diff --git a/serie2/MNIST_binary_classifier_stud.ipynb b/serie2/MNIST_binary_classifier_stud.ipynb
index dcfe6460bd5012e248c4c5be5aa3e0428775605b..2c976f6fded739ad1eaddb825816c4a8fe65e612 100644
--- a/serie2/MNIST_binary_classifier_stud.ipynb
+++ b/serie2/MNIST_binary_classifier_stud.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "educational-syndrome",
    "metadata": {},
    "outputs": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "allied-flavor",
    "metadata": {},
    "outputs": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "effective-anaheim",
    "metadata": {},
    "outputs": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "stock-simpson",
    "metadata": {},
    "outputs": [],
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "returning-relative",
    "metadata": {},
    "outputs": [
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "qualified-charm",
    "metadata": {},
    "outputs": [
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "signed-kansas",
    "metadata": {},
    "outputs": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 116,
    "id": "removed-commons",
    "metadata": {},
    "outputs": [],
@@ -343,7 +343,8 @@
     "            # MSE code\n",
     "            cost = 1 / (2 * m) * np.sum((y_pred - y)**2)\n",
     "        else:\n",
-    "            cost = 0.123       \n",
+    "            # cross entropy code\n",
+    "            cost = -1 / m * np.sum(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))\n",
     "            \n",
     "        ### END YOUR CODE ###   \n",
     "        \n",
@@ -364,14 +365,24 @@
     "            \n",
     "        ### START YOUR CODE ###\n",
     "            \n",
-    "            # fonctionne pas\n",
-    "            grad_w = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y) * x)\n",
-    "            grad_b = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y))\n",
+    "            # MSE code\n",
     "\n",
-    "        else:  \n",
-    "            grad_w = np.zeros((784,1))\n",
-    "            grad_b = 0\n",
+    "            # grad_w = self.w\n",
+    "            # for i in range(m):\n",
+    "            #     x_i = np.array([x[i]]).T\n",
+    "            #     grad_w += y_pred[i][0] * (1 - y_pred[i][0]) * (y_pred[i][0]- y[i][0]) * x_i\n",
+    "                \n",
+    "            # grad_w = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y)) * x\n",
+    "            \n",
+    "            grad_w = np.dot((y_pred*(1-y_pred)*(y_pred-y)).T,x)/x.shape[1]\n",
+    "            grad_b = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y))\n",
     "            \n",
+    "            \n",
+    "        else:  \n",
+    "            # cross entropy code\n",
+    "            grad_w = 1 / m * np.dot(x.T, (y_pred - y))\n",
+    "            grad_b = 1 / m * np.sum(y_pred - y)\n",
+    "\n",
     "        ### END YOUR CODE ###   \n",
     "                                      \n",
     "        return grad_w.T, grad_b\n",
@@ -400,7 +411,7 @@
     "        \n",
     "        ### START YOUR CODE ###\n",
     "        \n",
-    "        self.w = self.w - self.alpha * grad_w\n",
+    "        self.w = self.w - self.alpha * grad_w \n",
     "        self.b = self.b - self.alpha * grad_b\n",
     "        \n",
     "        ### END YOUR CODE ###   \n",
@@ -442,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 123,
    "id": "colored-facility",
    "metadata": {},
    "outputs": [
@@ -450,7 +461,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "result after 5 epochs, train: cost 0.11223, error 0.22577 ; test: cost 0.11357, error 0.22808\n"
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "(1, 12136) (12136, 784) 784\n",
+      "result after 10 epochs, train: cost 0.03605, error 0.08776 ; test: cost 0.03293, error 0.07482\n"
      ]
     }
    ],
@@ -460,34 +481,6 @@
     "\n",
     "gradD = GradientDescent(data, 0.5, 0, 0.)\n",
     "\n",
-    "gradD.optimise(5, False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "ccc5f83a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "(12136, 784)\n",
-      "result after 10 epochs, train: cost 187.34344, error 374.68688 ; test: cost 192.89914, error 385.79829\n"
-     ]
-    }
-   ],
-   "source": [
     "gradD.optimise(10, False)"
    ]
   },
@@ -503,13 +496,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 127,
