From 8c47a1c50e651549b76351fe56c520330dcee3eb Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Thu, 24 Mar 2022 11:22:30 +0000
Subject: [PATCH] adding output

---
 ... - Object Detection and Segmentation.ipynb | 830 ++++--------------
 1 file changed, 190 insertions(+), 640 deletions(-)

diff --git a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb
index e5ef704..a7a8616 100644
--- a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb	
+++ b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb	
@@ -38,15 +38,15 @@
       "Found 2 image links\n",
       "Saved 2 images\n",
       "Found 2 image links\n",
-      "Saved 2 images\n",
-      "Found 2 image links\n",
-      "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/4/48/Angelina_Jolie_%2848462859552%29.jpg - cannot identify image file <_io.BytesIO object at 0x7f869f91d4d0>\n",
+      "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/thumb/c/c0/Robert_De_Niro_KVIFF_portrait.jpg/1200px-Robert_De_Niro_KVIFF_portrait.jpg - cannot identify image file <_io.BytesIO object at 0x7fae297b5770>\n",
       "Saved 1 images\n",
       "Found 2 image links\n",
       "Saved 2 images\n",
       "Found 2 image links\n",
       "Saved 2 images\n",
       "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
       "Saved 2 images\n"
      ]
     }
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -146,12 +146,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 420 images belonging to 8 classes.\n"
+      "Found 480 images belonging to 8 classes.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -168,7 +168,7 @@
        "       0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -310,8 +310,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 420 images belonging to 8 classes.\n",
-      "Found 70 images belonging to 8 classes.\n"
+      "Found 480 images belonging to 8 classes.\n",
+      "Found 83 images belonging to 8 classes.\n"
      ]
     }
    ],
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -372,8 +372,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/20\n",
-      " 6/24 [======>.......................] - ETA: 21s - loss: 2.0385 - accuracy: 0.1833"
+      "Epoch 1/20\n"
      ]
     },
     {
@@ -388,8 +387,45 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "21/24 [=========================>....] - ETA: 3s - loss: 2.0452 - accuracy: 0.1619WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 480 batches). You may need to use the repeat() function when building your dataset.\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 2.0452 - accuracy: 0.1619 - val_loss: 2.0012 - val_accuracy: 0.1571\n"
+      "24/24 [==============================] - 30s 1s/step - loss: 2.1493 - accuracy: 0.1146 - val_loss: 2.0780 - val_accuracy: 0.1625\n",
+      "Epoch 2/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 2.0772 - accuracy: 0.1437 - val_loss: 2.0687 - val_accuracy: 0.2250\n",
+      "Epoch 3/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 2.0690 - accuracy: 0.1667 - val_loss: 2.0434 - val_accuracy: 0.2125\n",
+      "Epoch 4/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 2.0251 - accuracy: 0.2229 - val_loss: 1.9642 - val_accuracy: 0.3500\n",
+      "Epoch 5/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.9674 - accuracy: 0.2438 - val_loss: 1.8757 - val_accuracy: 0.3000\n",
+      "Epoch 6/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.8926 - accuracy: 0.2750 - val_loss: 1.7863 - val_accuracy: 0.3625\n",
+      "Epoch 7/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.8776 - accuracy: 0.2979 - val_loss: 1.7791 - val_accuracy: 0.3125\n",
+      "Epoch 8/20\n",
+      "24/24 [==============================] - 35s 1s/step - loss: 1.7989 - accuracy: 0.3146 - val_loss: 1.7242 - val_accuracy: 0.3125\n",
+      "Epoch 9/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.7280 - accuracy: 0.3438 - val_loss: 1.6277 - val_accuracy: 0.3750\n",
+      "Epoch 10/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.6853 - accuracy: 0.3667 - val_loss: 1.6015 - val_accuracy: 0.4375\n",
+      "Epoch 11/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.5953 - accuracy: 0.3750 - val_loss: 1.5688 - val_accuracy: 0.3875\n",
+      "Epoch 12/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.5313 - accuracy: 0.4062 - val_loss: 1.5065 - val_accuracy: 0.5375\n",
+      "Epoch 13/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.5017 - accuracy: 0.4271 - val_loss: 1.5153 - val_accuracy: 0.4625\n",
+      "Epoch 14/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.4794 - accuracy: 0.4437 - val_loss: 1.5480 - val_accuracy: 0.4625\n",
+      "Epoch 15/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.4285 - accuracy: 0.4563 - val_loss: 1.4185 - val_accuracy: 0.4500\n",
+      "Epoch 16/20\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.4327 - accuracy: 0.4437 - val_loss: 1.4999 - val_accuracy: 0.4375\n",
+      "Epoch 17/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.2907 - accuracy: 0.5083 - val_loss: 1.4782 - val_accuracy: 0.4125\n",
+      "Epoch 18/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.2766 - accuracy: 0.5292 - val_loss: 1.4866 - val_accuracy: 0.4500\n",
+      "Epoch 19/20\n",
+      "24/24 [==============================] - 27s 1s/step - loss: 1.2222 - accuracy: 0.5375 - val_loss: 1.5021 - val_accuracy: 0.4500\n",
+      "Epoch 20/20\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.2967 - accuracy: 0.4854 - val_loss: 1.4795 - val_accuracy: 0.4625\n"
      ]
     }
    ],
@@ -415,7 +451,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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O4o5b9XeyucsxxnAqLh6T1X4L/wPehaDTm3mPPReOn7vMqIX7uKVuOXo1q+yy83ZuVJFpL/TkUPBrBMkJHvVdcN11Bmfnxno2Stlhy8+QkmCNumn3tLVweskq1p3B9OFwOdbTEWbsyGo4HwnNBmW8PbCV1aG7Zw6EjrtmszGGXcfO8cGCvdw6ajkh7y5l6I8biTiTwbDK8CWwbz7c8lLOVxhzkTdm7SLFGN7t09jl7fXF/Hzo1OteqNcNVo6C88dden5P00SgVFZSU2DjOAjq8M94+PIN4JGlcOvLsGMafH2ztchKfrNtMviVgPrdM9/n5ieh1h2w4N9wchcA4SfjGL04jDtGr6D756v5buVBgsoWZUTHWoQePsOdn6xgzPIDJKU4FntJTrRq/peuBW1GuOGJXWvhrhMs2n2SZ+6oa+/Inc7vQkoiLHnTvmt4gJaYUCor4Yvg3FHo/M7Vj3v7wm3/tpZHnPE4TOgHwQ/DnW+B37WTl9wu8RLsngWN+mZd48fLC/p+S/LXbTk//l6G+X7ItpNJiECbGmV4uH0NujSqSJlifgDc36Y6b87exQcL9jJraxTv9m1Cq6gJEBNurTfs4+emJ/iPCwnJvDl7F/UrFueRDi5a6SwzpWtC26dg1cdWU6GH+kJcTbJs88uHgoODTWho1pM8lHKZX/rCqb3w7I7Ma/QkXbZq6qz9CkoFQZ8xUP1mt4Z5je2/wfRHYOh8CGqX4S6RZy8xb/tx5m4/Tonjq/nF933+LNKZyPbv061JJcqXyLxA2sJdJ3hz9i6Sz59gVeEX8a7RHt/7f7Pr2WTpzdm7GL/2MNNHtHVPB27CBfiyNRQrD4/+6ZHRUbkhIpuMMcEZbdOmIaUyc3o/HPjTWlQ9q0JtvoWtJoOh88Ckwo9drbH0SfHuizW9bZOs0s/Vrk5IJ8/HM271Ifp9/RftP1jGe3/sxUugY5e7uRjyFJ0uL2Boyc1ZJgGwOlAXP38rYyvPQ1ISuftIL+ZuP5Z1Z7INtkXEMn7tYe5vU919o3j8isFdb8PxrbBlgnuuaTNtGlIqM6E/gJcvtHzQuf2D2sGINbDoVVjzuVXTp+83ULm5rWFe4/xxOLgMOrzw93rAMRcSeGbyVv46cBpjoEGlErzUuR49mlaiehnHSlkpr8PxdTDnWajcMtsF5YtFb6V5zDxONR9JUmQQT/66hd/rRfJW78ZumWGbnJLKv6fvoHxxP17sbPOyl+k17g8bv4el/4WGvT02g9pVbL0jEJEuIrJPRPaLyDVFR0RkqIhEi8hWx9cjdsajlNMSL8KWidYfefqhl1nxKwY9P4V7f4f4WPj+Dlj+AaQk2RXptXb8Zt2ZNP1ntNCohftYdzCGp2+vw5Lnb+WPZzrwxG21/0kCYPV79P8eEGt+QVYxp6bC/JegeCXKd/sPM0e24/UeDdlwyOpM/nZFms5km/z412F2Hz/Pmz0buX9Sl4g1ae7yWVj+vnuvbQPbEoGIeANfAV2BhsBgEWmYwa5TjDHNHV/f2xWPUjmy4zdIOAchj+bu+DqdYORaaNQPlv8PfrgTove5NsbMbJ8Cga2hbG0Adh87z5TQCB5sG8Rzd9aldvksOrNLVYden0PUJvjz7cz32zrRmrHs6By/Mht38fO30qFOOd77Yy89v1jN5qMumqyWTsSZS4xeHEanBuXp0riiLdfIVqWmVv2mDd9ZM8+vY3beEYQA+40xB40xicBkoLeN11PKNYyBDWOhQhOomodRIYVLQf+xMHA8xB6Fb2+BY1tcF2dGTuyAkzv/njtgjOGdebsJKOzL07fXce4cjfpAq2HWbOr9S67dfjnWGj5Z9SZoMvCqTZUDCjP2gWC+vb8V5y4n0X/MGl6buZPz8a67IzLG8PqsnYjAf3u7fs5Ajtz+GvgVt6qtXmcDb9KyMxFUASLS/BzpeCy9/iKyXUSmiUjVjE4kIsNFJFREQqOjr5Op/er6dXSd9WYa8ohrFlRp1AdGrLXG9M9/yWpWscu2yVa/RqN+ACzZc4o1B2J4tlPdnJVc6PIelG/oKFl98uptKz6ESzFW00gmr8+VzuShbYOYuP4InT5ewdztx0h2QXPRvB3HWbYvmhfuqkeVAA+v/1ukNNz+Khxakf9nmmfB06OG5gBBxpimwGJgfEY7GWO+M8YEG2OCy5Ur59YAVQG0cSz4lbzm026eFK8Ad/4XIjdaTTd2SEmG7VOtuQ1FSpOYnMq783ZTq1xRhtxULWfn8i0MA8ZZQyVnDP8neZ3aCxu+hVYPZtsJXszPhzd6NmLmE+0oX8KPJ3/dQtP/LmLI2HWMXrSP5ftOce5yzu4Uzl1O4r9zdtOkSkmGtg3K2XOyS6thUKGxVWIj6bKno8kVO0cNRQFpP+EHOh77mzEmJs2P3wMf2hiPUtmLOwm7Z1t9A4WKZr9/TjQdBBt/sJaLrN89T2vjZujgMrh46u9Koz+vPczhmEv8OKx17mrjlG8AXd+HOc/AX59C++dgwb+s1+X215w+TdPAAGaObMfCXSfZcCiGTUfP8uWy/aQa64aiTvlitKpemlbVS9GqeimCyhTJtLnngwV7ibmQwI9DW+PtlU/KPnv7WKW9f+oOf30OHf/l6YhyzM5EsBGoIyI1sBLAIGBI2h1EpJIx5krRjl7A9d3joq5/m8dDahK0tmEAm5cXdPsQxt5u1au5K4vO2NzYNtnql6hzF2cvJvL50nBuqVuO2+qVz/05Wz4IB5ZZE+YSL8DB5VaTUNGyOTqNj7cX3ZtWontTqw7RxYRktkXEsunIWTYdPcu87ceYtOEoAKWLFqJltVIEB1mJoUmVkvj7erPpyBl+XX+Uh9vXoHGVkrl/TnYIam81x60eDc0HQ0AO78A8zLZEYIxJFpEngYWANzDOGLNLRN4CQo0xs4GnRaQXkAycAYbaFY9S2UpJstbZrXUHlMl6ecNcq9LKWrBl3Rho+QCUdbIDNzvx560F6lvcDz6F+HTeTi4mpvBq9zyuFywCPT+zRgit+hjKNbBKaeRRUT8f2tYuS9vaVkJJTTXsj75gJQbH15I9Vt+Er7fQqHJJouMSqBJQmOfvrJvn69virrdh3x/WPJK7f/Z0NDli64QyY8x8YH66x15P8/2/gX/bGYNSTts7z1pUpcdoe69zxxtW89OCl636PK7okN49C5Ljodkgwk/GMWH9UQaHVKVuheJ5P3fhABjwo1VptcforGdZ55KXl1C3QnHqVijO4BDr03TMhQQ2H7XuGjYfOcuFhGQ+G9Scon75dB5syUBrEt+yd+DgCqh5q6cjcprWGlLqip96wNkj8MxW++vHrP0KFr4Cg6dAvS55P9+P3eHCCXgylKE/bWTTkbMsf7Hj38XilJskxcNXIeBbBB5fbUvSzC2tNaRUdk7tgcOroPVD7ikiFjIcytaz7gqSE/J2rrNHrLUHmg1iRfhplu+L5unb62gS8ARff+j8P4jeY5UouU5oIlAKrLox3n7Q4gH3XM/b1xqRc/aQdXeQFzumApDcaCDvzN1N9TJFeKBtdRcEqXKlfneoeRssexcunvZ0NE7RRKCcd501Izot/rw14qZxfyhaxpZLrDsYw20fLWfpnjSTs2rdDvV7wMqP4Pyx3J3YGCv2oA5MCoPwUxf4d9cG+PlcH6WRb0gi1nDSxItZl+nIRzQRKOccWAbvVIAJ/a1ibPl1ecbc2DbZGhoZYk/Nw1Pn43ny1y0cOn2RxydsYsHONMsc3vUOpCbD4tczP0FWojZBzH4uNRjA6MVhtKlZms6NclAkT9mjXD0IeQw2jYdjWz0dTbY0ESjnhC20/j0dBrNGwkd1YNJgawGUhAuejS0vjLGahSq3tIZ2ulhySipPTtrCxYRkfh/RlqaBATzx6xZmbXXMrSxdw1oHecdvcGRtzi+wbRL4+PPVyUbEXk7itR4NPVt7R/2j47+s+RZ//F++v5vWRKCcE7Heqmj5zHZ45E9o/aj1SWf6IzCqNkx9wBrCeL1NsT+0Ek7vy32V0Wx8tCiMDYfO8F6/JrSqXoqfHwqhdVApnp2ylamhjlJc7Z+DElXgj5esNZKdlZwIO3/nQo0ufLc+moGtAmlUOZ9NtCrI/EtaQ4Uj1luJPh/TRKCyl3QZTmyHqq2t9s/AVtDlf/DcLhj2hzVB6sgaKxmMqg2/P2JNrMnraBh32DgWCpf+u0ibKy3efZJvVhxgyE3V6NPCqrdY1M+HH4eG0L52Wf5v2nZ+WXfEKtlw19tW5dDNGZbbylj4Irh8lh/ibsLX24sX73Lz4iwqe83vhcotrBXrEuI8HU2mNBGo7B3bYrVjpy/J7OUF1dtC94/g+b3wwCyrw3X/Epg0CEbVgZkjrZ/duTCLs85Fwt751gxf36yXZsypiDOXeGHqVhpXKcHrPa5ehqNwIW/GPhBMpwbleW3mTr5fddBKRNXbwdK34dIZ5y6ybRKJhcvx+eFARnasle3yksoDvLyg6yhrjseqjz0dTaY0EajsRay3/g0MyXwfbx+o2dFa1OTFcGvGbP3uVmneCf3ho7pWCeb81HQU+qO1klfwQy49bXxSCiMmbgJgzL2t8Pe9dgSPv683X9/biq6NK/LOvD18tfyANdIkPhaWv5f9RS6dwYQtZG5qOyoGFOORDjVd+hyUC1VtDc2GwJov3bc4UQ5pIlDZi9gAZWo7P7TS2xfq3Al9x1hJYdCvUOs2ayWnRa/aG6uzkhOsZpi6XaxVuVzo7bm72Rl1no/vbp7l2r2FfLz4YnAL+jSvzKiF+xi9ww/T6iGr8/rkrqwvsvN3JDWJsedv4l9d62eYbFQ+0ulNq9rsL33h7GFPR3MNTQQqa8Y4OoqzuBvIiq+/dWcwYBzc/KT1JueBBTz2n7rAE79uZs/x89YDu2fDxWiXDxmduSWKieuP8titNbmzYfbDOH28vfj47ubcE1yVz5eG81nKQIx/yWxXvErZOokwqlO4ajN6Oip6qnyseAWr6TTxIvzUE2Ijsj/GjTQRqKydOWitRlU1l4kgrTvegErNYdYTbv1DiE9KYeTETczbfpx+X69hzrZjVidx6VpQ83aXXSf8ZBz/nr6DkBqleSkHHbfeXsJ7/Zpwf5vqfLo2hnllH7HKXeyemfEBp/fjfWwTU5Pa63DR60nFJvDATIg/B+N75H4SoQ00EaisRWyw/s3L2r1X+BSy7gxSU62RRSnJeT+nE96au5uwkxf4eGAzGlUuwZjJMyBiPSnBD1udeS5wMSGZxydsoqifD18OboFPDheC8fIS3urdiEfa1+Dp8GYc86+NWfgqJF66Zt/zG34hxQiJDfrRolopl8Sv3KRyC7h/OlyMgfE9Ie6EpyMCNBGo7ESst9baLVffNecrUwt6fAIR62DF+645ZxbmbT/Or46mmv6tAvn10Ta8VWktl4wfI3bW5+zFxDxfwxjDy9N3cOj0RT4f3DzXo3dEhP90b8DI2+ryzLkhyPlIUld9cvVOqakkbZ7EGpryeI92eY5deUBgMNw3Dc4fh/G94ILn12HXRKCyFrnR+sV10SdnAJoOhOb3WTV2Dq103XnTiThziZenb6d51YC/x9gXSjpH8PklHK/Wk+VHEun55Wp2Rp3L03UmrDvCnG3HeOGuerStlbOVu9ITEV7sXI9bOvViVkpbUlZ/QtLpQ39v37dxAWWSTxJXtz+VPb1wu8q9am3g3qkQexR+7m3dIXiQJgKVufjz1ugVVzQLpdftQ2sk0u+P2lKhMSkllacnbwEDXwxu8c+avVsmQvJlanV/lqmP30xKqqH/mDXM2BKZq+tsi4jl7bl7uK1eOUbc6rpVzZ66ow4XO7xOUqoX28c9RUJyCqmphqPLfuQi/tzae5jLrqU8JKg9DJkMZw7AL72dnz9iA00EKnNRoYBxTUdxeoWKWv0Fl8/CzBFWv4ELfbI4jC1HY/lfvyb/DOFMTbVGLVVtAxWb0LxqAHOeak/zqgE8N2Ubb83ZTVKK83HEXkpk5MTNlCvuxyf3NMfLxYupD7nrZvbWHU6rS6v49Lux/L4+nDaXV3GqaleKFnPxwvfKM2p2hHsmWvMLfunrsWKOmghU5iI2AAJVMlzUKO8qNbWqb4YvgvVjXHba1eGnGbPiAINaV6Vns8r/bDjwp1X/P01dobLF/JjwyE0MaxfEuL8Ocd/36zl9IfvSGKmphuenbuNUXDxf3duSgCKFXBZ/Wi3vfpULRQLpe+Jz1swZR3G5TPXbXDsBTnlYnU5w9y/W3ffEAdaduJtpIlCZi9gA5RtaE2HsEvIo1OsOi9+wSlnkUXRcAs9N3UqtcsV4o2ejqzduHAtFy0ODXlc97OvtxRs9G/HJPc3YGhFLzy9Wsz0yNsvrjFlxgD/3nuK1Hg1pXjUgz3FnytefYr1GUdcrircL/URC0cp4BbW373rKM+p1gYE/QtRm+PVut1f01USgMpaaanUU29EslJYI9P4SilWA34bl6dNQaqrhhd+2cf5yEl8OaUHhQmlm20ZuskpptxpqDWPNQN8Wgfw+oi1eIgz4Zi2/hWY812HtgRg+XrSPns0qc38bN6wEVq8r1LqDYlzGr+Vg13bcq/yjQU8Y8IM1Um