machine-learning - 在给定输入示例的情况下获取中间层的激活值

Question

假设我已将顺序模型定义如下:

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

这是打印的模型:

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
}

简单地使用 net:forward(input) 返回应用 LogSoftMax 后最后一层的输出，这是我不想要的。相反，我想获得一些中间层的激活(例如模块 6)。

那么，在输入输入时如何获得每个中间层的激活？即，我向网络提供一个输入示例，并希望提取第 6 层(卷积层)的激活结果，而不仅仅是最后一层。

谢谢

Answer 1

通过 net:get(6).output (参见 get 和 output )。