Try to handle warnings

src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp

@@ -56,11 +56,11 @@ AtrousConvolution<
     outSize(layer.outSize),
     kernelWidth(layer.kernelWidth),
     kernelHeight(layer.kernelHeight),
+    weights(layer.weights),
     strideWidth(layer.strideWidth),
     strideHeight(layer.strideHeight),
     inputWidth(layer.inputWidth),
     inputHeight(layer.inputHeight),
-    weights(layer.weights),
     outputWidth(layer.outputWidth),
     outputHeight(layer.outputHeight),
     dilationWidth(layer.dilationWidth),

src/mlpack/methods/ann/layer/fast_lstm_impl.hpp

@@ -52,15 +52,15 @@ FastLSTM<InputDataType, OutputDataType>::FastLSTM(
     inSize(layer.inSize),
     outSize(layer.outSize),
     rho(layer.rho),
+    weights(layer.weights),
     forwardStep(layer.forwardStep),
-    grad(layer.grad),
     backwardStep(layer.backwardStep),
     gradientStep(layer.gradientStep),
+    grad(layer.grad),
     batchSize(layer.batchSize),
     batchStep(layer.batchStep),
     gradientStepIdx(layer.gradientStepIdx),
     rhoSize(layer.rho),
-    weights(layer.weights),
     bpttSteps(layer.bpttSteps)
 {
   Reset();

src/mlpack/methods/ann/layer/gru_impl.hpp

@@ -43,11 +43,11 @@ GRU<InputDataType, OutputDataType, CustomLayers...>::GRU(
     gradientStep(layer.gradientStep),
     outParameter(layer.outParameter),
     allZeros(layer.allZeros),
-    prevError(layer.prevError),
     prevOutput(layer.prevOutput),
-    deterministic(layer.deterministic),
     backIterator(layer.backIterator),
-    gradIterator(layer.gradIterator)
+    gradIterator(layer.gradIterator),
+    prevError(layer.prevError),
+    deterministic(layer.deterministic)
 {
   CopyVisitor<CustomLayers...> copyVisitor;
 

src/mlpack/methods/ann/layer/highway_impl.hpp

@@ -42,13 +42,13 @@ template<typename InputDataType, typename OutputDataType,
 Highway<InputDataType, OutputDataType, CustomLayers...>::Highway(
   const Highway& layer) :
     inSize(layer.inSize),
+    networkOwnerships(layer.networkOwnerships),
     model(layer.model),
+    weights(layer.weights),
     reset(layer.reset),
     width(layer.width),
-    weights(layer.weights),
-    networkOwnerships(layer.networkOwnerships),
-    networkOutput(layer.networkOutput),
-    height(layer.height)
+    height(layer.height),
+    networkOutput(layer.networkOutput)
 {
   for (size_t i = 0; i < layer.network.size(); ++i)
   {

src/mlpack/methods/ann/layer/lstm_impl.hpp

@@ -30,14 +30,14 @@ LSTM<InputDataType, OutputDataType>::LSTM(
     inSize(layer.inSize),
     outSize(layer.outSize),
     rho(layer.rho),
+    weights(layer.weights),
     forwardStep(layer.forwardStep),
     backwardStep(layer.backwardStep),
     gradientStep(layer.gradientStep),
     batchSize(layer.batchSize),
     batchStep(layer.batchStep),
     rhoSize(layer.rhoSize),
-    bpttSteps(layer.bpttSteps),
-    weights(layer.weights)
+    bpttSteps(layer.bpttSteps)
 {
   Reset();
 }

src/mlpack/methods/ann/layer/mean_pooling_impl.hpp

@@ -63,8 +63,8 @@ MeanPooling<InputDataType, OutputDataType>::MeanPooling(
     outSize(layer.outSize),
     inputWidth(layer.inputWidth),
     inputHeight(layer.inputHeight),
-    reset(layer.reset),
     outputWidth(layer.outputWidth),
+    reset(layer.reset),
     outputHeight(layer.outputHeight),
     deterministic(layer.deterministic),
     offset(layer.offset),

src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp

@@ -188,6 +188,7 @@ TransposedConvolution<
     const TransposedConvolution& layer) :
     inSize(layer.inSize),
     outSize(layer.outSize),
+    weights(layer.weights),
     kernelWidth(layer.kernelWidth),
     kernelHeight(layer.kernelHeight),
     strideWidth(layer.strideWidth),
@@ -198,7 +199,6 @@ TransposedConvolution<
     padHTop(layer.padHTop),
     inputWidth(layer.inputWidth),
     inputHeight(layer.inputHeight),
-    weights(layer.weights),
     paddingForward(layer.paddingForward),
     paddingBackward(layer.paddingBackward),
     outputWidth(layer.outputWidth),

src/mlpack/methods/ann/layer/weight_norm_impl.hpp

@@ -43,8 +43,8 @@ WeightNorm<InputDataType, OutputDataType, CustomLayers...>::WeightNorm(
     const WeightNorm& layer) :
     layerWeightSize(layer.layerWeightSize),
     layerGradients(layer.layerGradients),
-    layerWeights(layer.layerWeights),
-    weights(layer.weights)
+    weights(layer.weights),
+    layerWeights(layer.layerWeights)
 {
   Reset();
 }