/SoAyHDttFf6NUxqL3QsJ5+xMBQJHS0H8sxB6BeS/kunb796sPsjIsmtd6NKR+RcddTEoyrBgF4+6C4hWh9cNZnqNxlZLMeao9rYNK8dK07bw+ayeJyf/0G5w6H89Tk7YQVLYo7/Vr4p7JXCLQbRRUb28lMnXjatQX+n4Hh1fD5MGQFO+Wy2oiUBmLdOFEMmdUbwsd/22tv7ttUo4P3xoRy4cL9tG1cUXuvama9WB0GPxwJyx7Bxr2gRFrrGSQjdJFCzF+WAjDb6nJz2uPcO/36zgVF3/VIjPf3NeKYn4+OY4z18rUgmHzIKCa+66pPKPpQOjzNRxcAVPuc0s5dzf+JqvrSsQGKFIGSruxqmWHF6x5BfNesBbBKVvHqcPOxyfx1KTNVCjhz/v9miLGwLoxsPS/4FsEBv5kfdLKAR9vL17p1oDGVUryf9O20fOL1dxcswwbDp1h9N3NqFuheC6eoFJOaj4EUhJhzjMw9UG4++dMmzRdQe8IVMYi1lt3A+6sY+PlDf2+Ax9/q7/AidtiYwyvTN/Bsdh4Ph/cnJIJUdbU/YX/tobmjVyX4ySQVq9mlZkxsh1+Pt7M3HqMITdVo1/LwFyfTymntRoK3T6CsD9g2jBb1/TQRKCudTEGYvZbn8rdrURl6DMGTu5wakH3qaERzN1+nOc71aHV6dkwph0c3wa9v4LBk62qj3nUoFIJZj/Zjg/6N7lmkRmlbBXyKHR+D/bOhenDbavPpU1D6lqRG61/3dU/kF69LtBmJKz7GmreapWxzsD+U3G8MXsX3WvAyGOvwMpFENTBal91cVt6QJFC3NNa2+eVB9w8ElKTrA9GlZpaa1y7mCYCda2I9eDlY1VK9JROb8KRv6yS1ZWaQcmrm2Pik1J4cuJm+vqs490zPyHRCdD1Q2j9qA6vVDeeds9A8cqZfijKK/2LUdeK2GDVTi+U+epatvPxgwE/Wu2ivz96zS3xJ7PW8uSZd3nPfIpX2drw+Gq46TFNAurG1XSgbX+T+lejrpaSBMc2e65ZKK0ytaD7aDi6BlaO+vvhTYsm8siOIXT12QR3vA4PLYSytT0YqFLXN20aUlc7uROSLrlnIpkzmt0DB5fByg+hUjMubp9Fq92TOeRTg4CH5uBdpZmnI1TquqeJQF3NlSuSuUq3j6y4Jg/GHy/Gmj50fuxTfMvr6lxKuYI2DamrRay3OqVK5qOx8n7F4O6fCS91KwMTXqdCv/eopklAKZfRRKCuFuGGQnM5ZIxh4pHi3HXiMeq0uoNeaUtLK6XyzNZEICJdRGSfiOwXkZez2K+/iBgRsanwvXLK+WNw7mi+ahY6H5/Ek5O28J8ZO+lQpxxv9NIJXUq5mm19BCLiDXwF3AlEAhtFZLYxZne6/YoDzwDr7YpFOSmf9Q9si4jlqUlbiIq9zL+61OexW2q6fBUwpZS9dwQhwH5jzEFjTCIwGeidwX5vAx8A7qm3qjIXudGq81OxiUfDMMbw/aqDDPhmDSmphqmPtWFEx1qaBJSyiVOJQESmi0h3EclJ4qgCpF3ZI9LxWNrztgSqGmPmZXP94SISKiKh0dHROQhB5UjEems2sY1VDrNz5mIij4wP5Z15e7itXnnmPd2eVtVLeywepQoCZ9/YvwaGAOEi8r6I1MvrhR1JZTTwQnb7GmO+M8YEG2OCy5Url9dLq4wkxcOxrR7tKN5w6AzdPlvFqvDT/LdXI769v5VtawErpf7hVB+BMWYJsERESgKDHd9HAGOBCcaYjOqjRgFV0/wc6HjsiuJAY2C5Y5WnisBsEelljAnN8TNReXN8q1XYKtD9iSAl1fDVsv18uiSMaqWLMH1kWxpXKen2OJQqqJzuLBaRMsB9wP3AFmAi0B54EOiYwSEbgToiUgMrAQzCuqsAwBhzDiib5vzLgRc1CXjI3x3F7k0Ep87H8+yUraw5EEOf5pV5p28T9678pZRyLhGIyAygHvAL0NMYc9yxaYqIZPjGbYxJFpEngYWANzDOGLNLRN4CQo0xs/MevnKZiPVQqgYUK++2S64Ii+b5KVu5lJjChwOaMrBVoHvWAFZKXcXZj16fG2OWZbTBGJPp2H9jzHxgfrrHMlxtxBjT0clYlKsZY90R1LrdLZdLSknl40VhfLPiAPUqFOfLIS2oo0s/KuUxziaChiKyxRgTCyAipYDBxpivbYtMuU/sEbh4CqravyJZ5NlLPD1pC5uPxjI4pBpv9GyIv6+37ddVSmXO2VFDj15JAgDGmLPAo7ZEpNzPTRPJFu46QbfPVhF28gJfDG7Be/2aaBJQKh9w9o7AW0TEGGPg71nDOq7vRhGxHgoVg/L2lW+YsSWS56Zso2lgSb4Y3ILqZYradi2lVM44mwgWYHUMf+v4+THHY+pGELEeqrQCL3s+nR8/d5nXZ+2idVApJj7ShkI+WutQqfzE2b/IfwHLgBGOr6XA/9kVlHKjhAtwcpdtzULGGP5v2naSUwwfDWymSUCpfMjZCWWpwBjHl7qRRG0Ck2pbIvh1w1FWhZ/m7d6NtDlIqXzK2XkEdYD3gIaA/5XHjTE1bYpLucuVjuJA11cAjzhziXfn7aFd7TLce1N1l59fKeUazt6n/4h1N5AM3Ab8DEywKyjlRpEboFx9KBzg0tOmphpe/G0bXiJ8OKCZVg5VKh9zNhEUNsYsBcQYc8QY8ybQ3b6wlFukplp3BDaUlRi/9jDrD53htR4NqBJQ2OXnV0q5jrOjhhIc1ULDHWUjooBi9oWl3CImHOJjXd4/cDD6Ah8s2Mtt9cpxd3DV7A9QSnmUs3cEzwBFgKeBVljF5x60KyjlJhGOReFcWHE0xdEk5Ofjzfv9m2rtIKWuA9neETgmj91jjHkRuAAMsz0q5R4RG6BwKShT22Wn/H7VQTYfjeXTe5pToYR/9gcopTwu2zsCY0wKVrlpdZ2LT0q5+oGIDdbdgJdrxvaHnYzj40VhdG5Ugd7NK7vknEop+zn7DrBFRGaLyP0i0u/Kl62RKZeasO4Ijd9YyLjVhzDGwKUzcHqfyzqKk1JSeWHqNor5+/Bu3ybaJKTUdcTZzmJ/IAZIW6fYANNdHpFyuZgLCXywYC/+vt68NXc3O6LO8X7TE/iByxLBmOUH2BF1jq/vbUnZYn4uOadSyj2cnVms/QLXsY8W7eNyYgp/PNOBBTtPMHpJGCGHZjFIvJHKLfN8/l3HzvH50nB6NqtMtyaVXBCxUsqdnJ1Z/CPWHcBVjDEPuTwi5VI7Is8xeWMED7erQZ0KxalToTiNqpSgyOT/sYdqnDkaT/s6uR8JnJhsNQmVKlqIt3o1cmHkSil3cbaPYC4wz/G1FCiBNYJI5WPGGN6YvZMyRQvxdKc6fz9+e50yhPgeJLxQAx4Yt57vVh7AUWE8xz5fGs7eE3G817cJpYpqZXKlrkfONg39nvZnEZkErLYlIuUyM7dGsfloLB8OaEoJf99/NpzajVfSJTr36k2XPRX53/y9bI88x4cDmlKkkPMLx2+LiGXMigMMaBVIp4YVbHgGSil3yO24wTqA+1Y5Vzl2ISGZ9+bvpVlgSQa0DLx6o2MimX+Nm/lqSEv+1aU+83ccp9/XazgSc9Gp88cnpfDCb9soX9yP13vat6CNUsp+TiUCEYkTkfNXvoA5WGsUqHzqyz/3cyougTd7Nbq24FvEBihWEQKqISKM6FiLn4aFcPxcPD2/WM3yfaeyPf/oxWHsP3WBD/qnu9tQSl13nEoExpjixpgSab7qpm8uUvnHodMX+WH1QQa0CqRFtVLX7hC5wVqoPs1Y/1vqlmPOk+2pHFCYYT9t5Ktl+zPtN9h4+AxjVx1kyE3VuKVuObuehlLKTZy9I+grIiXT/BwgIn1si0rlydtzd+Pn483/dal37ca4k3D2cIaF5qqVKcL0kW3p2bQyoxbuY8SEzVxISL5qn0uJybz42zaqBBTmlW4NbHoGSil3craP4A1jzLkrPxhjYoE3bIlI5cmfe0/y595TPHNHHcoXz6DWT6RjIZpMKo4WKeTDZ4Oa82r3BizafYK+X/3Fweh/Boh98MdejsRcYtSAZhTzc75jWSmVfzmbCDLaT98F8pmE5BTenruHWuWK8mDboIx3ilgP3oWgUrNMzyMiPNKhJhMevomYi4n0/vIvlu45yZr9pxm/9ghD2wZxc60y9jwJpZTbOftmHioio4GvHD8/AWyyJySVWz/+dZhDpy/y80MhmS8SH7ERKjUHn+zLQLStXZbZT7bj8QmbeHh8KCUL+1KjbFH+1aW+awNXSnmUs3cETwGJwBRgMhCPlQxUPnHyfDxfLA3nzoYVMu/ATU6AY1tyVF8osFQRpj3eln4tq3A5KYWPBjajcCFvF0WtlMoPnJ1QdhF42eZYVB68/8deklINr3XPYkz/8e2QkpDjFcn8fb0ZfXdz3u3TRJOAUjcgZ0cNLRaRgDQ/lxKRhbZFpXIk9PAZZmyJYniHmlQrUyTzHf/uKM5dxVFNAkrdmJxtGirrGCkEgDHmLDqzOF9ISTW8OWcXFUv4M/K2WlnvHLEeAqpB8YruCU4pdV1wNhGkiki1Kz+ISBAZVCNV7jc1NIKdUed5pXuDrOsEGWPNKHbxQvVKqeufs6OG/gOsFpEVgAAdgOG2RaWccu5SEqMW7iMkqDQ9m2azDsC5CIg7rolAKXUNZzuLF4hIMNab/xZgJnDZxrjUFReiYc8s2Dkdjq4Fk/r3ppLAZoATwH+dPF9ga9fHqJS6rjm7MM0jwDNAILAVaAOs5eqlK5WrXDoDe+dab/6HVlhv/mXrwc1PgK/VGXz6QgK/bjhK48olub2+k901RctlOZFMKVUwOds09AzQGlhnjLlNROoD/7MvrAIo/jzsm2+9+R/4E1KToFQNaP8cNO4P5Rv+XSTOGMPT369nl895lj/QEXRBGKVUHjibCOKNMfEigoj4GWP2ikgGFc2uJiJdgM8Ab+B7Y8z76bY/jjUxLQVrxbPhxpjdOXsK17HEixC2wHrzD19sjfEvWRXaPG69+VdqflWF0CsW7DzBmgMxvN27ka4KppTKM2cTQaRjHsFMYLGInAWOZHWAiHhjlaS4E4gENorI7HRv9L8aY75x7N8LGA10ydEzuN4kxcP+xdabf9gCSLpkrQ0QPMx6868SDF6ZD+a6nJjCO/P2UL9icQaHVMt0P6WUcpazncV9Hd++KSLLsPopF2RzWAiw3xhzEEBEJgO9gb8TgTHmfJr9i3IjD0m9GAOL/gN75kJiHBQpA80GWW/+1W4GL+cma3278gBRsZeZPLwNPt65XWBOKaX+keMKosaYFU7uWgWISPNzJHDN2EUReQJ4HihEJp3PIjIcx3DVatWu00/Baz6D7VOh+WDrzT/oFvDO2csfefYSY5YfoEfTSrSpqdU/lVKu4fGPlMaYr4wxtbCWvnw1k32+M8YEG2OCy5W7TlfEClsEQe2g91dQ6/YcJwGA/83fgwi6IIxSyqXsTARRQNU0Pwc6HsvMZKCPjfF4TuxRiN4DdTrn+hTrD8Ywf8cJnuhYm8oBhV0YnFKqoLMzEWwE6ohIDREpBAwCZqfdQUTqpPmxOxBuYzyeE+aoz1c394lgwvqjBBTx5dFbarooKKWUsti2ypgxJllEngQWYg0fHWeM2SUibwGhxpjZwJMi0glIAs4CD9oVj0eFL7LmBJSpnavDz8cnsWjXCe4Oroq/r1YAVUq5lq3LTRpj5gPz0z32eprvn7Hz+vlC4iU4tBJaDc1wToAz/thxnITkVPq1rOLa2JRSinzQWXzDO7wKkuOhzl25PsX0zVHULFuU5lUDXBeXUko5aCKwW9hC8C0KQe1zdXjEmUusP3SGfi2rILm8o1BKqaxoIrCTMVb/QM2OTi0Wn5GZW6yBVn1aaLOQUsoemgjsdGqPtQ5A3dw1CxljmL4liptqlCawVBZLUCqlVB5oIrBTuGPYaC77B7ZGxHLo9EX6twx0YVBKKXU1TQR2ClsEFZtCicq5Onz65ij8fLzo2kTXGFZK2UcTgV0un7UWi8/lJLKE5BTmbD9G50YVKe7v6+LglFLqH5oI7LJ/KZiUXJeVWLY3mthLSTp3QCllO00EdglfZJWartIyV4dP3xxJ2WJ+tK9d1sWBKaXU1TQR2CE1xVpxrPadTq8zkNbZi4ks23eKPs0r65oDSinb6buMHSJD4fKZXA8bnbv9GEkphn46Wkgp5QaaCOwQvhDEG2rdkavDf98cRf2KxWlYuYSLA1NKqWtpIrBD2CKo1gYKB+T40APRF9gaEatzB5RSbqOJwNXORcHJHbmeRDZzSxReAr2b527ugVJK5ZQmAlcLX2T9m4v5A6mphumbo2hfpxzlS/i7ODCllMqYJgJXC18EJatBufo5PnTD4TNExV6mv84dUEq5kSYCV0qKh4PLrdFCuSgZPX1zJEULeXNXQy0poZRyH00ErnRkNSRdytVs4vikFObvOEHXJpUoXEiXo1RKuY8mAlcKWwQ+haFGhxwfumj3SS4kJGtJCaWU22kicBVjrPkDNW4B38I5Pnz65kgql/SnTY0yNgSnlFKZ00TgKqfD4ezhXM0mPhUXz8qwaPq2rIKXly5HqZRyL00ErvL3IjQ57x+YvfUYqQb6ttBJZEop99NE4CphC6F8QwiomuNDp2+OollgSWqXL2ZDYEoplTVNBK4Qfw6Ors3VbOI9x8+z+/h5LTCnlPIYTQSucGAZpCbnajbxjC1R+HgJPZtpSQmllGcUrERgjD3nDV8E/gEQGJKjw1JSDTO3RHFb/fKULlrIntiUUiobBScR7JkDP3azZv+6UmqqlQhq3wHePjk69K/9pzkVl6AlJZRSHlVwEoG3HxxdA4tede15j2+Bi9G5Gi00fXMkJQv7clv98q6NSSmlcqDgJIK6d8HNT8LGsbBnruvOG7YIEKjdKUeHXUhIZsGuE/RoWgk/Hy0poZTynIKTCADueAMqNYdZT0BshGvOGb4QAltD0ZzNCP5jx3Hik1J1tJBSyuMKViLwKQQDxlmLy09/FFKS83a+uJNwbEuuZhNP3xxFUJkitKwWkLcYlFIqjwpWIgAoUwt6fGKN+1/xQd7OtX+x9W8O+weiYi+z7lAM/VoGIrkoV62UUq5U8BIBQNOB0Pw+WDkKDq3M/XnCFkLxylCxSY4Om7klCmOgbwsdLaSU8ryCmQgAun0IZWrD9OFw8XTOj09OtCaS5XARGmMM0zdHEhJUmqqli+T8ukop5WIFNxEUKmr1F1w6AzNH5nyy2dG1kBiX42ah7ZHnOBB9UdcdUErlG7YmAhHpIiL7RGS/iLycwfbnRWS3iGwXkaUiUt3OeK5RqSnc9Y418mfdmJwdG7bQmptQ89YcHTZjSxSFfLzo1rRSzq6nlFI2sS0RiIg38BXQFWgIDBaRhul22wIEG2OaAtOAD+2KJ1Mhj0K97rD4dWsEkLPCF0JQe+vOwkmJyanM3naMuxpWoIS/by6CVUop17PzjiAE2G+MOWiMSQQmA73T7mCMWWaMueT4cR3g/kH1ItD7SyhWHqY9BAlx2R8TcwBi9ue4yNyKsGjOXEzUZiGlVL5iZyKoAqSdtRXpeCwzDwN/ZLRBRIaLSKiIhEZHR7swRIcipaH/99YKY3Ofz76/IHyR9W8Oy05P3xxJ2WKF6FCnXO7iVEopG+SLzmIRuQ8IBkZltN0Y850xJtgYE1yunE1votXbwq0vw46psG1S1vuGLYSydaF0DadPf+5SEkv3nKJXsyr4eueLl10ppQB7E0EUkHa5rkDHY1cRkU7Af4BexpgEG+PJ3i0vQvX2MO9Faw3ijCRcgCN/5ehuwBjDJ0vCSExJ1WYhpVS+k7O6yTmzEagjIjWwEsAgYEjaHUSkBfAt0MUYc8rGWJzj5Q39x8KYdjBtGDy8BHz9r97n4HJISXS6f8AYw6iF+/hpzWGGtg2icZWSro9bKZWlpKQkIiMjiY93cRn6fMjf35/AwEB8fZ0fkGJbIjDGJIvIk8BCwBsYZ4zZJSJvAaHGmNlYTUHFgN8cpRaOGmN62RWTU0pUhj5jYNI91kiibukGMoUvBL8SUO1mp0736ZJwvl5+gCE3VeONnukHTSml3CEyMpLixYsTFBR0Q5d1McYQExNDZGQkNWo433Rt5x0Bxpj5wPx0j72e5vuc1W52l3pdoM1IWPc11OwI9btZjxsD4Yuh1m3gnX22/fLPcD5bGs7AVoG807vxDf0LqFR+Fh8ff8MnAQARoUyZMuR0UI32Wmam05tQsSnMGgnnHF0bJ7ZD3HGnZhN/u+IAHy0Ko2+LKrzfvyleXjf2L6BS+d2NngSuyM3z1ESQGR8/GPgTpCTB749YJavDrgwbvTPLQ8etPsR7f+ylR9NKjBrQFG9NAkqpfEwTQVbK1ILuH1tLXK4cZfUPVG5pTT7LxC9rD/PW3N10bVyRT+5pjo8OFVWqwIuNjeXrr7/O8XHdunUjNjbW9QGlo+9S2Wk2CJoNhpUfQmRolqOFJm84ymuzdtGpQQU+G9RC5wsopYDME0FyctaLY82fP5+AgACbovqHrZ3FN4xuH0HEBjhzINP5A9M2RfLvGTvoWK8cX93bgkI+mgSUyo/+O2cXu4+dd+k5G1YuwRs9G2W6/eWXX+bAgQM0b94cX19f/P39KVWqFHv37iUsLIw+ffoQERFBfHw8zzzzDMOHDwcgKCiI0NBQLly4QNeuXWnfvj1r1qyhSpUqzJo1i8KFC7skfn23coZfMRg8CTq+Yq15nM6srVG8NG0b7WuX5Zv7Wuli9Eqpq7z//vvUqlWLrVu3MmrUKDZv3sxnn31GWFgYAOPGjWPTpk2Ehoby+eefExMTc805wsPDeeKJJ9i1axcBAQH8/vvvLotP7wicVa4edPzXNQ/P236c56Zs5aYapfnu/mD8fTUJKJWfZfXJ3V1CQkKuGuf/+eefM2PGDAAiIiIIDw+nTJkyVx1To0YNmjdvDkCrVq04fPiwy+LRRJAHC3ae4OnJW2hVvRQ/PNiawoU0CSilsle06D/l65cvX86SJUtYu3YtRYoUoWPHjhnOgPbz8/v7e29vby5fvuyyeLRpKJeW7jnJU5M20zSwJD8OC6Gon+ZUpVTGihcvTlxcxiXuz507R6lSpShSpAh79+5l3bp1bo5O7whyZUVYNCMmbKZBpRKMfyiEYpoElFJZKFOmDO3ataNx48YULlyYChUq/L2tS5cufPPNNzRo0IB69erRpk0bt8cnJqdr9XpYcHCwCQ0N9dj1/9p/mod+2kitcsWY9GgbShbRlcaUyu/27NlDgwYNPB2G22T0fEVkkzEmOKP9tWkoB9YdjOHh8RupUbYoEx65SZOAUuqGoInASSvDonnop40ElirChEduonTRQp4OSSmlXEIbt7ORnJLK6MVhjFlxgLrli/PLwyGULeaX/YFKKXWd0ESQhajYyzw9aQubjpxlcEhVXu/RSIeIKqVuOJoIMrFo1wlemradlFTD54Nb0KtZZU+HpJRSttBEkE5Ccgrvzd/LT2sO07hKCb4c3JKgskWzP1Appa5T2lmcxuHTF+k/Zg0/rTnMsHZB/D6irSYBpZTbFStWDIBjx44xYMCADPfp2LEjrhpKr3cEDrO2RvGfGTvx9hLGPhDMnQ0rZH+QUkrZqHLlykybNs326xT4RHA5MYU3Z+9iSmgEwdVL8dngFlQJcE1pV6VUPvTHy3Bih2vPWbEJdH0/080vv/wyVatW5YknngDgzTffxMfHh2XLlnH27FmSkpJ455136N2791XHHT58mB49erBz504uX77MsGHD2LZtG/Xr13dpraECnQjCTsbxxMTN7I++wBO31eK5TnV1RTGllMvdc889PPvss38ngqlTp7Jw4UKefvppSpQowenTp2nTpg29evXKdM3hMWPGUKRIEfbs2cP27dtp2bKly+IrkInAGMPkjRH8d84uivn58vNDIXSoU87TYSml3CGLT+52adGiBadOneLYsWNER0dTqlQpKlasyHPPPcfKlSvx8vIiKiqKkydPUrFixQzPsXLlSp5++mkAmjZtStOmTV0WX4FLBHHxSbwyYydzth2jfe2yjL6nGeWL+3s6LKXUDW7gwIFMmzaNEydOcM899zBx4kSio6PZtGkTvr6+BAUFZVh+2h0KVDvIjshz9PhiNfN3HOelzvX4+aEQTQJKKbe45557mDx5MtOmTWPgwIGcO3eO8uXL4+vry7Jlyzhy5EiWx99yyy38+uuvAOzcuZPt27e7LLYCc0fwW2gEr8zYQdlifkwe3obWQaU9HZJSqgBp1KgRcXFxVKlShUqVKnHvvffSs2dPmjRpQnBwMPXr18/y+BEjRjBs2DAaNGhAgwYNaNWqlctiKzCJoGa5otxRvwLv9WtCKS0Yp5TygB07/hmtVLZsWdauXZvhfhcuXACsxet37twJQOHChZk8ebItcRWYRNCqemla3a93AUoplV6B6iNQSil1LU0ESqkC4XpbjTG3cvM8NREopW54/v7+xMTE3PDJwBhDTEwM/v45Gw1ZYPoIlFIFV2BgIJGRkURHR3s6FNv5+/sTGBiYo2M0ESilbni+vr7UqFHD02HkW9o0pJRSBZwmAqWUKuA0ESilVAEn11svuohEA1kX5chcWeC0C8NxNY0vbzS+vMvvMWp8uVfdGJNhmeXrLhHkhYiEGmOCPR1HZjS+vNH48i6/x6jx2UObhpRSqoDTRKCUUgVcQUsE33k6gGxofHmj8eVdfo9R47NBgeojUEopda2CdkeglFIqHU0ESilVwN2QiUBEuojIPhHZLyIvZ7DdT0SmOLavF5EgN8ZWVUSWichuEdklIs9ksE9HETknIlsdX6+7Kz7H9Q+LyA7HtUMz2C4i8rnj9dsuIi3dGFu9NK/LVhE5LyLPptvH7a+fiIwTkVMisjPNY6VFZLGIhDv+LZXJsQ869gkXkQfdFNsoEdnr+P+bISIBmRyb5e+CzTG+KSJRaf4fu2VybJZ/7zbGNyVNbIdFZGsmx7rlNcwTY8wN9QV4AweAmkAhYBvQMN0+I4FvHN8PAqa4Mb5KQEvH98WBsAzi6wjM9eBreBgom8X2bsAfgABtgPUe/L8+gTVRxqOvH3AL0BLYmeaxD4GXHd+/DHyQwXGlgYOOf0s5vi/lhtjuAnwc33+QUWzO/C7YHOObwItO/A5k+fduV3zptn8MvO7J1zAvXzfiHUEIsN8Yc9AYkwhMBnqn26c3MN7x/TTgDhERdwRnjDlujNns+D4O2ANUcce1Xag38LOxrAMCRKSSB+K4AzhgjMntTHOXMcasBM6kezjt79l4oE8Gh3YGFhtjzhhjzgKLgS52x2aMWWSMSXb8uA7IWd1iF8vk9XOGM3/veZZVfI73jruBSa6+rrvciImgChCR5udIrn2j/Xsfxx/DOaCMW6JLw9Ek1QJYn8Hmm0Vkm4j8ISKN3BsZBlgkIptEZHgG2515jd1hEJn/8Xny9buigjHmuOP7E0CFDPbJD6/lQ1h3eBnJ7nfBbk86mq/GZdK0lh9evw7ASWNMeCbbPf0aZutGTATXBREpBvwOPGuMOZ9u82as5o5mwBfATDeH194Y0xLoCjwhIre4+frZEpFCQC/gtww2e/r1u4ax2gjy3VhtEfkPkAxMzGQXT/4ujAFqAc2B41jNL/nRYLK+G8j3f083YiKIAqqm+TnQ8ViG+4iID1ASiHFLdNY1fbGSwERjzPT0240x540xFxzfzwd8RaSsu+IzxkQ5/j0FzMC6/U7LmdfYbl2BzcaYk+k3ePr1S+PklSYzx7+nMtjHY6+liAwFegD3OhLVNZz4XbCNMeakMSbFGJMKjM3k2h79XXS8f/QDpmS2jydfQ2fdiIlgI1BHRGo4PjUOAman22c2cGV0xgDgz8z+EFzN0Z74A7DHGDM6k30qXumzEJEQrP8ntyQqESkqIsWvfI/Vqbgz3W6zgQcco4faAOfSNIG4S6afwjz5+qWT9vfsQWBWBvssBO4SkVKOpo+7HI/ZSkS6AP8H9DLGXMpkH2d+F+yMMW2/U99Mru3M37udOgF7jTGRGW309GvoNE/3VtvxhTWqJQxrNMF/HI+9hfVLD+CP1aSwH9gA1HRjbO2xmgi2A1sdX92Ax4HHHfs8CezCGgGxDmjrxvhqOq67zRHDldcvbXwCfOV4fXcAwW7+/y2K9cZeMs1jHn39sJLScSAJq536Yax+p6VAOLAEKO3YNxj4Ps2xDzl+F/cDw9wU236stvUrv4NXRtFVBuZn9bvgxtfvF8fv13asN/dK6WN0/HzN37s74nM8/tOV37s0+3rkNczLl5aYUEqpAu5GbBpSSimVA5oIlFKqgNNEoJRSBZwmAqWUKuA0ESilVAGniUApN3JURp3r6TiUSksTgVJKFXCaCJTKgIjcJyIbHDXkvxURbxG5ICKfiLWOxFIRKefYt7mIrEtT27+U4/HaIrLEUfxus4jUcpy+mIhMc6wHMNFdlW+VyowmAqXSEZEGwD1AO2NMcyAFuBdrRnOoMaYRsAJ4w3HIz8C/jDFNsWbCXnl8IvCVsYrftcWamQpWxdlngYZYM0/b2fyUlMqSj6cDUCofugNoBWx0fFgvjFUwLpV/iotNAKaLSEkgwBizwvH4eOA3R32ZKsaYGQDGmHgAx/k2GEdtGseqVkHAatuflVKZ0ESg1LUEGG+M+fdVD4q8lm6/3NZnSUjzfQr6d6g8TJuGlLrWUmCAiJSHv9cero719zLAsc8QYLUx5hxwVkQ6OB6/H1hhrNXnIkWkj+McfiJSxJ1PQiln6ScRpdIxxuwWkVexVpXywqo4+QRwEQhxbDuF1Y8AVonpbxxv9AeBYY7H7we+FZG3HOcY6ManoZTTtPqoUk4SkQvGmGKejkMpV9OmIaWUKuD0jkAppQo4vSNQSqkCThOBUkoVcJoIlFKqgNNEoJRSBZwmAqWUKuD+H+lI20Siqb4TAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -427,7 +463,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -513,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -538,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -548,7 +584,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -612,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -628,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -675,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -690,7 +726,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_2 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