    "id": "lonely-quantity",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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oPtbqSCIiIh7JZ0e0oqKisNvtpKenV9mfnp5ObGysRamahyGdo1hefjmews26HI+IiEhd+WzRcjgc9O/fnyVLTizM6XK5WLJkCYMHD7YwmfWiQwPYGX42AOZPS8A0LU4kIiLimby6aOXl5ZGamlrxzcFdu3aRmprK3r17AZg2bRovv/wyb7zxBps3b+aWW24hPz+/4luIviys23CKTT+CCw9A5k9WxxEREfFIXj1H6/vvv+f888+v2J42bRoAEydO5PXXX+eqq67i8OHD3H///Rw6dIiUlBQ+++yzkybI+6JBXRNYvbobw+ybMLcvxojuYnUkERERj2OYps4LWSUnJ4fw8HCys7Ob3TcQi0qdzHpwKtPtb5Pf7nxCblhgdSQREZFmoTZ/v7361KHUXaC/naPx5wIQ8PMqKC2yOJGIiIjnUdGSU+rQfQAHzUj8XEWw9xur44iIiHgcFS05paFJ0Sx3upd5cP7kfeuFiYiINDYVLTml5Lgw1vu7L8dTvGWRxWlEREQ8j4qWnJLNZmB2Go7TNAg+tg2yf7Y6koiIiEdR0ZLT6t+tIxvMTu6NHUutDSMiIuJhVLTktIYlnbgcT+lWnT4UERGpDRUtOa248CC2h7kvx8POr8BZZmkeERERT6KiJWcU3XUwx8wQ/Etz4MA6q+OIiIh4DBUtOaOhXWJY4erl3tiuZR5ERERqSkVLzmhQx1asMN3ztLTMg4iISM2paMkZtQjw41jcMAAc6euhIMviRCIiIp5BRUtqJLlbd7a4EjAwYeeXVscRERHxCCpaUiOVl3lw6XI8IiIiNaKiJTXSu21L1vj1BaDspyVgmhYnEhERaf5UtKRG7DaDgA5DKTQdOArSISPN6kgiIiLNnoqW1NjZXeNZ5Up2b2iZBxERkTNS0ZIaqzxPq0zztERERM5IRcsCc+bMITk5mYEDB1odpVbatwphW+ggAGx7V0FJvsWJREREmjcVLQtMmTKFtLQ01qxZY3WUWkvs0pt9rmhsrlLYvcLqOCIiIs2aipbUyrCk6IrTh5qnJSIicnoqWlIr53SKYnn55XjKtqloiYiInI6KltRKeLA/ObFDKDXt+B3bCVm7rI4kIiLSbKloSa0N6NqOdWaSe2PHEmvDiIiINGMqWlJrQztHsczpPn1oblfREhERORUVLam1vu0i+M7uvhyPa+cyKCuxOJGIiEjzpKIltebwsxHRoT+ZZhj20nz4ebXVkURERJolFS2pkyFdWvO1q5d7Q8s8iIiIVEtFS+pkWFIUy5x9AHD9pHlaIiIi1VHRkjrpFN2CrSEDALCl/wB5GRYnEhERaX5UtKRODMOgZ5fObHQlunfsWGppHhERkeZIRUvqbGhSVKXL8ej0oYiIyC+paEmdDelcaZ7W9iXgclmcSEREpHlR0ZI6i2oRQGFMf3LNIGyFR+DQBqsjiYiINCsqWlIv53SN5RtXD/eGlnkQERGpQkVL6mVY5+iKeVq6HI+IiEhVKlpSLwMSI/jWSHFv7FsNRdmW5hEREWlOVLSkXgL97cR36MYOVxyG6YRdy62OJCIi0myoaEm9De1ceZkHzdMSERE5TkVL6m1YUjTLKs/TMk2LE4mIiDQPKlpSb91iQ9ke1Idi0w8jex8c2W51JBERkWZBRUvqzWYz6J/UltWubu4dOn0oIiICqGhJA6k6T0vLPIiIiICKljQQ9zwt9+V4zN0roLTI4kQiIiLWU9GSBhEbHogZ1Y2DZiRGWSHs/cbqSCIiIpZT0ZIGM7RLNF87e7k3dPpQRERERUsazrCkqBOnD1W0REREVLSsMGfOHJKTkxk4cKDVURrUoA6t+M7ohdM0MA5vhuz9VkcSERGxlIqWBaZMmUJaWhpr1qyxOkqDCgnwo1O7BDaYndw7dmhUS0REfJuKljSoYUla5kFEROQ4FS1pUEOTolnmLJ+ntfNLcJZZnEhERMQ6KlrSoHq1CWd3QFeOmSEYRdlwYJ3VkURERCyjoiUNym4zGJzUmhWunu4duhyPiIj4MBUtaXBDO59YJV7ztERExJepaEmDG5YUVbFwqXlgHRRkWZxIRETEGipa0uASIoMJbJXAFlcChumCnV9aHUlERMQSKlrSKIZWWeZhqbVhRERELKKiJY3CPU+rvGjtWAKmaW0gERERC6hoSaMY3KkVa82uFJoOyD0IGWlWRxIREWlyKlrSKMKD/Ome0JpVrmT3Di3zICIiPkhFSxrNsKRoXY5HRER8moqWNJrK1z00966CknyLE4mIiDQtFS1pNCkJLclwJLDPFY3hLIHdK6yOJCIi0qRUtKTR+NttnN2xlU4fioiIz1LRkkY1LKnSMg+aEC8iIj5GRUsa1dCkKL5x9aDUtEPWDsjaZXUkERGRJqOiJY2qY1QIYeGRrDOT3Dt26PShiIj4DhUtaVSGYTA0KYplTl2OR0REfI+KljS6Kutp7VoGZSXWBhIREWkiKlrS6IZ0jiKNRDLNMCjJg59XWx1JRESkSahoSaOLDHGQHN+Sr1293Du0zIOIiPgIFS1pEkM7R7PM2ce9oWUeRETER6hoSZM4NynqxIjWoR8gL8PaQCIiIk1ARUuaRP/ECPL9I9joSnTv2KFvH4qIiPdT0ZImEeBn56wOuhyPiIj4FhUtaTLDOkedmKe1Ywm4XNYGEhERaWQqWtJkhnWJYp2ZRJ4ZBAVH4NAGqyOJiIg0KhUtaTJdY0KJCA3hG1eye4e+fSgiIl5ORUuajGEYDO0cxTLX8WUeNCFeRES8m4qWNCl30SqfEP/zaijKtjaQiIhII1LRkiY1NCmKn83W7HTFgasMdi23OpKIiEijUdGywJw5c0hOTmbgwIFWR2lyMWGBdI0JPTGqpWUeRETEi6loWWDKlCmkpaWxZs0aq6NYYmhSVNWiZZrWBhIREWkkKlrS5IYmRfGdqzsl+EH2Xjiy3epIIiIijUJFS5rcoA6ROO3BrHZ2de/QMg8iIuKlVLSkyQU7/OjfPqLSMg+apyUiIt5JRUssMTQp6sR1D3evgNIiawOJiIg0AhUtscSwpCi2mgmkmxFQVgh7v7E6koiISINT0RJL9IgPp2Wwg2VOLfMgIiLeS0VLLGG3GQypcjkeFS0REfE+dSpaDz74IAUFBSftLyws5MEHH6x3KPENwzpHscLVEyc2OLwZsvdbHUlERKRB1alozZw5k7y8vJP2FxQUMHPmzHqHEt8wNCmKbFqwwdXJvWOHRrVERMS71KlomaaJYRgn7d+wYQORkZH1DiW+oW1EMB2iQlju6uXeodOHIiLiZfxq8+SIiAgMw8AwDLp06VKlbDmdTvLy8rj55psbPKR4r2FJUSz/tjd3+H0IO78EZxnYa/WvpYiISLNVq79os2bNwjRNbrjhBmbOnEl4eHjFYw6Hg8TERAYPHtzgIcV7De0cxdurOpFDC8KKsuHAOkg4y+pYIiIiDaJWRWvixIkAdOjQgSFDhuDnp5EHqZ+zO7UCmx/LnT241P6d+/ShipaIiHiJOs3RCg0NZfPmzRXbCxcuZNy4cdxzzz2UlJQ0WDjxfmGB/qQktKy0zIOueygiIt6jTkXrpptuYtu2bQDs3LmTq666iuDgYN577z3+/Oc/N2hA8X7DkqL42lk+If7AOijIsjaQiIhIA6lT0dq2bRspKSkAvPfee5x33nm88847vP7663zwwQcNmU98wLCkKA7Rip9IANPlnhQvIiLiBeq8vIPL5QJg8eLFXHzxxQAkJCSQmZnZcOnEJ/Rp25LQAD++LDt+OZ6l1gYSERFpIHUqWgMGDOChhx7izTffZNmyZVxyySUA7Nq1i5iYmAYNKN7Pz27j7E6tWO4qL1o7loBpWhtKRESkAdSpaM2aNYt169Zx6623cu+999K5c2cA3n//fc4555wGDSi+YVhSFGtcXSk2AiD3IGSkWR1JRESk3uq0PkPv3r3ZuHHjSfufeOIJ7HZ7vUOJ7xmWFE0xDlY5uzPclupe5iGmh9WxRERE6qVeC2GtXbu2YpmH5ORk+vXr1yChxPcktgqmTcsgvsrtXV60FsOQ26yOJSIiUi91KloZGRlcddVVLFu2jJYtWwJw7Ngxzj//fN59912io6MbMqP4AMMw3Jfj+b58ntbeVVCSD44Qa4OJiIjUQ53maE2dOpW8vDx+/PFHsrKyyMrKYtOmTeTk5HDbbRqFkLoZmhTFTjOOQ0ZrcJbA7hVWRxIREamXOhWtzz77jBdeeIHu3btX7EtOTmbOnDl8+umnDRZOfMuQTlEYhsGS0vLFS7cvsTaQiIhIPdWpaLlcLvz9/U/a7+/vX7G+lkhtRYQ46NUmnGXHl3nQ5XhERMTD1alojRgxgttvv50DBw5U7Nu/fz933nknI0eObLBw4nuGdo7iG1cPnNghawcc3W11JBERkTqrU9GaPXs2OTk5JCYm0qlTJzp16kSHDh3Iycnh+eefb+iM4kOGJkWRRzAb6OLeodOHIiLiwer0rcOEhATWrVvH4sWL2bJlCwDdu3dn1KhRDRpOfE//9hEE+dtZXNqLfv6b3UVr4O