diff --git a/.gitignore b/.gitignore index 5389b59..67ce61b 100644 --- a/.gitignore +++ b/.gitignore @@ -86,3 +86,4 @@ report/mlp-cw2-template.bbl report/mlp-cw2-template.blg venv +saved_models diff --git a/data/VGG38_BN_RC_accuracy_performance.pdf b/data/VGG38_BN_RC_accuracy_performance.pdf new file mode 100644 index 0000000..d6cdadf Binary files /dev/null and b/data/VGG38_BN_RC_accuracy_performance.pdf differ diff --git a/data/VGG38_BN_RC_loss_performance.pdf b/data/VGG38_BN_RC_loss_performance.pdf new file mode 100644 index 0000000..a6ced50 Binary files /dev/null and b/data/VGG38_BN_RC_loss_performance.pdf differ diff --git a/notebooks/Plot_Results.ipynb b/notebooks/Plot_Results.ipynb index a0e0fb3..851013d 100644 --- a/notebooks/Plot_Results.ipynb +++ b/notebooks/Plot_Results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -64,19 +64,22 @@ "output_type": "stream", "text": [ "VGG_08 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n", - "VGG_38 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n" + "VGG_38 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n", + "VGG38_BN ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n", + "VGG38_BN_RC ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n" ] } ], "source": [ "result_dict = collect_experiment_dicts(target_dir=experiment_dir)\n", + "\n", "for key, value in result_dict.items():\n", " print(key, list(value.keys()))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -158,825 +161,33 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { + "image/png": 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GPvvss0yYMIGbbrqJk08+mWeeeYZbb72VSy+9NODn9fjjjzNp0iQmT57MZZddRmhoKG+//TZxcXF+bzN9+nSmTZuG0+kM+HF8iY+P57zzzuOJJ57A7XZz2mmn8fHHH2O1WrnxxhuZOXMm119/PVVVVdx6661t3l9qaip//etfWbRoERMmTGDhwoV+tx03bhz33HMPzz77LLNmzeLDDz/sUF3w4XjiiScYP348l112GWeffTaqqvL6668bmWa3283ChQs56aSTuPjiixkyZAiLFy8GtGz0okWLmD17Nueeey5ms5lnn322y/dZUbu62rkXKy4ubrUepTMpikLMgd2UPPg3SB+M+Z5/dsvjis6jKAqpqank5+d3+SQB0XXkOPYPnX0cq6qqOjQ7Xhw+fXa86NsO5zj6+/xZrVYSExMDug/J0HYj63BPhvbQQdSeatothBBCCNHPSA1tNzLHJ0FUDFRVQE4WDB3V07skhBBCdKsPPviA2267zed16enprFy5ss891m233cYHH3zg87pzzz3XGI7vCXl5eZx00kl+r1+1alWLUpW+SALabqStGDYMddsm1IP7UCSgFUIIcYSZM2dOkxW/LBaLUfOq12h21WN568zHuuWWW/yuUqbXKfeU5ORkVqxY0er1/YEEtN1t8DDYtkkmhgkhhDgiRURENFkAoCtraJs/VldJSEggISGhyx+nIywWi7GMb38mNbTdTBmkLbCgZktAK4QQQgjRGSSg7WaNK4bloNpkYpgQQgghxOGSgLabKTHxEB0Lqhtys3p6d4QQQggh+jwJaHuCXnYgdbRCCCGEEIdNAtoeoNfRInW0QgghhBCHrVcFtB9++CHz58/n3//+d6vb7dy5k9tuu42LL76YG2+8sdV2FL2RTAwTQgghRHssWbKE0aNHB7Tt3//+d0499dQu3qPepdcEtHv37uXLL79k0KBBrW5XVFTEokWLGD16NIsXL2bevHm8+uqrrFu3rpv2tBMMGqr9Pz8X1dbQs/sihBCiz7nsssu44IILfF63adMmBgwYwLZt2wD49NNP+c1vfsOYMWMYOnQoM2bM4Oabb2b79u1Nbme323nuuec47bTTGDZsGKNGjWL27NksXryYgoICY7vXXnuN2bNnM3LkSEaOHMncuXP5+uuvm9xXbW0tCxcu5Oijj2bo0KHMnDmT1157LeDnN3XqVAYMGMCAAQPIyMhg8uTJ/PWvf6WiosLY5rvvvmPAgAHMmjULl8vV5PajR49myZIl7X6soUOHcuKJJ/Lcc8+1WFJ5+/btXH311UyYMIEhQ4Ywffp0brnlFvbt2xfw8xJdp1cEtA0NDTz11FNcc801hIeHt7rtihUrSEhI4PLLLyc9PZ1TTjmFk08+mWXLlnXT3h4+JSYOYuK0iWE5+3t6d4QQQvQxF110EWvXriU3N7fFdUuWLGHs2LGMHz+ehx56iOuuu46xY8fy6quvsnLlSh599FEGDRrEI488YtzGZrNx0UUX8dRTTzF//nw++OADPvvsM+68807Ky8t59dVXjW1TU1NZsGABy5cvZ/ny5UybNo0rr7yS3bt3G9vce++9rFq1iqeeeopVq1Zx1VVXcdddd/H5558H/Bz/9re/8cMPP7Bhwwaeeuop1q1bx1133dViu+zsbN57772A77e1x1q1ahXXXnstjzzyCG+88YZx/RdffMHcuXOx2+3Gc/rnP/9JZGQkjz322GE9tugcvWJhhZdeeolJkyZx1FFH+V06Trdnzx6OOuqoJpdNnDiRlStX4nQ6sVhaPiWHw9GkabOiKISGhhr/7g764xj/Hzwc9cf1kL0PZfjYbtkHcXiaH0PRN8lx7B+68jiqqorNpba9YRcINisBPafZs2eTkJDA0qVLufnmm43L6+vr+eSTT7j99tvZvHkzzz77LPfffz+///3vjW0GDhzI8ccf3yQD+eKLL7JhwwY+++wzxo0bZ1yemZnJSSed1GTbOXPmNNmX22+/nddff50tW7YwcuRIADZv3sz555/PCSecAMDvfvc73njjDbZu3cppp50W0GsRERFBUlISoAXR559/Pp988kmL7a644goef/xxzjnnHEJCQgK679Ye67e//S3/+c9/WL16NZdccgn19fXcfPPNzJo1i5dfftm4zcCBA5k8eTKVlZWt3rfb7eaYY47hT3/6E5deeqlx+bZt2zj99NP57rvvGDRoEM8//zxLly4lOzubmJgYTj31VO688842E32BcLvd/N///R9vvPEGZWVlDBs2jDvuuIOTTz4Z0LLz9913H8uXL6eyspLExER+97vfcdNNNwFaCcM777xDSUkJsbGx/OpXv+KBBx447P3ydrif5R4PaNeuXUtWVhaLFi0KaPuKigqio6ObXBYdHY3L5aK6uprY2NgWt/nwww+bnL1lZmayePFiEhMTD2/nA5RbXscbm3I41R7M0QNTAKgcO5GqH9cTUpRHfGpqt+yH6BwpKSk9vQuiE8hx7B866zjW19cbS6