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Summary of work done during GSoC

GSoC17 is at the end and I want to thank my mentors and the Octave community for giving me the opportunity to participate in this unique experience. During this Google Summer of Code, my goal was to implement from scratch the Convolutional Neural Networks package for GNU Octave. It  will be integrated with the already existing nnet package. This was a very interesting project and a stimulating experience for both the implemented code and the theoretical base behind the algorithms treated. A part has been implemented in Octave and an other part in Python using the Tensorflow API. Code repository All the code implemented during these months can be found in my public repository:    https://bitbucket.org/cittiberto/gsoc-octave-nnet/commits/all (my username: citti berto , bookmark enrico ) Since I implemented a completely new part of the package, I pushed the entire project in three commits and I wait for the community approving for preparing a PR for the official packa
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Deep learning functions

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Hi there, the second part of the project is finishing. This period was quite interesting because I had to dive into the theory behind Neural Networks  In particular [1], [2], [3] and [4] were very useful and I will sum up some concepts here below. On the other hand,  coding became more challenging and t he focus was on the python layer and in particular the way to structure the class in order to make everything scalable and generalizable. Summarizing the situation, in the first period I implemented all the Octave classes for the user interface. Those are Matlab compatible and they call some Python function in a seamless way. On the Python side, the TensorFlow API is used to build the graph of the Neural Network and perform training, evaluation and prediction. I implemented the three core functions: trainNetwork, SeriesNetwork and trainingOptions. To do this, I used a Python class in which I initialize an object with the graph of the network and I store this object as attribute of S
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End of the first work period

Hi all, this is the end of this first period of GSoC! It was a challenging but very interesting period and I am very excited about the next two months. It is the first time where I have the opportunity to make something that has a real value, albeit small, on someone else's work. Even when I have to do small tasks, like structure the package or learn Tensorflow APIs, I do it with enthusiasm because I have very clear in mind the ultimate goal and the value that my efforts would bring. It may seem trivial, but for a student this is not the daily bread :) I really have fun coding for this project and I hope this will last until the end! Speaking about the project, I've spent some time wondering which was the best solution to test the correct installation of the Tensorflow Python APIs on the machine. The last solution was putting the test in a function __nnet_init__ and calling it in the PKG_ADD (code in inst/__nnet_init__ in my repo [1]). Regarding the code, in this last day
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Train a Convolutional Neural Network for Regression

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Hello, I spent the last period working mostly on Tensorflow, studying the APIs and writing some examples in order to explore the possible implementations of neural networks. For this goal, I chose an interesting example proposed in the Matlab examples at [1]. The dataset is composed by 5000 images, rotated by an angle α, and a corresponding integer label (the rotation angle α). The goal is to make a regression to predict the angle of a rotated image and straighten it up. All files can be found in  tests/examples/cnn_linear_model  in my repo [2]. I have kept the structure as in the Matlab example, but I generated a new dataset starting from LeCun's MNIST digits (datasets at [3]). Each image was rotated by a random angle between 0° and 70°, in order to keep the right orientation of the digits (code in dataset_generation.m ). In Fig. 1 some rotated digits with the corresponding original digits. Figure 1. rotated images in columns 1,3,5 and originals in columns 2,4,6
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Package structure

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Hello! During the first period of GSoC I have worked mostly on analyzing the Matlab structure of the net package in order to guarantee the compatibility throughout the whole project. The focus of the project is on the convolutional neural networks, about which I will write the next post. Regarding the package structure, the core will be composed by three parts: Layers : there are 11 types of layers that I defined as Octave classes, using classdef . These layers can be concatenated in order to create a Layer object defining the architecture of the network. This will be the input for the training function.  Training : the core of the project is the training function, which takes as input the data, the layers and some options and returns the network as output.  Network :   the network object has three methods (activations, classify and predict) that let the user compute the final classification and prediction.  Figure 1: conv nnet flowchart  I have already implement
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Introductory Post

Hello, I'm Enrico Bertino, and this is the blog I'll be using to track my work for Google Summer of Code 2017. The project will consist in rifare the neural network package with a focus on convolutional neural networks. Here my proposal. Something about myself: I’m an Mathematical Engineering student and I live in Milan. After a BSc in Politecnico di Milano I did a master double degree both in Ecole Centrale de Nantes, France, and Politecnico di Milano. In the two years in France I attended engineering classes with spec in virtual reality and in Italy I am enrolled in the last year of MSc major Statistics and Big Data. During my project, I will leverage on the Pytave project in order to exploit Python code for the implementation of the package. This is almost necessary because nowadays there is a strong tendency to open-researching in the deep learning field, where even the biggest companies release their frameworks and open their code. The level of this resources is ver
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