diff --git a/course_material/week_14/.ipynb_checkpoints/2.1-a-first-look-at-a-neural-network-checkpoint.ipynb b/course_material/week_14/.ipynb_checkpoints/2.1-a-first-look-at-a-neural-network-checkpoint.ipynb
index 063c3be..891caf3 100644
--- a/course_material/week_14/.ipynb_checkpoints/2.1-a-first-look-at-a-neural-network-checkpoint.ipynb
+++ b/course_material/week_14/.ipynb_checkpoints/2.1-a-first-look-at-a-neural-network-checkpoint.ipynb
@@ -1,18 +1,18 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First, Get Ready!\n",
+    "![spiderman](https://media.giphy.com/media/EU42nA5hrKn16/giphy.gif)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 113,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#!pip install keras\n",
     "\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +105,7 @@
        "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +132,7 @@
        "(10000, 28, 28)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
        "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +175,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -199,7 +199,7 @@
     "import matplotlib.image as mpimg\n",
     "import scipy\n",
     "\n",
-    "i = 100 #choose a random component\n",
+    "i = 0 #choose a random component\n",
     "img = train_images[i]\n",
     "imgplot = plt.imshow(img)\n",
     "plt.show()\n",
@@ -220,11 +220,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/jpeg": 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xbEcqiaKNJyeiIIijVRwre3RGihhVHV6svRcUtOnGGxDc8RUiUtGa1IK0bu6OfiXK0nHRRtdpbW2l/JvRnw9aDoVIyveebVK+t3Z8THv3ZPjXnxNcSCaqvNFSUtGntaL4c3B3tdq6V3fgdFclrEU81OahWirSi37z3o5tfD1qbtlefflsYl1L8VZW9WrPr8S/CYp057fZe3ue8tg81Fqq/bi7tLT4vyMdr67yy4l+vQRqvea6crq5zuTlnpxfdY6MVZHornDHA5ZxTlLKn7KbXDad1nlMZK9R/wB6zn7rp5UMPW66ag6jy5VotLFlBXki/EQW1I47lbxmozcZJ3O7h6ueK3nAOngalmlvR1/jv3HP3PjuUI2RbcrpyvFMdHVmOfynjXH/AE4O0mrt9leZyJYd81Kpt9pK/e9bnVwuBdaOfbKUpN92tkvoTgoU4Vq2Hre5JxV90rbTz+vW10nxxcJl1TV1s1ttst481TpSzLPKbTSi9F8TTyjyfzE3leem9YvejJhI57545UtiSJ/0/tp5Kqq2R7dq3P8AtjpJrcuBw5yy1M0Va2xdx1qNTNqth38evn1j1Lvw9Tu0K6lV3NFgcE+oWxZKx52Fy2ph2tmq3FcWZ1cTGLNMRYDk1ZMBBJBFLLan3hLaE9gTARiTV013DsUDI1b217r08H3iRRfWp2bcevbHqZnlT3a93WhQ0fzJ7k+4RPUlUZ7vqCw8t31Jgsqe946jx1i1u1KuZnu+oyoyXV9SYp0hsG03Z9WpTzMl1FtBZE3K+pZE1gUCxIsyhlOzkVIawyiDQQrV1ZmSo3B6vQ2NCVIKS2algyVZuzte37lEIN66JLb1l7jtu9Cu13or9w0ktWQskut/3gE5X2kqjJ7XbuWrLqdC2xfF6vgTWsjI6DlsXxNsLyt1vrfUOqS69fHyNmDwzm1uH2pbE4XCuT7jqQpqCshoRUVlRNjWYzqUhrEIsjEKSxDgi502K0ZalU5DLgsKudlTkutyjucX5G5orqUr2adpJ3jJbUzHrzsalPjeRFUhJ0moztst7L4bDzeJw+Ji8soTf/m2j0kuWXSsq0Nv54e6/FdRRiOWqLV9W/A5yYzb6cnkzAyjLNWjHJ1QWy+97yzlzA06Li6X51dJO+r2WCvylOostGm31XtcvwuBkpKpVlmmti6o/wDJqeWfv7V+Boc3SjHrSV/E0AFjq0hs8lin/qS8WesZ5LFK1Wot05/+zOftvybCtKWrS8S7EVFban4MxEnHGgbMLL2ocDGacL+Xx/k6/wAf6x7/AB6PCS0saEY8M7PxNh2rnL8VciU1KdWhKTTjJyUdilF/1cS3lrkxSjno2VVRyuOmWcV/JnrYZ85GtSllrRVk76SW58XxJnyzb2a0HCXhdP8Av9Zy9Rbu9R5ueKnC8ZZou+qzW18GNTlJxzu7v7qb29516+NoyT9qL06zJiMZTnlsnKSVlZGcWetYYpyTutb28Wd3C4XLFLiZ+T8C7qpNWt7sd3e+86qR08+V1WqKGVMsuSjWJpMhRXwilqtJfuawsXE1zIxto9HuGsbK1BSXf1Mxu6dpbf3Od843PWggkCKVoH7qJsRHY1uegUjQtiyxFiDPXjoY5VJxvaz8TpSjdGKcLMlajJ61Pu+pKxU+76lzopgsOhqYr5+X9uTGq3q7fUuVBDRoIunKrnHuQOTZoVBDcyhqcq1EjKM6ae/i0SonVxLlBxHyBk72EV5RMrL8veyHHvYVhq0Ve+WT2abEXRpfDuWhdOL0V39Ayvf9ERdIoW2IlIdRe8IQk3a+r7uoqaehRzvu6zrUY5VZf1C0KChFfTzLYo3JjFupSGsRchsVqGzDwqNf25UkOjLUalUutLeBWykHiVHbqRcWNEWMtTlNbEkUS5Rl1IDe4COjHcuCOdLGTfWVutJ9YHUSitwOpHejkuT3kagdR14byHiobzl2CwHReLieaxy/1qjXXJvidJ3Obil/qP4fsc/f415UWAlknJtCRppO1jM0dLIdf42PbbRxEdNTpQrRa2o4KiWQm0dbXOTHdTW9CzpqSs0mtzVzlKb3liryXWRpolydSf5FxaLKWFhD3YpeC1M8cXIsjjd6HxGmxJVHFxYzrRKHBMolW3BTuwjUmSVIsiyhimvRUl39T3MuAg5ivez2r695LNWKo3V17y2eRki7o52Y6S6CYLbxJsTFar4oilsQ4jtBlAryldSjc0WJsRXP5hk8yzbkJyDF1iVFjRpM2c2SqepMNZObZPNs15AyDDXNCxNgOzzgCQAVgSSgK373wJCO1smwEG3BUfzP+ozUoZpJHWy5Uo/FmvMS1F7skErIWT6uJpIkCEOkZaiYhOoo7WZ8Ricui2mGc3LayNNlbGrYjFObe0UmxBFibE2JsAtibDWCwC2Cw1gsFLYLDWCwFbic3GxtU+COtY5vKK/1F/2/yzHv8WMYWJYHJs9CN6kV3o6kqZz8Ar1l3Js6tjr4/GfTNYC6cBLG2EwZakU2LISAaxNiUTYKUlDWCxA0HvNsEraGGw0JNbCmN9hkiilWvozdSo5ouS6i6hacL+PUt4rQbGNe772AjMOJpZZZvyvb47zeyupFNNPrFiRjyohrZ4oIXTcXtX7DSWhydA4hYZ9T7iAqLDAiSBcpKQwARYJLQZEgLl72GUaOxE2A5IASdXEWIJACCVsbAiXuvgAtNaIYlBa7tvA2cn0vzPxNlODlL9xKccsEt/7GiCywb62dY52qa0tdNmwqGn1cRSVqJiRXqZY940TJXleXgZrUUtdYrRYyGRSEpBYlATYLDJDKIUlgsWKA3NkFVgsXc2TzYVRYLF/Nk82Bnsc7lNe1H/tf7nY5swcpUPbpOTtFtwb3X2GfX4s/XIYprxGBq03aUJLdua7jK09z026HJrY3clw1lLwR0bFXJ9G1LxdzRlOnn8ZquwsoF2UjKaRnygkXSgV5TSHgywqii6JBFgsNlJykUhKQ2UmxRCNuFxDV0ntVn3ox2Gi7MDcwQsHdDGmVtRXSkuvb4lLLqTveO/Z4lUkVGPFxtaa6tH4CmqpG6a3mKlss9sXY5+o35p4bPBtDCw/MvBjGG0gBIASQMQBJBKAmPWALaSBxySCTq4i4EEgBE37q+JJEl7XwAksw0bz8Cs04GO1ln6lbV1Ftad0kim4Tf7HRgkndgQSjLQnK0TGX4h7EUma1EEEgkFRYnKOkNYqaiES6NMWCL0hhKhUx1TGQ6ZGlXNhzZemMRWbmw5s06EXQGfmynE4VVIOEtj+j3mxsi4HEfLValFUK0I1MitGT9mduq+/xEpVqmJfNqEIUk80mvalfq16jt1KcZe9FStsukxopJWSSW5KyM8xPjI6Vkkti0QjibJ2KmkVqMzRBocEI6QERiJOmXRgyJRNxzqhUyyEBlEthEtiSkyBkNOUhwM43rM4kZS+UBGiKqsA7RFgLqEuovMkHZmtFZCHqrW+9XFLHrDwf7/8AwqVnZirLLV7pL6m5mTHx9lS7LT+BL+LCx97xTX8jC31i+9cBzk6AkAAkkACpAAIBjMgFsQHgocvVltUH8LGmHpFp7VPxtI4SA9GRx16ePLtBq7zL/wAdhuoV41IqcHeL6zxLO1yNjlGKpW1u34mbB6KIqd2/GwtKqr6u23qIhNbyCxs3YNewc6VRW2nTwq9hGvKVayJksWW02zACAlGVZqz9oQaptYpmtIGSIHigGSHsQiTbNNFFqEjoh0CHQ4iGMtJuK5AKyKnMRmEYAPmFcxJzsjl4vHNXy622vqQ/B1eeW8lVb9Z5n16o3dT+FlY20MXNe+rLT2ls1J1DHaciqUgpzzR06ldlchVhswKYhKI00U5DuJTTL0ajNKoDxiA0WVnDwS6xqlNLVaouowjLxEnCzsLP7WX+mdoqkjTKJTJGVUNEWLJISwVCNVN6GY0UdhYlOWQekl3X+ohMSs0jKMTDNCS3pmiSEktAOdSeakn12NEtXfqevEzYTZJbpNF9P3I911wZyroYAJRFSAAFSAABJMeviQStoHy2JZzMuzLgz09OjCnFJJaK17amWvJyZ26cpHD9XluOjyTg2qim+rYi5UzbhIWRNMbPyPgNEia9mK+JKCIktDq4X3Ecqew6eCf+mvBGvLNWsiW0exXLaaQDIUlEVmntYo1Re0KYaCLIiIsRSmJIJNMrHbS3xHRSmWJgixMYRMa5lqJEkyWxGwqGyGyBWVlm5Rr5Kcnu/c8rjMQ4wbv3LxPTcp081KXE8pynRbgmup3st28xf2NT8cz1id75nxPS+jvK+ZunVSlJK8X2luZ5U7XovSXrCqT/AP5xv8Wx/JJzTz9uPWYSftSSuknprfRrYaXEowyUq1Vw9xNRXwWv1Ncok8/fMazKqsAzFuFWQLYspgWo1GasTGRWiyKKyvovVF1dWehTBFstRvwz6qZTNF0iqRlpTIrZbJFbAixfQ2FJfRWhRYSgsTYIRiyRZJCtFRxqFO9Srq/f3l8Ke3V6PfvX/wBKsLrUq/8AeaYr2n3xvwf/ACcq6xHN97Dm+98RwIpVHxGSJCwBYLEkkAltI2ajWADzdSNzI4myqzNLadXOEUbs3UY6FFKBuwsLziuq934LVhKit71t1kCFbvJsdFREthv5OleFjAzTybLVrvNeWa3yeiEqbRxaqsjTJbhcUm4VVV2iFtVaFJitwyLEVIsQiU4EAaRKZZFlQyYFyZJWmOmRU2DKSSRVbiVtFzFsUVShdHFxvJ0ldwV1u614HfyhkJZKR4ifJ8drhrfVHQwOCnKyhFxjvasl4I9LzC3FkaaRnn/WtV4TDqnBRWxDzLBJIpFEhbFzgCpmWhSReohCBYkaYojAdIEiSoZDpiEolWFkVyLJFciKqkVlkhLARY00loUJGqK0KiUiSCQFYs9jGZViZZacnuTA5XJ+ud75s1te1Dxa4ozcmr/ST3ts01XaN+zZ8DlXU1gHkrNikEWJsBIBYLEgFAAAHmKsiuMbg3cemjq5LacTZhVpUl2YPi3b+WZol98tB7pzS8cq/wD0WM1UkOhEMgJJws8tRECT0aa6ixK7LeuhbWV43M1OV4pminrFo6xzrKmSE1ZimWkspZcJUj1ma1FaLIsrGiyRVgEXJNMpJTFJAdMeLK4lsERYdASkNlI0rCxZlIsAtgJsAASCQ6iQKkTlHsTYKqykqJZYLEVEUOkEUMkVmgAsBUCHQqGRGiSKpFsimRAkhSWADUo3ZoFpRshyoAJIYCmHliploy3uyN5x+VpZ6tOn33Yv4sW4WGWnFdyLZRugiMcXREfdg/8AFX8dhJFNexbdJr4bf5GCoJAAAAAAJIADycVqXxK4ItidXJYi7Ee7Sj3OXxb/APhVFaj13edtyS4FZKNEiwIgcWUboZAUaeTql1lfgb6Wj8dDj055Zp9T0OvH2lddf7nTzWLEYmHX/bmdHRglUpvT2uvxRgnCzLUlQTa4JDpGW2WcbMhM1VKd0ZGrOxlViYxUmOmVDkiklRKL6bKB4sEa0hiuEixGGxYiwxNgqtoLD2JSAVIaxNgAAJRIEBYmxIAiQAAAACJRLZAs2RSyZVJjSZW2BBZShdixjc0RVkUSMQFwiRSbgAk3ZNnDw75yvOp1LRG7lfE5KbS96WiMuCoZYJfH4mfVb8xrQwqGObYpbZrwf8EkRftrvTX8/wAEgAAAAAAAAAAeYii1I84uXKvZp8H5jLl+r2afCXmdscNempL2lxETvJvezzq9Iay/LT4S8xY8v1l+Wnwl5lwenA810hrdmnwl5k9Ia3Zp8JeYwemQyPL9Iq3Zp8JeZPSOt2afCXmMHp5xumauTsRf2ZO3V4Pqf93njuklfs0+EvMiPpFWUsyjTv4St+5fPxL9fSMKnGo09L7fEuxWEvqlr1+Z8/j6b4pW9ig7K13Gd3/uLen+L/Tw/wAs/uOmzMcubux6lws7EpHj6nppiZO7p0F4Rn9xX0wxPYo/LL7jm6vbWKa1G/ieP6YYnsUfll9wdMMT2KPyz+4mK9K1YmLPLT9K68tsKPyy+4TpNX7FL5ZeZcHsEyUePXpRX7FL5ZfcT0qxHYpfLL7gj2KJPHdK8R2KXyy+4OleI7FL5ZfcVHtYTsaITPBdLMR2KXyy+4ePpfiV+Sj8svuJYse/QHg16aYnsUfln9xPTXFfp0Pln9xMa17yxNjwXTbFfp0Pln9xPTfFfp0Pln9ww172wHgum+K/TofLP7iOm2K/TofLP7hhr3wHgem2K/TofLP7g6bYr9Oh8s/uGGvfXJPAdNsV+nQ+Wf3E9N8V+nQ+Wf3DDXvgPA9N8V+nQ+Wf3B03xX6dD5Z/cMNe+uFzwPTfFfp0Pln9wdN8V+nQ+Wf3DDXvZSK5SPCP00xPYo/LP7iOmWJ7FH5Z/cTDXuGwSueG6Y4nsUfln9w0fTTEr/p0Pln9ww176EbDngOm+K/TofLP7g6b4r9Oh8s/uLhr34HgOm+K/TofLP7g6b4r9Oh8s/uGJr31yHKyPBdNsV+nQ+Wf3FdX0yxMotZKKvujK/8A7DF16SrPnsR/hD9zekeEoektemrRhS+MZeZd0vxPYo/LL7jF82tz1HtkSeI6X4nsUfll9xPS/E9ij8svuJxTuPZ1HZxe6SLXtPDS9LcQ1Zwo/LL7hn6Y4l/ko/LL7hxTuPbAeI6YYnsUfll9wdMMT2KPyy+4cVe49vcLniOmGJ7FH5ZfcHS/E9ij8svuHFO49vci54jpfiexR+WX3B0vxPYo/LL7hxU7jgAAHZyAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB/9k=\n",
       "text/html": [
        "\n",
        "        <iframe\n",
@@ -237,10 +238,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11094b940>"
+       "<IPython.lib.display.YouTubeVideo at 0x653af7780>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,11 +278,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
