DeepLearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import ,division,print_function\n",
    "import os\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from keras.layers import Conv2D,MaxPool2D,Dense,Dropout,Flatten\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "tf.__version__\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(tf.keras.Model):\n",
    "  \"\"\"A simple linear model.\"\"\"\n",
    "\n",
    "  def __init__(self):\n",
    "    super(Net, self).__init__()\n",
    "    self.l1 = tf.keras.layers.Dense(5)\n",
    "\n",
    "  def call(self, x):\n",
    "    return self.l1(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.save_weights('easy_checkpoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/home/roopesh/PythonFiles/colorectal-histology-mnist/hmnist_64_64_L.csv\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel0000</th>\n",
       "      <th>pixel0001</th>\n",
       "      <th>pixel0002</th>\n",
       "      <th>pixel0003</th>\n",
       "      <th>pixel0004</th>\n",
       "      <th>pixel0005</th>\n",
       "      <th>pixel0006</th>\n",
       "      <th>pixel0007</th>\n",
       "      <th>pixel0008</th>\n",
       "      <th>pixel0009</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel4087</th>\n",
       "      <th>pixel4088</th>\n",
       "      <th>pixel4089</th>\n",
       "      <th>pixel4090</th>\n",
       "      <th>pixel4091</th>\n",
       "      <th>pixel4092</th>\n",
       "      <th>pixel4093</th>\n",
       "      <th>pixel4094</th>\n",
       "      <th>pixel4095</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>134</td>\n",
       "      <td>99</td>\n",
       "      <td>119</td>\n",
       "      <td>130</td>\n",
       "      <td>142</td>\n",
       "      <td>169</td>\n",
       "      <td>152</td>\n",
       "      <td>139</td>\n",
       "      <td>117</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>89</td>\n",
       "      <td>73</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>126</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>77</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114</td>\n",
       "      <td>116</td>\n",
       "      <td>136</td>\n",
       "      <td>152</td>\n",
       "      <td>132</td>\n",
       "      <td>100</td>\n",
       "      <td>151</td>\n",
       "      <td>150</td>\n",
       "      <td>127</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>157</td>\n",
       "      <td>159</td>\n",
       "      <td>205</td>\n",
       "      <td>182</td>\n",
       "      <td>143</td>\n",
       "      <td>129</td>\n",
       "      <td>89</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>82</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>103</td>\n",
       "      <td>93</td>\n",
       "      <td>98</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>115</td>\n",
       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>109</td>\n",
       "      <td>128</td>\n",
       "      <td>89</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>143</td>\n",
       "      <td>140</td>\n",
       "      <td>139</td>\n",
       "      <td>129</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>141</td>\n",
       "      <td>137</td>\n",
       "      <td>101</td>\n",
       "      <td>...</td>\n",
       "      <td>231</td>\n",
       "      <td>199</td>\n",
       "      <td>183</td>\n",
       "      <td>195</td>\n",
       "      <td>179</td>\n",
       "      <td>134</td>\n",
       "      <td>142</td>\n",
       "      <td>158</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4097 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0000  pixel0001  pixel0002  pixel0003  pixel0004  pixel0005  \\\n",
       "0        134         99        119        130        142        169   \n",
       "1         55         64         74         63         74         75   \n",
       "2        114        116        136        152        132        100   \n",
       "3         86         82         88         85        103         93   \n",
       "4        168        143        140        139        129        123   \n",
       "\n",
       "   pixel0006  pixel0007  pixel0008  pixel0009  ...  pixel4087  pixel4088  \\\n",
       "0        152        139        117         87  ...        112         89   \n",
       "1         71         73         70         77  ...         79         85   \n",
       "2        151        150        127        205  ...        128        157   \n",
       "3         98        109        104        115  ...         79         80   \n",
       "4        123        141        137        101  ...        231        199   \n",
       "\n",
       "   pixel4089  pixel4090  pixel4091  pixel4092  pixel4093  pixel4094  \\\n",
       "0         73        100        120        120        126        140   \n",
