In[35]:= feats = RandomSample[feats];
Length@feats
RandomSample::lrwl: The set of items to sample from,
Join[normalFeats,virusFeats,bacFeats], should be a non-empty list or a rule
weights -> choices.
Out[36]= 1

In[20]:= trainedNet =
NetTrain[learnNet, trainFeat, ValidationSet -> testFeat,
MaxTrainingRounds -> 35, TargetDevice -> "GPU"]

NetTrain::jitgen: Generator function cannot be used when the net to be
trained has not been fully specified.

NetTrain::nfspec: Cannot train net: input of first layer is not fully
specified.

Out[20]= $Failed

"C:\Users\El_Elyon`s\Desktop\prj\"

SetDirectory["/Users/El_Elyon`s/Desktop/prj/"]

"C:\Users\El_Elyon`s\Desktop\prj"

normalFiles = FileNames[Except["."] ~~ "*.jpeg", NotebookDirectory[] <>
"train/NORMAL"];

virusFiles =FileNames[Except["."] ~~ "*_virus_*.jpeg",
NotebookDirectory[] <> "train/PNEUMONIA"];
bacFiles =FileNames[Except["."] ~~ "*_bacteria_*.jpeg",
  NotebookDirectory[] <> "train/PNEUMONIA"];

normalImg = Import /@ normalFiles;

virusImg = Import /@ virusFiles;

bacImg = Import /@ bacFiles;

ClearAll[cropFinder];
cropFinder[x_, img_] := Module[{mid, flatValues, cropLeft, cropRight},
mid = Ceiling[Last[ImageDimensions[img]]/(x)];
flatValues =
Flatten@ImageData[ImageTake[ColorConvert[img, "Grayscale"], {mid, mid}]];
cropLeft = LengthWhile[flatValues, # < 0.16 &];
cropRight = LengthWhile[Reverse[flatValues], # < 0.16 &];
List[cropLeft, cropRight]
]

ClearAll[widthResize];
widthResize[img_] :=
Block[{ flatValues, cropLeft, cropRight, meanLeft, meanRight, list1, list2,
list3, list4, list5, resizedImage},
list1 = cropFinder[10, img];
list2 = cropFinder[2, img];
list3 = cropFinder[4, img];
list4 = cropFinder[1.5, img];
list5 = cropFinder[1, img];
meanLeft = meanLeft /. x_ /; x > 300 :> 0;
meanLeft = -(Max[{list1[[1]], list2[[1]], list3[[1]], list4[[1]],
   list5[[1]]}]);
meanRight = meanRight /. x_ /; x > 300 :> 0;
meanRight = -((Max[{list1[[2]], list2[[2]], list3[[2]], list4[[2]],
    list5[[2]]}]));
resizedImage = ImagePad[img, {{meanLeft, meanRight}, {0, 0}}];
ImageResize[resizedImage, {512, 512}]
]

normNorm =
ConformImages[
Table[If[RandomReal[] > .8,
 If[RandomReal[] > .5, rotateImage[i], shearImage[i]], i], {i,
 normalImg}], {512, 512}];
virusNorm =
ConformImages[
Table[If[RandomReal[] > .8,
 If[RandomReal[] > .5, rotateImage[i], shearImage[i]], i], {i,
 virusImg}], {512, 512}];

bacNorm = ConformImages[
Table[If[RandomReal[] > .8,
 If[RandomReal[] > .5, rotateImage[i], shearImage[i]], i], {i,
 bacImg}], {512, 512}];

RayNet = NetModel["ResNet-50 Trained on ImageNet Competition Data"]

NetChain[ <> ]

In[3]:= FixedNet = NetTake[RayNet, 23]

Out[3]= NetChain[ <> ]

In[4]:= AdjustedNet =
NetReplacePart[FixedNet, "Input" -> NetEncoder[{"Image", {512, 512}}]]

Out[4]= NetChain[ <> ]

In[5]:= learnNet = NetChain[{
DropoutLayer[0.3],
LinearLayer[3],
SoftmaxLayer[]
},
"Output" -> NetDecoder[{"Class", {0, 1, 2}}]
]

Out[5]= NetChain[ <> ]

normalFeats = AdjustedNet[normNorm, TargetDevice -> "GPU"];

virusFeats = AdjustedNet[virusNorm, TargetDevice -> "GPU"];

bacFeats = AdjustedNet[bacNorm, TargetDevice -> "GPU"];

In[33]:= feats = Join[Map[# -> 0 &, normalFeats], Map[# -> 1 &, virusFeats],
Map[# -> 2 &, bacFeats]];

In[35]:= feats = RandomSample[feats];
Length@feats

In[35]:= RandomSample::lrwl: The set of items to sample from,
Join[normalFeats,virusFeats,bacFeats], should be a non-empty list or a rule
weights -> choices.

Out[36]= 1

trainFeat = feats[[1 ;; 5000]];

testFeat = feats[[5000 ;;]];

In[20]:= trainedNet =
NetTrain[learnNet, trainFeat, ValidationSet -> testFeat,
MaxTrainingRounds -> 35, TargetDevice -> "GPU"]

In[20]:= NetTrain::jitgen: Generator function cannot be used when the net to
be trained has not been fully specified.

In[20]:= NetTrain::nfspec: Cannot train net: input of first layer is not
fully specified.

Out[20]= $Failed``