Simplify matrixVectorAdd interface

zhongkaifu · zhongkaifu · commit ad9a7cfd8ede · 2016-02-11T08:43:10.000-08:00
diff --git a/RNNSharp/BiRNN.cs b/RNNSharp/BiRNN.cs
@@ -262,7 +262,7 @@ public SimpleCell[][] InnerDecode(Sequence pSequence, out SimpleCell[][] outputH
                 seqOutput[curState] = InitSimpleCell(L2);
                 SimpleCell[] outputCells = seqOutput[curState];
 
-                matrixXvectorADD(outputCells, mergedHiddenLayer[curState], Hidden2OutputWeight, 0, L2, 0, L1, 0);
+                matrixXvectorADD(outputCells, mergedHiddenLayer[curState], Hidden2OutputWeight, L2, L1, 0);
 
                 for (int i = 0; i < L2; i++)
                 {
diff --git a/RNNSharp/LSTMRNN.cs b/RNNSharp/LSTMRNN.cs
@@ -740,7 +740,7 @@ public override void computeHiddenLayer(State state, bool isTrain = true)
 
         public override void computeOutput(double[] doutput)
         {
-            matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, 0, L2, 0, L1, 0);
+            matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, L2, L1, 0);
             if (doutput != null)
             {
                 for (int i = 0; i < L2; i++)
diff --git a/RNNSharp/RNN.cs b/RNNSharp/RNN.cs
@@ -586,39 +586,36 @@ protected double NormalizeErr(double err)
             return err;
         }
 
-        public void matrixXvectorADD(SimpleCell[] dest, SimpleCell[] srcvec, Matrix<double> srcmatrix, int from, int to, int from2, int to2, int type)
+        public void matrixXvectorADD(SimpleCell[] dest, SimpleCell[] srcvec, Matrix<double> srcmatrix, int DestSize, int SrcSize, int type)
         {
             if (type == 0)
             {
                 //ac mod
-                Parallel.For(0, (to - from), parallelOption, i =>
+                Parallel.For(0, DestSize, parallelOption, i =>
                 {
-                    SimpleCell cell = dest[i + from];
+                    SimpleCell cell = dest[i];
                     double[] vector_i = srcmatrix[i];
                     cell.cellOutput = 0;
-                    for (int j = 0; j < to2 - from2; j++)
+                    for (int j = 0; j < SrcSize; j++)
                     {
-                        cell.cellOutput += srcvec[j + from2].cellOutput * vector_i[j];
+                        cell.cellOutput += srcvec[j].cellOutput * vector_i[j];
                     }
                 });
 
             }
             else
             {
-                Parallel.For(0, (to - from), parallelOption, i =>
+                Parallel.For(0, DestSize, parallelOption, i =>
                 {
-                    SimpleCell cell = dest[i + from];
+                    SimpleCell cell = dest[i];
                     cell.er = 0;
-                    for (int j = 0; j < to2 - from2; j++)
+                    for (int j = 0; j < SrcSize; j++)
                     {
-                        cell.er += srcvec[j + from2].er * srcmatrix[j][i];
+                        cell.er += srcvec[j].er * srcmatrix[j][i];
                     }
-                });
 
-                for (int i = from; i < to; i++)
-                {
-                    dest[i].er = NormalizeErr(dest[i].er);
-                }
+                    cell.er = NormalizeErr(cell.er);
+                });
             }
         }
 
diff --git a/RNNSharp/SimpleRNN.cs b/RNNSharp/SimpleRNN.cs
@@ -137,7 +137,7 @@ public override void computeHiddenLayer(State state, bool isTrain = true)
 
             //hidden(t-1) -> hidden(t)
             neuHidden = InitSimpleCell(L1);
-            matrixXvectorADD(neuHidden, neuLastHidden, mat_hiddenBpttWeight, 0, L1, 0, L1, 0);
+            matrixXvectorADD(neuHidden, neuLastHidden, mat_hiddenBpttWeight, L1, L1, 0);
 
             //Apply feature values on hidden layer
             var sparse = state.SparseData;
@@ -171,7 +171,7 @@ public override void computeHiddenLayer(State state, bool isTrain = true)
         public override void computeOutput(double[] doutput)
         {
             //Calculate output layer
-            matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, 0, L2, 0, L1, 0);
+            matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, L2, L1, 0);
             if (doutput != null)
             {
                 for (int i = 0; i < L2; i++)
@@ -187,7 +187,7 @@ public override void computeOutput(double[] doutput)
         public override void ComputeHiddenLayerErr()
         {
             //error output->hidden for words from specific class    	
-            matrixXvectorADD(neuHidden, OutputLayer, Hidden2OutputWeight, 0, L1, 0, L2, 1);
+            matrixXvectorADD(neuHidden, OutputLayer, Hidden2OutputWeight, L1, L2, 1);
 