     "from keras import models\n",
+    "from keras.models import load_model\n",
     "from keras import layers\n",
     "\n",
     "network_dense = models.Sequential()\n",
@@ -321,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -350,7 +352,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -383,7 +385,7 @@
        "(60000, 784)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -430,25 +432,25 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "60000/60000 [==============================] - 3s 42us/step - loss: 0.0515 - accuracy: 0.9845 - val_loss: 0.0615 - val_accuracy: 0.9810\n",
+      "60000/60000 [==============================] - 3s 48us/step - loss: 0.5031 - accuracy: 0.8446 - val_loss: 0.2363 - val_accuracy: 0.9281\n",
       "Epoch 2/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0405 - accuracy: 0.9874 - val_loss: 0.0585 - val_accuracy: 0.9810\n",
+      "60000/60000 [==============================] - 3s 50us/step - loss: 0.1930 - accuracy: 0.9414 - val_loss: 0.1414 - val_accuracy: 0.9568\n",
       "Epoch 3/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0298 - accuracy: 0.9914 - val_loss: 0.0685 - val_accuracy: 0.9793\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.1282 - accuracy: 0.9596 - val_loss: 0.1076 - val_accuracy: 0.9663\n",
       "Epoch 4/10\n",
-      "60000/60000 [==============================] - 2s 34us/step - loss: 0.0237 - accuracy: 0.9928 - val_loss: 0.0780 - val_accuracy: 0.9772\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0938 - accuracy: 0.9709 - val_loss: 0.1239 - val_accuracy: 0.9594\n",
       "Epoch 5/10\n",
-      "60000/60000 [==============================] - 2s 40us/step - loss: 0.0201 - accuracy: 0.9940 - val_loss: 0.0557 - val_accuracy: 0.9826\n",
+      "60000/60000 [==============================] - 3s 46us/step - loss: 0.0701 - accuracy: 0.9782 - val_loss: 0.0737 - val_accuracy: 0.9769\n",
       "Epoch 6/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.0575 - val_accuracy: 0.9832\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0561 - accuracy: 0.9825 - val_loss: 0.0741 - val_accuracy: 0.9784\n",
       "Epoch 7/10\n",
-      "60000/60000 [==============================] - 2s 36us/step - loss: 0.0131 - accuracy: 0.9960 - val_loss: 0.0606 - val_accuracy: 0.9837\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0426 - accuracy: 0.9870 - val_loss: 0.0626 - val_accuracy: 0.9806\n",
       "Epoch 8/10\n",
-      "60000/60000 [==============================] - 2s 42us/step - loss: 0.0095 - accuracy: 0.9974 - val_loss: 0.0717 - val_accuracy: 0.9813\n",
+      "60000/60000 [==============================] - 3s 46us/step - loss: 0.0365 - accuracy: 0.9888 - val_loss: 0.0665 - val_accuracy: 0.9794\n",
       "Epoch 9/10\n",
-      "60000/60000 [==============================] - 2s 42us/step - loss: 0.0102 - accuracy: 0.9971 - val_loss: 0.0612 - val_accuracy: 0.9833\n",
+      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0282 - accuracy: 0.9913 - val_loss: 0.1454 - val_accuracy: 0.9582\n",
       "Epoch 10/10\n",
-      "60000/60000 [==============================] - 2s 40us/step - loss: 0.0067 - accuracy: 0.9980 - val_loss: 0.0633 - val_accuracy: 0.9840\n"
+      "60000/60000 [==============================] - 2s 38us/step - loss: 0.0211 - accuracy: 0.9941 - val_loss: 0.1131 - val_accuracy: 0.9673\n"
      ]
     }
    ],
@@ -458,6 +460,26 @@
     "                      epochs=10, batch_size=1024) # setting the hyper parameters of the neural network"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving my model\n",
+    "network_dense.save('my_model_den.h5')  # creates a HDF5 file 'my_model.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading my model\n",
+    "network_dense = load_model('my_model_den.h5')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -470,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -487,19 +509,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "test_acc_dense: 0.984000027179718\n"
+      "test_accuracy with DNN: 0.9672999978065491\n"
      ]
     }
    ],
    "source": [
-    "print('test_acc_dense:', test_acc_dense)"
+    "print('test_accuracy with DNN:', test_acc_dense)"
    ]
   },
   {
@@ -527,12 +549,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -547,8 +569,8 @@
     "\n",
     "# Plot training & validation accuracy values\n",
     "plt.figure(figsize=(20,10))\n",
-    "plt.plot(history.history['accuracy'])\n",
-    "plt.plot(history.history['val_accuracy'])\n",
+    "plt.plot(history_dense.history['accuracy'])\n",
+    "plt.plot(history_dense.history['val_accuracy'])\n",
     "plt.title('Model accuracy')\n",
     "plt.ylabel('Accuracy')\n",
     "plt.xlabel('Epoch')\n",
@@ -558,12 +580,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -578,8 +600,8 @@
     "\n",
     "# Plot training & validation accuracy values\n",
     "plt.figure(figsize=(20,10))\n",
-    "plt.plot(history.history['loss'])\n",
-    "plt.plot(history.history['val_loss'])\n",
+    "plt.plot(history_dense.history['loss'])\n",
+    "plt.plot(history_dense.history['val_loss'])\n",
     "plt.title('Model loss')\n",
     "plt.ylabel('Loss')\n",
     "plt.xlabel('Epoch')\n",
@@ -596,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +630,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -622,7 +644,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(28, 28)\n"
+      "Shape =  (28, 28)\n",
+      "Label =  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
      ]
     }
    ],
@@ -630,30 +653,33 @@
     "%pylab inline\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.image as mpimg\n",
-    "img = test_images[0]\n",
+    "\n",
+    "#######\n",
+    "i = 32\n",
+    "#######\n",
+    "\n",
+    "img = test_images[i]\n",
     "imgplot = plt.imshow(img)\n",
     "plt.show()\n",
-    "print(img.shape)"
+    "print('Shape = ', img.shape)\n",
+    "print('Label = ',test_labels_cat[i])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([7, 2, 1, ..., 4, 5, 6])"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction =  3\n"
+     ]
     }
    ],
    "source": [
-    "network.predict_classes(test_images_rsh)"
+    "print('Prediction = ', network_dense.predict_classes(test_images_rsh_dense)[i])"
    ]
   },
   {
@@ -672,22 +698,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x62f10db00>"
+       "<matplotlib.image.AxesImage at 0x62f63c7b8>"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -701,24 +744,24 @@
    "source": [
     "# Lookinf at raw image\n",
     "from PIL import Image \n",
-    "img_file = \"./sample_handwrittings/seh.jpg\"\n",
+    "img_file = \"./sample_handwrittings/my_003.png\"\n",
     "img = Image.open(img_file)\n",
     "plt.imshow(img)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAUGVYSWZNTQAqAAAACAACARIAAwAAAAEAAQAAh2kABAAAAAEAAAAmAAAAAAADoAEAAwAAAAEAAQAAoAIABAAAAAEAAAMgoAMABAAAAAEAAAMHAAAAAKGoEDUAAACBSURBVHicxZFBDoAgDAR3QV/gA/z/20y8eFXWA0FbiFw0kQtNhzbTQuH5hA57AYc7jCnfaqECwAQgUjUkEPcStW3L+6MjRFNK1cwIhQ7zMHrm29KZVZVKkjA+CgG8Sz/8lcmkariuZn/XbrMZgQ0tLCu19ldbzQAWucnaOXu2f8MTE4UhKBhbdcAAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=L size=28x28 at 0x631391D30>"
+       "<PIL.Image.Image image mode=L size=28x28 at 0x63D5200F0>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,17 +778,17 @@
     "im1 = im.resize(newsize) \n",
     "\n",
     "# Separate Color Channels [RGB]\n",
-    "red, green, blue = im1.split()\n",
+    "C, M, Y, K = im1.split()\n",
     "\n",
     "# Shows the image in image viewer  \n",
-    "img_resize_file = \"/Users/smirs/Desktop/my_handwritting_2.jpg\"\n",
-    "red.save(img_resize_file)\n",
-    "red"
+    "img_resize_file = \"~/Desktop/my_handwritting_2.jpg\"\n",
+    "C.save(img_resize_file)\n",
+    "C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
@@ -766,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [
     {
@@ -775,7 +818,7 @@
        "(784,)"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,20 +832,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.03529412, 0.        , 0.00784314, 0.        , 0.        ,\n",
-       "       0.03529412, 0.        , 0.        , 0.04313725, 0.01960784,\n",
-       "       0.        , 0.01568627, 0.        , 0.02352941, 0.        ],\n",
+       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.03137255,\n",
+       "       0.        , 0.01176471, 0.01568627, 0.01960784, 0.        ,\n",
+       "       0.03921568, 0.        , 0.        , 0.        , 0.        ],\n",
        "      dtype=float32)"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -815,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,46 +868,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([3])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precitction = 3\n"