       "1         86         77         68         66         65         68   \n",
       "2        159        205        182        143        129         89   \n",
       "3        109        128         89         85         80         63   \n",
       "4        183        195        179        134        142        158   \n",
       "\n",
       "   pixel4095  label  \n",
       "0        195      2  \n",
       "1         69      2  \n",
       "2        122      2  \n",
       "3         48      2  \n",
       "4        149      2  \n",
       "\n",
       "[5 rows x 4097 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data[\"label\"]\n",
    "data.drop([\"label\"],axis=1, inplace=True)\n",
    "X = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "g = sns.countplot(Y, palette=\"winter_r\")\n",
    "plt.suptitle(\"Amount of each classes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    625\n",
       "3    625\n",
       "6    625\n",
       "2    625\n",
       "5    625\n",
       "1    625\n",
       "8    625\n",
       "4    625\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X / 255.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZRUwgTPe2sUZ5bB/rjdSjdfZ6QeyZWDVPBPJQhm1j8WRVNcumeV/h3Vt2qLoD82yi+t4D23h8T+lzmbqA5yM6Z85TDFkGL1U7tdk2PsH3QcwEfNW3cQTNmSt5bg5OaJ26O8M3XTau52r3ftb7Y1k94St7ZxrljgTPR6Gi95PGZnkToziobakux/M6MDDVKF/cd0y1uzR3kMcb1Tf4g/ktAID/+6t3YeipiaaKu3+ze3i0Cfxi9/BoEyy76S0ZShhW/ExOC4KKkhZ9Y0Uh6t0vPL/O0X3ISK7MkK6rCutVVYSfW5NRckp4jHWY8VebqwlVHc6OWlp6e5lzmRbkG1NaDalm+DypW0yQMdXUhYmuPqVNMAlh97tyO5ukkhEtgqdFIPzG9Jiq64+x6Ls+Pt4od0R0JOGACJjfENOheVc/zqJ7xx4+T0sMkT3AdTXtUIhSP593525+L/Xu0mK8i3E7GwlZ380XZyKysVGOrtHvueloV6M8q7tHVqoQGb1kBru58rhQ0XoGZlS7VV08pz0rtTfnti4W11+R29Uo2/k+WGF76Sd2vUHV9WeDG7lQ1feDhH+ze3i0Cfxi9/BoEyyrGF9PAnNnBDJSYtyIUS9hMTO7V4siHSK2IT3G7SJVIwYnxS5y3VoZRJ0Q0+qtpR4kZvVnSSJRXCHaaccvJau6uBbBayISRloPAMAJMRDF1kRl8U4Wn9/zip+puvPSLBK+Ms30ArNmPuTsR1s7XeG4IKuLGoaEWRGt81RZm1A2dbIn4s7cQKNcMcE0Hedxu1/bvFPV3XaIPdKKR1mE3f8uE6iyThOQSIyOC11sjEXu5IRuJ+nDqoY7RVpbElN6slKjPAflTi7PTOvd/qkkX9v5M8dV3dW9+xrlJ4rr0QpfOry9UX7V2j2q7o9W/hgA8OpU67nwb3YPjzaBX+weHm0Cv9g9PNoEy+pBt+b8bnfjl18JAPjGgZeoumKJlecNfVrvGP4O6zGSijle0GMv9grPuG6tW0kiAEnIYM1mkkzhBDpgod9LL7kTONmF11w9q+04yhsup81hmIs1rfulCx9Rzd7S/WijvNVsLBTF9ZwSLBpFQ1Yu9e8OYwetCCX1WJVD+s5N6Oty/zxfl7MS2px0ZpzP+4Z91zfKu0c1+cMHt/6wUX4mv0bVffenFzXKmXUcFvmOM/R8PDbNpMaxiN4jeWjX5kY5fYi92qwJUE6BsVKq+8DeL5KuOz3CDRN5Y3IVxBOWYELq+vK+rSX1IOc2ch/pLdq0l58ONhoG//tnUTpw1HvQeXi0M/xi9/BoEyyr6W18PosvPBGQSGzbcFTVPXGURbHDYzo6pbZa8LBznADGLtbSSlQEp5RWafG53CUIAqQZyjzuskICSk1okVCa9mR2jvk+PY6uvVyux/QUT1zEfb5r20OqbkbYfC7rYHPMy9MHVbuCEMmPV3X/krxBlhMmZ1J8kdxC8nezdR7T25+4XrUr38ki+fwqLZpe+kr2BBvKs/nrJas12/j5STYV/mB0q6qL9rGMnEmyrvSVPZeodpV93H/WSLAD43ye1VRrL03J3W6nRorTlazhlsuI+RZemmUTXVSP8Y0WKxpuubzwEBX5CCQvIwB0HBXj/zcdTNMd8tSPTbW2o/o3u4dHm8Avdg+PNoFf7B4ebYJl1dkTE4T1XwoOOZLZouqyq1kPtfpfbSXbQgqr2P5V6TEmI2G2kNFlAODiIm+Y4Hnv2WMIGQ4Ik4ZJxlbpYf211MPjmNiqn5mlyzmUbt2KKVV3YY7NV9d0PKvqLkiwG6VUKQvGbCb1bat7S7PZYnq5bLe/ol07Pz90daP82D3Mtd7/qO4vAd646NBbMNi7mwn+R6/gdsPxbtXuoQMciZbJag782ji7t87uYptXzGTGzY7K+8W49G4Q8yEubdrkbJMZgOf7jT5fak1UEhNewlLHtia6itDF7WWR5KXKJGgJPlWE5nN/T/s3u4dHm+Cki52IPk9EI0T0pPiul4h+QER7wv89i/Xh4eFx+rEUMf6fAHwWwD+L724CcJdz7pNEdFP4+SMn66geI8yvCA4ZMV5E2WGR2qag5ZfYLinGCkKDQ1q8TcxyXT1mTE15FttIpDdKDmpPpFoXi+ozW7QsVhfhYbObuPzxX/qKavdL2cFGec5pVSMvzH4Tdc1TdkjIfnERmpciLXJmhKhaMbJeRrSVM3DfvFab/uUY8+jv26kjrfoe4/6lI+L8Cv1ukN5jMmUUAMytFz8UxfRBfc7SlJqY0fz4ndJE5fi8KhnDlS+6rKZbcxtKk9fkWfrWL6zl2UqN6P4T4haJVIxnnBDj5T1tLpniUawazzjJiShxggkw0VqdaHj9LRLBeNI3u3PuRwBMQCDeBuCWsHwLgOvh4eHxC42fV2df5ZwbBIDw/8pWDYnoA0S0g4h2VIv5Vs08PDxOMU75brxz7mYANwNAemC9KwwEcoZ18s8MS+8m3YcUnzsPcWXiuGGNELvnLqlPLTLJgRQuzbu81T7NnTZ5LovSNphmbgvLZp983Zcb5XNNEMjjZd6p74hoeS7veFxWPAcEbbOQ4Wy74RqrGrvLq1TdjjkO/Pjus5yaKPGMZmSQ873CzLeLiCyxIuOopefuPMwy8tgFmgWkkhOqwJN8zpZnLjovRXVdJz0dFTGJaSf5C9PjWvaNCM+4wsrWhCA9T3P/6TE939Ey91nqNIQpKeFdJzK1WnUiKoKlbCCMurwys5fx1pNivQ3WWRDxa4uQsfy8b/ZhIloNAOH/kZO09/DwOM34eRf7twDcEJZvAPDNF2Y4Hh4epwpLMb39C4CfAjiHiI4S0Y0APgngtUS0B8Brw88eHh6/wDipzu6ce3eLqlc/14NFakBqPNBXLNGjjDCzEUnSrJMY5U0+mtfKZmU9s0CWu7WJh9ZwZNT8Cta7isaclF/Hx9p06RFV989bbmuUJeHDwWqXapcirpM6OqBJIyyBY8qS6YeYNZP1hfErG+XvPniRqut5jM9n3VFW7OZXGK9EyYVuCBalbi7TZtlorfGtPK65s/TY04e5TpqrbGovqXvae6KY4HOppeXegSF/ELqyM16PMXEvpaYE+cO47mPyHL5O1bRNK8bjqBgzWVlw+Evvt6ghNCl0CKLRktXnxX6EJNEwWzryOtm5qmaD/utmT0TCe9B5eLQJ/GL38GgTLGsgTDXrMPayQG5LDOlDZ8R+fnJci+fRWf5c7WIvq+HXrFDtZrcIL7xV2k7UlePoiWKFj72mU3vQ3bj+vkb5ipTOojlaY9VgqMYmuhURfayiEN0rJohF2lkkvxsA1MWztyx+968TF6t2P7yH+ftW7zRECLMsF1dy3J8V++KCI80OUZqeZtfxuQxdZgJEZvlzbo8+gDSPlXpYTI3r6UZZaEA2AMpFBZefCGSymVoXg4tx24wIgIoZjriIuOUKa8w4RDll3MtyIqfB1Lmt+RwTU3zsatZ44Qn3E2lesybolFA9yp3GU3Ao+DxifqPatK7y8PB4McEvdg+PNoFf7B4ebYJl1dnP7RrBndd9GgDwtMmH/FevYEueJJ8EgGqBXVqzvWy7ef1Gnefs9V1PNMpnxDXHuXyq2UixVpA6OqB18aywkVje9Yjgbv/a9KWq7nbBlz93VOvskaIkOBB67qweb99uSaKo64rdPEbpOmrdKyXBoo0im1vDfci02GRy02UGW8+jtDjKaYzatMzCJFjt1LameDcroHKEuaxmrygK9+R5Ezk3sJrvg6E+EYltfG5TB3hg8Rkzpyt5HosXadth/Qneu8keaR7pBwC5o8Ll1rphb2i+H2F19pogx5D5EwDOmWD58CX8m93Do03gF7uHR5tgWcX4kiPsrwRiVm9Um6tuWv/dRnlijY5Ekx5p0iQ1ENOpj+pChhmtabewosjdJHnRs6RdnXpF7t5ek9cpK8T1uOhjZ1l70H3y4Bsb5WP3aGKIrn0sznUZj7RqqnnZhoNJM5r0HgMAedodQnSUxAcAUFgpzHIx4xUmtAtF3FA25h7p/aY1HuXlRlVq2S49LExjx+3tyJ/lFOezmlSk3McDiXVolSoV47r1a5njLxPX1/ZwB4v484Oak12OvzprTIwi1ZeMuqxmjMei8AZMTei6xCT/rtwjTaKtZfKq8cJbuF8sqYWEf7N7eLQJ/GL38GgTLKsYX3cRzNQDeUOK1QBwrMpi1FMFvRvfJYi+uoT4n43o7co1om4VGXGOmrsWVYyI/ISQYadqWpw7IiiX//fjr2iUkzt1u9wxFp97qppMQZILFPr1s1Z5RYmqpAnakJv/1vstKUTEWpI7sRxxi3nXpceE55oQ8W0fNj1RqzHK/vPrzY77DI8jOt+6v5pI3VRebQKGysI7bdrw+o2u5jGJdFKvOnO3anfdqqca5QdXb1Z1jw9zdtnCMa1ixsSYZXCRJemQBBXSoxAAUoIKW3roFbWDqNrhtzFTkXAlW2IP1aZ1lYeHx4sJfrF7eLQJ/GL38GgTLKvOPlrJ4e+HrmlaN1NmW9P6rE6ZdH6Go8+uTB1qlK3KePvs+Y3yA9Na7zo8y3sC0/N8rELBKFdH2XaVMOlvZdTRlofZ7FdPaNbcUi/3WerWz1NJnHhCQJzQt6KiS+v9JnXg1KTeE5AEE/O9fOzkjG6XnOJ2klRyMVgTnSSlqBgTYH6N+CBCuRKThhhCWMBihthCQuqyySmtl8+exRNEWT1ZTngYJnbxtf3xs9tUu39by+miqaKvWW4/j1lr7PoaRkQ5OaHnQ0YZWjOovL6yPzsfRZGli8w+S6Ot19k9PDz8YvfwaBMsqxhfKCexY3+QtTPXqWWULT026Qzj3inOCPrV4vZG+fHHN6l23U/zsys9ZtM/8ee+IpfJWbmHzTN148FUF6as+QH24rLieHyO7SxVE2QiRWbjoIfEjGtal5jT5yI5yWWWUgBIj0rTG7eby+lB1oSHnjXjSHG9Lu4Qm45ofqXgTDeEDMIREU7qW8Y2JDhAYJwelceeJKHIHtcD6dovzF8JLeLP9zd/n6UMv3zsSWGmNCmeZjZwuaQT3iI90jzlkzVnyrmKmVwpco5VPgUz3/E50U7H+zSuoTe9eXh4+MXu4dEu8Ivdw6NNsKw6e6RAyD0aKBsuqpWOgyU2jR00vyNhmpDc32cc0y6wJHJyuYR+jlUyMoKKy9YFVOpMpQ7dhzSRyPS8ySntAlrq4f7nVus+FotKohZRZPY3U2eLMa7Upqb0KOvmJREpVu0wOnWhtQmwInRFqYdWNN8IqjluGJ/Wg5S6Y7FTkGis19eslue5ik2a21Hsp9Rqrfcp5HVJTBmlVZyLHH8lq/uQZi5LRhkV5szOA7oud5Q3V2LTfG5jlxhiEjHGuOk/LvLdSb55m9Zc7iFZd9zGNfQ6u4eHx1LSP60nonuI6BkieoqIPhR+30tEPyCiPeH/npP15eHhcfqwFDG+CuDDzrmdRNQB4GEi+gGA9wG4yzn3SSK6CcBNAD6y6MGKDr27AhvBYhFUkZKxOUSae3hJ0RwA6t3SBGNIHcRnmZLYisjSDJKYMyQDwqQmedsktzoAFFcI/rg5VYWOw625yKQXmvKE6zOqgPhZx257CYUpSAzfcq1LfjOy6X+FiCg54iw3W+5g66i6aobb5g6LyLY92r4m+emsOiE/S1NT3dwO0iR4gulJtK0KM1/cmL86jvK1ze3VqcBpWJiFayYnU293ozi/he1yMZPCLH5ERO2ZFFKFVc0jEG0qK3meVowvd9ebfi9x0je7c27QObczLM8CeAbAWgBvA3BL2OwWANefrC8PD4/Th+eksxPRJgAXA3gAwCrn3CAQPBAArGzxmw8Q0Q4i2lEp55s18fDwWAYsebETUQ7A1wD8nnNu5mTtF+Ccu9k5t905tz2eyJ78Bx4eHqcESzK9EVEcwUL/onPu6+HXw0S02jk3SESrAYy07iGAi1DDfbSSNuYTYWaIVFqbcSR3tmVKkfq25EUHtL7jWnwP6Ogwy6c+u4EVqvw6mWJa95EZbN2/dHWVuj0A1JI8ssyQcEU1oVaSBNKmBpb6vDy2jZyTJD/WTVWSJaZGW3OVy30Fu0dCdWFOEq6zNjouJsxOdoxyL0Hy0FumF7nvYnnTJfFl9jh/nzuudePspyIAACAASURBVO/sftbTaVxHXTqhp7s1/aouv5lNbHIO4iat9Nwa3oCY3aRvinofX0SKibTSGX1xSyXpu6z7z6bCvbCk2VMQWMpuPAH4BwDPOOf+UlR9C8ANYfkGAN88WV8eHh6nD0t5s18F4NcBPEFEj4bffQzAJwHcSkQ3AjgM4B2nZogeHh4vBE662J1zP8YJyWwaeHWL75uiHgfmVkcbZQkS8ld81ngwCWlGZWSyzlLKjNPa1KSjjHQfxV4Wt+ZXW3MSi0jUK0Svw9obsCS4vy3XujS3nZhGubVXm4QUz00WLdWHnCtJ2AgAMeFBF9XZlJARXO5RIWbX0vpcyl2C71w7jCmTY1ZEHJ5AgCE842pWzhTjl6a9xLTl2xd9mHRYJNRDpVKZe6c0IHSlAaM3ibZ145kpz6ea4rq5dca8tlHoJDETkZnkupqYhMKcvjlTQqx3i+V5agHvQefh0Sbwi93Do02wrIEwEkkTsCADLqwIK7nWlfhlHlVKTDO74JI0otgnRNOc3r2MFLlTFzVjFKJ77ACL7p0H9LGkqG530qU4nRozu8riakjx3O72t/oNAFTTzdvF54xaU2xeBoCoIG8o9Qox1fQt5zthjLFSrB+/QHhHGmIImT7AevnFZZ9CbK0aC26lo7XaREJNkKpA2QQ52c8SiszDqF7K21BYCebXml3xRVglpEgeT7BIn0ho80R3mi/UeT1Dqq4c6qaDJq2VhH+ze3i0Cfxi9/BoE/jF7uHRJlhWnZ1qQDI0m1j9ryJNUiatr/SkiolMzyZTMhIig3PVmKSkbhWRxIBlE1EWE/pfr9Z/pD4VF2l2i4aEsNIpcqXZVMZCd7ZqnPwozY3WC2+x/Q1p8pIEljaCKiqisqy5auJCPmCsIPYw9KHg4jJdsZ7HksjHRvM8yLolSixL850xuYq0xAlhjk2YSMJ5oZfLKD0AqIr+k5PiuFalFia6qjExFvuamzMB470nOd8NmUdV7N0kBnS68rW97L3XEecNmv6UPtHuOP9updkkGQuZOWIRc7MI+De7h0ebwC92D482wbKK8bWuOmbeEogmxXEtxyfGWQbKHNNiVOcRFp+VGWTUHECIqnUj3kpzmAxAIZv6VngwxQ7rMcpADakWzA9o0UmqAqkhPZCE4EUo9ptUzGWpygjSiNnWgR+ZQdeyrijMZjINMwCUxXzMbdIyrTRXSRNmPaP7qGf5d8W4rksOso1KctXVOvSx6mKKk0PariXFemnCTI/o+ZAedZGK9fLjsuQXrBp1Qqo5ixGa2LpSH59bajPrkd1JrQJe3H+0Uf6VFQ+qunPifFPIO2miZtKa1/hkuiNaFZiqB8wc34i0zqHl3+weHm0Cv9g9PNoEfrF7eLQJllVnd9UISsOBbpEe1LqszFGWntB6ncp/JZCc1u3iM6wnVXJa34mW+VQTgjixkjX631zrCK2KcNOUpIHUb/xZR1mhl2meAaDcKaP7LHMimtZJggcLO8aiIAeLCctN1ZjX5jby3MVn9DM/Mc1t8+sFcYPRy2NZ3vBw01oJrgsijosu28vjM3poXbiK7uvuU3WVsea+v6WVZhwzfC8lx3Vb6d46dYFI7ZzS946rSmZKQ54i9jA2nzOo6n593c8a5TUxtu0lyNybwn4cN3XTYoMpLuysvVG9odQXHWuUCybqLV8P7rnFYuH8m93Do03gF7uHR5tgedM/lYCOfYHIUsnoOineVkx6YWnukGJxLG+4vJJCnBvTJogkS0AneJOpMRaESG746isr2f5TE8can9bipvTikvzpgDbjZIZbm82kpGc96CSKK/Rn6WEoOePmNuhxpEZ4/OUuw5d2LqtDkbgQ4414WxfibT2nB1kTEYNn55iesMcQtp+TZLE4sV6Lt//z4Osb5cEpDqPrzOgwvQ2dLD4P5TWLxrER5nWnKeH+ZsyIZ28ebpTXZTUH3RkZtvH+RvfDaIUJIY7XjEBdERfX1qnPyran57SySO6wldFAZ4vR8+Cg8/DweHHAL3YPjzbB8gbC1DkjpqUUliQMVRsIIyQuKRbbQJJKjjtxRgSX1MaREos60Vm9k17P8U56pKjJA2SW2OQUi5Lr9o6pdpVV7Ok0c4YW8edXSHIMM35hGVDkEobwQaWomoWGpN0WAR1ydxzQu9S0Vqs88qaoFkTGW0NTnEjx/Fxzzi5Vd9/hMxrlr+2+qFE+f7Xezb5wDXuW9Uf1yXxs83ca5aEqi+M/nTlTtasLMfiNfU+ouom1rHodK3EfOyfWq3bHZ1j870rq+RgXZpivz52n6q5Ks6VB7qRHTdhQUYyxbsTxgvjcEWlNPiH7z5ibf+wEAr8T4d/sHh5tAr/YPTzaBH6xe3i0CZZVZ6/HgcLqoGzJHFPSNGY43wuruO30eSLlca/2xkqKzLqRij61zBj/LiJSQ8VTuh251t5q1azYExBjjBe03h+bYvtX5z7dR6mT9T/r/aYsMuIxLM2SgE5xZNM/SaJKabKrGtOYExz48UXIEBFtXVcu8nxsTE2oupddwCf+0xnW36creg9jqMr7G1viug+p925