             //Apply drop out on error in hidden layer
             for (int i = 0; i < L1; i++)
@@ -254,7 +254,7 @@ void learnBptt(State state)
                 });
 
                 //propagates errors hidden->input to the recurrent part
-                matrixXvectorADD(neuLastHidden, neuHidden, mat_hiddenBpttWeight, 0, L1, 0, L1, 1);
+                matrixXvectorADD(neuLastHidden, neuHidden, mat_hiddenBpttWeight, L1, L1, 1);
 
                 for (int a = 0; a < L1; a++)
                 {

Original file line number	Diff line number	Diff line change
`@@ -262,7 +262,7 @@ public SimpleCell[][] InnerDecode(Sequence pSequence, out SimpleCell[][] outputH`
`262`	`262`	`seqOutput[curState] = InitSimpleCell(L2);`
`263`	`263`	`SimpleCell[] outputCells = seqOutput[curState];`
`264`	`264`
`265`		`- matrixXvectorADD(outputCells, mergedHiddenLayer[curState], Hidden2OutputWeight, 0, L2, 0, L1, 0);`
	`265`	`+ matrixXvectorADD(outputCells, mergedHiddenLayer[curState], Hidden2OutputWeight, L2, L1, 0);`
`266`	`266`
`267`	`267`	`for (int i = 0; i < L2; i++)`
`268`	`268`	`{`
Original file line number	Diff line number	Diff line change
`@@ -740,7 +740,7 @@ public override void computeHiddenLayer(State state, bool isTrain = true)`
`740`	`740`
`741`	`741`	`public override void computeOutput(double[] doutput)`
`742`	`742`	`{`
`743`		`- matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, 0, L2, 0, L1, 0);`
	`743`	`+ matrixXvectorADD(OutputLayer, neuHidden, Hidden2OutputWeight, L2, L1, 0);`
`744`	`744`	`if (doutput != null)`
`745`	`745`	`{`
`746`	`746`	`for (int i = 0; i < L2; i++)`
Original file line number	Diff line number	Diff line change
`@@ -586,39 +586,36 @@ protected double NormalizeErr(double err)`
`586`	`586`	`return err;`
`587`	`587`	`}`
`588`	`588`
`589`		`- public void matrixXvectorADD(SimpleCell[] dest, SimpleCell[] srcvec, Matrix<double> srcmatrix, int from, int to, int from2, int to2, int type)`
	`589`	`+ public void matrixXvectorADD(SimpleCell[] dest, SimpleCell[] srcvec, Matrix<double> srcmatrix, int DestSize, int SrcSize, int type)`
`590`	`590`	`{`
`591`	`591`	`if (type == 0)`
`592`	`592`	`{`
`593`	`593`	`//ac mod`
`594`		`- Parallel.For(0, (to - from), parallelOption, i =>`
	`594`	`+ Parallel.For(0, DestSize, parallelOption, i =>`
`595`	`595`	`{`
`596`		`- SimpleCell cell = dest[i + from];`
	`596`	`+ SimpleCell cell = dest[i];`
`597`	`597`	`double[] vector_i = srcmatrix[i];`
`598`	`598`	`cell.cellOutput = 0;`
`599`		`- for (int j = 0; j < to2 - from2; j++)`
	`599`	`+ for (int j = 0; j < SrcSize; j++)`
`600`	`600`	`{`
`601`		`- cell.cellOutput += srcvec[j + from2].cellOutput * vector_i[j];`
	`601`	`+ cell.cellOutput += srcvec[j].cellOutput * vector_i[j];`
`602`	`602`	`}`
`603`	`603`	`});`
`604`	`604`
`605`	`605`	`}`
`606`	`606`	`else`
`607`	`607`	`{`
`608`		`- Parallel.For(0, (to - from), parallelOption, i =>`
	`608`	`+ Parallel.For(0, DestSize, parallelOption, i =>`
`609`	`609`	`{`
`610`		`- SimpleCell cell = dest[i + from];`
	`610`	`+ SimpleCell cell = dest[i];`
`611`	`611`	`cell.er = 0;`
`612`		`- for (int j = 0; j < to2 - from2; j++)`
	`612`	`+ for (int j = 0; j < SrcSize; j++)`
`613`	`613`	`{`
`614`		`- cell.er += srcvec[j + from2].er * srcmatrix[j][i];`
	`614`	`+ cell.er += srcvec[j].er * srcmatrix[j][i];`
`615`	`615`	`}`
`616`		`- });`
`617`	`616`
`618`		`- for (int i = from; i < to; i++)`
`619`		`- {`
`620`		`- dest[i].er = NormalizeErr(dest[i].er);`
`621`		`- }`
	`617`	`+ cell.er = NormalizeErr(cell.er);`
	`618`	`+ });`
`622`	`619`	`}`
`623`	`620`	`}`
`624`	`621`