[systemml] branch master updated: [MINOR][DOC] Updated Deep Learning documentation

[ni...@apache.org]

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niketanpansare pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemml.git


The following commit(s) were added to refs/heads/master by this push:
     new 392f3d2  [MINOR][DOC] Updated Deep Learning documentation
392f3d2 is described below

commit 392f3d2c8a9d7fd9f1c05454636536d5b4d9e155
Author: Niketan Pansare <np...@us.ibm.com>
AuthorDate: Fri Mar 22 19:47:00 2019 -0700

    [MINOR][DOC] Updated Deep Learning documentation
    
    - Also, fixed javadoc errors.
---
 docs/deep-learning.md                                                | 1 +
 src/main/java/org/apache/sysml/api/ScriptExecutorUtils.java          | 1 +
 .../sysml/runtime/instructions/gpu/context/GPUMemoryManager.java     | 5 +++--
 src/main/python/systemml/mllearn/keras2caffe.py                      | 2 +-
 4 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/deep-learning.md b/docs/deep-learning.md
index 2dbb4bb..968c959 100644
--- a/docs/deep-learning.md
+++ b/docs/deep-learning.md
@@ -207,6 +207,7 @@ keras_model.add(Flatten())
 keras_model.add(Dense(512, activation='relu'))
 keras_model.add(Dropout(0.5))
 keras_model.add(Dense(10, activation='softmax'))
+keras_model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True))
 keras_model.summary()
 
 # Scale the input features
diff --git a/src/main/java/org/apache/sysml/api/ScriptExecutorUtils.java b/src/main/java/org/apache/sysml/api/ScriptExecutorUtils.java
index c9d1a5d..5e59204 100644
--- a/src/main/java/org/apache/sysml/api/ScriptExecutorUtils.java
+++ b/src/main/java/org/apache/sysml/api/ScriptExecutorUtils.java
@@ -104,6 +104,7 @@ public class ScriptExecutorUtils {
 	 * @param api API used to execute the runtime program
 	 * @param performHOPRewrites should perform hop rewrites
 	 * @param maintainSymbolTable whether or not all values should be maintained in the symbol table after execution.
+	 * @param init whether to initialize hadoop execution
 	 * @return compiled runtime program
 	 */
 	public static Program compileRuntimeProgram(String script, Map<String,String> nsscripts, Map<String, String> args, String[] allArgs,
diff --git a/src/main/java/org/apache/sysml/runtime/instructions/gpu/context/GPUMemoryManager.java b/src/main/java/org/apache/sysml/runtime/instructions/gpu/context/GPUMemoryManager.java
index ce22a7e..cf579ec 100644
--- a/src/main/java/org/apache/sysml/runtime/instructions/gpu/context/GPUMemoryManager.java
+++ b/src/main/java/org/apache/sysml/runtime/instructions/gpu/context/GPUMemoryManager.java
@@ -517,14 +517,15 @@ public class GPUMemoryManager {
 	}
 	
 	/**
-	 * Clears up the memory used by non-dirty pointers.
+	 * Clears up the memory used by non-dirty pointers except output and locked matrix objects.
+	 * 
+	 * @param outputMatrixObjects list of output matrix objects
 	 */
 	public void clearTemporaryMemory(HashSet<MatrixObject> outputMatrixObjects) {
 		Set<Pointer> donotClearPointers =  new HashSet<>();
 		// First clean up all GPU objects except:
 		// 1. Output matrix objects
 		// 2. GPU objects that are currently being used (i.e. locked)
-		// 3. Matrix object are 
 		Set<GPUObject> allGPUObjects = new HashSet<>(matrixMemoryManager.getGpuObjects());
 		for (GPUObject gpuObj : allGPUObjects) {
 			boolean isOutput = outputMatrixObjects.contains(gpuObj.mat);
diff --git a/src/main/python/systemml/mllearn/keras2caffe.py b/src/main/python/systemml/mllearn/keras2caffe.py
index 39a9755..19cde10 100755
--- a/src/main/python/systemml/mllearn/keras2caffe.py
+++ b/src/main/python/systemml/mllearn/keras2caffe.py
@@ -296,7 +296,7 @@ def getDropoutParam(layer):
         if not supported:
             raise Exception('noise_shape=' + str(layer.noise_shape) + ' is not supported for Dropout layer with input_shape='
                             + str(layer.input_shape))
-    return {'dropout_ratio': l.rate}
+    return {'dropout_ratio': layer.rate}
 
 layerParamMapping = {
     keras.layers.InputLayer: lambda l: