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<div class="title">mlpack::nn::SparseAutoencoder< OptimizerType > Class Template Reference</div> </div>
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<p>A sparse autoencoder is a neural network whose aim to learn compressed representations of the data, typically for dimensionality reduction, with a constraint on the activity of the neurons in the network.
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<a href="classmlpack_1_1nn_1_1SparseAutoencoder.html#details">More...</a></p>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:ad379594f83f6cb4c42269ada1449b28b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ad379594f83f6cb4c42269ada1449b28b">SparseAutoencoder</a> (const arma::mat &data, const size_t <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a529226bc5ab4906b3f0af1c081fc46e0">visibleSize</a>, const size_t <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#afc447ba7a9904ec25d1b7e12b95dcc00">hiddenSize</a>, const double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#acd9e03beffadd0a2be11bd72f0e0a593">lambda</a>=0.0001, const double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ad7493b113fc0585bf94cc2fabcafbee2">beta</a>=3, const double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a20a6d4a33fa571ec6062fa65e0dab2b8">rho</a>=0.01)</td></tr>
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<tr class="memdesc:ad379594f83f6cb4c42269ada1449b28b"><td class="mdescLeft"> </td><td class="mdescRight">Construct the sparse autoencoder model with the given training data. <a href="#ad379594f83f6cb4c42269ada1449b28b">More...</a><br /></td></tr>
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<tr class="separator:ad379594f83f6cb4c42269ada1449b28b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a19066e100db4c4d4748ac1574b993df0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a19066e100db4c4d4748ac1574b993df0">SparseAutoencoder</a> (OptimizerType< <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoderFunction.html">SparseAutoencoderFunction</a> > &optimizer)</td></tr>
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<tr class="memdesc:a19066e100db4c4d4748ac1574b993df0"><td class="mdescLeft"> </td><td class="mdescRight">Construct the sparse autoencoder model with the given training data. <a href="#a19066e100db4c4d4748ac1574b993df0">More...</a><br /></td></tr>
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<tr class="separator:a19066e100db4c4d4748ac1574b993df0"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ac226d403f39a50066c2baaed164e9928"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ac226d403f39a50066c2baaed164e9928">Beta</a> (const double b)</td></tr>
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<tr class="memdesc:ac226d403f39a50066c2baaed164e9928"><td class="mdescLeft"> </td><td class="mdescRight">Sets the KL divergence parameter. <a href="#ac226d403f39a50066c2baaed164e9928">More...</a><br /></td></tr>
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<tr class="separator:ac226d403f39a50066c2baaed164e9928"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a71119d36c88bfb32e1e79844880b5151"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a71119d36c88bfb32e1e79844880b5151">Beta</a> () const </td></tr>
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<tr class="memdesc:a71119d36c88bfb32e1e79844880b5151"><td class="mdescLeft"> </td><td class="mdescRight">Gets the KL divergence parameter. <a href="#a71119d36c88bfb32e1e79844880b5151">More...</a><br /></td></tr>
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<tr class="separator:a71119d36c88bfb32e1e79844880b5151"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acf0c2f96e5c9d062c2172166fe3f97de"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#acf0c2f96e5c9d062c2172166fe3f97de">GetNewFeatures</a> (arma::mat &data, arma::mat &features)</td></tr>
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<tr class="memdesc:acf0c2f96e5c9d062c2172166fe3f97de"><td class="mdescLeft"> </td><td class="mdescRight">Transforms the provided data into the representation learned by the sparse autoencoder. <a href="#acf0c2f96e5c9d062c2172166fe3f97de">More...</a><br /></td></tr>
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<tr class="separator:acf0c2f96e5c9d062c2172166fe3f97de"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ae0a6316ebf98fca173e3d6eef1e663bf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ae0a6316ebf98fca173e3d6eef1e663bf">HiddenSize</a> (const size_t hidden)</td></tr>
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<tr class="memdesc:ae0a6316ebf98fca173e3d6eef1e663bf"><td class="mdescLeft"> </td><td class="mdescRight">Sets size of the hidden layer. <a href="#ae0a6316ebf98fca173e3d6eef1e663bf">More...</a><br /></td></tr>
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<tr class="separator:ae0a6316ebf98fca173e3d6eef1e663bf"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab19aede380b2d940411c31e91c94bcc4"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ab19aede380b2d940411c31e91c94bcc4">HiddenSize</a> () const </td></tr>
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<tr class="memdesc:ab19aede380b2d940411c31e91c94bcc4"><td class="mdescLeft"> </td><td class="mdescRight">Gets the size of the hidden layer. <a href="#ab19aede380b2d940411c31e91c94bcc4">More...</a><br /></td></tr>
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<tr class="separator:ab19aede380b2d940411c31e91c94bcc4"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aefa60fd1fafc5de36b3748e98964ec41"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#aefa60fd1fafc5de36b3748e98964ec41">Lambda</a> (const double l)</td></tr>
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<tr class="memdesc:aefa60fd1fafc5de36b3748e98964ec41"><td class="mdescLeft"> </td><td class="mdescRight">Sets the L2-regularization parameter. <a href="#aefa60fd1fafc5de36b3748e98964ec41">More...</a><br /></td></tr>
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<tr class="separator:aefa60fd1fafc5de36b3748e98964ec41"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aefc446629ae17dd765057a3a828792fd"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#aefc446629ae17dd765057a3a828792fd">Lambda</a> () const </td></tr>
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<tr class="memdesc:aefc446629ae17dd765057a3a828792fd"><td class="mdescLeft"> </td><td class="mdescRight">Gets the L2-regularization parameter. <a href="#aefc446629ae17dd765057a3a828792fd">More...</a><br /></td></tr>
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<tr class="separator:aefc446629ae17dd765057a3a828792fd"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a033c62a32fb5d84ab8b78459cf745b64"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a033c62a32fb5d84ab8b78459cf745b64">Rho</a> (const double r)</td></tr>
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<tr class="memdesc:a033c62a32fb5d84ab8b78459cf745b64"><td class="mdescLeft"> </td><td class="mdescRight">Sets the sparsity parameter. <a href="#a033c62a32fb5d84ab8b78459cf745b64">More...</a><br /></td></tr>
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<tr class="separator:a033c62a32fb5d84ab8b78459cf745b64"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a02f3ae8b0af824280a221b4032a450dd"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a02f3ae8b0af824280a221b4032a450dd">Rho</a> () const </td></tr>
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<tr class="memdesc:a02f3ae8b0af824280a221b4032a450dd"><td class="mdescLeft"> </td><td class="mdescRight">Gets the sparsity parameter. <a href="#a02f3ae8b0af824280a221b4032a450dd">More...</a><br /></td></tr>
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<tr class="separator:a02f3ae8b0af824280a221b4032a450dd"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a6ee241219a3382b7c93b0dcea61fba7a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a6ee241219a3382b7c93b0dcea61fba7a">Sigmoid</a> (const arma::mat &x, arma::mat &output) const </td></tr>
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<tr class="memdesc:a6ee241219a3382b7c93b0dcea61fba7a"><td class="mdescLeft"> </td><td class="mdescRight">Returns the elementwise sigmoid of the passed matrix, where the sigmoid function of a real number 'x' is [1 / (1 + exp(-x))]. <a href="#a6ee241219a3382b7c93b0dcea61fba7a">More...</a><br /></td></tr>
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<tr class="separator:a6ee241219a3382b7c93b0dcea61fba7a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afb23b66c9aebec148cae8849e0e280d6"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#afb23b66c9aebec148cae8849e0e280d6">VisibleSize</a> (const size_t visible)</td></tr>
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<tr class="memdesc:afb23b66c9aebec148cae8849e0e280d6"><td class="mdescLeft"> </td><td class="mdescRight">Sets size of the visible layer. <a href="#afb23b66c9aebec148cae8849e0e280d6">More...</a><br /></td></tr>
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<tr class="separator:afb23b66c9aebec148cae8849e0e280d6"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af393b28a274675ca7489619bf53d9309"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#af393b28a274675ca7489619bf53d9309">VisibleSize</a> () const </td></tr>
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<tr class="memdesc:af393b28a274675ca7489619bf53d9309"><td class="mdescLeft"> </td><td class="mdescRight">Gets size of the visible layer. <a href="#af393b28a274675ca7489619bf53d9309">More...</a><br /></td></tr>
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<tr class="separator:af393b28a274675ca7489619bf53d9309"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
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Private Attributes</h2></td></tr>
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<tr class="memitem:ad7493b113fc0585bf94cc2fabcafbee2"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ad7493b113fc0585bf94cc2fabcafbee2">beta</a></td></tr>
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<tr class="memdesc:ad7493b113fc0585bf94cc2fabcafbee2"><td class="mdescLeft"> </td><td class="mdescRight">KL divergence parameter. <a href="#ad7493b113fc0585bf94cc2fabcafbee2">More...</a><br /></td></tr>
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<tr class="separator:ad7493b113fc0585bf94cc2fabcafbee2"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afc447ba7a9904ec25d1b7e12b95dcc00"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#afc447ba7a9904ec25d1b7e12b95dcc00">hiddenSize</a></td></tr>
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<tr class="memdesc:afc447ba7a9904ec25d1b7e12b95dcc00"><td class="mdescLeft"> </td><td class="mdescRight">Size of the hidden layer. <a href="#afc447ba7a9904ec25d1b7e12b95dcc00">More...</a><br /></td></tr>
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<tr class="separator:afc447ba7a9904ec25d1b7e12b95dcc00"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acd9e03beffadd0a2be11bd72f0e0a593"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#acd9e03beffadd0a2be11bd72f0e0a593">lambda</a></td></tr>
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<tr class="memdesc:acd9e03beffadd0a2be11bd72f0e0a593"><td class="mdescLeft"> </td><td class="mdescRight">L2-regularization parameter. <a href="#acd9e03beffadd0a2be11bd72f0e0a593">More...</a><br /></td></tr>
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<tr class="separator:acd9e03beffadd0a2be11bd72f0e0a593"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a806addcafdd2ee7740ba1dc235b03585"><td class="memItemLeft" align="right" valign="top">arma::mat </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a806addcafdd2ee7740ba1dc235b03585">parameters</a></td></tr>