+     ]
     }
    ],
    "source": [
     "# Look at the Predicted Value!\n",
-    "network.predict_classes(image_rsh_arr)"
+    "print('Precitction =', network_dense.predict_classes(image_rsh_arr)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.25623765e-11, 8.46728653e-07, 1.35030930e-07, 9.99975562e-01,\n",
-       "        2.19083507e-08, 2.11385632e-05, 4.02669407e-13, 8.96447787e-08,\n",
-       "        1.78690584e-06, 4.83480392e-07]], dtype=float32)"
+       "[<matplotlib.lines.Line2D at 0x63e09d390>]"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Look at the propabilities\n",
-    "network.predict(image_rsh_arr)"
+    "plot(network_dense.predict(image_rsh_arr)[0].tolist())"
    ]
   },
   {
@@ -1014,7 +1064,7 @@
    "metadata": {},
    "source": [
     "# OK, let's do it!\n",
-    "![spiderman](https://media.giphy.com/media/ifpUMDEBZy3OU/giphy.gif)\n",
+    "![spiderman](https://media.giphy.com/media/hAxSXWmRnRnJm/giphy.gif)\n",
     "\n",
     "## Let's Design and Train and Evaluate our own CNN!\n",
     "**Is it hard to implement? Nope! It's piece'a cake! Thanks Keras! You are the best!**"
@@ -1022,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1063,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -1072,7 +1122,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1096,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
@@ -1105,7 +1155,7 @@
        "(60000, 28, 28, 1)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1116,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1126,7 +1176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1185,7 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "59392/60000 [============================>.] - ETA: 1s - loss: 0.5371 - accuracy: 0.8467"
+      "54272/60000 [==========================>...] - ETA: 11s - loss: 0.4773 - accuracy: 0.8577"
      ]
     },
     {
@@ -1145,7 +1195,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-16a94499a740>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m history_cnn = network_cnn.fit(train_images_rsh_cnn, train_labels_cat, # training imput features (images in this example!) and their assigned labels\n\u001b[1;32m      2\u001b[0m                        \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images_rsh_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels_cat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# this line is optional: telling the model to evaluate the model as it gets trained!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                        epochs=10, batch_size=1024) # setting the hyper parameters of the neural network\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-178-16a94499a740>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m history_cnn = network_cnn.fit(train_images_rsh_cnn, train_labels_cat, # training imput features (images in this example!) and their assigned labels\n\u001b[1;32m      2\u001b[0m                        \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images_rsh_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels_cat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# this line is optional: telling the model to evaluate the model as it gets trained!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                        epochs=10, batch_size=1024) # setting the hyper parameters of the neural network\n\u001b[0m",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3508\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3509\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3510\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3512\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -1165,15 +1215,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving my model\n",
+    "network_cnn.save('my_model_cnn.h5')  # creates a HDF5 file 'my_model.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading my model\n",
+    "network_cnn = load_model('my_model_cnn.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 8s 798us/step\n",
-      "test_acc_convoltional: 0.9894999861717224\n"
+      "10000/10000 [==============================] - 8s 755us/step\n",
+      "test_acc_cnn: 0.988099992275238\n"
      ]
     }
    ],
@@ -1184,19 +1254,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'history_cnn' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-1c630425fc14>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot training & validation accuracy values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_cnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_cnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'history_cnn' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
+       "<Figure size 1440x720 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1214,12 +1292,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -1251,22 +1329,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x639c8a470>"
+       "<matplotlib.image.AxesImage at 0x63cb69550>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1280,24 +1358,24 @@
    "source": [
     "# Lookinf at raw image\n",
     "from PIL import Image \n",
-    "img_file = \"./sample_handwrittings/seh.jpg\"\n",
+    "img_file = \"./sample_handwrittings/my_002.png\"\n",
     "img = Image.open(img_file)\n",
     "plt.imshow(img)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAUGVYSWZNTQAqAAAACAACARIAAwAAAAEAAQAAh2kABAAAAAEAAAAmAAAAAAADoAEAAwAAAAEAAQAAoAIABAAAAAEAAAMgoAMABAAAAAEAAAMHAAAAAKGoEDUAAACBSURBVHicxZFBDoAgDAR3QV/gA/z/20y8eFXWA0FbiFw0kQtNhzbTQuH5hA57AYc7jCnfaqECwAQgUjUkEPcStW3L+6MjRFNK1cwIhQ7zMHrm29KZVZVKkjA+CgG8Sz/8lcmkariuZn/XbrMZgQ0tLCu19ldbzQAWucnaOXu2f8MTE4UhKBhbdcAAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=L size=28x28 at 0x639CEB128>"
+       "<PIL.Image.Image image mode=L size=28x28 at 0x63E347668>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1314,17 +1392,17 @@
     "im1 = im.resize(newsize) \n",
     "\n",
     "# Separate Color Channels [RGB]\n",
-    "red, green, blue = im1.split()\n",
+    "C, M, Y, K = im1.split()\n",
     "\n",
     "# Shows the image in image viewer  \n",
-    "img_resize_file = \"/Users/smirs/Desktop/my_handwritting_2.jpg\"\n",
-    "red.save(img_resize_file)\n",
-    "red"
+    "img_resize_file = \"~/Desktop/my_handwritting_2.jpg\"\n",
+    "C.save(img_resize_file)\n",
+    "C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
@@ -1354,7 +1432,7 @@
        "(28, 28, 1)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1368,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1379,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,75 +1467,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([3])"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction =  2\n"
+     ]
     }
    ],
    "source": [
-    "# Look at the Predicted Value!\n",
-    "network_cnn.predict_classes(image_rsh_arr)"
+    "# Look at the Predicted Value\n",
+    "print('Prediction = ', network_cnn.predict_classes(image_rsh_arr)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[2.0868050e-08, 8.6770592e-07, 1.8318027e-02, 9.3462396e-01,\n",
-       "        4.6201958e-06, 1.0233928e-03, 1.3826156e-06, 4.8718606e-11,\n",
-       "        1.5667746e-03, 4.4461008e-02]], dtype=float32)"
