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From kaip...@apache.org
Subject [4/9] 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 05:31:01 GMT
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f94ec89f/doc/notebook/mlp.ipynb
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diff --git a/doc/notebook/mlp.ipynb b/doc/notebook/mlp.ipynb
deleted file mode 100755
index ea21ee6..0000000
--- a/doc/notebook/mlp.ipynb
+++ /dev/null
@@ -1,419 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Train a multi-layer perceptron (MLP) model \n",
-    "\n",
-    "In this notebook, we are going to use PySINGA to train a MLP model for classifying 2-d points into two categories (i.e., positive and negative). We use this example to illustrate the usage of PySINGA's modules. Please refer to the [documentation page](http://singa.apache.org/en/docs/index.html) for the functions of each module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To import PySINGA modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from singa import tensor\n",
-    "from singa import optimizer\n",
-    "from singa import loss\n",
-    "from singa import layer\n",
-    "from singa.proto import model_pb2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Task is to train a MLP model to classify 2-d points into the positive and negative categories.\n",
-    "\n",
-    "## Training data generation\n",
-    "\n",
-    "The following thress steps would be conducted to generate the training data.\n",
-    "1. draw a boundary line in the 2-d space \n",
-    "2. generate data points in the 2-dspace\n",
-    "3. label the data points above the boundary line as positive points, and label other points as negative points."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We draw the boundary line as $y=5x+1$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# generate the boundary\n",
-    "f = lambda x: (5 * x + 1)\n",
-    "bd_x = np.linspace(-1., 1, 200)\n",
-    "bd_y = f(bd_x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We generate the datapoints by adding a random noise to the data points on the boundary line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# generate the training data\n",
-    "x = np.random.uniform(-1, 1, 400)\n",
-    "y = f(x) + 2 * np.random.randn(len(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We label the data points above the boundary line as positive points with label 1 and other data points with label 0 (negative)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcfcf3b5e50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# convert training data to 2d space\n",
-    "label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)])\n",
-    "data = np.array([[a,b] for (a, b) in zip(x, y)], dtype=np.float32)\n",
-    "\n",
-    "plt.plot(bd_x, bd_y, 'k', label = 'boundary')\n",
-    "plt.plot(x[label], y[label], 'ro', ms=7)\n",
-    "plt.plot(x[~label], y[~label], 'bo', ms=7)\n",
-    "plt.legend(loc='best')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create the MLP model\n",
-    "\n",
-    "1. We will create a MLP by with one dense layer (i.e. fully connected layer).\n",
-    "2. We use the Softmax function to get compute the probability of each category for every data point.\n",
-    "3. We use the cross-entropy as the loss function.\n",
-    "4. We initialize the weight matrix following guassian distribution (mean=0, std=0.1), and set the bias to 0.\n",
-    "5. We creat a SGD updater to update the model parameters.\n",
-    "\n",
-    "2 and 3 are combined by the SoftmaxCrossEntropy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2) (2,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create layers\n",
-    "layer.engine = 'singacpp'\n",
-    "dense = layer.Dense('dense', 2, input_sample_shape=(2,))\n",
-    "p = dense.param_values()\n",
-    "print p[0].shape, p[1].shape\n",
-    "\n",
-    "# init parameters\n",
-    "p[0].gaussian(0, 0.1) # weight matrix\n",
-    "p[1].set_value(0) # bias\n",
-    "\n",
-    "# setup optimizer and loss func\n",
-    "opt = optimizer.SGD(lr=0.05)\n",
-    "lossfunc = loss.SoftmaxCrossEntropy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Each layer is created with a layer name and other meta data, e.g., the dimension size for the dense layer. The last argument is the shape of a single input sample of this layer.\n",
-    "* **param_values()** returns a list of tensors as the parameter objects of this layer\n",
-    "* SGD optimzier is typically created with the weight decay, and momentum specified. The learning rate could be specified at creation or passed in when the optimizer is applied."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train the model\n",
-    "\n",
-    "We run 1000 iterations to train the MLP model. \n",
-    "1. For each iteration, we compute the gradient of the models parameters and use them to update the model parameters.\n",
-    "2. Periodically, we plot the prediction from the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "training loss =  0.245654\n",
-      "training loss =  0.236532\n",
-      "training loss =  0.228489\n",
-      "training loss =  0.221329\n",
-      "training loss =  0.214903\n",
-      "training loss =  0.209094\n",
-      "training loss =  0.203810\n",
-      "training loss =  0.198976\n",
-      "training loss =  0.194531\n",
-      "training loss =  0.190426\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcfd26b55d0>"
-      ]
-     },
-     "metadata": {}

<TRUNCATED>


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