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The main part of work was training \ a classifier to detect non-planar rotations of an imaged 3D object (Ironman \ helmet). Most of the work focused on the special case of nonplanar \ rotations about the vertical axis in the image plane. 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"Type" -> "Catenate", "Arrays" -> Association[], "Parameters" -> Association["Level" -> 1, "$InputShapes" -> { NeuralNetworks`TensorT[{64, 15, 15}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{64, 15, 15}, NeuralNetworks`RealT]}, "$InputCount" -> 2, "$OutputShape" -> NeuralNetworks`TensorT[{128, 15, 15}, NeuralNetworks`RealT]], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{64, 15, 15}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{64, 15, 15}, NeuralNetworks`RealT]}], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{128, 15, 15}, NeuralNetworks`RealT]]]], "Edges" -> { NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", 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-> Association[ "Output" -> NeuralNetworks`TensorT[{16, 15, 15}, NeuralNetworks`RealT]]], "expand1x1" -> Association[ "Type" -> "Convolution", "Arrays" -> Association[ "Weights" -> NeuralNetworks`Private`DummyRawArray[{64, 16, 1, 1}], "Biases" -> NeuralNetworks`Private`DummyRawArray[{64}]], "Parameters" -> Association[ "OutputChannels" -> 64, "KernelSize" -> {1, 1}, "Stride" -> {1, 1}, "PaddingSize" -> {0, 0}, "Dilation" -> {1, 1}, "Dimensionality" -> 2, "InputChannels" -> 16, "$GroupNumber" -> 1, "$InputSize" -> {15, 15}, "$OutputSize" -> {15, 15}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{16, 15, 15}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{64, 15, 15}, NeuralNetworks`RealT]]], "relu_expand1x1" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {64, 15, 15}], "Inputs" -> Association[ "Input" -> 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Association[ "Output" -> NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT]]], "relu_expand3x3" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {128, 7, 7}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT]]], "concat" -> Association[ "Type" -> "Catenate", "Arrays" -> Association[], "Parameters" -> Association["Level" -> 1, "$InputShapes" -> { NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT]}, "$InputCount" -> 2, "$OutputShape" -> NeuralNetworks`TensorT[{256, 7, 7}, NeuralNetworks`RealT]], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{128, 7, 7}, NeuralNetworks`RealT]}], "Outputs" 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Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "relu_expand3x3" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {256, 3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "concat" -> Association[ "Type" -> "Catenate", "Arrays" -> Association[], "Parameters" -> Association["Level" -> 1, "$InputShapes" -> { NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]}, "$InputCount" -> 2, "$OutputShape" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]}], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]]]], "Edges" -> { NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "expand1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "expand3x3", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand3x3", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "expand3x3", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "concat", "Inputs", "Input"] -> { NeuralNetworks`NetPath[ "Nodes", "relu_expand1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand3x3", "Outputs", "Output"]}, NeuralNetworks`NetPath[ "Nodes", "squeeze1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath["Outputs", "Output"] -> NeuralNetworks`NetPath[ "Nodes", "concat", "Outputs", "Output"]}], "fire9" -> Association[ "Type" -> "Graph", "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Nodes" -> Association[ "squeeze1x1" -> Association[ "Type" -> "Convolution", "Arrays" -> Association[ "Weights" -> NeuralNetworks`Private`DummyRawArray[{64, 512, 1, 1}], "Biases" -> NeuralNetworks`Private`DummyRawArray[{64}]], "Parameters" -> Association[ "OutputChannels" -> 64, "KernelSize" -> {1, 1}, "Stride" -> {1, 1}, "PaddingSize" -> {0, 0}, "Dilation" -> {1, 1}, "Dimensionality" -> 2, "InputChannels" -> 512, "$GroupNumber" -> 1, "$InputSize" -> {3, 3}, "$OutputSize" -> {3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{64, 3, 3}, NeuralNetworks`RealT]]], "relu_squeeze1x1" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {64, 3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{64, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{64, 3, 3}, NeuralNetworks`RealT]]], "expand1x1" -> Association[ "Type" -> "Convolution", "Arrays" -> Association[ "Weights" -> NeuralNetworks`Private`DummyRawArray[{256, 64, 1, 1}], "Biases" -> NeuralNetworks`Private`DummyRawArray[{256}]], "Parameters" -> Association[ "OutputChannels" -> 256, "KernelSize" -> {1, 1}, "Stride" -> {1, 1}, "PaddingSize" -> {0, 0}, "Dilation" -> {1, 1}, "Dimensionality" -> 2, "InputChannels" -> 64, "$GroupNumber" -> 1, "$InputSize" -> {3, 3}, "$OutputSize" -> {3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{64, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "relu_expand1x1" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {256, 3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "expand3x3" -> Association[ "Type" -> "Convolution", "Arrays" -> Association[ "Weights" -> NeuralNetworks`Private`DummyRawArray[{256, 64, 3, 3}], "Biases" -> NeuralNetworks`Private`DummyRawArray[{256}]], "Parameters" -> Association[ "OutputChannels" -> 256, "KernelSize" -> {3, 3}, "Stride" -> {1, 1}, "PaddingSize" -> {1, 1}, "Dilation" -> {1, 1}, "Dimensionality" -> 2, "InputChannels" -> 64, "$GroupNumber" -> 1, "$InputSize" -> {3, 3}, "$OutputSize" -> {3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{64, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "relu_expand3x3" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {256, 3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]]], "concat" -> Association[ "Type" -> "Catenate", "Arrays" -> Association[], "Parameters" -> Association["Level" -> 1, "$InputShapes" -> { NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]}, "$InputCount" -> 2, "$OutputShape" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{256, 3, 3}, NeuralNetworks`RealT]}], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]]]], "Edges" -> { NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "expand1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "expand3x3", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_squeeze1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand3x3", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "expand3x3", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "concat", "Inputs", "Input"] -> { NeuralNetworks`NetPath[ "Nodes", "relu_expand1x1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "relu_expand3x3", "Outputs", "Output"]}, NeuralNetworks`NetPath[ "Nodes", "squeeze1x1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath["Outputs", "Output"] -> NeuralNetworks`NetPath[ "Nodes", "concat", "Outputs", "Output"]}], "drop9" -> Association[ "Type" -> "Dropout", "Arrays" -> Association[], "Parameters" -> Association["DropoutProbability" -> 0.5], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]]], "conv10" -> Association[ "Type" -> "Convolution", "Arrays" -> Association[ "Weights" -> NeuralNetworks`Private`DummyRawArray[{1000, 512, 1, 1}], "Biases" -> NeuralNetworks`Private`DummyRawArray[{1000}]], "Parameters" -> Association[ "OutputChannels" -> 1000, "KernelSize" -> {1, 1}, "Stride" -> {1, 1}, "PaddingSize" -> {0, 0}, "Dilation" -> {1, 1}, "Dimensionality" -> 2, "InputChannels" -> 512, "$GroupNumber" -> 1, "$InputSize" -> {3, 