+       "[<matplotlib.lines.Line2D at 0x65840eb70>]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAcHklEQVR4nO3dbYxc133f8e9/Zp+4O5ekuQ+zFkVpJXlnEjlo4IBQ3RgonNpIJTeR3qSFBLgPgRG9sZO4NlI4baEGKvqiSdG0RdW0qpsETV2rihK0QspWBRoXLYpaEP0QN5I6sxRNiaS0s0tSJO/uch/n3xczd7kcznKH3Jm9D/P7QAJn5t6988dQ+s3Zc849x9wdERFJv1zcBYiISHco0EVEMkKBLiKSEQp0EZGMUKCLiGTEQFxvPDEx4TMzM3G9vYhIKn3nO9+55O6T7Y7FFugzMzOcPn06rrcXEUklM3t3t2PqchERyQgFuohIRijQRUQyQoEuIpIRCnQRkYzYM9DN7LfNbMHM/nSX42Zm/8zMzpjZD8zsJ7pfpoiI7KWTFvrvAo/f4fgTwGzz32eB39p/WSIicrf2DHR3/5/AlTuc8hTwb73h28BRM/totwqUO3v97GXe/uB63GWISAJ0ow/9OHB+x/MLzdduY2bPmtlpMzu9uLjYhbeWr/7+n/AP/vPbcZchIgnQjUC3Nq+13TXD3V9095PufnJysu2dq3IXwtUNLnx4g0otjLsUEUmAbgT6BeDEjuf3A+934bqyh7mFJQAWwzWuLK/HXI2IxK0bgf4q8Neas10+CVxz9w+6cF3ZQ3X+Zsu8qla6SN/bc3EuM/sm8GlgwswuAH8PGARw938JnAI+B5wBVoCf71WxcqtKLSSfM7bqTrUW8smHx+MuSURitGegu/szexx34Itdq0g6Nldb4uP3HebcpWW10EUkvuVzZf8qtZBPlyYZHshRnV+KuxwRiZlu/U+pK8vrLIZrlIoBs8WASi2k8cuSiPQrBXpKRV0spemAcjHg2o0NFsK1mKsSkTipyyWlokAvFwOG8o3v5cp8SPHwSJxliUiM1EJPqWot5PDIAMXDw5SKhe3XRKR/KdBTqjq/RHk6wMwYLwwzURhWoIv0OQV6Crk7lVrIbDHYfq1ULFCpaaaLSD9ToKfQQrjGtRsblG8J9IC5Wki9rpkuIv1KgZ5CleYt/6UdgV6eDlhZ3+Li1RtxlSUiMVOgp9D2lMXmYGjjcXDLMRHpPwr0FKrWQiYKw4wXhrdfi8JdS+mK9C8FegpVaku3tM4BgpFB7jsycssKjCLSXxToKVOvO3O18Jb+80hpOtBMF5E+pkBPmYtXb7CyvkV5+vZALxcD3llcYnOrHkNlIhI3BXrK3BwQbdNCLwasb9Z598rKQZclIgmgQE+ZSpsZLpGo1a5+dJH+pEBPmep8yH1HRghGBm879shkATPNdBHpVwr0lKnUlii16T8HODSU58Fjo5qLLtKnFOgpsrlV553FpVtu+W9VKgZUNdNFpC8p0FPk3SsrrG/W2w6IRsrTAT+8tMza5tYBViYiSaBAT5FosLPdlMVIqRiwVXfOLi4fVFkikhAK9BSp1ELMGoOfu9GaLiL9S4GeItVayIPHRjk0lN/1nIcmxhjI2faKjCLSPxToKVKtLd2x/xxgaCDHw5NjGhgV6UMK9JRY29zih5eW79h/HmnMdFELXaTfKNBT4uziMlt137OFDo01Xd67ssLK+uYBVCYiSaFAT4k7reHSKtprdE7dLiJ9RYGeEpX5kIGc8dDE2J7nRt0yWgJApL8o0FOiWlvi4ckxhgb2/it74NgowwM55hToIn1FgZ4S1V02tWgnnzNmiwVtdiHSZxToKbCyvsl7V1Y6DnSA0lSgZXRF+kxHgW5mj5tZxczOmNnX2hx/wMy+ZWbfM7MfmNnnul9q/4oGN+8q0KcD5q+vcm1lo1dliUjC7BnoZpYHXgCeAB4FnjGzR1tO+7vAy+7+CeBp4F90u9B+Fg1udjIHPRKtyFhdUCtdpF900kJ/DDjj7mfdfR14CXiq5RwHDjcfHwHe716JMlcLGR7I8cCx0Y5/JlozXTcYifSPTgL9OHB+x/MLzdd2+jXg82Z2ATgF/GK7C5nZs2Z22sxOLy4u3kO5/alSW2K2WCCfs45/5r4jIxSGB9SPLtJHOgn0diniLc+fAX7X3e8HPgf8npnddm13f9HdT7r7ycnJybuvtk9V50NKU513twCYRTNdFOgi/aKTQL8AnNjx/H5u71L5AvAygLv/H2AEmOhGgf3u2soG89dXd9127k7KxYDKfIh76/eviGRRJ4H+BjBrZg+Z2RCNQc9XW855D/gMgJn9KI1AV59KF0SDmnfadm43pWLAhysbXFpa73ZZIpJAewa6u28CXwJeA96mMZvlTTN73syebJ72VeAXzOxPgG8Cf8PVLOyK7TVc7qWFPh2t6aJuF5F+MNDJSe5+isZg587Xntvx+C3gU90tTaDRf14YHuC+IyN3/bPRvPVKLeQnP6YeMJGs052iCVephcwWC5h1PsMlMlEY4iOjg5q6KNInFOgJ5u5U5sN76j+HxkyXUnNgVESyT4GeYJeW1vlwZeOubvlvVZ4OmKstaaaLSB9QoCfY3D3c8t+qVAwI1zb54Npqt8oSkYRSoCdY5S52KdqNNrsQ6R8K9ASr1kI+MjrIRGHonq8R3WGqJQBEsk+BnmCV+camFvcywyVyZHSQ4uFhtdBF+oACPaHcnbna0r76zyOlYqCpiyJ9QIGeUB9cWyVc29xX/3mkXAw4s7DEVl0zXUSyTIGeUPeyqcVuStMBqxt1zl9Z2fe1RCS5FOgJFQ1i3u2yue3sXAJARLJLgZ5QlVpI8fAwR0YH932t2akCoJkuIlmnQE+oai3sSv85wNjwACeOHVILXSTjFOgJtFV3ziws3fMaLu2Ui40lAEQkuxToCXT+ygqrG/V7WgN9N6ViwDuLS6xv1rt2TRFJFgV6AnXjlv9WpWLAZt05d3m5a9cUkWRRoCdQNHgZDWZ2w/ZMFw2MimSWAj2BKrWQE8cOMTbc0YZSHXl4cox8znTHqEiGKdATaK7W3QFRgJHBPDPjowp0kQxToCfM+maddxaXutp/HilPB1Q100UksxToCXPu8jKbde9JoM9OBZy7vMzqxlbXry0i8VOgJ0w0aNmrFro7nFlQK10kixToCTNXC8nnjIcnx7p+7ehLQv3oItmkQE+YSi1kZnyUkcF81689Mz7KUD6nJQBEMkqBnjDVLm1q0c5APscjUwUt0iWSUQr0BFnd2OLc5WVmu7Bk7m5KxYJmuohklAI9Qc4sLOHenU0tdlMqBly8eoNwdaNn7yEi8VCgJ0i1B2u4tIpuWJrTTBeRzFGgJ0ilFjKUzzEzPtqz94ha/+pHF8keBXqCVOdDHpkqMJDv3V/L8aOHGB3Ka6aLSAZ1lBxm9riZVczsjJl9bZdz/oqZvWVmb5rZv+9umf2hWluiVOzeCovt5HLG7FRBc9FFMmjPQDezPPAC8ATwKPCMmT3acs4s8KvAp9z948CXe1BrpoWrG1y8eqOn/eeRUjGgMq8+dJGs6aSF/hhwxt3Puvs68BLwVMs5vwC84O4fArj7QnfLzL5okLLbqyy2U54OuLS0xpXl9Z6/l4gcnE4C/ThwfsfzC83XdioBJTP732b2bTN7vN2FzOxZMzttZqcXFxfvreKMigYpezllMaIlAESyqZNAtzavecvzAWAW+DTwDPB1Mzt62w+5v+juJ9395OTk5N3WmmmVWsjoUJ7jRw/1/L22Z7oo0EUypZNAvwCc2PH8fuD9Nuf8J3ffcPcfAhUaAS8dqtZCZqcK5HLtvj+7ayoY5vDIgLajE8mYTgL9DWDWzB4ysyHgaeDVlnP+I/BTAGY2QaML5mw3C826ynxvNrVox8yam10o0EWyZM9Ad/dN4EvAa8DbwMvu/qaZPW9mTzZPew24bGZvAd8CfsXdL/eq6Ky5srzOpaW1A+k/j5SKjd2L3Ft7z0QkrTrahdjdTwGnWl57bsdjB77S/Ffu0kHc8t+qPB3wjdffYyFco3h45MDeV0R6R3eKJkAcgR6t6Kh+dJHsUKAnQGU+5PDIAMXDwwf2ntEdqepHF8kOBXoCVGsh5ekAs97PcImMF4aZKAyrhS6SIQr0mLl7cw2Xg+tuiZSnC1S1jK5IZijQY7YQrnHtxsaBznCJlIoBc7WQel0zXUSyQIEes6jLo5fbzu2mVAxYWd/i4tUbB/7eItJ9CvSY3Zzh0ttlc9uJunnUjy6SDQr0mFXmQyYKw4wXDm6GSyT6EtFmFyLZoECPWXVhifL0wbfOAYKRQY4fPcScAl0kExToMarXnblaGMsMl0ipWKBS00wXkSxQoMfo4tUbrKxvxRzoAe8sLLG5VY+tBhHpDgV6jKLByLgDfX2rzrnLK7HVICLdoUCPUSXGGS4RbXYhkh0K9BjN1UKOHz1EMDIYWw0fmypgpkAXyQIFeowqtaVYW+cAI4N5ZsbHFOgiGaBAj8nmVp13FuJZw6XV7FRBNxeJZIACPSbnLq+wvlVPRKCXpwPOXV5hdWMr7lJEZB8U6DGJujjiWJSrVakYsFV3zi4ux12KiOyDAj0m1VqIWWNQMm7Rl8rcgrpdRNJMgR6Tai1kZnyMkcF83KUwMz7GYN7Ujy6Scgr0mFTmQ2YT0DoHGBrI8dCEZrqIpJ0CPQarG1ucu7ySiP7zSKkYaNVFkZRToMfg7OIyW3VPxAyXSLkYcP7KDZbXNuMuRUTukQI9BtHgY6Ja6M1azmiPUZHUUqDHoDIfMpg3ZsbH4i5lWznavUjdLiKppUCPQbUW8tDEGEMDyfn4TxwbZXggR1UzXURSKzmJ0kcqMW9q0U4+Z8wWC2qhi6SYAv2Araxvcv7Kje0ujiQpFQPmtHuRSGop0A9YFJilBA2IRsrFgPnrq1xb2Yi7FBG5Bwr0AxZ1aSSyhR5tdqElAERSSYF+wKrzIcMDOU4cG427lNtE/fpaAkAknToKdDN73MwqZnbGzL52h/N+zszczE52r8RsqdRCZosF8jmLu5Tb3HdkhMLwgJYAEEmpPQPdzPLAC8ATwKPAM2b2aJvzAuCXgNe7XWSWzNWSsalFO2ZGqVhQoIukVCct9MeAM+5+1t3XgZeAp9qc9/eBXwdWu1hfplxb2WD++moi+88j5emAynyIu8ddiojcpU4C/ThwfsfzC83XtpnZJ4AT7v5Hd7qQmT1rZqfN7PTi4uJdF5t20WBjEme4RErFgA9XNri0tB53KSJylzoJ9HadvdvNNzPLAb8JfHWvC7n7i+5+0t1PTk5Odl5lRkSDjUntcoGbtanbRSR9Ogn0C8CJHc/vB97f8TwAfgz4H2Z2Dvgk8KoGRm9XrYUUhge478hI3KXsSjNdRNKrk0B/A5g1s4fMbAh4Gng1Ouju19x9wt1n3H0G+DbwpLuf7knFKVathZSKBcySN8MlMlEY4tjYkLajE0mhPQPd3TeBLwGvAW8DL7v7m2b2vJk92esCs8LdqcyHiVoyt51opota6CLpM9DJSe5+CjjV8tpzu5z76f2XlT2Xltb5cGUj0f3nkXIx4A++exF3T/RvEyJyK90pekCiQcY0BPpsMWBpbZP3r2kGqkiaKNAPSBpmuESibiGtjS6SLgr0AzK3EHJsbIiJwlDcpeypNKWpiyJppEA/IJX55M9wiRwZHWT68Ig2uxBJGQX6AXB3qglew6WdWa3pIpI6CvQD8P61VZbWNlMV6OXm7kVbda3pIpIWCvQDEA0uJn0O+k6l6YC1zTrvXVmJuxQR6ZAC/QBsT1mcSk+gl7Wmi0jqKNAPQKUWMn14hCOjg3GX0rHZYgHQ1EWRNFGgH4Bqc5eiNBkdGuDEsUOa6SKSIgr0HtuqO3O1pURvarGbcjFQl4tIiijQe+y9KyusbdYTvanFbkrFgLOLy6xv1uMuRUQ6oEDvsaiFm8oW+nTAZt05d3k57lJEpAMK9B6LBhXT1ocO2uxCJG0U6D1WqYWcOHaI0aGOVipOlIcnx8jnTP3oIimhQO+xai1MZXcLwPBAnpnxUbXQRVJCgd5D65t1zi4up+qW/1blac10EUkLBXoPnbu8zGbdU3XLf6tSMeDdKyusbmzFXYqI7EGB3kNp2tRiN+VigDucWViKuxQR2YMCvYeqtZB8znh4cizuUu7ZrGa6iKSGAr2HKvMhM+OjDA/k4y7lns2MjzKUz6kfXSQFFOg9VK2Fqe4/BxjI53hkqqA1XURSQIHeI6sbW7x7ZSXV/eeRcrHAXE196CJJp0DvkTMLS7in85b/VqXpgItXbxCubsRdiojcgQK9Ryrbt/xnINCnos0u1EoXSTIFeo9UayFD+Rwz46Nxl7Jv0TiABkZFkk2B3iOVWsgjUwUG8un/iI8fPcToUF5TF0USLv1pk1CNTS3St8JiO7mcMVsMmFtQoIskmQK9B8LVDS5evZHKTS12Uy4WqMyrD10kyRToPRANHkaDiVlQKgZcWlrj8tJa3KWIyC46CnQze9zMKmZ2xsy+1ub4V8zsLTP7gZn9dzN7sPulpsf2LkUZaqFH8+k100UkufYMdDPLAy8ATwCPAs+Y2aMtp30POOnufwZ4Bfj1bheaJpX5kNGhPMePHoq7lK7RTBeR5Oukhf4YcMbdz7r7OvAS8NTOE9z9W+6+0nz6beD+7paZLnMLIbPFgFzO4i6la6aCYY4cGlSgiyRYJ4F+HDi/4/mF5mu7+QLwX9odMLNnzey0mZ1eXFzsvMqUqcxnZ4ZLxMwoF7XZhUiSdRLo7ZqZ3vZEs88DJ4HfaHfc3V9095PufnJycrLzKlPk8tIal5bWMrGGS6vZYoHKfIh7279+EYlZJ4F+ATix4/n9wPutJ5nZZ4G/Azzp7n07FWJ7hksGA708HXB9dZPa9b796xVJtE4C/Q1g1sweMrMh4Gng1Z0nmNkngH9FI8wXul9mekQ332Rphkvk5kwXdbuIJNGege7um8CXgNeAt4GX3f1NM3vezJ5snvYbQAH4fTP7vpm9usvlMq8yH3Lk0CBTwXDcpXSdAl0k2QY6OcndTwGnWl57bsfjz3a5rtSq1kLKxQCz7MxwiRwbG2IyGNaaLiIJpTtFu8jdqcyHzGZshstOpWJBLXSRhFKgd1Ht+hrXVzcz2X8eKRUDqrUl6nXNdBFJGgV6F0Ut1yzOcImUiwE3Nra4ePVG3KWISAsFehf1Q6BHK0iqH10keRToXVSZD5kMhjk2NhR3KT0zO9UYH6ioH10kcRToXVSthZQyPCAKEIwMcvzoIQ2MiiSQAr1L6nWnWlvKdHdLpNRcAkBEkkWB3iUXr97gxsYW5X4I9OmAs4vLbG7V4y5FRHZQoHdJ1GLN0rZzuykXA9a36py7vLL3ySJyYBToXRINEkaDhlmmJQBEkkmB3iXVWsjxo4cIRgbjLqXnPjZVIGeauiiSNAr0LqnMZ3+GS2RkMM+D42NqoYskjAK9Cza36pxdXO6L/vOI1nQRSR4Fehecu7zC+la9L2a4RMrFgHOXV1jd2Iq7FBFpUqB3QT/c8t9qthiwVXfOLi7HXYqINCnQu6AyH2LWGCzsF9GKkup2EUkOBXoXVGshM+NjjAzm4y7lwMyMjzGYN63pIpIgCvQu6Ic1XFoNDeR4eKLAnAJdJDEU6Pu0urHFucsrfTUgGilNB2qhiySIAn2fzi4us1V3Zvsx0KcKnL9yg+W1zbhLEREU6PsWDQpmedu53UTz7ucWlmKuRERAgb5vlVrIYN6YGR+Lu5QDF3UzVbUEgEgiKND3aa4W8vBEgaGB/vsoTxwbZWQwp6mLIgnRfynUZZVa2Fe3/O+UzxmzUxoYFUkKBfo+LK9tcv7KDUp9dENRq1mt6SKSGAr0fYgGA/u1hQ6NfvTa9TWurqzHXYpI31Og70M0GNiPc9Ajpe0lADTTRSRuCvR9qNZCRgZznDg2GncpsSlr9yKRxFCg70OlFjI7FZDPWdylxOajR0YIhgcU6CIJoEDfh2otZLbP1nBpZWbMFgvajk4kARTo9+jqyjq162t93X8eKU8HVGsh7h53KSJ9raNAN7PHzaxiZmfM7Gttjg+b2X9oHn/dzGa6XWjSRIOA/TzDJVIqBny4ssHi0lrcpYj0tT0D3czywAvAE8CjwDNm9mjLaV8APnT3jwG/CfzDbheaNNtruKiFvv0ZzGmmi0isBjo45zHgjLufBTCzl4CngLd2nPMU8GvNx68A/9zMzHvwO/jLb5znX/+vs92+7F27vLxOMDzAR4+MxF1K7KLfUr7y8vc5PDIYczWSRA7U3aHxD+7e/BMcb/zZTIt2x+reuIq3+/nm406uDYCBAWZgWPPPxniQ3XL89mPWPCF6PddyDtF197j2lz9b4md//L6uf86dBPpx4PyO5xeAP7vbOe6+aWbXgHHg0s6TzOxZ4FmABx544J4KPjo6mIiByFngsZlj23+J/WyiMMwXf+oRfnhJ+4vK7m6Gou0I1Naw2xmCjee5XONg6+ttg7T1vO3AbfwZhfwt4b/9+OYXAOzyxUD05XDzi6Lecl77L5Zbv3iOjvam4dNJoLdLrNaWdyfn4O4vAi8CnDx58p5a7z/98Wl++uPT9/Kj0kO/8hd/JO4SRPpeJ4OiF4ATO57fD7y/2zlmNgAcAa50o0AREelMJ4H+BjBrZg+Z2RDwNPBqyzmvAn+9+fjngD/uRf+5iIjsbs8ul2af+JeA14A88Nvu/qaZPQ+cdvdXgX8D/J6ZnaHRMn+6l0WLiMjtOulDx91PAadaXntux+NV4C93tzQREbkbulNURCQjFOgiIhmhQBcRyQgFuohIRlhcswvNbBF49x5/fIKWu1D7nD6PW+nzuEmfxa2y8Hk86O6T7Q7EFuj7YWan3f1k3HUkhT6PW+nzuEmfxa2y/nmoy0VEJCMU6CIiGZHWQH8x7gISRp/HrfR53KTP4laZ/jxS2YcuIiK3S2sLXUREWijQRUQyInWBvteG1f3CzE6Y2bfM7G0ze9PMfjnumpLAzPJm9j0z+6O4a4mbmR01s1fM7P81/zv5c3HXFBcz+5vN/0/+1My+aWaZ3DsyVYHe4YbV/WIT+Kq7/yjwSeCLffxZ7PTLwNtxF5EQ/xT4r+7+I8CP06efi5kdB34JOOnuP0ZjGfBMLvGdqkBnx4bV7r4ORBtW9x13/8Ddv9t8HNL4n/V4vFXFy8zuB/4S8PW4a4mbmR0G/jyNvQpw93V3vxpvVbEaAA41d1Qb5fZd1zIhbYHebsPqvg4xADObAT4BvB5vJbH7J8DfAupxF5IADwOLwO80u6C+bmZjcRcVB3e/CPwj4D3gA+Cau/+3eKvqjbQFekebUfcTMysAfwB82d2vx11PXMzsZ4AFd/9O3LUkxADwE8BvufsngGWgL8eczOwjNH6Tfwi4Dxgzs8/HW1VvpC3QO9mwum+Y2SCNMP+Gu/9h3PXE7FPAk2Z2jkZX3F8ws38Xb0mxugBccPfot7ZXaAR8P/os8EN3X3T3DeAPgZ+MuaaeSFugd7JhdV8wM6PRP/q2u//juOuJm7v/qrvf7+4zNP67+GN3z2QrrBPuPg+cN7Ny86XPAG/FWFKc3gM+aWajzf9vPkNGB4g72lM0KXbbsDrmsuLyKeCvAv/XzL7ffO1vN/d/FQH4ReAbzcbPWeDnY64nFu7+upm9AnyXxuyw75HRJQB067+ISEakrctFRER2oUAXEckIBbqISEYo0EVEMkKBLiKSEQp0EZGMUKCLiGTE/wfB3HazbzB29gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Look at the propabilities\n",
-    "network_cnn.predict(image_rsh_arr)"