JcWRY3OSYfmZ+baN8YfKoqlsluNYTwuQaX6nndFTs6Vh9WMJ6rnWL6xQXF3Ciruc7I0xiBbNZI01vKdFuVVTfmxXHfT5t5vHrk9sBAOPVH7Qcu3+ze3i0CZaS6y1FRA8S0WNE9BQR/Un4/WYieoCI9hDRV4gocbK+PDw8Th+WIsaXAFzrnJsLs7n+mIjuAPAHAD7lnPsyEX0OwI0A/naxjlzMobQyEFNiU1qUme8XgR+Gx7wmYiyk+F8c0Kag+fVCVM/r/vMiWELysFNdT0FUWF0yozr9jgxIqXTy7yod2o2tJtIAFbuN15nUPBZ71C7iNSelTJv+qSo41KMixZMU2wEgJsxmlTn9nO7oNyRvC+3mDWm64O/712MXqKq/OOerjfK1A/sb5SGTb0uK8ccNcWBGpJq9M39Oo3ygpDOpHsjz/D87t0rVvXfV/Y1yRExqd1STP2yKsbj/hCE33FHY0ijvn9fBOscKbM7LCf64izuPqHaXZ1it2RLX/HGzwvPun6cub5S/f/xc1W7oKJMdZg7oa7FgsZuZuB+tcNI3uwuwcPXj4Z8DcC2A28LvbwFw/cn68vDwOH1Yks5ORNEwg+sIgB8A2Adgyjm38Ho4CmBti99+gIh2ENGO2ly+WRMPD49lwJIWu3Ou5py7CMA6AJcBOK9Zsxa/vdk5t905tz2ayzZr4uHhsQx4TqY359wUEd0L4AoA3UQUC9/u6wAcP3kHBCoFz5faam2ukm6Z6WN6WPUk61okSAaiBaMPC5ILS7QgecwjBdaRJDkkoPcEZrTKhMQE/06OIzGt9wdk4FHdpISWWwQ2x1qsIIkTRX/mMSoDx2zm3vya5sSJVNBjzK1gvTxvuMbzs7xJUhfmtXhO2/nqST7A5KwOY3yiyO6ov737mka5sLtbtZPmQZvrrZXnaCVnXH/FlkNyQvfxX4jfS3mZNtmQV5y5gSPzjk7oMZaO80sqNm/498W+iCQJfXJW3zyfz72Gx99jUjZP8Tz2Ps19JKf0GNfHxb1f0sQWpa7g+lpTrMRSduP7iag7LKcBvAbAMwDuAfD2sNkNAL55sr48PDxOH5byZl8N4BYiiiJ4ONzqnPs2ET0N4MtE9KcAHgHwD6dwnB4eHs8TJ13szrnHAVzc5Pv9CPT3nwtd3Yb3usbRSTYNsUyV7OqS+9uEjcWEeGTSOqHeXMR3acOZPssHjxa1yGbFzMahbBoqYVnpPGB40oUoVos37w8AosLLz6YqmlvN51Y1JCDSTFmTkW7GlFcosuzbldP6xPg4XwuKi0iumO7k7H4WfetGn/hf//pW7p8DwxBdZ8g85LUw0yHNlFJEThlRPT0q0lUV9RirQrWLi2sbqeh7Z3Qnqx3FbUYWFvdIx1P65owVeVwF4ZWXmtCqxoqnuQ+ZagoASHjbRSpcLncZdVZ4XFoP1AX10M6hhPeg8/BoE/jF7uHRJljWQBhEXENsnp7W8mdmL4uV8+dosfJ/X/GlRvn2iUsa5bv2mO1ygVhWi2KVEp9qPc/yYXTS7vwLD7SifhZGWmRWTZnUSnInvW5EdSlWJvJWnBOccWtYzMyvM5lJhVpDRS2ORuZ5zHJnNjppLAYTLKqPbdTeWPE0n0Auw1aTnowmdTg02cNjPKK939Y8KETTqpjTkh6HtE4UtPMbCmL3PN3Hat/snDZxzM4I1ctYaCRtuLxmNk2UpDKPTRhPQdnfnKGxLspzk9leDcV3jzhvY12JyYy6cZHx1gRKRaT4b/pYjKew8fuTN/Hw8HgxwC92D482gV/sHh5tguXV2auEWKgjJ41eVBOx+NdtfVrVPZBn8gNJutfXowkKZwpsd6qUDSmFfKxJi5QhYpSecRGTzjkmVFZpGrPmDkmmWezTlckp/l3JkFKUu0S0nOCeT0wZzvcx4TK2iK4mxxs3umZJpHMuT+lrITMhTRb4WFMx7e6c2s3zvWaXtg9WsnwuMn22nDcAiAmntp49+mR69oi00quZELK8QfdxxiUcYbapQ+d/+tnxTY3yzIgwKc7rvYNST2uz1poLmFN+ZH61quveLbw7xc/secp0YVbflqnApe5tiUalR2S5w6aoWiigJfyb3cOjTeAXu4dHm2B50z/VOEOozVJT2sQmnkpdi1gPTW5slCVBwMsHNMFbRMhRPxo6Q9UNHRQEE0JMs4EwmSMs0p5ARSZEJGkyqnQYUV2kqEqbLK6FVfx8ne9vHYSTHpaBNsa010LsC+qEKUiIktazzEX4BLpP8Axk0V1m0I0bU2Fqki+iNTVJUTUuAnxsRl6ZI6CS1nVKpBXmqZ5n9KFGDmxolPdt1nzw6TOZu27tBhbxjx0wGWOFGB8x5ruLVjCv3b3bdLba4hibH9V4jRgv58PmC5DmWWl+tWnKFImLeU1HW2d9asC/2T082gR+sXt4tAn8YvfwaBMsq87uIkA1TJVLWvXBFWcxKeHhuR5VtyHHKXn7RXKzZETzh78ix/nGbBTW1/YLHU1ExHXtse6VzaOYAMPXLg7dvVcrTOUc9zmx1ZAdCE/gWMG4VK7mfuaFrdDy6EvEtAerSmkt9bpaUp9ndphPQOr5ADDfx7dFTXCMZEb0fCdm2DZZ7EuYOrl3ILj+e/QtJ91Wyx2tz1OZQU0zSQTa/ayuqxxlIooJ4dGb1aeizIM2f97dh89ulPMTmq89I/qUqa/jc62ve91442oTqSwbs7AIFLWpwL27rIeHRwN+sXt4tAmW14MOLG64czTT7PauQ41yvFuLxWclhxrlmTrL/zYtrkwLdEVOm+Vui21vlHsfE3x34/pYMtrM8nllh5rbN8pZ88wUw7JipRRp43kte00KHr757Syzldbr81wpPAfzJS0+Dx9mJo3cAT6X5KTuIyHEVisCynEV1/KYps7S8mdKcPJ1HtCRivU4z0lhFY/RmjOlqhHVtISKmEOa9iy5iRTxJY8fAKTEeWdEZJs1/cqIMsv1X9jF3nsJ87viSp6relaYIjO6j6iIRiSrQojoNmmaLHVpUT0xK0yYxtSZX7vwe7SEf7N7eLQJ/GL38GgTLO9ufBSodAaiyAfP/7GqWxPnHXebmidFLKf1x1mEna3rLf24kM0GYlOqLnOIRdDcIMtRilQA2vNJBq0A2nNN7ghb77TkdGt3Jsk/ZjnGYq9kD6/vbWP+zqm6FtVlMFDWyIRDF7Ls+8XxKxvlHxiij8ST3K5rn6E2FmK83OmuGg+3vODCK3VrMpKY8LaTu88y4ATQorr1ZoxK2mZRrGQt6QeXs8cW8zqTfHS2DxkIo6rQeYDL0soAaEtGsZuvZ9mI4PLesSm7KiJll7S8uIg+VlEYlJKTqgqp0fA4JnhLjaF1lYeHx4sJfrF7eLQJ/GL38GgTLKvOHk1W0bUlUDauzmqbVF3YYGrGRaoibC1STy8b5Wp7nHXl9+5/o6rrf0x4e3Xz76rGk096M0UMv7ccltS94/O6nYxiknoiAIxcyufy6jc9rOr+26q7GuWoiHi6t7BBtbtjjNMjb85qsoaX5tgT8b+u/LdG+brux1W7L2/k1MA/feIsVZcV6YCzg3xu2RHDsS8+Sq9BACh1N9e3bcoraRKsG3OS9GqT3PlUs3q5SKWdU1WIS284MURJ3gFos58138loP6vPVxLCpCb2DjIjeh9E1iUnWxNJxuYFT7/ZC5JmSxv1Vu4IBmacSvVxWld5eHi8mLDkxR6mbX6EiL4dft5MRA8Q0R4i+goRJU7Wh4eHx+nDcxHjP4QgoeOCO9GfA/iUc+7LRPQ5ADcC+NvFOoiQQyYRiNOjNW1/GIhyzqSacZHKRFjGkuJ+rzHR3T3f2yjvv02Lpj01FuOlx1LqBPOaa9oOAOJzLEtGhCdcpUPLdrPC6yx/zZyqu/Xyv2uUL0xoj7SHy+z+9MGn3tMojw3q/FIkAnke6dRkDff28Hmv72T7zIVdOsnuVT2ck+maa3arun2XrWyU7zzMJrsJk4E1NcLzI3nXASA1KcRR4Ylo+fqcCkAxPHbCC00GyWT0ZVfqxPxKXVdYI7j8BId81AQQSVOhFYUrWenhpuvkPSLHcQK5hDCJ2T5qwuxX7BUBUNQ6O3Bqqrm5V5JfWCzpzU5E6wC8CcDfh58JwLUAbgub3ALg+qX05eHhcXqwVDH+0wD+EMxlugLAVJibHQCOAljb7IdE9AEi2kFEOyrT882aeHh4LAOWkp/9zQBGnHNy67hZ4HFT+cE5d7Nzbrtzbnu8K92siYeHxzJgKTr7VQDeSkRvBJBCoLN/GkA3EcXCt/s6AMcX6QMAUK1FMTod2EaeLa5RdRd2jjXKx6vGviGQEf6AEzXtovnh229olNfu0X6DpS7uU5owrI4j9XJrNpN62Nw63o+c3KrH+PbXsyvwH/XvUHXTdR7XrorWu97/yG82ytUnOdIqd+G0aveq9Xu4zoSK3T3IRAuP7GaizsmNeq6m59nmaHO4xYWda0vPRKM8+hJ9rLkiK5/ViD6XWJQ/j4zz/kzsuFZYZf68+Iy+Hcvdwrw5K/ZZDImndOmN6y0S5X4qdV5rGkvMcGX6iGaviOTF/FSMQu/EWCJC304ahook3y/5LXoPZvQiQRYiUlPHDRHo/ACPufv8Qd19yKZCu1v7y570ze6c+6hzbp1zbhOAdwG42zn3HgD3AHh72OwGAN88WV8eHh6nD8/Hzv4RAH9ARHsR6PD/cJL2Hh4epxHPyYPOOXcvgHvD8n4Alz2no5FDNBTv9s/3q6oIS60neNClRI6jorDV3Hj/DapdL1PQneCNpY4lzGsn8J0Lsd560I2fzyLowPVMtvGZTd9S7TYKN7zdFS3OjdSYX+8rY5eruvJunoTIuSyPfm7bF1S7h+a3cP+FAVU3PMwi4oqf8rEr39PtpFCfT2rOPxn5lxdS6+C1qhmom2XwesFE8HWwOBmNiRRJRsqU0WaW2CI5IUxli3C4SfUqaTj2s8N87MSU4N2rmUi/MWPPa4H69Iz+nOffRbKZpmUAcHGen4hJ6yTNgNIsZ8+zYz+f6CHSaag2nx9o0W6R/E/eg87Do03gF7uHR5tgeckrahHMzwS7wGNlnRF0rMYyS8oSMtRYvP1fh17XKMf3a1NeYo5FM0uPbL2zFlA23m+SO234pVqOeusv3d8of6z/p41yoa77nhVuYnFD8PaNyUsb5Xv3aC8/l+W2/2krH+v3nn6Xajf5LHsKdj2rxbbz7h1plKkkxOwOPd+yjgo6OiW/jV0mxs/nOUis0rvU1UMcdZIyO8eZnVJs5e+taJod4mtdTS3t3ZMfMBxxA0IVMJvlcqd+bg3LyPMrbfZevpeqOV3XKegMVz5g5jHGY6lm+eRqSX1fFQWF9uxGQ18uVA+ZSqzcZfmh+XfZI7qPozPBNavMmQlu+msPD48XNfxi9/BoE/jF7uHRJlhe8ooCoevRwJPowdmzVd2fXcq6ypv7HlN1902f0yjv3csmpBVHTKTVOCuH5U59aolpVubqgnDA6vJST//Me/9O1Z0hSDHvLLBe+2h+o2o3WGLz1/G89pbavZ9NJolhPcbr38T7AA9Mbm6Uq9/V6YXP2MlmuejTB1UdhIkHXbzX4YwOWRpgc9vR1+jo5NxW9pqrSl76ZzQzRFS8Kspnai88FxH8/kXBhd6jr9nMFh6XTFkNAKlRPoBMkV3qM956Yr9ARrYBQFV4QdbEqdjUW+lhmcZbVWnPyS06WrPUIdN0ieMacs6yMC3bPQHUm3v55Q7pd7Eyy5mA8gVvu8XSQPk3u4dHm8Avdg+PNsFpyOIalLt3aTHn2f0sqj+0bbOqozzLR117uJw7rt2xasJ0I8V2AIiUBfGE8J4avEKb7z76q7c2ypentLfUHXlNFLGAc9OtY4BKNT3F0RlxLhePqbofDZ3RKBe+t6pR7n9Gm8Zix1jMrm/QAUW1Dpb1hq5kM9HcJmN6lLz3+jRR/7cV3EyIn1Hj/ZY7KjwbR/Q8SrG1sIl/2DOgDzY9LTzNxnWQzPxqkVopzeOPzhpzqRBpE8aDLjXRnAPfBkBVRAqvkkn/JMV4S2iSnuBxyT5LnXqM2aHmPHMAML2ZVUepCtQMP2JhrUgdZtSVBU9ES5oh4d/sHh5tAr/YPTzaBH6xe3i0CZZXZyfmAi/2WeZBLvb9WLv8ySisuXX8faFfD79rH5t/XEw/x8rdrNgNX8aK0Tvf9kPV7oo0R7ONmai3rUkmDBiIsq6WMcSAw+mDjfJvT2hX11qOfzfxtDaprb6fdbL+KdbTE0d13rrSGcyqmF/dmtQ3Icg0ex/X8yGjwyKGRENywEuiiOS05THnOqvPp8V2ROdBnp9S9wrVLiNU9rrhLCls5H0XSgndeFpfd6m/lrWlE8UV3Gmxn8fvevSAKcbHSmd0ru7VXbzPUKrqYw9NsSmuOsgnE5sz6ZaneE7jeX0timJKJBeJTWEtTZj1E8yDwbWwhJ4S/s3u4dEm8Ivdw6NNsPwpm7sCcSM53jqlrUodBKCWFOmUFhlxpYvFf2v6GL6KRc6VW0Yb5bmqNvc8VORUS98cvUjVPXKYTW8RwbFWGdNmp85dfGxp+gGAlYITLTGr6wor+dlb7OZxxQY0GbrkMbeIi9RFklvcenRJ7jRL4CFJOyRfe6xkSEXG+KLFp7VYrNQo8bPsoEn7PMDXbOJ8Y04SorsT5BjVXnOstdyuI6c9+S5bzWrZeVlWw7YkRlS7TXFhzjSycEbcnBnDsCF5KGbFzXn79CWq3Vf3XdwoTx/SXnjpoebpoudXmcg8cdrVDl2XHgzmO9I8uDOoa13l4eHxYoJf7B4ebYJlFeOpxruUUb3hiZLILFRLahElI0S/jmMspxSNp1OhTxAEbDZZOue4z9n7WCy+I6pF5LtmrmiUu/dqcXEgKdIRiR1ry1XnRDSCFZGlOJ1fo1WNSlZ6WYnfGbFSEkBUOrUqUBcZTdPHo6KdpfUXqZusaL1BjD/HImx0zmyXO7YEnBA8IoJaEquYp237uiOq3YUdxxrlzUktWhfFiRbqrNZcIqwdALBCpAebNewYXYI5oyBk5BN4DmUEitF4JO9h0UyjpDO/beKljfKDIzrzLgnxv5421o8uPmD2KJcjVT2QvtfyXOXL2gozFgsITWyAjIR/s3t4tAn8YvfwaBP4xe7h0SZYdtPbQuB+YtqYWWQWHWM+SI+J6Cqpp1sOAKGuZQZNiiBBCx4riXTCJa0/yXRB8yv09FSlt5eIfrIc9ZIkwepQVaGXt9aigbJIA1QzOl5mHZNXvGr9PlX3+DhHwR2LCQ+9hO5j03o2P17Vv1/VvbfnZ43y8SqbiXaVNFf5pgS7ya2PaS+/NUJnTxLP45zT+yBFYQIsmr2JKTF5UqdW+rVpZzEtboqi2FiwOvuEqOuI6ChDebyHizry8eaD1zTKxw7yfMcnLJEpl6PGfJecam5aTpo0V4eHmGj0Y9vvUHX71gR7T7fkTP4rAf9m9/BoEyzpzU5EBwHMAqgBqDrnthNRL4CvANgE4CCAX3HOTbbqw8PD4/TiuYjxr3LOSbaFmwDc5Zz7JBHdFH7+yGIdUJ25v2a3mCyaq/ONcucdmutMisy1lOTrMml0ROCA5ScvdwqTxqTwEMtpcUt6MFkigMRM89RQdhylLmGi6zAmQOGFVtEU5KjHheguTGrZtZqvfftqNl/tm9HBNCOTLHb/yuUPNspX5XardpcYM5cavzid9YLZYr1huZDTI81aALBfBIxI8bniNCNDSriFRRYhUJsVovqsqYsLMTtqlCMprhddXPxGk5tsFOc2bW6eL05ymq6vPHmpqkvsYe9JqW1FtSaApLjnQJZ4QtSJovV6jB3muftE/Y2q7rIzDwIASjZCRuD5iPFvA3BLWL4FwPXPoy8PD49TjKUudgfg+0T0MBF9IPxulXNuEADC/yub/ZCIPkBEO4hoRy2fb9bEw8NjGbBUMf4q59xxIloJ4AdEtOukvwjhnLsZwM0AkFq73m5Ae3h4LBOWtNidc8fD/yNEdDuCVM3DRLTaOTdIRKsBtFYCQ+Q653HFdU8AAAYLnapu7yALBkb9Q34t6y6Sc9sG6svIOVsnTW+yP+WWCiAxLfozqXWnzxS6uMjDVY/rPlyCdcjkqD6Z9Ig4thF0pI5GNT7WXFwr9/cVzmyU4wmte757645G+X3dD6AVpF5uXUzLSxT4rH7cqk7q1HFjNkugtZ4uxyH7s7q9rLMmNelm2x3hiLizzTV7ssLt/uMTv6bqpvcxx356WM+NNPFKXne5t2RhSSmkzi5JQKLm/ssKbvuZCb330bc1uJlii+x7nPSqElGWiDoWygBeB+BJAN8CsJAg/QYA3zxZXx4eHqcPS3mzrwJwOwU7iDEAX3LOfY+IHgJwKxHdCOAwgHecumF6eHg8X5x0sTvn9gPY1uT7cQCvfUWzMgAAEWRJREFUfi4HK9ejOJoPwts25SZU3T7qb5RndTYllFdziFwkyWJgtkPbN6pVwSmf1rLS9pVsrrqsgz3GvjF8sWr32G6OVrp8q/ZO+x/rv9Uof2WGp+Qn42eqdtNlFrHGBrQIPn2ETWPxWSNYCalN8o0lxownX0FEYXVpNeFr+5hw44k+9qa7ulefyzbBtTcQ1V5XHcIcJsXiwiJmHQv5u7qwYVoRPCKjwRYhUNOqgCH9qMebtgOALXE2qcnZft2T71XtRh9inn7p0QYAK8bEGGOGUEIMpeMYq1T5lXqukjPc0DjQIT4jfreGTYyWxEWd9su1S8tPjge5FuYqmoxFwnvQeXi0Cfxi9/BoE/jF7uHRJljWqLc1ySl8fHOwab9F2sIAvHeOI3r2Flapute95Ckud3PZujzuLXE652RER1e9JfdMo3zfPG8KJKK6j5dt3dsoX9OjXUxnBbF5Shz7iSM631rkOOvsZhiQqcJqKaO8ueZlo4Yql9t6xbDMjHJo3iOjmxrlx7LrVDvJjd7foXX2tVm2P56R5ei4V4k5BIA1MXZcXcwMl3fSddZEgwk9PWKU2RRqTdtZ3X5NjE1qKeOK+t38lkb5T+5lJ8/+n+px9AozV6RqSCWFSTQ+r+viecFFL46dmGsdTVkzpuX5TaynF1aJCDjj8k0X8P6Dq+ilG7s7dDGfNp0L+De7h0ebwC92D482wbKK8WlyuCARmMQKxtHnTQNPNMqTfZpM4Ze7Hm6U+4WbXMFIjheJSC5LhHC8ytFJt4+wue3obLdqly+xSPWzvTp19JcGLmuUO5PC7GfGIfgPEamZqDfxs4oO7kMtLUgaZwUJ4THjMabSYWlZL79OtK0LkXBei3eFKJ/noYgeyKEsR9LtzLH4//3suardqgyL//0prQqcmeFrsS7BZtYBQ3KRr7c2FT0ryDKmBXPINTntrT0i1Jz/eeg6VTf0PSabGFDz2Focj5asSiKIKo1o7SKC7KSX352VnL7uxX4RadlrRPyUUCXF/bJ247hq92sb2CPyL77zVlW36nDQh7w3LPyb3cOjTeAXu4dHm2BZxfhd+T68fMf7AQCvXf+sqnv/ip80yrOGU+xghXfqf1hmT7uOqE71c2X6QKO8KqqfY/fPs0i4cx/vxtOklstk9s2k2eke2819jAtJLGEk0VJf6xw8sTmRIXVE9y8DauZX8gHmztD9xSf5sqVM+FHmmPBWE9KhJdGQlgBL9FEv8xfzQvyfH8uodsM1FvclTzwA3J07u1HO5Vh32dyjPSdTMTZXTJV0Gq1cnPWhK3tYtXu6tFa1+6t7Xt8o9z2kr3tXnueukua69Li2wkipvtyh+5g6o/mcAjpoq9QtM+May8IEz39ppa57y0WPNcqbUiy69xmykE/vZofV9XfpgaSOBpaRSPl5BMJ4eHi8OOAXu4dHm8Avdg+PNsGy6uz1WgSzE0EU2Pfvv0LVPftG9po7r3NI1X1n//mNsnucSS+S2oqDvxAqn81tJrnce/ZwubhC67Lz64R+bNUfmYW4iz3QzlqjFectHax3HS90qbrReY6CGxzqUXVdD7HyLyPips/T51LNCK+tiDapxfNSb+Tv7VxJsksbXSX3DqjO45DEIYDe37CopXlcs108+c8az6+ODOvza3JaR12RZHaPrx3laL6Jnw2odquf5vlIjeskgsVekepZbDlMd+iNiqqwPhbWmPx5SaEfm1POrWKT47a+1vwtjx8TXpazepPnmWk+n72zvCd14J5Nqt3G7/L8RI/pnHmuHo65YjYVBPyb3cOjTeAXu4dHm4Cca+1x80Jj4Pxe9+tfCswHX7tbi/Hdu1g+mn2NJmerDrL81fNE66CE9AjLrSdQcQnxa3oLi5VTr9QEGJGI8KSK6U7O6Gfa/LECy8HDh3tVu0iRn6HxmdapoSzXXkKQJkgTj4kZ0udmxErLRb8AmyK7MMBzV+3Vol88x43rQozHoBY/a/J3xkwZm2bxOS68AS3vnvQirCfMvSi6TI7zh66D2hQp4pOQmNXXrCTIPSbOl+YvY87s5vvgzFVjqu7MDg4GurJjr6q7PMXi9JoYz89oTZOnyFF9cvg1qu6+r17SKK//jvCaOzKo2iHBqoeb1/dtPfz8QO37mHETTfUr/2b38GgT+MXu4dEm8Ivdw6NNsKymt7lKAj8dCSLJar2G1UG4yFaGtFtmROiDNUEkMH61VkSTR7iP3CHoOkH4J3O2df1I829LM1T+LN3/03k2nyT3s3624rghIRTqYHpC64YySsryzddErjeZm862y4gwr1K3fl5bt9gF5NdqXVbp22XdR22IbZiRUmvzWmyUdUhL0iEJM7v38LFzh7WLs9TLqzltDisI0sbELJ9zOWfcWc8RcxXTdfVkc1NkrFNf2/dt5YiyN3c+puq2iFWyo6TvzdtnX9Io3zHMJuK9e7V5MCJIQgfu1/fL+rs5iq82zu7EFNPLM5JgMy6l9X3rtoWkp4/eh1bwb3YPjzaBX+weHm2CZRXjK9UYjg4HXmOxER3ZNnENmyoihpu77w4WmUdexiLh77z0HtUufhmLeo/Orld1z05xeqnjBzlaq+cx64HG5diEFivX/pBF3/g028OcER2rGe5Tco8BQC0peNhXtn7WSgIFa6Kb2Sh4400EVS3Dc5A+xu36HjUHELxw8316HBWRmUvS/GWG9bE6D7IobNNb11Ji0FJUz5pbTqg1hX5dN7+CO508T6btNp5869mL7ZK1x1TdeTn2xhwu84ntHNWcfN84wuL43BptYpwSrnd3PHaBqksd5vtYmki7jblUXs/YvOG9v+wMrituapSLK/T9N3Umz2npJfoA79waELw89W5zYAH/ZvfwaBMsabETUTcR3UZEu4joGSJ6GRH1EtEPiGhP+L/n5D15eHicLizJg46IbgFwn3Pu74koASAD4GMAJpxznySimwD0OOc+slg/6YH17sz3/AEAID5r6HrFLrKlTp7byGLPf77ue41yr0lbtK+kKagl+mKCL02QAtw5qcWyB/71wkbZEhWsfER4lsVa71JLsdW2k95vpc5Iy7paQnidFc1cZQS3nJGKJfdZTATF5Ab1pB6/ikXCC6/UXmF7v3lWo7zqARYLI1XDhTfOc1rv0rvUpT7e0Y8WxU56lx6wVHOqST1X0iJRFec8t8GkkKpIy4WeKydUQmlZqHXo+UgO87ikJQEASj3ch/XMlFmAXYTbpUZ1H8mp1mQh8yuFZ58gwHjH63+i2r2nhy0G68x1n64H5/PWN47h8ccrTW/OpWRx7QRwDYB/AADnXNk5NwXgbQBuCZvdAuD65j14eHj8ImApYvwWAKMA/pGIHiGivw9TN69yzg0CQPh/ZbMfE9EHiGgHEe2oFfLNmnh4eCwDlrLYYwAuAfC3zrmLAeQB3LTUAzjnbnbObXfObY9mWkRpeHh4nHIsxfR2FMBR59yCwnAbgsU+TESrnXODRLQaQOvI/RD1OJBfE+gkXXt0XUnQt2cu1nzZf37uHY1yVpCy/2j2HNVu1yzr7JmYdukaj3N41cHIikZ5Q1oTIN67iX+37g79LJQRVNUU12UHTUiZRF33ES2x0hcraL2xluS2UpetZEwfghs8aoQl6R0YqXH58Fu0Lvu3136+Ue6Pzqq6d/V/iMckTGiJPfq6uLzQ5yenVV16lvdrXZxvM3KaVHJ0m/DWMySNhQGegzQHnmHlQ6oZir0iWtA6Zkabv89KPfr7hJgCa+oUFjvkDmt1uPMwb+xEBdljfpVWzGvC4W1yqz7Pa172ZKN8Qz/r6VsT+rqM1njMeyvGZBymnqraMEiBk77ZnXNDAI4Q0cLKejWApwF8C8ANC2ME8M2T9eXh4XH6sFSnmt8B8MVwJ34/gN9A8KC4lYhuBHAYwDtOzRA9PDxeCCxpsTvnHgWwvUnVq5t81xLZXBHbrwz44nfWtAgu+c8r961Qdf9l8J2N8kde/t1GebjUqdpt7z7cKHdZxgeBwyXuf8+c3ld8+6U7GuUfPP4yVSdFtnG20KEe096AMujGSlV1YVKz3nWShGFujeBrf5k2Mb5k7fFG+ek7z1Z1/Y/yGI9fw33c+fq/VO0yImPq3YVNqi6eF9z5I6wn1I5pMoVIt+DXMybc2h7m8I+t5wAi6tDeaVJ9S106qequWnW0Ue5N8Dhu23mpatd3P4vM6XGtGsmgmWpKnNeUnnvpARif0+eSE5assqYUxOE3czmS42Ov6NFqzSsG9jXKb+nW7oznxNkUfFywmxypalWgO1IWdTpt2b5ycB/P1fVxJbwHnYdHm8Avdg+PNoFf7B4ebYJljXorTKfw2J1B2l9Kar2o9xm2mWT2ahPPgfcwEcCbc5wj7qUitxugdRqLWeGjeFGKmS2+XtdbEXWR6nn6fO0v2/ckf65sETaeS7T969gRrU9JkEijjJy2E60bYJ31jzff1Si/PqOtmr/87NsbZbs1MfNbrP99Y9s/8nhNWNrtc5x+uWBy6xUFGWNklskmqjVD9DjNpiFK2IRx3Fab3vR1l+6tn73wS6quX+Tyi4tkbBderTnT/6zzDY3y7DMdqi49zGXpWjx/ld4H6e/izxNz2vW3r4Ov76sGdqu6X+rc2SiviYmoyEVMYFN17XM7LPJAp4Sv+FBNp9K+fZr3Kr554EJVlz8enPfwrLFpC/g3u4dHm8Avdg+PNsGy8sYT0SiAQwD6AIydpPmpxi/CGAA/Dgs/Do3nOo6Nzrn+ZhXLutgbByXa4ZxrZrdvqzH4cfhxLOc4vBjv4dEm8Ivdw6NNcLoW+82n6bgSvwhjAPw4LPw4NF6wcZwWnd3Dw2P54cV4D482wbIudiK6joieJaK9IUnlch3380Q0QkRPiu+WnR2XiNYT0T0hQ+9TRPSh0zEWIkoR0YNE9Fg4jj8Jv99MRA+E4/hKGNJ8ykFE0ZDy7NunaxxEdJCIniCiR4loR/jd6bhHThmT87ItdiKKAvgbAG8AsBXAu4lo6zId/p8AXGe+uwnAXc65swDchedAtfU8UAXwYefceQCuAPDBcA6WeywlANc657YBuAjAdUR0BYA/B/CpcByTAG48xeNYwIcAPCM+n65xvMo5d5EwdZ2Oe+QzAL7nnDsXwDYE8/LCjMM5tyx/AF4G4E7x+aMAPrqMx98E4Enx+VkAq8PyagDPLtdYxBi+CeC1p3MsCGjBdwK4HIHzRqzZ9TqFx18X3sDXAvg2AgaA0zGOgwD6zHfLel0AdAI4gHAv7YUex3KK8WsByAiGo+F3pwtLYsc9VSCiTQAuBvDA6RhLKDo/ioA78AcA9gGYcs4tRHMs1/X5NIA/BLAQHbLiNI3DAfg+ET1MRB8Iv1vu6/K8mJxPhuVc7M3CgNrSFEBEOQBfA/B7zrmZk7U/FXDO1ZxzFyF4s14G4LxmzU7lGIjozQBGnHMPy6+XexwhrnLOXYJAzfwgEV2zDMe0eF5MzifDci72owBktsV1AI63aLscGA5ZcbFUdtwXAkQUR7DQv+ic+/rpHAsAuCDhx70I9hC6iWghEHQ5rs9VAN5KRAcBfBmBKP/p0zAOOOeOh/9HANyO4AG43NelGZPzJS/UOJZzsT8E4KxwpzUB4F0IGGpPF5adHZeICEFmnWecc5IUblnHQkT9RNQdltMAXoNgI+geAAvB8qd8HM65jzrn1jnnNiG4H+52zr1nucdBRFki6lgoA3gdgCexzNfFnWom51O98WE2Gt4IYDcC/fC/LeNx/wXAIIAKgqfnjQh0w7sA7An/9y7DOK5GIJI+DuDR8O+Nyz0WAC8B8Eg4jicB/HH4/RYADwLYC+CrAJLLeI1eCeDbp2Mc4fEeC/+eWrg3T9M9chGAHeG1+QaAnhdqHN6DzsOjTeA96Dw82gR+sXt4tAn8YvfwaBP4xe7h0Sbwi93Do03gF7uHR5vAL3YPjzaBX+weHm2C/x/Xl2tB8Fjr9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X.iloc[75].to_numpy()\n",
    "img = img.reshape(64,64)\n",
    "plt.imshow(img)\n",
    "plt.suptitle(\"An example of image on the dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.values.reshape(-1,64,64,1)       # shaping for the Keras\n",
    "Y = Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding \n",
    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding for better results\n",
    "Y = to_categorical(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_val, y_train, y_val = train_test_split(X, Y, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape:  (4000, 64, 64, 1)\n",
      "x_val.shape:  (1000, 64, 64, 1)\n",
      "y_train.shape:  (4000, 9)\n",
      "y_val.shape:  (1000, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train.shape: \",x_train.shape)\n",
    "print(\"x_val.shape: \",x_val.shape)\n",
    "print(\"y_train.shape: \",y_train.shape)\n",
    "print(\"y_val.shape: \",y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 128)       3328      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 64)        73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               1048832   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 9)                 297       \n",
      "=================================================================\n",
      "Total params: 1,181,705\n",
      "Trainable params: 1,181,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters = 128, kernel_size = (5,5),padding = 'same',activation ='relu', input_shape = (64,64,1)))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(256,activation = \"relu\"))          \n",
    "    model.add(Dense(64,activation = \"relu\"))\n",
    "    model.add(Dense(32,activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(9, activation = \"softmax\"))\n",
    "    model.compile(optimizer = \"Adam\" , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "    return model\n",
    "model=create_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/21\n",
      "200/200 [==============================] - 1230s 6s/step - loss: 1.3107 - accuracy: 0.4347 - val_loss: 1.0814 - val_accuracy: 0.5460\n",
      "Epoch 2/21\n",
      "200/200 [==============================] - 1249s 6s/step - loss: 0.9630 - accuracy: 0.6072 - val_loss: 1.0180 - val_accuracy: 0.6220\n",
      "Epoch 3/21\n",
      "200/200 [==============================] - 1235s 6s/step - loss: 0.8869 - accuracy: 0.6446 - val_loss: 0.7681 - val_accuracy: 0.7140\n",
      "Epoch 4/21\n",
      "200/200 [==============================] - 1225s 6s/step - loss: 0.8575 - accuracy: 0.6626 - val_loss: 0.9691 - val_accuracy: 0.6500\n",
      "Epoch 5/21\n",
      "200/200 [==============================] - 1236s 6s/step - loss: 0.8176 - accuracy: 0.6790 - val_loss: 0.9844 - val_accuracy: 0.6260\n",
      "Epoch 6/21\n",
      "200/200 [==============================] - 1238s 6s/step - loss: 0.8193 - accuracy: 0.6709 - val_loss: 0.8332 - val_accuracy: 0.6840\n",
      "Epoch 7/21\n",
      "200/200 [==============================] - 1273s 6s/step - loss: 0.7873 - accuracy: 0.6932 - val_loss: 1.1086 - val_accuracy: 0.6130\n",
      "Epoch 8/21\n",
      "200/200 [==============================] - 1241s 6s/step - loss: 0.7441 - accuracy: 0.7104 - val_loss: 0.7103 - val_accuracy: 0.7380\n",
      "Epoch 9/21\n",
      "200/200 [==============================] - 1255s 6s/step - loss: 0.6803 - accuracy: 0.7398 - val_loss: 0.6703 - val_accuracy: 0.7490\n",
      "Epoch 10/21\n",
      "200/200 [==============================] - 1235s 6s/step - loss: 0.6515 - accuracy: 0.7547 - val_loss: 0.6030 - val_accuracy: 0.7730\n",
      "Epoch 11/21\n",
      "200/200 [==============================] - 1229s 6s/step - loss: 0.6562 - accuracy: 0.7527 - val_loss: 0.6444 - val_accuracy: 0.7660\n",
      "Epoch 12/21\n",
      "200/200 [==============================] - 1237s 6s/step - loss: 0.6158 - accuracy: 0.7687 - val_loss: 0.5836 - val_accuracy: 0.7700\n",
      "Epoch 13/21\n",
      "200/200 [==============================] - 1243s 6s/step - loss: 0.5779 - accuracy: 0.7803 - val_loss: 0.7424 - val_accuracy: 0.7380\n",
      "Epoch 14/21\n",
      "200/200 [==============================] - 1243s 6s/step - loss: 0.5876 - accuracy: 0.7765 - val_loss: 0.5890 - val_accuracy: 0.7720\n",
      "Epoch 15/21\n",
      "200/200 [==============================] - 1238s 6s/step - loss: 0.5879 - accuracy: 0.7810 - val_loss: 0.4675 - val_accuracy: 0.8160\n",
      "Epoch 16/21\n",
      "200/200 [==============================] - 1240s 6s/step - loss: 0.5543 - accuracy: 0.7927 - val_loss: 0.4978 - val_accuracy: 0.8240\n",
      "Epoch 17/21\n",
      "200/200 [==============================] - 1239s 6s/step - loss: 0.5654 - accuracy: 0.7883 - val_loss: 0.4532 - val_accuracy: 0.8520\n",
      "Epoch 18/21\n",
      "200/200 [==============================] - 1232s 6s/step - loss: 0.5489 - accuracy: 0.7956 - val_loss: 0.6062 - val_accuracy: 0.7810\n",
      "Epoch 19/21\n",
      "200/200 [==============================] - 1228s 6s/step - loss: 0.5280 - accuracy: 0.8020 - val_loss: 0.5321 - val_accuracy: 0.8150\n",
      "Epoch 20/21\n",
      "200/200 [==============================] - 1231s 6s/step - loss: 0.5320 - accuracy: 0.8003 - val_loss: 0.4897 - val_accuracy: 0.8180\n",