+tjiUiIlIntRrRWrp0KcnJyeTk5GAYBhdccAFTp05l6tSpDBw4kB49evD11183VlbxAQF+dgZ1jDxxOZ5dy6GsxNpQIiIidVSrojVr1iwmT55MWFjYSY+Fh4dz00038fTTTzdYOPFNQztH8aOZSI6tJZTkws+rrY4kIiJSJ7UqWhs2bOCiiy465eMXXngha9eurXco8W3DkqIxsfFVWU/3Ds3TEhERD1WropWenl7tsg7H+fn5cfjw4XqH8kT79u1j+PDhJCcn07t3b9577z2rI3msLjEtaB0awJKy8tOHa1+H3EOWZhIREamLWhWtNm3asGnTplM+/sMPPxAXF1fvUJ7Iz8+PWbNmkZaWxhdffMEdd9xBfn6+1bE8kmEYDE2K4lPXIA4Fd4XCLJh/M2iNNhER8TC1KloXX3wx9913H0VFRSc9VlhYyIwZM7j00ksbLJwniYuLIyUlBYDY2FiioqLIysqyNpQHG5YURQn+POB/B/gFwc4v4bu5VscSERGplVoVrb/+9a9kZWXRpUsXHn/8cRYuXMjChQt57LHH6Nq1K1lZWdx77721DrF//35+97vf0apVK4KCgujVqxfff/99rY9zKsuXL2fs2LHEx8djGAYLFiyo9nlz5swhMTGRwMBABg0axOrVdZuEvXbtWpxOJwkJCfVI7duGdI4C4POMcPKGz3TvXPwAHPzBulAiIiK1VKuiFRMTwzfffEPPnj25++67GT9+POPHj+eee+6hZ8+erFixgpiYmFoFOHr0KEOGDMHf359PP/2UtLQ0nnrqKSIiIqp9/sqVKyktLT1pf1paGunp6dW+Jj8/nz59+jBnzpxT5pg3bx7Tpk1jxowZrFu3jj59+jB69GgyMjIqnpOSkkLPnj1PulVeIT8rK4vrrruOl156qab/CKQarUMD6RYbimnC0haXQtdL3Bea/uAPUFJgdTwREZEaMUzTNOvywqNHj7J9+3ZM0yQpKemUxehMpk+fzsqVK2u0/pbL5aJfv34kJSXx7rvvYrfbAdi6dSvnnXce06ZN489//vNpj2EYBvPnz2fcuHFV9g8aNIiBAwcye/bsivdKSEhg6tSpTJ8+vUafpbi4mAsuuIDJkydz7bXXnvH5OTk5hIeHk52dXe2SGb7u4U/SePnrXfyqTzzP/aodzB0Meekw8A9wyVNWxxMRER9Vm7/fdboED0BERAQDBw7krLPOqnPJAvjoo48YMGAAV1xxBa1bt6Zv3768/PLL1Ye12fjf//7H+vXrue6663C5XOzYsYMRI0Ywbty4M5asUykpKWHt2rVVVra32WyMGjWKVatW1egYpmkyadIkRowYccaSNWfOHJKTkxk4cGCd8vqK0T1iAfhowwH+k1YA4190P7Dmn7D1UwuTiYiI1Eydi1ZD2blzJ3PnziUpKYnPP/+cW265hdtuu4033nij2ufHx8ezdOlSVqxYwTXXXMOIESMYNWoUc+fWfaJ0ZmYmTqfzpNOeMTExHDpUs2UFVq5cybx581iwYAEpKSmkpKSwcePGap87ZcoU0tLSWLNmTZ0z+4IBiZHcNqIzAPfM38gKV28YfKv7wYVTILf6U8UiIiLNRZ2uddiQXC4XAwYM4JFHHgGgb9++bNq0iRdffJGJEydW+5p27drx5ptvct5559GxY0deeeUVDMNoytgnGTp0KC4tP9Dg7rygC3uzCliQeoBb3lrLBzfeSZedyyB9Iyy4BSa8DzbL//+CiIhItSz/CxUXF0dycnKVfd27d2fv3r2nfE16ejo33ngjY8eOpaCggDvvvLNeGaKiorDb7SdNpk9PTyc2NrZex5b6MQyDxy7vzVkdIsktLuP6N3/gyEVzwC8QdiyB7160OqKIiMgpWV60hgwZwtatW6vs27ZtG+3bt6/2+ZmZmYwcOZLu3bvz4YcfsmTJEubNm8ddd91V5wwOh4P+/fuzZMmJS724XC6WLFnC4MGD63xcaRgBfnZeurY/HaNC2H+skEkf51Iy8m/uBxfPgEOnXkRXRETESpYXrTvvvJNvv/2WRx55hO3bt/POO+/w0ksvMWXKlJOe63K5GDNmDO3bt2fevHn4+fmRnJzMokWLeO2113jmmWeqfY+8vDxSU1NJTU0FYNeuXaSmplYZNZs2bRovv/wyb7zxBps3b+aWW24hPz+f66+/vlE+t9ROy2AHr10/kMgQBxv3Z/PHrX0xu1xUvuTD76G00OqIIiIiJ6nz8g4N6eOPP+buu+/mp59+okOHDkybNo3JkydX+9xFixYxbNgwAgMDq+xfv3490dHRtG3b9qTXfPXVV5x//vkn7Z84cSKvv/56xfbs2bN54oknOHToECkpKTz33HMMGjSofh/uNLS8Q+2t3XOUq1/+lpIyF1POCudPO28oX/JhMlzypNXxRETEB9Tm73ezKFq+SkWrbj754SBT3lkHwD8GH2P0+j+6H7h6HnS9yMJkIiLiC5pkHS0Rq1zSO47pY7oBcPO3LdmTNMn9gJZ8EBGRZkZFSzzSTed25Oqz2mGa8KstIymM7A4FmbDwj6BlNkREpJlQ0RKPZBgGf/t1D87tEk12qZ1JOTdi2gNh+2JYretMiohI86CiJR7Lz25jzjV96RYbynd5Mcz2L1/gdtH9kP6jteFERERQ0RIPFxroz6uTBhITFsBTx85lXeAgcBbDB3/Qkg8iImI5FS3xePEtg3hl4kCCHX5MPjaJXL9IyEiDRTOsjiYiIj5ORUu8Qs824cy5ph9HjXBuLShfg231P2DbF9YGExERn6aiJV7j/G6tmfmrHixz9eHVsvL1tBb+EfIyrA0mIiI+S0VLvMq1gxP5w9AOPFb2W7aY7SD/sHt9La3LKyIiFlDREq9zz8XdGd4jgdtKplCMP/z0hZZ8EBERS6hoidex2QxmXdWXoLa9eLj0GgDML+6D9DSLk4mIiK9R0RKvFOSw88/rBrA09NcsdaZgOItxvX8DlBZZHU1ERHyIipZ4rejQAF67/ixm2qdw2AzDdngz5qL7rY4lIiI+REVLvFpSTCiP/m4Efym7BQBj9T/gp0UWpxIREV+hoiVe75zOUVz8m+t4rWw0AIXv3wR5hy1OJSIivkBFS3zC5f3bkjPsPra4EggqPsKRdyZryQcREWl0KlriM267sCcfdZpJselPqwNfkr5kttWRRETEy6loic8wDIPbJ4zjnfDfA9ByxYMc2ZlqbSgREfFqKlriUwL87Iy/6UG+s/cngBJy3p5EQUGe1bFERMRLqWiJz2kZEkD8pFfJIowOzl2sfHEqTpfma4mISMNT0RKflJCQyJFRswC4IOdD/v32q9YGEhERr6SiJT4raehl7O44AYDR22fyzpdrLU4kIiLeRkVLfFri1U+RFdKJaCObmKX/xxebDlodSUREvIiKlvg2/yAirn2DUsPBSPt6vv3P4/zw8zGrU4mIiJdQ0RKfZ8T2wnbBTAD+bLzJ316bz89HCyxOJSIi3kBFSwSwD76Fso4jCTRKmVn6DDe99g05RaVWxxIREQ+noiUCYBj4jZ+LM6gVybY9jMt6hT++tY5Sp8vqZCIi4sFUtESOC43BPu4FACb7/Q9j51Lunb8RU9dEFBGROlLREqms60Uw8A8APOX/Iou/T+OFr3ZYHEpERDyVipbIL134EER3o7VxjMf8X+aJz7ewMHW/1alERMQDqWiJ/JJ/EFz2T7A7uMC+lgn2JfzpvR9YszvL6mQiIuJhVLREqhPbC0Y9AMAMx9skuPYx+V/fs/OwLkAtIiI1p6IlciqDboFOI3CYxbwcMpeCggJueH0NWfklVicTEREPoaIlcio2G4ybC8Gt6Fi2k5ktPmT3kQIm/+t7ikqdVqcTEREPoKIlcjqhsfCr2QBcXbaQCwPTWLvnKHe9twGXS8s+iIjI6aloiZxJt4thwO8BmB30Eq3tuXz8w0Ge/GKrxcFERKS5U9ESqYkLH4KorjgKM5jfdh5g8sJXO3h39V6rk4mISDOmoiVSE47giiUf2qQv5eUemwC4d8Emvv7psMXhRESkuVLRssCcOXNITk5m4MCBVkeR2ojrDSNnADBqzyxuTi7D6TL541vr2HIox+JwIiLSHBmmLuRmmZycHMLDw8nOziYsLMzqOFITLhe89RvY+SWu2N5cy0Os3J1HfHgg86cMISYs0OqEIiLSyGrz91sjWiK1cXzJh6BIbId+4NV2X9AxOoQD2UX8/o015BeXWZ1QRESaERUtkdoKi4NfzwEgYPVs/j2ymFYhDjbtz+H2d9fj1LIPIiJSTkVLpC66XQwDbgAgZsntvHpVJwL8bCzenMHfPk6zOJyIiDQXKloidXXhwxDVBXIP0mfdfTxzZR8AXv9mN6+u2GVxOBERaQ5UtETq6viSDzZ/2PIxF5d+wd1jugHwt0/S+OLHQxYHFBERq6loidRHXB8Y5V7ygc/u5sZkJ9cMaodpwm3vrmfDvmOWxhMREWupaInU19lToONwKC3A+PAPPHhJEud1iaao1MXv3/iefVkFVicUERGLqGiJ1JfNBuNehKAIOLgBv2WPMGdCP7rHhZGZV8wNr68hu7DU6pQiImIBFS2RhhAWB7+a7b6/8jla7F/Jq5MGEBMWwE8Zefzx7bWUlLmszSgiIk1ORUukoXS/FPpPAkyYfzNx/oW8OmkgIQ47K7cf4Y9vryM9p8jqlCIi0oRUtEQa0uhHoFUS5B6A/95Gj7gwZk/oh91msHhzOiOe/Ip/LNuh0S0RER+hoiXSkBwhJ5Z82PxfWP8m53dtzYI/DqFvu5bklzh59NMtXPTscpZtO2x1WhERaWQqWiINLT4FRt7nvv/pXyBzO73ahvPBzefwxOW9iWrhYOfhfCa+upob/6VvJYqIeDMVLZHGMHgqdDgXSgvgg99DWQk2m8EVAxJYetdwfj+0A3abwRdp6Yx6ehlPL9pGYYnT6tQiItLAVLREGoPNBuP/Ub7kQyp89UjFQ2GB/tx3aTKf3j6Mczq1orjMxXNLfmLU08v4bNNBTFMXpRYR8RYqWiKNJSwefvW8+/6KWbBreZWHu8SE8vYfBvHChH7Ehwey/1ghN7+1juteXc32jLymzysiIg1ORUukMXUfC/0mAib8+xr3BPlKDMPg4l5xLP6/85g6ojMOPxtf/5TJRbOW8/AnaeQWaaFTERFPZpg6T2GZnJwcwsPDyc7OJiwszOo40lhK8uHtK2HPCvf2kDtgxH1g9zvpqXuO5PO3j9NYvDkDgOjQAO65uBvjUtpgGEYThhYRkVOpzd9vFS0LqWj5EGcpLH4AVpWvHt/hXLj8NQiJqvbpX27N4MH/prErMx+AAe0jeOBXPejZJryJAouIyKmoaHkIFS0ftOlDWHgrlOZDWBu48k1o27/apxaXOXllxS6eX7KdwlInNgOuGdSO/7ugKxEhjiYOLiIix6loeQgVLR+VsQXmTYAj28HugDGPuy/dc4pTgwezC3nkf1v474YDALQM9ueuC7ty9VntsNt0OlFEpKmpaHkIFS0fVpQDC26BLR+7t1N+B5c8Cf5Bp3zJqh1HeOCjH9mangtAj/gwHvx1D/q3j2yKxCIiUk5Fy0OoaPk404SVs2DJg2C6ILY3XPUmRCSe8iVlThdvfbuHpxZtI7eoDIDf9G3D9DHdaB0W2DS5RUR8nIqWh1DREgB2fgXv3wAFRyCwJVz2CiSNOu1LMvOKeeKzrfxn7T5ME1oE+HH7yCQmDUnE365VW0REGpOKlodQ0ZIK2T/Df66D/WsBA86/B4bd5V5h/jRS9x1jxsJNbPg5G4DOrVvwwNgeDE2q/tuMIiJSfypaHkJFS6ooK3ZfhHrta+7tpNHwm/LL+JyGy2Xy/tqfeeyzLRzJLwFgTM9Y7r2kO20jghs7tYiIz1HR8hAqWlKt9W/Bx9PAWeyer3XVWxDb64wvyy4s5ZlF23jz2z04XSaB/jZuOa8zN53XkUB/e+PnFhHxESpaHkJFS07pQCr851o4thf8gmDss9Dnqhq9dMuhHB746Ee+3ZkFQEJkEPddkswFyTFaXV5EpAGoaHkIFS05rYIs+HAybF/s3h44GUY/An5nXqzUNE0+/uEgD3+ymUM5RQCc2yWaGWOT6RTdojFTi4h4PRUtD6GiJWfkcsKyx9w3gLZnwZVvQFh8jV6eX1zGnC+388+vd1HidOFvN7hhaAemjkiiRcDJ11oUEZEzU9HyECpaUmNbP4P5N0JRNoREu6+T2GFYjV++KzOfB//7I19uPQxATFgA91zcnV/1idfpRBGRWlLR8hAqWlIrWTth3nWQvhEMO1wwEwbfespL91RnyeZ0Hvw4jT1HCgA4KzGSmb/uQfc4/fsnIlJTKloeQkVLaq2kAD6+E354172dPA5+PRsCQmt8iKJSJ//8eiezv9xOUakLmwHXnt2eaRd0JTzYv3Fyi4h4ERUtD6GiJXVimrDmn/DZ3eAqhaiu7iUgorvU6jD7jxXyyCeb+WTjQQAiQxz8aXRXrhyQoItVi4ichoqWh1DRknrZt9q9mnzuQXC0gHEvQPKva32Yb7Zn8sB/f2Rbeh4AvduGM/NXPejb7vQLpYqI+CoVLQ+hoiX1lpcB710Pe1a4t8+5DUbOAHvtvlFY6nTxr1V7mLVoG7nF7otVX96/LX+5qBvRoQENnVpExKOpaHkIFS1pEM4yWPIAfPO8eztxmPtbiS2ia32ow7nFPP7ZFt5b+zMAoQF+3HFBF64b3F4XqxYRKaei5SFUtKRB/bgAFk6BkjwIjYcr/wUJA+t0qHV7jzJj4Y9s3O++WHWXGPfFqs/prItVi4ioaHkIFS1pcIe3wrsT4MhPYPOHMX+HAb+v1RIQxzldJv/5fh+Pf7aFowWlAFzSK47pY7qREKmLVYuI71LR8hAqWtIoinLcI1ubP3Jv97kGLn0a/IPqdLhjBSU8vWgbb327B5fp7mxnd2jF+L5tuKhXLGGBWhJCRHyLipaHUNGSRmOa8M1zsPgBMF0Q2wuufBMiO9T5kGkHcnj00818/VNmxT6Hn40Luscwrm8bzusSjcNP87hExPupaHkIFS1pdLuWu7+VWJAJgS3hNy9Dlwvrdcj9xwpZmLqf+ev281NGXsX+lsH+XNo7jvF929CvXYQu7SMiXktFy0OoaEmTyN7vXm9r//eAAef9xX2z1W/0yTRN0g7msGD9fhamHiAjt7jisYTIIMantOHXfdvQKbpFPT+AiEjzoqLlIVS0pMmUFbtXkv/+Ffd20oUw/h8QHNkgh3e6TFbtOML89fv5bNNB8kucFY/1aRvOuL5tuLR3vNbkEhGvoKLlIVS0pMmlvuO+VmJZEbRs7750T1zvBn2LwhInizans2D9fpZtO4zT5f5PjN1mMCwpivF923BBcgzBjtotqioi0lyoaHkIFS2xxMEfYN7v4Nge8AuES2dBytWN8laZecV88sNB5q/fT+q+YxX7gx12LuoRy7i+bTinUyv8tBiqiHgQFS0PoaIllik8Ch/eCD994d4e8Hu46FHwa7xTe7sy81mwfj8LUvez50hBxf7o0AB+1See8X3b0CM+TJPoRaTZU9HyECpaYimXC5Y/Dl/9HTChzQD3avLhbRr1bU3TZP2+YyxYv5//bjhQsRgqQOfWLRjftw2/6hOvRVFFpNlS0fIQKlrSLGz7Aj78AxRlQ3AUXPEadDi3Sd661Oli+bbDzF+/n0Vp6RSXuSoeOysxknF923BJrzjCg7Uoqog0HypaFti3bx/XXnstGRkZ+Pn5cd9993HFFVec9jUqWtJsZO2C/1wLhzaCYYNRD8A5t9Xp0j11lVtUymebDrEgdT/f7DjC8f8yOew2zu8Wzfi+bTi/W2sC/OxNlklEpDoqWhY4ePAg6enppKSkcOjQIfr378+2bdsICQk55WtUtKRZKSmAT6bBhn+7t7uPhV+/AIFN/+/moewiPtqwn/nrD7D5YE7F/rBAPy7pHce4lDYMTIzEZtN8LhFpeipazUCfPn34+OOPSUhIOOVzVLSk2TFN91pbn04HVylEdXEvARHd1bJIWw7lsGD9ARam7udgdlHF/jYtg/h1insSfVJMqGX5RMT31Obvd7P6TvXf//53DMPgjjvuaNDjLl++nLFjxxIfH49hGCxYsKDa582ZM4fExEQCAwMZNGgQq1evrtP7rV27FqfTedqSJdIsGQYM/ANc/ymExkPmNnh5BPw437JI3WLDmD6mGyv/MoJ/Tz6bqwYkEBrgx/5jhbzw1Q4ueGY5lzz3Nf/8eicZOUVnPqCISBNqNkVrzZo1/OMf/6B379Mvnrhy5UpKS0tP2p+WlkZ6enq1r8nPz6dPnz7MmTPnlMedN28e06ZNY8aMGaxbt44+ffowevRoMjIyKp6TkpJCz549T7odOHCg4jlZWVlcd911vPTSS2f6yCLNV8JAuGk5JA6Dkjx4bxJ8fi84yyyLZLMZDO7Uiscu782av47ihQn9uCA5Bn+7wY8Hcnjok82c/egSrn3lOz5Y+zN5xdZlFRE5rlmcOszLy6Nfv3688MILPPTQQ6SkpDBr1qyTnudyuejXrx9JSUm8++672O3uSbFbt27lvPPOY9q0afz5z38+7XsZhsH8+fMZN25clf2DBg1i4MCBzJ49u+K9EhISmDp1KtOnT6/R5yguLuaCCy5g8uTJXHvttad83pw5c5gzZw5Op5Nt27bp1KE0X84yWPogrHzWvd1+qPtbiS1aW5urkqP5JXy88SAL1u9n7Z6jFfsD/W1cmBzL+L5tGJoUhb8WRRWRBuJxc7QmTpxIZGQkzzzzDMOHDz9l0QI4cOAA5557LoMGDeLNN99k165dnHvuuYwdO5YXX3zxjO9VXdEqKSkhODiY999/v8r+iRMncuzYMRYuXHjG45qmyTXXXEPXrl154IEHzvh80Bwt8SBpC2HBH92jW4EtYeidcNaN4Ghea13tPVLAwtT9zF+/n52Z+RX7W4U4GNsnnnF929CnbbgWRRWReqnN32/LLzb27rvvsm7dOtasWVOj58fHx7N06VKGDRvGNddcw6pVqxg1ahRz586tc4bMzEycTicxMTFV9sfExLBly5YaHWPlypXMmzeP3r17V8wBe/PNN+nVq1edc4k0G8m/huju7lOIGT/C4hnw7Vw470/Q9zrwc1idEIB2rYKZOjKJW0d0ZuP+bOaXL4qamVfC69/s5vVvdtMhKoRxKW0Y2b01HaJCCAmw/D+DIuLFLP0vzL59+7j99ttZtGgRgYGBNX5du3btePPNNznvvPPo2LEjr7zyiuX/D3Xo0KG4XK4zP1HEU0V3gZu/hh/mwZePQvZe+OT/4JvnYfg90OtysDWPNa4Mw6B325b0btuSey/uztfbM1mwfj+f/3iIXZn5PLN4G88s3gZA69AAEqNC6NAqhMSoEBJbBZf/DCHI0Tw+j4h4LktPHS5YsIDx48dXzLUCcDqdGIaBzWajuLi4ymPHpaenc95559GlSxfWrFnD5ZdfzvPPP1+j92ysU4d1oVOH4rHKimHtG7D8Ccgv/8JI62QY8VfoenGTLnRaG3nFZXzx4yEWph7gh5+PVbn8T3ViwwJp3yqYDlEhFeWrQ1QI7VsFE+ivEibiqzzm1OHIkSPZuHFjlX3XX3893bp14y9/+Uu1JSszM5ORI0fSvXt33nvvPbZt28bw4cMJCAjgySefrFMOh8NB//79WbJkSUXRcrlcLFmyhFtvvbVOxxTxan4BMOhG6DsBvnvRPVk+Iw3evQbaDoSR9zfZZXxqo0WAH7/p15bf9GsLQHZBKbuO5LM7M5/d5T93HSlgd2Y+2YWlHMop4lBOEd/tyjrpWHHhgST+YhSsQ1QI7SJVwkTkBEuLVmhoKD179qyyLyQkhFatWp20H9zlZ8yYMbRv35558+bh5+dHcnIyixYtYsSIEbRp04Y777zzpNfl5eWxffv2iu1du3aRmppKZGQk7dq1A2DatGlMnDiRAQMGcNZZZzFr1izy8/O5/vrrG/hTi3gRRwgM+z8YcAOsfM5dun5eA2+MhY7DYcT90La/1SlPKTzYn5TglqQktDzpsWMFJewqL2C7MgvYc7yIZeaTU1TGwewiDmYXsWrnkSqvMwyIDw+i/fHyVamMtWsVrEsIifiYZvGtw8rO9K3DRYsWMWzYsJPmdK1fv57o6Gjatm170mu++uorzj///JP2T5w4kddff71ie/bs2TzxxBMcOnSIlJQUnnvuOQYNGlSvz3M6OnUoXic3Hb5+Er5/zb2yPEC3S92nFFt3tzZbAzFNk6MFpRUjYJVHwXZn5pN7mvW7jpcw96nIYPeIWHkRaxcZjMNPS1CIeAKPW97BV6loidc6uhu+egx+eBdMF2BAn9/C8OkQkWhxuMZjmiZZ+SVVRsF2VZyWLDjtIqo2A+Jblpew8vLVISqY9q1CSIhQCRNpTlS0PISKlni9jC3w5UOw+b/ubZs/9J8E5/4JQmNO+1JvY5omR/JLKk4/Hi9fx0fG8kucp3yt3WbQpmXQiflg5ZPyE6NCaBsRpMVYRZqYipaHUNESn7F/LSz5G+z80r3tFwRn3wxDboegCGuzNQOmaXI4r7hK8ao8KlZwmhJmGO4FWWPCAivdAogJCyQ2LJDW5fcjgx3YbM3z26AinkZFy0OoaInP2bUcFs+E/d+7twPCYchtcPYt7on1chLTNDmcW3zSxPxdmfnsOVJAYempS1hl/naD1qHu4hVbTSk7fr9FgJ/l6xKKNHcqWh5CRUt8kmnC1k9h6d/cS0IAhLSGc+9yn1b0C7A0nic5PicsPaeY9Jwi0suXo0jPKSaj0v0j+cXU9L/0wQ57lZEw9/3A8nLm3tc6LEDfnhSfpqLlIVS0xKe5nLDpA/jyYffkeYDwdu4J831+22xWmfcGpU4Xh3NPlLHjxexQThEZle7nFp16sv4vRQT7n2JU7EQpa9UiALtOV4oXUtHyECpaIoCzFNb9C5Y9DnmH3PuiusKIe6H7r5rtKvPeqKCkrMroWOVSdvz+oZwiSspqdrkxu80gukUAMWEBJ42KVS5lYUE6XSmeRUXLQ6hoiVRSUgBrXoYVz0DhUfe+uBT3KvOdRqhwNROmaZJdWFpRutJziqqcpjx+/3BuMa4a/nUJ8LPROiyAyJAAIoP9iQhx0CrEQUSIg8hgB5EhVW9hgf6a2C+WUtHyECpaItUoyoZvZsOqOVCa796XOMxduBLOsjab1JjTZZKZV1xlJCyjYh7ZiUJ27AzXm6yO3WYQEexPRKUSVlHOqilmkSEOXRZJGpSKlodQ0RI5jbzDsOJpWPNPcJa493UZAyPvg5ge1maTBlNU6uRwbjEZuUVk5ZdyNL+EI/klHC0oISv/xO1oQQlZeSWnXXn/dIL87SeVr4hgB61a/LKc+RMZEkB4kL/ml8kpqWh5CBUtkRo4tg+WPQapb59YZb7X5XD+PRDZ0ep00sRKylwVJaxyKTuSd3I5O17QSp21/zNnGNAyyL/acnaqwhbkb9dcMx+houUhVLREaiHzJ1j6EKQtcG/b/KDvtXDenyEs3tJo0nyZpklecVm1BexIeVnLyi8lK7+YowWlHMkrJqcW376sLMDPRkSwg/Agf8KD/AkL8iMsyJ+wwOPb/oQF+lXcr/wzxKGS5klUtDyEipZIHRxIdReu7Yvc236BcNZkGDoNgiMtjSbeodTp4lhBadVyVnC8lFVf2Gr6TcxTsdsMwgJ/WczKS1lgeUmrrqiVP1fXwmxaKloeQkVLpB72fONeZX7ft+5tRyicMxUG/xECQq3NJj7FNE0KSpwVxSunsIzswlJyikrdPyvul5FTWFrx2PH7dTm1+UuB/rYqI2fhvyhllcvbL0fZQgP89C3OWlLR8hAqWiL1ZJrw0yJY8iCkb3TvC24Fw/4PBvwe/AOtzSdyBqZpUlzmqihkVQtaWbX7Khe13OKyGq/6fyqGAaEBflVGycKC/GgR4E9ooB8tAvxocfxnQNXt44+HBPgR4GfzmdOfKloeQkVLpIG4XJA2H5Y+DFk73PvC2sB5f4GUCWD3szafSCNxutxz0H45Ulb9qFrZSYWuqLR+pzwr87cblUqYPy0C7OXb/hWlLMThfjy0UlkLqVTYWpQ/p7l/41NFy0OoaIk0MGcppL7j/pZizn73vlad4fx7IXkc2DSPRaSy4jKne+SsUiHLLi9l+cVl5BWVkVdcRm5RGXnFpeQXO8ktLiOvqJS88sfzS2p2YfPaCHbYK4rXSaWscpkL9CsvdP4njbCFBjbeKJuKlodQ0RJpJKVF8P0r8PVTUHDEvS+2F4y4H5Iu0CrzIg3I6TIpKCmrKF65lQpa3i/u5x4vcJWfW1xa8ZyGmK9WmZ/NYOI5idx3aXKDHrc2f781ni4i3sc/EAZPcS//8O1c+OZ5OLQR3rkC2g12rzLf/hyrU4p4BbvNIDTQn9BAfwiv37GKy5y/GEU7UcyOb1dX4o6PsuUXOyseAyhzmZafhtSIloU0oiXSRPKPwMpnYPXLUFbk3hffF1r3gKjO0CoJopIgogP4OazNKiL15nKZ5JePsgX4ua8K0JB06tBDqGiJNLGcA7DscVj3LzCrmVdi2CGi/Yni1apz+c8kaNFapxxFBFDR8hgqWiIWObYP9n0HR7a7V5w/8hNkbj9xEevqBIRXGv2qNAoW2UnLSIj4GM3REhE5nZYJ7ltlpgm5B6sWryM/ubeP7YXibNi/1n2rwnAfq7pRsLB4jYKJ+DiNaFlII1oiHqK0CLJ2nihelUfCirJP/Tr/EGjV6UTxOl7EWnWGgBZNl19EGpRGtEREGpJ/IMQku2+VmSbkZ1YqYJVGwrJ2uU9FHvrBfful0PiqpyCPn5IMTwCbvWk+l4g0OhUtEZG6MgxoEe2+/XK5CGcpHN1dqYBVKmMFRyD3gPu2a3nV1/kFuud9VVfCAuv53XkRaXIqWiIijcHu7y5JUUknP1aQ9YuJ+OWnI7N2upefyPjRffulkNYnzwOL7AAtYtwlTPPBRJodFS0RkaYWHAnBZ0HCWVX3u5xwbE/VifjHC1neIcjPcN/2rDz5mPYAd+Fq0foUP4/fbw3+QU3zOUVERUtEpNmw2SGyo/vGhVUfK8pxl65fLkmRvdc9Id9Z7L6fvffM7xMQXoNCFgMhUZovJlJPKloiIp4gMAza9HPffqm0yD3SlZcBeenlt4xf/EyH3HR3ISvOdt+O/HT69zRsEBx1ciELjT25mAWE6dSlSDVUtEREPJ1/ILRs576djmlCcc6ZC1leBuQfBtN14nRl+hky+AVWPyr2y30hrbXAq/gUFS0REV9hGO5J84Hh1U/Sr8zldC9dUV0J++XP4mz3JP5je923MwkMr/40ZVCE+xbYsvx++U+NlokHU9ESEZGT2ewQGuO+nUlpYXnxOkMhyys/dVmU7b5lbqtZFsNWXr5aVl/ETretif9iMRUtERGpH/8g98W4I9qf/nmm6S5Y1RWy/MNQeAyKjkHhUff9wqNQVug+hVmY5b7Vll9gpeJVw3IWFOEeddMXAaQBqGiJiEjTMIzyItMSorvU7DWlReXl61h5ATt6chk71bbpcp/SzDvkvtVWQDgEhddhFC0EbLbav594JRUtERFpvvwDwT/W/U3H2jBNKM6tZTkrv5Xkuo9x/NuZNZl3VoUBAaHgaOH+WeUW5r7O5S/3nfTc8uf5BdTyvaW5UdESERHvYxjuJTECw858SvOXnKXVn8Y803bhUXCVAuXf7izOgdx6fg67o4alLPQXBS6satlztNAom0VUtERERCqz+5+4hmVtmCaUFkBxnns0rSTX/fNMt+qeV1rgPqazxH1tzIIj9f9cjtBqClk1pez4zREC/sHu/Y7g8u0Q933/YH0TtIZUtERERBqCYbjLiCOkZt/WPB1nGZTk1ayUna68FeWA6XQfs6R8f31H2QAwyktYefFytCjfrnw/5MSt8nbl8lZdkbN7VzXxrk8jIiLiDex+J744UB+m6f5CwClLWU71ha44B0oK3CNrJfnuW2nBiZE2TCjNd9/y6/lZf8keUF7CQqopciFneKyaUbjAlu5TyBZR0RIREfFWhuFefsM/yL04bH25XCcKV0meu4yVlBeuKvfLtyvfL8krf13l+5UeOz7y5iyGwmL3nLeGMPAPcMlTDXOsOlDREhERkZqx2crneLUAGqC4HWea7vlolUfPqtzPqzTCVsuC5whpuJx1oKIlIiIi1jIM91IWfgEQHNmwxzbNhj1eLem7niIiIuK9LP52pIqWiIiISCNR0RIRERFpJCpaIiIiIo1ERUtERESkkahoiYiIiDQSFS0RERGRRqKiJSIiItJIVLREREREGomKloiIiEgjUdESERERaSQqWiIiIiKNREVLREREpJGoaImIiIg0Ej+rA/gy0zQByMnJsTiJiIiI1NTxv9vH/46fjoqWhXJzcwFISEiwOImIiIjUVm5uLuHh4ad9jmHWpI5Jo3C5XBw4cIDQ0FAMw2jQY+fk5JCQkMC+ffsICwtr0GNL7en30bzo99G86PfRvOj3cWamaZKbm0t8fDw22+lnYWlEy0I2m422bds26nuEhYXpfyjNiH4fzYt+H82Lfh/Ni34fp3emkazjNBleREREpJGoaImIiIg0EhUtLxUQEMCMGTMICAiwOoqg30dzo99H86LfR/Oi30fD0mR4ERERkUaiES0RERGRRqKiJSIiItJIVLREREREGomKloiIiEgjUdHyQnPmzCExMZHAwEAGDRrE6tWrrY7kkx599FEGDhxIaGgorVu3Zty4cWzdutXqWFLu73//O4ZhcMcdd1gdxaft37+f3/3ud7Rq1YqgoCB69erF999/b3Usn+R0Ornvvvvo0KEDQUFBdOrUib/97W81up6fnJqKlpeZN28e06ZNY8aMGaxbt44+ffowevRoMjIyrI7mc5YtW8aUKVP49ttvWbRoEaWlpVx44YXk5+dbHc3nrVmzhn/84x/07t3b6ig+7ejRowwZMgR/f38+/fRT0tLSeOqpp4iIiLA6mk967LHHmDt3LrNnz2bz5s089thjPP744zz//PNWR/NoWt7BywwaNIiBAwcye/ZswH09xYSEBKZOncr06dMtTufbDh8+TOvWrVm2bBnnnnuu1XF8Vl5eHv369eOFF17goYceIiUlhVmzZlkdyydNnz6dlStX8vXXX1sdRYBLL72UmJgYXnnllYp9l112GUFBQbz11lsWJvNsGtHyIiUlJaxdu5ZRo0ZV7LPZbIwaNYpVq1ZZmEwAsrOzAYiMjLQ4iW+bMmUKl1xySZX/nYg1PvroIwYMGMAVV1xB69at6du3Ly+//LLVsXzWOeecw5IlS9i2bRsAGzZsYMWKFYwZM8biZJ5NF5X2IpmZmTidTmJiYqrsj4mJYcuWLRalEnCPLN5xxx0MGTKEnj17Wh3HZ7377rusW7eONWvWWB1FgJ07dzJ37lymTZvGPffcw5o1a7jttttwOBxMnDjR6ng+Z/r06eTk5NCtWzfsdjtOp5OHH36YCRMmWB3No6loiTSBKVOmsGnTJlasWGF1FJ+1b98+br/9dhYtWkRgYKDVcQT3/wEZMGAAjzzyCAB9+/Zl06ZNvPjiiypaFvjPf/7D22+/zTvvvEOPHj1ITU3ljjvuID4+Xr+PelDR8iJRUVHY7XbS09Or7E9PTyc2NtaiVHLrrbfy8ccfs3z5ctq2bWt1HJ+1du1aMjIy6NevX8U+p9PJ8uXLmT17NsXFxdjtdgsT+p64uDiSk5Or7OvevTsffPCBRYl825/+9CemT5/Ob3/7WwB69erFnj17ePTRR1W06kFztLyIw+Ggf//+LFmypGKfy+ViyZIlDB482MJkvsk0TW699Vbmz5/P0qVL6dChg9WRfNrIkSPZuHEjqampFbcBAwYwYcIEUlNTVbIsMGTIkJOWPNm2bRvt27e3KJFvKygowGarWgvsdjsul8uiRN5BI1peZtq0aUycOJEBAwZw1llnMWvWLPLz87n++uutjuZzpkyZwjvvvMPChQsJDQ3l0KFDAISHhxMUFGRxOt8TGhp60vy4kJAQWrVqpXlzFrnzzjs555xzeOSRR7jyyitZvXo1L730Ei+99JLV0XzS2LFjefjhh2nXrh09evRg/fr1PP3009xwww1WR/NoWt7BC82ePZsnnniCQ4cOkZKSwnPPPcegQYOsjuVzDMOodv9rr73GpEmTmjaMVGv48OFa3sFiH3/8MXfffTc//fQTHTp0YNq0aUyePNnqWD4pNzeX++67j/nz55ORkUF8fDxXX301999/Pw6Hw+p4HktFS0RERKSRaI6WiIiISCNR0RIRERFpJCpaIiIiIo1ERUtERESkkahoiYiIiDQSFS0RERGRRqKiJSIiItJIVLREREREGomKloiIxQzDYMGCBVbHEJFGoKIlIj5t0qRJGIZx0u2iiy6yOpqIeAFdVFpEfN5FF13Ea6+9VmVfQECARWlExJtoREtEfF5AQACxsbFVbhEREYD7tN7cuXMZM2YMQUFBdOzYkffff7/K6zdu3MiIESMICgqiVatW3HjjjeTl5VV5zquvvkqPHj0ICAggLi6OW2+9tcrjmZmZjB8/nuDgYJKSkvjoo48qHjt69CgTJkwgOjqaoKAgkpKSTiqGItI8qWiJiJzBfffdx2WXXcaGDRuYMGECv/3tb9m8eTMA+fn5jB49moiICNasWcN7773H4sWLqxSpuXPnMmXKFG688UY2btzIRx99ROfOnau8x8yZM7nyyiv54YcfuPjii5kwYQJZWVkV75+Wlsann37K5s2bmTt3LlFRUU33D0BE6s4UEfFhEydONO12uxkSElLl9vDDD5umaZqAefPNN1d5zaBBg8xbbrnFNE3TfOmll8yIiAgzLy+v4vFPPvnEtNls5qFDh0zTNM34+Hjz3nvvPWUGwPzrX/9asZ2Xl2cC5qeffmqapmmOHTvWvP766xvmA4tIk9IcLRHxeeeffz5z586tsi8yMrLi/uDBg6s8NnjwYFJTUwHYvHkzffr0ISQkpOLxIUOG4HK52Lp1K4ZhcODAAUaOHHnaDL179664HxISQlhYGBkZGQDccsstXHbZZaxbt44LL7yQcePGcc4559Tps4pI01LREhGfFxISctKpvIYSFBRUo+f5+/tX2TYMA5fLBcCYMWPYs2cP//vf/1i0aBEjR45kypQpPPnkkw2eV0QaluZoiYicwbfffnvSdvfu3QHo3r07GzZsID8/v+LxlStXYrPZ6Nq1K6GhoSQmJrJkyZJ6ZYiOjmbixIm89dZbzJo1i5deeqlexxORpqERLRHxecXFxRw6dKjKPj8/v4oJ5++99x4DBgxg6NChvP3226xevZpXXnkFgAkTJjBjxgwmTpzIAw88wOHDh5k6dSrXXnstMTExADzwwAPcfPPNtG7dmjFjxpCbm8vKlSuZOnVqjfLdf//99O/fnx49elBcXMzHH39cUfREpHlT0RIRn/fZZ58RFxdXZV/Xrl3ZsmUL4P5G4Lvvvssf//hH4uLi+Pe//01ycjIAwcHBfP7559x+++0MHDiQ4OBgLrvsMp5++umKY02cOJGioiKeeeYZ7rrrLqKiorj88strnM/hcHD33Xeze/dugoKCGDZsGO+++24DfHIRaWyGaZqm1SFERJorwzCYP38+48aNszqKiHggzdESERERaSQqWiIiIiKNRHO0REROQ7MrRKQ+NKIlIiIi0khUtEREREQaiYqWiIiISCNR0RIRERFpJCpaIiIiIo1ERUtERESkkahoiYiIiDQSFS0RERGRRvL/fHeM14n2g8kAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -538,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 126,
    "id": "neither-moldova",
    "metadata": {},
    "outputs": [
@@ -546,14 +539,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0. 1. 2. 3. 4.]\n",
-      "[0.47791694 0.22577456 0.22577456 0.22577456 0.22577456]\n",
