svn commit: r1708035 - /incubator/singa/site/trunk/content/markdown/docs/layer.md

[wa...@apache.org]

Author: wangwei
Date: Mon Oct 12 04:07:02 2015
New Revision: 1708035

URL: http://svn.apache.org/viewvc?rev=1708035&view=rev
Log:
update docs for output layers

Modified:
    incubator/singa/site/trunk/content/markdown/docs/layer.md

Modified: incubator/singa/site/trunk/content/markdown/docs/layer.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/layer.md?rev=1708035&r1=1708034&r2=1708035&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/layer.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/layer.md Mon Oct 12 04:07:02 2015
@@ -52,7 +52,7 @@ These layers are categorized according t
   * Input layers for loading records (e.g., images) from disk files, HDFS or network into memory.
   * Neuron layers for feature transformation, e.g., [convolution](../api/classsinga_1_1ConvolutionLayer.html), [pooling](../api/classsinga_1_1PoolingLayer.html), dropout, etc.
   * Loss layers for measuring the training objective loss, e.g., Cross Entropy loss or Euclidean loss.
-  * Output layers for outputting the prediction results (e.g., probabilities of each category), e.g., onto disk.
+  * Output layers for outputting the prediction results (e.g., probabilities of each category) or features into persistent storage, e.g., disk or HDFS.
   * Connection layers for connecting layers when the neural net is partitioned.
 
 #### Input layers
@@ -95,33 +95,33 @@ It may do some preprocessing like [stand
 The data for preprocessing is loaded by and parsed in a virtual function, which is implemented by
 its subclasses.
 
-#### ProtoRecordInputLayer
+##### RecordInputLayer
 
 It is a subclass of SingleLabelRecordLayer. It parses the value field from one
-tuple into a SingleLabelImageRecord, which is generated by Google Protobuf according
+tuple into a RecordProto, which is generated by Google Protobuf according
 to common.proto.  It can be used to store features for images (e.g., using the pixel field)
-or other objects (using the data field). The key field is not used.
+or other objects (using the data field). The key field is not parsed.
 
-    type: kProtoRecordInput
+    type: kRecordInput
     store_conf {
       has_label: # default is true
       ...
     }
 
-#### CSVRecordInputLayer
+##### CSVInputLayer
 
 It is a subclass of SingleLabelRecordLayer. The value field from one tuple is parsed
 as a CSV line (separated by comma). The first number would be parsed as a label if
 `has_label` is configured in `store_conf`. Otherwise, all numbers would be parsed
 into one row of the `data_` Blob.
 
-    type: kCSVRecordInput
+    type: kCSVInput
     store_conf {
       has_label: # default is true
       ...
     }
 
-#### ImagePreprocessLayer
+##### ImagePreprocessLayer
 
 This layer does image preprocessing, e.g., cropping, mirroring and scaling, against
 the data Blob from its source layer. It deprecates the RGBImageLayer which
@@ -215,7 +215,7 @@ the mean of each pixel over all training
       meanfile: "Image_Mean_File_Path"
     }
 
-#### PrefetchLayer
+##### PrefetchLayer
 
 [PrefetchLayer](../api/classsinga_1_1PrefetchLayer.html) embeds other input layers
 to do data prefeching.  It will launch a thread to call the embedded layers to load and extract features.
@@ -248,6 +248,44 @@ The layers on top of the PrefetchLayer s
 layers as their source layers. For example, the "rgb" and "label" should be
 configured to the `srclayers` of other layers.
 
+
+#### Output Layers
+
+Output layers get data from their source layers and write them to persistent storage,
+e.g., disk files or HDFS (to be supported).
+
+##### RecordOutputLayer
+
+This layer gets data (and label if it is available) from its source layer and converts it into records of type
+RecordProto. Records are written as (key = instance No., value = serialized record) tuples into Store, e.g., KVFile. The configuration of this layer
+should include the specifics of the Store backend via `store_conf`.
+
+    layer {
+      name: "output"
+      type: kRecordOutput
+      srclayers:
+      store_conf {
+        backend: "kvfile"
+        path:
+      }
+    }
+
+##### CSVOutputLayer
+This layer gets data (and label if it available) from its source layer and converts it into
+a string per instance with fields separated by commas (i.e., CSV format). The shape information
+is not kept in the string. All strings are written into
+Store, e.g., text file. The configuration of this layer should include the specifics of the Store backend via `store_conf`.
+
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers:
+      store_conf {
+        backend: "textfile"
+        path:
+      }
+    }
+
 #### Neuron Layers
 
 Neuron layers conduct feature transformations.