Underfitting and Overfitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "N = 10000 # number of data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randint(0,100,size=(N, 8)), columns=list('ABCDEFGH'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>87</td>\n",
       "      <td>79</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>98</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>98</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>93</td>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>67</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D   E   F   G   H\n",
       "0   8  24  67  87  79  48  10  94\n",
       "1  52  98  53  66  98  14  34  24\n",
       "2  15  60  58  16   9  93  86   2\n",
       "3  27   4  31   1  13  83   4  91\n",
       "4  59  67   7  49  47  65  61  14"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = np.linspace(0, 4*np.pi, N)\n",
    "data = 15.0*np.sin(t+0.001) + 0.5 + np.random.randn(N) # create artificial data with noise\n",
    "# data = 3.0*np.sin(t+100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['target']=data +20\n",
    "# len(df['A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_poly(date, degree):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['A']=(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['B']=df['B']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['C']=df['C']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['D']=df['D']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['E']=df['E']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['F']=df['F']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['G']=df['G']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n",
    "df['H']=df['H']+(25.0*np.sin(t+0.001) + 0.5 + np.random.randn(N))+30\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d0feb8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RnaIkIEkSFi1ahOLiYixZsgSnTp2qU4Y1cOPV4/HA4wmMpZKSkgKnU9l4JqKsFGgSjfKm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\nCLExSgKEEGJjlAQIIcTG/j9O19PbHn+jEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e96518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.084792</td>\n",
       "      <td>54.280600</td>\n",
       "      <td>99.606257</td>\n",
       "      <td>116.899194</td>\n",
       "      <td>109.528866</td>\n",
       "      <td>78.724891</td>\n",
       "      <td>39.971491</td>\n",
       "      <td>124.704883</td>\n",
       "      <td>19.722735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.357058</td>\n",
       "      <td>129.335490</td>\n",
       "      <td>84.479174</td>\n",
       "      <td>96.278852</td>\n",
       "      <td>129.508894</td>\n",
       "      <td>45.504254</td>\n",
       "      <td>65.217745</td>\n",
       "      <td>53.503791</td>\n",
       "      <td>20.749521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.723062</td>\n",
       "      <td>88.984993</td>\n",
       "      <td>88.592325</td>\n",
       "      <td>46.891401</td>\n",
       "      <td>40.075978</td>\n",
       "      <td>123.538610</td>\n",
       "      <td>117.819881</td>\n",
       "      <td>34.078082</td>\n",
       "      <td>21.634473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.669397</td>\n",
       "      <td>34.489531</td>\n",
       "      <td>60.757485</td>\n",
       "      <td>32.560400</td>\n",
       "      <td>43.860465</td>\n",
       "      <td>112.540084</td>\n",
       "      <td>33.922024</td>\n",
       "      <td>120.418458</td>\n",
       "      <td>21.055577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.325972</td>\n",
       "      <td>95.499055</td>\n",
       "      <td>38.261552</td>\n",
       "      <td>78.516679</td>\n",
       "      <td>77.379959</td>\n",
       "      <td>96.125619</td>\n",
       "      <td>92.681642</td>\n",
       "      <td>44.115223</td>\n",
       "      <td>18.345650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A           B          C           D           E           F  \\\n",
       "0  31.084792   54.280600  99.606257  116.899194  109.528866   78.724891   \n",
       "1  30.357058  129.335490  84.479174   96.278852  129.508894   45.504254   \n",
       "2  30.723062   88.984993  88.592325   46.891401   40.075978  123.538610   \n",
       "3  30.669397   34.489531  60.757485   32.560400   43.860465  112.540084   \n",
       "4  29.325972   95.499055  38.261552   78.516679   77.379959   96.125619   \n",
       "\n",
       "            G           H     target  \n",
       "0   39.971491  124.704883  19.722735  \n",
       "1   65.217745   53.503791  20.749521  \n",
       "2  117.819881   34.078082  21.634473  \n",
       "3   33.922024  120.418458  21.055577  \n",
       "4   92.681642   44.115223  18.345650  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n",
      "Warning: Your H2O cluster version is too old (3 months and 12 days)! Please download and install the latest version from http://h2o.ai/download/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>5 hours 2 mins</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.0.10</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>3 months and 12 days !!!</td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_cqqpff</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>836 Mb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.11 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------\n",