+      " input_5 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
       "                                                                 \n",
       " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
       "                                                                 \n",
@@ -788,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "Found 480 files belonging to 8 classes.\n",
-      "Found 80 files belonging to 8 classes.\n"
+      "Found 83 files belonging to 8 classes.\n"
      ]
     }
    ],
@@ -820,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -860,7 +896,7 @@
        "(480, 4, 4, 512)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -878,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -887,7 +923,7 @@
        "(480, 8)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -898,15 +934,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(80, 4, 4, 512)\n",
-      "(80, 8)\n"
+      "(83, 4, 4, 512)\n",
+      "(83, 8)\n"
      ]
     }
    ],
@@ -917,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -934,26 +970,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model\"\n",
+      "Model: \"model_2\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_3 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
+      " input_6 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
       "                                                                 \n",
-      " flatten_1 (Flatten)         (None, 8192)              0         \n",
+      " flatten_3 (Flatten)         (None, 8192)              0         \n",
       "                                                                 \n",
-      " dense_2 (Dense)             (None, 256)               2097408   \n",
+      " dense_6 (Dense)             (None, 256)               2097408   \n",
       "                                                                 \n",
-      " dropout_1 (Dropout)         (None, 256)               0         \n",
+      " dropout_3 (Dropout)         (None, 256)               0         \n",
       "                                                                 \n",
-      " dense_3 (Dense)             (None, 8)                 2056      \n",
+      " dense_7 (Dense)             (None, 8)                 2056      \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 2,099,464\n",
@@ -969,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -977,65 +1013,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "15/15 [==============================] - 2s 104ms/step - loss: 45.7096 - accuracy: 0.3167 - val_loss: 27.5370 - val_accuracy: 0.4250\n",
+      "15/15 [==============================] - 2s 102ms/step - loss: 44.8126 - accuracy: 0.2958 - val_loss: 25.7535 - val_accuracy: 0.4096\n",
       "Epoch 2/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 17.4056 - accuracy: 0.6062 - val_loss: 23.3888 - val_accuracy: 0.4250\n",
+      "15/15 [==============================] - 1s 90ms/step - loss: 13.9936 - accuracy: 0.6562 - val_loss: 17.8460 - val_accuracy: 0.4699\n",
       "Epoch 3/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 10.0907 - accuracy: 0.7000 - val_loss: 27.9630 - val_accuracy: 0.4000\n",
+      "15/15 [==============================] - 1s 95ms/step - loss: 11.5003 - accuracy: 0.7000 - val_loss: 25.4290 - val_accuracy: 0.4578\n",
       "Epoch 4/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 8.1440 - accuracy: 0.7729 - val_loss: 22.5127 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 7.5189 - accuracy: 0.7500 - val_loss: 21.8036 - val_accuracy: 0.4819\n",
       "Epoch 5/30\n",
-      "15/15 [==============================] - 1s 79ms/step - loss: 5.6496 - accuracy: 0.8271 - val_loss: 29.9891 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 1s 80ms/step - loss: 5.2865 - accuracy: 0.8313 - val_loss: 20.3280 - val_accuracy: 0.4699\n",
       "Epoch 6/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 4.4128 - accuracy: 0.8562 - val_loss: 23.5226 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 4.7574 - accuracy: 0.8417 - val_loss: 30.6355 - val_accuracy: 0.4699\n",
       "Epoch 7/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 4.9496 - accuracy: 0.8729 - val_loss: 23.6873 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 80ms/step - loss: 4.5704 - accuracy: 0.8521 - val_loss: 25.3694 - val_accuracy: 0.4819\n",
       "Epoch 8/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 3.2162 - accuracy: 0.8667 - val_loss: 29.5564 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 78ms/step - loss: 3.4702 - accuracy: 0.8771 - val_loss: 22.3616 - val_accuracy: 0.5783\n",
       "Epoch 9/30\n",
-      "15/15 [==============================] - 1s 96ms/step - loss: 2.5075 - accuracy: 0.9146 - val_loss: 26.9293 - val_accuracy: 0.4500\n",
+      "15/15 [==============================] - 1s 78ms/step - loss: 2.9711 - accuracy: 0.8875 - val_loss: 27.0335 - val_accuracy: 0.5060\n",
       "Epoch 10/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 3.2294 - accuracy: 0.8917 - val_loss: 29.0235 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 86ms/step - loss: 2.8419 - accuracy: 0.9125 - val_loss: 33.1303 - val_accuracy: 0.4578\n",
       "Epoch 11/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 2.2690 - accuracy: 0.9125 - val_loss: 28.3215 - val_accuracy: 0.4375\n",
+      "15/15 [==============================] - 1s 84ms/step - loss: 3.0754 - accuracy: 0.8917 - val_loss: 22.9139 - val_accuracy: 0.5422\n",
       "Epoch 12/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 3.3047 - accuracy: 0.8979 - val_loss: 25.3148 - val_accuracy: 0.4875\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 1.9240 - accuracy: 0.9354 - val_loss: 27.3888 - val_accuracy: 0.5060\n",
       "Epoch 13/30\n",
-      "15/15 [==============================] - 1s 98ms/step - loss: 1.9621 - accuracy: 0.9312 - val_loss: 26.1054 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 96ms/step - loss: 2.5214 - accuracy: 0.9125 - val_loss: 26.3481 - val_accuracy: 0.5301\n",
       "Epoch 14/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 3.0970 - accuracy: 0.9146 - val_loss: 35.0209 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 1s 77ms/step - loss: 1.1142 - accuracy: 0.9542 - val_loss: 31.6295 - val_accuracy: 0.4458\n",
       "Epoch 15/30\n",
-      "15/15 [==============================] - 1s 78ms/step - loss: 1.3575 - accuracy: 0.9458 - val_loss: 29.6890 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 77ms/step - loss: 2.7297 - accuracy: 0.9250 - val_loss: 27.2849 - val_accuracy: 0.5542\n",
       "Epoch 16/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 1.2133 - accuracy: 0.9479 - val_loss: 30.5464 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 98ms/step - loss: 2.8289 - accuracy: 0.9312 - val_loss: 37.3016 - val_accuracy: 0.4940\n",
       "Epoch 17/30\n",
-      "15/15 [==============================] - 1s 79ms/step - loss: 1.8159 - accuracy: 0.9396 - val_loss: 33.3255 - val_accuracy: 0.4750\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 1.7244 - accuracy: 0.9333 - val_loss: 32.6699 - val_accuracy: 0.5181\n",
       "Epoch 18/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 1.4012 - accuracy: 0.9396 - val_loss: 30.1178 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.6433 - accuracy: 0.9625 - val_loss: 34.0047 - val_accuracy: 0.4578\n",
       "Epoch 19/30\n",
-      "15/15 [==============================] - 1s 100ms/step - loss: 2.6479 - accuracy: 0.9438 - val_loss: 32.6207 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 2.1252 - accuracy: 0.9208 - val_loss: 32.4775 - val_accuracy: 0.4940\n",
       "Epoch 20/30\n",
-      "15/15 [==============================] - 1s 80ms/step - loss: 1.1343 - accuracy: 0.9521 - val_loss: 33.7492 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 93ms/step - loss: 1.9605 - accuracy: 0.9417 - val_loss: 38.5654 - val_accuracy: 0.4819\n",
       "Epoch 21/30\n",
-      "15/15 [==============================] - 1s 80ms/step - loss: 1.4898 - accuracy: 0.9542 - val_loss: 32.9078 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 1.2957 - accuracy: 0.9521 - val_loss: 34.1380 - val_accuracy: 0.5060\n",
       "Epoch 22/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 1.4119 - accuracy: 0.9583 - val_loss: 35.3497 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 0.9360 - accuracy: 0.9688 - val_loss: 35.2782 - val_accuracy: 0.4819\n",
       "Epoch 23/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 2.7630 - accuracy: 0.9125 - val_loss: 33.3531 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 90ms/step - loss: 1.6199 - accuracy: 0.9521 - val_loss: 42.6388 - val_accuracy: 0.4096\n",
       "Epoch 24/30\n",
-      "15/15 [==============================] - 1s 89ms/step - loss: 1.1729 - accuracy: 0.9625 - val_loss: 37.2347 - val_accuracy: 0.4750\n",
+      "15/15 [==============================] - 1s 81ms/step - loss: 1.3869 - accuracy: 0.9563 - val_loss: 36.0665 - val_accuracy: 0.5181\n",
       "Epoch 25/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 1.0788 - accuracy: 0.9500 - val_loss: 36.1926 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 68ms/step - loss: 0.9746 - accuracy: 0.9646 - val_loss: 33.6369 - val_accuracy: 0.4940\n",
       "Epoch 26/30\n",
-      "15/15 [==============================] - 1s 101ms/step - loss: 0.8924 - accuracy: 0.9604 - val_loss: 33.2011 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 0.9503 - accuracy: 0.9708 - val_loss: 35.7475 - val_accuracy: 0.4819\n",
       "Epoch 27/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 1.4114 - accuracy: 0.9563 - val_loss: 32.9735 - val_accuracy: 0.5500\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 0.5366 - accuracy: 0.9771 - val_loss: 32.7837 - val_accuracy: 0.4819\n",
       "Epoch 28/30\n",
-      "15/15 [==============================] - 1s 81ms/step - loss: 1.0081 - accuracy: 0.9583 - val_loss: 35.2063 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 1.1540 - accuracy: 0.9583 - val_loss: 32.0780 - val_accuracy: 0.5060\n",
       "Epoch 29/30\n",
-      "15/15 [==============================] - 1s 93ms/step - loss: 1.0290 - accuracy: 0.9583 - val_loss: 35.6483 - val_accuracy: 0.4875\n",
+      "15/15 [==============================] - 1s 84ms/step - loss: 1.2960 - accuracy: 0.9542 - val_loss: 39.2627 - val_accuracy: 0.4819\n",
       "Epoch 30/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 0.6959 - accuracy: 0.9667 - val_loss: 35.5286 - val_accuracy: 0.5000\n"
+      "15/15 [==============================] - 1s 92ms/step - loss: 0.9093 - accuracy: 0.9771 - val_loss: 36.2351 - val_accuracy: 0.4940\n"
      ]
     }
    ],
