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Posted to commits@singa.apache.org by ka...@apache.org on 2017/01/03 05:31:04 UTC

[7/9] incubator-singa git commit: SINGA-289 Update SINGA website automatically using Jenkins Add Dockerfile for generating html files from doc/. Add a shell script (jenkins_doc.sh) to build the documentation and udpate svn repo. Update the tool/jenkins/R

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f94ec89f/doc/en/docs/notebook/rbm.ipynb
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+{
+ "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": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import gzip\n",
+    "import argparse\n",
+    "import cPickle\n",
+    "import utils\n",
+    "import Image\n",
+    "import urllib\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def load_train_data():\n",
+    "    # download the data for the first time running\n",
+    "    print 'downloading data'\n",
+    "    urllib.urlretrieve('https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz', 'data.bin')\n",
+    "    print 'finished data downloading'\n",
+    "    f = gzip.open('data.bin', 'rb')\n",
+    "    train_set, valid_set, test_set = cPickle.load(f)\n",
+    "    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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import PySINGA modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data ..................\n",
+      "downloading data\n",
+      "finished data downloading\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": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Reconstruction error per image = 17.937706\n",
+      "Epoch 1, Reconstruction error per image = 10.943875\n",
+      "Epoch 2, Reconstruction error per image = 9.583109\n",
+      "Epoch 3, Reconstruction error per image = 8.912214\n",
+      "Epoch 4, Reconstruction error per image = 8.491555\n",
+      "Epoch 5, Reconstruction error per image = 8.196870\n",
+      "Epoch 6, Reconstruction error per image = 7.985176\n",
+      "Epoch 7, Reconstruction error per image = 7.813920\n",
+      "Epoch 8, Reconstruction error per image = 7.697145\n",
+      "Epoch 9, Reconstruction error per image = 7.588426\n"
+     ]
+    },
+    {
+     "data": {
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