server activations properly

IsaacSherman · Jul 13, 2015 · 4209192 · 4209192
1 parent eff7c78
commit 4209192
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Showing 4 changed files with 26 additions and 22 deletions.
diff --git a/.../main/java/org/deeplearning4j/plot/iterationlistener/ActivationMeanIterationListener.java b/.../main/java/org/deeplearning4j/plot/iterationlistener/ActivationMeanIterationListener.java
@@ -59,17 +59,10 @@ public void invoke() {
     public void iterationDone(Model model, int iteration) {
         if(iteration % this.iteration == 0) {
             Layer l = (Layer) model;
-            INDArray weights = Transforms.sigmoid(l.activationMean());
-            if(weights.rank() < 4) {
-                if(weights.rank() == 2) {
-                    weights = weights.reshape(1,1, weights.rows(), weights.columns());
-                }
-                else if(weights.rank() == 3) {
-                    weights = weights.reshape(Ints.concat(new int[]{1}, weights.shape()));
-                }
-            }
+            INDArray activationMean = l.activate();
+            INDArray weights = Transforms.sigmoid(activationMean);
+
 
-
             BufferedImage image = ImageLoader.toImage(weights);
             try {
                 ImageIO.write(image, "png", outputFile);

diff --git a/deeplearning4j-core/src/main/java/org/deeplearning4j/util/ImageLoader.java b/deeplearning4j-core/src/main/java/org/deeplearning4j/util/ImageLoader.java
@@ -18,9 +18,9 @@
 
 package org.deeplearning4j.util;
 
-import org.deeplearning4j.plot.FilterRenderer;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.ops.transforms.Transforms;
 import org.nd4j.linalg.util.ArrayUtil;
 
 import java.awt.*;
@@ -135,16 +135,17 @@ public static  BufferedImage toBufferedImageRGB(INDArray arr) {
      * @return
      */
     public static BufferedImage toImage(INDArray matrix) {
-        BufferedImage img = new BufferedImage(matrix.size(-2), matrix.size(-1), BufferedImage.TYPE_INT_ARGB);
+        BufferedImage img = new BufferedImage(matrix.size(-1), matrix.size(-2), BufferedImage.TYPE_INT_RGB);
         if(matrix.isMatrix()) {
+            INDArray toRound = matrix;
             WritableRaster r = img.getRaster();
             int[] equiv = new int[matrix.length()];
             for(int i = 0; i < equiv.length; i++) {
-                equiv[i] = (int) matrix.getScalar(i).getDouble(i);
+                equiv[i] = (int) toRound.linearView().getDouble(i) * 256;
             }
 
 
-            r.setDataElements(0,0,matrix.rows(),matrix.columns(),equiv);
+            r.setDataElements(0,0,matrix.columns(),matrix.rows(),equiv);
         }
 
         else {

diff --git a/deeplearning4j-ui/src/main/java/org/deeplearning4j/ui/activation/ActivationsResource.java b/deeplearning4j-ui/src/main/java/org/deeplearning4j/ui/activation/ActivationsResource.java
@@ -18,11 +18,14 @@
 
 package org.deeplearning4j.ui.activation;
 
+import org.apache.commons.compress.utils.IOUtils;
+
 import javax.activation.MimetypesFileTypeMap;
 import javax.ws.rs.*;
 import javax.ws.rs.core.MediaType;
 import javax.ws.rs.core.Response;
-import java.io.File;
+import javax.ws.rs.core.StreamingOutput;
+import java.io.*;
 import java.util.Collections;
 
 /**
@@ -33,7 +36,7 @@
 @Path("/activations")
 @Produces(MediaType.TEXT_HTML)
 public class ActivationsResource {
-    private String imagePath = "render.png";
+    private String imagePath = "activations.png";
 
     @GET
     public RenderView get() {
@@ -57,14 +60,24 @@ public Response image() {
             throw new WebApplicationException(404);
         }
 
-        File f = new File(imagePath);
+        final File f = new File(imagePath);
 
         if (!f.exists()) {
             throw new WebApplicationException(404);
         }
 
-        String mt = new MimetypesFileTypeMap().getContentType(f);
-        return Response.ok(f, mt).build();
+        return Response.ok().entity(new StreamingOutput(){
+            @Override
+            public void write(OutputStream output) throws IOException, WebApplicationException {
+                FileInputStream fis = new FileInputStream(f);
+                byte[] bytes = IOUtils.toByteArray(fis);
+                fis.close();
+                output.write(bytes);
+                output.flush();
+
+
+            }
+        }).build();
     }
 
 

diff --git a/deeplearning4j-ui/src/main/resources/org/deeplearning4j/ui/activation/render.ftl b/deeplearning4j-ui/src/main/resources/org/deeplearning4j/ui/activation/render.ftl
@@ -11,9 +11,6 @@
         },3000);
     </script>
     <style type="text/css">
-        #embed {
-            background: #2F4F4F;
-        }
     </style>
 </head>