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<tr class="memdesc:a806addcafdd2ee7740ba1dc235b03585"><td class="mdescLeft"> </td><td class="mdescRight">Parameters after optimization. <a href="#a806addcafdd2ee7740ba1dc235b03585">More...</a><br /></td></tr>
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<tr class="separator:a806addcafdd2ee7740ba1dc235b03585"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a20a6d4a33fa571ec6062fa65e0dab2b8"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a20a6d4a33fa571ec6062fa65e0dab2b8">rho</a></td></tr>
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<tr class="memdesc:a20a6d4a33fa571ec6062fa65e0dab2b8"><td class="mdescLeft"> </td><td class="mdescRight">Sparsity parameter. <a href="#a20a6d4a33fa571ec6062fa65e0dab2b8">More...</a><br /></td></tr>
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<tr class="separator:a20a6d4a33fa571ec6062fa65e0dab2b8"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a529226bc5ab4906b3f0af1c081fc46e0"><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#a529226bc5ab4906b3f0af1c081fc46e0">visibleSize</a></td></tr>
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<tr class="memdesc:a529226bc5ab4906b3f0af1c081fc46e0"><td class="mdescLeft"> </td><td class="mdescRight">Size of the visible layer. <a href="#a529226bc5ab4906b3f0af1c081fc46e0">More...</a><br /></td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<div class="textblock"><h3>template<template< typename > class OptimizerType = mlpack::optimization::L_BFGS><br />
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class mlpack::nn::SparseAutoencoder< OptimizerType ></h3>
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<p>A sparse autoencoder is a neural network whose aim to learn compressed representations of the data, typically for dimensionality reduction, with a constraint on the activity of the neurons in the network. </p>
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<p>Sparse autoencoders can be stacked together to learn a hierarchy of features, which provide a better representation of the data for classification. This is a method used in the recently developed field of deep learning. More technical details about the model can be found on the following webpage:</p>
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<p><a href="http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial">http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial</a></p>
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<p>An example of how to use the interface is shown below:</p>
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<div class="fragment"><div class="line">arma::mat data; <span class="comment">// Data matrix.</span></div><div class="line"><span class="keyword">const</span> <span class="keywordtype">size_t</span> vSize = 64; <span class="comment">// Size of visible layer, depends on the data.</span></div><div class="line"><span class="keyword">const</span> <span class="keywordtype">size_t</span> hSize = 25; <span class="comment">// Size of hidden layer, depends on requirements.</span></div><div class="line"></div><div class="line"><span class="comment">// Train the model using default options.</span></div><div class="line"><a class="code" href="classmlpack_1_1nn_1_1SparseAutoencoder.html#ad379594f83f6cb4c42269ada1449b28b">SparseAutoencoder</a> encoder1(data, vSize, hSize);</div><div class="line"></div><div class="line"><span class="keyword">const</span> <span class="keywordtype">size_t</span> numBasis = 5; <span class="comment">// Parameter required for L-BFGS algorithm.</span></div><div class="line"><span class="keyword">const</span> <span class="keywordtype">size_t</span> numIterations = 100; <span class="comment">// Maximum number of iterations.</span></div><div class="line"></div><div class="line"><span class="comment">// Use an instantiated optimizer for the training.</span></div><div class="line">SparseAutoencoderFunction saf(data, vSize, hSize);</div><div class="line">L_BFGS<SparseAutoencoderFunction> optimizer(saf, numBasis, numIterations);</div><div class="line">SparseAutoencoder<L_BFGS> encoder2(optimizer);</div><div class="line"></div><div class="line">arma::mat features1, features2; <span class="comment">// Matrices for storing new representations.</span></div><div class="line"></div><div class="line"><span class="comment">// Get new representations from the trained models.</span></div><div class="line">encoder1.GetNewFeatures(data, features1);</div><div class="line">encoder2.GetNewFeatures(data, features2);</div></div><!-- fragment --><p>This implementation allows the use of arbitrary mlpack optimizers via the OptimizerType template parameter.</p>
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<dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">OptimizerType</td><td>The optimizer to use; by default this is L-BFGS. Any mlpack optimizer can be used here. </td></tr>
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</table>
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</dd>
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</dl>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00068">68</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
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template<template< typename > class OptimizerType = mlpack::optimization::L_BFGS> </div>
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<td class="memname"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::<a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">SparseAutoencoder</a> </td>
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<td>(</td>
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<td class="paramtype">const arma::mat & </td>
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<td class="paramname"><em>data</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">const size_t </td>
