From 8e609477756fc8e1331dd61d5c5c383378157fb7 Mon Sep 17 00:00:00 2001 From: Cirilli Simon <cirillisimon@pop-os.localdomain> Date: Sun, 5 Mar 2023 11:59:29 +0100 Subject: [PATCH] Version control is awful --- serie2/MNIST_binary_classifier_stud.ipynb | 90 +++++++++++------------ 1 file changed, 41 insertions(+), 49 deletions(-) diff --git a/serie2/MNIST_binary_classifier_stud.ipynb b/serie2/MNIST_binary_classifier_stud.ipynb index 9a1b699..dcfe646 100644 --- a/serie2/MNIST_binary_classifier_stud.ipynb +++ b/serie2/MNIST_binary_classifier_stud.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "educational-syndrome", "metadata": {}, "outputs": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 20, "id": "removed-commons", "metadata": {}, "outputs": [], @@ -288,6 +288,7 @@ " std = np.std(x_train)\n", "\n", " #separate normalisation of train and test data\n", + " # znorm\n", " self.data['x_train'] = (self.data['x_train'] - me) / std\n", " self.data['x_test'] = (self.data['x_test'] - me) / std\n", " \n", @@ -364,8 +365,8 @@ " ### START YOUR CODE ###\n", " \n", " # fonctionne pas\n", - " grad_w = 1 / m * np.dot(x.T, (y_pred - y))\n", - " grad_b = 1 / m * np.sum(y_pred - y)\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", "\n", " else: \n", " grad_w = np.zeros((784,1))\n", @@ -382,8 +383,8 @@ " \"\"\" \n", " \n", " ### START YOUR CODE ###\n", - " \n", - " return 1 / (1 + np.exp(-np.dot(x, self.w) - self.b))\n", + " z = np.dot(x, self.w) + self.b\n", + " return 1 / (1 + np.exp(-z))\n", " \n", " ### END YOUR CODE ### \n", " \n", @@ -441,23 +442,15 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 21, "id": "colored-facility", "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "shapes (784,) and (1,) not aligned: 784 (dim 0) != 1 (dim 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [98], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m data \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mx_train\u001b[39m\u001b[39m'\u001b[39m : x_train, \u001b[39m'\u001b[39m\u001b[39my_train\u001b[39m\u001b[39m'\u001b[39m : y_train, \u001b[39m'\u001b[39m\u001b[39mx_test\u001b[39m\u001b[39m'\u001b[39m : x_test, \u001b[39m'\u001b[39m\u001b[39my_test\u001b[39m\u001b[39m'\u001b[39m : y_test}\n\u001b[1;32m 4\u001b[0m gradD \u001b[39m=\u001b[39m GradientDescent(data, \u001b[39m0.5\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m0.\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m gradD\u001b[39m.\u001b[39moptimise(\u001b[39m5\u001b[39m, \u001b[39mFalse\u001b[39;00m)\n", - "Cell \u001b[0;32mIn [97], line 180\u001b[0m, in \u001b[0;36mGradientDescent.optimise\u001b[0;34m(self, epochs, debug)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[39mperforms epochs number of gradient descend steps and appends result to output array\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[39m\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[39mArguments:\u001b[39;00m\n\u001b[1;32m 177\u001b[0m \u001b[39mdebug -- False (default)/True; get info on each gradient descend step\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[39mfor\u001b[39;00m i0 \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m0\u001b[39m,epochs):\n\u001b[0;32m--> 180\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate()\n\u001b[1;32m 181\u001b[0m res_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mappend_result(i0)\n\u001b[1;32m 182\u001b[0m \u001b[39mif\u001b[39;00m debug \u001b[39mand\u001b[39;00m np\u001b[39m.\u001b[39mmod(i0,\u001b[39m1\u001b[39m) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n", - "Cell \u001b[0;32mIn [97], line 158\u001b[0m, in \u001b[0;36mGradientDescent.update\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[39m#predicted outcome for train [0] and test data [1]\u001b[39;00m\n\u001b[1;32m 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39my_pred \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredict(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata[\u001b[39m'\u001b[39m\u001b[39mx_train\u001b[39m\u001b[39m'\u001b[39m]), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredict(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata[\u001b[39m'\u001b[39m\u001b[39mx_test\u001b[39m\u001b[39m'\u001b[39m])]\n\u001b[0;32m--> 158\u001b[0m grad_w, grad_b \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgrad_cost()\n\u001b[1;32m 160\u001b[0m \u001b[39m### START YOUR CODE ###\u001b[39;00m\n\u001b[1;32m 