I wrote the following code to make an interactive GUI for Mathematica's Neural Network example of

Digit Classification

Here is the Code

DynamicModule[{l = {}},
   Dynamic[s = 
     Graphics[{EdgeForm[Thick], White, Rectangle[{0, 0}, {2, 2}], 
       PointSize[.04], Black, 
       Point[l]}, {PlotRange -> {{0, 2}, {0, 
          2}}}]], {"MouseDragged" :> (l = 
       Append[l, MousePosition["Graphics"]])}]}]

and Then I feed $s$ into the Neural Network Dynamically

Dynamic[Thread[Image[s] -> lenet[Image[s]]]]

This works. But makes the whole thing slow if I am drawing.(I have an Amd Fx-4350 and 16 gigs of RAM). You may have also noticed that I am plotting points, which may not be the best choice for drawing. If you want to improve my code from any direction, you are welcome to do that

  • $\begingroup$ Am I the only one who gets URLFetch::invhttp: Transferred a partial file. and can't run the example? $\endgroup$ – Kuba May 1 '17 at 17:23
  • $\begingroup$ Let us continue this discussion in chat. $\endgroup$ – Dip773 May 1 '17 at 17:32

The last example in the Classify can be used as a start. lowriniak has translated the code from the cell in the example in the answer here. The example uses logistic regression but the classifier can be changed to a neural network easily:

lenet = NetModel["LeNet Trained on MNIST Data"];

(*Inputs for the canvas and brush size*)

{xsize = 64, ysize = 64, thickness = 3},

(*Makes the dynamic environment for variables to update and track each other*)

    (*Set up the initial graphics objects (so different drawing canvases basically*)

    imgdata = ImageData[Image[Table[1, {ysize}, {xsize}]]],
    p1 = {53, 23},
    p2 = {53, 23}, 
    blank = ImageData[Image[Table[1, {ysize}, {xsize}]]]
  (*Deploy makes it harder to accidentally delete your interface*)
    (*Grid formats the elements*)
        (*EventHandler will watch what your mouse does, you can customise the gestures here*)
          (*This is the thing that the event handler watches*)
            (*This checks the image is valid then constructs it*)
              Framed[Image[imgdata, ImageSize -> {160, 160}]], 
            (*This means only one symbol is watched for updates, not all of them*)
            TrackedSymbols :> {imgdata}
             (*This defines what click and drag does*)
            "MouseDown" :> (
              p1 = (p2 = PixelPos[]); 
              (*A click paints a dot*)
              PaintDot[imgdata, p1];
            (*A drag paints a line*)
            "MouseDragged" :> (p1 = p2; p2 = PixelPos[]; 
            PaintLine[imgdata, p1, p2]; Null)
      (*Buttons for clearing the canvas and outputting the data.  You can make your own actions here*)
      {Button["clear", imgdata = blank]}
    Frame -> True
  ,"  ",
  (*Here is where all the painting tools are defined*)
  Initialization :> {
    (*This finds the mouse position in the graphics and rounds it to the nearest pixel (I think)*)
    PixelPos[] := Replace[
      {{  i_, j_} :>Round[{ysize - j, i}], _ :>   None}
    (*This takes a position {i1, j1} and makes a disk of the data around that point of radius 2.5 into 0 values (i.e. black)*)
    Attributes[PaintDot] = {HoldFirst},
    PaintDot[data_Symbol,  p : {i1_, j1_}] := Block[
      {dimx = Length[First[data]], dimy = Length[data]},
          EuclideanDistance[N[{i, j}],  N[p]] < (thickness*(3/4)),
          Part[data, i, j] = 0.
        {i,  Max[i1 - thickness, 1], Min[i1 + thickness, dimx]}, 
        {j,  Max[j1 - thickness, 1],  Min[j1 + thickness, dimy]}
    (*This takes a start and end point, interpolates between them, and makes a line of thickness defined in the With statement as with PaintDot*)
    Attributes[PaintLine] = {HoldFirst}, 
    PaintLine[data_, {i1_,   j1_}, {i2_, j2_}] := Block[
      {dimx = Length[First[data]], dimy = Length[data], indices, ib, ie, jb, je}, 
      indices = interpolatePoints[N[{i1, j1}], N[{i2, j2}], (thickness*(3/4))]; 
      {ib, ie} =  Sort[{i1, i2}]; 
      {jb, je} =  Sort[{j1, j2}]; 
      {{ib, jb}, {ie, je}} =  
        {Clip[#1, {1, dimy}], Clip[#2, {1, dimx}]} & @@ Transpose[{{ib, jb} - thickness, {ie, je} + thickness}]
          Min[Map[EuclideanDistance[N[{i, j}], #] & , indices]] < (thickness*(3/4)), 
          Part[data, i, j] =  0.
        {i, ib, ie}, 
        {j, jb, je}

  (*This checks how far apart two points are and if they are further than 3 pixels apart, breaks up the line into segments of length 3*)
  interpolatePoints[start_, stop_] := Module[
    {dist, unit},
    dist = N[ EuclideanDistance[start, stop]]; 
      dist < thickness, 
      Return[{start, stop}]];
      unit = Normalize[stop - N[start]];
      Append[stop][Table[start + i unit, {i, 0, dist, thickness}]]
    (*This I think does the same as before but with a generalised step size*)
    interpolatePoints[p1_, p2_, r_] :=  Module[{d, v},
      d = EuclideanDistance[p1, p2];
      If[d < 2 r, Return[{p1, p2}]];
      v = Normalize[p2 - p1]; 
        Append[p2][Table[p1 + i v, {i,  0., d, r}]], 

enter image description here

It runs smoothly on my 4-year-old macbook pro.

  • $\begingroup$ Excellent! Although it uses more lines. It is exactly what I needed $\endgroup$ – Dip773 May 3 '17 at 6:05

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