def load_model(path, epoch, pretrain):
pool = util.DataPool()
x_dim, y_dim, timestamp = parse_params(path)
xs = util.Gaussian_sampler(mean=np.zeros(x_dim), sd=1.0)
ys = find_y_sampler()
if data == 'mnist' or data == 'cifar10':
from main_density_est_img import RoundtripModel
g_net = model.Generator_img(input_dim=x_dim,
output_dim=y_dim,
name='g_net',
nb_layers=2,
nb_units=256,
dataset=data,
is_training=False)
h_net = model.Encoder_img(input_dim=y_dim,
output_dim=x_dim,
name='h_net',
nb_layers=2,
nb_units=256,
dataset=data)
dx_net = model.Discriminator(input_dim=x_dim,
name='dx_net',
nb_layers=2,
nb_units=128)
dy_net = model.Discriminator_img(input_dim=y_dim,
name='dy_net',
nb_layers=2,
nb_units=128,
dataset=data)
RTM = RoundtripModel(g_net,
h_net,
dx_net,
dy_net,
xs,
ys,
data,
pool,
batch_size=64,
nb_classes=10,
alpha=10.0,
beta=10.0,
df=1,
is_train=False)
else:
from main_density_est import RoundtripModel
g_net = model.Generator(input_dim=x_dim,
output_dim=y_dim,
name='g_net',
nb_layers=10,
nb_units=512)
h_net = model.Generator(input_dim=y_dim,
output_dim=x_dim,
name='h_net',
nb_layers=10,
nb_units=256)
dx_net = model.Discriminator(input_dim=x_dim,
name='dx_net',
nb_layers=2,
nb_units=128)
dy_net = model.Discriminator(input_dim=y_dim,
name='dy_net',
nb_layers=4,
nb_units=256)
RTM = RoundtripModel(g_net,
h_net,
dx_net,
dy_net,
xs,
ys,
data,
pool,
batch_size=64,
alpha=10.0,
beta=10.0,
df=1,
is_train=False)
RTM.load(pre_trained=pretrain, timestamp=timestamp, epoch=epoch)
return RTM
nb_units=256)
dx_net = model.Discriminator(input_dim=x_dim,
name='dx_net',
nb_layers=4,
nb_units=256)
dy_net1 = model.Discriminator(input_dim=y_dim1,
name='dy_net1',
nb_layers=4,
nb_units=256)
dy_net2 = model.Discriminator(input_dim=y_dim2,
name='dy_net2',
nb_layers=4,
nb_units=256)
pool = util.DataPool()
#xs = util.Mixture_sampler_v2(nb_classes=nb_classes,N=10000,dim=x_dim,sd=1)
xs = util.Mixture_sampler(nb_classes=nb_classes, N=10000, dim=x_dim, sd=1)
#ys = util.DataSampler() #scRNA-seq data
#ys = util.RA4_Sampler('scrna')
ys = util.RA4CoupleSampler()
#ys = util.scATAC_Sampler()
#ys = util.GMM_sampler(N=10000,n_components=nb_classes,dim=y_dim,sd=8)
CRTM = CoupleRTM(g_net1, g_net2, h_net1, h_net2, dx_net, dy_net1, dy_net2,
xs, ys, nb_classes, data, pool, batch_size, alpha, beta,
gamma, is_train)
if args.train:
CRTM.train(epochs=epochs, patience=patience)