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‎Dimensionality_Reduction/AE1d/CMakeLists.txt

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cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
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# Project Name
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project(AE1d CXX)
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# Directory Name
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set(SUB_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../../utils)
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set(SRC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src)
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set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
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# Create Executable File
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set(SRCS
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${SRC_DIR}/main.cpp
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${SRC_DIR}/train.cpp
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${SRC_DIR}/valid.cpp
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${SRC_DIR}/test.cpp
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${SRC_DIR}/loss.cpp
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${SRC_DIR}/networks.cpp
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)
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add_subdirectory(${SUB_DIR} build)

‎Dimensionality_Reduction/AE1d/README.md

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# AE1d
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This is the implementation of "Autoencoder" corresponding to 1-dimensional shape.<br>
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Original paper: G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006. [link](https://science.sciencemag.org/content/313/5786/504.abstract)
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## Usage
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### 1. Build
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Please build the source file according to the procedure.
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~~~
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$ mkdir build
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$ cd build
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$ cmake ..
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$ make -j4
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$ cd ..
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~~~
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### 2. Dataset Setting
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#### Recommendation
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- Normal Distribution Dataset<br>
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This is the dataset of 300-dimensional data of 1-dimensional shape that has a training set of 100,000 pieces and a test set of 1,000 pieces.<br>
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Link: [official](https://github.com/koba-jon/normal_distribution_dataset)
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- THE MNIST DATABASE of handwritten digits<br>
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This is the dataset of 28x28 grayscale for handwritten digits in 10 classes that has a training set of 60000 images and a test set of 10000 images.<br>
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Link: [official](http://yann.lecun.com/exdb/mnist/)
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#### Setting
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Please create a link for the dataset.<br>
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The following hierarchical relationships are recommended.
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~~~
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datasets
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|--Dataset1
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| |--train
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| | |--data1.dat
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| | |--data2.csv
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| | |--data3.txt
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| |
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| |--valid
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| |--test
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|
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|--Dataset2
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|--Dataset3
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~~~
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You should substitute the path of training data for "<training_path>", test data for "<test_path>", respectively.<br>
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The following is an example for "NormalDistribution".
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~~~
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$ cd datasets
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$ git clone https://github.com/koba-jon/normal_distribution_dataset.git
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$ ln -s normal_distribution_dataset/NormalDistribution ./NormalDistribution
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$ cd ..
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~~~
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### 3. Training
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#### Setting
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Please set the shell for executable file.
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~~~
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$ vi scripts/train.sh
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~~~
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The following is an example of the training phase.<br>
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If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.
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~~~
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#!/bin/bash
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DATA='NormalDistribution'
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./AE1d \
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--train true \
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--epochs 300 \
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--dataset ${DATA} \
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--nd 300 \
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--nz 1 \
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--batch_size 64 \
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--gpu_id 0
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~~~
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#### Run
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Please execute the following to start the program.
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~~~
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$ sh scripts/train.sh
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~~~
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### 4. Test
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#### Setting
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Please set the shell for executable file.
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~~~
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$ vi scripts/test.sh
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~~~
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The following is an example of the test phase.<br>
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If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.
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~~~
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#!/bin/bash
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DATA='NormalDistribution'
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./AE1d \
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--test true \
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--dataset ${DATA} \
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--test_in_dir "test" \
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--test_out_dir "test" \
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--nd 300 \
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--nz 1 \
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--gpu_id 0
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~~~
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If you want to test the reconstruction error of the data, the above settings will work.<br>
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If you want to test the denoising of the data, set "test_in_dir" to "directory of noisy data" and "test_out_dir" to "directory of output ground truth".<br>
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However, the two file names must correspond.
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#### Run
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Please execute the following to start the program.
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~~~
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$ sh scripts/test.sh
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~~~
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‎Dimensionality_Reduction/AE1d/datasets/datasets.txt

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1. What kind of dataset do you need?
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Please prepare files of 1-dimensional shape in which the data is written line-by-line.
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Example (7-dimensional data of 1-dimensional shape):
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---------------------------------------------------------
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0.125
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1.552
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2.452
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4.552
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2.563
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4.532
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2.413
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---------------------------------------------------------
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2. What kind of hierarchical relationship do you need?
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The following hierarchical relationships are recommended.
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---------------------------------------------------------
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datasets
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|--Dataset1
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| |--train
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| | |--data1.dat
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| | |--data2.csv
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| | |--data3.txt
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| |
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| |--valid
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| | |--data4.dat
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| | |--data5.csv
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| | |--data6.txt
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| |
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| |--test
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| |--data7.dat
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| |--data8.csv
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| |--data9.txt
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|
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|--Dataset2
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|--Dataset3
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---------------------------------------------------------

‎Dimensionality_Reduction/AE1d/scripts/test.sh

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#!/bin/bash
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DATA='NormalDistribution'
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./AE1d \
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--test true \
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--dataset ${DATA} \
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--test_in_dir "test" \
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--test_out_dir "test" \
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--nd 300 \
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--nz 1 \
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--gpu_id 0

‎Dimensionality_Reduction/AE1d/scripts/train.sh

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#!/bin/bash
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DATA='NormalDistribution'
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./AE1d \
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--train true \
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--epochs 300 \
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--dataset ${DATA} \
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--nd 300 \
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--nz 1 \
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--batch_size 64 \
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--gpu_id 0

‎Dimensionality_Reduction/AE1d/src/loss.cpp

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#include <iostream>
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#include <string>
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// For External Library
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#include <torch/torch.h>
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// For Original Header
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#include "loss.hpp"
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#include "losses.hpp"
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// -----------------------------------
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// class{Loss} -> constructor
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// -----------------------------------
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Loss::Loss(const std::string loss){
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if (loss == "l1"){
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this->judge = 0;
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}
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else if (loss == "l2"){
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this->judge = 1;
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}
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else{
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std::cerr << "Error : The loss fuction isn't defined right." << std::endl;
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std::exit(1);
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}
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}
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// -----------------------------------
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// class{Loss} -> operator
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// -----------------------------------
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torch::Tensor Loss::operator()(torch::Tensor &input, torch::Tensor &target){
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if (this->judge == 0){
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static auto criterion = torch::nn::L1Loss(torch::nn::L1LossOptions().reduction(torch::kMean));
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return criterion(input, target);
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}
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static auto criterion = torch::nn::MSELoss(torch::nn::MSELossOptions().reduction(torch::kMean));
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return criterion(input, target);
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}

‎Dimensionality_Reduction/AE1d/src/loss.hpp

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#ifndef LOSS_HPP
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#define LOSS_HPP
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#include <string>
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// For External Library
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#include <torch/torch.h>
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// -------------------
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// class{Loss}
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// -------------------
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class Loss{
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private:
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int judge;
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public:
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Loss(){}
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Loss(const std::string loss);
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torch::Tensor operator()(torch::Tensor &input, torch::Tensor &target);
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};
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#endif

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