@@ -1085,12 +1121,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+qAvNG0SzJzOXjxdu5/2ft7PrQA5t42K4fmAHruzXjrh6v45cLSxUHvl8FR8u2M6YM+J59JKuVrVjTBkCmQxOA8ap6m88rx8GUNUni22zGrhQVXeI+yvOUNVyJyGxZBA463dlctmLP3Baxya8fWP/o268G3Zn8uAny1melMHQ7i15fFh3msWWXkrYsf8Qj32xmtlrU+nUvD6PD+vOaR2P7fufV1DIrDW7mfDTVhZs2U9URBjDerXmt6fF06VVA/746QomL07i9iEd+b/fnGyJwJhyBHIEchtgR7HXScCpJbZZDozEVSWNAGJFpImq7vNjXKYKcvIKuPujpcRGR/Kvy0855sZ7UotYPr39dF7/fgv/mbWBnzfP47Fh3bm056+lhJy8Al77bjMvzUkkPEz400WdGXNGQpkNvZHhYVzUoxUX9WjFul0HeHf+NqYuSWbSoiRaN4wmJSPHGnuN8RF/lgwuxz313+J5fT1wqqreWWyb1sCLQALwHTAK6K6q6SWONRYYC9C+ffu+27Zt80vMpmzjpq3mnZ+28s6Y/gw5uXm5227cnckDk1ewfEc6v+nWgr8N786alAOMm7aarfsOcXHPVjxycRdaNYypdBwZh/L4ZPEOPl2SzMjebcqc0sAYc7QaXU1UYvv6wDpVbVveca2aqPoVrTR186AE/nJJV6/2yS8o5M0ftvDMrA2ECeTkFXJC03o8NqwbgztZQ6Ex1S2Q1UQLgU4ikgAkA1cB15QIrimwX1ULgYdxPYtMDZJ6IIcHJ6+ga6sG/N+FJ3u9X0R4GLed1ZFzu7Tg6Rnr6NWuEbcMTqhVs2waE0r8lgxUNV9E7gRm4rqWvqWqq0XkcWCRqk4DhgBPiojiqonu8Fc8pvIKC5U/TFrOocP5PH917yrdyE9sXp/Xf1vmw4gxpobw66ylqjodmF7ivUeLfT0ZmOzPGEzVvfHDZn5I3MuTI3twYnP/TN1sjKkZbG4iU6qVSRn8a+Z6LuzWkqtsKmFjgp4lA3OMg7n53D1xKU3rR/GUTSVsTEiwxW2C3KKt+3nh20QWb0ujY7N6dGnVgM4tYz3/N6Bh3WMnBXvsi9Vs3XeQj24dWOFatcaY4GDJIAipKj8m7uPFORv5efN+GterwyU9W7F9/yFmrt7FxIW/jgVs3TDaJYZWsXRu2YD0Q4eZtCiJO88+kYFerghmjKn9LBkEEVXl23WpvPBtIst2pNOiQRR/uaQrVw9oR906EUe2Sc3MZe3OA6zdmcm6XQdYu/MAczfsocCzyEjv9o2457xOgbwUY0w1s2QQBAoKlRmrdvHinETW7jxA27gY/j6iO5f3bXtMd1ARoUWDaFo0iD5qJHFufgGJqVkkpmZxWscmFa4FYIwJLpYMarH8gkKmLU/hpTmJbNpzkBOa1eOZK07hsl6tK30zj4oIp1vrhnRr3dBP0RpjajJLBrVUYmoWY99bxOY9B+ncMpYXr+nN0O6tKrWoizHGFLFkUAv9sHEvt3+wmKiIMF67vi/nd21h3T+NMcfFkkEt88GCbTz6+WpObFafN2/sR9u4uoEOyRgTBCwZ1BIFhco/pq/lzR+2MOTkZrxwdW9ibeFwY4yPWDKoBbJy87nno6V8sy6VMWfE8+eLuhBhvX2MMT5kyaCGS07P5uZ3FrIxNYu/De/O9QM7BDokY0wQsmRQgy3bkc4tExaRm1fA2zf258yTbFEYY4x/WDKoob5ckcL9k5bTvEEUH916Kp1axAY6JGNMELNkUMOoKi9+m8gzszbQPz6O8df1pUn9qECHZYwJcpYMapiiRDCydxueHNXDlok0xlQLSwY1SGJqJi98m8glPVvxzJWn2EAyY0y1sf6JNURhofLQpyupGxXOuMu6WSIwxlQrSwY1xAe/bGfRtjQeubgrTa2NwBhTzSwZ1AC7MnJ4+ut1DDqxKaP6tAl0OMaYEGTJoAZ49PNV5BcW8vcR3a16yBgTEH5NBiJyoYisF5FEEXmolM/bi8gcEVkqIitE5CJ/xlMTzVi1k/+t2c29551Ehyb1Ah2OMSZE+S0ZiEg48BIwFOgKXC0iXUts9ggwSVV7A1cBL/srnpooIzuPRz9fTddWDbhlUEKgwzHGhDB/lgwGAImqullVDwMTgWEltlGggefrhkCKH+OpcZ6esY69Wbk8PaqnTTxnjAkof96B2gA7ir1O8rxX3DjgOhFJAqYDd5V2IBEZKyKLRGTRnj17/BFrtftly34+XLCdm85IoEdbW2rSGBNYgX4cvRp4R1XbAhcB74nIMTGp6muq2k9V+zVrVvsna8vNL+DhKStoGxfDHy44KdDhGGOMX5NBMtCu2Ou2nveKuxmYBKCq84FooKkfY6oRXpqziU17DvL3ET2oW8cGgRtjAs+fyWAh0ElEEkSkDq6BeFqJbbYD5wKISBdcMgiOeqAybNidyStzExneqzVn2ZTUxpgawm/JQFXzgTuBmcBaXK+h1SLyuIhc5tnsfuBWEVkOfATcqKrqr5gCrbBQeXjKSupHRfCXS0p2rDLGmMDxax2Fqk7HNQwXf+/RYl+vAc7wZww1yQcLtrF4WxrPXHGKTUttjKlRAt2AHDJ2ZmTz9Iz1DO7UlJE25YQxpoaxZFBN/vr5ajflxPAeNuWEMabGsWRQDZZuT+N/a3Zz1zmdaN+kbqDDMcaYY1gyqAYvz91Ew5hIbjg9PtChGGNMqSwZ+NmG3ZnMWrObG06Pp36UjSkwxtRMlgz8bPzcTcREhjPGSgXGmBrMkoEf7dh/iM+Xp3D1gPbE1asT6HCMMaZMlgz86PXvNxMmcOuZNj21MaZms2TgJ3syc/l44Q5G9m5Lq4YxgQ7HGGPKZcnAT976cQuHCwq57awTAh2KMcZUyJKBHxzIyeP9+du4qHsrTmhWP9DhGGNMhSwZ+MF787eRmZvP7UM6BjoUY4zxiiUDH8vJK+DtH7dw5knN6N7GVjAzxtQOlgx8bNKiHezNOswdViowxtQilgx8KK+gkFfnbaZvhzgGJDQOdDjGGOM1SwY+NG1ZCsnp2fx+SEebmdQYU6tYMvCRwkLllXmb6NwylnM6Nw90OMYYUymWDHxk1trdJKZmcbuVCowxtZAlAx9QVV6eu4n2jetycY9WgQ7HGGMqzZKBD8zftI/lO9K57awTiAi3b6kxpvaxO5cPvDx3E81ioxjVp22gQzHGmCrxazIQkQtFZL2IJIrIQ6V8/h8RWeb5t0FE0v0Zjz8s35HOD4l7uWVQAtGR4YEOxxhjqsSrpbdEZArwJvC1qhZ6uU848BJwPpAELBSRaaq6pmgbVb2v2PZ3Ab0rEXuN8PLcRBpER3DtwA6BDsUYY6rM25LBy8A1wEYReUpETvZinwFAoqpuVtXDwERgWDnbXw185GU8NUJiaiYzV9uSlsaY2s+rZKCqs1X1WqAPsBWYLSI/icgYEYksY7c2wI5ir5M87x1DRDoACcC3ZXw+VkQWiciiPXv2eBNytXh3/jbqRIRxoy1paYyp5bxuMxCRJsCNwC3AUuA5XHKY5YM4rgImq2pBaR+q6muq2k9V+zVr1swHpzt+h/MLmbY8hQu6tqBJ/ahAh2OMMcfF2zaDqcDJwHvApaq60/PRxyKyqIzdkoF2xV639bxXmquAO7yJpaaYsz6V9EN5jOprPYiMMbWftxXdz6vqnNI+UNV+ZeyzEOgkIgm4JHAVrt3hKCLSGYgD5nsZS43w6eIkmtaPYvCJTQMdijHGHDdvq4m6ikijohciEicivy9vB1XNB+4EZgJrgUmqulpEHheRy4ptehUwUVW1cqEHzv6Dh5mzPpXhvVrbIDNjTFDwtmRwq6q+VPRCVdNE5FZcL6Myqep0YHqJ9x4t8XqclzHUGF+uSCGvQBlpg8yMMUHC28facCk2+5pnDEEd/4RU8326JJkurRrQtXWDQIdijDE+4W0ymIFrLD5XRM7FjQeY4b+waq7E1CyW70hnVJ9Se8kaY0yt5G010R+B24DbPa9nAW/4JaIabsqSJMIELuvVOtChGGOMz3iVDDxTULzi+ReyCguVqUuTOfOkZjSPjQ50OMYY4zNeVROJSCcRmSwia0Rkc9E/fwdX0/y8eR87M3JsdlJjTNDxts3gbVypIB84G3gXeN9fQdVUny5JJjYqgvO7tgh0KMYY41PeJoMYVf0GEFXd5ukOerH/wqp5Dubm8/WqnVzcs5VNVW2MCTreNiDnikgYbtbSO3Ejiuv7L6yaZ+bqXRw6XGBjC4wxQcnbksE9QF3gbqAvcB1wg7+CqommLEmmXeMY+nWIC3QoxhjjcxWWDDwDzEar6gNAFjDG71HVMDszsvlx017uPqcTYWFS8Q7GGFPLVFgy8EwrPagaYqmxpi5NRhVG2kAzY0yQ8rbNYKmITAM+AQ4WvamqU/wSVQ2iqkxZkky/DnF0aFIv0OEYY4xfeJsMooF9wDnF3lMg6JPByuQMElOz+MeIHoEOxRhj/MbbEcgh105QZMqSZOpEhHFxz1aBDsUYY/zG25XO3saVBI6iqjf5PKIapGhpy/O7tqBhTFlLPRtjTO3nbTXRl8W+jgZGACm+D6dmmbs+lf0HD9sMpcaYoOdtNdGnxV+LyEfAD36JqAaZsiSZpvXrMLhTs0CHYowxflXVNRs7Ac19GUhNk37oMN+s282wXm2ItKUtjTFBzts2g0yObjPYhVvjIGh9sWKnZ2lLqyIyxgQ/b6uJYv0dSE0zZUkSnVvG0rWVLW1pjAl+3q5nMEJEGhZ73UhEhvstqgDbtCeLpdvTGdmnDcWWfjbGmKDlbWX4X1U1o+iFqqYDf/VLRDXA1CXJhAkM72VVRMaY0OBtMihtO28mubtQRNaLSKKIPFTGNld6VlBbLSIfehmPX322LJnBnZrRvIEtbWmMCQ3eJoNFIvKsiHT0/HsWWFzeDp7ZTl8ChgJdgatFpGuJbToBDwNnqGo34N7KXoCvZeXmk5SWzaknNA50KMYYU228TQZ3AYeBj4GJQA5wRwX7DAASVXWzqh727DesxDa3Ai+pahqAqqZ6G7i/pKRnA9CmUUyAIzHGmOrjbW+ig0Cp1TzlaAPsKPY6CTi1xDYnAYjIj0A4ME5VZ5Q8kIiMBcYCtG/fvpJhVE6yJxm0jbNkYIwJHd72JpolIo2KvY4TkZk+OH8EbgDbEOBq4PXi5ymiqq+paj9V7desmX9HAyenuWTQ2koGxpgQ4m01UVNPDyIAPNU6FY1ATgbaFXvd1vNecUnANFXNU9UtwAZccgiYlPRsIsKE5rHWeGyMCR3eJoNCETlSPyMi8ZQyi2kJC4FOIpIgInWAq4BpJbb5DFcqQESa4qqNNnsZk18kp2fTsmE04ba8pTEmhHg7a+mfgR9EZB4gwGA8dfhlUdV8EbkTmIlrD3hLVVeLyOPAIlWd5vnsAhFZAxQAD6rqvipei0+kpGdb47ExJuR424A8Q0T64RLAUtwTfbYX+00Hppd479FiXyvwB8+/GiE5LZuBJzQJdBjGGFOtvJ2o7hbgHly9/zJgIDCfo5fBrPXyCwrZdSCHNtaTyBgTYrxtM7gH6A9sU9Wzgd5Aur+CCpRdB3IoVOtJZIwJPd4mgxxVzQEQkShVXQec7L+wAiMlPQewAWfGmNDjbQNykqf//2fALBFJA7b5K6hASU4/BFjJwBgTerxtQB7h+XKciMwBGgLHjBSu7axkYIwJVd6WDI5Q1Xn+CKQmSErLpnG9OsTUCQ90KMYYU61scd9ibIyBMSZUWTIoJjk9m9aNbBoKY0zosWTgoaqekkHdQIdijKmJlrwLn98BeTmBjsQvKt1mEKzSD+Vx6HCBlQyMMcda/jFMu8t9nZ0OV0yA8OC6fVrJwMPWMTDGlGrDTPjsdogfDOc/Duu+hC/vBa1ors7aJbhS23EoSgY2xsAYc8S2+TDpt9CyO1z1IUQ3gNxM+O5fULcJnP9YoCP0GSsZeNhyl36SlwMvnwZL3gt0JKElOx0+vQWe6wVbfwh0NLXTrlXw4Who2Bau/dQlAoCz/wz9boIf/ws/Ph/QEH3JkoFHclo20ZFhNK5XJ9ChBJe1X0DqGlj4eqAjCR3bF8D4wbBqChTkwYRL4du/Q0F+oCOrPfZvgfdHQp16cP1UqF9shUURuOjf0G0EzPoLLH0/cHH6kCUDj5SMbFo3ikHEFrXxqSUT3P87l8PexMDGEuwKC2Dev+Dtoe6GdfP/4I4FcMrV8N0/4Z2LIC3oZpHxvczd8N5wKDjsEkGjUtZdDwuHEa/CCWe7huV1X1V7mL5mycAjOc0GnAFwcJ+7qfjCvk2w9XvofwsgsOpT3xy3MgoLITut+s9b3TKSYcJlMOcJ6D4Sfvc9tO0HUfVh+Msw6k1IXftriaEmOXwIDu717THzcyFzV+X3y053JYKsPXDtZGjeuextI6Jg9PvQujd8MqbWV8dZMvBITs8J7WRQWAg/PgfPnATf+KhRbMkEkHA480HocDqsmly9PTDyD8OHV8J/e8KBlOo7b3Vb+yWMPwNSlsLw8TDydYhuePQ2PS53CaLZSTB5jOsvf/hgYOIF98Cx6VuY+jv4dyd4vg+kb/fNsVXdzfmZk+GtC2HRW949EBw+BB9dBXvWw+j3XDKtSFR9lzTi4uHDq1wJuJayZADk5BWwNys3dHsSZe5yT0OzHoWoBvDLG8f/NJ1/GJZ9CCddCLEt3dPq3g2we7VvYq5IYaHrDpg4y9305vy9es5bnfKy4cv74ONroVEHd7PvdbWrIipNXDyM+RoGPwBLP4BXz6r+m9euVfC/R+A/3eC9EbBuOnQdDloAn/3e/dyO19L3Yf1X7riH9rvv0b9Pgo+vc21Y+bnH7lOQ55Lk9p9h5Gtw4rnen69uY1edFNMI3h/lSsS1kCUDYGdGCM9WuuF/8MoZ7o/gkv/CDV9A3kFY+OZxHvdrOLgH+t7gXncd7koJqyYfb8QVU4UZf3TnOm8cDLzd3fx2rfL/uavL7jXw2tnuqff0u+DmWdCkY8X7hUfCuX+BG6bB4Sx44zyY/7J/S2wHUlyp8+XTXQnm51dc1coV78ADG2D4S3DhU65KccErx3eutK0w4yE3JuDyt12bydi5rqpy+88uIfz7JJcgti9w111YCJ/fCRtmwMXPuAeXymrYxiUELYR3h9fKkqhoLRs40a9fP120aJFPj/nDxr1c9+YCPrp1IKd1DJH1j/NzYdZf3R9f825w+Vu/1o++P8o9Md67CiKrOCL7vZGwZx3cu9I1thW9t28j3LOi7KdXX5j7NMz9B5x2J1zwhCvlPN8L2vSD66uxvvzwQdg0x1WR1W3su+MufBNm/smV4ka8AieeV7XjHNwH0+6E9dPhxPOhy6W+ixEgP8c1rG75DlBo2x96joZuI6Feib8zVfjoald1dNs8aN6l8ucrLIB3LoHdq+D2H49t+C3Ih81zYMXHrmotP9uVlpp0ciXIsx+Bsx6s6tU6KUtdDA3bwY1fHXudASQii1W1zLovG3TGr2MMQmb08Z4N8OlNsGslDLjNjaosftM/4x7XHXH5R9BvTOWPn77d/VGf9X+/JgKA7qPg899D8mLv6mOr4pfXXSLoda1LBCLuRnzmg656IvGbylUBVFZhAWye++sNJ+8gtOgBN37pqhGO1/yXXCI48TzXPlC8y2Nl1WviBlItfMPzvZl1/PGVFBfvfg96ji6/5CIClz3vxqRMGQu3fAMRlezmPf9F2P4TDH+l9B5A4RHQ6Xz3LzfTVRmt+BgSZ8PA38OZD1TufKVp3dt9Tz+4AsYPclVOCYOP/7jVwK8lAxG5EHgOCAfeUNWnSnx+I/AvINnz1ouq+kZ5x/RHyeDZWRt44duNrP/bUOpEBHHNmSosfQ++/iNERLteJicPLX2718+GnANw58Kjb+jemPMPmPdPuHfF0X+UORnwrxOh380w9Kmy96+qlZPdQKuTh8KV7x09d0x+LrzYzz1N3/Zd5a+pPKousa742MWQtcs14HYbAS26w4yH3VPx9VMg8jgeOJZ9BJ/9zj3BXzHBt9eQc8DdIH1Jwlx7UWVKgWu/cFU5Zz4I5zzi/X67V8NrQ6DTBa6HT2XOmZvlGoJ9KWUZfHqzaz8YfD8MechV0QVQwEoGIhIOvAScDyQBC0VkmqquKbHpx6p6p7/i8EZKejYtYqODOxFkp8MX98CazyDhTBjxGjRoVfq2Iq508MmNrgqhMtUHhQWuAe/Ec499Ootu6P5YV0+F3/zdtzezxNmuZ0qH012VV8lJxCKi4Ny/uj/Q5ROh97XHf86MZFj5iUsCqWsgLBJO+g30vBI6/ebX0lbdxjD5ZtfDZfT7VZvgbP3XrgdQwpmum6gvv3fgRtcWjbANpC6XwinXwPfPuO9hu/4V75OfC1Nuc79flz5X+SpIXycCgNa9YOw89+D1/b9dVdmoNyCug+/P5SP+vPsNABJVdbOqHgYmAsP8eL4qS07Lpk0wVxHtXO76l6/70t0Qr/+s7ERQpMtlroj/w38r17iYOBsOJEOfG0r/vPtI9+S87Sfvj1mRHQvh4+tdm8fVH5X99N19FLTuA98+4boRVlXyEleN9p9uMPuvUKe+a3h8YANc9QF0HXZ0tVv3UXDxv12j+rS7Kt9jZttPLjG36umqICKiqh57bTD0KWjQBqbe5l3317lPwu6VcNkLUK+p/+PzVlR91zh++Vuu/Wz8oKqNtVF1v+Nf3e/aJPzEn8mgDbCj2Oskz3sljRKRFSIyWUTalXYgERkrIotEZNGePXt8HmjR6OOgtHej68KnhXDTTBj8B++eKsPCXQNs8iLYPt/78y2eAPWal179BK6raWQ93/UqSl0LH17hqiOum3Js//riRFw7QmYK/PxS1c63e7Ubnbp3I5z1R7hrCdwyy/VWKa+RuP8tbk6b5R+6+nlvE+yulZ75cdq5/uxRsVWLuzaJbujq/fdvct2dy7P9Z9dTqff1Zf/OBVr3UZ4xHifD5Jvgsztc1VRF9m+GuU/BC33gzfNciduPPeICXS/yBRCvqj2BWcCE0jZS1ddUtZ+q9mvW7DgazEpRWKjsDNYBZxnJLhFImOtKWNlG217XupkZf3zOu+0zd7nueb2uKbt+tE4990e75nPXt/t4pG93PZTCozzzxzSveJ/4M+Dki+GH59wo08pI2+rOF1nXTfVw9sPedecscuaDrsH+55fgh2cr3n7/Zne+qFh3fTXpqdffEgbDwDtc43bi7NK3yc1ypYeGbeHCJ6s3vsoqPsZj2Qfw2lmuXaGkQ/tdb7E3L4Dne7tk0KANDHsJHtgIfa73W4j+TAbJQPEn/bb82lAMgKruU9WiESBvAH39GE+p9mblcrigkDbBtqjNof0uEeRkwHWfVu6mVaROXXfz2jADUtdVvP3S993goT6/LX+7Hpe77p6b51Y+piJZe9z15R10DbNx8d7ve/5jkHcI5lWiETsr1fUfz88pe76aioi4/vQ9roBvHofF75S9beYud77CfM/5Si00B7dzH4Vmnd0YgEP7j/38f392cy2NeLV2lJiOjPH4wlVTvnGe6x2WlwNrpsHEa90YiK/+4Br0zxsH961yPdF6X+f3Nh1/JoOFQCcRSRCROsBVwLTiG4hI8Yrry4C1foynVElFU1cHU5tBbpbr2pa21dWhtzql6scacKt7Ev7phfK3Kyx0ywLGD6448XQ8x1UFrKxiVVFejqsaykiGaz6BFt0qt3/TTq7L7KK3XTfbiuRkeOar2e2Zr6YKfeCLhIW5KpATz3cDn1Z/duw22WmuRHBwrztfs5Orfr7aLDLa3egP7oHpJbp9bvifS6an3+U6DdQmCYPdOIhOF7huwk+1h0nXQ9JCOPU2uO17+P18GHSfK/VUE78lA1XNB+4EZuJu8pNUdbWIPC4il3k2u1tEVovIcuBu4EZ/xVOWlGBb1CY/13XNS1nqRnjGDzq+49Vt7J5KVnxc/qjKLfMgfRv0vbHiY0ZEuV4j675yUypU1pwn3PVd/ia0P7Xy+wOc9ZBLcrPHlb9dXrYbDJW6zs1X403vloqER8KV77ruplNuPbqEdPiQm+Nm7wa46n1oW+2F5ZqldS/3s1r16a8PD0WD5Zp3q1z305qkbmPX2eCyF1y16nWfwn1rXC+7Vj39OyizDH5tM1DV6ap6kqp2VNW/e957VFWneb5+WFW7qeopqnq2qnpRF+FbyWlBtKhNYYGrQ908x/2Sdb7IN8c97Q5X/fNzOVMFLJkAMXHQ+RLvjtn9cjicCRsrOdBp64/w04tucZHOF1du3+LqN4NB97g5bLb+WPo2BfmuwW/bTzBifNVH+pamTl245mNocqKrHkhe7NpQPrkRdiyAUa+7EpRxT8ht+rneNAdS4Kv7XLXRyFdrd88qEVeleul/3e9WgNdUDnQDcsClpGcTGx1BbHRgB4QcN1VXlF491fWY8UU/+iJx8W4A1aK3XZVJSQf3utG2p1zt/fQV8YOhXrPK9SrKOeAGXcXFw/l/836/sgy8A2Jbu949Jbt7Fha6bqDrp8NF/3LtHL4WE+d6QNVtDO9fDpNugI0z4ZJn3ffbOOERrrooP9fNQrrmczj7T9CyR6AjCyohnwyS04NkHYM5/3CTlp1xr6tH9bXT73ZP8ovePvaz5R9BYV7ZYwtKEx7hJq/bMNP7ka8zH4aMJDfE3xcDherUddUMKUtgdbE5i1TdClbLP4Qhf3LtJv7SoJUb9xEW7kop5zziSj3maE1PhAv+5qoi2w10gyKNT1kyCIZupT+PdytZ9b7e9UDwh9a94IQhrqqo+BTAqm5sQbtTy18IpDQ9LvdMZja94m3XTXe9lQbdB+0GVO485TnlKjdlxDeP/XpdP/7XzXMzYKybV8ffmnR03Q5Hvu66HprS9b/Fzcd05bu+H4FtLBkkpx2q3T2JVkxy0zV3udRNQe3PhqfT73ajh1d+8ut72+e7mUgrUyoo0nYANGhb8ajMg3vhi7tdtcBZD1X+POUJC/c8cW6HX15ziW32ONemceHT1deQ17STm8bCll0tm4hbryG2RaAjCUohPWtpZk4eB3Lyq6cn0aH9bpoGX0pd6xZwiR8MI9/wfwNUx3PcDJw/Pu/mjwkLczfPqAbQbXjljxcW5qan+Pll9/0pbQSvqptTKScDfjut8jNZeqPjOdDxXJjzpJvW+MTzXPfPsJB/VjIhJKSTQUp6NS1qk7kbXhoAOem+P3arXm6+mqquO1AZRRPYTbnFNXS2H+gmvut1rRtZXBXdR8FPz8PaaaV3S132oZtT6YInoEXX44m+fBf8zc0d06afq4bwR9IxpgYL8WRQTWMM5v7DrSo14tWq3zRLI2FuFsvqHH3ZbbgbPfvjc5C+w9X5961CFVGRVqdA446uqqhkMkjf7mZ97DDI9fzxpxbd4Pb5bmRxnbr+PZcxNVBIJ4Ok6ljUJnWtG5nb/1bXWFnbhUe6cQcz/ugma2vV6/hGOIu4huR5/3RTMMS2dO8XFro1ccGtu1AdVTaVbQA3JoiEdKVoSno2keFCs/p+HLgyyzPF8Vl/9N85qlvv6yC6ERzaW/E8RN7oNhLQo6dm+Plltybu0Kdq9BzwxgSLkE4GyWnZtGoYQ1iYn3pwbJ7n6tYH/6FGrYV63KLqw+l3QkxjN+na8Wre2XXvLOpVlLrWVUWdfLFrjzDG+F1IJ4OU9Gxa+2u20sJCN7K1YTs49Xf+OUcgDX4A7lvtu5kUu4+CpF/cMoFTxrp2kKqsWmWMqZKQTgZu9LGfGgtXfgK7VsA5fzm+dW9rKhHfNrR2H+n+f3+U+75d9vzxLfZujKmUkE0GeQWF7D6Q4591DPKy4du/uYZVX1SjhIK4eDeLZ9oW6HXd8U1CZ4yptJDtTbQrI4dC9dM6BgvGQ8YOtzqRDVzy3qm/Awmv+atWGROEQjYZ+G2MwcF98P2z0Ok3cMJZvj12sOtxuX9mBzXGVChkH1uT0/20jsF3/3QDzM5/3LfHNcYYPwqdksGKSW4iMo/TM3KYUieb+M8aldJjRVzvlgFjK1fNs2+TW8C7z29tAJMxplYJnWQQHnnUtA0HFHLDwwgrrWvkof1uhG3ibDdhmbe9WmaPg/AoNwe+McbUIqGTDLqNOGr1qCfe+oX06MNMu76UNYJV3RP+zD/DK6d7ljw8t/zjb1/gJlsb8rBNsWuMqXVCt80g7VDZ7QUibnWrsXOgbhN4f6QbQJZ/uPTtVd3n9Vv6Z5UxY4zxs5BMBqpKSnpOxT2JWnRzCaHfzfDTC/Dm+a5doKQ1n7vRs2f/ybezkhpjTDXxazIQkQtFZL2IJIpImUtUicgoEVER6efPeIqkHcojO6/Au55EkTFugfLRH7j1V8cPhmUfudIAuNLC7HHQrIubwM0YY2ohv7UZiEg48BJwPpAELBSRaaq6psR2scA9wAJ/xVJSlcYYdLkEWvd28+Z89jvY9A1c/Cws+8CNmr12sq3LakwNlZeXR1JSEjk5OYEOxe+io6Np27YtkZGRldrPnw3IA4BEVd0MICITgWHAmhLb/Q14GnjQj7EcJSmtiusYNGwDN0xzg8rmPgk7foHcA5Bwllsq0RhTIyUlJREbG0t8fDwSxJMfqir79u0jKSmJhISESu3rz2qiNsCOYq+TPO8dISJ9gHaq+pUf4zjGcY0+DguHsx6EMV+7qqLsdLdkYhD/ghlT2+Xk5NCkSZOgTgQAIkKTJk2qVAIKWNdSEQkDngVu9GLbscBYgPbt2x/3uZPTs4mJDCeubuWKUUdpfyrc/qNbmrFl9+OOyRjjX8GeCIpU9Tr9WTJIBtoVe93W816RWKA7MFdEtgIDgWmlNSKr6muq2k9V+zVrdvzTGhetY3DcvxzRDSwRGGOCgj+TwUKgk4gkiEgd4CpgWtGHqpqhqk1VNV5V44GfgctUdZEfYwI86xjE2aLnxpjqkZ6ezssvv1zp/S666CLS09N9H1Ap/JYMVDUfuBOYCawFJqnqahF5XEQu89d5vZGSnu2fdQyMMaYUZSWD/Pz8cvebPn06jRo18lNUR/Nrm4GqTgeml3jv0TK2HeLPWIrk5BWwN+uw72crNcbUCo99sZo1KQd8esyurRvw10u7lfn5Qw89xKZNm+jVqxeRkZFER0cTFxfHunXr2LBhA8OHD2fHjh3k5ORwzz33MHbsWADi4+NZtGgRWVlZDB06lEGDBvHTTz/Rpk0bPv/8c2JifHcfC7kRyH5bx8AYY8rw1FNP0bFjR5YtW8a//vUvlixZwnPPPceGDRsAeOutt1i8eDGLFi3i+eefZ9++fcccY+PGjdxxxx2sXr2aRo0a8emnn/o0xtCZqM7Db+sYGGNqhfKe4KvLgAEDjhoH8PzzzzN16lQAduzYwcaNG2nSpMlR+yQkJNCrVy8A+vbty9atW30aU8glAysZGGMCrV69X+cwmzt3LrNnz2b+/PnUrVuXIUOGlDpOICoq6sjX4eHhZGdn+zSmkKsmSk7LJkygZUNrQDbGVI/Y2FgyMzNL/SwjI4O4uDjq1q3LunXr+Pnnn6s5OifkSgbJ6Tm0aBBNZHjI5UFjTIA0adKEM844g+7duxMTE0OLFr+ueXLhhRcyfvx4unTpwsknn8zAgQMDEmMIJoNy1jEwxhg/+fDDD0t9Pyoqiq+//rrUz4raBZo2bcqqVauOvP/AAw/4PL6Qezz2ah0DY4wJMSGVDAoLlZ0Z2bSp7GylxhgT5EIqGezJyiWvQK1kYIwxJYRUMjiyjoElA2OMOUpIJYOiMQZWTWSMMUcLqWSQbAPOjDGmVCGVDFLSs2kYE0n9qJDrUWuMqUXq168PQEpKCpdffnmp2wwZMoRFi3w3439IJYPktGwrFRhjao3WrVszefLkajlXSD0iJ6dn09YWtTEmtH39EOxa6dtjtuwBQ58q8+OHHnqIdu3acccddwAwbtw4IiIimDNnDmlpaeTl5fHEE08wbNiwo/bbunUrl1xyCatWrSI7O5sxY8awfPlyOnfubHMTHY9kW9TGGBMAo0ePZtKkSUdeT5o0iRtuuIGpU6eyZMkS5syZw/3334+qlnmMV155hbp167J27Voee+wxFi9e7NMYQ6ZkcCAnj8ycfOtJZEyoK+cJ3l969+5NamoqKSkp7Nmzh7i4OFq2bMl9993Hd999R1hYGMnJyezevZuWLVuWeozvvvuOu+++G4CePXvSs2dPn8YYMsnApq42xgTSFVdcweTJk9m1axejR4/mgw8+YM+ePSxevJjIyEji4+NLnbq6uoRMNVGKLWpjjAmg0aNHM3HiRCZPnswVV1xBRkYGzZs3JzIykjlz5rBt27Zy9z/zzDOPTHa3atUqVqxY4dP4QqZkkJxmycAYEzjdunUjMzOTNm3a0KpVK6699louvfRSevToQb9+/ejcuXO5+99+++2MGTOGLl260KVLF/r27evT+EImGbRoEM35XVvQtH5UxRsbY4wfrFz5ay+mpk2bMn/+/FK3y8rKAiA+Pv7I1NUxMTFMnDjRb7GFTDK4oFtLLuhWesOMMcaEOr+2GYjIhSKyXkQSReShUj7/nYisFJFlIvKDiHT1ZzzGGGNK57dkICLhwEvAUKArcHUpN/sPVbWHqvYC/gk86694jDGhrbw+/MGkqtfpz5LBACBRVTer6mFgInDU8DpVPVDsZT0gNH5axphqFR0dzb59+4I+Iagq+/btIzq68oNr/dlm0AbYUex1EnBqyY1E5A7gD0Ad4JzSDiQiY4GxAO3bt/d5oMaY4Na2bVuSkpLYs2dPoEPxu+joaNq2bVvp/QLegKyqLwEvicg1wCPADaVs8xrwGkC/fv2CO7UbY3wuMjKShISEQIdRo/mzmigZaFfsdVvPe2WZCAz3YzzGGGPK4M9ksBDoJCIJIlIHuAqYVnwDEelU7OXFwEY/xmOMMaYMfqsmUtV8EbkTmAmEA2+p6moReRxYpKrTgDtF5DwgD0ijlCoiY4wx/ie1rXVdRPYA5U/iUbamwF4fhlMTBNs1Bdv1QPBdU7BdDwTfNZV2PR1UtVlZO9S6ZHA8RGSRqvYLdBy+FGzXFGzXA8F3TcF2PRB811SV6wmZWUuNMcaUzZKBMcaYkEsGrwU6AD8ItmsKtuuB4LumYLseCL5rqvT1hFSbgTHGmNKFWsnAGGNMKSwZGGOMCZ1kUNHaCrWNiGwtthbEokDHUxUi8paIpIrIqmLvNRaRWSKy0fN/XCBjrIwyrmeciCR7fk7LROSiQMZYWSLSTkTmiMgaEVktIvd43q+VP6dyrqfW/pxEJFpEfhGR5Z5reszzfoKILPDc8z72zARR9nFCoc3As7bCBuB83OypC4GrVXVNQAM7DiKyFeinqrV2oIyInAlkAe+qanfPe/8E9qvqU56kHaeqfwxknN4q43rGAVmq+u9AxlZVItIKaKWqS0QkFliMm0PsRmrhz6mc67mSWvpzEhEB6qlqlohEAj8A9+Bmg56iqhNFZDywXFVfKes4oVIyqHBtBVP9VPU7YH+Jt4cBEzxfT6AWTV5YxvXUaqq6U1WXeL7OBNbipqevlT+ncq6n1lIny/My0vNPcUsCTPa8X+HPKFSSQWlrK9TqXwDcD/t/IrLYs95DsGihqjs9X+8CWgQyGB+5U0RWeKqRakV1SmlEJB7oDSwgCH5OJa4HavHPSUTCRWQZkArMAjYB6aqa79mkwnteqCSDYDRIVfvglhW9w1NFEVTU1WHW9nrMV4COQC9gJ/BMQKOpIhGpD3wK3FtihcJa+XMq5Xpq9c9JVQs8ywe3xdWEdK7sMUIlGVR2bYUaT1WTPf+nAlNxvwDBYLenXreofjc1wPEcF1Xd7flDLQRepxb+nDz10J8CH6jqFM/btfbnVNr1BMPPCUBV04E5wGlAIxEpmpm6wnteqCSDCtdWqE1EpJ6n8QsRqQdcAKwqf69aYxq/TmV+A/B5AGM5bkU3TI8R1LKfk6dx8k1grao+W+yjWvlzKut6avPPSUSaiUgjz9cxuI4ya3FJ4XLPZhX+jEKiNxGAp6vYf/l1bYW/BzaiqhORE3ClAXBrUnxYG69HRD4ChuCm290N/BX4DJgEtMdNVX6lqtaKRtkyrmcIrupBga3AbcXq2ms8ERkEfA+sBAo9b/8JV89e635O5VzP1dTSn5OI9MQ1EIfjHvAnqerjnvvERKAxsBS4TlVzyzxOqCQDY4wxZQuVaiJjjDHlsGRgjDHGkoExxhhLBsYYY7BkYIwxBksGxlQrERkiIl8GOg5jSrJkYIwxxpKBMaURkes8c8QvE5FXPROBZYnIfzxzxn8jIs082/YSkZ89k5xNLZrkTEROFJHZnnnml4hIR8/h64vIZBFZJyIfeEbFGhNQlgyMKUFEugCjgTM8k38VANcC9YBFqtoNmIcbYQzwLvBHVe2JG9la9P4HwEuqegpwOm4CNHAzZd4LdAVOAM7w8yUZU6GIijcxJuScC/QFFnoe2mNwE7EVAh97tnkfmCIiDYFGqjrP8/4E4BPP3FFtVHUqgKrmAHiO94uqJnleLwPicQuSGBMwlgyMOZYAE1T14aPeFPlLie2qOpdL8flhCrC/Q1MDWDWRMcf6BrhcRJrDkfV+O+D+XopmgbwG+EFVM4A0ERnsef96YJ5nFa0kERnuOUaUiNStzoswpjLsicSYElR1jYg8gltJLgzIA+4ADgIDPJ+l4toVwE0PPN5zs98MjPG8fz3wqog87jnGFdV4GcZUis1aaoyXRCRLVesHOg5j/MGqiYwxxljJwBhjjJUMjDHGYMnAGGMMlgyMMcZgycAYYwyWDIwxxgD/D37zcgoDkShuAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1102,7 +1138,7 @@
     },
     {
      "data": {
-      "image/png": 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Y1ZXwyR0Q3xOO/71n2xceAxe9AcYFMy6zdxiekrveXvxL8+CKD2zaqR0KqACQGBPGvsoayiq1HlDAy06HfXkw4V5I6gc//M+OYFHuWTcH3rnS5tgvfddebA/W/1S49ksbGF4+w6aKDrbwSZsqOuNRCPVCZ2diHzh/uv28Z//WM5/xzuXw8kSoqYSrPrZDUNupgAoAybWTwTQNpDIX2N99T4RxN8LOZbB1oU+b1G5smAvvXG5z65fOtGmfhnQcBL/6CrqOsXcLXz1o774ACjbDN4/AkHNhwGnea2//U+DkP0P6TBvoW2LL9/DK2Xb28dVzoPMIz7TRRwIqACTp4vCqVuZ8mx+O6wIjL4bIRPjhKV+3qnXkbbAjdh7pZ7/FZ3wM1W5+Kdo0H2ZcYnP1l70HkfGH3yc6Ca74EEZdBt/8C9690o7Aqi32NvHhFp2OW479nQ00X/7FnkNzrP8CXp8CMalwzRxI7ufZNvpAQI0Cqi0Ip7OBA1xVuf22P+Zq+3dYFBxxLXzzqJ28k9S3ddpRUwVI4+UOPGnnMvj2MVtoLSTCfjPe8p3t/4joYC+Qw6dCz/EQFHzo/pu/hbcutimzK2ZDVKL77x0SBuf8D1IG2xE/O5fD3m1w+kM2CHubCJz7tM3dz7zGzhRO6On+/umzbGd36lAb+KKTvdbU1hRQASDZKQmdp/WAAtv2H6G6HPpM+GXbEb+G75+wdwFnPeb9NuwrsJ2IkfFw5cf2AukNxthRNt89ZtNe4R3guDvgqOttUbWaati8AFa+a4fFLn0VYjvbES3DL4DOafbiufUHeHOavWhe8WHTLv61ROCYm20AmXWtrd1z5HUePuFG1HYKv3CiHbc//AL7bT6mE8R0hNhOEJV8aEBe/DJ8fDv0ONqO9ono0Hpt9rKACgBJWhFUgU3/BIVAr/G/bItNhREXwvI34aQ/Ne8C567qCluqoGATuKrtXISJ//Dse7hcdhjmd4/BjiUQ3RFO+SuMvebAnH1wiB3B0u8UO0pm/RzbWfvjczZfntQPBp4Bi6fbb+pXzLaBoyUGToRblkFwWOvd/dRK6mvH679/A3z1QD0vEPvtvjYohEbC2o+h/2kw9RW/mzAYUAEgKiyEiNAgTQEFuk3z7dDF8NgDt4+76ZfFRo6/0zvvbYwdjbL1ezj/JXs3sugp6HUsDDqj5cevqbIX8O//YycnxfeEMx+DtEshNKLxfcOiYNgU+1O2B9bMhlXvwsL/2sJqV35kA6UnxHT0zHGao98pcOcGW5CtJMf5yYaS3b88Ls62v3PXwegr4Ix/e+8uzYcCKgCALQutKaAAtq8Adq2wwz8PljoE+p4MP70Ax9zinen7X/8TVr4NJ/7JpiAGn22DwAc3wPXfQXz35h+7rBBePRd2LYeOQ2HKizB0cvO+ZUcmwJgr7U9xtv0m3Nhon/YoNNKmtJrSF+BnAmoUENg0kN4BBLDNXwPGDv+szzE3229+q971/HuveBsWPAQjL/ll0lNIOFzwsq0eOfMap2O4GSqK4Y0LIHu1vbO44XsYMdUzKZbYVP+7+CsgEAOAzgYObJkLbMGyLqPrf77Pifbb8w9PeXZi2Jbv7dDLXsfZUgl1SwYk9YVznoCsn+Crvzf92FVldnTOjqW2dPDwC9pdSQLlG4EXAGLCdVWwQLZpvr0IN/TNWASOvgly1nhuZam8DXbsfGJvmPZa/bnkYefbYanfPwEbvnT/2NWV8Pbldjjn5OdsSkkpNwVeAIgOI7+0EqPT/gNPQSYUbm04/VOrdnhgS2eNgq0V88YFdtTRJe80Xud+4kO2hPD7v4GinYc/dk21HU658Ut7VzFiasvbqwJK4AWAmDAqql2Uaj2gwFNb/qHu+P/6hITb8embvrI59eaqKrff/It320qZh1uiMDQSpv6f3W/mtY1XKHW54MMbIWO2nUk75srmt1MFrMALALo4fODaNB/iutmx7Ycz9hpb7+WHp5v3Xi6XHdmz/UeY/Kz769sm97eLoW9baDuM62OMXTpx5dt20ZVxNzSvjSrgBVwASNTJYIHJVWNnxPaZ4F4HaVQipF0Cq96xwyCbav6DsPo9OOV+OxSzKUZOs3Vzvv33of0QxsDnf4QlL9sZvZ4uoawCSsAFgP0VQXUkUGDZtRzKCw+f/69r3I12WOZPzzftvZa9Dt8+aicQjb+tafvWmvSILVb33nU2hVRr/j/sxLGjrrff/pVqAa8FABGJEJGfRGSFiKwWkb8623uLyI8islFE3haRVp1eV3sHoCuDBZjaCpC9T3B/n6S+MOhMOzO4srTx17pqbB/DBzfCR7faO40zH2v+cMywKNsfUFECs35lj//d47aa5ugrbN5fh3qqFvLmHUAFcJIxZiSQBkwUkXHAP4HHjTH9gD3AtV5swyGSnIqgeXoHEFgyF0Dq8KbXsTn6ZlsWYcVbhz5nDOxeZatbPj7UzsJdM9uWl77wVQgObVmbOw6CMx+FLd/aY8+931brPOs/evFXHuG1UhDGjrMscf4MdX4McBJwibP9FeB+4BlvteNgEaHBxISHtK0U0K4VdsRHtzG+bol/qtxnO2OP+k3T9+0xzk4a++FpGHMNBAXZNW5XvQsr37HzBYJCbLGw4f+AgZM8u7JV2qW2DPPKGTDoLDjvmfpLNSvVDF6tBSQiwcASoB/wFLAJKDTG1I5vywK6NrDvdcB1AD169PBouxLb2uLws34NFUVw+2r9x+0NWxfa5fv6NCH/X6u2hPHMa+Dze2F3Omz9zj7X/Sg4898wZLJd9MQbROyooP6n2kleLb2rUKoOrwYAY0wNkCYi8cD7wKAm7Ps88DzA2LFjPTprKykmrO2MAspdB3nr7OPMBdDv5NZ9/8p9sOT/YORF3i2B7EuZ823p4R5HN2//wedCfA/48VlI6v9LIbfDjev3lLAo+35KeVirVAM1xhSKyHzgaCBeREKcu4BuwI7WaENdSdFh7Cwsb+23rV/GR/Z3WIzNM7d2AFj6iv1mu/o9u9BHWHTrvn9ryFxgUznNreUeHGL/25QXQeeRmn9XfsObo4BSnG/+iEgkcCqQAcwHar/OXAl86K02NCQpOrztLAyf8ZGtTT/iQrs2a3lR6723MXahj9gudtGQd65sfjXKg49bsNnWqfG1khzITm9e+qeuxD7QJU0v/sqveHMUUGdgvoisBH4GvjTGfAzcDfxORDYCScBLXmxDvWpLQvu8HlDhNjs+fdBZtkRwdRmsacV4uPV7yFtvV8A663FbU+bDm+0s1uaqqYKPboEn0+BffezKV0tfa95kKk/I/Nr+Plz5B6UCkDdHAa0ERtWzPRM40lvv647E6DCqagxF5dV0iPRhp1rGx/b34LPtN8zEvrBiBoy+vHXe/+eX7PqmQyfb9EhJLsx/wC6Jd/qDTT9eRTG8exVsnGtr6dRUwYYvfklzdU6DAafbn86j7Igab8ucbwuwdR7p/fdSqp0JuBXB4JfF4fNLKnwbANZ+bGvPJ/W1f6ddbNcp3bMFEnp5971LcuyF+chf/5IbP/73UJpjq2DGdITxt7p/vKJd8OZUyF5jK1OOucpuN8amYNZ/boPBN4/YVbGiO9qRLf1Ps7+90fdgjM3/9z5eR1cpVY+AKwUBbWRx+JIcOzyxbv32ERfZ3yve9v77L3sdXFW2Bn0tEZj4Txg6Bb78i10g3R05GfDSqZCfCZe8/cvFv/aYnYbb4HLtF3DnJpjygr0or/0Y3r0SXjzVznj1tLwNULSj5fl/pfxUQAaARGc2cLMmg236CtZ91vJGrPsUMAcGgPjudrGSFW95djWqg7lctphYr+MgZcCBzwUF2eqVfSbY/oB1cxo/1uZv4KXT7Tj7qz+13+YbE5VoO7wveAnuzLTlDnIz7GpZnj7nTKf8Q1Pq/ygVQAIyAOxPATV1JFD5Xnj3ajtxq6WjdTI+goTekDr0wO1pl8CezXbmqrdsmmc7oMdeXf/zIeEw7XX7zf3dq2BbA21Z+S68NgXiOsOv5tpRMk0RHGL7H075K6z5wK6G5UmZC2wqzdvpNKXaKf8OAF//C+bce8jmhCinIFxT7wAW/s9WlKwsdj89Up/yvXZ0yuCzDh1WOPgcW4e+Jcc/nMXTIToFBjWyfGB4LFw6E+K6wJsX2jRPLWNsqeL3fmXH118zx06Uaq5jfmvTTvP+6rllGGuqbAkFTf8o1SD/DgD7Cuzszd2rDtgcFhJEXERI0/oASnLtQuFDzoXu4+xxXc1cVWz9Fzb/PvicQ58Lj7HbV39gF/v2tL1ZsH4OjLq8/rVp64pJgcvfs3cEr02Bwu22ZtHHt8O8v9nCZJfNanyZQ3eIwLn/g5TBtuTCni0tOx7YeQ2VxZr+UaoR/h0AJtwNEfH2LuCg/HJyTDh5TVkV7LvH7Tj9E/9oV2Das9leSJsjYzbEdIKuY+t/Pu1iqNjr9BN42NJX7X8Ld5cQTOgFl71nyyG/NhlmXGz7D479HUx+3gYHTwiLhoteB+Oycwcq97XseJkLALH9HEqpevl3AIhMgJP+aMvpZsw+4ClbEM7NO4C9O+DnF22Z35SBduJWh+6wqBlFTCv32XHyg89qeBx8r+MgrqudE+BJNVWw5BXod0rT8uKdhsHFb9l+g41z7aSxU+7z/Dj+xD5w/ku24NpHt7asU3jTfOgyyn/rGynlAf4dAABGXwUdh9ia7VW/1P9JiglzfxTQN/+y30xPuNv+HRxiJzpt+RZ2rWxaezZ9BVX7Dhz9c7CgYBgxDTbO8+wM2nWfQcluOKIZSzD0Gm9H+Vw9x66X6y39T7VBe9U7zQuwYDvos37W2b9KHYb/B4DgEJj4kP32+sP/9m9OjA53rw8gf5MtZTD2akjo+cv20VdAaLTtC2iKjI/snUnP8Y2/buTFYGrshdBTFk+3i6L3P615+3cbCz2O8lx7GnLsHfYu64s/2Y7cptr6vf1vp/l/pRrl/wEA7DfBQWfBt4/ZGatAcoxdE8DlOkyaYcFDtpTwcQctvh0ZD6MutQuDuPstvaYK1n8GAyYdvq57ygDoOsZzaaD8TXZc/Jgr2/6s2KAgu/BJUl87DHVvlvv7VlfYekohkbZev1KqQYERAABO+7sdeTPvr4AtCe0yUFjWSPXL7NWwaqZdSSo29dDnj7reToBaPN29Nmz51g4BbSz9U9fIi20Zhaammeqz5GWQYDv6pz2IiIOL3rQX9LcvOyB9d4iqclj7qV1A/ZF+diLd4LM810GtlJ8KnACQ2AfG3WgvDllLSHQmgzW6MthXD9rx8A3VxEnqCwMm2kXDG7tA1cr4yKaN3E1NDDsfgkLrX4+2KarKYdkbdoHzuM4tO1ZrSu4PU56DncvgkzsO7BSuKrPF9Gb9yl70Z1xs6w0NOcfOXziv1VYZVardCqxicMf/3l5M59xN8gn2oppXUkm/jvW8NmsxrPvErv7U2EiScTfCq+dA+iybEmqIq8ZesPqf6v6asVGJMHCiTTOd+rfmLweYMRvKCrzbeestg86E4++yHfGpQ6FDNztreP3nUFkCkYkwbLKdn9H7BF0yUakmCKwAEB4LJ/8FPryJXrs+BZIbHgn01d8hKhnGXd/4MXsfbyt6LnralnFoaMGQrJ9tpU130z+1Rl5i7xw2zrPBoDkWT7d3QL1PaN7+vjbhXrtuwufOrO6oJLtE4pDzoNexetFXqpkCJwVUa+Ql0DmN1J8eIpLy+lNAmV/biUTH/c4GjcaI2Ilh2ek2x9+QjI9sZ3JTR+D0P9Ve8JqbBspeDdt+sFU/W6P+vjcEBcH5L9q7sStmwx3rbcnpvifqxV+pFminV4QWCAqCSf8kuGQXN4R8RN7BdwDG2G//cV1hrJvj5YdPtXcLDY1bN8amYfpMsJ2bTREcao+/7lMo29O0fQEWvwzB4ZDWSHqqPYjoACfcCX1OsEN7lVItFngBAGwBs2Hnc13Ix9Ts2Xbgc+vn2HTNCXdBaIR7xwuNsJOr1n1mh1sebPcqOw+hqemfWiMvtqON0t9r2n4VJXYY6dDzIDqpee+tlPJbbgUAEblVROLEeklElopIM2cTtRGn/BUQTtz231+2uVx2Ra7EPk3/xjz2WggKgR+fO/S5jI9AgmDgGc1ra+eRdjZzU+cEpM+0BdHaY+evUsrr3L0DuMYYUwScBiQAlwMPe61VrSG+Ox/FTGVMyQLY8r3dtvo9m8s/8Y9Nzy3HptqOyWWvQ1nhgc9lfGRn/kYnN6+tIjDyIsj6CfI2urePMXbN345DdEKUUqpe7gaA2qEtZwCvGWNW19nWbn2Xeik5kgxz7rETjub/w47oGTqleQccdwNUldogUCtvg13xqrnpn1rDL7R3Ee52Bu9cCrtX2m//DY1MUkoFNHd705aIyBdAb+BeEYkFXN5rVuuIi+3A41zKQ7ufsIueFGyCi95q/miZziOh57E2DXTU9bazMuMj+9ygM1vY2M52cZOVb9u7gbJCuzhN2R7nx3lcuy0nw046GzGtZe+rlPJb7gaAa4E0INMYs09EEoEG1hNsPxKjw3ii7Ej+0f8oJHOBrc8/cFLLDjruBnj7UjuJbMi5NgB0GW0nMLVU2iUw61r4XwPrCITF2kJzkfG2cN34W5s+6kgpFTDcDQBHA8uNMaUichkwGvDwAq6tLzkmDBAKT3iQhE9+Bac90PJ0ycBJttb+omdsMbedS+Hk+zzRXLt+bk2VLeYWEe9c7J0LfkQHHROvlGoSdwPAM8BIERkJ3AG8CLwKtNOppVaSUw8oO2YgCbcs90yuPCjYpn/m3GNHFEH9Sz8299hpF3vmWEqpgOdusrvaGGOAc4H/GWOeAg4zRbbtS4yuszi8JztK0y616ZgVb9l1bpP7ee7YSinlIe4GgGIRuRc7/PMTEQkC2n2+waaAIK8pi8O7IyIORjtll1s6+kcppbzE3QAwDajAzgfYDXQDHvFaq1pJUrRNAeU3ZXF4d427EXocoykbpVSb5VYAcC76bwAdROQsoNwY86pXW9YKOkSGEhwk7i8O3xTx3eGaz+ysYqWUaoPcLQVxIfATMBW4EPhRRC7wZsNaQ1CQkBAVdmhBOKWUCgDujgL6I3CEMSYHQERSgLnATG81rLUkRYc1viqYUkr5KXf7AIJqL/6O/Cbs26YlxYQ1vCiMUkr5MXfvAOaIyOdAbSGaacCn3mlS60qKCSd9x15fN0MppVqdWwHAGHOniJwPjHc2PW+Med97zWo9SdFh3hkFpJRSbZzbSysZY2YBs7zYFp9Iig6jqLyaymoXYSF+kdVSSim3NHrFE5FiESmq56dYRIoOs293EZkvImtEZLWI3OpsTxSRL0Vkg/M7wZMn1FSJzmQwrwwFVUqpNqzRAGCMiTXGxNXzE2uMOVyZyWrgDmPMEGAccJOIDAHuAeYZY/oD85y/fWb/ZDAdCaSUCjBey3kYY3YZY5Y6j4uBDKArtp7QK87LXgHO81Yb3FFbDkJHAimlAk2rJL1FpBcwCvgRSDXG7HKe2g2kNrDPdSKyWEQW5+bmeq1t+wvCaQpIKRVgvB4ARCQG23l8m7Ou8H5OhVFT337GmOeNMWONMWNTUlK81r7aktB5OhJIKRVgvBoARCQUe/F/wxjznrM5W0Q6O893BnIa2r81xEWEEBos5OsdgFIqwHgtAIiIAC8BGcaYx+o8NRu40nl8JfCht9rgDhEhMTrMrgmglFIBxO15AM0