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<td class="paramname"><em>visibleSize</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">const size_t </td>
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<td class="paramname"><em>hiddenSize</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const double </td>
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<td class="paramname"><em>lambda</em> = <code>0.0001</code>, </td>
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<td class="paramtype">const double </td>
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<td class="paramname"><em>beta</em> = <code>3</code>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">const double </td>
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<td class="paramname"><em>rho</em> = <code>0.01</code> </td>
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<td></td>
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<td>)</td>
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<p>Construct the sparse autoencoder model with the given training data. </p>
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<p>This will train the model. The parameters 'lambda', 'beta' and 'rho' can be set optionally. Changing these parameters will have an effect on regularization and sparsity of the model.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">data</td><td>Input data with each column as one example. </td></tr>
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<tr><td class="paramname">visibleSize</td><td>Size of input vector expected at the visible layer. </td></tr>
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<tr><td class="paramname">hiddenSize</td><td>Size of input vector expected at the hidden layer. </td></tr>
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<tr><td class="paramname">lambda</td><td>L2-regularization parameter. </td></tr>
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<tr><td class="paramname">beta</td><td>KL divergence parameter. </td></tr>
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<tr><td class="paramname">rho</td><td>Sparsity parameter. </td></tr>
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template<template< typename > class OptimizerType = mlpack::optimization::L_BFGS> </div>
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<td class="memname"><a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::<a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">SparseAutoencoder</a> </td>
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<td>(</td>
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<td class="paramtype">OptimizerType< <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoderFunction.html">SparseAutoencoderFunction</a> > & </td>
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<td class="paramname"><em>optimizer</em></td><td>)</td>
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<p>Construct the sparse autoencoder model with the given training data. </p>
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<p>This will train the model. This overload takes an already instantiated optimizer and uses it to train the model. The optimizer should hold an instantiated <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoderFunction.html" title="This is a class for the sparse autoencoder objective function. ">SparseAutoencoderFunction</a> object for the function to operate upon. This option should be preferred when the optimizer options are to be changed.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">optimizer</td><td>Instantiated optimizer with instantiated error function. </td></tr>
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<h2 class="groupheader">Member Function Documentation</h2>
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<td class="memname">void <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::Beta </td>
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<td>(</td>
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<p>Sets the KL divergence parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00160">160</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00193">mlpack::nn::SparseAutoencoder< OptimizerType >::beta</a>.</p>
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<td class="memname">double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::Beta </td>
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<td>(</td>
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<p>Gets the KL divergence parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00166">166</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00193">mlpack::nn::SparseAutoencoder< OptimizerType >::beta</a>.</p>
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<td class="memname">void <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::GetNewFeatures </td>
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<td>(</td>
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<p>Transforms the provided data into the representation learned by the sparse autoencoder. </p>
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<p>The function basically performs a feedforward computation using the learned weights, and returns the hidden layer activations.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">data</td><td>Matrix of the provided data. </td></tr>
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<p>Sets size of the hidden layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00136">136</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Gets the size of the hidden layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00142">142</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00189">mlpack::nn::SparseAutoencoder< OptimizerType >::hiddenSize</a>.</p>