162\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mw \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mw \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malpha \u001b[39m*\u001b[39m grad_w\n", - "Cell \u001b[0;32mIn [97], line 124\u001b[0m, in \u001b[0;36mGradientDescent.grad_cost\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m y_pred \u001b[39m=\u001b[39m y_pred[i]\n\u001b[1;32m 123\u001b[0m grad_b \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m/\u001b[39m m \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39msum(y_pred \u001b[39m-\u001b[39m y)\n\u001b[0;32m--> 124\u001b[0m grad_w \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m/\u001b[39m m \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39;49mdot(x, (y_pred \u001b[39m-\u001b[39;49m y))\n\u001b[1;32m 126\u001b[0m \u001b[39m# compute gradient of cost function wrt w and b with x[i].T\u001b[39;00m\n\u001b[1;32m 127\u001b[0m \u001b[39m# grad_w = 1 / m * np.dot(x.T, (y_pred - y))\u001b[39;00m\n\u001b[1;32m 128\u001b[0m \u001b[39m# grad_b = 1 / m * np.sum(y_pred - y)\u001b[39;00m\n\u001b[1;32m 129\u001b[0m \n\u001b[1;32m 130\u001b[0m \u001b[39melse\u001b[39;00m: \n\u001b[1;32m 131\u001b[0m grad_w \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((\u001b[39m784\u001b[39m,\u001b[39m1\u001b[39m))\n", - "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: shapes (784,) and (1,) not aligned: 784 (dim 0) != 1 (dim 0)" + "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" ] } ], @@ -473,6 +466,7 @@ { "cell_type": "code", "execution_count": 88, + "id": "ccc5f83a", "metadata": {}, "outputs": [ { @@ -509,13 +503,13 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 25, "id": "lonely-quantity", "metadata": {}, "outputs": [ { "data": { - 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", 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/8fv09HQAQGpqKj744ANUVFSgqKjI+HzLxIKpU6e2Os6aNWvw9NNPm/36SqUSr776Ku69915oNBrj9ujoaOzatQs+Pj4WZweAxx57DNeuXcNLL72E0tJSxMTEYPv27R3eW85eJI0OwLrt9xkKuKLdQOUVwGOg1LGIiIjsgkII3ufB3lRVVcHDwwOVlZVwd3eXJMPl67W49+97kKl5BROU54D4l4B7/z9JshARUffdunULxcXFCA0NhbOzs9RxZKuurg4XL17E4MGD20y2NOfvt+w+A0fyMMjLBdFBHrcnM/CecEREstbyGfKGhgaJk8hbbW0tALRZ/clckrdQyX4ljQ7Aih9+hj9rPoLT9SLg8hEgeILUsYiIyAJqtRouLi64du0aHBwcoFTyGpA5hBCora1FeXk5+vfv3+6kSnOwgCOrSYwMwKvZ57ClKRazVfsN94RjAUdEJEsKhQIBAQEoLi7G999/L3Uc2erfv3+P3EqMBRxZzSAvF0QP6o8NP0wxFHCns4AZrwGa7i1CTERE0tBoNAgLC2Mb1UIODg7dvvLWggUcWdWDUQF49fIIlKn84ddQCpz9NxD9mNSxiIjIQkql0qyVjsg62MAmq0qM8oeAEp/U/9ywQfuJtIGIiIjsAAs4sqogTxeMCe6Pjbp7DRuK9wM/XZI2FBERkcyxgCOrS4oKwA/CB/maGAACOLle6khERESyxgKOrG5mVAAAYE1N8/qvWt4TjoiIqDtYwJHVBfZ3xtgQT2zTjUeDyhW4cRH4/mupYxEREckWCziyiaSoANTBCfsdWiYzrJM2EBERkYyxgCObSIwy3LTw7crmG/meyQLqqyVMREREJF8s4MgmAjycMS7EE8fECFQ6DwIaa4CzX0odi4iISJZYwJHNJI0OAKDAVuX9hg0neE84IiIiS7CAI5tJjAyAQgGs/HE8BBTA9weA68VSxyIiIpIdFnBkM/4eThgf4oUSDMAVr+bPwp38VNpQREREMsQCjmzK0EYFMpvuM2zQfgro9RImIiIikh8WcGRTiZH+UCiAf5VHQK9xByovARe/kjoWERGRrLCAI5vydXdCbKgX6qFBofcDho28JxwREZFZWMCRzbW0Ud+vnWzYULAZuFUlYSIiIiJ5YQFHNjejuY26odQPjZ7DgKY6oGCT1LGIiIhkgwUc2ZyvmxMmDPYCoECe10zDRrZRiYiITMYCjiSRNDoQAPD29XGAQglcOgT8WCRxKiIiInlgAUeSmDHKH0oFsKdEjbrgqYaNvApHRERkEhZwJAkfN0f8bMgAAMCBftMMG09+Cuh1EqYiIiKSBxZwJJmW2airS0YATv2BqitA8T5pQxEREckACziSTEsbNe9qHW6GPWTYyAXuiYiIusQCjiQzoJ8jJg31BgBsd0gwbDy3Baj7SbpQREREMsACjiQ1M8rQRv2guD/gEw403QLObJQ2FBERUS/HAo4kNX2UH1RKBc6U3MT14XMMGzkblYiIqFMs4EhShjaqYTbqZv1kQKECfjgKXCuUOBkREVHvxQKOJJfU3EbdcK4RCGu+pQivwhEREXWIBRxJbvoof6iUChSUVKF0yMOGjSfXA7omaYMRERH1UizgSHKerhpMHmaYjbqxOhJw9gKqS4Hv9kicjIiIqHdiAUe9woPNbdQvT1cAox81bDzxsYSJiIiIei8WcNQrTBvlB7VSgXOlN3EpZJZhY2E2UHtd2mBERES9EAs4GZg1axY8PT0xe/ZsqaNYTX8XDX4eZmijbioZAPhFAboG4PQXEicjIiLqfVjAycDChQvx0UcfSR3D6lpu6rv1VAkwZp5ho5ZLaxEREd2NBZwMTJ06FW5ublLHsLrpEf5wUClQWHYT3wUkAko1cPUEUFYgdTQiIqJeRfICbv/+/UhOTkZgYCAUCgU2bdrU6fjly5dj/PjxcHNzg6+vL1JSUlBYWGj2GFtmz8jIQGhoKJycnDBhwgQcOXKkx7PYAw8XB/y8eTbqv79tBIbPMDzBq3BEREStSF7A1dTUIDo6GhkZGSaN37dvH9LS0nD48GHk5OSgsbER06ZNQ01NjVlj7nTw4EE0Nja22V5QUICysrJuZc/MzER6ejqWLVuGvLw8REdHY/r06SgvLwcAxMTEIDIyss3X1atXTfp52Juk0YEAgK35V4GY5jbqqc8AXdv3h4iIqM8SvQgAkZWVZdY+5eXlAoDYt2+fRWN0Op2Ijo4Ws2fPFk1NTcbt586dE35+fuK1117rVvbY2FiRlpbW6vUCAwPF8uXLTTpuiz179ohHHnnEpLGVlZUCgKisrDTrNXqDn2obxLDfbxUhv9siCq/8KMTfhwqxzF2Ic9lSRyMiIrIqc/5+S34FrrsqKysBAF5eXhaNUSqVyM7OxokTJ/DUU09Br9ejqKgIcXFxSElJwQsvvGBxtoaGBhw/fhwJCQmtXi8hIQGHDh2y+LgdycjIQEREBMaPH9/jx7YVD2cH3BfmAwDYeqYCGP2Y4Qm2UYmIiIxkXcDp9XosWrQIkydPRmRkpMVjAgMDsXv3bhw4cABz585FXFwcEhISsHr16m7lq6iogE6ng5+