wHrt+wCoRWe5s+wPwMPCOiFwLbMUWl/OppOhwHQWklAo4XgsAxpjvgIaW2TrZW+/bHEkxYZoCUkoFHJ36Sm05CA0ASqnAogEAOxJI6wEppQKNBgDsXIDSyhrKq2p83RSllGo1GgCoMxtY+wGUUgFEAwCQ6M3F4ZVSqo3SAAB0jLUBYHtBmY9bopRSrUcDADCkSxzxUaF8uWa3r5uilFKtRgMAEBocxGlDUpmXkUNFtXYEK6UCgwYAx6ThnSmuqOb7jXm+bopSSrUKDQCO8X2TiY0I4dNVmgZSSgUGDQCOsJAgTh2cypdrsqmqcfm6OUop5XUaAOqYNLwze8uq+GFTvq+bopRSXqcBoI7j+icTHRbMZ+m7Dv9ipZRq5zQA1BERGsxJg1P5YnU21ZoGUkr5OQ0ABzljWCfySyv5aUuBr5uilFJepQHgICcMTCEiNIjPdDSQUsrPaQA4SFRYCCcO7Mic1btxuepdr14ppfyCBoB6TBzWidziCpZs2+PrpiillNdoAKjHSYM6EhYSxKerdDSQUsp/aQCoR2xEKMf3T2FOuqaBlFL+SwNAAyYN68SuveWsyCr0dVOUUsorNAA04JTBqYQGC5+l62ggpZR/0gDQgA5RoYzvl8xn6bswRtNASin/owGgEZOGdWJ7QRmrdxb5uilKKeVxGgAaceqQTgQHiY4GUkr5JQ0AjUiMDuPoPkl8lr5b00BKKb+jAeAwJg7rxOa8UtZlF/u6KUop5VEaAA7j9KGdEEFXClNK+R0NAIeREhvOkb0SmaNrBCil/IwGADdMGtaJ9dklbMzRNJBSyn9oAHDDxGGdAbREtFLKr2gAcEOnDhGM7hGvs4KVUn5FA4CbzhjemTW7itiSV+rrpiillEdoAHDTxGGdAPQuQCnlNzQAuKlbQhQjunXQ0UBKKb/htQAgItNFJEdE0utsSxSRL0Vkg/M7wVvv7w2ThnVmRdZesvbs83VTlFKqxbx5B/B/wMSDtt0DzDPG9AfmOX+3G5OcNNAcTQMppfyA1wKAMeYboOCgzecCrziPXwHO89b7e0Ov5GiGdI5j+nebWa+lIZRS7Vxr9wGkGmNqk+i7gdSGXigi14nIYhFZnJub2zqtc8NDU4ZTWWOY/NT3fLFa7wSUUu2XzzqBjS2v2WCJTWPM88aYscaYsSkpKa3YssaN7B7PR78dT9+OMVz32hL+O2+DVgpVSrVLrR0AskWkM4DzO6eV398jOneI5J3fHM15aV3495fruenNpeyrrPZ1s5RSqklaOwDMBq50Hl8JfNjK7+8xEaHBPD4tjT+cMYg56buZ8vRCthfo6CClVPvhzWGgbwE/AANFJEtErgUeBk4VkQ3AKc7f7ZaIcN3xfZl+1RHsKCzj3Ke+Z1Fmvq+bpZRSbpH2kL8eO3asWbx4sa+b0ajM3BJ+9epituXv475zhnLZUT0QEV83SykVwERkiTFmbEPP60xgD+mTEsMHN43n+AEp/PmDdP7wfjqV1S5fN0sppRqkAcCD4iJCeeGKsdw4oS9v/bSNS19cROG+Sl83Syml6qUBwMOCg4S7Jg7iyYtHsSJrL1dM/4mi8ipfN0sppQ6hAcBLzhnZhWcuHc2anUVc8/LPOkxUKdXmaADwopMHp/LERaNYum0P1726hPKqGl83SSml9tMA4GVnjujMIxeM5LuNedz0xlLtGFZKtRkaAFrB+WO68cB5w5i3Nofb315OdY0GAaWU74X4ugGB4rJxPSmvquGBTzIIDwni0akjCQrSeQJKKd/RANCKfnVcH8oqa/j3l+uJDAvmgfOG6WQxpZTPaABoZTef1I+yqhqeXrCJiNBg/nTmYA0CSimf0ADQykSEO08fyL7KGl76bjNRYcHccdpAXzdLKRWANAD4gIhw39lDKK+q4b9fbSQyLJgbJ/TzdbOUUgFGA4CPiAgPTh5OeVUN/5qzjooqFzdM6EtEaLCvm6aUChAaAHwoOEh4dOpIXAaemLeBt3/ezm9P7sfUMd0JC9ERukop79KrjI+FBAfx5MWjePPXR9ElPoI/vp/OyY8tYNaSLGpcbb9Ut1Kq/dL1ANoQYwwL1uXy6BfrWL2ziL4p0fzu1IFMGtbJ7TkD1TUu1u4uZum2PXRPiOLEQR293GqlVFt1uPUANAC0QcYY5qTv5rEv17Mhp4QhneO447QBnDSo4yFDRgtKK1m2bQ9Ltu5h6bY9rNi+l7I6NYemjunG/ecMJTpcs31KBRoNAO1Yjcswe8UO/jN3A1vz95HWPZ7rT+hLQWklS7buYdm2PWTmlQIQEiQM6RLH6B4JjO6ZQFq3eN5ZvJ2nFmykd1I0T148imFdO/j4jJRSrUkDgB+oqnExc0kWT87bwK695QAkRYcxqkcCY3omMLpHPCO6xRMZdugIooWb8rj97eUUlFZy98RBXDO+t5agUCpAaADwI+VVNSzKzKdXUjQ9k6LcnkFcUFrJXTNXMjcjmwkDU3h06kiSY8K93FqllK/pmsB+JCI0mAkDO9IrObpJ5SMSo8N44Yox/O3coSzclM/E/3zLN+tzvdhSpVR7oAEgQIgIVxzdiw9vGk9CVChXTP+Jhz7N0PUJlApgGgACzODOccy++VguOaoHz32TyQXPLmSz05HcllVU17Aqa6+upaCUB2kfQACbk76Lu2aupKrGcNrQVE4a1JEJAzrSISq0WcfLKS7nq4wc5mbksDKrkOMHpHDJUT0Y1T2+2RVPc4rLefPHbby+aBt5JRUMTI3lj2cO5vgBKc06Xl1F5VWs3L6XI3sn6sxr5Ze0E1g1akdhGY9/uZ6v1uZQUFpJcJAwpmcCJw/qyMmDO9I3JabBi7cxhoxdxczLyGbu2hxWbC8EoGt8JMO7duCbDbnsq6xhUKdYLj2qB+eO6kpchHvBZVXWXl7+fjMfr9xFZY2LEwemcMKAFKZ/v4VtBfs4YUAKfzxzMANSY5t8znklFbz8/WZeXbiV4opqRnTrwJMXjaJXcnSTj6VUW6YBQLmlxmVYkVXIVxk5zFubQ8auIgB6JEZxkhMMjuydCMCizALmZWQzLyOHHYVlAKR1j+eUwR05eXAqgzrFIiIUl1cxe8VO3vxxG6t3FhEZGsw5I7twyVE9GNGtwyGBpbrGxeers3n5+80s3rqH6LBgLhjTjSuP6UWflBjApoJeXbiVJ7/aQGlFNRcf2YPbTx3g1qimHYVlvPBNJjN+3kZFtYtJwzpxdN9kHv18HdU1Lh6YPIzJo7p58j9rqzLGsHRbIdHhwQzqFOfr5jRo2bY9/HPOWhKiwvjrOUPpGBfh6yb5LQ0Aqll2Fpbx1docvlqbw/cb86iodhHtzDMorawhIjSI4/qncMrgjpw4qCMdYxv+R2yMYWXWXt78cRuzV+ykrKqGoV3iuOSoHpyb1pXqGhdv/bSd137Yws695XRPjOTKo3tx4RHdG7xjKCit5Ml5G3ht0VYiQ4O56cR+XD2+V73VVDfmlPDs15v4YNkOACaP6spvTuhLv442qOwoLOP2Gcv5aUsBU0Z35W/nDiOmnc2cXrK1gEc+X8eizAJE4Mqje/H70we2qfPYUVjGPz9by+wVO0mOCae4vIqI0GDuP2cI56V11YWRvEADgGqxssoafsjM46u1ORgDJw/uyDF9k5tVurqovIoPl+3gjR+3sXZ3MVFhwbiMobzKxTF9k7h6fG9OGtSRYDcnq23MKeHhzzKYm5FD1/hI7pk0iLNGdEZEWJW1l6cXbGTO6t2EhwRx0RE9+PXxfegaH3nIcaprXPxv/kaenLeBHolR/Pfi0Qzv1vZnTq/euZd/f2FTeMkx4dx0Yl+25JXy6qKtdIqL4O/nDuOUIak+bWNpRTXPLNjEC99mAnDd8X24/oS+7C4q566ZK1mydQ+nDE7lH5OH6d2Ah2kAUG2SMYbl2wt5Z/F2gkS4/OieLUpbLNyYxwOfZLBmVxGjesQTEx7CtxvyiI0I4Yqje3L1+N5upYl+2lzArTOWkVdSwV2nD+LaY9vmzOlNuSU89uV6Plm5i7iIEK6f0JerjulFVJj9xr902x7unbWKddnFnDG8E/ef3fqplhqXYeaS7Tz6xXpyiys4N60Ld00cdEAArnEZXv5+M498vo7wkCDuP2cok0fp3YCnaABQAaPGZZi1NItHP1+HyxiuObY3l43r6XbHc63CfZXcPWsln6/O5oQBduZ0SqxnZk4bY9iUW8oPmfn8mJlPjcswIDWWgZ3sT8/EKEKCGx6RlLVnH0/M3cCspVlEhAZz7bG9+dVxfegQeeg5VtW4eP6bTJ6Yt4HwkCDumTSIi4/o0SoBbeGmPB742Abk0T3i+fNZQxjVI6HB12fmlnCnczdw8qCO/GPKcFL1bqDFNACogONyGQy4nUaqjzGGN37cxt8/XkNsRCiPTxvJcf2bPvTUGMPmPHvBX5RZwKLMfHKLKwDoFBdBZFgwW/JLqf1nGBYSRL+UGAZ1imWAExQGpsYSEiQ8NX8jb/60DRHh8nE9uWFCX7fuajbnlfLH91excFM+Y3sm8NCU4fR3Y/SUy2XYVVTO5txSyqpqCA8JIjwkiIjQYMJDg4gIsb/DQ4KJcH5vzS/lH5+uZW5G9iEpucM5+G7gvrOHMmW03g20hAYApVpg3e5ibn5zKRtySji2XzIJ0WHERYQQGxFKXKTzOyKEuIhQYp3tIrBk6x4WZeazKDOf7CJ7we8YG87RfZMY1yeJo/sk7a/nVFZZw8acEtZlF7NudxHrsktYv7uY3UXlB7QlOEi4cGw3fntSf7rU04/RGGMMM5dk8eCnGZRWVHPDhH7c6CxBuqe0ksy8UjbnlbI5r4TNeaVk5payJb+U8qqmT7yLCQ/hxhP7cs343s3qJ9qcV8qd765gsQ/uBkoqqlmZVcjy7YUs21bIiu2FBInQPzWGAamxDEiNoX9qLP07xhDbxDtLX9AAoFQLlVXW8O8v1vHTlgKKy6spKquiqLyKqprG/+0kx9Re8BM5uk8SvZtYw6lwXyXrs0tYt7uI3OIKpozu1uK5CnklFTzw8Ro+WL6TlNhwqmpcFO6r2v98SJDQIzGK3snR9iclmt5J0cRGhFJRXUN5lYuK6hoqql2UV9nfFVU1lFe7qKhyERIsXDi2e4tTZjUuwysLt/Cvz9fiMrb6bWRoMBGhwUSGBR/0OMj+HRZMXEQoCVFhJEbX/g4jITqM+MjQQ1JrLpdhY24Jy7cVsmz7HpZtK2R9djG1C/H1To4mrXs8AqzPKWZDdgkVdUqndI2P3B8Y+neMoXdyNEFBgjEGl7HHrzEGY8BVZ5vLGOKjwuiZFEVSdJhX73A0ACjlBcYYKqpdFJVXUVRWTXF5FUXl9ndltYsR3eLpm9K0C35r+np9Lq8v2kpKbDh9ai/2ydF0T4witJE+iNa2Ja+U1xdtpai8irIqF2WVNZRX1VBWVXPA432V9u/KRkqFdIgMtQEhygaDjJ1FFFdUAxAXEUJajwRGdY8nrUc8ad3iSYgOO2D/Gpdhe8E+1mcXsyGnhPXZxazPLmFTTkmj79uYmPAQeiZFOT/R9EqKokdiNL2So0iNjWhxf40GAKVUwCivqmHPvkoKSivZU1pFwb5KCvf/XUnBvir2lFZSVlXD4M6xjOqeQFqPeHonRTf7Yltd42JrwT62FewDIEiEIIFgEaT2cdAvj4NEKCitZEt+KVvz97HV+b19z74D7irDQ4LokRjFs5ePoa8zEbKpDhcAfDJLREQmAk8AwcCLxpiHfdEOpZR/iQgNpnOHSDp3aFofSUuEBAfRNyWm2RfpWtU1LnbtLWdr/j4nONjAkBAVdvidm6nVA4CIBANPAacCWcDPIjLbGLOmtduilFJtRUhwEN0To+ieGMWx/ZNb5T19kew7EthojMk0xlQCM4BzfdAOpZQKaL4IAF2B7XX+znK2HUBErhORxSKyODdXV69SSilPazvd/QcxxjxvjBlrjBmbktLy2u9KKaUO5IsAsAPoXufvbs42pZRSrcgXAeBnoL+I9BaRMOAiYLYP2qGUUgGt1UcBGWOqReRm4HPsMNDpxpjVrd0OpZQKdD6ZB2CM+RT41BfvrZRSymqzncBKKaW8q12UghCRXGBrM3dPBvI82Jy2wN/OSc+n7fO3c/K384H6z6mnMabBYZTtIgC0hIgsbqwWRnvkb+ek59P2+ds5+dv5QPPOSVNASikVoDQAKKVUgAqEAPC8rxvgBf52Tno+bZ+/nZO/nQ8045z8vg9AKaVU/QLhDkAppVQ9NAAopVSA8usAICITRWSdiGwUkXt83Z6WEpEtIrJKRJaLSLtcI1NEpotIjoik19mWKCJfisgG53eCL9vYFA2cz/0issP5nJaLyBm+bGNTiEh3EZkvImtEZLWI3Opsb8+fUUPn1C4/JxGJEJGfRGSFcz5/dbb3FpEfnevd206ttcaP5a99AM7KY+ups/IYcHF7XnlMRLYAY40x7XYCi4gcD5QArxpjhjnb/gUUGGMedgJ1gjHmbl+2010NnM/9QIkx5lFftq05RKQz0NkYs1REYoElwHnAVbTfz6ihc7qQdvg5iYgA0caYEhEJBb4DbgV+B7xnjJkhIs8CK4wxzzR2LH++A9CVx9ogY8w3QMFBm88FXnEev4L9x9kuNHA+7ZYxZpcxZqnzuBjIwC7Y1J4/o4bOqV0yVonzZ6jzY4CTgJnOdrc+I38OAG6tPNbOGOALEVkiItf5ujEelGqM2eU83g2k+rIxHnKziKx0UkTtJl1Sl4j0AkYBP+Inn9FB5wTt9HMSkWARWQ7kAF8Cm4BCY0y18xK3rnf+HAD80bHGmNHAJOAmJ/3gV4zNSbb3vOQzQF8gDdgF/NunrWkGEYkBZgG3GWOK6j7XXj+jes6p3X5OxpgaY0wadkGtI4FBzTmOPwcAv1t5zBizw/mdA7yP/eD9QbaTp63N1+b4uD0tYozJdv6BuoAXaGefk5NXngW8YYx5z9ncrj+j+s6pvX9OAMaYQmA+cDQQLyK1Jf7dut75cwDwq5XHRCTa6cBCRKKB04D0xvdqN2YDVzqPrwQ+9GFbWqz2QumYTDv6nJwOxpeADGPMY3WearefUUPn1F4/JxFJEZF453EkdqBLBjYQXOC8zK3PyG9HAQE4w7r+wy8rjz3o2xY1n4j0wX7rB7uQz5vt8XxE5C1gArZ0bTZwH/AB8A7QA1v2+0JjTLvoWG3gfCZg0woG2AL8pk7+vE0TkWOBb4FVgMvZ/Adszry9fkYNndPFtMPPSURGYDt5g7Ff4t8xxvzNuUbMABKBZcBlxpiKRo/lzwFAKaVUw/w5BaSUUqoRGgCUUipAaQBQSqkApQFAKaUClAYApZQKUBoAlPIyEZkgIh/7uh1KHUwDgFJKBSgNAEo5ROQyp876chF5zim4VSIijzt11+eJSIrz2jQRWeQUEnu/tpCYiPQTkblOrfalItLXOXyMiMwUkbUi8oYzO1Upn9IAoBQgIoOBacB4p8hWDXApEA0sNsYMBb7GzvQFeBW42xgzAjvDtHb7G8BTxpiRwDHYImNgK1DeBgwB+gDjvXxKSh1WyOFfolRAOBkYA/zsfDmPxBY8cwFvO695HXhPRDoA8caYr53trwDvOrWauhpj3gcwxpQDOMf7yRiT5fy9HOiFXchDKZ/RAKCUJcArxph7D9go8ueDXtfc2il1a7LUoP/2VBugKSClrHnABSLSEfavgdsT+2+ktsLiJcB3xpi9wB4ROc7ZfjnwtbPaVJaInOccI1xEolrzJJRqCv0WohRgjFkjIn/CrrgWBFQBNwGlwJHOcznYfgKw5XafdS7wmcDVzvbLgedE5G/OMaa24mko1SRaDVSpRohIiTEmxtftUMobNAWklFIBSu8AlFIqQOkdgFJKBSgNAEopFaA0ACilVIDSAKCUUgFKA4BSSgWo/weXxu33JIap0QAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1132,23 +1168,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3/3 [==============================] - 0s 9ms/step - loss: 35.5286 - accuracy: 0.5000\n"
+      "3/3 [==============================] - 0s 7ms/step - loss: 36.2351 - accuracy: 0.4940\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[35.52861785888672, 0.5]"
+       "[36.235076904296875, 0.4939759075641632]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1161,7 +1197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We reach a validation accuracy of about 46% — much worse than we achieved in the\n",
+    "We reach a validation accuracy of about 49% — much worse than we achieved in the\n",
     "previous section with the small model trained from scratch. \n",
     "\n",
     "The learning curves indicate that we’re overfitting almost from the start—\n",
@@ -1227,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1260,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1277,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1286,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1323,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1338,7 +1374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1381,7 +1417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1396,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1404,101 +1440,63 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/50\n",
-      "15/15 [==============================] - 168s 11s/step - loss: 96.2850 - accuracy: 0.3958 - val_loss: 124.8547 - val_accuracy: 0.1375\n",
+      "15/15 [==============================] - 166s 11s/step - loss: 91.1297 - accuracy: 0.4229 - val_loss: 82.8456 - val_accuracy: 0.1205\n",
       "Epoch 2/50\n",
-      "15/15 [==============================] - 161s 11s/step - loss: 34.7940 - accuracy: 0.5771 - val_loss: 117.0612 - val_accuracy: 0.2000\n",
+      "15/15 [==============================] - 182s 12s/step - loss: 34.8701 - accuracy: 0.5875 - val_loss: 109.8627 - val_accuracy: 0.2169\n",
       "Epoch 3/50\n",
-      "15/15 [==============================] - 164s 11s/step - loss: 32.3914 - accuracy: 0.5688 - val_loss: 100.1375 - val_accuracy: 0.2250\n",
+      "15/15 [==============================] - 162s 11s/step - loss: 36.2388 - accuracy: 0.5521 - val_loss: 94.7938 - val_accuracy: 0.2410\n",
       "Epoch 4/50\n",
-      "15/15 [==============================] - 162s 11s/step - loss: 24.2779 - accuracy: 0.6146 - val_loss: 98.1699 - val_accuracy: 0.2125\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 28.6837 - accuracy: 0.6146 - val_loss: 99.4623 - val_accuracy: 0.2410\n",
       "Epoch 5/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 21.5480 - accuracy: 0.6646 - val_loss: 86.7904 - val_accuracy: 0.2500\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 23.9077 - accuracy: 0.6229 - val_loss: 89.0602 - val_accuracy: 0.2771\n",
       "Epoch 6/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 19.2203 - accuracy: 0.6625 - val_loss: 97.0842 - val_accuracy: 0.2500\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 18.6794 - accuracy: 0.6583 - val_loss: 91.0110 - val_accuracy: 0.2651\n",
       "Epoch 7/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 19.1349 - accuracy: 0.6833 - val_loss: 92.7148 - val_accuracy: 0.2875\n",
+      "15/15 [==============================] - 153s 10s/step - loss: 19.0228 - accuracy: 0.6854 - val_loss: 83.4722 - val_accuracy: 0.3012\n",
       "Epoch 8/50\n",
-      "15/15 [==============================] - 153s 10s/step - loss: 18.4256 - accuracy: 0.6917 - val_loss: 89.8846 - val_accuracy: 0.3000\n",
+      "15/15 [==============================] - 152s 10s/step - loss: 18.7669 - accuracy: 0.6729 - val_loss: 81.9506 - val_accuracy: 0.2651\n",
       "Epoch 9/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 15.9491 - accuracy: 0.7312 - val_loss: 81.9328 - val_accuracy: 0.3375\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 16.7555 - accuracy: 0.6896 - val_loss: 73.5456 - val_accuracy: 0.2892\n",
       "Epoch 10/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 15.8089 - accuracy: 0.7000 - val_loss: 91.3705 - val_accuracy: 0.3000\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 14.7009 - accuracy: 0.7083 - val_loss: 91.4017 - val_accuracy: 0.3253\n",
