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)