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CodeReport_Samira.rtf
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CodeReport_Samira.rtf
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{\rtf1\ansi\ansicpg1252\cocoartf1404\cocoasubrtf470
{\fonttbl\f0\froman\fcharset0 Times-Roman;\f1\fnil\fcharset0 Menlo-Regular;\f2\fnil\fcharset0 LucidaGrande;
}
{\colortbl;\red255\green255\blue255;\red0\green0\blue0;}
\margl1440\margr1440\vieww37840\viewh21320\viewkind0
\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
\f0\fs48 \cf2 MAIN FILES:
\fs36 \
\
\'97
\b \CocoaLigature0 cifar-10-batches-t7
\b0 : original data. contains the images in the CIFAR-10.\
\
\'97
\b provider.lua
\b0 : I ran this file at the beginning of my experiment. It does some preprocessing on the data like normalizing the images. After running this file, it generates
\b \cf0 provider.t7
\b0 . After this step, we will work with provider.t7 to load training and test points. We don\'92t work with
\b cifar-10-batches-t7
\b0 anymore
\b .
\b0 \
\
\'97
\b provider.t7 :
\b0 Contains the preprocessed images. See the explanation of
\b provider.lua.\
\
\'97 models :
\b0 This folder contain the models. We use
\b vgg_bn_drop
\b0 .\
\
\'97
\b logs
\b0 : This folder contains the result of experiments like the plots and the report. The important file in this folder is
\b logs/vgg/trainedModel.net
\b0 . This is the model that I trained (with accuracy 91% on the test set). In our experiments, we work with saved model.\
\
\'97
\b train.lua :
\b0 This is the original code for training the network. It saves the trained network in
\b logs/vgg/trainedModel.net.
\b0 \
\
\'97
\b \cf2 imageTofeature.lua :
\b0 This code converts a set of images to features that the saved network generates. I used this code to get features of train and test images. These features are saved as \cf0 trainFeature_originalLabels.dat, trainFeature_learntLabels.dat, testFeature_learntLabels.dat, testFeature_originalLabels.dat.\
\
\'97
\b trainFeature_originalLabels.dat :
\b0 This is the output of
\b imageTofeature.lua
\b0 when the input is the train set. This data set is a table. Each row of the table has three components: featureTensor, softLabels, hardLabel (\ul original label of the point\ulnone ). To load this file do points = torch.load('trainFeature_originalLabels.dat\'92). Use points[i][1], points[i][2], points[i][3] to get feature vector, soft labels and original hard label of the i^\{th\} training point.\
\
\'97
\b trainFeature_learntLabels.dat :
\b0 This is the output of
\b imageTofeature.lua
\b0 when the input is the train set. This data set is a table. Each row of the table has three components: featureTensor, softLabels, hardLabel (\ul the index of softLabels with maximum value\ulnone ). To load this file do points = torch.load('trainFeature_learntLabels.dat\'92). Use points[i][1], points[i][2], points[i][3] to get feature vector, soft labels and network generated hard label of the i^\{th\} training point.\
\
\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
\cf0 \'97
\b testFeature_originalLabels.dat :
\b0 This is the output of
\b imageTofeature.lua
\b0 when the input is the test set. This data set is a table. Each row of the table has three components: featureTensor, softLabels, hardLabel (\ul original label of the point\ulnone ). To load this file do points = torch.load('testFeature_originalLabels.dat\'92). Use points[i][1], points[i][2], points[i][3] to get feature vector, soft labels and original hard label of the i^\{th\} training point.\
\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
\cf0 \
\pard\tx560\tx1120\tx1680\tx2240\tx2800\tx3360\tx3920\tx4480\tx5040\tx5600\tx6160\tx6720\pardirnatural\partightenfactor0
\f1\fs22 \cf0 \'97
\f0\b\fs36 testFeature_learntLabels.dat :
\b0 This is the output of
\b imageTofeature.lua
\b0 when the input is the test set. This data set is a table. Each row of the table has three components: featureTensor, softLabels, hardLabel (\ul the index of softLabels with maximum value\ulnone ). To load this file do points = torch.load('testFeature_learntLabels.dat\'92). Use points[i][1], points[i][2], points[i][3] to get feature vector, soft labels and network generated hard label of the i^\{th\} training point.
\b \
\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
\cf0 \
\f1\b0\fs22 \
\f0\fs36 \'97
\b critical.lua :
\b0 \
\
\f2\fs48 TESTING
\f0 :\
\
\'97
\b\fs36 \cf2 example_classify.lua :
\b0 This code is for testing the network. You can input an image to it and get the output probabilities of each class. There is a folder
\b test
\b0 which contains some 32*32 images and I use them as input to this code.
\fs48 \cf0 \
\
\'97
\b\fs36 test :
\b0 This folder contains a few images that I picked myself to test the code on them. I use them as input to
\b example_classify.lua .\
\
\'97 }