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From wan...@apache.org
Subject [04/17] incubator-singa git commit: SINGA-268 Add IPython notebooks to the documentation
Date Fri, 02 Dec 2016 05:13:07 GMT
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/7da4455e/doc/notebook/mlp.ipynb
----------------------------------------------------------------------
diff --git a/doc/notebook/mlp.ipynb b/doc/notebook/mlp.ipynb
new file mode 100755
index 0000000..0f0153b
--- /dev/null
+++ b/doc/notebook/mlp.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# pls install PySinga before running the code\n",
+    "from singa import tensor\n",
+    "from singa import device\n",
+    "from singa import optimizer\n",
+    "from singa import loss\n",
+    "from singa import layer\n",
+    "from singa import initializer\n",
+    "from singa.proto.model_pb2 import kTrain\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f0a6529af90>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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 nQluWusrNxdzO+Z80fS2uNvPZbG201af1t9g+KekCiheAI+W7iYi6fQassf17uVTxBnBVj2MaKktS\n+G3ftsCHmAbWNJxfWn5vKLWbz/KDnwttz0i6CPixxWOcLL/+JGk3xZ/kKfzVcm0auKzDmCh0nE/b\nvzYc75W0VdK5tn+uKcblpKvc7PVST6t1vgPAWkmXS1oJ3EfRFBZzTQAPlccPAm/OHiDpTElnl8dn\nAbcDX9UVYJ+rkmsTwAMAktYBp/5dXos5Os5n4xq0pBGKbeUp+q2J1rWyq9xcknf87UgaB14Azqdo\n+jpke1TSxcArtu+2/ZekjcA+ihenbbaP1B3rgNgC7JL0CHCc4nLYNM4nxTLR7vLyGCuAHbb39Srg\nftIq1yRtKH7sl23vkXSXpG+B34CHexlzP6syn8A9kh4DTgN/APf2LuL+Jul1YD1wnqTvgM3AShaY\nm2ngiogYMr1e6omIiJql8EdEDJkU/oiIIZPCHxExZFL4IyKGTAp/RMSQSeGPiBgyKfwREUPmH9tV\n9Mrph8RbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0a6842b090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# generate the boundary\n",
+    "f = lambda x: (5 * x + 1)\n",
+    "x_tr = np.linspace(-1., 1, 200)\n",
+    "y_tr = f(x_tr)\n",
+    "\n",
+    "\n",
+    "# generate the training data\n",
+    "x = np.random.uniform(-1, 1, 400)\n",
+    "y = f(x) + 2 * np.random.randn(len(x))\n",
+    "\n",
+    "\n",
+    "# convert training data to 2d space\n",
+    "dat = np.array([[a,b] for (a, b) in zip(x, y)], dtype=np.float32)\n",
+    "label = []\n",
+    "posx, posy = [], []\n",
+    "negx, negy = [], []\n",
+    "for (a, b) in zip(x, y):    \n",
+    "    if 5.0 * a + 1.0 < b:\n",
+    "        l = 0\n",
+    "        negx.append(a)\n",
+    "        negy.append(b)\n",
+    "    else:\n",
+    "        l = 1\n",
+    "        posx.append(a)\n",
+    "        posy.append(b)\n",
+    "    label.append(l)\n",
+    "    \n",
+    "label = np.asarray(label, dtype=np.int32)\n",
+    "\n",
+    "#plot the training data and the boundary\n",
+    "plt.plot(x_tr, y_tr, '--k', label='truth line')\n",
+    "plt.legend(loc='best')\n",
+    "plt.plot(posx, posy, 'ro', ms=7)\n",
+    "plt.plot(negx, negy, 'bo', ms=7)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2L, 2L) (2L,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "tdat = tensor.from_numpy(dat)\n",
+    "tlbl = tensor.from_numpy(label)\n",
+    "# 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)\n",
+    "p[1].set_value(0)\n",
+    "\n",
+    "# setup optimizer and loss func\n",
+    "opt = optimizer.SGD(lr=0.03)\n",
+    "lossfunc = loss.SoftmaxCrossEntropy()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.704925477505\n",
+      "0.466226756573\n",
+      "0.39766433835\n",
+      "0.352448284626\n",
+      "0.320274919271\n",
+      "0.296036958694\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0a64dad5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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