       "Epoch 11/50\n",
-      "15/15 [==============================] - 161s 11s/step - loss: 14.6212 - accuracy: 0.7104 - val_loss: 85.8852 - val_accuracy: 0.3375\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 14.5194 - accuracy: 0.6958 - val_loss: 86.6846 - val_accuracy: 0.3494\n",
       "Epoch 12/50\n",
-      "15/15 [==============================] - 165s 11s/step - loss: 14.2607 - accuracy: 0.7375 - val_loss: 79.6010 - val_accuracy: 0.3625\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 14.1941 - accuracy: 0.7229 - val_loss: 79.6319 - val_accuracy: 0.3494\n",
       "Epoch 13/50\n",
-      "15/15 [==============================] - 166s 11s/step - loss: 14.5002 - accuracy: 0.7229 - val_loss: 81.6204 - val_accuracy: 0.3750\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 14.7247 - accuracy: 0.7250 - val_loss: 69.4316 - val_accuracy: 0.4217\n",
       "Epoch 14/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 11.8944 - accuracy: 0.7167 - val_loss: 78.6013 - val_accuracy: 0.3625\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 11.2260 - accuracy: 0.7458 - val_loss: 80.7598 - val_accuracy: 0.3133\n",
       "Epoch 15/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 11.1978 - accuracy: 0.7563 - val_loss: 97.3765 - val_accuracy: 0.3250\n",
+      "15/15 [==============================] - 181s 12s/step - loss: 10.3544 - accuracy: 0.7583 - val_loss: 76.6999 - val_accuracy: 0.4096\n",
       "Epoch 16/50\n",
-      "15/15 [==============================] - 156s 10s/step - loss: 17.9992 - accuracy: 0.6958 - val_loss: 79.1425 - val_accuracy: 0.3500\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 12.8780 - accuracy: 0.6958 - val_loss: 60.4076 - val_accuracy: 0.4096\n",
       "Epoch 17/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 14.9720 - accuracy: 0.7104 - val_loss: 71.0003 - val_accuracy: 0.3750\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 13.8035 - accuracy: 0.7542 - val_loss: 78.4311 - val_accuracy: 0.3855\n",
       "Epoch 18/50\n",
-      "15/15 [==============================] - 153s 10s/step - loss: 10.8481 - accuracy: 0.7521 - val_loss: 93.2622 - val_accuracy: 0.3500\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 14.4556 - accuracy: 0.7271 - val_loss: 62.9922 - val_accuracy: 0.4337\n",
       "Epoch 19/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 11.0923 - accuracy: 0.7625 - val_loss: 63.9837 - val_accuracy: 0.4250\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 13.1251 - accuracy: 0.7563 - val_loss: 63.2732 - val_accuracy: 0.4458\n",
       "Epoch 20/50\n",
-      "15/15 [==============================] - 152s 10s/step - loss: 11.5035 - accuracy: 0.7500 - val_loss: 70.1764 - val_accuracy: 0.4000\n",
+      "15/15 [==============================] - 162s 11s/step - loss: 10.3900 - accuracy: 0.7708 - val_loss: 72.5239 - val_accuracy: 0.3855\n",
       "Epoch 21/50\n",
-      "15/15 [==============================] - 152s 10s/step - loss: 9.8748 - accuracy: 0.7958 - val_loss: 67.4912 - val_accuracy: 0.4125\n",
+      "15/15 [==============================] - 161s 11s/step - loss: 8.2427 - accuracy: 0.8021 - val_loss: 76.2614 - val_accuracy: 0.3494\n",
       "Epoch 22/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 9.0496 - accuracy: 0.7854 - val_loss: 68.4334 - val_accuracy: 0.4000\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 10.2765 - accuracy: 0.7875 - val_loss: 61.1442 - val_accuracy: 0.4096\n",
+      "Epoch 23/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 8.8797 - accuracy: 0.7750 - val_loss: 66.7209 - val_accuracy: 0.3614\n",
       "Epoch 24/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 10.7076 - accuracy: 0.7563 - val_loss: 76.1538 - val_accuracy: 0.3750\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 8.3769 - accuracy: 0.7896 - val_loss: 55.9643 - val_accuracy: 0.4458\n",
       "Epoch 25/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 9.3391 - accuracy: 0.7667 - val_loss: 73.5574 - val_accuracy: 0.4000\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 6.8533 - accuracy: 0.8083 - val_loss: 65.5897 - val_accuracy: 0.4217\n",
       "Epoch 26/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 11.0616 - accuracy: 0.7646 - val_loss: 65.0781 - val_accuracy: 0.4375\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 11.2915 - accuracy: 0.7563 - val_loss: 70.5098 - val_accuracy: 0.4217\n",
       "Epoch 27/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 10.4314 - accuracy: 0.7875 - val_loss: 73.0751 - val_accuracy: 0.4375\n",
+      "15/15 [==============================] - 180s 12s/step - loss: 7.8575 - accuracy: 0.7812 - val_loss: 85.2074 - val_accuracy: 0.3976\n",
       "Epoch 28/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 8.8000 - accuracy: 0.7937 - val_loss: 56.8248 - val_accuracy: 0.4125\n",
+      "15/15 [==============================] - 164s 11s/step - loss: 9.7024 - accuracy: 0.7583 - val_loss: 60.2458 - val_accuracy: 0.4458\n",
       "Epoch 29/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 6.1402 - accuracy: 0.8208 - val_loss: 61.1947 - val_accuracy: 0.4250\n",
-      "Epoch 31/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 6.7533 - accuracy: 0.8125 - val_loss: 72.0019 - val_accuracy: 0.4000\n",
-      "Epoch 32/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 7.7121 - accuracy: 0.8250 - val_loss: 59.1955 - val_accuracy: 0.4625\n",
-      "Epoch 33/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 8.7896 - accuracy: 0.7771 - val_loss: 66.8333 - val_accuracy: 0.4250\n",
-      "Epoch 34/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 7.4364 - accuracy: 0.8167 - val_loss: 66.7417 - val_accuracy: 0.4000\n",
-      "Epoch 35/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 6.2558 - accuracy: 0.8042 - val_loss: 65.5794 - val_accuracy: 0.4250\n",
-      "Epoch 36/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 6.1949 - accuracy: 0.8354 - val_loss: 54.5587 - val_accuracy: 0.4375\n",
-      "Epoch 37/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 6.6892 - accuracy: 0.8125 - val_loss: 63.7705 - val_accuracy: 0.4375\n",
-      "Epoch 38/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 5.5650 - accuracy: 0.8208 - val_loss: 64.0116 - val_accuracy: 0.4750\n",
-      "Epoch 39/50\n",
-      "15/15 [==============================] - 159s 11s/step - loss: 6.0108 - accuracy: 0.8271 - val_loss: 70.6677 - val_accuracy: 0.4250\n",
-      "Epoch 40/50\n",
-      "15/15 [==============================] - 159s 11s/step - loss: 6.9199 - accuracy: 0.8417 - val_loss: 56.8354 - val_accuracy: 0.4500\n",
-      "Epoch 41/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 4.1278 - accuracy: 0.8583 - val_loss: 58.6842 - val_accuracy: 0.4625\n",
-      "Epoch 42/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 7.9539 - accuracy: 0.8125 - val_loss: 73.3030 - val_accuracy: 0.4625\n",
-      "Epoch 43/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 7.7179 - accuracy: 0.8062 - val_loss: 58.1898 - val_accuracy: 0.4500\n",
-      "Epoch 44/50\n",
-      "15/15 [==============================] - 180s 12s/step - loss: 5.4911 - accuracy: 0.8417 - val_loss: 63.1966 - val_accuracy: 0.4500\n",
-      "Epoch 45/50\n",
-      "15/15 [==============================] - 161s 11s/step - loss: 6.8619 - accuracy: 0.8354 - val_loss: 53.5746 - val_accuracy: 0.4625\n",
-      "Epoch 46/50\n",
-      "15/15 [==============================] - 159s 11s/step - loss: 6.8473 - accuracy: 0.8083 - val_loss: 55.5791 - val_accuracy: 0.4625\n",
-      "Epoch 47/50\n",
-      "15/15 [==============================] - 159s 11s/step - loss: 5.0699 - accuracy: 0.8458 - val_loss: 63.1028 - val_accuracy: 0.4500\n",
-      "Epoch 48/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 4.7313 - accuracy: 0.8646 - val_loss: 50.6113 - val_accuracy: 0.4875\n",
-      "Epoch 49/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 6.3342 - accuracy: 0.8188 - val_loss: 61.1096 - val_accuracy: 0.4500\n",
-      "Epoch 50/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 5.7529 - accuracy: 0.8396 - val_loss: 60.3737 - val_accuracy: 0.4375\n"
+      " 1/15 [=>............................] - ETA: 2:05 - loss: 4.9456 - accuracy: 0.8438"
      ]
     }
    ],
@@ -1519,34 +1517,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(history.history['accuracy'])\n",
     "plt.plot(history.history['val_accuracy'])\n",
@@ -1566,27 +1539,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3/3 [==============================] - 21s 6s/step - loss: 60.3737 - accuracy: 0.4375\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[60.3736572265625, 0.4375]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.evaluate(validation_dataset)"
    ]
@@ -1654,68 +1609,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "cnObzTupGhLV",
     "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"vgg16\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
-      "                                                                 \n",
-      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
-      "                                                                 \n",
-      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
-      "                                                                 \n",
-      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
-      "                                                                 \n",
-      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
-      "                                                                 \n",
-      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
-      "                                                                 \n",
-      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
-      "                                                                 \n",
-      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
-      "                                                                 \n",
-      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
-      "                                                                 \n",
-      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
-      "                                                                 \n",
-      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
-      "                                                                 \n",
-      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
-      "                                                                 \n",
-      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
-      "                                                                 \n",
-      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
-      "                                                                 \n",
-      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
-      "                                                                 \n",
-      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
-      "                                                                 \n",
-      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
-      "                                                                 \n",
-      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
-      "                                                                 \n",
-      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 14,714,688\n",
-      "Trainable params: 7,079,424\n",
-      "Non-trainable params: 7,635,264\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "conv_base.summary()"
    ]
@@ -1748,40 +1649,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "tBXYN1t2GhLc",
     "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "layer name = input_2, shape = [(None, 150, 150, 3)], trainable = False\n",
-      "layer name = block1_conv1, shape = (None, 150, 150, 64), trainable = False\n",
-      "layer name = block1_conv2, shape = (None, 150, 150, 64), trainable = False\n",
-      "layer name = block1_pool, shape = (None, 75, 75, 64), trainable = False\n",
-      "layer name = block2_conv1, shape = (None, 75, 75, 128), trainable = False\n",
