用torch写一个残差神经网络代码

以下是使用 Torch 实现的残差神经网络代码：

require 'nn'

function residualBlock(nChannels)
  local convModule = nn.Sequential()
    :add(nn.SpatialConvolution(nChannels, nChannels, 3,3, 1,1, 1,1))
    :add(nn.SpatialBatchNormalization(nChannels))
    :add(nn.ReLU(true))
    :add(nn.SpatialConvolution(nChannels, nChannels, 3,3, 1,1, 1,1))
    :add(nn.SpatialBatchNormalization(nChannels))
  return nn.Sequential()
    :add(nn.ConcatTable()
      :add(convModule)
      :add(nn.Identity()))
    :add(nn.CAddTable())
    :add(nn.ReLU(true))
end

function resNet(nChannels, nClasses, nResiduals)
  local nets = nn.Sequential()
  nets:add(nn.SpatialConvolution(3, nChannels, 3,3, 1,1, 1,1))
  nets:add(nn.SpatialBatchNormalization(nChannels))
  nets:add(nn.ReLU(true))
  for i=1,nResiduals do
    nets:add(residualBlock(nChannels))
  end
  nets:add(nn.SpatialAveragePooling(8,8))
  nets:add(nn.View(nChannels))
  nets:add(nn.Linear(nChannels, nClasses))
  nets:add(nn.LogSoftMax())
  return nets
end

model = resNet(64, 10, 5) -- 5 residual blocks

希望这个代码能够帮到您！