Switch weight init to f order; remove unnecessary labels/params dups …

…in MultiLayerNetwork
vovietanh · May 2, 2016 · b401fa1 · b401fa1
1 parent 844e896
commit b401fa1
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Showing 2 changed files with 14 additions and 12 deletions.
diff --git a/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java b/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java
@@ -1341,8 +1341,8 @@ public INDArray labelProbabilities(INDArray examples) {
      */
     @Override
     public void fit(INDArray data, INDArray labels) {
-        setInput(data.dup());
-        setLabels(labels.dup());
+        setInput(data);
+        setLabels(labels);
         update(TaskUtils.buildTask(data, labels));
 
         if (layerWiseConfigurations.isPretrain()) {
@@ -1375,7 +1375,7 @@ public void fit(INDArray data, INDArray labels) {
 
     @Override
     public void fit(INDArray data) {
-        setInput(data.dup());
+        setInput(data);
         update(TaskUtils.buildTask(data));
         pretrain(data);
     }

diff --git a/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/weights/WeightInitUtil.java b/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/weights/WeightInitUtil.java
@@ -74,37 +74,39 @@ public static INDArray initWeights(int[] shape, float min, float max) {
    * @return a matrix of the specified dimensions with the specified
    * distribution based on the initialization scheme
    */
-  public static INDArray initWeights(int[] shape, WeightInit initScheme,
-                                     Distribution dist) {
+  public static INDArray initWeights(int[] shape, WeightInit initScheme, Distribution dist) {
+
+    //Note: using f order here as params get flattened to f order
+
     INDArray ret;
     switch (initScheme) {
       case DISTRIBUTION:
         ret = dist.sample(shape);
         return ret;
       case NORMALIZED:
-        ret = Nd4j.rand(shape, Nd4j.getRandom());
+        ret = Nd4j.rand('f', shape);
         return ret.subi(0.5).divi(shape[0]);
       case RELU:
-        return Nd4j.randn(shape).muli(FastMath.sqrt(2.0 / shape[0]));   //N(0, 2/nIn)
+        return Nd4j.randn('f',shape).muli(FastMath.sqrt(2.0 / shape[0]));   //N(0, 2/nIn)
       case SIZE:
         return uniformBasedOnInAndOut(shape, shape[0], shape[1]);
       case UNIFORM:
         double a = 1 / (double) shape[0];
-        return Nd4j.rand(shape, -a, a, Nd4j.getRandom());
+        return Nd4j.rand('f',shape).muli(2*a).subi(a);
       case VI:
-        ret = Nd4j.rand(shape, Nd4j.getRandom());
+        ret = Nd4j.rand('f',shape);
         int len = 0;
         for (int aShape : shape) {
           len += aShape;
         }
         double r = Math.sqrt(6) / Math.sqrt(len + 1);
-        ret.muli(2).muli(r).subi(r);
+        ret.muli(2*r).subi(r);
         return ret;
       case XAVIER:
-        ret = Nd4j.randn(shape).divi(FastMath.sqrt(shape[0] + shape[1]));
+        ret = Nd4j.randn('f',shape).divi(FastMath.sqrt(shape[0] + shape[1]));
         return ret;
       case ZERO:
-        return Nd4j.create(shape);
+        return Nd4j.create(shape,'f');
     }
 
     throw new IllegalStateException("Illegal weight init value");