      "Epoch 21/21\n",
      "200/200 [==============================] - 1408s 7s/step - loss: 0.5313 - accuracy: 0.8006 - val_loss: 0.5503 - val_accuracy: 0.7980\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fde8d787dd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint_path=\"/home/roopesh/PythonFiles/taininig_dl/cp-{epoch:04d}.ckpt\"#\"taininig_dl/cp-{epoch:04d}.ckpt\"\n",
    "checkpoint_dir=os.path.dirname(checkpoint_path)\n",
    "cp_callback=tf.keras.callbacks.ModelCheckpoint(checkpoint_path,verbose=1,save_weights_only=True,save_freq=2)#to create checkpoints after every 5 epoch .period=5\n",
    "model =create_model()\n",
    "datagen = ImageDataGenerator(\n",
    "        rotation_range=0.5, \n",
    "        zoom_range = 0.5, \n",
    "        width_shift_range=0.5,  \n",
    "        height_shift_range=0.5, \n",
    "        horizontal_flip=True, \n",
    "        vertical_flip=True)\n",
    "\n",
    "datagen.fit(x_train)\n",
    "\n",
    "model.fit_generator(datagen.flow(x_train,y_train, batch_size=200),\n",
    "                              epochs = 21, validation_data = (x_val,y_val), steps_per_epoch=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checkpoint\t\t    DeepLearning_old.ipynb\t\t taining_dl\r\n",
      "colorectal-histology-mnist  easy_checkpoint.data-00000-of-00001\r\n",
      "DeepLearning.ipynb\t    easy_checkpoint.index\r\n"
     ]
    }
   ],
   "source": [
    "checkpoint_path=\"/home/roopesh/PythonFiles/\"\n",
    "checkpoint_dir=os.path.dirname(checkpoint_path)\n",
    "!ls {checkpoint_dir}\n",
    "#latest=tf.train.latest_checkpoint(checkpoint_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict the values from the validation dataset\n",
    "Y_pred = model.predict(x_val)\n",
    "# Convert predictions classes to one hot vectors \n",
    "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
    "# Convert validation observations to one hot vectors\n",
    "Y_true = np.argmax(y_val,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "# plot the confusion matrix\n",
    "f,ax = plt.subplots(figsize=(18, 16))\n",
    "sns.heatmap(confusion_mtx, annot=True, linewidths=0.01,cmap=\"summer_r\", fmt= '.1f',ax=ax)\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('dl_acc_80.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 14s 14ms/step\n",
      "Untrained accuracy is :15.00%\n"
     ]
    }
   ],
   "source": [
    "model=create_model()\n",
    "loss,acc=model.evaluate(x_val,y_val)\n",
    "print(\"Untrained accuracy is :{:5.2f}%\".format(100*acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/roopesh/PythonFiles/easy_checkpoint'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint_path=\"/home/roopesh/PythonFiles/\"\n",
    "checkpoint_dir=os.path.dirname(checkpoint_path)\n",
    "latest=tf.train.latest_checkpoint(checkpoint_dir)\n",
    "latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_7 (Conv2D)            (None, 64, 64, 128)       3328      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 32, 32, 64)        73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 256)               1048832   \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 9)                 297       \n",
      "=================================================================\n",
      "Total params: 1,181,705\n",
      "Trainable params: 1,181,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model=keras.models.load_model('dl_acc_80.h5')\n",
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 13s 13ms/sample - loss: 0.5503 - accuracy: 0.7980\n",
      "Trained accuracy is :79.80%\n"
     ]
    }
   ],
   "source": [
    "#checkpoint_path=\"/home/roopesh/PythonFiles/\"\n",
    "#model.load_weights(latest)\n",
    "loss,acc=new_model.evaluate(x_val,y_val)\n",
    "print(\"Trained accuracy is :{:5.2f}%\".format(100*acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('dl_acc_80.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save_weights(./checkpoints/my_checkpoints)#save the weights\n",
    "#model=create_model()\n",
    "#model.load_weights('./checkpoints/my_checkpoints')\n",
    "#loss,acc=model.evaluate()\n",
    "#Save the entire model\n",
    "#model =create_model()\n",
    "#model.fit_generator(datagen.flow(x_train,y_train, batch_size=200),epochs = 20, validation_data = (x_val,y_val), steps_per_epoch=500)\n",
    "#model.save('my_model.h5')\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#datagen = ImageDataGenerator(\n",
    " #       rotation_range=0.5, \n",
    "  #      zoom_range = 0.5, \n",
    "   #     width_shift_range=0.5,  \n",
    "    #    height_shift_range=0.5, \n",
    "     #   horizontal_flip=True, \n",
    "      #  vertical_flip=True)\n",
    "\n",
    "#datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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