+    "plot(network_cnn.predict(image_rsh_arr)[0].tolist())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Let's do something more fun!"
+    "# You don't have to always train model on your own data!\n",
+    "# Let's do something more fun!\n",
+    "![sheldon](https://media.giphy.com/media/9nhE1iPKLnGcU/giphy.gif)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fatal: destination path 'darknet' already exists and is not an empty directory.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# Download the model! i.e. \"Darknet\"\n",
+    "# More details on Darknet and Yolo can be found here: https://pjreddie.com/darknet/yolo/\n",
     "!git clone https://github.com/pjreddie/darknet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download the weights [pretrained model]\n",
+    "!wget https://pjreddie.com/media/files/yolov3.weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -1466,7 +1557,7 @@
        "'/Users/smirs/git_projects/deep-learning-with-python-notebooks'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,11 +1569,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 199,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
+     ]
+    }
+   ],
    "source": [
-    "os.chdir('./darknet/')"
+    "# Change directory and display where we are!\n",
+    "os.chdir('./darknet/')\n",
+    "!pwd"
    ]
   },
   {
@@ -1568,38 +1669,30 @@
     }
    ],
    "source": [
+    "# Setup \"Darknet\"\n",
     "!make"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 208,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pwd"
+    "# Display the raw image that you want to use for Object Detection!\n",
+    "!open ~/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "!open /Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
@@ -1715,24 +1808,61 @@
       "  105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs\n",
       "  106 yolo\n",
       "Loading weights from /Users/smirs/Downloads/yolov3.weights...Done!\n",
-      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg: Predicted in 31.430375 seconds.\n",
-      "person: 100%\n"
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg: Predicted in 25.008658 seconds.\n",
+      "laptop: 96%\n",
+      "laptop: 86%\n",
+      "laptop: 82%\n",
+      "laptop: 78%\n",
+      "laptop: 71%\n",
+      "chair: 59%\n",
+      "chair: 52%\n",
+      "cup: 87%\n",
+      "person: 99%\n",
+      "backpack: 72%\n",
+      "person: 99%\n",
+      "backpack: 60%\n",
+      "bottle: 68%\n",
+      "cup: 58%\n",
+      "person: 98%\n",
+      "person: 96%\n",
+      "person: 94%\n",
+      "person: 93%\n",
+      "person: 92%\n",
+      "person: 91%\n",
+      "person: 72%\n",
+      "chair: 97%\n",
+      "chair: 96%\n",
+      "chair: 93%\n",
+      "chair: 87%\n",
+      "chair: 86%\n",
+      "chair: 82%\n",
+      "chair: 75%\n",
+      "chair: 73%\n"
      ]
     }
    ],
    "source": [
-    "!./darknet detect cfg/yolov3.cfg ~/Downloads/yolov3.weights /Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg"
+    "# Run Darknet! [in order to do Object Detection in your target image]\n",
+    "!./darknet detect cfg/yolov3.cfg ~/Downloads/yolov3.weights ~/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Display the post-proccessed image!\n",
     "!open ./predictions.jpg"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thank You!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/course_material/week_14/2.1-a-first-look-at-a-neural-network.ipynb b/course_material/week_14/2.1-a-first-look-at-a-neural-network.ipynb
index 063c3be..891caf3 100644
--- a/course_material/week_14/2.1-a-first-look-at-a-neural-network.ipynb
+++ b/course_material/week_14/2.1-a-first-look-at-a-neural-network.ipynb
@@ -1,18 +1,18 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First, Get Ready!\n",
+    "![spiderman](https://media.giphy.com/media/EU42nA5hrKn16/giphy.gif)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 113,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#!pip install keras\n",
     "\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +105,7 @@
        "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +132,7 @@
        "(10000, 28, 28)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
        "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +175,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -199,7 +199,7 @@
     "import matplotlib.image as mpimg\n",
     "import scipy\n",
     "\n",
-    "i = 100 #choose a random component\n",
+    "i = 0 #choose a random component\n",
     "img = train_images[i]\n",
     "imgplot = plt.imshow(img)\n",
     "plt.show()\n",
@@ -220,11 +220,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/jpeg": 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\n",
       "text/html": [
        "\n",
        "        <iframe\n",
@@ -237,10 +238,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11094b940>"
+       "<IPython.lib.display.YouTubeVideo at 0x653af7780>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,11 +278,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
     "from keras import models\n",
+    "from keras.models import load_model\n",
     "from keras import layers\n",
     "\n",
     "network_dense = models.Sequential()\n",
@@ -321,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -350,7 +352,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -383,7 +385,7 @@
        "(60000, 784)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -430,25 +432,25 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "60000/60000 [==============================] - 3s 42us/step - loss: 0.0515 - accuracy: 0.9845 - val_loss: 0.0615 - val_accuracy: 0.9810\n",
+      "60000/60000 [==============================] - 3s 48us/step - loss: 0.5031 - accuracy: 0.8446 - val_loss: 0.2363 - val_accuracy: 0.9281\n",
       "Epoch 2/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0405 - accuracy: 0.9874 - val_loss: 0.0585 - val_accuracy: 0.9810\n",
+      "60000/60000 [==============================] - 3s 50us/step - loss: 0.1930 - accuracy: 0.9414 - val_loss: 0.1414 - val_accuracy: 0.9568\n",
       "Epoch 3/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0298 - accuracy: 0.9914 - val_loss: 0.0685 - val_accuracy: 0.9793\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.1282 - accuracy: 0.9596 - val_loss: 0.1076 - val_accuracy: 0.9663\n",
       "Epoch 4/10\n",
-      "60000/60000 [==============================] - 2s 34us/step - loss: 0.0237 - accuracy: 0.9928 - val_loss: 0.0780 - val_accuracy: 0.9772\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0938 - accuracy: 0.9709 - val_loss: 0.1239 - val_accuracy: 0.9594\n",
       "Epoch 5/10\n",
-      "60000/60000 [==============================] - 2s 40us/step - loss: 0.0201 - accuracy: 0.9940 - val_loss: 0.0557 - val_accuracy: 0.9826\n",
+      "60000/60000 [==============================] - 3s 46us/step - loss: 0.0701 - accuracy: 0.9782 - val_loss: 0.0737 - val_accuracy: 0.9769\n",
       "Epoch 6/10\n",
-      "60000/60000 [==============================] - 2s 35us/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.0575 - val_accuracy: 0.9832\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0561 - accuracy: 0.9825 - val_loss: 0.0741 - val_accuracy: 0.9784\n",
       "Epoch 7/10\n",
-      "60000/60000 [==============================] - 2s 36us/step - loss: 0.0131 - accuracy: 0.9960 - val_loss: 0.0606 - val_accuracy: 0.9837\n",
+      "60000/60000 [==============================] - 3s 44us/step - loss: 0.0426 - accuracy: 0.9870 - val_loss: 0.0626 - val_accuracy: 0.9806\n",
       "Epoch 8/10\n",
-      "60000/60000 [==============================] - 2s 42us/step - loss: 0.0095 - accuracy: 0.9974 - val_loss: 0.0717 - val_accuracy: 0.9813\n",
+      "60000/60000 [==============================] - 3s 46us/step - loss: 0.0365 - accuracy: 0.9888 - val_loss: 0.0665 - val_accuracy: 0.9794\n",
       "Epoch 9/10\n",
-      "60000/60000 [==============================] - 2s 42us/step - loss: 0.0102 - accuracy: 0.9971 - val_loss: 0.0612 - val_accuracy: 0.9833\n",
+      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0282 - accuracy: 0.9913 - val_loss: 0.1454 - val_accuracy: 0.9582\n",
       "Epoch 10/10\n",
-      "60000/60000 [==============================] - 2s 40us/step - loss: 0.0067 - accuracy: 0.9980 - val_loss: 0.0633 - val_accuracy: 0.9840\n"
+      "60000/60000 [==============================] - 2s 38us/step - loss: 0.0211 - accuracy: 0.9941 - val_loss: 0.1131 - val_accuracy: 0.9673\n"
      ]
     }
    ],
@@ -458,6 +460,26 @@
     "                      epochs=10, batch_size=1024) # setting the hyper parameters of the neural network"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving my model\n",
+    "network_dense.save('my_model_den.h5')  # creates a HDF5 file 'my_model.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading my model\n",
+    "network_dense = load_model('my_model_den.h5')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -470,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -487,19 +509,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "test_acc_dense: 0.984000027179718\n"
+      "test_accuracy with DNN: 0.9672999978065491\n"
      ]
     }
    ],
    "source": [
-    "print('test_acc_dense:', test_acc_dense)"
+    "print('test_accuracy with DNN:', test_acc_dense)"
    ]
   },
   {
@@ -527,12 +549,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -547,8 +569,8 @@
     "\n",
     "# Plot training & validation accuracy values\n",
     "plt.figure(figsize=(20,10))\n",
-    "plt.plot(history.history['accuracy'])\n",
-    "plt.plot(history.history['val_accuracy'])\n",
+    "plt.plot(history_dense.history['accuracy'])\n",
+    "plt.plot(history_dense.history['val_accuracy'])\n",
     "plt.title('Model accuracy')\n",
     "plt.ylabel('Accuracy')\n",
     "plt.xlabel('Epoch')\n",
@@ -558,12 +580,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -578,8 +600,8 @@
     "\n",
     "# Plot training & validation accuracy values\n",
     "plt.figure(figsize=(20,10))\n",
-    "plt.plot(history.history['loss'])\n",
-    "plt.plot(history.history['val_loss'])\n",
+    "plt.plot(history_dense.history['loss'])\n",
+    "plt.plot(history_dense.history['val_loss'])\n",
     "plt.title('Model loss')\n",
     "plt.ylabel('Loss')\n",
     "plt.xlabel('Epoch')\n",
@@ -596,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +630,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -622,7 +644,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(28, 28)\n"
+      "Shape =  (28, 28)\n",
+      "Label =  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
      ]
     }
    ],
@@ -630,30 +653,33 @@
     "%pylab inline\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.image as mpimg\n",
-    "img = test_images[0]\n",
+    "\n",
+    "#######\n",
+    "i = 32\n",
+    "#######\n",
+    "\n",
+    "img = test_images[i]\n",
     "imgplot = plt.imshow(img)\n",
     "plt.show()\n",
-    "print(img.shape)"
+    "print('Shape = ', img.shape)\n",
+    "print('Label = ',test_labels_cat[i])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([7, 2, 1, ..., 4, 5, 6])"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction =  3\n"
+     ]
     }
    ],
    "source": [
-    "network.predict_classes(test_images_rsh)"
+    "print('Prediction = ', network_dense.predict_classes(test_images_rsh_dense)[i])"
    ]
   },
   {
@@ -672,22 +698,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x62f10db00>"
+       "<matplotlib.image.AxesImage at 0x62f63c7b8>"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAQi0lEQVR4nO3dfcxkZXnH8e/VRWjxpYALdAusuzRbWmraQjaU1tY0UhWoZW2qyRJTiW5CmmKrtUaW8of+KbVVa9JirGCxoSD1JW4abNlssU2TsrqLIOCKLoi4srJofYs26urVP879pMPe8+zzPHPmzJyZ+X6SzcycOc+ce8/M/Oa6z7xckZlI0qCfmPYAJPWPwSCpYjBIqhgMkioGg6SKwSCp0lkwRMSlEfFwRByMiJ1dbUfS+EUXn2OIiHXA54EXA4eATwFXZuZnx74xSWPXVcVwEXAwMx/NzB8AtwPbOtqWpDE7oaPbPQv48sDlQ8CvLbfy+vXrc9OmTR0NRRLA/v37v5aZp69m3a6CIYYse9qcJSKuBq4G2LhxI/v27etoKJIAIuJLq123q6nEIeCcgctnA08MrpCZ783MrZm59fTTVxVikiakq2D4FLAlIjZHxInAdmBXR9uSNGadTCUy82hEvA74N2AdcHNmPtTFtiSNX1fHGMjMO4E7u7p9Sd3xk4+SKgaDpIrBIKliMEiqGAySKgaDpIrBIKliMEiqGAySKgaDpIrBIKliMEiqGAySKgaDpEpnX7tWP0UM+9W94eyEvrgMhgWwljBY7u8MicXiVEJSxWCYYxExcrUw7La0OJxKzKGunsRLt+u0Yv6NXDFExDkRcXdEHIiIhyLi9WX5aRGxOyK+UE5PHd9wdTzjrBC02NpMJY4Cf56ZvwhcDFwTEecDO4E9mbkF2FMuS5ohIwdDZh7OzHvL+e8AB2ha020Dbimr3QK8vO0gtbJJVgpWJfNvLMcYImITcAGwFzgzMw9DEx4RccY4tqGaT1B1pfW7EhHxLODDwBsy89tr+LurI2JfROx76qmn2g5D0hi1CoaIeAZNKNyamR8pi5+MiA3l+g3AkWF/a+/K0fXhIOO0t69utXlXIoCbgAOZ+Y6Bq3YBV5XzVwEfG314OpZPSE1Cm2MMLwD+EHggIu4ry/4CeBtwR0TsAB4HXtluiJImbeRgyMz/ApZ7+bpk1NtVzSpBk+ZHonvOUNA0+JHonjIQNE1WDJIqBkMPWS1o2gyGnjEU1AcGg6SKBx97wkpBfWLF0AOGgvrGYJBUMRgkVQyGKZrGtyT9vUathgcfF4SBoLWwYpBUMRgWgNWC1sqpxJwyDNSGFYOkisEwh6wW1JZTiTliIGhcrBimKDPH9mQ2FDROBoOkyjgazqyLiE9HxL+Uy5sjYm9pavvBiDix/TC1nHFWHdKScVQMr6fpW7nkBuCdpantN4AdY9jGXBv1iW0gqCttO1GdDfwu8L5yOYAXAR8qq9jUVppBbSuGdwFvBn5cLj8X+GZmHi2XD9F0wNYKlqYEK1UBq11PaqNNi7qXAUcyc//g4iGrDn0E29R2eYNP/mP/SZPQpmJ4AXBFRDwG3E4zhXgXcEpELH0+4mzgiWF/bFNbqb9GDobMvC4zz87MTcB24N8z81XA3cArymo2tZVmUBefY7gWeGNEHKQ55nBTB9uQ1KGxfCQ6Mz8BfKKcfxS4aBy3K2k6/OSjpIrBIKliMEiqGAySKgaDpIrBIKliMEiqGAySKgbDgrGztlbDYJBUMRgkVQwGSRWDQSPxR2Pmmw1nFoQHHbUWVgySKgaDpIrBoDXz+ML8MxgkVQwGSZW2nahOiYgPRcTnIuJARPx6RJwWEbtL78rdEXHquAYraTLaVgx/A/xrZv4C8Cs0PSx3AntK78o95bKkGdKmE9VzgBdSfh4+M3+Qmd8EttH0rAR7V0ozqU3FcC7wFPD+iPh0RLwvIp4JnJmZhwHK6RljGKekCWoTDCcAFwI3ZuYFwHdZw7TB3pVSf7UJhkPAoczcWy5/iCYonoyIDQDl9MiwP7Z3pdRfbXpXfhX4ckScVxZdAnwW2EXTsxLsXdkLfk9Ca9X2S1R/AtwaEScCjwKvoQmbOyJiB/A48MqW25A0Ya2CITPvA7YOueqSNrcrabr82vUccwqhUfmRaEkVKwatmt+qXBxWDJIqBoOkisGgVXEasVgMBkkVg0FSxWCQVDEYJFUMBkkVg0FSxWCQVDEYJFUMhjnlNyvVhsEgqWIwSKoYDHPIaYTa8vcYdFx+eWoxte1d+WcR8VBEPBgRt0XET0bE5ojYW3pXfrD8UKykGdKmRd1ZwJ8CWzPz+cA6YDtwA/DO0rvyG8COcQxU0uS0PcZwAvBTEXECcDJwGHgRTfMZsHflTHMasbjaNJz5CvBXNL0jDgPfAvYD38zMo2W1Q8BZbQcpabLaTCVOpelsvRn4WeCZwGVDVh36smPvSqm/2kwlfgf4YmY+lZk/BD4C/AZwSplaAJwNPDHsj+1dqXGIiLH+U6NNMDwOXBwRJ0ezR5d6V94NvKKsY+9KjWwaT2QDpNHmGMNemoOM9wIPlNt6L3At8MaIOAg8F7hpDOPUHJrlJ9+sjHNUbXtXvgV4yzGLHwUuanO7kqbLTz7OkVl49ZqFMa5VRMzdW7sGg8ZuHp/8i8YvUUmqWDGoNSuE+ZtOGAwaiWEw35xKSKpYMWioYWWxVcLiMBh0XIbBYjIYNJSBsNg8xiCpYjBIqhgMc8LSf7rm6TMMYDBIGsJgkFQxGKSW5m0aAb5dqRlxvCfftI6vzGMgLLFikFSxYlBvjPIKPOlqYZ6rhEEGg6aqzRPNUOjOilOJiLg5Io5ExIMDy06LiN2lP+Xu0mOCaLw7Ig5GxGci4sIuBy+pG6s5xvAPwKXHLNsJ7Cn9KfeUy9A0nNlS/l0N3DieYWpWZeZx/41i0r/O3Gass2rFYMjM/wT+55jF22j6UsLT+1NuAz6QjXtoms9sGNdgNRvaPvGPx+nDZIz6rsSZmXkYoJyeUZafBXx5YD17V0ozaNxvVw6Lc3tXLoCuq4RpNHdZ1GoBRg+GJ5emCOX0SFl+CDhnYD17V07INB/EXW17Wp2eFvGYwrFGDYZdNH0p4en9KXcBry7vTlwMfGtpyqH50mWFAH6acdpW/BxDRNwG/DawPiIO0bSkextwR0TsoGlu+8qy+p3A5cBB4HvAazoYs6SOrRgMmXnlMlddMmTdBK5pOyiNZunVbpyvtpN8BZ3mb0pYKTyd35XQsgyFxWUwSKr4XYk5lJkjvwJP+tXTg4z9ZDDMqcEH/kpPvmk8SZw69JvBsAD69EQwEGaDxxgkVQwGTYzVwuxwKqFO2e9iNlkxSKpYMWjs+lQlOIUYjcGgsehTGICB0JZTCUkVKwa11qdqwUphPAwGjaRPYbDEUBgfg0Fr1qdQMAy64TEGSRUrBq2KVcJiMRj0NH0KgEGGwWQ5lZBUsWJQb6uEJVYLkzdqU9u3R8TnSuPaj0bEKQPXXVea2j4cES/tauBqZ1pNXNbC/g7TM2pT293A8zPzl4HPA9cBRMT5wHbgl8rf/F1ErBvbaNVa38MADIQ+GKmpbWbelZlHy8V7aDpOQdPU9vbM/H5mfpGmv8RFYxyvpAkYx8HH1wIfL+dX3dTW3pXdG5wuWCloLVoFQ0RcDxwFbl1aNGS1ofe0vSu7MwshsKTrVncazcjvSkTEVcDLgEvy/+/VVTe1ldRfI1UMEXEpcC1wRWZ+b+CqXcD2iDgpIjYDW4BPth+mVjIr04VBVgn9NWpT2+uAk4Dd5YF4T2b+UWY+FBF3AJ+lmWJck5k/6mrwi26WQmCJYTAbog931NatW3Pfvn3THsbMMRi0FhGxPzO3rmZdP/k4g2YxEMBQmCUGgzpnIMweg2HGzEq1YBjMNr9dKalixaCxslKYDwaDRmYIzC+nEpIqVgxaNSuExWEw6LgMg8VkMMyYzOz0LUuDQOAxBklDWDHIKkEVg2EGLT2R20wpDAMdj1MJSRUrhhk2+Kq/UvVghaC1MBjmhE98jZNTCUkVg0FSxWCQVBmpd+XAdW+KiIyI9eVyRMS7S+/Kz0TEhV0MWlK3Ru1dSUScA7wYeHxg8WU0Pxm/BbgauLH9ECVN2ki9K4t3Am/m6Z2mtgEfyMY9wCkRsWEsI5U0MaM2nLkC+Epm3n/MVavuXSmpv9b8OYaIOBm4HnjJsKuHLBv6BntEXE0z3WDjxo1rHYakDo1SMfwcsBm4PyIeo+lPeW9E/Axr6F1pU1upv9YcDJn5QGaekZmbMnMTTRhcmJlfpeld+ery7sTFwLcy8/B4hyypa6t5u/I24L+B8yLiUETsOM7qdwKPAgeBvwf+eCyjlDRRKx5jyMwrV7h+08D5BK5pPyxJ0+QnHyVVDAZJFYNBUsVgkFQxGCRVDAZJFYNBUsVgkFQxGCRVDAZJFYNBUsVgkFQxGCRVDAZJlehDa7OIeAr4LvC1aY+lWE9/xgL9Go9jGa5PY4Hh43leZq7q59J6EQwAEbEvM7dOexzQr7FAv8bjWIbr01ig/XicSkiqGAySKn0KhvdOewAD+jQW6Nd4HMtwfRoLtBxPb44xSOqPPlUMknpi6sEQEZdGxMOlQ/bOKWz/nIi4OyIORMRDEfH6svytEfGViLiv/Lt8QuN5LCIeKNvcV5adFhG7I+IL5fTUCYzjvIH/+30R8e2IeMMk98uwTuvL7YuuO60vM5a3R8TnyvY+GhGnlOWbIuJ/B/bReyYwlmXvl4i4ruyXhyPipavaSGZO7R+wDngEOBc4EbgfOH/CY9hA0zAH4NnA54HzgbcCb5rCPnkMWH/Msr8EdpbzO4EbpnA/fRV43iT3C/BC4ELgwZX2BXA58HGaNokXA3snMJaXACeU8zcMjGXT4HoT2i9D75fyWL4fOImmg9wjwLqVtjHtiuEi4GBmPpqZPwBup+mYPTGZeTgz7y3nvwMcoH+NeLcBt5TztwAvn/D2LwEeycwvTXKjObzT+nL7otNO68PGkpl3ZebRcvEempaMnVtmvyxnG3B7Zn4/M79I0wzqopX+aNrB0Kvu2BGxCbgA2FsWva6UiTdPonwvErgrIvaXxr8AZ2Zp9VdOz5jQWJZsB24buDyN/bJkuX0x7cfSa2kqliWbI+LTEfEfEfFbExrDsPtlpP0y7WBYdXfsrkXEs4APA2/IzG8DN9I08P1V4DDw1xMaygsy80LgMuCaiHjhhLY7VEScCFwB/HNZNK39spKpPZYi4nrgKHBrWXQY2JiZFwBvBP4pIp7T8TCWu19G2i/TDoZVd8fuUkQ8gyYUbs3MjwBk5pOZ+aPM/DFNH84Vy69xyMwnyukR4KNlu08ulcXl9MgkxlJcBtybmU+WcU1lvwxYbl9M5bEUEVcBLwNelWVSX8r2r5fz+2nm9T/f5TiOc7+MtF+mHQyfArZExObyyrSdpmP2xEREADcBBzLzHQPLB+envw88eOzfdjCWZ0bEs5fO0xzcepBmn1xVVrsK+FjXYxlwJQPTiGnsl2Msty8m3mk9Ii4FrgWuyMzvDSw/PSLWlfPnAltomj13OZbl7pddwPaIOCkiNpexfHLFG+zqyOkajrBeTvNOwCPA9VPY/m/SlFafAe4r/y4H/hF4oCzfBWyYwFjOpTmCfD/w0NL+AJ4L7AG+UE5Pm9C+ORn4OvDTA8smtl9oAukw8EOaV74dy+0LmpL5b8vj6AFg6wTGcpBm/r70uHlPWfcPyv13P3Av8HsTGMuy9wtwfdkvDwOXrWYbfvJRUmXaUwlJPWQwSKoYDJIqBoOkisEgqWIwSKoYDJIqBoOkyv8BE5RITnqifcYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -701,24 +744,24 @@
    "source": [
     "# Lookinf at raw image\n",
     "from PIL import Image \n",
-    "img_file = \"./sample_handwrittings/seh.jpg\"\n",
+    "img_file = \"./sample_handwrittings/my_003.png\"\n",
     "img = Image.open(img_file)\n",
     "plt.imshow(img)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAUGVYSWZNTQAqAAAACAACARIAAwAAAAEAAQAAh2kABAAAAAEAAAAmAAAAAAADoAEAAwAAAAEAAQAAoAIABAAAAAEAAAMgoAMABAAAAAEAAAMHAAAAAKGoEDUAAACBSURBVHicxZFBDoAgDAR3QV/gA/z/20y8eFXWA0FbiFw0kQtNhzbTQuH5hA57AYc7jCnfaqECwAQgUjUkEPcStW3L+6MjRFNK1cwIhQ7zMHrm29KZVZVKkjA+CgG8Sz/8lcmkariuZn/XbrMZgQ0tLCu19ldbzQAWucnaOXu2f8MTE4UhKBhbdcAAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=L size=28x28 at 0x631391D30>"
+       "<PIL.Image.Image image mode=L size=28x28 at 0x63D5200F0>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,17 +778,17 @@
     "im1 = im.resize(newsize) \n",
     "\n",
     "# Separate Color Channels [RGB]\n",
-    "red, green, blue = im1.split()\n",
+    "C, M, Y, K = im1.split()\n",
     "\n",
     "# Shows the image in image viewer  \n",
-    "img_resize_file = \"/Users/smirs/Desktop/my_handwritting_2.jpg\"\n",
-    "red.save(img_resize_file)\n",
-    "red"
+    "img_resize_file = \"~/Desktop/my_handwritting_2.jpg\"\n",
+    "C.save(img_resize_file)\n",
+    "C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
@@ -766,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [
     {
@@ -775,7 +818,7 @@
        "(784,)"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,20 +832,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.03529412, 0.        , 0.00784314, 0.        , 0.        ,\n",
-       "       0.03529412, 0.        , 0.        , 0.04313725, 0.01960784,\n",
-       "       0.        , 0.01568627, 0.        , 0.02352941, 0.        ],\n",
+       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.03137255,\n",
+       "       0.        , 0.01176471, 0.01568627, 0.01960784, 0.        ,\n",
+       "       0.03921568, 0.        , 0.        , 0.        , 0.        ],\n",
        "      dtype=float32)"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -815,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,46 +868,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([3])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precitction = 3\n"
+     ]
     }
    ],
    "source": [
     "# Look at the Predicted Value!\n",
-    "network.predict_classes(image_rsh_arr)"
+    "print('Precitction =', network_dense.predict_classes(image_rsh_arr)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.25623765e-11, 8.46728653e-07, 1.35030930e-07, 9.99975562e-01,\n",
-       "        2.19083507e-08, 2.11385632e-05, 4.02669407e-13, 8.96447787e-08,\n",
-       "        1.78690584e-06, 4.83480392e-07]], dtype=float32)"
+       "[<matplotlib.lines.Line2D at 0x63e09d390>]"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Look at the propabilities\n",
-    "network.predict(image_rsh_arr)"
+    "plot(network_dense.predict(image_rsh_arr)[0].tolist())"
    ]
   },
   {
@@ -1014,7 +1064,7 @@
    "metadata": {},
    "source": [
     "# OK, let's do it!\n",
-    "![spiderman](https://media.giphy.com/media/ifpUMDEBZy3OU/giphy.gif)\n",
+    "![spiderman](https://media.giphy.com/media/hAxSXWmRnRnJm/giphy.gif)\n",
     "\n",
     "## Let's Design and Train and Evaluate our own CNN!\n",
     "**Is it hard to implement? Nope! It's piece'a cake! Thanks Keras! You are the best!**"
@@ -1022,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1063,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -1072,7 +1122,7 @@
        "(60000, 28, 28)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1096,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
@@ -1105,7 +1155,7 @@
        "(60000, 28, 28, 1)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1116,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1126,7 +1176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1185,7 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/10\n",
-      "59392/60000 [============================>.] - ETA: 1s - loss: 0.5371 - accuracy: 0.8467"
+      "54272/60000 [==========================>...] - ETA: 11s - loss: 0.4773 - accuracy: 0.8577"
      ]
     },
     {
@@ -1145,7 +1195,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-16a94499a740>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m history_cnn = network_cnn.fit(train_images_rsh_cnn, train_labels_cat, # training imput features (images in this example!) and their assigned labels\n\u001b[1;32m      2\u001b[0m                        \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images_rsh_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels_cat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# this line is optional: telling the model to evaluate the model as it gets trained!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                        epochs=10, batch_size=1024) # setting the hyper parameters of the neural network\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-178-16a94499a740>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m history_cnn = network_cnn.fit(train_images_rsh_cnn, train_labels_cat, # training imput features (images in this example!) and their assigned labels\n\u001b[1;32m      2\u001b[0m                        \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images_rsh_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels_cat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# this line is optional: telling the model to evaluate the model as it gets trained!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                        epochs=10, batch_size=1024) # setting the hyper parameters of the neural network\n\u001b[0m",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3508\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3509\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3510\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3512\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -1165,15 +1215,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving my model\n",
+    "network_cnn.save('my_model_cnn.h5')  # creates a HDF5 file 'my_model.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading my model\n",
+    "network_cnn = load_model('my_model_cnn.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 8s 798us/step\n",
-      "test_acc_convoltional: 0.9894999861717224\n"
+      "10000/10000 [==============================] - 8s 755us/step\n",
+      "test_acc_cnn: 0.988099992275238\n"
      ]
     }
    ],
@@ -1184,19 +1254,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'history_cnn' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-1c630425fc14>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot training & validation accuracy values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_cnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_cnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'history_cnn' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
+       "<Figure size 1440x720 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1214,12 +1292,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -1251,22 +1329,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x639c8a470>"
+       "<matplotlib.image.AxesImage at 0x63cb69550>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1280,24 +1358,24 @@
    "source": [
     "# Lookinf at raw image\n",
     "from PIL import Image \n",
-    "img_file = \"./sample_handwrittings/seh.jpg\"\n",
+    "img_file = \"./sample_handwrittings/my_002.png\"\n",
     "img = Image.open(img_file)\n",
