def main_lower(x_minus, x_plus, y_minus, y_plus, plot=False, num=0):
# x1 = (x_minus + x_plus) / 2
# x1 = x_minus
x1 = binary_search_x1(x_minus, x_plus, y_minus, y_plus)
# y1 = binary_search_y1(x_minus, x_plus, y_minus, y_plus)
x2, z = find_x2(x_minus, x_plus, y_minus, y_plus, x1)
a, b, c = get_abc_lower(x1, x2, z, x_minus, x_plus, y_minus, y_plus)
volume = utils.get_volume(a, b, c, x_minus, x_plus, y_minus, y_plus)
if plot:
utils.plot_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a[num], b[num], c[num])
# x = torch.linspace(x_minus.item(), x_plus.item(),100)
# v = torch.zeros(x.shape)
# g = torch.zeros(x.shape)
# for i in range(len(x)):
# x2,z = find_x2(x_minus, x_plus, y_minus, y_plus, torch.Tensor([x[i]]))
# a,b,c = get_abc_lower(x[i],x2,z,x_minus, x_plus, y_minus, y_plus)
# v[i] = utils.get_volume(a,b,c,x_minus, x_plus, y_minus, y_plus)
# g[i] = estimate_gradient_lower(torch.Tensor([x[i]]), 1e-3, x_minus, x_plus, y_minus, y_plus)
# # v =
# plt.figure()
# plt.plot(x.numpy(),v.numpy())
# plt.figure()
# plt.plot(x.numpy(),g.numpy())
return a, b, c, volume, x1, x2
def main_upper(x_minus, x_plus, y_minus, y_plus, plot=False, num=0):
y1 = binary_search_y1(x_minus, x_plus, y_minus, y_plus)
y2, z = find_y2(x_minus, x_plus, y_minus, y_plus, y1)
a, b, c = get_abc_upper(y1, y2, z, x_minus, x_plus, y_minus, y_plus)
volume = utils.get_volume(a, b, c, x_minus, x_plus, y_minus, y_plus)
if plot:
utils.plot_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a[num], b[num], c[num])
return a, b, c, volume, y1, y2
def main_lower(x_minus,
x_plus,
y_minus,
y_plus,
plot=False,
num=0,
print_info=True):
if print_info:
print('4th orthant lower: using third.main_upper function')
x_minus_new = -x_plus
x_plus_new = -x_minus
a, b, c = third.main_upper(x_minus_new,
x_plus_new,
y_minus,
y_plus,
print_info=print_info)
b = -b
c = -c
if plot:
utils.plot_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a[num], b[num], c[num])
return a.detach(), b.detach(), c.detach()
def validate(a_l,
b_l,
c_l,
a_u,
b_u,
c_u,
x_minus,
x_plus,
y_minus,
y_plus,
verify_and_modify_all=False,
max_iter=100,
plot=False,
eps=1e-5,
print_info=True):
# eps =1e-5
original_shape = c_l.shape
a_l_new = a_l.view(-1) #.data.clone()
b_l_new = b_l.view(-1) #.data.clone()
c_l_new = c_l.view(-1) #.data.clone()
a_u_new = a_u.view(-1) #.data.clone()
b_u_new = b_u.view(-1) #.data.clone()
c_u_new = c_u.view(-1) #.data.clone()
x_minus_new = x_minus.view(-1) #.data.clone()
x_plus_new = x_plus.view(-1) #.data.clone()
y_minus_new = y_minus.view(-1) #.data.clone()
y_plus_new = y_plus.view(-1) #.data.clone()
N = a_l_new.size(0)
if verify_and_modify_all:
max_iter = N
for i in range(max_iter):
if verify_and_modify_all:
n = i
else:
n = torch.randint(0, N, [1])
n = n.long()
hl_fl, hu_fu = plot_2_surface(x_minus_new[n],
x_plus_new[n],
y_minus_new[n],
y_plus_new[n],
a_l_new[n],
b_l_new[n],
c_l_new[n],
a_u_new[n],
b_u_new[n],
c_u_new[n],
plot=plot)
