# other
randseed = 0
gce_path = './pretrained_models/mnist_38_gce'
retrain_gce = False # train explanatory VAE from scratch
save_gce = False # save/overwrite pretrained explanatory VAE at gce_path
# --- initialize ---
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if randseed is not None:
np.random.seed(randseed)
torch.manual_seed(randseed)
ylabels = range(0, len(data_classes))
# --- load data ---
from load_mnist import load_mnist_classSelect
X, Y, tridx = load_mnist_classSelect('train', data_classes, ylabels)
vaX, vaY, vaidx = load_mnist_classSelect('val', data_classes, ylabels)
ntrain, nrow, ncol, c_dim = X.shape
x_dim = nrow * ncol
# --- load classifier ---
from models.CNN_classifier import CNN
classifier = CNN(len(data_classes)).to(device)
checkpoint = torch.load('%s/model.pt' % classifier_path, map_location=device)
classifier.load_state_dict(checkpoint['model_state_dict_classifier'])
# --- train/load GCE ---
from models.CVAE import Decoder, Encoder
if retrain_gce:
encoder = Encoder(K + L, c_dim, x_dim).to(device)
decoder = Decoder(K + L, c_dim, x_dim).to(device)
Nbeta = 100
lam = 0.05
batch_size = 64
lr = 5e-4
# other
randseed = 0
# --- initialize ---
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if randseed is not None:
np.random.seed(randseed)
torch.manual_seed(randseed)
# --- load data ---
from load_mnist import load_mnist_classSelect
X, Y, tridx = load_mnist_classSelect('train', data_classes,
range(0,len(data_classes)))
vaX, vaY, vaidx = load_mnist_classSelect('val', data_classes,
range(0,len(data_classes)))
ntrain, nrow, ncol, c_dim = X.shape
x_dim = nrow*ncol
# --- load classifier ---
from models.CNN_classifier import CNN
classifier = CNN(len(data_classes)).to(device)
checkpoint = torch.load('%s/model.pt' % classifier_path,
map_location=device)
classifier.load_state_dict(checkpoint['model_state_dict_classifier'])
# --- initialize VAE ---
from models.CVAE import Decoder, Encoder
encoder = Encoder(K+L, c_dim, x_dim).to(device)