Classification with torch model exported from digits - lua 5.1 - lua

Question

I'm very new to deep learning and i'm trying to obtain a classification with lua.
I've installed digits with torch and lua 5.1 and i've train the following model :
After that, i've made a classification with the digits server to test the exemple and here is the result :
I've exported the model and now i'm trying to do a classification with the following lua code :
local image_url = '/home/delpech/mnist/test/5/04131.png'
local network_url = '/home/delpech/models/snapshot_30_Model.t7'
local network_name = paths.basename(network_url)
print '==> Loading network'
local net = torch.load(network_name)
--local net = torch.load(network_name):unpack():float()
net:evaluate()
print(net)
print '==> Loading synsets'
print 'Loads mapping from net outputs to human readable labels'
local synset_words = {}
--for line in io.lines'/home/delpech/models/labels.txt' do table.insert(synset_words, line:sub(11)) end
for line in io.lines'/home/delpech/models/labels.txt' do table.insert(synset_words, line) end
print 'synset words'
for line in io.lines'/home/delpech/models/labels.txt' do print(line) end
print '==> Loading image and imagenet mean'
local im = image.load(image_url)
print '==> Preprocessing'
local I = image.scale(im,28,28,'bilinear'):float()
print 'Propagate through the network, sort outputs in decreasing order and show 10 best classes'
local _,classes = net:forward(I):view(-1):sort(true)
for i=1,10 do
print('predicted class '..tostring(i)..': ', synset_words[classes[i]])
end
But here is the output :
delpech#delpech-K55VD:~/models$ lua classify.lua
==> Downloading image and network
==> Loading network
nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> output]
(1): nn.MulConstant
(2): nn.SpatialConvolution(1 -> 20, 5x5)
(3): nn.SpatialMaxPooling(2x2, 2,2)
(4): nn.SpatialConvolution(20 -> 50, 5x5)
(5): nn.SpatialMaxPooling(2x2, 2,2)
(6): nn.View(-1)
(7): nn.Linear(800 -> 500)
(8): nn.ReLU
(9): nn.Linear(500 -> 10)
(10): nn.LogSoftMax
}
==> Loading synsets
Loads mapping from net outputs to human readable labels
synset words
0
1
2
3
4
5
6
7
8
9
==> Loading image and imagenet mean
==> Preprocessing
Propagate through the network, sort outputs in decreasing order and show 5 best classes
predicted class 1: 4
predicted class 2: 8
predicted class 3: 0
predicted class 4: 1
predicted class 5: 9
predicted class 6: 6
predicted class 7: 7
predicted class 8: 2
predicted class 9: 5
predicted class 10: 3
And this is actually not the classification provided by digits...

Answer 1

OK, after searching in the digits code source, it looked like i've missed two things :
you have to get the mean image in the job folder and make the following pre-process :
print '==> Preprocessing'
for i=1,im_mean:size(1) do
im[i]:csub(im_mean[i])
end
and the fact that i had to load my images in this way and multiply every pixel to 255.
local im = image.load(image_url):type('torch.FloatTensor'):contiguous();
im:mul(255)
Here is the total anwser :
require 'image'
require 'nn'
require 'torch'
require 'paths'
local function main()
print '==> Downloading image and network'
local image_url = '/home/delpech/mnist/test/7/03079.png'
local network_url = '/home/delpech/models/snapshot_30_Model.t7'
local mean_url = '/home/delpech/models/mean.jpg'
print '==> Loading network'
local net = torch.load(network_url)
net:evaluate();
print '==> Loading synsets'
print 'Loads mapping from net outputs to human readable labels'
local synset_words = {}
for line in io.lines'/home/delpech/models/labels.txt' do table.insert(synset_words, line) end
print '==> Loading image and imagenet mean'
local im = image.load(image_url):type('torch.FloatTensor'):contiguous();--:contiguous()
im:mul(255)
local I = image.scale(im,28,28,'bilinear'):float()
local im_mean = image.load(mean_url):type('torch.FloatTensor'):contiguous();
im_mean:mul(255)
local Imean = image.scale(im,28,28,'bilinear'):float()
print '==> Preprocessing'
for i=1,im_mean:size(1) do
im[i]:csub(im_mean[i])
end
local _,classes = net:forward(im):sort(true);
for i=1,10 do
print('predicted class '..tostring(i)..': ', synset_words[classes[i]])
end
end
main()