# print('hl-fl max', hl_fl.max())
# print('hu-fu min', hu_fu.min())
if print_info:
print(
'tanh sigmoid iter: %d num: %d hl-f max %.6f mean %.6f hu-f min %.6f mean %.6f'
% (i, n, hl_fl.max(), hl_fl.mean(), hu_fu.min(), hu_fu.mean()))
if hl_fl.max() > eps: #we want hl_fl.max() < 0
print(x_minus_new[n], x_plus_new[n], y_minus_new[n], y_plus_new[n],
a_l_new[n], b_l_new[n], c_l_new[n], a_u_new[n], b_u_new[n],
c_u_new[n])
plot_surface(x_minus_new[n], x_plus_new[n], y_minus_new[n],
y_plus_new[n], a_l_new[n], b_l_new[n], c_l_new[n])
print('hl-f max', hl_fl.max())
raise Exception('lower plane fail')
break
if hl_fl.max() > 0 and verify_and_modify_all:
c_l_new[n] = c_l_new[n] - hl_fl.max() * 2
if hu_fu.min() < -eps: # we want hu_fu.min()>0
print(x_minus_new[n], x_plus_new[n], y_minus_new[n], y_plus_new[n],
a_l_new[n], b_l_new[n], c_l_new[n], a_u_new[n], b_u_new[n],
c_u_new[n])
plot_surface(x_minus_new[n], x_plus_new[n], y_minus_new[n],
y_plus_new[n], a_u_new[n], b_u_new[n], c_u_new[n])
print('hu-f min', hu_fu.min())
raise Exception('upper plane fail')
break
if hu_fu.min() < 0 and verify_and_modify_all:
c_u_new[n] = c_u_new[n] - hu_fu.min() * 2
c_l_new = c_l_new.view(original_shape)
c_u_new = c_u_new.view(original_shape)
return c_l_new, c_u_new
if __name__ == '__main__':
# x_minus = torch.Tensor([0,0.3])
# x_plus = torch.Tensor([0.1,2])
# y_minus = torch.Tensor([0,0.1])
# y_plus = torch.Tensor([0.1,1])
x_minus = torch.Tensor([0.9062])
x_plus = torch.Tensor([0.9295])
y_minus = torch.Tensor([0.1032])
y_plus = torch.Tensor([5.3253])
# a0 = torch.zeros(x_minus.shape, device = x_minus.device)
# b0 = torch.zeros(x_minus.shape, device = x_minus.device)
z10 = torch.tanh(x_minus) * torch.sigmoid(y_minus)
z20 = torch.tanh(x_minus) * torch.sigmoid(y_minus)
z30 = torch.tanh(x_minus) * torch.sigmoid(y_minus)
a, b, c = train_lower(z10,
z20,
z30,
x_minus,
x_plus,
y_minus,
y_plus,
first.qualification_loss_lower_standard,
'1l',
lr=1e-2,
max_iter=500)
num = 0
plot_surface(x_minus[num], x_plus[num], y_minus[num], y_plus[num], a[num],
b[num], c[num])
loss5 = torch.clamp(loss5, min=confidence)
loss = loss1 + loss2 + loss4 + loss5
return loss, valid
import train_activation_plane
def main_upper(x_minus, x_plus, y_minus, y_plus, print_info = True):
z10 = torch.tanh(x_plus) * torch.sigmoid(y_minus)
z20 = torch.tanh(x_plus) * torch.sigmoid(y_minus)
z30 = torch.tanh(x_plus) * torch.sigmoid(y_minus)
a,b,c = train_activation_plane.train_upper(z10,z20,z30,
x_minus, x_plus, y_minus, y_plus, qualification_loss,
'23u', lr=1e-2,
max_iter = 500, print_info = print_info)
return a.detach(),b.detach(),c.detach()
if __name__ == '__main__':
x_minus = torch.Tensor([-2])
x_plus = torch.Tensor([-0.1])
y_minus = torch.Tensor([-2])
y_plus = torch.Tensor([2])
num = 0
a_u, b_u, c_u = main_upper(x_minus, x_plus, y_minus, y_plus, print_info = False)
plot_surface(x_minus[num], x_plus[num],y_minus[num], y_plus[num],
a_u[num],b_u[num],c_u[num])
