Python venv

Start a python environment here in this directory

python -m venv .

Then we start the env using

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

# 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, 4 models are implemented.

• ResNet-18
• ResNet-50
• DenseNet121
• Inception

Fine tuning

Preparation

Lets say we want to finetune Inception. In Tune.py we have to adjust the variables accordingly like so:

# 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.

# 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

# open terminal, cd to project root and run
python Tune.py