require 'nn'
require 'rnn'
maxzero =
1
if maxzero ==
1 then
y_ = torch.Tensor{{
1,
5,
2},{
0,
5,
2},{
0,
0,
0}}
y = torch.Tensor{{
2,
3,
1},{
0,
5,
4},{
0,
0,
0}}
y = torch.Tensor{{
2,
3,
1},{
0,
5,
4},{
1,
1,
1}}
else
y_ = torch.Tensor{{
1,
5,
2},{
0,
5,
2}}
y = torch.Tensor{{
2,
3,
1},{
0,
5,
4}}
end
print(y_,y)
c1 = nn.MSECriterion()
c2 = nn.MaskZeroCriterion(nn.MSECriterion(),
1)
o1 = c1:forward(y_,y)
o2 = c2:forward(y_,y)
print(o1,o2)
c3 = nn.DistKLDivCriterion()
c4 = nn.MaskZeroCriterion(nn.DistKLDivCriterion(),
1)
o3 = c3:forward(y_,y)
o4 = c4:forward(y_,y)
print(o3,o4)
if maxzero ==
1 then
y_ = torch.Tensor{{-
1.20397280433,-
2.30258509299,-
0.51082562376},{-
2.30258509299,-
0.22314355131,-
2.30258509299},{
0,
0,
0}}
y = torch.Tensor{
3,
2,
3}
else
y_ = torch.Tensor{{-
1.20397280433,-
2.30258509299,-
0.51082562376},{-
2.30258509299,-
0.22314355131,-
2.30258509299}}
y = torch.Tensor{
3,
2}
end
c5 = nn.ClassNLLCriterion()
c6 = nn.MaskZeroCriterion(nn.ClassNLLCriterion(),
1)
o5 = c5:forward(y_,y)
o6 = c6:forward(y_,y)
print(o5,o6)
