[torch] remove layers from the model

odel = nn.Sequential()
model:add(nn.Linear(10, 20))
model:add(nn.Linear(20, 20))
model:add(nn.Linear(20, 30))
model:remove(2)
> model
nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.Linear(10 -> 20)
(2): nn.Linear(20 -> 30)
}

require 'nn'
require 'rnn'
require 'os'
require 'cunn'
featdim=10
hiddenSize=5
temperature=2
numTargetClasses=21
batch=3
seq=4
model = nn.Sequencer(
nn.Sequential()
:add(nn.FastLSTM(featdim, hiddenSize):maskZero(1))
:add(nn.MaskZero(nn.Linear(hiddenSize, numTargetClasses),1))
:add(nn.MaskZero(nn.MulConstant(1/temperature),1))
--:add(nn.MaskZero(nn.LogSoftMax(),1))
)
print(model)
input={}
for i=1,seq do
table.insert(input,torch.rand(batch,featdim))
end
out1=model:forward(input)
local m = model.modules
--m[1].module.modules[3]=nil
m[1].module.modules[3]=nn.Identity()
print(model)
out2=model:forward(input)
for i=1,seq do
print(out1[i]*2-out2[i])
end

nn.Sequencer @ nn.Recursor @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.FastLSTM(10 -> 5)
(2): nn.MaskZero @ nn.Linear(5 -> 21)
(3): nn.MaskZero @ nn.MulConstant
}
nn.Sequencer @ nn.Recursor @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.FastLSTM(10 -> 5)
(2): nn.MaskZero @ nn.Linear(5 -> 21)
(3): nn.Identity
}
0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
[torch.DoubleTensor of size 3x21]

Columns 1 to 10
0.1501 -0.0584 -0.1265  0.1189 -0.0639  0.1172 -0.0891  0.1156 -0.0944  0.2143
0.1466 -0.0396 -0.1348  0.1289 -0.0446  0.1270 -0.0739  0.1079 -0.0979  0.2021
0.1440 -0.0401 -0.1134  0.1281 -0.0608  0.1034 -0.0582  0.0809 -0.1187  0.2039

Columns 11 to 20
0.1220 -0.0880  0.1872  0.1126  0.1963  0.0020  0.0096 -0.1716  0.1589 -0.2559
0.1368 -0.0833  0.1888  0.1275  0.1928  0.0062  0.0279 -0.1468  0.1529 -0.2321
0.1367 -0.1016  0.2025  0.1276  0.1887  0.0198  0.0378 -0.1519  0.1527 -0.2348

Columns 21 to 21
0.0528
0.0514
0.0532
[torch.DoubleTensor of size 3x21]

Columns 1 to 10
0.1626 -0.0541 -0.1265  0.1148 -0.0606  0.1298 -0.0956  0.1248 -0.0810  0.2055
0.1406 -0.0517 -0.1321  0.1217 -0.0596  0.1040 -0.0814  0.1172 -0.1044  0.2122
0.1515 -0.0570 -0.1184  0.1281 -0.0723  0.0901 -0.0719  0.0834 -0.1072  0.2081

Columns 11 to 20
0.1230 -0.0808  0.2008  0.1037  0.2006 -0.0072  0.0060 -0.1646  0.1551 -0.2592
0.1364 -0.0880  0.1557  0.1126  0.2007  0.0040  0.0115 -0.1521  0.1662 -0.2402
0.1402 -0.0989  0.1979  0.1155  0.1838  0.0034  0.0276 -0.1636  0.1467 -0.2400

Columns 21 to 21
0.0455
0.0398
0.0434
[torch.DoubleTensor of size 3x21]

Columns 1 to 10
0.1576 -0.0524 -0.1315  0.1203 -0.0593  0.1163 -0.0893  0.1187 -0.0870  0.2037
0.1274 -0.0541 -0.1538  0.1317 -0.0486  0.0992 -0.0824  0.1237 -0.1050  0.2171
0.1407 -0.0447 -0.1118  0.1192 -0.0642  0.1090 -0.0678  0.0982 -0.1176  0.2117

Columns 11 to 20
0.1362 -0.0816  0.1843  0.1056  0.1979 -0.0084  0.0111 -0.1536  0.1543 -0.2453
0.1477 -0.0807  0.1195  0.1227  0.1965  0.0000  0.0120 -0.1453  0.1678 -0.2224
0.1234 -0.1008  0.1901  0.1213  0.1986  0.0223  0.0233 -0.1609  0.1651 -0.2495

Columns 21 to 21
0.0364
0.0373
0.0573
[torch.DoubleTensor of size 3x21]