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# Higher-level functions
from sunlab.common.distribution.adversarial_distribution import AdversarialDistribution
from sunlab.common.scaler.adversarial_scaler import AdversarialScaler
from sunlab.common.data.utilities import import_dataset
from .adversarial_autoencoder import AdversarialAutoencoder
def create_aae(
dataset_file_name,
model_directory,
normalization_scaler: AdversarialScaler,
distribution: AdversarialDistribution or None,
magnification=10,
latent_size=2,
):
"""# Create Adversarial Autoencoder
- dataset_file_name: str = Path to the dataset file
- model_directory: str = Path to save the model in
- normalization_scaler: AdversarialScaler = Data normalization Scaler Model
- distribution: AdversarialDistribution = Distribution for the Adversary
- magnification: int = The Magnification of the Dataset"""
dataset = import_dataset(dataset_file_name, magnification)
model = AdversarialAutoencoder(
model_directory, distribution, normalization_scaler
).init(dataset.dataset, latent_size=latent_size)
return model
def create_aae_and_dataset(
dataset_file_name,
model_directory,
normalization_scaler: AdversarialScaler,
distribution: AdversarialDistribution or None,
magnification=10,
batch_size=1024,
shuffle=True,
val_split=0.1,
latent_size=2,
):
"""# Create Adversarial Autoencoder and Load the Dataset
- dataset_file_name: str = Path to the dataset file
- model_directory: str = Path to save the model in
- normalization_scaler: AdversarialScaler = Data normalization Scaler Model
- distribution: AdversarialDistribution = Distribution for the Adversary
- magnification: int = The Magnification of the Dataset"""
model = create_aae(
dataset_file_name,
model_directory,
normalization_scaler,
distribution,
magnification=magnification,
latent_size=latent_size,
)
dataset = import_dataset(
dataset_file_name,
magnification,
batch_size=batch_size,
shuffle=shuffle,
val_split=val_split,
scaler=model.scaler,
)
return model, dataset
def load_aae(model_directory, normalization_scaler: AdversarialScaler):
"""# Load Adversarial Autoencoder
- model_directory: str = Path to save the model in
- normalization_scaler: AdversarialScaler = Data normalization Scaler Model
"""
return AdversarialAutoencoder(model_directory, None, normalization_scaler).load()
def load_aae_and_dataset(
dataset_file_name,
model_directory,
normalization_scaler: AdversarialScaler,
magnification=10,
):
"""# Load Adversarial Autoencoder
- dataset_file_name: str = Path to the dataset file
- model_directory: str = Path to save the model in
- normalization_scaler: AdversarialScaler = Data normalization Scaler Model
- magnification: int = The Magnification of the Dataset"""
model = load_aae(model_directory, normalization_scaler)
dataset = import_dataset(
dataset_file_name, magnification=magnification, scaler=model.scaler
)
return model, dataset
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