apaam/mlpack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Remove unnecessary element function for the convolutional network and add sparse autoencoder training routine that makes use of the batch size.

Browse Source
This commit is contained in:
marcus
2016-01-31 23:35:00 +01:00
parent 1896937d03
commit f34ae33e2c
1 changed files with 55 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/methods/ann/trainer/trainer.hpp
+55 -24
View File
@@ -88,8 +88,10 @@ class Trainer
if (shuffle)
index = arma::shuffle(index);
Train(trainingData, trainingLabels);
Evaluate(validationData, validationLabels);
Train<InputType, OutputType, NetworkType>(
trainingData, trainingLabels);
Evaluate<InputType, OutputType, NetworkType>(
validationData, validationLabels);
if (validationError <= tolerance)
break;
@@ -132,8 +134,9 @@ class Trainer
* @param data Data used to train the network.
* @param target Labels used to train the network.
*/
template<typename InputType, typename OutputType>
void Train(InputType& data, OutputType& target)
template<typename InputType, typename OutputType, typename ModelType>
typename std::enable_if<!NetworkTraits<ModelType>::IsSAE, void>::type
Train(InputType& data, OutputType& target)
{
// Reset the training error.
trainingError = 0;
@@ -156,14 +159,55 @@ class Trainer
trainingError /= index.n_elem;
}
/**
* Train the sparse autoencoder on the given dataset.
*
* @param data Data used to train the network.
*/
template<typename InputType, typename OutputType, typename ModelType>
typename std::enable_if<NetworkTraits<ModelType>::IsSAE, void>::type
Train(InputType& data, OutputType& /* unused */)
{
// Reset the training error.
trainingError = 0;
arma::uvec indices(batchSize);
if (index.n_elem > batchSize)
{
for (size_t i = 0; i < index.n_elem; i += batchSize)
{
for (size_t j = 0; j < batchSize; j++)
indices(j) = index(j + i);
MatType input = data.rows(indices);
net.FeedForward(input, input, error);
trainingError += net.Error();
net.FeedBackward(input, error);
net.ApplyGradients();
}
trainingError /= (index.n_elem / batchSize);
}
else
{
net.FeedForward(data, data, error);
trainingError += net.Error();
net.FeedBackward(data, error);
net.ApplyGradients();
}
}
/**
* Evaluate the network on the given dataset.
*
* @param data Data used to train the network.
* @param target Labels used to train the network.
*/
template<typename InputType, typename OutputType>
void Evaluate(InputType& data, OutputType& target)
template<typename InputType, typename OutputType, typename ModelType>
typename std::enable_if<!NetworkTraits<ModelType>::IsSAE, void>::type
Evaluate(InputType& data, OutputType& target)
{
// Reset the validation error.
validationError = 0;
@@ -177,6 +221,10 @@ class Trainer
validationError /= ElementCount(data);
}
template<typename InputType, typename OutputType, typename ModelType>
typename std::enable_if<NetworkTraits<ModelType>::IsSAE, void>::type
Evaluate(InputType& data, OutputType& target) { /* Nothing to do here */ }
/*
* Create a Col object which uses memory from an existing matrix object.
* (This approach is currently not alias safe)
@@ -185,24 +233,7 @@ class Trainer
* @param sliceNum Provide a Col object of the specified index.
*/
template<typename eT>
typename std::enable_if<!NetworkTraits<NetworkType>::IsCNN,
arma::Mat<eT> >::type
Element(arma::Mat<eT>& input, const size_t colNum)
{
return arma::Mat<eT>(input.colptr(colNum), input.n_rows, 1, false, true);
}
/*
* Create a Mat object which uses memory from an existing matrix object.
* (This approach is currently not alias safe)
*
* @param data The reference data.
* @param sliceNum Provide a Mat object of the specified index.
*/
template<typename eT>
typename std::enable_if<NetworkTraits<NetworkType>::IsCNN,
arma::Mat<eT> >::type
Element(arma::Mat<eT>& input, const size_t colNum)
arma::Mat<eT> Element(arma::Mat<eT>& input, const size_t colNum)
{
return arma::Mat<eT>(input.colptr(colNum), input.n_rows, 1, false, true);
}