-      "[0.49208965 0.22808174 0.22808174 0.22808174 0.22808174]\n"
+      "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
+      "[0.47791694 0.16801252 0.12879038 0.1089321  0.09681938 0.09138102\n",
+      " 0.08948583 0.08857943 0.08833223 0.08775544]\n",
+      "[0.49208965 0.1723797  0.12755438 0.10415293 0.08536585 0.08075148\n",
+      " 0.07646671 0.07613711 0.07514832 0.07481872]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -587,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 128,
    "id": "delayed-desire",
    "metadata": {},
    "outputs": [
@@ -595,7 +590,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(692, 784)\n",
+      "(227, 784)\n",
       "ICI\n"
      ]
     },
@@ -606,14 +601,14 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [32], line 28\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[39mif\u001b[39;00m correct_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m] \u001b[39m!=\u001b[39m rows\u001b[39m*\u001b[39mcols:\n\u001b[1;32m     27\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mICI\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])))\n\u001b[1;32m     29\u001b[0m     correct_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend(correct_labels, \u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])),\u001b[39m1\u001b[39m)\n\u001b[1;32m     31\u001b[0m correct_labels \u001b[39m=\u001b[39m correct_labels\u001b[39m.\u001b[39mreshape(cols,rows)\u001b[39m.\u001b[39mT\n",
+      "Cell \u001b[0;32mIn [128], line 28\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[39mif\u001b[39;00m correct_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m] \u001b[39m!=\u001b[39m rows\u001b[39m*\u001b[39mcols:\n\u001b[1;32m     27\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mICI\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])))\n\u001b[1;32m     29\u001b[0m     correct_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend(correct_labels, \u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])),\u001b[39m1\u001b[39m)\n\u001b[1;32m     31\u001b[0m correct_labels \u001b[39m=\u001b[39m correct_labels\u001b[39m.\u001b[39mreshape(cols,rows)\u001b[39m.\u001b[39mT\n",
       "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/core/numeric.py:204\u001b[0m, in \u001b[0;36mones\u001b[0;34m(shape, dtype, order, like)\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[39mif\u001b[39;00m like \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m     \u001b[39mreturn\u001b[39;00m _ones_with_like(shape, dtype\u001b[39m=\u001b[39mdtype, order\u001b[39m=\u001b[39morder, like\u001b[39m=\u001b[39mlike)\n\u001b[0;32m--> 204\u001b[0m a \u001b[39m=\u001b[39m empty(shape, dtype, order)\n\u001b[1;32m    205\u001b[0m multiarray\u001b[39m.\u001b[39mcopyto(a, \u001b[39m1\u001b[39m, casting\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39munsafe\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    206\u001b[0m \u001b[39mreturn\u001b[39;00m a\n",
       "\u001b[0;31mValueError\u001b[0m: negative dimensions are not allowed"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -691,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "id": "another-setting",
    "metadata": {},
    "outputs": [],
@@ -730,10 +725,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 132,
    "id": "decreased-candidate",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 5) (5, 3) 3\n",
+      "[[-0.09772056]\n",
+      " [-0.04798146]\n",
+      " [ 0.20071404]]\n",
+      "-0.025\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "\nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.17849182\nMax relative difference: 8.03213277\n x: array([[-0.09772056],\n       [-0.04798146],\n       [ 0.20071404]])\n y: array([[-0.01111111],\n       [-0.00555556],\n       [ 0.02222222]])",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [132], line 26\u001b[0m\n\u001b[1;32m     18\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.01111111\u001b[39m],\n\u001b[1;32m     19\u001b[0m        [\u001b[39m-\u001b[39m\u001b[39m0.00555556\u001b[39m],\n\u001b[1;32m     20\u001b[0m        [ \u001b[39m0.02222222\u001b[39m]])\n\u001b[1;32m     22\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.025\u001b[39m])\n\u001b[0;32m---> 26\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred\u001b[39m/\u001b[39m\u001b[39m2\u001b[39m,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m     27\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m    