added a BS version of 01_Conv

Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.bs.cntk

@@ -0,0 +1,86 @@
+# Simple CIFAR-10 convnet
+
+command = Train:Eval
+
+makeMode = false ; traceLevel = 1 ; deviceId = 0
+
+RootDir = "." ; DataDir  = "$RootDir$" ; ModelDir = "$RootDir$/Output/Models"
+
+modelPath = "$ModelDir$/01_Convolution"
+
+Train = [
+    action = "train"
+
+    BrainScriptNetworkBuilder = [
+        imageShape = 32:32:3
+        labelDim = 10
+
+        Subtract128 (x) = x - Constant (128)
+
+        model = Sequential (
+            Subtract128 :
+            ConvolutionalLayer {32, (5:5), activation = ReLU, init = "gaussian", initValueScale = 0.0043} :
+            MaxPoolingLayer {(3:3), stride = (2:2)} :
+            ConvolutionalLayer {32, (5:5), activation = ReLU, init = "gaussian", initValueScale = 1.414} :
+            MaxPoolingLayer {(3:3), stride = (2:2)} :
+            ConvolutionalLayer {64, (5:5), activation = ReLU, init = "gaussian", initValueScale = 1.414} :
+            MaxPoolingLayer {(3:3), stride = (2:2)} :
+            DenseLayer {64, activation = ReLU, init = "gaussian", initValueScale = 12} :
+            Dropout :
+            LinearLayer {labelDim, init = "gaussian", initValueScale = 1.5}
+        )
+
+        # inputs
+        features = Input {imageShape}
+        labels   = Input {labelDim}
+
+        # apply model to features
+        z = model (features)
+
+        # connect to system
+        ce   = CrossEntropyWithSoftmax (labels, z)
+        errs = ErrorPrediction         (labels, z)
+        top5Errs = ErrorPrediction (labels, z, topN=5)  # only used in Eval action
+
+        featureNodes    = (features)
+        labelNodes      = (labels)
+        criterionNodes  = (ce)
+        evaluationNodes = (errs)
+        outputNodes     = (z)
+    ]
+
+    SGD = [
+        epochSize = 49984
+        minibatchSize = 64
+        learningRatesPerMB = 0.01*10:0.003*10:0.001
+        momentumPerMB = 0.9*20:0.99
+        maxEpochs = 30
+        L2RegWeight = 0.03
+        dropoutRate = 0*5:0.5
+        firstMBsToShowResult = 10
+        numMBsToShowResult = 500
+    ]
+
+    reader = [
+        readerType = "CNTKTextFormatReader"
+        file = "$DataDir$/Train_cntk_text.txt"
+        input = [
+            features = [ dim = 3072 ; format = "dense" ]
+            labels   = [ dim = 10 ;   format = "dense" ]
+        ]
+    ]
+]
+
+Eval = [
+    action = "eval"
+    minibatchSize = 16
+    evalNodeNames = errs:top5Errs  # also test top-5 error rate
+    reader = [
+        readerType = "CNTKTextFormatReader"
+        file = "$DataDir$/Test_cntk_text.txt"
+        input = [
+            features = [ dim = 3072 ; format = "dense" ]
+            labels   = [ dim = 10 ;   format = "dense" ]
+        ]
+    ]
+]