-      "layer name = block2_conv2, shape = (None, 75, 75, 128), trainable = False\n",
-      "layer name = block2_pool, shape = (None, 37, 37, 128), trainable = False\n",
-      "layer name = block3_conv1, shape = (None, 37, 37, 256), trainable = False\n",
-      "layer name = block3_conv2, shape = (None, 37, 37, 256), trainable = False\n",
-      "layer name = block3_conv3, shape = (None, 37, 37, 256), trainable = False\n",
-      "layer name = block3_pool, shape = (None, 18, 18, 256), trainable = False\n",
-      "layer name = block4_conv1, shape = (None, 18, 18, 512), trainable = False\n",
-      "layer name = block4_conv2, shape = (None, 18, 18, 512), trainable = False\n",
-      "layer name = block4_conv3, shape = (None, 18, 18, 512), trainable = False\n",
-      "layer name = block4_pool, shape = (None, 9, 9, 512), trainable = False\n",
-      "layer name = block5_conv1, shape = (None, 9, 9, 512), trainable = True\n",
-      "layer name = block5_conv2, shape = (None, 9, 9, 512), trainable = True\n",
-      "layer name = block5_conv3, shape = (None, 9, 9, 512), trainable = True\n",
-      "layer name = block5_pool, shape = (None, 4, 4, 512), trainable = True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "conv_base.trainable = True\n",
     "for layer in conv_base.layers[:-4]:\n",
@@ -1814,30 +1689,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "4YBjFhSVGhLh",
     "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "layer name = input_4, shape = [(None, 150, 150, 3)], trainable = False\n",
-      "layer name = sequential_1, shape = (None, 150, 150, 3), trainable = False\n",
-      "layer name = tf.__operators__.getitem, shape = (None, 150, 150, 3), trainable = False\n",
-      "layer name = tf.nn.bias_add, shape = (None, 150, 150, 3), trainable = False\n",
-      "layer name = vgg16, shape = (None, None, None, 512), trainable = False\n",
-      "layer name = flatten_2, shape = (None, 8192), trainable = False\n",
-      "layer name = dense_4, shape = (None, 256), trainable = False\n",
-      "layer name = dropout_2, shape = (None, 256), trainable = False\n",
-      "layer name = dense_5, shape = (None, 8), trainable = False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.compile(loss=\"categorical_crossentropy\",\n",
     "    optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
@@ -1923,7 +1782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1932,29 +1791,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "WoDOi_F8GhL5",
     "outputId": "17c21c92-2a5d-4e21-c367-57e818046762"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  2   0   0   0   0   2   0   6 ], angelina jolie\n",
-      "[  0   2   0   1   0   0   7   0 ], brad pitt\n",
-      "[  0   0   5   0   0   2   0   3 ], catherine deneuve\n",
-      "[  0   0   0   5   2   1   2   0 ], johnny depp\n",
-      "[  0   1   0   0   5   0   4   0 ], leonardo dicaprio\n",
-      "[  1   0   0   0   0   2   0   7 ], marion cotillard\n",
-      "[  0   1   0   0   0   0   9   0 ], robert de niro\n",
-      "[  0   0   0   0   0   1   0   9 ], sandra bullock\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.metrics import confusion_matrix\n",
     "import sys\n",
@@ -1981,53 +1825,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "nNp0qChLGhL-",
     "outputId": "f22e9bfe-e5da-4d57-fbdc-2ea55d6681e7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wrong classification for: sandra bullock\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "matched to: marion cotillard\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Choose the class label you want to check\n",
     "clbl = 7\n",
@@ -2650,7 +2455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2660,19 +2465,7 @@
     "id": "HGiDmuejGhM8",
     "outputId": "ce5ca013-96e5-4766-d2ed-b4cde9b3ca94"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cloning into 'Mask_RCNN'...\n",
-      "remote: Enumerating objects: 956, done.\u001b[K\n",
-      "remote: Total 956 (delta 0), reused 0 (delta 0), pack-reused 956\u001b[K\n",
-      "Receiving objects: 100% (956/956), 111.84 MiB | 30.52 MiB/s, done.\n",
-      "Resolving deltas: 100% (570/570), done.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!git clone https://github.com/matterport/Mask_RCNN.git"
    ]
@@ -2729,7 +2522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2739,157 +2532,7 @@
     "id": "aEUeZhX5GhNB",
     "outputId": "be5de5a1-e821-477c-ce28-91bb9f8c3194"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.18.2)\n",
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-      "Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (0.29.15)\n",
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-      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.4->nbconvert; extra == \"all\"->IPython[all]->-r requirements.txt (line 12)) (1.1.1)\n",
-      "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert; extra == \"all\"->IPython[all]->-r requirements.txt (line 12)) (0.5.1)\n",
-      "Requirement already satisfied: pytz>=2015.7 in /usr/local/lib/python3.6/dist-packages (from babel!=2.0,>=1.3->Sphinx>=1.3; extra == \"all\"->IPython[all]->-r requirements.txt (line 12)) (2018.9)\n",
-      "WARNING:root:Fail load requirements file, so using default ones.\n",
-      "running install\n",
-      "running bdist_egg\n",
-      "running egg_info\n",
-      "creating mask_rcnn.egg-info\n",
-      "writing mask_rcnn.egg-info/PKG-INFO\n",
-      "writing dependency_links to mask_rcnn.egg-info/dependency_links.txt\n",
-      "writing top-level names to mask_rcnn.egg-info/top_level.txt\n",
-      "writing manifest file 'mask_rcnn.egg-info/SOURCES.txt'\n",
-      "reading manifest template 'MANIFEST.in'\n",
-      "writing manifest file 'mask_rcnn.egg-info/SOURCES.txt'\n",
-      "installing library code to build/bdist.linux-x86_64/egg\n",
-      "running install_lib\n",
-      "running build_py\n",
-      "creating build\n",
-      "creating build/lib\n",
-      "creating build/lib/mrcnn\n",
-      "copying mrcnn/visualize.py -> build/lib/mrcnn\n",
-      "copying mrcnn/parallel_model.py -> build/lib/mrcnn\n",
-      "copying mrcnn/model.py -> build/lib/mrcnn\n",
-      "copying mrcnn/utils.py -> build/lib/mrcnn\n",
-      "copying mrcnn/config.py -> build/lib/mrcnn\n",
-      "copying mrcnn/__init__.py -> build/lib/mrcnn\n",
-      "creating build/bdist.linux-x86_64\n",
-      "creating build/bdist.linux-x86_64/egg\n",
-      "creating build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/visualize.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/parallel_model.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/model.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/utils.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/config.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "copying build/lib/mrcnn/__init__.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/visualize.py to visualize.cpython-36.pyc\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/parallel_model.py to parallel_model.cpython-36.pyc\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/model.py to model.cpython-36.pyc\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/utils.py to utils.cpython-36.pyc\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/config.py to config.cpython-36.pyc\n",
-      "byte-compiling build/bdist.linux-x86_64/egg/mrcnn/__init__.py to __init__.cpython-36.pyc\n",
-      "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying mask_rcnn.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying mask_rcnn.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying mask_rcnn.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying mask_rcnn.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "zip_safe flag not set; analyzing archive contents...\n",
-      "creating dist\n",
-      "creating 'dist/mask_rcnn-2.1-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
-      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
-      "Processing mask_rcnn-2.1-py3.6.egg\n",
-      "Removing /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
-      "Copying mask_rcnn-2.1-py3.6.egg to /usr/local/lib/python3.6/dist-packages\n",
-      "mask-rcnn 2.1 is already the active version in easy-install.pth\n",
-      "\n",
-      "Installed /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
-      "Processing dependencies for mask-rcnn==2.1\n",
-      "Finished processing dependencies for mask-rcnn==2.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "os.chdir('./Mask_RCNN')\n",
@@ -2934,7 +2577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2944,24 +2587,7 @@
     "id": "kKXRZ1vTGhNG",
     "outputId": "9f0df55c-755f-4e11-a6c3-e8b7418eefcb"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: mask-rcnn\n",
-      "Version: 2.1\n",
-      "Summary: Mask R-CNN for object detection and instance segmentation\n",
-      "Home-page: https://github.com/matterport/Mask_RCNN\n",
-      "Author: Matterport\n",
-      "Author-email: waleed.abdulla@gmail.com\n",
-      "License: MIT\n",
-      "Location: /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
-      "Requires: \n",
-      "Required-by: \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip3 show mask-rcnn"
    ]
@@ -3081,7 +2707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3091,15 +2717,7 @@
     "id": "_TWgehzsNOSV",
     "outputId": "73225d99-e9df-4d1c-c733-a092c97e336c"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from google.colab import drive\n",
     "drive.mount('/content/drive')"
@@ -3107,7 +2725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3117,26 +2735,7 @@
     "id": "46t9gwLdGhNR",
     "outputId": "842b58f4-2678-4ad9-bbcf-aac4656392b7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# load coco model weights\n",
     "rcnn.load_weights('/content/drive/My Drive/mask_rcnn_coco.h5', by_name=True)"
@@ -3261,7 +2860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3271,32 +2870,7 @@
     "id": "VbLvAtkvGhNk",
     "outputId": "1db15efd-d2a8-4a0c-fcac-e00ab09e24c7"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.patches.Rectangle at 0x7fc347f515c0>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8CWpCYMvSU0YfElq4n8BzPZcum6w2goAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from matplotlib import pyplot\n",
@@ -3368,7 +2942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3378,19 +2952,7 @@
     "id": "XscAeWiLGhNq",
     "outputId": "8c0f20a6-1ff0-4162-f7a0-d2ed64370872"
    },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'keras'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-b598b8f517eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmrcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmrcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMaskRCNN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from keras.preprocessing.image import load_img\n",
     "from keras.preprocessing.image import img_to_array\n",
@@ -3510,7 +3072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3520,19 +3082,7 @@
     "id": "mIlhDj57GhNz",
     "outputId": "9e57f9b3-97af-4cb5-c389-6d6f2435ddc7"
    },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'mrcnn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-8236acdb0d4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmrcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay_instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# get dictionary for first prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# show photo with bounding boxes, masks, class labels and scores\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[0mdisplay_instances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rois'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'masks'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'class_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scores'\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;31mModuleNotFoundError\u001b[0m: No module named 'mrcnn'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mrcnn.visualize import display_instances\n",
     "# get dictionary for first prediction\n",
-- 
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