This is the implementation of "Autoencoder" corresponding to 1-dimensional shape.
Original paper: G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006. link

Please build the source file according to the procedure.

$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ cd ..

• Normal Distribution Dataset
  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.
  Link: official

• THE MNIST DATABASE of handwritten digits
  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.
  Link: official

Please create a link for the dataset.
The following hierarchical relationships are recommended.

datasets
|--Dataset1
|    |--train
|    |    |--data1.dat
|    |    |--data2.csv
|    |    |--data3.txt
|    |
|    |--valid
|    |--test
|
|--Dataset2
|--Dataset3

The following is an example for "NormalDistribution".

$ cd datasets
$ git clone https://github.com/koba-jon/normal_distribution_dataset.git
$ ln -s normal_distribution_dataset/NormalDistribution ./NormalDistribution
$ cd ..

Please set the shell for executable file.

$ vi scripts/train.sh

The following is an example of the training phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='NormalDistribution'

./AE1d \
    --train true \
    --epochs 300 \
    --dataset ${DATA} \
    --nd 300 \
    --nz 1 \
    --batch_size 64 \
    --gpu_id 0

Please execute the following to start the program.

$ sh scripts/train.sh

Please set the shell for executable file.

$ vi scripts/test.sh

The following is an example of the test phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='NormalDistribution'

./AE1d \
    --test true \
    --dataset ${DATA} \
    --test_in_dir "test" \
    --test_out_dir "test" \
    --nd 300 \
    --nz 1 \
    --gpu_id 0

If you want to test the reconstruction error of the data, the above settings will work.
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".
However, the two file names must correspond.

Please execute the following to start the program.

$ sh scripts/test.sh