Source code for "Towards a Deeper Understanding of Adversarial Losses"
Below we assume the working directory is the repository root.
Using pipenv (recommended)
Make sure
pipenvis installed. (If not, simply runpip install --user pipenv.)
# Install the dependencies
pipenv install
# Activate the virtual environment
pipenv shellUsing pip
# Install the dependencies
pip install -r requirements.txt# Download the training data
./scripts/download_data.sh
# Store the training data to shared memory
./scripts/process_data.shYou can also download the MNIST handwritten digit database manually here.
We provide several shell scripts for easy managing the experiments. (See
scripts/README.md for a detailed documentation.)
Below we assume the working directory is the repository root.
Run the following command to set up a new experiment with default settings.
# Set up a new experiment (for one run only)
./scripts/setup_exp.sh -r 1 "./exp/my_experiment/"Modify the configuration files for different experimental settings. The
configuration file can be found at ./exp/my_experiment/config.yaml.
Train the model by running the following command.
# Train the model (on GPU 0)
./scripts/run_train.sh -c -g 0 "./exp/my_experiment/"For each run, there will be three folders created in the experiment folder.
logs/: contain all the logs createdmodel/: contain the trained modelsrc/: contain a backup of the source codeNote that the validation results can be found in the logs/ folder.