updated SLUHandsOn tests

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

@@ -46,11 +46,12 @@ DenseLayer{outDim, bias = true, activation=(x=>x), init='heNormal', initValueSca
 
 # EmbeddingLayer -- create a linear embedding layer
 EmbeddingLayer {outDim,                                   # dimension of embedding
+                init='heNormal', initValueScale=1,
                 embeddingPath = '', transpose = false} =  # load a fixed embedding from a path instead
 {
     shape = if transpose then (Inferred : outDim) else (outDim : Inferred)
     E = if embeddingPath == ''
-        then ParameterTensor {shape, init='heNormal'}  # learnable
+        then ParameterTensor {shape, init=init, initValueScale=initValueScale}  # learnable
         else ParameterTensor {shape, initFromFilePath = embeddingPath, learningRateMultiplier = 0}  # fixed from file
     TimesOp = if transpose then TransposeTimes else Times
     apply (x) = TimesOp (E, x)    # x is expected to be sparse one-hot

Tutorials/SLUHandsOn/SLUHandsOn.cntk

@@ -21,9 +21,9 @@ TrainTagger = {
         hiddenDim = 300
 
         model = Sequential (
-            EmbeddingLayer {embDim} :                            # embedding
-            RecurrentLSTMLayer {hiddenDim, goBackwards=false} :  # LSTM
-            DenseLayer {labelDim, initValueScale=7}              # output layer
+            EmbeddingLayer {embDim, init='uniform'} :                            # embedding
+            RecurrentLSTMLayer {hiddenDim, goBackwards=false, init='uniform'} :  # LSTM
+            DenseLayer {labelDim, init='uniform', initValueScale=7}              # output layer
         )
 
         # features

Tutorials/SLUHandsOn/SLUHandsOn_Solution1.cntk

@@ -20,11 +20,11 @@ TrainTagger = {
         hiddenDim = 300
 
         model = Sequential (
-            EmbeddingLayer {embDim} :                            # embedding
+            EmbeddingLayer {embDim, init='uniform'} :                            # embedding
             BatchNormalizationLayer {normalizationTimeConstant=2048} :           ##### added
-            RecurrentLSTMLayer {hiddenDim, goBackwards=false} :  # LSTM
+            RecurrentLSTMLayer {hiddenDim, goBackwards=false, init='uniform'} :  # LSTM
             BatchNormalizationLayer {normalizationTimeConstant=2048} :           ##### added
-            DenseLayer {labelDim, initValueScale=7}              # output layer
+            DenseLayer {labelDim, init='uniform', initValueScale=7}              # output layer
         )
 
         # features

Tutorials/SLUHandsOn/SLUHandsOn_Solution2.cntk

@@ -22,12 +22,12 @@ TrainTagger = {
         LookaheadLayer (x) = Splice (x : FutureValue (0, x, defaultHiddenActivation=0))
 
         model = Sequential (
-            EmbeddingLayer {embDim} :                            # embedding
+            EmbeddingLayer {embDim, init='uniform'} :                            # embedding
             LookaheadLayer :                   ##### added
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
-            RecurrentLSTMLayer {hiddenDim, goBackwards=false} :  # LSTM
+            RecurrentLSTMLayer {hiddenDim, goBackwards=false, init='uniform'} :  # LSTM
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
-            DenseLayer {labelDim, initValueScale=7}              # output layer
+            DenseLayer {labelDim, init='uniform', initValueScale=7}              # output layer
         )
 
         # features

Tutorials/SLUHandsOn/SLUHandsOn_Solution3.cntk

@@ -20,17 +20,17 @@ TrainTagger = {
         hiddenDim = 150
 
         BiRecurrentLSTMLayer {outDim} = {
-            F = RecurrentLSTMLayer {outDim, goBackwards=false}
-            G = RecurrentLSTMLayer {outDim, goBackwards=true}
+            F = RecurrentLSTMLayer {outDim, goBackwards=false, init='uniform'}
+            G = RecurrentLSTMLayer {outDim, goBackwards=true, init='uniform'}
             apply (x) = Splice (F(x):G(x))
         }.apply
 
         model = Sequential (
-            EmbeddingLayer {embDim} :
+            EmbeddingLayer {embDim, init='uniform'} :
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
             BiRecurrentLSTMLayer {hiddenDim} :
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
-            DenseLayer {labelDim, initValueScale=7}
+            DenseLayer {labelDim, init='uniform', initValueScale=7}
         )
 
         # features

Tutorials/SLUHandsOn/SLUHandsOn_Solution4.cntk

@@ -20,17 +20,17 @@ TrainTagger = {
         hiddenDim = 150
 
         BiRecurrentLSTMLayer {outDim} = {
-            F = RecurrentLSTMLayer {outDim, goBackwards=false}
-            G = RecurrentLSTMLayer {outDim, goBackwards=true}
+            F = RecurrentLSTMLayer {outDim, goBackwards=false, init='uniform'}
+            G = RecurrentLSTMLayer {outDim, goBackwards=true, init='uniform'}
             apply (x) = Splice (BS.Sequences.Last(F(x)):BS.Sequences.First(G(x)))
         }.apply
 
         model = Sequential (
-            EmbeddingLayer {embDim} :
+            EmbeddingLayer {embDim, init='uniform'} :
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
             BiRecurrentLSTMLayer {hiddenDim} :
             BatchNormalizationLayer {normalizationTimeConstant=2048} :
-            DenseLayer {intentDim, initValueScale=7}
+            DenseLayer {intentDim, init='uniform', initValueScale=7}
         )
 
         # features