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Containers

Complex neural networks are easily built using container classes:

  • Container : abstract class inherited by containers ;
    • Sequential : plugs layers in a feed-forward fully connected manner ;
    • Parallel : applies its ith child module to the ith slice of the input Tensor ;
    • Concat : concatenates in one layer several modules along dimension dim ;
      • DepthConcat : like Concat, but adds zero-padding when non-dim sizes don't match;

See also the Table Containers for manipulating tables of Tensors.

Container

This is an abstract Module class which declares methods defined in all containers. It reimplements many of the Module methods such that calls are propagated to the contained modules. For example, a call to zeroGradParameters will be propagated to all contained modules.

add(module)

Adds the given module to the container. The order is important

get(index)

Returns the contained modules at index index.

size()

Returns the number of contained modules.

Sequential

Sequential provides a means to plug layers together in a feed-forward fully connected manner.

E.g. creating a one hidden-layer multi-layer perceptron is thus just as easy as:

mlp = nn.Sequential()
mlp:add( nn.Linear(10, 25) ) -- 10 input, 25 hidden units
mlp:add( nn.Tanh() ) -- some hyperbolic tangent transfer function
mlp:add( nn.Linear(25, 1) ) -- 1 output

print(mlp:forward(torch.randn(10)))

which gives the output:

-0.1815
[torch.Tensor of dimension 1]

remove([index])

Remove the module at the given index. If index is not specified, remove the last layer.

model = 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)
}

insert(module, [index])

Inserts the given module at the given index. If index is not specified, the incremented length of the sequence is used and so this is equivalent to use add(module).

model = nn.Sequential()
model:add(nn.Linear(10, 20))
model:add(nn.Linear(20, 30))
model:insert(nn.Linear(20, 20), 2)
> model
nn.Sequential {
  [input -> (1) -> (2) -> (3) -> output]
  (1): nn.Linear(10 -> 20)
  (2): nn.Linear(20 -> 20)      -- The inserted layer
  (3): nn.Linear(20 -> 30)
}

Parallel

module = Parallel(inputDimension,outputDimension)

Creates a container module that applies its ith child module to the ith slice of the input Tensor by using select on dimension inputDimension. It concatenates the results of its contained modules together along dimension outputDimension.

Example:

 mlp=nn.Parallel(2,1);     -- iterate over dimension 2 of input
 mlp:add(nn.Linear(10,3)); -- apply to first slice
 mlp:add(nn.Linear(10,2))  -- apply to first second slice
 print(mlp:forward(torch.randn(10,2)))

gives the output:

-0.5300
-1.1015
 0.7764
 0.2819
-0.6026
[torch.Tensor of dimension 5]

A more complicated example:

mlp=nn.Sequential();
c=nn.Parallel(1,2)
for i=1,10 do
 local t=nn.Sequential()
 t:add(nn.Linear(3,2))
 t:add(nn.Reshape(2,1))
 c:add(t)
end
mlp:add(c)

pred=mlp:forward(torch.randn(10,3))
print(pred)

for i=1,10000 do     -- Train for a few iterations
 x=torch.randn(10,3);
 y=torch.ones(2,10);
 pred=mlp:forward(x)

 criterion= nn.MSECriterion()
 local err=criterion:forward(pred,y)
 local gradCriterion = criterion:backward(pred,y);
 mlp:zeroGradParameters();
 mlp:backward(x, gradCriterion); 
 mlp:updateParameters(0.01);
 print(err)
end

Concat

module = nn.Concat(dim)

Concat concatenates the output of one layer of "parallel" modules along the provided dimension dim: they take the same inputs, and their output is concatenated.

mlp=nn.Concat(1);
mlp:add(nn.Linear(5,3))
mlp:add(nn.Linear(5,7))
print(mlp:forward(torch.randn(5)))

which gives the output:

 0.7486
 0.1349
 0.7924
-0.0371
-0.4794
 0.3044
-0.0835
-0.7928
 0.7856
-0.1815
[torch.Tensor of dimension 10]

DepthConcat

module = nn.DepthConcat(dim)

DepthConcat concatenates the output of one layer of "parallel" modules along the provided dimension dim: they take the same inputs, and their output is concatenated. For dimensions other than dim having different sizes, the smaller tensors are copied in the center of the output tensor, effectively padding the borders with zeros.

