def create(cls, args, train='train.pt', validation='validation.pt'):
enl, dnl = AutoEncoder.get_non_linearity(args.nonlinearity)
return Trainer(AutoEncoder(encoder_sizes=args.encoder,
encoding_dimension=args.dimension,
encoder_non_linearity=enl,
decoder_non_linearity=dnl,
decoder_sizes=args.decoder),
DataLoader(load(join(args.data, train)),
batch_size=args.batch,
shuffle=True,
num_workers=cpu_count()),
DataLoader(load(join(args.data, validation)),
batch_size=32,
shuffle=False,
num_workers=cpu_count()),
lr=args.lr,
weight_decay=args.weight_decay,
path=args.data)
def create_model(loaded):
'''
Create Autoencoder from data that has previously been saved
Parameters:
loaded A model that has been loaded from a file
Returns:
newly created Autoencoder
'''
old_args = loaded['args_dict']
enl, dnl = AutoEncoder.get_non_linearity(old_args['nonlinearity'])
product = AutoEncoder(encoder_sizes=old_args['encoder'],
encoding_dimension=old_args['dimension'],
encoder_non_linearity=enl,
decoder_non_linearity=dnl,
decoder_sizes=old_args['decoder'])
product.load_state_dict(loaded['model_state_dict'])
return product