fX6vtfn5+KC0tNfk4CQkJmDNnDrKzsxEUFNRh8ZeWloaCggIcPXq0W7mlljS6eTZqfglE9BOGjYXbgZofJUxFRETUe6ilDtAdaWlpOH36NA4cONCtMQAQHByMtWvXYsqUKRgyZAjee+89KBSKno5skV27dkkdwaYSIvygUSlxobwa53EPRgTEACVaIH8D8LNfSx2PiIhIcrK9ArdgwQJs2bIFe/bsQVBQkMVjWpSVlWH+/PlITk5GbW0tFi9e3O2M3t7eUKlUbSZClJWVwd/fv9vHt1fuTg64b3hzG/XUHZMZtFxai4iICJBhASeEwIIFC5CVlYXdu3dj8ODBFo25U0VFBeLj4xEeHo6NGzciNzcXmZmZWLJkSbeyajQajB07Frm5ucZter0eubm5mDhxYreObe+SRhsK3C35JRCRjwAqDVCaD5SckjgZERGR9CRvoVZXV+PChQvG74uLi6HVauHl5YXg4GCsWrUKWVlZxiIoLS0N69atw+bNm+Hm5mb8LJmHhwecnZ1NHtNCr9cjMTERISEhyMzMhFqtRkREBHJychAXF4eBAwd2eDWuq+wAkJ6ejtTUVIwbNw6xsbFYsWIFampq8Mwzz/TQT9A+JYT7QaNW4rtrNSi86YCRIxKBgs2Adh0QMFrqeERERNKy+pzYLuzZs0cAaPOVmpoqhBBi2bJlIiQkxDi+vbEAxJo1a8wac6edO3eKurq6Ntvz8vLE5cuXLc7eYuXKlSI4OFhoNBoRGxsrDh8+bOqPxyJyvo3Inf7rw6Mi5HdbxOs7zglRuMNwO5HXBgvRWC91NCIioh5nzt9vhRBC2K5cJFuoqqqCh4cHKisr4e7uLnUci23WXsHC9VoM8XZF7uLJULwZCVSXAo99DIQnSx2PiIioR5nz91t2n4GjviO+pY1aUYOzZXVAdMs94dZJG4yIiEhiLOCo1+rnqMb9I5pno965tNb5HUB1uYTJiIiIpMUCjno149qop0ogvIcDA8cBQmdYH5WIiKiPYgFHvVr8SF84qpW4+GMtzlytAmLmGp7QfgLw45tERNRHsYCjXs3VUY24kb4ADEtrIfJhQOUIlBcYVmcgIiLqg1jAUa83M6p5bdRTJRBO/YHwBw1PcDIDERH1USzgqNeLG+kLJwclLl2/q42avwFoqpc2HBERkQRYwFGvd2cbdcupEmDI/YBbIFB3AyjcJnE6IiIi22MBR7KQFNU8GzX/KoRCCUQ/bnhC+4mEqYiIiKTBAo5k4f6RPnB2UOHy9TrkX6m8fU+4C7uAm6XShiMiIrIxFnAkCy4aNeLCm2ejnioBvIcBgyYAQg+cXC9xOiIiIttiAUey8WDzbNQtp0oghLh9FU67jveEIyKiPoUFHMnG1BG+cNGocOWnOpz8oRIYNQtQOwMVhcCV41LHIyIishkWcCQbzhoV4sP9AABbT10FnNyBiP8wPMnJDERE1IewgCNZSYryBwBk55c2t1Fb7gn3BdBYJ2EyIiIi22EBR7JyZxtVe/knIPQ+wGMQUF8JnNsqdTwiIiKbYAFHsuLkoEKCsY1aAiiVQPQThie5tBYREfURLOBIdpJGG2ajZueXQK8XQExzAVe0G6i8ImEyIiIi22ABR7IzZbgPXDUqXK28hROXfwK8hgAhkwEI4BTvCUdERPaPBRzJjpODCg9E3NFGBW5PZjjxCe8JR0REdo8FHMlS0mjD2qjGNmpECuDgClwvAi4fkTYcERGRlbGAI1m6N8wbbo5qlFbdQt6lG4BjPyDiIcOT2o+lDUdERGRlLOBIlu5so25paaOOaV5a63QW0FArUTIiIiLrYwFHsjWzeW3Ubaeb26jBk4D+IUDDTeDsvyVOR0REZD0s4Ei27h1uaKOWVdXj+KUbhnvCGRe459JaRERkv1jAkWw5qlV4YNRds1GjHzf8W7wf+OmSRMmIiIisiwUcydqDd9zUV6cXgGcIMPg+AAI4yXvCERGRfWIBR7L282E+cHNSo/xmPY5dvG7YeGcbVa+XLhwREZGVsIAjWdOolZg+yh8AsDW/uY0angxo3IAbF4FLh6QLR0REZCUs4Ej2bq+NWmpoo2pcgVEphic5mYGIiOwQCziSvclDveHh7ICK6nocKW5uo475heHfM5uA+mrJshEREVkDCziSPY1aiWkta6PmXzVsHDQB8BoKNNYAZ7+UMB0REVHPYwFHdqGljbr9dHMbVaFovcA9ERGRHWEBR3Zh8rCWNmoDvin+0bAx+nEACuD7A8D1YknzERER9SQWcGQXHFRKzGiZjdpyU1+PIGDIVMPjk59KE4yIiMgKWMCR3bizjdqka77/W8tkBu2nvCccERHZDRZwZDcmDh0ATxcH/FjTgG9aZqOOTAIcPYDKS8DFr6QNSERE1ENYwJHdcFApMSPS0Ebd0tJGdXAGIh82PNaukygZERFRz2IBJwOzZs2Cp6cnZs+eLXWUXi8pKhAAsP10ye02asvSWgWbgVtVEiUjIiLqOSzgZGDhwoX46KOPpI4hCz8b4gUvVw1u1Dbi0HfNs1GDxgHew4GmOqBgk6T5iIiIegILOBmYOnUq3NzcpI4hC2rVHWujtrRReU84IiKyM7Io4Pbv34/k5GQEBgZCoVBg06ZNnY5fvnw5xo8fDzc3N/j6+iIlJQWFhYWS5crIyEBoaCicnJwwYcIEHDlypMez0G0PtsxGPVOKxpY26ujHAYUSuHwY+LFIwnRERETdJ4sCrqamBtHR0cjIyDBp/L59+5CWlobDhw8jJycHjY2NmDZtGmpqajrc5+DBg2hsbGyzvaCgAGVlZRbnyszMRHp6OpYtW4a8vDxER0dj+vTpKC8vN46JiYlBZGRkm6+rV6+adL7U2oTBXhjgqsFPtY04VNTcRnUPAIbGGx5zgXsiIpI7ITMARFZWlln7lJeXCwBi37597T6v0+lEdHS0mD17tmhqajJuP3funPDz8xOvvfaaxbliY2NFWlpaq9cKDAwUy5cvN+sc9uzZIx555JFOx6xatUqEh4eL4cOHCwCisrLSrNewJ7/feEqE/G6LeGHDydsbT28UYpm7EP8IF0LX1PHOREREEqisrDT577csrsB1V2VlJQDAy8ur3eeVSiWys7Nx4sQJPPXUU9Dr9SgqKkJcXBxSUlLwwgsvWPS6DQ0NOH78OBISElq9VkJCAg4dOmTRMTuTlpaGgoICHD16tMePLTdJ7bVRhycCTv2BqivAd3sly0ZERNRddl/A6fV6LFq0CJMnT0ZkZGSH4wIDA7F7924cOHAAc+fORVxcHBISErB69WqLX7uiogI6nQ5+fn6ttvv5+aG0tNTk4yQkJGDOnDnIzs5GUFCQVYo/ezNh8AB499Ogsq4RBy9UGDY6OAFRcwyPeU84IiKSMbsv4NLS0nD69GmsX7++y7HBwcFYu3YtMjMzoVar8d5770GhUNggZed27dqFa9euoba2Fj/88AMmTpwodaReT6VUIDHScBXOOBsVuD0b9dwWoO4n2wcjIiLqAXZdwC1YsABbtmzBnj17EBQU1OX4srIyzJ8/H8nJyaitrcXixYu79fre3t5QqVRtJkGUlZXB39+/W8emrrW0UXecKUVDU3MbNXAM4BsBNN0CzmyUMB0REZHl7LKAE0JgwYIFyMrKwu7duzF48OAu96moqEB8fDzCw8OxceNG5ObmIjMzE0uWLLE4h0ajwdixY5Gbm2vcptfrkZuby6toNjA+1As+bo6outV0u4165z3h2EYlIiKZkkUBV11dDa1WC61WCwAoLi6GVqvFpUuXAACrVq1CfHy8cXxaWho+/vhjrFu3Dm5ubigtLUVpaSnq6uraPb5er0diYiJCQkKM7dOIiAjk5ORgzZo1ePPNNy3KBQDp6el499138eGHH+Ls2bN47rnnUFNTg2eeeaYHfjLUGUMb9a61UQEg6lFAoQJ+OApc6/n7AxIREVmd9SfFdt+ePXsEgDZfqampQgghli1bJkJCQozj2xsLQKxZs6bD19i5c6eoq6trsz0vL09cvnzZolwtVq5cKYKDg4VGoxGxsbHi8OHD5v4IzGLONGR7d7ioQoT8bouIXLZd3Gq849YhnzxmuKXIzpekC0dERHQHc/5+K4QQwtZFI1lXVVUVPDw8UFlZCXd3d6njSEqnF5i4PBflN+vx/tPjEDeyeUZwwZfAZ08C/fyBxWcAlVraoERE1OeZ8/dbFi1UIkuplArMjDJMZmjVRh0+A3D2AqpLge/2SJSOiIjIMizgyO61zEbNOVOG+iadYaNaA4x+1PD4xMcSJSMiIrIMCziye2ODPeHn7oib9U346nzF7Sdi5hn+LcwGaq9LE46IiMgCLODI7invaKNuzb+jjRowGvCLAnQNwOkvJEpHRERkPhZw1Cc82NJGLSjDrUbd7SfGNF+F034iQSoiIiLLsICjPmHMIE8EeDihur4J+89fu/1E