this is for holding javascript data
chengds added section_First_experiment_2_classes__.tex
almost 9 years ago
Commit id: 9e9a2eaa0b34e02436f63a7793be36fbf28920d9
deletions | additions
diff --git a/section_First_experiment_2_classes__.tex b/section_First_experiment_2_classes__.tex
new file mode 100644
index 0000000..9d11435
--- /dev/null
+++ b/section_First_experiment_2_classes__.tex
...
\section{First experiment: 2 classes}
The first idea is to select the images of those users that have te trait minor
and equal to the first quartile and greater and equal than the third quartile of
a given distribution trait, and assign label 0/1 respectively. In this case the
structure of the net was the same as the one of ImageNet, but we modified the
last layer in order to allow to the net to learn the new data representation. In
this case we want to classify an image belonging to a low or high level of a trait,
so it is a binary classification. We had to treat each trait independently, so we
build 10 files containing the path and the target for each trait in a unique txt
file in this case as the labels are integer (randomly sampled for the training set).
With this idea we have finally some results.
In Table 1 we can see the accuracy obtained form the fine-tuning for training
and testing phase.
