# Loss functions
adversarial_loss = torch.nn.MSELoss()
categorical_loss = torch.nn.CrossEntropyLoss()
continuous_loss = torch.nn.MSELoss()
# Loss weights
lambda_cat = 1
lambda_con = 0.1
# Initialize generator and discriminator
categorical_size = config.n_classes if config.n_classes else dataset.n_studies
print('categorical_size:', categorical_size)
#import ipdb; ipdb.set_trace()
generator = Generator(config.latent_dim, categorical_size, config.code_dim,
dataset.n_genes)
discriminator = Discriminator(categorical_size, config.code_dim,
dataset.n_genes)
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
categorical_loss.cuda()
continuous_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Optimizers
# load config
with open(args.config, 'r') as f:
y = yaml.load(f, Loader=yaml.SafeLoader)
opt = argparse.Namespace(**y)
# Loss functions
adversarial_loss = torch.nn.MSELoss()
categorical_loss = torch.nn.CrossEntropyLoss()
continuous_loss = torch.nn.MSELoss()
# Loss weights
lambda_cat = 1
lambda_con = 0.1
# Initialize generator and discriminator
generator = Generator(opt)
discriminator = Discriminator(opt)
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
categorical_loss.cuda()
continuous_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Configure data loader
data = CuratedBreastCancerData(opt.batch_size, test_split=0.0) # train this unsupervised model on all data
f.write(arg + '\n')
f.close()
unloader = transforms.ToPILImage()
encoder = Encoder(c_size, z_size)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
g = Generator(c_size + z_size)
g.apply(weights_init)
d = Discriminator()
d.apply(weights_init)
q = Q(c_size)
q.apply(weights_init)
dq = D_Q_commonlayer()
dq.apply(weights_init)
for i in [dq, d, q, g, encoder]:
i.cuda()
#i.apply(weights_init)
trainer = Trainer(g, dq, d, q, encoder, batch_size, img_size, c_size, z_size,