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From kaip...@apache.org
Subject [2/9] 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
Date Tue, 03 Jan 2017 05:30:59 GMT
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f94ec89f/doc/notebook/regression.ipynb
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diff --git a/doc/notebook/regression.ipynb b/doc/notebook/regression.ipynb
deleted file mode 100755
index 4e81a20..0000000
--- a/doc/notebook/regression.ipynb
+++ /dev/null
@@ -1,278 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Train a linear regression model\n",
-    "\n",
-    "In this notebook, we are going to use the tensor module from PySINGA to train a linear regression model. We use this example to illustrate the usage of tensor of PySINGA. Please refer the [documentation page](http://singa.apache.org/en/docs/tensor.html) to for more tensor functions provided by PySINGA. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To import the tensor module of PySINGA, run "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from singa import tensor"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The ground-truth\n",
-    "\n",
-    "Our problem is to find a line that fits a set of 2-d data points.\n",
-    "We first plot the ground truth line, "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fce59cef510>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce59cef550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "a, b = 3, 2\n",
-    "f = lambda x: a * x + b\n",
-    "gx = np.linspace(0.,1,100)\n",
-    "gy = [f(x) for x in gx]\n",
-    "plt.plot(gx, gy,  label='y=f(x)')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend(loc='best')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generating the trainin data\n",
-    "\n",
-    "Then we generate the training data points by adding a random error to sampling points from the ground truth line.\n",
-    "30 data points are generated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fce43e79390>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce59e405d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nb_points = 30\n",
-    "\n",
-    "# generate training data\n",
-    "train_x = np.asarray(np.random.uniform(0., 1., nb_points), np.float32)\n",
-    "train_y = np.asarray(f(train_x) + np.random.rand(30), np.float32)\n",
-    "plt.plot(train_x, train_y, 'bo', ms=7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training via SGD\n",
-    "\n",
-    "Assuming that we know the training data points are sampled from a line, but we don't know the line slope and offset. The training is then to learn the slop (k) and intercept (b) by minimizing the error, i.e. ||kx+b-y||^2. \n",
-    "1. we set the initial values of k and b (could be any values).\n",
-    "2. we iteratively update k and b by moving them in the direction of reducing the prediction error, i.e. in the gradient direction. For every iteration, we plot the learned line."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def plot(idx, x, y):\n",
-    "    global gx, gy, axes\n",
-    "    # print the ground truth line\n",
-    "    axes[idx/5, idx%5].plot(gx, gy, label='y=f(x)')     \n",
-    "    # print the learned line\n",
-    "    axes[idx/5, idx%5].plot(x, y, label='y=kx+b')\n",
-    "    axes[idx/5, idx%5].legend(loc='best')\n",
-    "\n",
-    "# set hyper-parameters\n",
-    "max_iter = 15\n",
-    "alpha = 0.1\n",
-    "\n",
-    "# init parameters\n",
-    "k, b = 2.,0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SINGA tensor module supports basic linear algebra operations, like `+ - * /`, and advanced functions including axpy, gemm, gemv, and random function (e.g., Gaussian and Uniform).\n",
-    "\n",
-    "SINGA Tensor instances could be created via **tensor.Tensor()** by specifying the shape, and optionally the device and data type. Note that every Tensor instance should be initialized (e.g., via **set_value()** or random functions) before reading data from it. You can also create Tensor instances from numpy arrays,\n",
-    "\n",
-    "* numpy array could be converted into SINGA tensor via **tensor.from_numpy(np_ary)** \n",
-    "* SINGA tensor could be converted into numpy array via **tensor.to_numpy()**; Note that the tensor should be on the host device. tensor instances could be transferred from other devices to host device via **to_host()**\n",
-    "\n",
-    "Users cannot read a single cell of the Tensor instance. To read a single cell, users need to convert the Tesnor into a numpy array.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.4457921346\n",
-      "6.52662099202\n",
-      "5.04807383219\n",
-      "3.90897369385\n",
-      "3.03137512207\n",
-      "2.35523325602\n",
-      "1.83428827922\n",
-      "1.43290456136\n",
-      "1.12362861633\n",
-      "0.885310490926\n",
-      "0.701658376058\n",
-      "0.560119374593\n",
-      "0.451024500529\n",
-      "0.366924413045\n",
-      "0.3020805041\n"
-     ]
-    },
-    {
-     "data": {
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