Z_dim = 2 X_dim = 2 h_dim = 128 c_dim = mode_num cnt = 0 num = '0' # else: # print("you have already creat one.") # exit(1) G = model.G_Net(Z_dim + c_dim, X_dim, h_dim).cuda() D = model.D_Net(X_dim, 1, h_dim).cuda() E = model.E_Net(X_dim, 10, h_dim).cuda() # G_fake = model.Direct_Net(X_dim+c_dim, 1, h_dim).cuda() G.apply(model.weights_init) D.apply(model.weights_init) E.apply(model.weights_init) """ ===================== TRAINING ======================== """ lr = 1e-4 G_solver = optim.Adam(G.parameters(), lr=lr) D_solver = optim.Adam(D.parameters(), lr=lr) E_solver = optim.Adam(E.parameters(), lr=lr * 3) ones_label = Variable(torch.ones(mb_size)).cuda() zeros_label = Variable(torch.zeros(mb_size)).cuda() criterion = nn.BCELoss()
Z_dim = 2 X_dim = 2 h_dim = 128 c_dim = mode_num cnt = 0 num = '0' # else: # print("you have already creat one.") # exit(1) G = model.G_Net(Z_dim + c_dim, X_dim, h_dim).cuda() D = model.D_Net(X_dim, 1, h_dim).cuda() E = model.E_Net(X_dim + c_dim, 1, h_dim).cuda() # G_fake = model.Direct_Net(X_dim+c_dim, 1, h_dim).cuda() G.apply(model.weights_init) D.apply(model.weights_init) E.apply(model.weights_init) """ ===================== TRAINING ======================== """ lr = 1e-4 G_solver = optim.Adam(G.parameters(), lr=lr) D_solver = optim.Adam(D.parameters(), lr=lr) E_solver = optim.Adam(E.parameters(), lr=lr * 10) ones_label = Variable(torch.ones(mb_size)).cuda() zeros_label = Variable(torch.zeros(mb_size)).cuda() criterion = nn.BCELoss()