You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@singa.apache.org by ji...@apache.org on 2015/08/17 16:36:44 UTC
svn commit: r1696296 -
/incubator/singa/site/trunk/content/markdown/docs/mlp.md
Author: jinyang
Date: Mon Aug 17 14:36:44 2015
New Revision: 1696296
URL: http://svn.apache.org/r1696296
Log:
CMS commit to singa by jinyang
Modified:
incubator/singa/site/trunk/content/markdown/docs/mlp.md
Modified: incubator/singa/site/trunk/content/markdown/docs/mlp.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/mlp.md?rev=1696296&r1=1696295&r2=1696296&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/mlp.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/mlp.md Mon Aug 17 14:36:44 2015
@@ -19,23 +19,48 @@ Notice: Licensed to the Apache Softwa
This example will show you how to use SINGA to train a MLP model using mnist dataset.
### Prepare for the data
-* Run the command `make download` and `make create` in the folder `example/mnist/` to download mnist dataset and prepare for the training and testing datashard. If you got the error no Makefile detected, rename Makefile.example to Makefile.
+* First go to the `example/mnist/` folder for preparing the dataset. There should be a makefile example called Makefile.example in the folder. Run the command `cp Makefile.example Makefile` to generate the makefile.
+Then run the command `make download` and `make create` in the current folder to download mnist dataset and prepare for the training and testing datashard.
### Set model and cluster configuration.
-* If you just want to use the training model provided in this example, you can just use model.conf file in current directory.
- In this example, we define a neurualnet that contains 5 hidden layer.
-fc+tanh is the hidden layer(fc is for the inner product part, and tanh is for the non-linear activation function),
-and the final softmax layer is represented as fc+loss (inner product and softmax).
-For each layer, we define its name, input layer(s), basic configurations (e.g. number of nodes, parameter initialization settings).
+* If you just want to use the training model provided in this example, you can just use job.conf file in current directory. Fig. 1 gives an example of MLP struture. In this example, we define a neurualnet that contains 5 hidden layer. fc+tanh is the hidden layer(fc is for the inner product part, and tanh is for the non-linear activation function), and the final softmax layer is represented as fc+loss (inner product and softmax). For each layer, we define its name, input layer(s), basic configurations (e.g. number of nodes, parameter initialization settings). If you want to learn more about how it is configured, you can go to [Model Configuration](http://singa.incubator.apache.org/docs/model-config.html) to get details.
+
+<div style = "text-align: center">
+<img src = "../images/mlp_example.png" style = "width: 280px"> <br/>Fig. 1: MLP example </img>
+</div>
### Run SINGA
-* Run the command `./bin/singa-run.sh -workspace=examples/mnist`
-in the root folder of SINGA
+* All script of SINGA should be run in the root folder of SINGA.
+First you need to start the zookeeper service if zookeeper is not started. The command is `./bin/zk-service start`.
+Then you can run the command `./bin/singa-run.sh -conf examples/mnist/job.conf` to start a SINGA job using examples/mnist/job.conf as the job configuration.
+After it is started, you should get a screenshots like the following:
+
+ xxx@yyy:zzz/incubator-singa$ ./bin/singa-run.sh -conf examples/mnist/job.conf
+ Unique JOB_ID is 1
+ Record job information to /tmp/singa-log/job-info/job-1-20150817-055231
+ Executing : ./singa -conf /xxx/incubator-singa/examples/mnist/job.conf -singa_conf /xxx/incubator-singa/conf/singa.conf -singa_job 1
+ E0817 07:15:09.211885 34073 cluster.cc:51] proc #0 -> 192.168.5.128:49152 (pid = 34073)
+ E0817 07:15:14.972231 34114 server.cc:36] Server (group = 0, id = 0) start
+ E0817 07:15:14.972520 34115 worker.cc:134] Worker (group = 0, id = 0) start
+ E0817 07:15:24.462602 34073 trainer.cc:373] Test step-0, loss : 2.341021, accuracy : 0.109100
+ E0817 07:15:47.341076 34073 trainer.cc:373] Train step-0, loss : 2.357269, accuracy : 0.099000
+ E0817 07:16:07.173364 34073 trainer.cc:373] Train step-10, loss : 2.222740, accuracy : 0.201800
+ E0817 07:16:26.714855 34073 trainer.cc:373] Train step-20, loss : 2.091030, accuracy : 0.327200
+ E0817 07:16:46.590946 34073 trainer.cc:373] Train step-30, loss : 1.969412, accuracy : 0.442100
+ E0817 07:17:06.207080 34073 trainer.cc:373] Train step-40, loss : 1.865466, accuracy : 0.514800
+ E0817 07:17:25.890033 34073 trainer.cc:373] Train step-50, loss : 1.773849, accuracy : 0.569100
+ E0817 07:17:51.208935 34073 trainer.cc:373] Test step-60, loss : 1.613709, accuracy : 0.662100
+ E0817 07:17:53.176766 34073 trainer.cc:373] Train step-60, loss : 1.659150, accuracy : 0.652600
+ E0817 07:18:12.783370 34073 trainer.cc:373] Train step-70, loss : 1.574024, accuracy : 0.666000
+ E0817 07:18:32.904942 34073 trainer.cc:373] Train step-80, loss : 1.529380, accuracy : 0.670500
+ E0817 07:18:52.608111 34073 trainer.cc:373] Train step-90, loss : 1.443911, accuracy : 0.703500
+ E0817 07:19:12.168465 34073 trainer.cc:373] Train step-100, loss : 1.387759, accuracy : 0.721000
+ E0817 07:19:31.855865 34073 trainer.cc:373] Train step-110, loss : 1.335246, accuracy : 0.736500
+ E0817 07:19:57.327133 34073 trainer.cc:373] Test step-120, loss : 1.216652, accuracy : 0.769900
+
+After the training of some steps (depends on the setting) or the job is finished, SINGA will checkpoint the current parameter. In the next time, you can train (or use for your application) by loading the checkpoint. Please refer to [Checkpoint](http://singa.incubator.apache.org/docs/checkpoint.html) for the use of checkpoint.
### Build your own model
-* If you want to specify you own model, then you need to decribe it in the model.conf file.
-It should contain the neurualnet structure, training algorithm(backforward or contrastive divergence etc.),
-SGD update algorithm(e.g. Adagrad), number of training/test steps and training/test frequency,
-and display features and etc. SINGA will read model.conf as a Google protobuf class [ModelProto](https://github.com/apache/incubator-singa/blob/master/src/proto/model.proto).
-You can also refer to the [programming model](http://singa.incubator.apache.org/docs/programming-model.html) to get details.
+* If you want to specify you own model, then you need to decribe it in the job.conf file. It should contain the neurualnet structure, training algorithm(backforward or contrastive divergence etc.), SGD update algorithm(e.g. Adagrad), number of training/test steps and training/test frequency, and display features and etc. SINGA will read job.conf as a Google protobuf class [JobProto](../src/proto/job.proto). You can also refer to the [Programmer Guide](http://singa.incubator.apache.org/docs/programmer-guide.html) to get details.
+