Cannot get mnist example working

[kennedymj97]

I am trying to get the mnist example working.

I have got all the tests passing and am using data downloaded from kaggle.

The output when I run it:
stack runghc examples/main/mnist.hs data/train.csv data/test.csv
Training convolutional neural network...
Failed reading: takeWhile1

Have I got the right dataset? Am I just missing something or just being stupid?

[leftaroundabout]

It seems that those CSV files on Kaggle contain an extra (and patently silly) “header” line

label,1x1,1x2,1x3,1x4,1x5,1x6,1x7,1x8,1x9,1x10,1x11,1x12,1x13,1x14,1x15,1x16,1x17,1x18,1x19,1x20,1x21,1x22,1x23,1x24,1x25,1x26,1x27,1x28,2x1,2x2,2x3,2x4,2x5,2x6,2x7,2x8,2x9,2x10,2x11,2x12,2x13,2x14,2x15,2x16,2x17,2x18,2x19,2x20,2x21,2x22,2x23,2x24,2x25,2x26,2x27,2x28,3x1,3x2,3x3,3x4,3x5,3x6,3x7,3x8,3x9,3x10,3x11,3x12,3x13,3x14,3x15,3x16,3x17,3x18,3x19,3x20,3x21,3x22,3x23,3x24,3x25,3x26,3x27,3x28,4x1,4x2,4x3,4x4,4x5,4x6,4x7,4x8,4x9,4x10,4x11,4x12,4x13,4x14,4x15,4x16,4x17,4x18,4x19,4x20,4x21,4x22,4x23,4x24,4x25,4x26,4x27,4x28,5x1,5x2,5x3,5x4,5x5,5x6,5x7,5x8,5x9,5x10,5x11,5x12,5x13,5x14,5x15,5x16,5x17,5x18,5x19,5x20,5x21,5x22,5x23,5x24,5x25,5x26,5x27,5x28,6x1,6x2,6x3,6x4,6x5,6x6,6x7,6x8,6x9,6x10,6x11,6x12,6x13,6x14,6x15,6x16,6x17,6x18,6x19,6x20,6x21,6x22,6x23,6x24,6x25,6x26,6x27,6x28,7x1,7x2,7x3,7x4,7x5,7x6,7x7,7x8,7x9,7x10,7x11,7x12,7x13,7x14,7x15,7x16,7x17,7x18,7x19,7x20,7x21,7x22,7x23,7x24,7x25,7x26,7x27,7x28,8x1,8x2,8x3,8x4,8x5,8x6,8x7,8x8,8x9,8x10,8x11,8x12,8x13,8x14,8x15,8x16,8x17,8x18,8x19,8x20,8x21,8x22,8x23,8x24,8x25,8x26,8x27,8x28,9x1,9x2,9x3,9x4,9x5,9x6,9x7,9x8,9x9,9x10,9x11,9x12,9x13,9x14,9x15,9x16,9x17,9x18,9x19,9x20,9x21,9x22,9x23,9x24,9x25,9x26,9x27,9x28,10x1,10x2,10x3,10x4,10x5,10x6,10x7,10x8,10x9,10x10,10x11,10x12,10x13,10x14,10x15,10x16,10x17,10x18,10x19,10x20,10x21,10x22,10x23,10x24,10x25,10x26,10x27,10x28,11x1,11x2,11x3,11x4,11x5,11x6,11x7,11x8,11x9,11x10,11x11,11x12,11x13,11x14,11x15,11x16,11x17,11x18,11x19,11x20,11x21,11x22,11x23,11x24,11x25,11x26,11x27,11x28,12x1,12x2,12x3,12x4,12x5,12x6,12x7,12x8,12x9,12x10,12x11,12x12,12x13,12x14,12x15,12x16,12x17,12x18,12x19,12x20,12x21,12x22,12x23,12x24,12x25,12x26,12x27,12x28,13x1,13x2,13x3,13x4,13x5,13x6,13x7,13x8,13x9,13x10,13x11,13x12,13x13,13x14,13x15,13x16,13x17,13x18,13x19,13x20,13x21,13x22,13x23,13x24,13x25,13x26,13x27,13x28,14x1,14x2,14x3,14x4,14x5,14x6,14x7,14x8,14x9,14x10,14x11,14x12,14x13,14x14,14x15,14x16,14x17,14x18,14x19,14x20,14x21,14x22,14x23,14x24,14x25,14x26,14x27,14x28,15x1,15x2,15x3,15x4,15x5,15x6,15x7,15x8,15x9,15x10,15x11,15x12,15x13,15x14,15x15,15x16,15x17,15x18,15x19,15x20,15x21,15x22,15x23,15x24,15x25,15x26,15x27,15x28,16x1,16x2,16x3,16x4,16x5,16x6,16x7,16x8,16x9,16x10,16x11,16x12,16x13,16x14,16x15,16x16,16x17,16x18,16x19,16x20,16x21,16x22,16x23,16x24,16x25,16x26,16x27,16x28,17x1,17x2,17x3,17x4,17x5,17x6,17x7,17x8,17x9,17x10,17x11,17x12,17x13,17x14,17x15,17x16,17x17,17x18,17x19,17x20,17x21,17x22,17x23,17x24,17x25,17x26,17x27,17x28,18x1,18x2,18x3,18x4,18x5,18x6,18x7,18x8,18x9,18x10,18x11,18x12,18x13,18x14,18x15,18x16,18x17,18x18,18x19,18x20,18x21,18x22,18x23,18x24,18x25,18x26,18x27,18x28,19x1,19x2,19x3,19x4,19x5,19x6,19x7,19x8,19x9,19x10,19x11,19x12,19x13,19x14,19x15,19x16,19x17,19x18,19x19,19x20,19x21,19x22,19x23,19x24,19x25,19x26,19x27,19x28,20x1,20x2,20x3,20x4,20x5,20x6,20x7,20x8,20x9,20x10,20x11,20x12,20x13,20x14,20x15,20x16,20x17,20x18,20x19,20x20,20x21,20x22,20x23,20x24,20x25,20x26,20x27,20x28,21x1,21x2,21x3,21x4,21x5,21x6,21x7,21x8,21x9,21x10,21x11,21x12,21x13,21x14,21x15,21x16,21x17,21x18,21x19,21x20,21x21,21x22,21x23,21x24,21x25,21x26,21x27,21x28,22x1,22x2,22x3,22x4,22x5,22x6,22x7,22x8,22x9,22x10,22x11,22x12,22x13,22x14,22x15,22x16,22x17,22x18,22x19,22x20,22x21,22x22,22x23,22x24,22x25,22x26,22x27,22x28,23x1,23x2,23x3,23x4,23x5,23x6,23x7,23x8,23x9,23x10,23x11,23x12,23x13,23x14,23x15,23x16,23x17,23x18,23x19,23x20,23x21,23x22,23x23,23x24,23x25,23x26,23x27,23x28,24x1,24x2,24x3,24x4,24x5,24x6,24x7,24x8,24x9,24x10,24x11,24x12,24x13,24x14,24x15,24x16,24x17,24x18,24x19,24x20,24x21,24x22,24x23,24x24,24x25,24x26,24x27,24x28,25x1,25x2,25x3,25x4,25x5,25x6,25x7,25x8,25x9,25x10,25x11,25x12,25x13,25x14,25x15,25x16,25x17,25x18,25x19,25x20,25x21,25x22,25x23,25x24,25x25,25x26,25x27,25x28,26x1,26x2,26x3,26x4,26x5,26x6,26x7,26x8,26x9,26x10,26x11,26x12,26x13,26x14,26x15,26x16,26x17,26x18,26x19,26x20,26x21,26x22,26x23,26x24,26x25,26x26,26x27,26x28,27x1,27x2,27x3,27x4,27x5,27x6,27x7,27x8,27x9,27x10,27x11,27x12,27x13,27x14,27x15,27x16,27x17,27x18,27x19,27x20,27x21,27x22,27x23,27x24,27x25,27x26,27x27,27x28,28x1,28x2,28x3,28x4,28x5,28x6,28x7,28x8,28x9,28x10,28x11,28x12,28x13,28x14,28x15,28x16,28x17,28x18,28x19,28x20,28x21,28x22,28x23,28x24,28x25,28x26,28x27,28x28

