Esempi in Python per E_Net

Esempio n. 1

File: gan_label_curve_triplet_nl_ng.py Progetto: beckybai/generative-models

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()

Esempio n. 2

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()