Training error. Help #21
Comments
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You have 2 classes but you give 10 output from the logsoftmax. |
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I changed the line net:add(nn.Linear(84, 10) to net:add(nn.Linear(84, 2), but the error remains. |
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Your batch size is very big. It could be the problem. Try with smaller batch. |
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Does not work. could you help me with the correct data? |
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net:add(nn.View(1699)) must be net:add(nn.View(5184)). I dont know what the problem is directly. It says something about batch size. Is this the full error message. |
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New error! nn.Sequential { StochasticGradient: training.../facedetect/torch/install/share/lua/5.1/nn/Container.lua:67: WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above. it looked at the beginning: |
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Ok. 16 x 11 x 11 would be correct. |
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I replaced it and did from the beginning: th> net:add(nn.SpatialConvolution(3, 6, 9, 9)) net = nn.Sequential() Error: StochasticGradient: training.../facedetect/torch/install/share/lua/5.1/nn/Container.lua:67: WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above. write 16x16x16 maybe I'm not there to change the value? |
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how to determine these parameters: 5x5 convolution kernel? reshapes from a 3D tensor of 16x5x5 into 1D tensor of 1655? |
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Convolution makes your pictures smaller by m-1 n-1 where mxn is your filter. Subsampling makes a division over size. Your pictures are 96x96. 5x5 convolution will make them 92x92 if used without padding. Search about convolution and pooling. |
Hello.
I teach a neural network for two of my classes.
Error occurs at the stage of training.
How to fix it?
th> require 'nn';
th> trainset = torch.load('animals_peoples2.t7')
th> testset = torch.load('animals_peoples2.t7')
th> classes = {'animals', 'peoples'}
th> print(trainset)
{
data : ByteTensor - size: 17299x3x96x96
label : ByteTensor - size: 17299
}
th> print(#trainset.data)
17299
3
96
96
[torch.LongStorage of size 4]
th> setmetatable(trainset,
..> {__index = function(t, i)
..> return {
..> t.data[i],
..> t.label[i]
..> }
..> end}
..> );
th> function trainset:size()
..> return self.data:size(1)
..> end
th> trainset.data = trainset.data:double()
th> print(trainset:size())
17299
th> print(trainset[33])
{
1 : DoubleTensor - size: 3x96x96
2 : 1
}
th> redChannel = trainset.data:select(2, 1)
th> print(#redChannel)
17299
96
96
[torch.LongStorage of size 3]
th> mean = {} -- store the mean, to normalize the test set in the future
th> stdv = {} -- store the standard-deviation for the future
th> for i=1,3 do -- over each image channel
..> mean[i] = trainset.data:select(2, 1):mean() -- mean estimation
..> print('Channel ' .. i .. ', Mean: ' .. mean[i])
..> trainset.data:select(2, 1):add(-mean[i]) -- mean subtraction
..>
..> stdv[i] = trainset.data:select(2, i):std() -- std estimation
..> print('Channel ' .. i .. ', Standard Deviation: ' .. stdv[i])
..> trainset.data:select(2, i):div(stdv[i]) -- std scaling
..> end
Channel 1, Mean: 0
Channel 1, Standard Deviation: 0
Channel 2, Mean: nan
Channel 2, Standard Deviation: 0
Channel 3, Mean: nan
Channel 3, Standard Deviation: 0
th> net = nn.Sequential()
th> net:add(nn.SpatialConvolution(3, 6, 9, 9)) -- 3 input image channels, 6 output channels, 5x5 convolution kernel
th> net:add(nn.ReLU()) -- non-linearity
th> net:add(nn.SpatialMaxPooling(2,2,2,2)) -- A max-pooling operation that looks at 2x2 windows and finds the max.
th> net:add(nn.SpatialConvolution(6, 16, 9, 9))
th> net:add(nn.ReLU()) -- non-linearity
th> net:add(nn.SpatialMaxPooling(2,2,2,2))
th> net:add(nn.View(1699)) -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 1655
th> net:add(nn.Linear(1699, 120)) -- fully connected layer (matrix multiplication between input and weights)
th> net:add(nn.ReLU()) -- non-linearity
th> net:add(nn.Linear(120, 84))
th> net:add(nn.ReLU()) -- non-linearity
th> net:add(nn.Linear(84, 10)) -- 10 is the number of outputs of the network (in this case, 10 digits)
th> net:add(nn.LogSoftMax()) -- converts the output to a log-probability. Useful for classification problems
th> criterion = nn.ClassNLLCriterion()
th> trainer = nn.StochasticGradient(net, criterion)
th> trainer.learningRate = 0.001
th> trainer.maxIteration = 5 -- just do 5 epochs of training.
th> trainer:train(trainset)
trainer:train(trainset)
StochasticGradient: training
/root/facedetect/torch/install/share/lua/5.1/nn/THNN.lua:110: Assertion `THIndexTensor_(size)(target, 0) == batch_size' failed. at /tmp/luarocks_nn-scm-1-1625/nn/lib/THNN/generic/ClassNLLCriterion.c:50
stack traceback:
[C]: in function 'v'
/root/facedetect/torch/install/share/lua/5.1/nn/THNN.lua:110: in function 'ClassNLLCriterion_updateOutput'
...ect/torch/install/share/lua/5.1/nn/ClassNLLCriterion.lua:43: in function 'forward'
...ct/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function 'train'
[string "_RESULT={trainer:train(trainset)}"]:1: in main chunk
[C]: in function 'xpcall'
/root/facedetect/torch/install/share/lua/5.1/trepl/init.lua:661: in function 'repl'
...tect/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:199: in main chunk
[C]: at 0x004064f0
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