The module is particularly useful for concatenating the output of Convolutions along the depth dimension (i.e. nOutputFrame). This is used to implement the DepthConcat layer of the Going deeper with convolutions article. The normal Concat Module can't be used since the spatial dimensions (height and width) of the output Tensors requiring concatenation may have different values. To deal with this, the output uses the largest spatial dimensions and adds zero-padding around the smaller Tensors.

inputSize = 3
outputSize = 2
input = torch.randn(inputSize,7,7)
mlp=nn.DepthConcat(1);
mlp:add(nn.SpatialConvolutionMM(inputSize, outputSize, 1, 1))
mlp:add(nn.SpatialConvolutionMM(inputSize, outputSize, 3, 3))
mlp:add(nn.SpatialConvolutionMM(inputSize, outputSize, 4, 4))
print(mlp:forward(input))

which gives the output:

(1,.,.) = 
 -0.2874  0.6255  1.1122  0.4768  0.9863 -0.2201 -0.1516
  0.2779  0.9295  1.1944  0.4457  1.1470  0.9693  0.1654
 -0.5769 -0.4730  0.3283  0.6729  1.3574 -0.6610  0.0265
  0.3767  1.0300  1.6927  0.4422  0.5837  1.5277  1.1686
  0.8843 -0.7698  0.0539 -0.3547  0.6904 -0.6842  0.2653
  0.4147  0.5062  0.6251  0.4374  0.3252  0.3478  0.0046
  0.7845 -0.0902  0.3499  0.0342  1.0706 -0.0605  0.5525

(2,.,.) = 
 -0.7351 -0.9327 -0.3092 -1.3395 -0.4596 -0.6377 -0.5097
 -0.2406 -0.2617 -0.3400 -0.4339 -0.3648  0.1539 -0.2961
 -0.7124 -1.2228 -0.2632  0.1690  0.4836 -0.9469 -0.7003
 -0.0221  0.1067  0.6975 -0.4221 -0.3121  0.4822  0.6617
  0.2043 -0.9928 -0.9500 -1.6107  0.1409 -1.3548 -0.5212
 -0.3086 -0.0298 -0.2031  0.1026 -0.5785 -0.3275 -0.1630
  0.0596 -0.6097  0.1443 -0.8603 -0.2774 -0.4506 -0.5367

(3,.,.) = 
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000 -0.7326  0.3544  0.1821  0.4796  1.0164  0.0000
  0.0000 -0.9195 -0.0567 -0.1947  0.0169  0.1924  0.0000
  0.0000  0.2596  0.6766  0.0939  0.5677  0.6359  0.0000
  0.0000 -0.2981 -1.2165 -0.0224 -1.1001  0.0008  0.0000
  0.0000 -0.1911  0.2912  0.5092  0.2955  0.7171  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000

(4,.,.) = 
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000 -0.8263  0.3646  0.6750  0.2062  0.2785  0.0000
  0.0000 -0.7572  0.0432 -0.0821  0.4871  1.9506  0.0000
  0.0000 -0.4609  0.4362  0.5091  0.8901 -0.6954  0.0000
  0.0000  0.6049 -0.1501 -0.4602 -0.6514  0.5439  0.0000
  0.0000  0.2570  0.4694 -0.1262  0.5602  0.0821  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000

(5,.,.) = 
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000  0.3158  0.4389 -0.0485 -0.2179  0.0000  0.0000
  0.0000  0.1966  0.6185 -0.9563 -0.3365  0.0000  0.0000
  0.0000 -0.2892 -0.9266 -0.0172 -0.3122  0.0000  0.0000
  0.0000 -0.6269  0.5349 -0.2520 -0.2187  0.0000  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000

(6,.,.) = 
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000  1.1148  0.2324 -0.1093  0.5024  0.0000  0.0000
  0.0000 -0.2624 -0.5863  0.3444  0.3506  0.0000  0.0000
  0.0000  0.1486  0.8413  0.6229 -0.0130  0.0000  0.0000
  0.0000  0.8446  0.3801 -0.2611  0.8140  0.0000  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
[torch.DoubleTensor of dimension 6x7x7]

Note how the last 2 of 6 filter maps have 1 column of zero-padding on the left and top, as well as 2 on the right and bottom. This is inevitable when the component module output tensors non-dim sizes aren't all odd or even. Such that in order to keep the mappings aligned, one need only ensure that these be all odd (or even).

Table Containers

While the above containers are used for manipulating input Tensors, table containers are used for manipulating tables :

These, along with all other modules for manipulating tables can be found here.