AlexNet model update.

Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk

@@ -50,19 +50,19 @@ Train = {
 
         # initValueScale are determined to generate Gaussians with variance of 0.01 and 0.005 (for the two DenseLayers)
         model = Sequential (
-            ConvolutionalLayer {96, (11:11), stride=(4:4), pad=false, init='gaussian', initValueScale=0.01} : ReLU : 
+            ConvolutionalLayer {96, (11:11), stride=(4:4), pad=false, init='gaussian', initValueScale=0.95265} : ReLU : 
             LRN {1.0, 2, 0.0001, 0.75} : 
             MaxPoolingLayer    {(3:3), stride=(2:2)} :
-            ConvolutionalLayer {192, (5:5), pad = true, init='gaussian', initValueScale=0.01, initBias=0.1} : ReLU : 
+            ConvolutionalLayer {192, (5:5), pad = true, init='gaussian', initValueScale=2.44978, initBias=0.1} : ReLU : 
             LRN {1.0, 2, 0.0001, 0.75} : 
             MaxPoolingLayer    {(3:3), stride=(2:2)} :
-            ConvolutionalLayer {384, (3:3), pad = true, init='gaussian', initValueScale=0.01} : ReLU : 
-            ConvolutionalLayer {384, (3:3), pad = true, init='gaussian', initValueScale=0.01, initBias=0.1} : ReLU : 
-            ConvolutionalLayer {256, (3:3), pad = true, init='gaussian', initValueScale=0.01, initBias=0.1} : ReLU : 
+            ConvolutionalLayer {384, (3:3), pad = true, init='gaussian', initValueScale=2.07857} : ReLU : 
+            ConvolutionalLayer {384, (3:3), pad = true, init='gaussian', initValueScale=2.93945, initBias=0.1} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true, init='gaussian', initValueScale=2.93945, initBias=0.1} : ReLU : 
             MaxPoolingLayer    {(3:3), stride=(2:2)} :
-            DenseLayer         {4096, activation=ReLU, init='gaussian', initValueScale=0.005, initBias=0.1} : Dropout :
-            DenseLayer         {4096, activation=ReLU, init='gaussian', initValueScale=0.005, initBias=0.1} : Dropout :
-            LinearLayer        {labelDim, init='gaussian', initValueScale=0.01}
+            DenseLayer         {4096, activation=ReLU, init='gaussian', initValueScale=2.40038, initBias=0.1} : Dropout :
+            DenseLayer         {4096, activation=ReLU, init='gaussian', initValueScale=1.6, initBias=0.1} : Dropout :
+            LinearLayer        {labelDim, init='gaussian', initValueScale=3.2}
         )
 
         # inputs