisibleSize() [1/2]
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inline |
Sets size of the visible layer.
Definition at line 89 of file sparse_autoencoder_function.hpp.
◆ VisibleSize() [2/2]
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inline |
Gets size of the visible layer.
Definition at line 95 of file sparse_autoencoder_function.hpp.
The documentation for this class was generated from the following file:
- /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp