[torch]Getting the Output of a Layer

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https://groups.google.com/forum/#!topic/torch7/R9DAdx95aqc

introduction

require 'cutorch'require 'cunn'require 'rnn'require 'os'tensor1 = torch.zeros(5,10)tensor1[3]=torch.rand(1,10)print(tensor1)input = {tensor1,torch.rand(5,10),torch.rand(5,10),torch.rand(5,10),torch.rand(5,10),torch.rand(5,10)}net = nn.Sequencer(   nn.Sequential()      :add(nn.MaskZero(nn.FastLSTM(10,3),1))      :add(nn.MaskZero(nn.Linear(3,4),1))      :add(nn.MaskZero(nn.LogSoftMax(),1)))output = net:forward(input)local m = net.modules--[[print("net")print(net)print("m")print(m)--]]for i = 1, #input do        print(output[i])        print(m[1].sharedClones[i].modules[1].output)endprint("net")print(net)print("m")print(m)

output

net nn.Sequencer @ nn.Recursor @ nn.Sequential {  [input -> (1) -> (2) -> (3) -> output]  (1): nn.MaskZero @ nn.FastLSTM(10 -> 3)  (2): nn.MaskZero @ nn.Linear(3 -> 4)  (3): nn.MaskZero @ nn.LogSoftMax}m   {  1 :     {      sharedClones :         {          1 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }          2 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }          3 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }          4 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }          5 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }          6 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }        }      step : 7      outputs :         {          1 : DoubleTensor - size: 5x4          2 : DoubleTensor - size: 5x4          3 : DoubleTensor - size: 5x4          4 : DoubleTensor - size: 5x4          5 : DoubleTensor - size: 5x4          6 : DoubleTensor - size: 5x4        }      output : DoubleTensor - size: 5x4      gradInput : DoubleTensor - empty      modules :         {          1 :             {              gradInput : DoubleTensor - empty              modules :                 {                  1 : {...}                  2 : {...}                  3 : {...}                }              _type : "torch.DoubleTensor"              output : DoubleTensor - size: 5x4            }        }      _gradOutputs : {...}      rho : 6      recurrentModule :         {          gradInput : DoubleTensor - empty          modules :             {              1 :                 {                  output : DoubleTensor - size: 5x3                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }              2 :                 {                  output : DoubleTensor - size: 5x4                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }              3 :                 {                  output : DoubleTensor - size: 5x4                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }            }          _type : "torch.DoubleTensor"          output : DoubleTensor - size: 5x4        }      nSharedClone : 6      _type : "torch.DoubleTensor"      gradInputs : {...}      module :         {          gradInput : DoubleTensor - empty          modules :             {              1 :                 {                  output : DoubleTensor - size: 5x3                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }              2 :                 {                  output : DoubleTensor - size: 5x4                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }              3 :                 {                  output : DoubleTensor - size: 5x4                  gradInput : DoubleTensor - empty                  nInputDim : 1                  batchmode : true                  zeroMask : ByteTensor - size: 5x1                  _type : "torch.DoubleTensor"                  _zeroMask : DoubleTensor - size: 5x1                  module : {...}                  modules : {...}                }            }          _type : "torch.DoubleTensor"          output : DoubleTensor - size: 5x4        }      rmInSharedClones : true    }}

可以看出m是一个table类型的变量. 所以看看想要它输出什么就能输出什么.
例如:

tensor1 = torch.zeros(5,10)tensor1[3]=torch.rand(1,10)print(tensor1)input = {tensor1,torch.rand(5,10),torch.rand(5,10),torch.rand(5,10),torch.rand(5,10),torch.rand(5,10)}net = nn.Sequencer(   nn.Sequential()      :add(nn.MaskZero(nn.FastLSTM(10,3),1))--      :add(nn.MaskZero(nn.Linear(3,4),1))--      :add(nn.MaskZero(nn.LogSoftMax(),1)))output = net:forward(input)local m = net.modules--[[print("net")print(net)print("m")print(m)--]]for i = 1, #input do        print(output[i])        print(m[1].sharedClones[i].modules[1].output)end)

test

require 'cutorch'require 'cunn'require 'rnn'require 'os'--[[net = nn.Sequencer(   nn.Sequential()      :add(nn.MaskZero(nn.FastLSTM(10,6),1))      :add(nn.MaskZero(nn.Linear(6,4),1))      :add(nn.MaskZero(nn.LogSoftMax(),1)))parameters, gradParameters = net:getParameters()lightModel = net:clone('weight','bias','running_mean','running_std')torch.save('model.t7',lightModel)--]]net=torch.load("model.t7")--[[tensor1 = torch.zeros(5,10)tensor1[3]=torch.Tensor{3,4,5,6,7,8,23,2,12,90}tensor2 = torch.ones(5,10)tensor2[{{1,2},{}}]=torch.Tensor{ {1,3,4,5,6,0,3,2,56,2}, {5,3,2,5,7,3,45,78,235,10}}tensor2[4]=torch.ones(1,10):fill(3.2)tensor2[5]=torch.zeros(1,10)input = {tensor1,tensor2}--]]--net=torch.load("/work1/t2g-shinoda2011/15M54105/trecvid/torch-lstm3/batch5_epoch5_hiddensize256_cw1/model_100ex_batch5_unit256_epoch70")--[[array = {}tensor1  = torch.zeros(5,10)tensor1[3]=torch.rand(1,10)tensor2 = torch.rand(5,10)tensor3 = torch.rand(5,10)tensor4 = torch.rand(5,10)tensor1=tensor1:cuda()tensor2=tensor2:cuda()tensor3=tensor3:cuda()tensor4=tensor4:cuda()table.insert(array, tensor1)table.insert(array, tensor2)table.insert(array, tensor3)table.insert(array, tensor4)file = torch.DiskFile('input.asc', 'w')file:writeObject(array)file:close()os.exit()--]]net:cuda()file = torch.DiskFile('input.asc', 'r')input = file:readObject()print(input)local m = net.modulesoutput = net:forward(input)--[[print("net")print(net)print("m")print(m)--]]model = (nn.MaskZero(nn.LogSoftMax(),1)):cuda()for seqj = 1, #input do    print(seqj)    res = m[1].sharedClones[seqj].modules[2].output    out1=output[seqj]    out2=model:forward(res)    print(out1-out2)end

test.lua得到的tep = m[1].sharedClones[seqj].modules[2].output[i]是取了net的第二层的输出.
out1与out2值相等.说明确实tep是第二层的输出.

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