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<p>Sets the L2-regularization parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00148">148</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00191">mlpack::nn::SparseAutoencoder< OptimizerType >::lambda</a>.</p>
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<p>Gets the L2-regularization parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00154">154</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00191">mlpack::nn::SparseAutoencoder< OptimizerType >::lambda</a>.</p>
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<p>Sets the sparsity parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00172">172</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00195">mlpack::nn::SparseAutoencoder< OptimizerType >::rho</a>.</p>
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template<template< typename > class OptimizerType = mlpack::optimization::L_BFGS> </div>
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<td class="memname">double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::Rho </td>
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<p>Gets the sparsity parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00178">178</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00195">mlpack::nn::SparseAutoencoder< OptimizerType >::rho</a>.</p>
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template<template< typename > class OptimizerType = mlpack::optimization::L_BFGS> </div>
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<td class="memname">void <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::Sigmoid </td>
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<td>(</td>
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<p>Returns the elementwise sigmoid of the passed matrix, where the sigmoid function of a real number 'x' is [1 / (1 + exp(-x))]. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00118">118</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<td class="memname">void <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::VisibleSize </td>
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<p>Sets size of the visible layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00124">124</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<td class="memname">size_t <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::VisibleSize </td>
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<td>(</td>
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<p>Gets size of the visible layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00130">130</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>References <a class="el" href="sparse__autoencoder_8hpp_source.html#l00187">mlpack::nn::SparseAutoencoder< OptimizerType >::visibleSize</a>.</p>
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<h2 class="groupheader">Member Data Documentation</h2>
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<td class="memname">double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::beta</td>
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<p>KL divergence parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00193">193</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Referenced by <a class="el" href="sparse__autoencoder_8hpp_source.html#l00160">mlpack::nn::SparseAutoencoder< OptimizerType >::Beta()</a>.</p>
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<td class="memname">size_t <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::hiddenSize</td>
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<p>Size of the hidden layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00189">189</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Referenced by <a class="el" href="sparse__autoencoder_8hpp_source.html#l00142">mlpack::nn::SparseAutoencoder< OptimizerType >::HiddenSize()</a>.</p>
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<td class="memname">double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::lambda</td>
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<p>L2-regularization parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00191">191</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Referenced by <a class="el" href="sparse__autoencoder_8hpp_source.html#l00148">mlpack::nn::SparseAutoencoder< OptimizerType >::Lambda()</a>.</p>
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<td class="memname">arma::mat <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::parameters</td>
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<p>Parameters after optimization. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00185">185</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<td class="memname">double <a class="el" href="classmlpack_1_1nn_1_1SparseAutoencoder.html">mlpack::nn::SparseAutoencoder</a>< OptimizerType >::rho</td>
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<p>Sparsity parameter. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00195">195</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Referenced by <a class="el" href="sparse__autoencoder_8hpp_source.html#l00172">mlpack::nn::SparseAutoencoder< OptimizerType >::Rho()</a>.</p>
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<p>Size of the visible layer. </p>
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<p>Definition at line <a class="el" href="sparse__autoencoder_8hpp_source.html#l00187">187</a> of file <a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a>.</p>
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<p>Referenced by <a class="el" href="sparse__autoencoder_8hpp_source.html#l00130">mlpack::nn::SparseAutoencoder< OptimizerType >::VisibleSize()</a>.</p>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<li>src/mlpack/methods/sparse_autoencoder/<a class="el" href="sparse__autoencoder_8hpp_source.html">sparse_autoencoder.hpp</a></li>
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