       "H2O cluster uptime:         5 hours 2 mins\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    3 months and 12 days !!!\n",
       "H2O cluster name:           H2O_from_python_user_cqqpff\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    836 Mb\n",
       "H2O cluster total cores:    2\n",
       "H2O cluster allowed cores:  2\n",
       "H2O cluster status:         locked, healthy\n",
       "H2O connection url:         http://localhost:54321\n",
       "H2O connection proxy:\n",
       "Python version:             2.7.11 final\n",
       "--------------------------  ---------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf=h2o.H2OFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate= hdf.split_frame(ratios=[0.75], seed=5010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl0_1 = H2ODeepLearningEstimator(seed =10,epochs =100,hidden=[2], model_id='dl0_1')\n",
    "dl0_1.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl1 = H2ODeepLearningEstimator(seed =10,epochs =100,hidden=[5,5], model_id='dl1')\n",
    "dl1.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl1_0 = H2ODeepLearningEstimator(seed =10,epochs =10,hidden=[5,5], model_id='dl1_0')\n",
    "dl1_0.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl1_1 = H2ODeepLearningEstimator(seed =10,epochs =100,hidden=[5,5], model_id='dl1_1')\n",
    "dl1_1.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl2 = H2ODeepLearningEstimator(seed =10,epochs =200,hidden=[15,15], model_id='dl2')\n",
    "dl2.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl3 = H2ODeepLearningEstimator(seed =10,epochs =500,hidden=[30,30], model_id='dl3')\n",
    "dl3.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl4 = H2ODeepLearningEstimator(seed =10,epochs =1000,hidden=[30,30,30], model_id='dl4')\n",
    "dl4.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl5 = H2ODeepLearningEstimator(seed =10,epochs =1000,hidden=[60,60,60], model_id='dl5')\n",
    "dl5.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl6 = H2ODeepLearningEstimator(seed =10,epochs =20000,hidden=[60,60,60], model_id='dl6')\n",
    "dl6.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training error: 1.7409381 ; 1.78644610143\n"
     ]
    }
   ],
   "source": [
    "print 'training error:', dl5.mse(train=True),';', dl6.mse(train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa542ef0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rkaj5i/G+8gfD8gghepc+WfCUHYT4RIpPeHt8/v1MGfE2Jg6I5uUi3wfUavo8KDuILi7q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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa31d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([dl0_1.mse(train=True), dl1.mse(train=True), dl1_0.mse(train=True), dl1_1.mse(train=True), dl2.mse(train=True),\n",
    "          dl3.mse(train=True),dl4.mse(train=True), dl5.mse(train=True), dl6.mse(train=True)])\n",
    "plt.plot([dl0_1.mse(valid=True), dl1.mse(valid=True), dl1_0.mse(valid=True), dl1_1.mse(valid=True), dl2.mse(valid=True), \n",
    "          dl3.mse(valid=True), dl4.mse(valid=True), dl5.mse(valid=True), dl6.mse(valid=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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77jkef/xxX0sMOk98Vyy5LxhdhhBCGMLnQ1g9e/bk5Zdfxu2uPuKvlGL58uX0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\nqDWPNm4y+qP3oH//Jaa+F1G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8AdOEO9CcDff6FaUUFOVX71UcOBYW+3fD0WxIaF69Z51y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\nDpiWZZGTk0M0GsUYs+wagMfjYWZmhlgsRiKRID8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8de0518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tjOSVe/xyqWwjN+JpMhu/K6XiRlIuQpQhZbOhtq9HrV2K1jga/fUP0cLrGB2r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\n3cLfKzUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e8a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl5.plot()\n",
    "dl0_1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc07ca20>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JafhuhdspcS18La7WP9gOL5gonelpPN49Clsf6wZGoP9v+CPaump3hHPhwN/1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f110f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction= dl0_1.predict(hdf_validate)\n",
    "\n",
    "prediction['target']=hdf_validate['target']\n",
    "output= prediction.as_data_frame()\n",
    "output.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc3bc320>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HHtYG9uNtMyQh0MFS2YZQP+6Vm2EY3BXGzcbfvrxRrx27bJ5D5aJYDiktdrYI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\ntQsVrr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3cec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction= dl5.predict(hdf_validate)\n",
    "\n",
    "prediction['target']=hdf_validate['target']\n",
    "output= prediction.as_data_frame()\n",
    "output.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc737320>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qfHP9dPMHMI8+71yBGoF+Ka7Z9wlP/eTKkoCzJ4AruU6SimIv6lh+ym/S2XXa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\nhJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa542c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction= dl6.predict(hdf_validate)\n",
    "\n",
    "prediction['target']=hdf_validate['target']\n",
    "output= prediction.as_data_frame()\n",
    "output.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}