3}, "$OutputSize" -> {3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{512, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{1000, 3, 3}, NeuralNetworks`RealT]]], "relu_conv10" -> Association[ "Type" -> "Elementwise", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> NeuralNetworks`ValidatedParameter[Ramp], "$Dimensions" -> {1000, 3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{1000, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{1000, 3, 3}, NeuralNetworks`RealT]]], "pool10" -> Association[ "Type" -> "Aggregation", "Arrays" -> Association[], "Parameters" -> Association[ "Function" -> Mean, "$Channels" -> 1000, "$InputDimensions" -> {3, 3}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{1000, 3, 3}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{1000}, NeuralNetworks`RealT]]], "classifier" -> Association[ "Type" -> "Linear", "Arrays" -> Association[ "Weights" -> NeuralNetworks`TensorT[{2, 1000}, NeuralNetworks`RealT], "Biases" -> NeuralNetworks`Nullable[ NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT]]], "Parameters" -> Association[ "OutputDimensions" -> {2}, "$OutputSize" -> 2, "$InputSize" -> 1000, "$InputDimensions" -> {1000}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{1000}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT]]], "output" -> Association[ "Type" -> "Graph", "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{3}, NeuralNetworks`RealT]], "Nodes" -> Association[ "part1" -> Association[ "Type" -> "Part", "Arrays" -> Association[], "Parameters" -> Association[ "PartSpecification" -> NeuralNetworks`ValidatedParameter[1], "$Dimensions" -> {}, "$Channels" -> 2, "$OutputDimensions" -> {}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{}, NeuralNetworks`RealT]]], "part2" -> Association[ "Type" -> "Part", "Arrays" -> Association[], "Parameters" -> Association[ "PartSpecification" -> NeuralNetworks`ValidatedParameter[2], "$Dimensions" -> {}, "$Channels" -> 2, "$OutputDimensions" -> {}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{}, NeuralNetworks`RealT]]], "constraint" -> Association[ "Type" -> "Threading", "Arrays" -> Association[], "Parameters" -> Association["Function" -> NeuralNetworks`ValidatedParameter[ NeuralNetworks`Private`ScalarFunctionObject[{ NeuralNetworks`Private`ScalarSymbol[1], NeuralNetworks`Private`ScalarSymbol[2]}, NeuralNetworks`Private`ScalarSymbol[6], Association[ NeuralNetworks`Private`ScalarSymbol[3] -> {Power, NeuralNetworks`Private`ScalarSymbol[1], 2.}, NeuralNetworks`Private`ScalarSymbol[4] -> {Power, NeuralNetworks`Private`ScalarSymbol[2], 2.}, NeuralNetworks`Private`ScalarSymbol[5] -> {Plus, NeuralNetworks`Private`ScalarSymbol[3], NeuralNetworks`Private`ScalarSymbol[4]}, NeuralNetworks`Private`ScalarSymbol[6] -> {Plus, -1., NeuralNetworks`Private`ScalarSymbol[5]}]]], "$Dimensions" -> {}], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{}, NeuralNetworks`RealT]}], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{}, NeuralNetworks`RealT]]], "reshape" -> Association[ "Type" -> "Reshape", "Arrays" -> Association[], "Parameters" -> Association["Dimensions" -> {1}, "$IDimensions" -> {}], "Inputs" -> Association[ "Input" -> NeuralNetworks`TensorT[{}, NeuralNetworks`RealT]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{1}, NeuralNetworks`RealT]]], "catenate" -> Association[ "Type" -> "Catenate", "Arrays" -> Association[], "Parameters" -> Association["Level" -> 1, "$InputShapes" -> { NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{1}, NeuralNetworks`RealT]}, "$InputCount" -> 2, "$OutputShape" -> NeuralNetworks`TensorT[{3}, NeuralNetworks`RealT]], "Inputs" -> Association["Input" -> { NeuralNetworks`TensorT[{2}, NeuralNetworks`RealT], NeuralNetworks`TensorT[{1}, NeuralNetworks`RealT]}], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{3}, NeuralNetworks`RealT]]]], "Edges" -> { NeuralNetworks`NetPath[ "Nodes", "constraint", "Inputs", "Input"] -> { NeuralNetworks`NetPath[ "Nodes", "part1", "Outputs", "Output"], NeuralNetworks`NetPath[ "Nodes", "part2", "Outputs", "Output"]}, NeuralNetworks`NetPath["Nodes", "catenate", "Inputs", "Input"] -> { NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath[ "Nodes", "reshape", "Outputs", "Output"]}, NeuralNetworks`NetPath["Nodes", "reshape", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "constraint", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "part1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath["Nodes", "part2", "Inputs", "Input"] -> NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath["Outputs", "Output"] -> NeuralNetworks`NetPath[ "Nodes", "catenate", "Outputs", "Output"]}]], "Edges" -> { NeuralNetworks`NetPath["Nodes", "conv1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Inputs", "Input"], NeuralNetworks`NetPath["Nodes", "relu_conv1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "conv1", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "pool1", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", 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NeuralNetworks`NetPath["Nodes", "pool5_pad", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "fire6", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "pool5", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "fire7", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "fire6", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "fire8", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "fire7", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "fire9", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "fire8", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "drop9", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "fire9", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "conv10", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "drop9", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "relu_conv10", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "conv10", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "pool10", "Inputs", "Input"] -> NeuralNetworks`NetPath[ "Nodes", "relu_conv10", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "classifier", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "pool10", "Outputs", "Output"], NeuralNetworks`NetPath["Nodes", "output", "Inputs", "Input"] -> NeuralNetworks`NetPath["Nodes", "classifier", "Outputs", "Output"], NeuralNetworks`NetPath["Outputs", "Output"] -> NeuralNetworks`NetPath["Nodes", "output", "Outputs", "Output"]}, "Inputs" -> Association["Input" -> NetEncoder["Image", Association[ "ImageSize" -> {64, 64}, "ColorSpace" -> "RGB", "ColorChannels" -> 3, "MeanImage" -> None], NeuralNetworks`TensorT[{3, 64, 64}, NeuralNetworks`RealT]]], "Outputs" -> Association[ "Output" -> NeuralNetworks`TensorT[{3}, NeuralNetworks`RealT]]], NeuralNetworks`Private`NetChain`opart, NeuralNetworks`Private`NetChain`part, NeuralNetworks`Private`NetChain`selected = Null}, DynamicBox[GridBox[{{ NeuralNetworks`Private`NetChain`MouseClickBoxes[ TagBox[ GridBox[{{ 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The number of training rounds is small, but results in \ a significant change. \ \>", "Text", CellChangeTimes->{{3.708255311983214*^9, 3.70825535764148*^9}, { 3.708255469490163*^9, 3.7082555322942343`*^9}, {3.708257502684031*^9, 3.708257507233417*^9}, {3.708258162552318*^9, 3.708258198473435*^9}},ExpressionUUID->"4e1590fc-8506-4c0e-9d4c-\ fb54e6322cac"], Cell["\<\ tunedSharpNet = NetTrain[ \tsharpNet, \ttrainingSet, \tValidationSet -> validationSet, \tLearningRateMultipliers -> { \t\t\"classifier\" -> 1, \t\t\"conv10\" -> 1, \t\t\"fire9\" -> 1, \t\t\"fire8\" -> None, \t\t\"fire7\" -> None, \t\t_ -> None \t}, MaxTrainingRounds -> 3 ]\ \>", "Program", CellChangeTimes->{ 3.708257514645706*^9, {3.708258125781391*^9, 3.708258158750299*^9}, { 3.7082582389940977`*^9, 3.708258257253385*^9}},ExpressionUUID->"fdce378d-54a2-4c3f-93e9-\ 7ee964795a0c"] }, Open ]], Cell[CellGroupData[{ Cell["Network 2b fine-tunes the trained network 2a. ", "Subsubsection", CellChangeTimes->{{3.7082526733766623`*^9, 3.708252706211863*^9}, { 3.708252801104948*^9, 3.708252805600053*^9}, {3.70825313858807*^9, 3.708253173073882*^9}, {3.7082532440871572`*^9, 3.708253249574711*^9}, { 3.7082533130205593`*^9, 3.7082534246641893`*^9}, {3.708253914646496*^9, 3.708253996618183*^9}, {3.708254300511797*^9, 3.708254362076926*^9}, { 3.708254770935215*^9, 3.708254840167417*^9}, {3.708255226795783*^9, 3.708255276582616*^9}, {3.708255368027326*^9, 3.708255377959703*^9}, { 3.708255419903204*^9, 3.7082554249648743`*^9}, {3.70825545513275*^9, 3.708255460546446*^9}, 3.708255542911585*^9},ExpressionUUID->"23242f28-8de2-41a1-aee2-\ c682f58ee7ab"], Cell["\<\ The learning multipliers are set to train all layers, but with larger weights \ on the last layers, small rates on the initial layers, and intermediate \ weights on the intervening layers.\ \>", "Text", CellChangeTimes->{ 3.708255449191958*^9, {3.7082555445974417`*^9, 3.7082555945463123`*^9}, { 3.7082575220525427`*^9, 3.70825752361644*^9}},ExpressionUUID->"54337235-18f6-4177-99ff-\ e7184256e7a3"], Cell["\<\ tunedSharpNet2alt = NetTrain[ \ttunedSharpNet, \ttrainingSet, \tValidationSet -> validationSet, \tLearningRateMultipliers -> { \t\t\"classifier\" -> 0.5, \t\t\"conv10\" -> 0.5, \t\t\"fire9\" -> 0.5, \t\t\"fire8\" -> 0.2, \t\t\"fire7\" -> 0.2, \t\t_ -> None}, \tMaxTrainingRounds -> 10 ]\ \>", "Program", CellChangeTimes->{{3.708257524192236*^9, 3.7082575262535267`*^9}, { 3.708258203648724*^9, 3.708258274028158*^9}},ExpressionUUID->"896631da-7f63-474d-a5a3-\ 50a82d4b3315"] }, Open ]] }, Closed]], Cell["Written Content / Lesson Plans", "TemplateSubsection", CellChangeTimes->{{3.708096199547399*^9, 3.708096214681534*^9}},ExpressionUUID->"a422f999-3421-4656-846a-\ a69ed019c6be"], Cell[CellGroupData[{ Cell["Conclusions in Detail", "TemplateSubsection", CellChangeTimes->{{3.549547456866158*^9, 3.549547459278479*^9}, 3.708095987661463*^9, {3.708096097865437*^9, 3.70809609806956*^9}},ExpressionUUID->"e688735c-3972-478e-a7ee-\ 8f0672e37d33"], Cell["\<\ It is known that previously trained large convolutional networks can be used \ as the starting point for new image identifications tasks, using transfer \ learning and fine-tuning. In this project we modified a previously trained \ image classification network to a regression network, retaining the trained \ initial stages. The goal was to measure rotations of a 3D object from \ images, focusing on the special case of rotations about the vertical axis in \ the image plane. This had some success, but there were complications in that the regression \ output satisfied a nonlinear constraint which was not enforced by the \ network, and resulted in more and more drift of the results during subsequent \ transfer learning and fine-tuning stages. The main technique introduced here is the addition of a network layer to \ dynamically enforce the nonlinear constraint on the output. Using several \ stages of transfer learning and fine tuning, this resulted in good results. \ We focused on one nonlinear constraint on two-dimensional output data. The \ same techniques should generalize to more nonlinear constraints on \ higher-dimensional output data, for example for the matrices describing the \ entire group of rotations in 3D space. \ \>", "Text", CellChangeTimes->{{3.7082588654688063`*^9, 3.708259374053566*^9}},ExpressionUUID->"5b100100-fb31-4a55-9a1b-\ 4f3ee1b3ffa3"] }, Closed]], Cell[CellGroupData[{ Cell["All Visualizations", "TemplateSubsection", CellChangeTimes->{{3.5495421749322853`*^9, 3.5495421791841507`*^9}, { 3.7080959928646717`*^9, 3.708095994448739*^9}, {3.708096104617906*^9, 3.708096104865819*^9}},ExpressionUUID->"f27ae70e-91c7-4506-8835-\ 21b281190700"], Cell["\<\ The generation of the main visualization was made as follows. 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