machine learning - Training data generator?

Python has generators which save memory, is there a technique for generating in memory examples for your training set "on the fly".

For example purposes, I constructed here a regressor for blur:

randomMask[img_] := 
 Module[{t, h, g, d = ImageDimensions[img]}, 
  t = Table[{PointSize@RandomReal[{0, .1}], 
     RandomChoice[{Point, 
        Rectangle[#, # + RandomReal[{-200, 200}, {2}]] &}]@
      RandomPoint[Rectangle[{0, 0}, d]]}, {RandomChoice[{0, 1, 2, 3, 
       4, 8, 14, 20, 50, 200}]}];
  g = Graphics[t, PlotRange -> Transpose[{{0, 0}, d}], ImageSize -> d];
  {g, Area@DiscretizeGraphics@g/Times @@ d}]


makeExample[img_] := Module[{g, v},
   {g, v} = randomMask[img];
   ImageCompose[img, SetAlphaChannel[Blur[img, 15], ColorNegate@g]] ->
     v
   ];

imgs = ConformImages[ExampleData /@ ExampleData["TestImage"], {100, 100}];

(* this is a large set that I don't want to precompute !!! *)

train = Table[makeExample@RandomChoice[imgs], {3000}]
test = Table[makeExample@RandomChoice[imgs], {500}];

convnet=NetChain[{
ConvolutionLayer[20,{5,5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2,2},{2,2}],
ConvolutionLayer[50,{5,5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2,2},{2,2}],

FlattenLayer[],
DotPlusLayer[500],
ElementwiseLayer[Ramp],
DotPlusLayer[50],
ElementwiseLayer[Ramp],
DotPlusLayer[1]
},
"Input"->NetEncoder[{"Image",{100,100}}],
"Output"->NetDecoder["Scalar"]
]


trainedConvnet = NetTrain[convnet, train, TargetDevice -> "GPU"]
output = trainedConvnet /@ Keys[test];

target = test // Values;
meanSquareLoss = Mean@Flatten[(#Output - #Target)^2, Infinity] &;

data = <|"Output" -> {{output}}, "Target" -> {{target}}|>;
N@meanSquareLoss@data