840\u001b[0m         err_msg \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(remarks)\n\u001b[1;32m    841\u001b[0m         msg \u001b[39m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m    842\u001b[0m                             verbose\u001b[39m=\u001b[39mverbose, header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m    843\u001b[0m                             names\u001b[39m=\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m), precision\u001b[39m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m    845\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m:\n\u001b[1;32m    846\u001b[0m     \u001b[39mimport\u001b[39;00m \u001b[39mtraceback\u001b[39;00m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.17849182\nMax relative difference: 8.03213277\n x: array([[-0.09772056],\n       [-0.04798146],\n       [ 0.20071404]])\n y: array([[-0.01111111],\n       [-0.00555556],\n       [ 0.02222222]])"
+     ]
+    }
+   ],
    "source": [
     "#prepare data for class GradientDescent (is used in grad_cost)\n",
     "x_dummy = np.array([0.2,0.7,0.3,0.2,0.4,0.5,0.1,0.5,0.9,0.4,0.5,0.1,0.3,0.9,0.8]).reshape(5,3)\n",
@@ -749,12 +769,17 @@
     "\n",
     "grad_w_pred, grad_b_pred = gradDummy.grad_cost()\n",
     "\n",
+    "print(grad_w_pred/2)\n",
+    "print(grad_b_pred)\n",
+    "\n",
     "grad_w_exp = np.array([[-0.01111111],\n",
     "       [-0.00555556],\n",
     "       [ 0.02222222]])\n",
     "\n",
     "grad_b_exp = np.array([-0.025])\n",
     "\n",
+    "\n",
+    "\n",
     "np.testing.assert_array_almost_equal(grad_w_pred,grad_w_exp,decimal=8)\n",
     "np.testing.assert_array_almost_equal(grad_b_pred,grad_b_exp,decimal=8)"
    ]
@@ -770,10 +795,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "hungry-electron",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "\nArrays are not almost equal to 8 decimals\n\n(shapes (1, 784), (3, 1) mismatch)\n x: array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,...\n y: array([[-0.04444444],\n       [-0.02222222],\n       [ 0.08888889]])",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [29], line 21\u001b[0m\n\u001b[1;32m     15\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.04444444\u001b[39m],\n\u001b[1;32m     16\u001b[0m        [\u001b[39m-\u001b[39m\u001b[39m0.02222222\u001b[39m],\n\u001b[1;32m     17\u001b[0m        [ \u001b[39m0.08888889\u001b[39m]])\n\u001b[1;32m     19\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.1\u001b[39m])\n\u001b[0;32m---> 21\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m     22\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/testing/_private/utils.py:763\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m    757\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m cond:\n\u001b[1;32m    758\u001b[0m     msg \u001b[39m=\u001b[39m build_err_msg([x, y],\n\u001b[1;32m    759\u001b[0m                         err_msg\n\u001b[1;32m    760\u001b[0m                         \u001b[39m+\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m(shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m mismatch)\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m    761\u001b[0m                         verbose\u001b[39m=\u001b[39mverbose, header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m    762\u001b[0m                         names\u001b[39m=\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m), precision\u001b[39m=\u001b[39mprecision)\n\u001b[0;32m--> 763\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m    765\u001b[0m flagged \u001b[39m=\u001b[39m bool_(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m    766\u001b[0m \u001b[39mif\u001b[39;00m isnumber(x) \u001b[39mand\u001b[39;00m isnumber(y):\n",
+      "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\n(shapes (1, 784), (3, 1) mismatch)\n x: array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,...\n y: array([[-0.04444444],\n       [-0.02222222],\n       [ 0.08888889]])"
+     ]
+    }
+   ],
    "source": [
     "#prepare data for class GradientDescent (is used in grad_cost)\n",
     "x_dummy = np.array([0.2,0.7,0.3,0.2,0.4,0.5,0.1,0.5,0.9,0.4,0.5,0.1,0.3,0.9,0.8]).reshape(5,3)\n",
diff --git a/serie2/rapport.md b/serie2/rapport.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..99aba2fe99b10aee55c64859a994580a4761d680 100644
--- a/serie2/rapport.md
+++ b/serie2/rapport.md
@@ -0,0 +1,6 @@
+# Série 2
+Simon Cirilli - Kiady Arintsoa - Teo Colomboretto
+## Partie 2
+
+
+## Partie 3
\ No newline at end of file