1BxAqQaungDKCqQLSEREZAYWcNQnKO9cG/XONqqrt2FGKsCrcEREJBss4KjPaJmNuuvuNmrLZIZTmYCuUYJkRERE5mEBR33GmEH9EejhhJoGHfbd2UYNewBw9QFqrgEXdkkXkIiIyEQs4KjPaDUb9c6b+qocgNGPGR6zjUpERDLAAo76FGMb9ezdbdS5hn8LtwM1P0qQjIiIyHQs4KhPiRnUHwP7O6O2QYe9heW3n/AbBQTEAPpGIH+DZPmIiIhMwQKO+hSFQmG8CtdqbVTg9mQGLZfWIiKi3o0FHPU5Sc2fg8s9W466hjvaqFGzAZUGKM0HSk5JlI6IiKhrLOCozxkd5IEgT2fUNeqw5842qosXMCLR8Fi7TppwREREJmABR32OQqEwXoXb2qaN+gvDv/mfAU0NNk5GRERkGhZw1Ce1fA5u97ly1DY03X5iaBzQzx+o/RH4dodE6YiIiDrHAo76pKiBHhjk1dxGPXfHTX1VaiC65Z5wbKMSEVHvxAKO+iRDGzUQALA1/2rrJ1tmo57fAVSXg4iIqLexqIB75ZVXUFtb22Z7XV0dXnnllW6HIrKFB+9oo9bU39FG9RkBDBwHCB1w6jOJ0hEREXXMogLu5ZdfRnV1dZvttbW1ePnll7sdisgWRgW6I2SAC2416rH73F1X2lpWZtB+Aghh+3BERESdsKiAE0JAoVC02X7y5El4eXl1OxSRLXQ6GzXyEUDlCJQXACVa24cjIiLqhFkFnKenJ7y8vKBQKDB8+HB4eXkZvzw8PPDAAw/g0UcftVZWoh7XMht1T2E5qu9sozr3B8IfNDw+wQXuiYiod1GbM3jFihUQQuCXv/wlXn75ZXh4eBif02g0CA0NxcSJE3s8JJG1RAS4Y7C3K4orapB7tgwPxQy8/WTMPOD0F4a1Uaf/FVA7SheUiIjoDmYVcKmpqQCAwYMHY/LkyVCrzdqdqNdRKBSYGeWPjD1F2HqqpHUBN2Qq4D4QqLoCFG4DRqVIFZOIiKgViz4D5+bmhrNnzxq/37x5M1JSUvD73/8eDQ28ez3JS8vtRPaev9a6japUAdGPGx5r2UYlIqLew6IC7le/+hXOnz8PAPjuu+/w2GOPwcXFBRs2bMALL7zQowGJrC08wA1DvF3R0KRH7tmy1k9GN89GvbALuFlq+3BERETtsKiAO3/+PGJiYgAAGzZswJQpU7Bu3Tp88MEH+OKLL3oyH5HVKRQK42SGLXfPRvUeBgyaAAg9cHK9BOmIiIjasvg2Inq9HgCwa9cuzJw5EwAwaNAgVFRU9Fw6IhtpKeD2FV7DzVuNrZ9sWZlBu473hCMiol7BogJu3Lhx+Mtf/oK1a9di3759SEpKAgAUFxfDz8+vRwMS2cIIPzcM9XFFg06PXXe3UUfNAtTOQEUhcOW4NAGJiIjuYFEBt2LFCuTl5WHBggV48cUXMWzYMADA559/jkmTJvVoQCJbMLRRm9dGvbuN6uQORPyH4TEnMxARUS+gEKLnekK3bt2CSqWCg4NDTx2SLFBVVQUPDw9UVlbC3d1d6jiycb7sJqa9uR8alRJH/5AAD+c7/nf83V7go4cARw9gSSHg4CxZTiIisk/m/P226Apci+PHj+Pjjz/Gxx9/jLy8PDg5ObF4I9ka7ueGMN9+hjZqwV1t1ND7AI9BQH0lcG6rNAGJiIiaWVTAlZeX4/7778f48ePxm9/8Br/5zW8wbtw4xMfH49q1az2dkchmZrasjZp/VxtVqQSinzA8ZhuViIgkZlEB9/zzz6O6uhpnzpzB9evXcf36dZw+fRpVVVX4zW9+09MZiWymZTbqV99eQ2Xd3bNRmwu4oj1A5RUbJyMiIrrNogJu+/bt+H//7/8hPDzcuC0iIgIZGRnYtm1bj4UjsrXhfm4Y7tcPjTqBnLvbqF5DgJDJAARw8lNJ8hEREQEWFnB6vb7dz7o5ODgY7w9HJFctS2ttPXW17ZO8JxwREfUCFhVwcXFxWLhwIa5evf0H7sqVK1i8eDHi4+N7LByRFJJG+wMAvvq2ApW1d7VRIx4CHFyB60XA5W8kSEdERGRhAbdq1SpUVVUhNDQUQ4cOxdChQzF48GBUVVVh5cqVPZ2xz5s1axY8PT0xe/ZsqaP0CcN83TDS3w1NeoEdBXetf+rYDxiVYnjMyQxERCQRiwq4QYMGIS8vD1u3bsWiRYuwaNEiZGdnIy8vD0FBQT2dsc9buHAhPvroI6lj9ClJLbNR776pLwDENC9wfzoLaKi1YSoiIiIDswq43bt3IyIiAlVVVVAoFHjggQfw/PPP4/nnn8f48eMxatQofPXVV9bK2mdNnToVbm5uUsfoU2Y2z0Y9eKECN2oaWj8ZPAnwDAUabgJn/237cERE1OeZVcCtWLECzz77bLt3B/bw8MCvfvUrvPHGG2YF2L9/P5KTkxEYGAiFQoFNmzb1yD46nQ5//OMfMXjwYDg7O2Po0KH485//jB5ceMLk7BkZGQgNDYWTkxMmTJiAI0eO9FgGso6hPv0QHuCOJr3AzrvbqEolEN18FY5tVCIikoBZBdzJkycxY8aMDp+fNm0ajh83b7HvmpoaREdHIyMjo0f3ee2117B69WqsWrUKZ8+exWuvvYa///3vHX5G7+DBg2hsbGyzvaCgAGVlZe3sYVqOzMxMpKenY9myZcjLy0N0dDSmT5+O8vJy45iYmBhERka2+bpzkgjZXlKUYTLDlvbaqNGPG/4t3g/8dMmGqYiIiAC1OYPLyso6XSpLrVabvRJDYmIiEhMTe3yfr7/+Gg899BCSkpIAAKGhofj000/bvfql1+uRlpaGsLAwrF+/HiqVCgBQWFiIuLg4pKen44UXXrAoxxtvvIFnn30WzzzzDADg7bffxtatW/H+++9j6dKlAACtVtvlOZPtzYwKwOs7z+Proh9xo6YBnq6a2096hgCD7zMUcCfXA1Pa/u+DiIjIWsy6Ajdw4ECcPn26w+dPnTqFgICAbofqCZMmTUJubi7Onz8PwHD18MCBA+0WXEqlEtnZ2Thx4gSeeuop6PV6FBUVIS4uDikpKe0Wb6ZoaGjA8ePHkZCQ0Oq1EhIScOjQIctOrBMZGRmIiIjA+PHje/zYfdEQn36ICHCHTi+w40xp2wHGe8J9AvD+h0REZENmFXAzZ87EH//4R9y6davNc3V1dVi2bBkefPDBHgvXHUuXLsXjjz+OkSNHwsHBAWPGjMGiRYswb968dscHBgZi9+7dOHDgAObOnYu4uDgkJCRg9erVFmeoqKiATqeDn59fq+1+fn4oLW2nIOhAQkIC5syZg+zsbAQFBXVY/KWlpaGgoABHjx61ODO11rK0Vpu1UQEgPBnQuAE3LgKXer4gJyIi6ohZLdQ//OEP2LhxI4YPH44FCxZgxIgRAIBz584hIyMDOp0OL774olWCmuuzzz7DJ598gnXr1mHUqFHQarVYtGgRAgMDkZqa2u4+wcHBWLt2LaZMmYIhQ4bgvffeg0KhsHHytnbt2iV1hD4rKSoA/7ujEF8X/Ygfq+sxoJ/j7Sc1roZ7wp1Ya7gKFzpZspxERNS3mHUFzs/PD19//TUiIyPxP//zP5g1axZmzZqF3//+94iMjMSBAwfaXG2Sym9/+1vjVbioqCg8+eSTWLx4MZYvX97hPmVlZZg/fz6Sk5NRW1uLxYsXdyuDt7c3VCpVm0kQZWVl8Pf379axyTZCvV0RObCljdrOZJYxvzD8e2YTUF9t02xERNR3mX0j35CQEGRnZ6OiogLffPMNDh8+jIqKCmRnZ2Pw4MHWyGiR2tpaKJWtT0+lUnW4VmtFRQXi4+MRHh6OjRs3Ijc3F5mZmViyZInFGTQaDcaOHYvc3FzjNr1ej9zcXEycONHi45JtGddGzW9nVvCgCYDXUKCxBijYbONkRETUV1m0EgMAeHp6Yvz48YiNjYWnp6fFAaqrq6HVao0zMYuLi6HVanHpkuHWDKtWrWqzvmpX+wBAcnIy/vrXv2Lr1q24ePEisrKy8MYbb2DWrFltMuj1eiQmJiIkJASZmZlQq9WIiIhATk4O1qxZgzfffNOi7ACQnp6Od999Fx9++CHOnj2L5557DjU1NcZZqdT7tazKcKjoR1RU17d+UqG4vTKDdp2NkxERUZ8lJLZnzx4BoM1XamqqEEKIZcuWiZCQELP2EUKIqqoqsXDhQhEcHCycnJzEkCFDxIsvvijq6+vbzbFz505RV1fXZnteXp64fPmyRdlbrFy5UgQHBwuNRiNiY2PF4cOHTf75WKKyslIAEJWVlVZ9nb7kwX9+JUJ+t0WsPXSx7ZM/XRZimYcQy9yF+PE7m2cjIiL7YM7fb4UQPbg0AfUKVVVV8PDwQGVlZburZpD53t5XhL9tO4eJQwbg0/k/aztg7SygaDcw5XfA/b+3fUAiIpI9c/5+W9xCJepLWtqo3xT/iGs369sOMN4T7lPeE46IiKyOBRyRCQZ5uSA6yAN6AWxv76a+I5MARw+g8hJw8SvbByQioj6FBRyRiYw39T3VzmxUB2cg8mHDYy5wT0REVsYCjshEM41t1Osov9l2NRLjPeEKvgRuVdkwGRER9TUs4IhMFOTpgphB/SEEsP10O23UgWMB7+FAUx1QsMnm+YiIqO9gAUdkhgeb26hbTrWzNuqd94Q7wTYqERFZDws4IjMkNrdRj168jrKqdtqoox8HFErg8mHgxyIbpyMior6CBRyRGQb2d8aYYEMbdVt+O1fh3AOAoc0rh3AyAxERWQkLOCIztdwTbmt7BRwAjGm+J9zJ9YBeZ6NURETUl7CAIzJTy2zUY9/fQGllO23U4YmAU3+g6grw3V6bZiMior6BBRyRmQL7O2NsiKehjXq6natwDk5A1BzDYy5wT0REVsACjsgCxjZqe7NRgduzUc9tAep+sk0oIiLqM1jAEVngzjZqSWVd2wGBYwDfCKDpFnBmo43TERGRvWMBR2QBfw8njA/1BABk57dzU1/eE46IiKyIBRyRhW63UdtZGxUARj8GKFTAlWPAtUIbJiMiInvHAo7IQolRAVAogLxLP+HKT+20Ufv5AmHTDI95TzgiIupBLOCILOTn7oTxIV4AOripL3DHPeEyAV2TjZIREZG9YwFH1A1Jna2NCgBh0wGXAUB1KVC024bJiIjInrGAI+qGxEh/KBSA9vJP+OFGbdsBag0Q9ajhMduoRETUQ1jAEXWDr7sTYkNb2qjtzEYFbs9GLcwGaq/bKBkREdkzFnBE3fRgSxu1o8/BBYwG/KIAXQNw+gsbJiMiInvFAo6om6ZH+kOpAE5e/gmXr7fTRgVuT2ZgG5WIiHoACziibvJ1c8KEwQMAANkdXYWLmgMo1cDVE0BZgQ3TERGRPWIBR9QDWmajbu2ogHP1BobPMDzmVTgiIuomFnBEPWBGcxv11A+VuPRjB23UmOY26qlMQNdou3BERGR3WMAR9QDvfo742RBDG7XDq3BhDwCuPkDNNeDCLhumIyIie8MCjqiH3G6jdrA2qsrBsD4qAJz42EapiIjIHrGAI+ohM0YZ2qinr1Th+x9r2h/Uck+489uBmgrbhSMiIrvCAo6ohwzo54hJQ70BdNJG9RsFBMQA+iYgf4PtwhERkV1hAUfUg4xt1I7WRgWAMb8w/MvZqEREZCEWcEQ9aPoof6iUCpy5WoXiig7aqJGPACoNUJoPlJyybUAiIrILLOCIepCXqwaThnZxU18XL2DETMNj7TobJSMiInvCAo6ohxnXRu2sjdpyT7j8z4CmBhukIiIie8ICjqiHTYvwh1qpwNmSKhRdq25/0NA4oJ8/UPsj8O0O2wYkIiLZYwFH1MM8XTWYNMwwGzW7o6twKjUQ3XxPOLZRiYjITCzgiKzgwagu1kYFbrdRz+8AqsttkIqIiOwFCzgiK5g2yg9qpQLnSm/iQnkHbVSfEcDAcYDQAac+s21AIiKSNRZwRFbQ30WDn4c1t1E7vQrXvDKD9hNACBskIyIie8ACjshKkqJMuKlv5COAyhEoLwBKtLYJRkREsscCjshKpkX4w0GlQGHZTXxbdrP9Qc79gfAHDY9PcGUGIiIyDQs4GZg1axY8PT0xe/ZsqaOQGTxcHHBvmA8AEycz5G8AmuptkIyIiOSOBZwMLFy4EB999JHUMcgCJrVRh0wF3AcCt34CCrNtkouIiOSNBZwMTJ06FW5ublLHIAskRPhBo1Li2/JqnO+ojapUAdGPGx7znnBERGQCyQu4/fv3Izk5GYGBgVAoFNi0aVOP7XPlyhX84he/wIABA+Ds7IyoqCgcO3bM5tkzMjIQGhoKJycnTJgwAUeOHOmxDNS7eTg74N7m2aidLq0V3Twb9cIuoKqTcUREROgFBVxNTQ2io6ORkZHRo/vcuHEDkydPhoODA7Zt24aCggL84x//gKenZ7vjDx48iMbGxjbbCwoKUFZWZnGOzMxMpKenY9myZcjLy0N0dDSmT5+O8vLbN26NiYlBZGRkm6+rV692eFySj6TmtVGz80sgOrpViPcwYNDPAKEHTmXaMB0REcmRWuoAiYmJSExM7PF9XnvtNQwaNAhr1qwxbhs8eHC7Y/V6PdLS0hAWFob169dDpVIBAAoLCxEXF4f09HS88MILFuV444038Oyzz+KZZ54BALz99tvYunUr3n//fSxduhQAoNVqOz2GqTIyMpCRkQGdTtcjx6Oe0dJGvVBejfNl1Rjh30E7PGYucPmw4Z5wkxcCCoVtgxIRkWxIfgXOWr788kuMGzcOc+bMga+vL8aMGYN333233bFKpRLZ2dk4ceIEnnrqKej1ehQVFSEuLg4pKSntFm+maGhowPHjx5GQkNDqtRISEnDo0CGLjtmZtLQ0FBQU4OjRoz1+bLKcu5MD7hvePBv1VCdXVUfNAtTOQMV54MpxG6UjIiI5stsC7rvvvsPq1asRFhaGHTt24LnnnsNvfvMbfPjhh+2ODwwMxO7du3HgwAHMnTsXcXFxSEhIwOrVqy3OUFFRAZ1OBz8/v1bb/fz8UFpaavJxEhISMGfOHGRnZyMoKMgqxR9Z14PNbdQtnbVRndyBiP8wPD7xsY2SERGRHEneQrUWvV6PcePG4dVXXwUAjBkzBqdPn8bbb7+N1NTUdvcJDg7G2rVrMWXKFAwZMgTvvfceFL2gjbVr1y6pI1A3xYf7QqNW4rtrNThXehPhAe7tD4yZZ/gM3OmNwIzlgIOzbYMSEZEs2O0VuICAAERERLTaFh4ejkuXLnW4T1lZGebPn4/k5GTU1tZi8eLF3crg7e0NlUrVZhJEWVkZ/P39u3Vskhc3JwdMNbZRO5llGnov4DEIqK8Ezm21UToiIpIbuy3gJk+ejMLCwlbbzp8/j5CQkHbHV1RUID4+HuHh4di4cSNyc3ORmZmJJUuWWJxBo9Fg7NixyM3NNW7T6/XIzc3FxIkTLT4uyVPLbNStnbVRlUog+gnDYy2X1iIiovZJXsBVV1dDq9UaZ2IWFxdDq9Uar5StWrUK8fHxZu0DAIsXL8bhw4fx6quv4sKFC1i3bh3+9a9/IS0trU0GvV6PxMREhISEIDMzE2q1GhEREcjJycGaNWvw5ptvWpQdANLT0/Huu+/iww8/xNmzZ/Hcc8+hpqbGOCuV+o74cD84qpUorqhBQUlVxwNjmgu4oj1A5RXbhCMiInkREtuzZ48A0OYrNTVVCCHEsmXLREhIiFn7tPj3v/8tIiMjhaOjoxg5cqT417/+1WGOnTt3irq6ujbb8/LyxOXLly3K3mLlypUiODhYaDQaERsbKw4fPtzlz6U7KisrBQBRWVlp1dch883/6KgI+d0W8dq2s50PfD9RiGXuQuz7X9sEIyIiyZnz91shREe9HJKrqqoqeHh4oLKyEu7uHXxYniTx5cmr+M2nJxA6wAV7lkzteJLMiU+Azf8NeA0Fnj/Oe8IREfUB5vz9lryFStSXxI/0haNaiYs/1uLM1U7aqBEPAQ6uwPUi4PI3tgtIRESywAKOyIZcHdWIG+kLwDCZoUOO/YBRKYbHnMxARER3YQFHZGPG2ainOpmNChiW1gKA01lAQ40NkhERkVywgCOysbiRvnByUOLS9VqcvtJJGzV4EuAZCjTcBM5usVk+IiLq/VjAEdmYi0aN+JGG5dW25HeyNqpSCUQ3X4XTcmktIiK6jQUckQRMb6M23xOueD/wU8eriBARUd/CAo5IAveP8IWzgwo/3KjDqR8qOx7YPxgYfJ/hsfZT24QjIqJejwUckQScNSrEhZswGxUAYn5h+Ff7CaDXWzkZERHJAQs4Iok8GGViGzU8GdC4AT99D1z62kbpiIioN2MBRySRqSN84aJR4cpPdTjZWRtV4wJEzjI81q6zTTgiIurVWMARScRZo0J8uGE26tZTncxGBYCYeYZ/z2wC6qutG4yIiHo9FnBEEkoytY06aIJhXdTGGqBgs43SERFRb8UCjkhCU0f4wFWjwtXKWzhx+aeOByoUt1dmYBuViKjPYwFHJCEnBxUSIlraqF3MRo1+HIAC+P4AcL3Y+uGIiKjXYgFHJLGWNmp2fgn0+k7aqB5BwND7DY9P8p5wRER9GQs4IondN9wH/RzVKKm8hROXb3Q+uGUyg/ZT3hOOiKgPYwFHJDEnBxUSmm/qu6WrNurIJMDRA6i8BFz8ygbpiIioN2IBR9QLJI0OBABsyy/tvI3q4AxEPmx4rP3EBsmIiKg3YgFH1AvcG+YNN0c1SqtuIe9SF23UMc1LaxV8Cdyqsn44IiLqdVjAEfUCTg4qPNA8G7XLNurAsYD3cKCpDjiTZYN0RETU27CAI+olkkabOBtVobhjMgPvCUdE1BexgCPqJX4e5g03JzXKb9bj2PddtFFHPwYolMDlw0DFBdsEJCKiXoMFHFEv4ahWYVqEPwAT1kZ1DwCGJRgen+RVOCKivoYFHFEv8mBLG/V0KXSdtVGBO5bW+hTQ66ycjIiIehMWcES9yORh3nB3UuPazXocvXi988EjZgJO/YGbV4Hv9toiHhER9RIs4Ih6EY1aiWmjWtqoXcxGVTsCUXMMjzmZgYioT2EBR9TLtMxG3WZOG/XcFqDuJ+sGIyKiXoMFHFEvM3moNzycHVBRXY8jxV20UQPHAL4RQNMt4MxG2wQkIiLJsYAj6mU0aiWmjzLc1HdrfhezURWK21fhTnBpLSKivoIFHFEv1LI26vbTpWjS6TsfPPoxQKECrhwDrhXaIB0REUmNBRxRLzRp6AD0d3FARXVD123Ufr5A2DTDYy5wT0TUJ7CAI+qFHFRKzGiejbolv4vZqAAwpnlprZOZgK7JismIiKg3YAFH1Eu1zEY1qY0aNh1wGQBUlwJFu22QjoiIpMQCjqiXmjhkADxdHHC9pgGHv+uijarWAFGPGh6zjUpEZPdYwBH1UmqVEjMim2/q29VsVOD2bNTCbKC2i4KPiIhkjQUcUS+WFGXGbNSA0YB/FKBrAE5/YYN0REQkFRZwRL3Yz4Z4wctVgxu1jTj03Y9d7xDTPJnhxMfWDUZERJJiAUfUi7Vqo3a1Nipg+Byc0gEo0QJlZ6wbjoiIJMMCjqiXezCqeTbqmVI0dtVGdR0ADJ9ueMwF7omI7BYLOKJeLnawF7z7afBTbSO+LjKhjTrmF4Z/T2UCukbrhiMiIkmwgJOBWbNmwdPTE7Nnz5Y6CkmgdRvVhNmowxIAVx+g5hpwYZeV0xERkRRYwMnAwoUL8dFHH0kdgyTUMht1x5kyNDR10UZVORjWRwU4mYGIyE6xgJOBqVOnws3NTeoYJCFDG9URlXWNOFhU0fUOLfeEO78dqDFhPBERyYrkBdz+/fuRnJyMwMBAKBQKbNq0qcf3+dvf/gaFQoFFixb1SGZzc2RkZCA0NBROTk6YMGECjhw50qM5yP6plAokmjMb1W8UEBAD6JuA/A3WDUdERDYneQFXU1OD6OhoZGRkWGWfo0eP4p133sHo0aM7HXfw4EE0Nrb9wHdBQQHKysoszpGZmYn09HQsW7YMeXl5iI6OxvTp01FeXm4cExMTg8jIyDZfV6+a8Hkn6jNa1kbdeaa06zYqcHsyA5fWIiKyO2qpAyQmJiIxMdEq+1RXV2PevHl499138Ze//KXDcXq9HmlpaQgLC8P69euhUqkAAIWFhYiLi0N6ejpeeOEFi3K88cYbePbZZ/HMM88AAN5++21s3boV77//PpYuXQoA0Gq1XZ6LKTIyMpCRkQGdTtcjx6PeZXyoF3zcHHHtZj0OXqjA/SN9O98h8hFgx++B0nyg5JRhpQYiIrILkl+Bs6a0tDQkJSUhISGh03FKpRLZ2dk4ceIEnnrqKej1ehQVFSEuLg4pKSntFm+maGhowPHjx1u9vlKpREJCAg4dOmTRMTuTlpaGgoICHD16tMePTdJTKRWY2dxG3WJKG9XFCxgx0/CY94QjIrIrdlvArV+/Hnl5eVi+fLlJ4wMDA7F7924cOHAAc+fORVxcHBISErB69WqLM1RUVECn08HPz6/Vdj8/P5SWlpp8nISEBMyZMwfZ2dkICgqySvFH8pA02jAbdWdBKeqbTLjS2rK0Vv5nQFODFZMREZEtSd5CtYbLly9j4cKFyMnJgZOTk8n7BQcHY+3atZgyZQqGDBmC9957DwqFwopJTbNrF+/lRQbjQjzh6+aI8pv1OPBtBeLD/TrfYWgc0M8fqC4Fvt0BhCfbJigREVmVXV6BO378OMrLy3HPPfdArVZDrVZj3759+Oc//wm1Wt3hZ8TKysowf/58JCcno7a2FosXL+5WDm9vb6hUqjaTIMrKyuDv79+tY1PfpFQqMLN5aS2TZqOq1EB0yz3hOJmBiMhe2GUBFx8fj/z8fGi1WuPXuHHjMG/ePGi1WuMkhTtVVFQgPj4e4eHh2LhxI3Jzc5GZmYklS5ZYnEOj0WDs2LHIzc01btPr9cjNzcXEiRMtPi71bQ82z0bNKSjDrUYz2qjf7gSqyzsfS0REsiB5C7W6uhoXLlwwfl9cXAytVgsvLy8EBwdj1apVyMrKalUEdbWPm5sbIiMjW72Oq6srBgwY0GY7YCiqEhMTERISgszMTKjVakRERCAnJwdxcXEYOHBgu1fjusoBAOnp6UhNTcW4ceMQGxuLFStWoKamxjgrlchc9wR7wt/dCaVVt/DVtxV4IKKLNqrPCGDgOODKMcP6qJOet01QIiKyGskLuGPHjuH+++83fp+eng4ASE1NxQcffICKigoUFRWZtY+5lEolXn31Vdx7773QaDTG7dHR0di1axd8fHwsyg4Ajz32GK5du4aXXnoJpaWliImJwfbt29tMbCAylVKpQGKUP9YcvIitp652XcABwJh5hgJOuw6YuADoBZ/tJCIiyymEEELqENSzqqqq4OHhgcrKSri7u0sdh6zg+PfX8cjqQ+jnqMaxPyTAyaHtxwJaqfsJ+McIoOkW8OweYOA9NslJRESmM+fvt11+Bo7I3o0Z5IkADydU1zdh//lrXe/g3B8Y+aDhMe8JR0QkeyzgiGSo1WzUfBNmowK3F7jP3wA01VspGRER2YLkn4EjIsskjQ7AeweKsat5NmqXbdQhUwH3gUDVFeDVgYCC//1GRGSxiIeAR96V7OVZwBHJ1JhB/TGwvzOu/FSHvYXXMCOyi3sLKlXAz54Ddv4B0DfaJiQRkb2S+P9HWcARyZRCocDMKH+8+1UxtuaXdF3AAYZbiIx+HNCxhUokGc4dtA8OLpK+PAs4IhlLGh2Id78qRu7ZMtQ16OCs6aKNCgD92r8tDhERyQc/BEMkY9FBHhjY3xm1DTrsLeQqC0REfQULOCIZUygUSGpeWmuLqbNRiYhI9ljAEclcUvPtRHafLUddgwlroxIRkeyxgCOSudFBHgjydEZdow572EYlIuoTWMARydydbdStp9hGJSLqC1jAEdmBB6MCAQC558pQ29AkcRoiIrI2FnBEdiByoDuCvVxwq1GP3efYRiUisncs4IjsANuoRER9Cws4IjthnI16rhw19WyjEhHZMxZwRHZiVKA7Qga4oL5Jj1y2UYmI7BoLOCI7oVAojFfhstlGJSKyayzgiOxIy+fg9hSWo5ptVCIiu8UCjsiORAS4Y7C3q6GNerZM6jhERGQlLOCI7MidbVTORiUisl8s4IjsTEsbde/5a7h5q1HiNEREZA0s4IjszEh/NwzxcUVDkx65ZzkblYjIHrGAI7IzCoUCDza3UbewjUpEZJdYwBHZoZnNbdT956+him1UIiK7wwKOyA6N8HPDUB9XNOj02FXA2ahERPaGBRyRHTKsjRoIAMjOZxuViMjesIAjslMPGtuoFaisYxuViMiesIAjslPD/dwQ5tuPbVQiIjvEAo7IjrXcE24r26hERHaFBRyRHWtZleGrb6+hspZtVCIie8ECjsiOhfm5YYSfGxp1AjsLSqWOQ0REPYQFHJGdYxuViMj+sIAjsnMzm9uoB76twE+1DRKnISKinsACjsjODfPth5H+bmjSC+w8w9moRET2gAUcUR/QMpmBbVQiIvvAAo6oD2hZG/XghQrcqGEblYhI7ljAEfUBQ336ITzA3dBG5WxUIiLZYwFH1Ee0LK215RTbqEREcqeWOgAR2cbMqAD8745CfF30I86VVqGfI3/9iYgs5eygwoB+jpK9Pv8fnKiPGOztilGB7jhztQozVnwldRwiIllLGh2AjLn3SPb6LOCI+pBfTRmKP2Tlo0GnlzoKdYMQUicgIgelQtLXZwEnA7NmzcLevXsRHx+Pzz//XOo4JGP/ER2I/4gOlDoGERF1EycxyMDChQvx0UcfSR2DiIiIegkWcDIwdepUuLm5SR2DiIiIegnJC7j9+/cjOTkZgYGBUCgU2LRpU4/ss3z5cowfPx5ubm7w9fVFSkoKCgsLJcmekZGB0NBQODk5YcKECThy5EiP5iAiIqK+RfICrqamBtHR0cjIyOjRffbt24e0tDQcPnwYOTk5aGxsxLRp01BTU9Pu+IMHD6KxsbHN9oKCApSVtb9+pCk5MjMzkZ6ejmXLliEvLw/R0dGYPn06ysvLjWNiYmIQGRnZ5uvq1asdHpeIiIj6MNGLABBZWVlW2ae8vFwAEPv27WvznE6nE9HR0WL27NmiqanJuP3cuXPCz89PvPbaaxbniI2NFWlpaa1eKzAwUCxfvrzLY95pz5494pFHHul0zKpVq0R4eLgYPny4ACAqKyvNeg0iIiKSTmVlpcl/vyW/AmcrlZWVAAAvL682zymVSmRnZ+PEiRN46qmnoNfrUVRUhLi4OKSkpOCFF16w6DUbGhpw/PhxJCQktHqthIQEHDp0yLIT6URaWhoKCgpw9OjRHj82ERER9R594