     "plt.imshow(img)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAUGVYSWZNTQAqAAAACAACARIAAwAAAAEAAQAAh2kABAAAAAEAAAAmAAAAAAADoAEAAwAAAAEAAQAAoAIABAAAAAEAAAMgoAMABAAAAAEAAAMHAAAAAKGoEDUAAACBSURBVHicxZFBDoAgDAR3QV/gA/z/20y8eFXWA0FbiFw0kQtNhzbTQuH5hA57AYc7jCnfaqECwAQgUjUkEPcStW3L+6MjRFNK1cwIhQ7zMHrm29KZVZVKkjA+CgG8Sz/8lcmkariuZn/XbrMZgQ0tLCu19ldbzQAWucnaOXu2f8MTE4UhKBhbdcAAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAKLGlDQ1BJQ0MgUHJvZmlsZQAAeJyVlgdUFNkShm/35MQAMwwZhpxzBsk5SY6iMsyQYYQhIyoiiyuwoohIUgRcooKrS5BFRUQxsAgoYF6QRUBZFwOiorKNbHzvvHfeqz516+vqe+pW973n9A8AKYuVkBAH8wMQz03meTvaMAODgpm4KYAFFMAH6ECHxU5KsPb0dAOI/RH/aW/HAbQWb2us1fr35//VBDjhSWwAIE+EUzhJ7HiEryHsyU7gJQMAiyAsl5acsMYbEKbzkAYRXptPj1xnzhqHrXP6lzm+3rYIFwKAJ7NYvEgAiGVInpnKjkTqELsQ1uZyorkIzyFswY5iITVIygirx8dvW2NfhJXD/lYn8h81w/6syWJF/snr7/LFxO3s3dyYfrr6OrruTFtWXHQYj5UczmFyw9P+zy/0P1h8XMof667tBDmc6+eDRH3EJYAdsAduyMUEfkAXyekgoztyZwtYIA5EgzDAQygZhAMOkuUiMS05PD15rZjttoQMXnRkVDLTGtnZcKYzl62pztTV1jECYO2crC/5+u6XVSEG/q9cAgMAEzsAUHV/5cLEAOhE+hQl/JWTbwCAGghARzY7hZe6nkOvDRhABFTk/IkCKSAHlIEG0rUhMANWyNu4AA/gC4LAFsAGUSAe6T8NZIHdIA8UgAPgMKgA1aAONIJT4AzoBD3gErgKboJhMAYegEkwA56DRfAWrEAQhIMoEA0ShaQhBUgN0oWMIQvIHnKDvKEgKBSKhLhQCpQF7YEKoGKoAqqBmqDvoHPQJeg6NALdg6ageegV9AFGwWSYDkvCirAWbAxbw66wL7wZjoQT4Uw4F94Pl8G18Em4A74E34TH4En4ObyEAigSioGSQWmgjFG2KA9UMCoCxUPtROWjSlG1qFZUN2oAdRs1iVpAvUdj0TQ0E62BNkM7of3QbHQieie6EF2BbkR3oPvRt9FT6EX0ZwwFI4FRw5hinDGBmEhMGiYPU4qpx7RjrmDGMDOYt1gsloFVwhphnbBB2Bjsdmwh9ii2DduLHcFOY5dwOJwoTg1njvPAsXDJuDxcOe4k7iJuFDeDe4cn4aXxungHfDCei8/Bl+Kb8Rfwo/hZ/AqBn6BAMCV4EDiEDEIR4QShm3CLMENYIQoQlYjmRF9iDHE3sYzYSrxCfEh8TSKRZEkmJC9SNCmbVEY6TbpGmiK9JwuSVcm25BByCnk/uYHcS75Hfk2hUBQpVpRgSjJlP6WJcpnymPKOj8anyefMx+HbxVfJ18E3yveCSqAqUK2pW6iZ1FLqWeot6gI/gV+R35afxb+Tv5L/HP8E/5IATUBHwEMgXqBQoFngusCcIE5QUdBekCOYK1gneFlwmoaiydFsaWzaHtoJ2hXaDB1LV6I702PoBfRT9CH6opCgkL6Qv1C6UKXQeaFJBoqhyHBmxDGKGGcY44wPwpLC1sLhwvuEW4VHhZdFxEWsRMJF8kXaRMZEPogyRe1FY0UPinaKPhJDi6mKeYmliR0TuyK2IE4XNxNni+eLnxG/LwFLqEp4S2yXqJMYlFiSlJJ0lEyQLJe8LLkgxZCykoqRKpG6IDUvTZO2kI6WLpG+KP2MKcS0ZsYxy5j9zEUZCRknmRSZGpkhmRVZJVk/2RzZNtlHckQ5Y7kIuRK5PrlFeWl5d/ks+Rb5+woEBWOFKIUjCgMKy4pKigGKexU7FeeURJSclTKVWpQeKlOULZUTlWuV76hgVYxVYlWOqgyrwqoGqlGqlaq31GA1Q7VotaNqI+oYdRN1rnqt+oQGWcNaI1WjRWNKk6Hpppmj2an5QkteK1jroNaA1mdtA+047RPaD3QEdVx0cnS6dV7pquqydSt17+hR9Bz0dul16b3UV9MP1z+mf9eAZuBusNegz+CToZEhz7DVcN5I3ijUqMpowphu7GlcaHzNBGNiY7LLpMfkvamhabLpGdNfzTTMYs2azeY2KG0I33Biw7S5rDnLvMZ80oJpEWpx3GLSUsaSZVlr+cRKzopjVW81a61iHWN90vqFjbYNz6bdZtnW1HaHba8dys7RLt9uyF7Q3s++wv6xg6xDpEOLw6KjgeN2x14njJOr00GnCWdJZ7Zzk/Oii5HLDpd+V7Krj2uF6xM3VTeeW7c77O7ifsj94UaFjdyNnR7Aw9njkMcjTyXPRM8fvLBenl6VXk+9dbyzvAd8aD5bfZp93vra+Bb5PvBT9kvx6/On+of4N/kvB9gFFAdMBmoF7gi8GSQWFB3UFYwL9g+uD17aZL/p8KaZEIOQvJDxzUqb0zdf3yK2JW7L+a3UraytZ0MxoQGhzaEfWR6sWtZSmHNYVdgi25Z9hP2cY8Up4cyHm4cXh89GmEcUR8xFmkceipyPsowqjVqIto2uiH4Z4xRTHbMc6xHbELsaFxDXFo+PD40/xxXkxnL7t0ltS982kqCWkJcwmWiaeDhxkefKq0+CkjYndSXTkR/yYIpyylcpU6kWqZWp79L8086mC6Rz0wczVDP2ZcxmOmR+ux29nb29L0sma3fW1A7rHTU7oZ1hO/t2ye3K3TWT7ZjduJu4O3b3jznaOcU5b/YE7OnOlczNzp3+yvGrljy+PF7exF6zvdVfo7+O/npon96+8n2f8zn5Nwq0C0oLPhayC298o/NN2Ter+yP2DxUZFh07gD3APTB+0PJgY7FAcWbx9CH3Qx0lzJL8kjeHtx6+XqpfWn2EeCTlyGSZW1lXuXz5gfKPFVEVY5U2lW1VElX7qpaPco6OHrM61lotWV1Q/eF49PG7NY41HbWKtaV12LrUuqcn/E8MfGv8bVO9WH1B/acGbsNko3djf5NRU1OzRHNRC9yS0jJ/MuTk8Cm7U12tGq01bYy2gtPgdMrpZ9+Ffjd+xvVM31njs63fK3xf1U5rz++AOjI6FjujOie7grpGzrmc6+s2627/QfOHhh6ZnsrzQueLLhAv5F5YvZh5cak3oXfhUuSl6b6tfQ8uB16+0+/VP3TF9cq1qw5XLw9YD1y8Zn6t57rp9XM3jG903jS82TFoMNj+o8GP7UOGQx23jG51DZsMd49sGLkwajl66bbd7at3nO/cHNs4NjLuN353ImRi8i7n7ty9uHsv76feX3mQ/RDzMP8R/6PSxxKPa39S+alt0nDy/JTd1OATnycPptnTz39O+vnjTO5TytPSWenZpjnduZ55h/nhZ5uezTxPeL6ykPeLwC9VL5RffP+r1a+Di4GLMy95L1dfFb4Wfd3wRv9N35Ln0uO38W9XlvPfib5rfG/8fuBDwIfZlbSPuI9ln1Q+dX92/fxwNX51NYHFY32RAijE4YgIAF4hOoESBABtGNFafOs67ne9A/1N+fwHXtd6X8wQgLpeAHyzAXBDYjkSFRGnWiGSE3FfKwDr6f3pv1tShJ7uei1SJyJNSldXXwcAgFMB4NPE6upK5+rqp3qk2fsA9L5d149rxn8SgOMZ2tomfnd6xLP/VbP9BoXqxSE6kHXzAAAAc0lEQVR4nI1SQQ7AIAwCs/9/uTsZgaqxp0YEKZWFc40Lhk96Aq4zDAO5Becxd6AJBEgAqLCuzDbVMC36FWH2OK4hGBiqT8x9/JTjVM1sL4bOW3Ei7U0Kcfa6bFTwDUxLxxAqmWm5gTrMSqj9PY+v1fs+s34hNhY97CodjQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=L size=28x28 at 0x639CEB128>"
+       "<PIL.Image.Image image mode=L size=28x28 at 0x63E347668>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1314,17 +1392,17 @@
     "im1 = im.resize(newsize) \n",
     "\n",
     "# Separate Color Channels [RGB]\n",
-    "red, green, blue = im1.split()\n",
+    "C, M, Y, K = im1.split()\n",
     "\n",
     "# Shows the image in image viewer  \n",
-    "img_resize_file = \"/Users/smirs/Desktop/my_handwritting_2.jpg\"\n",
-    "red.save(img_resize_file)\n",
-    "red"
+    "img_resize_file = \"~/Desktop/my_handwritting_2.jpg\"\n",
+    "C.save(img_resize_file)\n",
+    "C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
@@ -1354,7 +1432,7 @@
        "(28, 28, 1)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1368,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1379,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,75 +1467,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([3])"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction =  2\n"
+     ]
     }
    ],
    "source": [
-    "# Look at the Predicted Value!\n",
-    "network_cnn.predict_classes(image_rsh_arr)"
+    "# Look at the Predicted Value\n",
+    "print('Prediction = ', network_cnn.predict_classes(image_rsh_arr)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[2.0868050e-08, 8.6770592e-07, 1.8318027e-02, 9.3462396e-01,\n",
-       "        4.6201958e-06, 1.0233928e-03, 1.3826156e-06, 4.8718606e-11,\n",
-       "        1.5667746e-03, 4.4461008e-02]], dtype=float32)"
+       "[<matplotlib.lines.Line2D at 0x65840eb70>]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Look at the propabilities\n",
-    "network_cnn.predict(image_rsh_arr)"
+    "plot(network_cnn.predict(image_rsh_arr)[0].tolist())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Let's do something more fun!"
+    "# You don't have to always train model on your own data!\n",
+    "# Let's do something more fun!\n",
+    "![sheldon](https://media.giphy.com/media/9nhE1iPKLnGcU/giphy.gif)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fatal: destination path 'darknet' already exists and is not an empty directory.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# Download the model! i.e. \"Darknet\"\n",
+    "# More details on Darknet and Yolo can be found here: https://pjreddie.com/darknet/yolo/\n",
     "!git clone https://github.com/pjreddie/darknet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download the weights [pretrained model]\n",
+    "!wget https://pjreddie.com/media/files/yolov3.weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -1466,7 +1557,7 @@
        "'/Users/smirs/git_projects/deep-learning-with-python-notebooks'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,11 +1569,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 199,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
+     ]
+    }
+   ],
    "source": [
-    "os.chdir('./darknet/')"
+    "# Change directory and display where we are!\n",
+    "os.chdir('./darknet/')\n",
+    "!pwd"
    ]
   },
   {
@@ -1568,38 +1669,30 @@
     }
    ],
    "source": [
+    "# Setup \"Darknet\"\n",
     "!make"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 208,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/darknet\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pwd"
+    "# Display the raw image that you want to use for Object Detection!\n",
+    "!open ~/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "!open /Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
@@ -1715,24 +1808,61 @@
       "  105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs\n",
       "  106 yolo\n",
       "Loading weights from /Users/smirs/Downloads/yolov3.weights...Done!\n",
-      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg: Predicted in 31.430375 seconds.\n",
-      "person: 100%\n"
+      "/Users/smirs/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg: Predicted in 25.008658 seconds.\n",
+      "laptop: 96%\n",
+      "laptop: 86%\n",
+      "laptop: 82%\n",
+      "laptop: 78%\n",
+      "laptop: 71%\n",
+      "chair: 59%\n",
+      "chair: 52%\n",
+      "cup: 87%\n",
+      "person: 99%\n",
+      "backpack: 72%\n",
+      "person: 99%\n",
+      "backpack: 60%\n",
+      "bottle: 68%\n",
+      "cup: 58%\n",
+      "person: 98%\n",
+      "person: 96%\n",
+      "person: 94%\n",
+      "person: 93%\n",
+      "person: 92%\n",
+      "person: 91%\n",
+      "person: 72%\n",
+      "chair: 97%\n",
+      "chair: 96%\n",
+      "chair: 93%\n",
+      "chair: 87%\n",
+      "chair: 86%\n",
+      "chair: 82%\n",
+      "chair: 75%\n",
+      "chair: 73%\n"
      ]
     }
    ],
    "source": [
-    "!./darknet detect cfg/yolov3.cfg ~/Downloads/yolov3.weights /Users/smirs/git_projects/deep-learning-with-python-notebooks/images/mypic.jpg"
+    "# Run Darknet! [in order to do Object Detection in your target image]\n",
+    "!./darknet detect cfg/yolov3.cfg ~/Downloads/yolov3.weights ~/git_projects/deep-learning-with-python-notebooks/images/coder_girl_cnn.jpg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Display the post-proccessed image!\n",
     "!open ./predictions.jpg"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thank You!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
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