This document provides information about installing and using Torch 7, an open-source deep learning framework. It discusses installing Torch 7 on Linux, installing deep learning libraries for natural language processing and CUDA. It also gives examples of using Torch 7 for image processing and classification, including loading an image dataset and defining different types of neural network models like linear, MLP and convolutional networks.

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Torch 7
박천음 (parkce3@gmail.com)
Intelligent Software Lab.

2. Intelligent Software Lab.
Torch 7
Introduction: Installation

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Torch7 설치: Building
• Building (Linux, Ubuntu 12.04)
› sudo apt-get install lua5.2
› building Lua
› sudo apt-get install nodejs
› Torch는 nodejs가 제공하는 브라우저환경에서 실행되는
GFX.js를 사용..
› sudo apt-get install npm
› Torch 설치
› image 처리 예제 설치

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Torch7: Deep learning for NLP
• To install deep learning library
› sudo luarocks install dp
• For CUDA
› sudo luarocks install cunn

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Torch 7
Introduction: Examples

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Torch7 실행 명령어
• $ th
• $ luajit -lenv

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Torch7 이미치 처리 예제
• th
• th –lgfx.go -- gfx를 실행

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Torch7 이미지 분류 예제
• i = image.lena()
• gfx.image(i)

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Torch7 이미지 분류 예제 (2)
• require ‘nn’
• n = nn.SpatialConvolution(1, 64, 16, 16)
• gfx.image(n.weight, {zoom=2, legend=‘’})
• nn: Torch에서 사용하는
Neural Net. module
• nn.SpatialConvolution():
• 데이터셋을 학습시키는 함수
• 16x16 크기의 64개 필터를 주고,
해당 필터 별 “weight”를 이미지로 출력

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Torch7 이미지 분류 예제 (3)
• n = nn.SpatialConvolution(1, 16, 12, 12)
• res = n:forward(image.rgb2y(image.lena()))
• gfx.image(res, {zoom=0.25, legend=‘states’})
• forward(): output을
이미지로 출력

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Supervised Learning: step_1 data
• torch7으로 기계학습 진행
• https://github.com/clementfarabet/ipam-
tutorials/blob/master/th_tutorials/1_supervised/1_data.lua
• 예제에서 사용하는 dataset은 SVHN(Street View
House Number)
• SVHN
• real-world image dataset
• MNIST와 유사

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Supervised Learning: step_1 data cont`
• SVHN dataset
• 10 classes (digit 당 1개의 class)
• ex) digit: 1  label: 1, digit: 0  label: 10, digit: 9  label: 9
• train set: 73257 digits
• test set: 26032 digits
• additional extra training data: 531131 digits
• dataset format
• inputs: image feature “3*32*32”
• outputs: target result “10-dimensional”

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Supervised Learning: step_1 data cont`
• torch7으로 기계학습 진행
• https://github.com/clementfarabet/ipam-
tutorials/blob/master/th_tutorials/1_supervised/1_data.lua
• Data(train_set, test_set 다운)
› torch –lgfx.go –i 1_data.lua

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Supervised Learning: step_1 data cont`
• Data
› torch –lgfx.go –i 1_data.lua

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Supervised Learning: step_1 data cont`
• Data
› torch –lgfx.go –i 1_data.lua
Y U V

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Supervised Learning: step_2 model
• 2_model.lua, model 종류
› th –i 2_model.lua –model linear
› th –i 2_model.lua –model mlp
› th –i 2_model.lua –model convnet
• model 정의_linear
› model = nn.Sequential()
› model:add(nn.Reshape(ninputs))
› model:add(nn.Linear(ninputs, noutputs))
• model 정의_mlp
› model = nn.Sequential()
› model:add(nn.Reshape(ninputs))
› model:add(nn.Linear(ninputs, nhiddens))
› model:add(nn.Tanh())
› model:add(nn.Linear(nhiddens, noutputs))

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Thank you