addressed review feedback

.gitattributes

@@ -6,6 +6,7 @@ Dockerfile-GPU text
 *.counts text
 *.labels text
 *.feats text
+*.ctf text
 *.post text
 *.cpu text
 *.gpu text

Source/CNTK/BrainScript/BrainScriptParser.cpp

@@ -666,7 +666,9 @@ class Parser : public Lexer
         return id;
     }
 
-    map<wstring, int> infixPrecedence; // precedence level of infix operators
+    map<wstring, int> infixPrecedence;    // precedence level of infix operators
+    static const int unaryPrecedence = 90;  // for unary "-" and "!". 90 is below x., x[, x(, and x{, but above all others
+    // TODO: Would be more direct to fold this into the table below as well.
 public:
     Parser(SourceFile&& sourceFile, vector<wstring>&& includePaths)
         : Lexer(move(includePaths))
@@ -719,7 +721,7 @@ class Parser : public Lexer
         {
             operand = make_shared<Expression>(tok.beginLocation, tok.symbol + L"("); // encoded as +( -( !(
             ConsumeToken();
-            operand->args.push_back(ParseExpression(90, stopAtNewline)); // 90 is below x., x[, x(, and x{, but above all others
+            operand->args.push_back(ParseExpression(unaryPrecedence, stopAtNewline));
         }
         else if (tok.symbol == L"new") // === new class instance
         {

Source/CNTK/BrainScript/CNTKCoreLib/CNTK.core.bs

@@ -150,10 +150,10 @@ StabilizerLayer {} =
     f(x) = Stabilize (x)
 }.f
 
-# CorpusNormalizationLayer -- create a corpus-level feature-normalization layer
+# FeatureMVNLayer -- create a corpus-level feature-normalization layer
 # This can only be applied to features. Statistics are not shared across invocations,
 # which is semantically OK because the values are the same. However, it is not efficient.
-CorpusNormalizationLayer {} = MeanVarNorm
+FeatureMVNLayer {} = MeanVarNorm
 
 # Layers that exist in other tools that we will not have:
 # FlattenLayer{}: Not needed since DenseLayer() can handle tensors just fine.
@@ -202,8 +202,8 @@ Log                     = CNTK2.Log
 Minus                   = CNTK2.Minus
 Pass                    = CNTK2.Identity
 Plus                    = CNTK2.Plus
-RectifiedLinear         = CNTK2.Relu
-ReLU                    = CNTK2.Relu
+RectifiedLinear         = CNTK2.ReLU # deprecated
+ReLU                    = CNTK2.ReLU
 ReduceSum               = CNTK2.ReduceSum
 ReduceLogSum            = CNTK2.ReduceLogSum
 ReduceMin               = CNTK2.ReduceMin
@@ -315,8 +315,9 @@ CNTK2 = [
     PastValue(_, shape, timeStep = 1, defaultHiddenActivation = 0.1, tag='') = new ComputationNode [ operation = 'PastValue' ; inputs = _ ; shape = new TensorShape [ /*shape*/ ] /*plus the function args*/ ]
 
     // 10. NN-specific operations
-    // Changes: input -> _, RectifiedLinear -> Relu. [Use Relu to arrive at relu() in snake_case]
-    Relu(_, tag='') = new ComputationNode [ operation = 'RectifiedLinear' ; inputs = _ /*plus the function args*/ ]
+    // Changes: input -> _, RectifiedLinear -> ReLU
+    ReLU(_, tag='') = new ComputationNode [ operation = 'RectifiedLinear' ; inputs = _ /*plus the function args*/ ]
+    Relu = ReLU // [Use Relu to arrive at relu() in snake_case]
     Sigmoid(_, tag='') = new ComputationNode [ operation = 'Sigmoid' ; inputs = _ /*plus the function args*/ ]
     Softmax(_, tag='') = new ComputationNode [ operation = 'Softmax' ; inputs = _ /*plus the function args*/ ]
     Dropout(_, tag='') = new ComputationNode [ operation = 'Dropout' ; inputs = _ /*plus the function args*/ ]

Tests/EndToEndTests/Speech/QuickE2E/cntk.cntk

@@ -46,7 +46,7 @@ speechTrain = [
 
         # --- define the model
         model = Sequential (
-            CorpusNormalizationLayer{} :
+            FeatureMVNLayer{} :
             DenseLayer {H, activation=Sigmoid} :
             DenseLayer {H, activation=Sigmoid} :
             DenseLayer {J}