# Python venv
Start a python environment here in this directory
```py
python -m venv .
```

Then  we start the env using
```py
source ./bin/activate
```

We can then install whats needed with `pip`. for exampe
we can put all dependencies in some text file. say dependencies.txt
```py
# pip install
# already added dependencies.txt
pip install -r dependencies.txt

```

Downloading the data from google drive was impossible. So Downloaded them manualy
and They need to be put in the a ./data directory
The download url was available in the error log.
`https://drive.google.com/uc?id=0B7EVK8r0v71pZjFTYXZWM3FlRnM` 
this is the same location thats available in the official site

```
Root_dir/
└── data/
    └── celeba/
        ├── img_align_celeba.zip
        ├── list_attr_celeba.txt
        ├── list_bbox_celeba.txt
        ├── list_eval_partition.txt
        └── list_landmarks_align_celeba.txt

```

once this is manually done, We can run finetunning a selected model. For now, 8 models are implemented.
- ResNet-18
- ResNet-50
- DenseNet121
- Inception
- GoogleNet
- ShuffleNet
- EfficientNet
- WideResNet

## Fine tuning
### Preparation

Lets say we want to finetune **Inception**. In `Tune.py` we have to adjust the variables accordingly like so:
```py
# Set the class size. e.g
CLASS_SIZE = 50
# set the batch eg.
BATCH_SIZE = 16
# set the Tuning epochs. e.g
EPOCH = 20
# set the correct image size
# if ResNet or DenseNet, we set this to 224
RESOLUTION = 299 
# set the model architecture
arch = Architecture.INCEPTION
```
Other variable that we can change are those that are related to data size.  Namely Training sample size and full sample size. 
```py
# full sample size per class
SAMPLE_SIZE = 30

# Training sample size is then (full_sample - test_sample)
TRAINING_SMPLE = 28

# while at it, we can also set the learning rate
LR_RATE = 0.0001
```


### Rune the process
After we have set all necessary variables to our liking, we run the process by running Tune.py with python interpreter
```shell
# open terminal, cd to project root and run
python Tune.py
```