-
Notifications
You must be signed in to change notification settings - Fork 19
/
yHatMat2.lua
executable file
·79 lines (63 loc) · 3.12 KB
/
yHatMat2.lua
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
-- adapted from https://github.com/jarmstrong2/handwritingnet/blob/master/yHatMat.lua
require 'nn'
local YHat, parent = torch.class('nn.YHat', 'nn.Module')
function YHat:updateOutput(input)
local hat_e_t = input[{{},{1}}]
local hat_pi_t = input[{{},{2,21}}]
local hat_mu_1_t = input[{{},{22,41}}]
local hat_mu_2_t = input[{{},{42,61}}]
local hat_sigma_1_t = input[{{},{62,81}}]
local hat_sigma_2_t = input[{{},{82,101}}]
local hat_rho_t = input[{{},{102,121}}]
self.e_t_act = self.e_t_act or nn.Sigmoid():cuda()
self.pi_t_act = self.pi_t_act or nn.SoftMax():cuda()
self.sigma_1_t_act = self.sigma_1_t_act or nn.Exp():cuda()
self.sigma_2_t_act = self.sigma_2_t_act or nn.Exp():cuda()
self.rho_t_act = self.rho_t_act or nn.Tanh():cuda()
local e_t = self.e_t_act:forward(-hat_e_t)
local pi_t = self.pi_t_act:forward(hat_pi_t)
local mu_1_t = hat_mu_1_t:clone()
local mu_2_t = hat_mu_2_t:clone()
local sigma_1_t = self.sigma_1_t_act:forward(hat_sigma_1_t)
local sigma_2_t = self.sigma_2_t_act:forward(hat_sigma_2_t)
local rho_t = self.rho_t_act:forward(hat_rho_t)
local output = torch.cat(e_t:float(), pi_t:float(), 2)
output = torch.cat(output, mu_1_t:float(), 2)
output = torch.cat(output, mu_2_t:float(), 2)
output = torch.cat(output, sigma_1_t:float(), 2)
output = torch.cat(output, sigma_2_t:float(), 2)
output = torch.cat(output, rho_t:float(), 2)
self.output = output:cuda()
return self.output
end
function YHat:updateGradInput(input, gradOutput)
local hat_e_t = input[{{},{1}}]
local hat_pi_t = input[{{},{2,21}}]
local hat_mu_1_t = input[{{},{22,41}}]
local hat_mu_2_t = input[{{},{42,61}}]
local hat_sigma_1_t = input[{{},{62,81}}]
local hat_sigma_2_t = input[{{},{82,101}}]
local hat_rho_t = input[{{},{102,121}}]
local d_e_t = gradOutput[{{},{1}}]
local d_pi_t = gradOutput[{{},{2,21}}]
local d_mu_1_t = gradOutput[{{},{22,41}}]
local d_mu_2_t = gradOutput[{{},{42,61}}]
local d_sigma_1_t = gradOutput[{{},{62,81}}]
local d_sigma_2_t = gradOutput[{{},{82,101}}]
local d_rho_t = gradOutput[{{},{102,121}}]
local grad_e_t = d_e_t:clone() --self.e_t_act:backward(-hat_e_t, d_e_t)
local grad_pi_t = d_pi_t:clone() --self.pi_t_act:backward(hat_pi_t, d_pi_t)
local grad_mu_1_t = d_mu_1_t:clone()
local grad_mu_2_t = d_mu_2_t:clone()
local grad_sigma_1_t = d_sigma_1_t:clone() --self.sigma_1_t_act:backward(hat_sigma_1_t, d_sigma_1_t)
local grad_sigma_2_t = d_sigma_2_t:clone() --self.sigma_2_t_act:backward(hat_sigma_2_t, d_sigma_2_t)
local grad_rho_t = d_rho_t:clone() --self.rho_t_act:backward(hat_rho_t, d_rho_t)
local grad_input = torch.cat(grad_e_t:float(), grad_pi_t:float())
grad_input = torch.cat(grad_input, grad_mu_1_t:float())
grad_input = torch.cat(grad_input, grad_mu_2_t:float())
grad_input = torch.cat(grad_input, grad_sigma_1_t:float())
grad_input = torch.cat(grad_input, grad_sigma_2_t:float())
grad_input = torch.cat(grad_input, grad_rho_t:float())
self.gradInput = grad_input:cuda()
return self.gradInput
end