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From kaip...@apache.org
Subject [2/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks for SINGA V1 tutorial
Date Wed, 15 Mar 2017 09:16:28 GMT
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/regression.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/regression.ipynb b/doc/en/docs/notebook/regression.ipynb
index 4e81a20..a61aed6 100755
--- a/doc/en/docs/notebook/regression.ipynb
+++ b/doc/en/docs/notebook/regression.ipynb
@@ -60,7 +60,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fce59cef510>"
+       "<matplotlib.legend.Legend at 0x7f3c7ebe0050>"
       ]
      },
      "execution_count": 3,
@@ -69,9 +69,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce59cef550>"
+       "<matplotlib.figure.Figure at 0x7f3cce2f9cd0>"
       ]
      },
      "metadata": {},
@@ -109,7 +109,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fce43e79390>]"
+       "[<matplotlib.lines.Line2D at 0x7f3c7b71a410>]"
       ]
      },
      "execution_count": 4,
@@ -118,9 +118,9 @@
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce59e405d0>"
+       "<matplotlib.figure.Figure at 0x7f3c7ec4bcd0>"
       ]
      },
      "metadata": {},
@@ -142,14 +142,14 @@
    "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",
+    "Assuming that we know the training data points are sampled from a line, but we don't know the line slope and intercept. 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,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -165,7 +165,7 @@
     "\n",
     "# set hyper-parameters\n",
     "max_iter = 15\n",
-    "alpha = 0.1\n",
+    "alpha = 0.05\n",
     "\n",
     "# init parameters\n",
     "k, b = 2.,0."
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -196,28 +196,28 @@
      "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"
+      "loss at iter 0 = 8.897375\n",
+      "loss at iter 1 = 7.806258\n",
+      "loss at iter 2 = 6.850243\n",
+      "loss at iter 3 = 6.012600\n",
+      "loss at iter 4 = 5.278670\n",
+      "loss at iter 5 = 4.635614\n",
+      "loss at iter 6 = 4.072176\n",
+      "loss at iter 7 = 3.578499\n",
+      "loss at iter 8 = 3.145944\n",
+      "loss at iter 9 = 2.766943\n",
+      "loss at iter 10 = 2.434863\n",
+      "loss at iter 11 = 2.143896\n",
+      "loss at iter 12 = 1.888950\n",
+      "loss at iter 13 = 1.665564\n",
+      "loss at iter 14 = 1.469831\n"
      ]
     },
     {
      "data": {
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