# vat_tf

**Repository Path**: BolinLi-s/vat_tf

## Basic Information

- **Project Name**: vat_tf
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# vat_tf

<img src="https://github.com/takerum/vat_tf/raw/master/vat.gif" width="480">

Tensorflow implementation for reproducing the semi-supervised learning results on SVHN and CIFAR-10 dataset in the paper "Virtual Adversarial Training: a Regularization Method for Supervised and Semi-Supervised Learning" http://arxiv.org/abs/1704.03976

### Requirements
tensorflow-gpu 1.1.0, scipy 0.19.0(for ZCA whitening)

## Preparation of dataset for semi-supervised learning
On CIFAR-10

```python cifar10.py --data_dir=./dataset/cifar10/```

On SVHN

```python svhn.py --data_dir=./dataset/svhn/```

## Semi-supervised Learning without augmentation 
On CIFAR-10

```python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10/ --num_epochs=500 --epoch_decay_start=460 --epsilon=10.0 --method=vat```

On SVHN

```python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhn/ --num_epochs=120 --epoch_decay_start=80 --epsilon=2.5 --top_bn --method=vat```

## Semi-supervised Learning with augmentation 
On CIFAR-10

```python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=500 --epoch_decay_start=460 --aug_flip=True --aug_trans=True --epsilon=8.0 --method=vat```

On SVHN

```python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhnaug/ --num_epochs=120 --epoch_decay_start=80 --epsilon=3.5 --aug_trans=True --top_bn --method=vat```

## Semi-supervised Learning with augmentation + entropy minimization
On CIFAR-10

```python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=500 --epoch_decay_start=460 --aug_flip=True --aug_trans=True --epsilon=8.0 --method=vatent```

On SVHN

```python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhnaug/ --num_epochs=120 --epoch_decay_start=80 --epsilon=3.5 --aug_trans=True --top_bn --method=vatent```


## Evaluation of the trained model
On CIFAR-10

```python test.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=<path_to_log_dir> ```

On SVHN

```python test.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=<path_to_log_dir> --top_bn```