From 4c1a32f77a6b15206551c4ebd51e75c0c14106f7 Mon Sep 17 00:00:00 2001
From: Guillaume Pasero <guillaume.pasero@c-s.fr>
Date: Thu, 14 Dec 2017 15:32:34 +0100
Subject: [PATCH] BUG: wrong dimension detected when reading AE model
---
.../include/otbAutoencoderModel.txx | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
index 96eb2aa76e..33f1c28e24 100644
--- a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
+++ b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
@@ -179,6 +179,7 @@ AutoencoderModel<TInputValue,NeuronType>
shark::MaxIterations<> criterion(m_NumberOfIterationsFineTuning);
TrainNetwork(criterion, inputSamples_copy, ofs);
}
+ this->SetDimension(m_NumberOfHiddenNeurons[m_NumberOfHiddenNeurons.Size()-1]);
}
template <class TInputValue, class NeuronType>
@@ -387,8 +388,8 @@ AutoencoderModel<TInputValue,NeuronType>
// This gives us the dimension if we keep the encoder and decoder
size_t feature_layer_index = m_Net.layerMatrices().size()/2;
- // number of neurons in the feature layer (first dimension of the first decoder weight matrix)
- this->m_Dimension = m_Net.layerMatrix(feature_layer_index).size1();
+ // number of neurons in the feature layer (second dimension of the first decoder weight matrix)
+ this->SetDimension(m_Net.layerMatrix(feature_layer_index).size2());
}
template <class TInputValue, class NeuronType>
@@ -407,7 +408,8 @@ AutoencoderModel<TInputValue,NeuronType>
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
- data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data); // features layer for a network containing the encoder and decoder part
+ // features layer for a network containing the encoder and decoder part
+ data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data);
TargetSampleType target;
target.SetSize(this->m_Dimension);
@@ -432,7 +434,8 @@ AutoencoderModel<TInputValue,NeuronType>
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
- data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data); // features layer for a network containing the encoder and decoder part
+ // features layer for a network containing the encoder and decoder part
+ data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data);
unsigned int id = startIndex;
target.SetSize(this->m_Dimension);
--
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