simon-fuhaoyuan/lmser-pytorch

A pytorch implementation of LMSER, which is a bidirectional architecture with some built-in natures.

LMSER-pytorch

Introduction

A pytorch implementation of LMSER, which is a bidirectional architecture with some built-in natures. In this project, we implement the Dual Connection Weight (DCW) property of LMSER. We implemented both LMSER with and without the pseudo inverse constrain, and compare their performance on reconstructing MNIST and Fashion-MNIST with AutoEncoder. We also add the supervised learning to the middle-layer embedding vector (16-d vector in our implementation), which aims to test the supervised learning effect on original reconstruction task.

Prerequisites

• Linux, Ubuntu 16.04 or 18.04
• Python 3.6+
• Pytorch 1.4.0+
• CUDA 10.0+

Installation

1. Create a conda virtual environment and activate it.

conda create -n lmser python=3.6
conda activate lmser

2. Install Pytorch and torchvision following the official instruction.

3. Clone the LMSER-Pytorch repository.

git clone https://github.com/Simon-Fuhaoyuan/LMSER-pytorch

4. Install build requirements

cd LMSER-pytorch
pip install -r requirements.txt

Usage

We offer both training and testing codes.

To train a model, please run

python train.py [-h] [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--lr LR] \
                [--mean MEAN] [--std STD] [--dataset DATASET] \
                [--weight_dir WEIGHT_DIR] [--image_dir IMAGE_DIR] [--cpu_only] \
                [--no_test] \
                model

For example, to train AutoEncoder, run

python train.py AutoEncoder --epochs 50 --batch_size 64 --dataset mnist

To test a model, run

python test.py [-h] [--dataset DATASET] [--batch_size BATCH_SIZE] \
               [--mean MEAN] [--std STD] [--image_dir IMAGE_DIR] [--cpu_only] \
               model weight

For example, to test a trained model, whose weight model is saved at ./weight_dir/AutoEncoder.pth, run

python test.py AutoEncoder ./weight_dir/AutoEncoder.pth --dataset mnist

The usage of train_supervise.py and test_supervise.py is quite similar, but they will load the supervised version of model and train or test.

For more usages, please refer to experiments_command.txt.