This needs to be stripped before the parser can do its work; can be done with

diff --git a/examples/main/mnist.hs b/examples/main/mnist.hs
index 28be8cd..8805c8c 100644
--- a/examples/main/mnist.hs
+++ b/examples/main/mnist.hs
@@ -110,7 +110,7 @@ main = do
 readMNIST :: FilePath -> ExceptT String IO [(S ('D2 28 28), S ('D1 10))]
 readMNIST mnist = ExceptT $ do
   mnistdata <- T.readFile mnist
-  return $ traverse (A.parseOnly parseMNIST) (T.lines mnistdata)
+  return $ traverse (A.parseOnly parseMNIST) (tail $ T.lines mnistdata)
 
 parseMNIST :: A.Parser (S ('D2 28 28), S ('D1 10))
 parseMNIST = do

Not sure if that's really sufficient to get it working though... for me, the process runs out of memory...

[leftaroundabout]

Yeah, it does seem to work now – when I reduce the size of the datasets, it starts and improves accuracy during the training. Unfortunately, it doesn't run in constant memory, which is a big problem (but another issue).

[leftaroundabout]

Fortunately, the memory leak actually seems to be specific to the Inception-style layers. With the simpler model from the README, it runs seemingly in constant space. Nice!

[erikd]

It seems that those CSV files on Kaggle contain an extra (and patently silly) “header” line

Header lines are a common feature for CSV fines and they are not silly.

[leftaroundabout]

@erikd I don't mean the fact that there is a header, but the XxY information it contains.

[leftaroundabout]

Anyway... this issue has apparently been amply addressed with #97. I'll do some cherry-picking there and maybe try to submit that in form of usable PRs...