Update dl4j to match new op executioner api change (remove scalar/tra…

…nsform along dim)
jungleTail · Apr 4, 2016 · b8930ef · b8930ef
1 parent c174ddd
commit b8930ef
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Showing 3 changed files with 4 additions and 56 deletions.
diff --git a/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/layers/feedforward/rbm/RBM.java b/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/layers/feedforward/rbm/RBM.java
@@ -244,7 +244,7 @@ public  Pair<INDArray,INDArray> sampleHiddenGivenVisible(INDArray v) {
                 break;
             }
             case SOFTMAX: {
-                h1Sample = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", h1Mean), 0);
+                h1Sample = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", h1Mean));
                 break;
             }
             case BINARY: {
@@ -294,7 +294,7 @@ public Pair<INDArray,INDArray> sampleVisibleGivenHidden(INDArray h) {
                 break;
             }
             case SOFTMAX: {
-                v1Sample = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", v1Mean), 0);
+                v1Sample = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", v1Mean));
                 break;
             }
             case BINARY: {
@@ -342,7 +342,7 @@ public INDArray propUp(INDArray v,boolean training) {
             case BINARY:
                 return sigmoid(preSig);
             case SOFTMAX:
-                return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", preSig), 0);
+                return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", preSig));
             default:
                 throw new IllegalStateException("Hidden unit type should either be binary, gaussian, or rectified linear");
         }
@@ -382,7 +382,7 @@ public INDArray propDown(INDArray h) {
             case BINARY:
                 return sigmoid(vMean);
             case SOFTMAX:
-                return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", vMean), 0);
+                return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", vMean));
             default:
                 throw new IllegalStateException("Visible unit type should either be binary or gaussian");
         }

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
@@ -657,45 +657,6 @@ public List<INDArray> feedForward() {
         return feedForward(false);
     }
 
-
-    /**
-     * Compute input linear transformation (z)
-     * Compute activations (applies activation transformation to z)
-     *
-     * @return a pair of activations and corresponding derivatives
-     */
-    public Pair<List<INDArray>,List<INDArray>> feedForwardActivationsAndDerivatives(boolean training) {
-        INDArray currInput = input;
-
-        List<INDArray> activations = new ArrayList<>();
-        List<INDArray> derivatives = new ArrayList<>();
-        activations.add(currInput);
-
-        for (int i = 0; i < layers.length; i++) {
-            currInput = zFromPrevLayer(i, currInput,training); // w*x+b for each layer
-            //special case: row wise softmax
-            if (layers[i].conf().getLayer().getActivationFunction().equals("softmax"))
-                activations.add(Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("softmax", currInput.dup()), 1));
-            else
-                activations.add(Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(layerWiseConfigurations.getConf(i).getLayer().getActivationFunction(), currInput)));
-        }
-
-        currInput = this.input;
-        for (int i = 0; i < layers.length; i++) {
-            currInput = zFromPrevLayer(i, currInput,training); // w*x+b for each layer
-            INDArray dup = currInput.dup();
-            //special case: row wise softmax
-            if (layers[i].conf().getLayer().getActivationFunction().equals("softmax"))
-                derivatives.add(Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(layerWiseConfigurations.getConf(i).getLayer().getActivationFunction(), dup).derivative(), 1));
-            else
-                derivatives.add(Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(layerWiseConfigurations.getConf(i).getLayer().getActivationFunction(), dup).derivative()));
-        }
-        // Duplicating last layer derivative to keep pair list equal
-        derivatives.add(derivatives.get(layers.length - 1));
-        return new Pair<>(activations, derivatives);
-    }
-
-
     /**
      * Compute activations from input to output of the output layer
      *

diff --git a/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTest.java b/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTest.java
@@ -279,19 +279,6 @@ public void testGradientWithAsList(){
         net2.gradient();
     }
 
-
-    @Test
-    public void testFeedForwardActivationsAndDerivatives(){
-        MultiLayerNetwork network = new MultiLayerNetwork(getConf());
-        network.init();
-        DataSet data = new IrisDataSetIterator(1,150).next();
-        network.fit(data);
-        Pair result = network.feedForwardActivationsAndDerivatives(false);
-        List<INDArray> first = (List) result.getFirst();
-        List<INDArray> second = (List) result.getSecond();
-        assertEquals(first.size(), second.size());
-    }
-
     /**
      *  This test intended only to test activateSelectedLayers method, it does not involves fully-working AutoEncoder.
      */