out_dir = './out/gan_{}'.format(datetime.now())
out_dir = out_dir.replace(" ", "_")
print(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
sys.stdout = mutil.Logger(out_dir)
gpu = 0
torch.cuda.set_device(gpu)
mb_size = 600 # mini-batch_size
mode_num = 2
distance = 10
data = data_prepare.Data_2D_Circle(mb_size, mode_num, distance)
Z_dim = 4
X_dim = 2
h_dim = 128
c_dim = mode_num * 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_w(X_dim, 1, h_dim).cuda()
def add_mode(mode_num ,distance):
# mode_num = mn+1
# distance = distance * 1.5
return data_prepare.Data_2D_Circle(mb_size,mode_num,distance, noise_variance=0.5)
torch.cuda.set_device(gpu)
mb_size = 96 # mini-batch_size
# mode_num = 2
sample_point = 10000
# distance = 10
# start_points = np.array([[0,0],[0,1],[0,2]])
# end_points = np.array([[1,0],[1,1],[1,2]])
start_points = np.array([[0, 0]])
end_points = np.array([[1, 0]])
Z_dim = 2
X_dim = 2
h_dim = 16
# data = data_prepare.Straight_Line(90, start_points, end_points, type=1)
data = data_prepare.Data_2D_Circle(mb_size, R=2)
data_draw_m = data_prepare.Data_2D_Circle(8, R=2)
data_draw = data_draw_m.batch_next()
z_draw = Variable(torch.randn(sample_point, Z_dim)).cuda()
# c_dim = mode_num * mode_num
cnt = 0
num = '0'
# else:
# print("you have already creat one.")
# exit(1)
grid_num = 100
top_line = 3
out_dir = './out/gan_add_mode_{}'.format(datetime.now())
out_dir = out_dir.replace(" ", "_")
print(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
sys.stdout = mutil.Logger(out_dir)
gpu = 6
torch.cuda.set_device(gpu)
mb_size = 600 # mini-batch_size
mode_num = 2
distance = 5
data = data_prepare.Data_2D_Circle(mb_size, mode_num, distance, noise_variance=0.5)
Z_dim = 2
X_dim = 2
h_dim = 128
c_dim = mode_num * mode_num
cnt = 0
num = '0'
# else:
# print("you have already creat one.")
# exit(1)
G = model.G_Net(Z_dim , X_dim, h_dim).cuda()
D = model.D_Net(X_dim , 1, h_dim).cuda()