Obtaining intermediate layers' activation values given input example(s) - machine-learning

Question

Suppose I have defined my sequential model as follow:
require 'nn'
net = nn.Sequential()
net:add(nn.SpatialConvolution(1, 6, 5, 5)) -- 1 input image channel, 6 output channels, 5x5 convolution kernel
net:add(nn.ReLU()) -- non-linearity
net:add(nn.SpatialMaxPooling(2,2,2,2)) -- A max-pooling operation that looks at 2x2 windows and finds the max.
net:add(nn.SpatialConvolution(6, 16, 5, 5))
net:add(nn.ReLU()) -- non-linearity
net:add(nn.SpatialMaxPooling(2,2,2,2))
net:add(nn.View(16*5*5)) -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5
net:add(nn.Linear(16*5*5, 120)) -- fully connected layer (matrix multiplication between input and weights)
net:add(nn.ReLU()) -- non-linearity
net:add(nn.Linear(120, 84))
net:add(nn.ReLU()) -- non-linearity
net:add(nn.Linear(84, 10)) -- 10 is the number of outputs of the network (in this case, 10 digits)
net:add(nn.LogSoftMax()) -- converts the output to a log-probability. Useful for classification problems
And here's the model printed:
net
nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> output]
(1): nn.SpatialConvolution(1 -> 6, 5x5)
(2): nn.ReLU
(3): nn.SpatialMaxPooling(2x2, 2,2)
(4): nn.SpatialConvolution(6 -> 16, 5x5)
(5): nn.ReLU
(6): nn.SpatialMaxPooling(2x2, 2,2)
(7): nn.View(400)
(8): nn.Linear(400 -> 120)
(9): nn.ReLU
(10): nn.Linear(120 -> 84)
(11): nn.ReLU
(12): nn.Linear(84 -> 10)
(13): nn.LogSoftMax
}
Simply using net:forward(input) returns the last layer's output after LogSoftMax has been applied to which is what I do not want. Instead, I would like to get the activations of some of the intermediate layers (e.g. module 6).
So, How can I get the activations of each of the intermediate layers when feeding an input? i.e. I feed an input example to the network and want to extract the activation results of the 6th layer (a convolutional layer), not just the final layer.
Thanks

Answer 1

Via net:get(6).output (see get and output).