http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/6ef4bbee/doc/notebook/rbm.ipynb
----------------------------------------------------------------------
diff --git a/doc/notebook/rbm.ipynb b/doc/notebook/rbm.ipynb
index fe80a94..2fd2e54 100755
--- a/doc/notebook/rbm.ipynb
+++ b/doc/notebook/rbm.ipynb
@@ -1,35 +1,42 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train a RBM model\n",
+    "\n",
+    "This notebook woul train a Restricted Boltzmann Machine (RBM) over the 
MNIST dataset using PySINGA. The RBM model would learn a feature representation 
of a digit image like MNIST images.\n",
+    "Please refer to the previous two notebooks for basic usages of Tensor and 
other moduels of PySINGA.\n",
+    "\n",
+    "* Please intall Pillow to enable image display. 'conda install pillow'\n",
+    "\n",
+    "## Download the training data\n",
+    "\n",
+    "We will use a pre-processed version of the original MNIST dataset."
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# pls install PySinga before running the code\n",
     "import numpy as np\n",
     "import os\n",
     "import gzip\n",
     "import argparse\n",
     "import cPickle\n",
-    "from singa import initializer\n",
-    "from singa import utils\n",
-    "from singa import optimizer\n",
-    "from singa import device\n",
-    "from singa import tensor\n",
-    "\n",
-    "\n",
     "import utils\n",
     "import Image\n",
+    "import urllib\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "\n",
-    "from singa.proto import core_pb2\n",
     "\n",
-    "import urllib\n",
-    "# pls download the file \n",
     "def load_train_data():\n",
     "    # download the data for the first time running\n",
     "    print 'downloading data'\n",
@@ -40,13 +47,46 @@
     "    traindata = train_set[0].astype(np.float32)\n",
     "    validdata = valid_set[0].astype(np.float32)\n",
     "    print traindata.shape, validdata.shape\n",
-    "    return traindata, validdata\n",
-    "\n"
+    "    return traindata, validdata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import PySINGA modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from singa import initializer\n",
+    "from singa import optimizer\n",
+    "from singa import device\n",
+    "from singa import tensor\n",
+    "from singa.proto import core_pb2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "* Load training data\n",
+    "* Create the device. If GPU is available, please set **use_gpu=True**\n",
+    "* Create SGD optimzier with given momentum and weight decay\n",
+    "* Create parameter Tensor instances and initialize the parameters"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -55,39 +95,78 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Start intialization............\n",
       "Loading data ..................\n",
       "downloading data\n",
       "finished data downloading\n",
-      "(50000, 784) (10000, 784)\n",
-      "num_train_batch = 500 \n",
-      "Epoch 0\n",
-      "training errorsum = 13.922807\n",
-      "Epoch 1\n",
-      "training errorsum = 8.721448\n",
-      "Epoch 2\n",
-      "training errorsum = 7.938262\n",
-      "Epoch 3\n",
-      "training errorsum = 7.585506\n",
-      "Epoch 4\n",
-      "training errorsum = 7.358841\n",
-      "Epoch 5\n",
-      "training errorsum = 7.194983\n",
-      "Epoch 6\n",
-      "training errorsum = 7.065387\n",
-      "Epoch 7\n",
-      "training errorsum = 6.971709\n",
-      "Epoch 8\n",
-      "training errorsum = 6.883042\n",
-      "Epoch 9\n",
-      "training errorsum = 6.815583\n"
+      "(50000, 784) (10000, 784)\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt = optimizer.SGD(momentum=0.8, weight_decay=0.0002) \n",
+    "\n",
+    "hdim = 1000\n",
+    "vdim = 784\n",
+    "w = tensor.Tensor((vdim, hdim))\n",
+    "w.gaussian(0.0, 0.1)\n",
+    "vb = tensor.from_numpy(np.zeros(vdim, dtype = np.float32))\n",
+    "hb = tensor.from_numpy(np.zeros(hdim, dtype = np.float32))\n",
+    "\n",
+    "print 'Loading data ..................'\n",
+    "dat,_ = load_train_data()\n",
+    "\n",
+    "use_gpu = False\n",
+    "if use_gpu:\n",
+    "    dev = device.create_cuda_gpu()\n",
+    "else:\n",
+    "    dev = device.get_default_device()\n",
+    "\n",
+    "for t in [w, vb, hb]:\n",
+    "    t.to_device(dev)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the RBM model for 10 epochs, for each epoch\n",
+    "* Plot the weight matrix\n",
+    "* Update the model parameters using CD algorithm,\n",
+    "    * Get a mini-batch of training data\n",
+    "    * Do positive phase\n",
+    "    * Do negative phase\n",
+    "    * Compute reconstruction error\n",
+    "    * Compute parameter gradients and update the parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Reconstruction error per image = 7.513857\n",
+      "Epoch 1, Reconstruction error per image = 7.432445\n",
+      "Epoch 2, Reconstruction error per image = 7.368975\n",
+      "Epoch 3, Reconstruction error per image = 7.315392\n",
+      "Epoch 4, Reconstruction error per image = 7.263328\n",
+      "Epoch 5, Reconstruction error per image = 7.218916\n",
+      "Epoch 6, Reconstruction error per image = 7.169593\n",
+      "Epoch 7, Reconstruction error per image = 7.146370\n",
+      "Epoch 8, Reconstruction error per image = 7.112698\n",
+      "Epoch 9, Reconstruction error per image = 7.067219\n"
      ]
     },
     {
      "data": {
-      "image/png": 
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