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
ithchild module to theithslice of the input Tensor ; - Concat : concatenates in one layer several modules along dimension
dim;- DepthConcat : like Concat, but adds zero-padding when non-
dimsizes don't match;
- DepthConcat : like Concat, but adds zero-padding when non-
See also the Table Containers for manipulating tables of Tensors.
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.
Adds the given module to the container. The order is important
Returns the contained modules at index index.
Returns the number of contained modules.
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 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)
}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)
}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)
endmodule = 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]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).
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.