jYier0eixYtwuTJkxEZGdnumMDAQOzevRv33nsv5s6di0OHDiEhIQGrV6+2+HUrKiqg0+ng5+fXarufnx/OnTtn8nESEhJw8uRJ1NTUICgoCBs2bMDEiRMtzkVERETy1icKuLS0NJw+fRoHDhzodFxwcDDWrl2LKVOmYMiQIXjvvfegUEh7oz4A2LVrl9QRiIiIqBex+xbqggULsGXLFuzZswdBQUGdji0rK8P8+fORnJyM2tpaLF68uFuv7e3tDZVK1WYSRFlZGfz9/bt1bCIiIuq77LaAE0JgwYIFyMrKwu7duzF48OBOx1dUVCA+Ph7h4eHYuHEjcnNzkZmZiSVLllicQaPRYOzYscjNzTVu0+v1yM3NZQuUiIiILCZ5C7W6uhoXLlwwfl9cXAytVgsvLy8EBwdj1apVyMrKalUEdbUPYGibrlu3Dps3b4abmxtKS0sBAB4eHnB2dm6VQa/XIzExESEhIcjMzIRarUZERARycnIQFxeHgQMHtns1zpQc6enpSE1Nxbhx4xAbG4sVK1agpqYGzzzzTA/89IiIiKhPsv6k2M7t2bNHAGjzlZqaKoQQYtmyZSIkJMSsfYQQ7T4PQKxZs6bdHDt37hR1dXVttufl5YnLly9blL3FypUrRXBwsNBoNCI2NlYcPnzY1B+PRcyZhkxERES9gzl/vxVCCGHDepFsoKqqCh4eHqisrIS7u7vUcYiIiMgE5vz9ttvPwBERERHZK8k/A0c9r+WialVVlcRJiIiIyFQtf7dNaY6ygLNDN2/eBAAMGjRI4iRERERkrps3b8LDw6PTMfwMnB3S6/W4evUq3NzcevxGxFVVVRg0aBAuX75sl5+v4/nJn72fo72fH2D/58jzkz9rnaMQAjdv3kRgYCCUys4/5cYrcHZIqVR2edPi7nJ3d7fbX0yA52cP7P0c7f38APs/R56f/FnjHLu68taCkxiIiIiIZIYFHBEREZHMsIAjszg6OmLZsmVwdHSUOopV8Pzkz97P0d7PD7D/c+T5yV9vOEdOYiAiIiKSGV6BIyIiIpIZFnBEREREMsMCjoiIiEhmWMARERERyQwLOGojIyMDoaGhcHJywoQJE3DkyJFOx2/YsAEjR46Ek5MToqKikJ2dbaOkljHn/D744AMoFIpWX05OTjZMa579+/cjOTkZgYGBUCgU2LRpU5f77N27F/fccw8cHR0xbNgwfPDBB1bPaSlzz2/v3r1t3j+FQoHS0lLbBDbT8uXLMX78eLi5ucHX1xcpKSkoLCzscj85/Q5aco5y+j1cvXo1Ro8ebbzB68SJE7Ft27ZO95HT+2fu+cnpvWvP3/72NygUCixatKjTcVK8hyzgqJXMzEykp6dj2bJlyMvLQ3R0NKZPn47y8vJ2x3/99dd44okn8J//+Z84ceIEUlJSkJKSgtOnT9s4uWnMPT/AcKftkpIS49f3339vw8TmqampQXR0NDIyMkwaX1xcjKSkJNx///3QarVYtGgR/uu//gs7duywclLLmHt+LQoLC1u9h76+vlZK2D379u1DWloaDh8+jJycHDQ2NmLatGmoqanpcB+5/Q5aco6AfH4Pg4KC8Le//Q3Hjx/HsWPHEBcXh4ceeghnzpxpd7zc3j9zzw+Qz3t3t6NHj+Kdd97B6NGjOx0n2XsoiO4QGxsr0tLSjN/rdDoRGBgoli9f3u74Rx99VCQlJbXaNmHCBPGrX/3KqjktZe75rVmzRnh4eNgoXc8CILKysjod88ILL4hRo0a12vbYY4+J6dOnWzFZzzDl/Pbs2SMAiBs3btgkU08rLy8XAMS+ffs6HCO338G7mXKOcv49FEIIT09P8X//93/tPif390+Izs9Pru/dzZs3RVhYmMjJyRFTpkwRCxcu7HCsVO8hr8CRUUNDA44fP46EhATjNqVSiYSEBBw6dKjdfQ4dOtRqPABMnz69w/FSsuT8AKC6uhohISEYNGhQl/+lKTdyev+6IyYmBgEBAXjggQdw8OBBqeOYrLKyEgDg5eXV4Ri5v4emnCMgz99DnU6H9evXo6amBhMnTmx3jJzfP1POD5Dne5eWloakpKQ27017pHoPWcCRUUVFBXQ6Hfz8/Fpt9/Pz6/AzQ6WlpWaNl5Il5zdixAi8//772Lx5Mz7++GPo9XpMmjQJP/zwgy0iW11H719VVRXq6uokStVzAgIC8Pbbb+OLL77AF198gUGDBmHq1KnIy8uTOlqX9Ho9Fi1ahMmTJyMyMrLDcXL6Hbybqecot9/D/Px89OvXD46Ojvj1r3+NrKwsREREtDtWju+fOecnt/cOANavX4+8vDwsX77cpPFSvYdqqx6dSOYmTpzY6r8sJ02ahPDwcLzzzjv485//LGEyMsWIESMwYsQI4/eTJk1CUVER3nzzTaxdu1bCZF1LS0vD6dOnceDAAamjWI2p5yi338MRI0ZAq9WisrISn3/+OVJTU7Fv374Oixy5Mef85PbeXb58GQsXLkROTk6vn2zBAo6MvL29oVKpUFZW1mp7WVkZ/P39293H39/frPFSsuT87ubg4IAxY8bgwoUL1ohocx29f+7u7nB2dpYolXXFxsb2+qJowYIF2LJlC/bv34+goKBOx8rpd/BO5pzj3Xr776FGo8GwYcMAAGPHjsXRo0fx1ltv4Z133mkzVo7vnznnd7fe/t4dP34c5eXluOeee4zbdDod9u/fj1WrVqG+vh4qlarVPlK9h2yhkpFGo8HYsWORm5tr3KbX65Gbm9vh5xsmTpzYajwA5OTkdPp5CKlYcn530+l0yM/PR0BAgLVi2pSc3r+eotVqe+37J4TAggULkJWVhd27d2Pw4MFd7iO399CSc7yb3H4P9Xo96uvr231Obu9fezo7v7v19vcuPj4e+fn50Gq1xq9x48Zh3rx50Gq1bYo3QML30KpTJEh21q9fLxwdHcUHH3wgCgoKxPz580X//v1FaWmpEEKIJ598UixdutQ4/uDBg0KtVovXX39dnD17Vixbtkw4ODiI/Px8qU6hU+ae38svvyx27NghioqKxPHjx8Xjjz8unJycxJkzZ6Q6hU7dvHlTnDhxQpw4cUIAEG+88YY4ceKE+P7774UQQixdulQ8+eSTxvHfffedcHFxEb/97W/F2bNnRUZGhlCpVGL79u1SnUKnzD2/N998U2zatEl8++23Ij8/XyxcuFAolUqxa9cuqU6hU88995zw8PAQe/fuFSUlJcav2tpa4xi5/w5aco5y+j1cunSp2LdvnyguLhanTp0SS5cuFQqFQuzcuVMIIf/3z9zzk9N715G7Z6H2lveQBRy1sXLlShEcHCw0Go2IjY0Vhw8fNj43ZcoUkZqa2mr8Z599JoYPHy40Go0YNWqU2Lp1q40Tm8ec81u0aJFxrJ+fn5g5c6bIy8uTILVpWm6bcfdXyzmlpqaKKVOmtNknJiZGaDQaMWTIELFmzRqb5zaVuef32muviaFDhwonJyfh5eUlpk6dKnbv3i1NeBO0d24AWr0ncv8dtOQc5fR7+Mtf/lKEhIQIjUYjfHx8RHx8vLG4EUL+75+55yen964jdxdwveU9VAghhHWv8RERERFRT+Jn4IiIiIhkhgUcERERkcywgCMiIiKSGRZwRERERDLDAo6IiIhIZljAEREREckMCzgiIiIimWEBR0RERCQzLOCIiOyUQqHApk2bpI5BRFbAAo6IyAqefvppKBSKNl8zZsyQOhoR2QG11AGIiOzVjBkzsGbNmlbbHB0dJUpDRPaEV+CIiKzE0dER/v7+rb48PT0BGNqbq1evRmJiIpydnTFkyBB8/vnnrfbPz89HXFwcnJ2dMWDAAMyfPx/V1dWtxrz//vsYNWoUHB0dERAQgAULFrR6vqKiArNmzYKLiwvCwsLw5ZdfGp+7ceMG5s2bBx8fHzg7OyMsLKxNwUlEvRMLOCIiifzxj3/EI488gpMnT2LevHl4/PHHcfbsWQBATU0Npk+fDk9PTxw9ehQbNmzArl27WhVoq1evRlpaGubPn4/8/Hx8+eWXGDZsWKvXePnll/Hoo4/i1KlTmDlzJubNm4fr168bX7+goADbtm3D2bNnsXr1anh7e9vuB0BElhNERNTjUlNThUqlEq6urq2+/vrXvwohhAAgfv3rX7faZ8KECeK5554TQgjxr3/9S3h6eorq6mrj81u3bhVKpVKUlpYKIYQIDAwUL774YocZAIg//OEPxu+rq6sFALFt2zYhhBDJycnimWee6ZkTJiKb4mfgiIis5P7778fq1atbbfPy8jI+njhxYqvnJk6cCK1WCwA4e/YsoqOj4erqanx+8uTJ0Ov1KCwshEKhwNWrVxEfH99phtGjRxsfu7q6wt3dHeXl5QCA5557Do888gjy8vIwbdo0pKSkYNKkSRadKxHZFgs4IiIrcXV1bdPS7CnOzs4mjXNwcGj1vUKhgF6vBwAkJibi+++/R3Z2NnJychAfH4+0tDS8/vrrPZ6XiHoWPwNHRCSRw4cPt/k+PDwcABAeHo6TJ0+ipqbG+PzBgwehVCoxYsQIuLm5ITQ0FLm5ud3K4OPjg9TUVHz88cdYsWIF/vWvf3XreERkG7wCR0RkJfX19SgtLW21Ta1WGycKbNiwAePGjcPPf/5zfPLJJzhy5Ajee+89AMC8efOwbNkypKam4k9/+hOuXbuG559/Hk8++ST8/PwAAH/605/w61//Gr6+vkhMTMTNmzdx8OBBPP/88yble+mllzB27FiMGjUK9fX12LJli7GAJKLejQUcEZGVbN++HQEBAa22jRgxAufOnQNgmCG6fv16/Pd//zcCAgLw6aefIiIiAgDg4uKCHTt2YOHChRg/fjxcXFzwyCOP4I033jAeKzU1Fbdu3cKbb76JJUuWwNvbG7NnzzY5n0ajwf/8z//g4sWLcHZ2xr333ov169f3wJkTkbUphBBC6hBERH2NQqFAVlYWUlJSpI5CRDLEz8ARERERyQwLOCIiIiKZ4WfgiIgkwE+vEFF38AocERERkcywgCMiIiKSGRZwRERERDLDAo6IiIhIZljAEREREckMCzgiIiIimWEBR0RERCQzLOCIiIiIZOb/B9bltGoy1o9+AAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -534,17 +528,17 @@ "plt.semilogy(epochs, test_costs, label=\"test\")\n", "plt.ylabel('Cost')\n", "plt.xlabel('Epochs')\n", - "xmax = epochs[-1]\n", - "ymin = 2e-3\n", - "ymax = 1e-1\n", - "plt.axis([0,xmax,ymin,ymax])\n", + "# xmax = epochs[-1]\n", + "# ymin = 2e-3\n", + "# ymax = 1e-1\n", + "# plt.axis([0,xmax,ymin,ymax])\n", "plt.legend()\n", "plt.show() " ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 23, "id": "neither-moldova", "metadata": {}, "outputs": [ @@ -552,23 +546,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0. 1. 2. 3. 4. 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 0. 1. 2. 3. 4. 5. 6. 7. 8.\n", - " 9.]\n", - "[0.47791694 0.22577456 0.22577456 0.22577456 0.77430784 0.22577456\n", - " 0.22577456 0.22577456 0.22577456 0.22577456 0.22585695 0.22577456\n", - " 0.77430784 0.22577456 0.22577456 0.22577456 0.22577456 0.22585695\n", - " 0.22577456 0.77439024 0.22577456 0.22577456 0.22577456 0.22585695\n", - " 0.22585695]\n", - "[0.49208965 0.22808174 0.22808174 0.22808174 0.77191826 0.22808174\n", - " 0.22808174 0.22808174 0.22808174 0.22808174 0.22808174 0.22841134\n", - " 0.77191826 0.22808174 0.22808174 0.22808174 0.22808174 0.22808174\n", - " 0.22841134 0.77158866 0.22808174 0.22808174 0.22808174 0.22808174\n", - " 0.22808174]\n" + "[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" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -592,17 +577,17 @@ "plt.semilogy(epochs, test_error, label=\"test\")\n", "plt.ylabel('Error')\n", "plt.xlabel('Epochs')\n", - "xmax = epochs[-1]\n", - "ymin = 3e-3\n", - "ymax = 1e-1\n", - "plt.axis([0,xmax,ymin,ymax])\n", + "# xmax = epochs[-1]\n", + "# ymin = 3e-3\n", + "# ymax = 1e-1\n", + "# plt.axis([0,xmax,ymin,ymax])\n", "plt.legend()\n", "plt.show() " ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 32, "id": "delayed-desire", "metadata": {}, "outputs": [ @@ -610,7 +595,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(692, 784)\n" + "(692, 784)\n", + "ICI\n" ] }, { @@ -620,7 +606,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [70], line 23\u001b[0m\n\u001b[1;32m 20\u001b[0m correct_labels[correct_labels_bin \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m] \u001b[39m=\u001b[39m digit_2\n\u001b[1;32m 22\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[0;32m---> 23\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 25\u001b[0m correct_labels \u001b[39m=\u001b[39m correct_labels\u001b[39m.\u001b[39mreshape(cols,rows)\u001b[39m.\u001b[39mT\n\u001b[1;32m 27\u001b[0m \u001b[39mprint\u001b[39m(np\u001b[39m.\u001b[39marray(correct_labels, dtype \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m))\n", + "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", "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" ] @@ -656,9 +642,15 @@ "correct_labels_bin = y_test[(np.round(gradD.y_pred[1]) != y_test)[:,0]].T\n", "correct_labels = correct_labels_bin.copy()\n", "correct_labels[correct_labels_bin == 0] = digit_1\n", + "\n", + "\n", + "# ici problème\n", + "# print(correct_labels_bin)\n", "correct_labels[correct_labels_bin == 1] = digit_2\n", "\n", "if correct_labels.shape[1] != rows*cols:\n", + " print(\"ICI\")\n", + " print(-np.ones((1,rows*cols-correct_labels.shape[1])))\n", " correct_labels = np.append(correct_labels, -np.ones((1,rows*cols-correct_labels.shape[1])),1)\n", " \n", "correct_labels = correct_labels.reshape(cols,rows).T\n", @@ -668,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "endless-addition", "metadata": {}, "outputs": [ @@ -888,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "90f77759", "metadata": {}, "outputs": [ @@ -938,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "5e873ea8", "metadata": {}, "outputs": [ -- GitLab