E2ONxcsGRHp9xve31w8VhCrG0PnlqtVubPn8+7777LrbfeagQD77//Pg6Hg/nz5/PYY48RHh7OH/7wB5/JHm8ff/wxM2fO9LtMrD8ul4tPPvmEuro6pk6daryGU6dO5csvv+SSSy4hJSWFtWvXsn//fh566CGfS842v0xRFMxms3F5fn4+X3/9NUcffbRxmf6crrvuOj744AP+85//cMMNNxj3YbFYAlre1vuxVFXlu+++Y+/evQwdOhSr1cqKFSsoKyvjpptu8nl/gawQNm/ePD766KMmJxYff/wxU6ZMYdiwYcZr8PDDD5ORkcHBgwe57bbbePjhh3n00UebPN9AnpPJZEJRFGPbf/3rXzz//PM8/vjjjB8/nrfeeosrrriCNWvWMGTIEF544QW++OILXnrpJQYMGEBeXh6HDh3CarWybNkyXnzxRV544QVGjhxJUVERO3bsCOg4BiooKIjUw4yFejSgLSkp4d///jcLFy4kKCgo4Ns1j+L1M0d/0f28efM466yzWty+uLjYWD+6K720sYBPd5eTV1HPgCAbqqrijte+hOt2bcOen9/l+yAOn6IopKSkUFBQ0KK2SvQdchz7h84+jna73RjJczjdh31/HeVwOjAHWA04f/58nnnmGb755humTZsGwFtvvcUZZ5xBeHg4e/fuZeDAgaiqajw3PajRbd68maioKPbv38/xxx/fZDTz97//PatXrwa0mlTv7OiuXbs4++yzsdlshIeH89JLLzFkyBDj9vfddx+33HILEyZMwGKxYDKZeOyxxzj66KNbLHPra+lbVVV54IEHWLRoEW63m4aGBiZNmsRdd91lbKv/flssFv7yl7/wyCOPcOGFFxIVFWVcH8iSut6PpY/ohoSEcPnll+NwONi7V5vAPXjw4A4v0fvrX/+af/3rX2RlZZGeno7b7ebDDz/kpptuMu7zyiuvNLZPS0vjb3/7GwsWLOChhx5q8nwD2Qe3293kuD/77LNcf/31Riy0YMECvv32W5577jkefvhhcnJyGDx4MJMnTzY+W/qxys7OJjExkeOPPx6r1UpycjLjx48P6DgGym63k+8jFrJYLAEnH3s0oN2/fz+VlZXcfvvtxmVut5tdu3bxv//9j7feeguTqekHOyYmpklROEBVVRVms9nves1Wq9XvWUN3/KCdMyqWz34pZ92BMvaNiSYzNhgGDdGuLMjFXV+HEhLa5fshOoeqqhII9QNyHPuHrjiOwWaFJReM6NT7bM9jB2rYsGFMmTKFd955h2nTpnHgwAHWr1/PW2+9ZWzTPNFz4YUXMmfOHH744QduuummVl+7hx9+mLq6Ol555ZUWE6+HDh3KihUrqKqqYvny5fz5z3/m/fffZ8QI7XV75ZVX2LJlC6+++irp6emsX7+eO+64g6SkJE488cSAnt+1117L/PnzUVWVQ4cO8cgjj3DppZfywQcfYDabm2x70UUX8cILL/DMM8+wYMGCgO7f12OVlpayePFipk2bxjHHHAN0Tpwwbtw4hg0bxkcffcSNN97I999/T2lpKWeffbaxzdq1a3nqqafYs2cP1dXVuFwuGhoaqKurIywsrMOPXV1dTUFBgfF8dFOmTGHnzp2AdnJ04YUXMmPGDE4++WRmz57NzJkzATjrrLN46aWXOP744zn55JOZNWsWp556aptZ//Y63Ne5RyeFjR8/nscff5xHH33U+G/o0KFMnz6dRx99tEUwCzB8+HB++umnJpdt3bqVIUOGdPqL21mSI4KYNlA7Y/xwZykASlQsxCaAqkK2zJAUQojeQlEUQiymHvmvvXWEF110EcuXL6e6upolS5aQnp7OjBkzAK28Ljs7u0nWLDo6mszMzBalGpmZmS1m6ycnJ5OZmUlMTEyLxw0KCiIzM5MJEyawYMECxowZw0svvQRo5RuPPPII99xzD3PmzGHMmDFcccUVnH322Tz//PMBP7e4uDgyMzONjgL33XcfmzZtYu3atS22tVgs3Hrrrbz88stNOjK097GmTJnCiy++yIsvvmhkp4cM0RJQeqa2o/SyA4CPPvqImTNnEhcXB0Bubi6XXnopI0eO5IUXXuCzzz4zMrMdzXo252t0W79s/PjxrFu3jltuuYWGhgauvfZarrrqKgAGDBjA6tWrefjhhwkJCeGOO+7g3HPP7bT96iw9GtCGhoYycODAJv8FBwcTGRnJwIEDAW345OmnnzZuM2fOHEpKSnjttdfIzc3l66+/5uuvv2bu3Lk99TQCcu7YeADWZFdRWGMHQBmhTQZTN63psf0SQgjRd82dOxez2cyHH37Iu+++ywUXXGAEKeeccw61tbUBtcs655xzWL16dYtWXoFSVRW7Xftt04f6myelTCYTbnfHyzn0rGxDg+92l3PnzmXkyJE88cQTHX4M0EaCr7zySh544AFUVTUCz+eee87n9m1NCtPNmzePXbt28dNPP/Hpp59y7rnnGtdt3boVp9PJPffcY7Q660hg7ktkZCQpKSls2LChyeWbN29m+PDhTbY755xzeOyxx3juuedYvnw55eXlgBavzZkzhwceeIB3332XzZs38/PPP3fK/nWW3pnS9FJeXk5JSYnxd1JSEgsWLOC1117j888/JzY2liuuuILjjjuuB/eybUPjQpg6KJb12eV8/HM5V09JRpk2G3X9N6jrV6OefyVKcHBP76YQQog+JDw8nLPPPpvFixdTVVXF/PnzjeumTJnCNddcw/33309ubi5nnnkmaWlpFBYW8vbbb6MoihF0XnXVVXz11VfMnz+fm2++malTpxIdHc3+/ftZuXJlkyH+RYsWMWvWLNLS0qipqeHjjz/m+++/58033wS0wOj444/nwQcfJCQkhPT0dL7//nvef/997r777oCfW01NDUVFRUbJwUMPPURcXBxTpkzxe5sFCxZw8cUXt/dlbOHyyy/n2Wef5dNPP+Wss87i8ccf55prruHyyy/nyiuvJDMzk7KyMpYtW0ZeXp7fYNfbwIEDmTJlCn/9619xOp1NukUMGjQIp9PJK6+8wqmnnsrGjRt5/fXXD/t56K699lr+/ve/M2jQIMaOHcvSpUvZsWMHTz31FAAvvPACycnJjB07FkVR+O9//0tSUhLR0dEsWbIEt9vNpEmTCA0N5f333yckJIQBAwZ02v51hl4X0N57771N/vaesagbM2YMixcv7qY96jyXTh3E+uxyvthbwYXj4okcOR4SkqGkEHXLdyjHn9zTuyiEEKKPufDCC3n77beZOXNmiyDj7rvvZuLEifznP/9hyZIl1NfXk5CQwHHHHccnn3xCZGQkACEhISxZsoSXXnqJJUuWsGjRIlRVJSMjg5NPPtkYfgZtQvcf//hHioqKiIyMZPTo0bz55ptNamOfffZZFi1axE033URFRQUDBgzg1ltvbdK2qi2PP/64MYEtPj6eCRMm8PbbbxvD9L5Mnz6dadOm8c033wT8OL7Ex8dz3nnn8cQTT3DmmWdy2mmn8fHHH/P0009z4403UlNTQ2pqKtOmTePWW28N+H7nzZvHwoULOf/88432oaDV2N5zzz3G63bcccexYMEC/vSnPx3W89D9/ve/p6amhvvvv5/S0lKGDx/Oq6++apRThIeH88wzz5CVlYXZbGbChAm8/vrrmEwmoqOjefrpp7nvvvtwuVyMGjWKf//7360eh56gqEfwrIji4uJuqwHRZw1e9Mr37Ctr4KLxCVx4VALu/y5B/fhNGDEO8y0Pd8u+iI5RFIXU1FTy8/NlMlEfJsexf+js41hVVWXMjhfd63Bmx4ve43COo7/Pn9VqDbjLQa9YKexIoSgK53lqaf/7Szk2pxvlhFNAMcEv21ELD1HncPH8xgLe2VbSxr0JIYQQQgjohSUH/d3xGZGkRFgpqHHw5b5KfjUyAcZOgu2byV2zhkeCjyG3SiusP3tULGFWcxv3KIQQQvQdH3zwAbfddpvP69LT01m5cmWfe6zbbrvN70qn5557bqeXSZ588sk+lz0GWLx4cZMJZ0cKCWi7mdmkcM7oOJ7fWMhHu8o4fXgMphmnsiG/jifrRlJnsxvbFtc6GRQjAa0QQoj+Y86cOU1WJLNYLMaiAR1daSrQx/LWmY91yy23cO211/q8Tq9T7kyvv/663+H97loFtbeRgLYHnDIkmnd+KqGo1sG32VXkm4fyzrjLURWF0WFOKs2hHKp2UFTjYFCMdD4QQgjRf0RERDRZCKkra2ibP1ZXSUhICGgJ3M6Snp7ebY/VV0gNbQ8Itpj41chYAP65roC3t5ejKgqn5X3HvYc+YaAniC2qlSJ5IYQQQoi2SEDbQ84cEUuwWcHpVrGYFG4YFcQ1ez7C+tMGkiwuQAJaIYToTofT9F8I0TGd1W1GAtoeEhls5upjkjkqOYyHTx3InKOHwNBR4HaTWLgfkIBWCCG6S1hYGNXV1RLUCtHN6urqCO6EhaWkhrYHzR4aw+yhMcbfyvRTUff9TOLPG2Hg2RTVSEArhBDdwWKxEB4eTk1NTU/vyhEnKCjIWDZX9F0dOY6qqmKxWCSg7W+UKdNR33mJpMK9MFAytEII0Z0sFossrtDNZKGT/qE3HEcpOehFlJBQlGNnkNhQAUCVzUW9Q4a/hBBCCCFaIwFtL6NMm024q4EIRx0AxZKlFUIIIYRolQS0vc2QkZCYQmJDOSBlB0IIIYQQbZGAtpdRFAVl4lQjoC2UiWFCCCGEEK2SgLYXUiYdT5Keoa229fDeCCGEEEL0bhLQ9kZDR5JEPQBFhaU9vDNCCCGEEL2bBLS9kGIyk5SWDEBRRW0P740QQgghRO8mAW0vlTxqBABFDjOqrFwjhBBCCOGXBLS9VOLY0QBUWcNp2PtLD++NEEIIIUTvJQFtLxUZHkK4qi0hV/Tj1h7eGyGEEEKI3ksC2l4sKUQ7PIX7smRJQCGEEEIIPySg7cUS4yIBKGpQ4VBOD++NEEIIIUTvJAFtL5YcFQJAcUgs6g/f9/DeCCGEEEL0ThLQ9mJJEVYAikJiUX9c38N7I4QQQgjRO0lA24slhesBbRxk70UtLe7hPRJCCCGE6H0koO3FjIA2PAEA9cd1Pbk7QgghhBC9kgS0vZheclBlDqXBZEX9QQJaIYQQQojmJKDtxSKCzIRbtUNUHBILv+xAra7q4b0SQgghhOhdJKDt5fQsbXHGaFDdqD9t6OE9EkIIIYToXSw9vQMrVqxgxYoVFBdrE57S09M5//zzmTRpks/td+zYwX333dfi8n/84x8MGDCgS/e1JySFW8kqt1GceRTs/gZ183cwbXZP75YQQgghRK/R4wFtXFwcv/3tb0lJSQHgm2++4dFHH+XRRx8lIyPD7+2efPJJwsLCjL+joqK6fF97QqI+MSxpiHbBzh9Ra2tQwiN6cK+EEEIIIXqPHi85mDJlCpMnTyYtLY20tDQuuugiQkJC2LNnT6u3i46OJiYmxvjPZOrxp9IljE4HhED6YHA5ZZEFIYQQQggvPZ6h9eZ2u/n++++x2WyMGDGi1W1vvfVWHA4H6enpnHvuuYwbN87vtg6HA4fDYfytKAqhoaHGv7uD/jjtfbxkvYa21oHpmBm4cw/AxjUoM+YAUGVz8lNBHScMjMTUTc/lSNXRYyh6FzmO/YMcx/5BjmP/0BuOo6Kqqtpjj+5x8OBBFi5ciMPhICQkhD/+8Y9MnjzZ57aHDh1i586dDBkyBKfTyerVq/niiy+45557GDNmjM/bLF26lPfee8/4OzMzk8WLF3fJc+lsuwur+d1/NhIXZuXTczPJ/8OvwWQm7fXPMMfE8cf3fuT7rDIe+NUYTh+T0tO7K4QQQgjR7XpFQOt0OikpKaG2tpb169fz1Vdfcd9995Genh7Q7R955BEUReG2227zeb2/DG1xcTFOp7NTnkNbFEUhJSWFgoIC2vOS19hc/PbdXwB498KRmB/5KxzYi+ni6yiaMpurP94HwNmj4vjDlOQu2Xeh6egxFL2LHMf+QY5j/yDHsX/oquNosVhITEwMbNtOe9TDYLFYjElhQ4cOZd++fSxfvpyrr746oNuPGDGCNWvW+L3earVitVp9XtfdHyBVVdv1mGFWhTCriTqHm8IaOwOOmYF6YC/ujWtYETPF2O5gpU2+DLpJe4+h6J3kOPYPchz7BzmO/UNPHsdeOZNKVdUmGdW2ZGVlERMT03U71IMURWmcGFbjQJkyHQDn3l18tbfc2C6n0tYj+yeEEEII0dN6PKB966232LVrF0VFRRw8eJC3336bHTt2MGPGDOP6p59+2tj+008/ZcOGDeTn55OTk8Nbb73F+vXrOf3003vqKXQ5fXGFoloHSlwiDBvN5rhRlNvcRARph7C0zkmdw9WTuymEEEII0SN6vOSgsrKSp59+mvLycsLCwhg0aBALFy7kqKOOAqC8vJySkhJje6fTyeuvv05ZWRlBQUFkZGRw++23+51E1h8YGdpaLWutTJnBFzu0f88ZFsPKrCrK653kVNoZmRDaY/sphBBCCNETejygve6661q9/oYbbmjy9znnnMM555zTlbvU6zQPaEvGHscP+UUAzI51sLcsiPJ6J7mVNglohRBCCHHE6fGSA9E27xpagK+KVFTFxLjyvaTuWkdGdDAAOZX2HttHIYQQQoieIgFtH+BdQ+tyq3yxrxKAOfnrUTetISMqCJCJYUIIIYQ4MvV4yYFom56hrWhwsS63mtI6J5FBJqaW7gSXg3RXFQA5VZKhFUIIIcSRRzK0fUBEkIlQi3ao3tqqTZA7eUg01tHjAUjfswHQShIanO6e2UkhhBBCiB4iAW0foCiKUXaQ68nCzhkWgzJFa20WtWkVUcFmVCBPsrRCCCGEOMJIQNtH6GUHAGMSQ8mIDkaZNBUsFsjPISNEy8xKHa0QQgghjjQS0PYReoYWtOwsgBIWAeOOBmBAdT4gnQ6EEEIIceSRgLaPSPZkaMODTJwwMNK43DT9VAAyDvwISIZWCCGEEEceCWj7iGMGRBAfZuGi8QkEW7wO27ijISae9PKDgGRohRBCCHHkkbZdfURaVBCvzBvW4nLFbEaZfioZK5YDUFBjx+FyYzXLuYoQQgghjgwS9fQDyvRTiXVUE+asx61KpwMhhBBCHFkkoO0HlPhElHFHk15bBDS29hJCCCGEOBJIQNtPmE6cQ0ZdIQAHy+t7eG+EEEIIIbqPBLT9xfhjSHfXAJBzsKCHd0YIIYQQovtIQNtPKGYzGUPSAciRDK0QQgghjiAS0PYjA6dOASDfFIGj4FAP740QQgghRPeQgLYfSUxPJUR14DRZyP92TU/vjhBCCCFEt5CAth8xKQoDQhUAcnbuQXU6eniPhBBCCCG6ngS0/UxGciwAuUoEbN3Yw3sjhBBCCNH1JKDtZzJiQwDICU/GvfrzHt4bIYQQQoiuJwFtP5MRHQRAblgS7PwBtay4h/dICCGEEKJrSUDbzwyMDgYgLyIZFwrq5u96eI+EEEIIIbqWBLT9TFK4FatJwa5YKA6JRd30bU/vkhBCCCFEl5KAtp8xmxQGRHnKDsKTYf9u1FIpOxBCCCFE/yUBbT+k19HmDBoPgLp5bU/ujhBCCCFEl5KAth/K8NTR5iaPAAio7EBV1S7dJyGEEEKIriIBbT9kdDoIigNFgaxfUEuL/G7/5b4Kzn17N1sLartrF4UQQgghOo0EtP2QnqHNqXWhjhgHtF52sHJ/JW4VNubVdMv+CSGEEEJ0Jglo+6HUyCAsJmhwqhROmAmAusl3QOtyq+wpbQCgqEaWyhVCCCFE32Pp6R1YsWIFK1asoLhYm4mfnp7O+eefz6RJk/zeZufOnbz22mvk5uYSGxvL2WefzZw5c7prl3s9i0lhaFwIu0sa2J02jhTFpJUdlBSiJCQ32Ta7wobNpdXPFkpAK4QQQog+qMcztHFxcfz2t79l0aJFLFq0iHHjxvHoo4+Sk5Pjc/uioiIWLVrE6NGjWbx4MfPmzePVV19l3bp13bznvdvoxDAAfq4xwYixgO+yg90l9ca/C2scMjlMCCGEEH1Ojwe0U6ZMYfLkyaSlpZGWlsZFF11ESEgIe/bs8bn9ihUrSEhI4PLLLyc9PZ1TTjmFk08+mWXLlnXznvduYxJDAdhZXIcyZTrgu+zAO6Ctd7qpsbu7ZweFEEIIITpJjwe03txuN2vXrsVmszFixAif2+zZs4ejjjqqyWUTJ05k//79OJ3O7tjNPmGUJ6DNqbRTM24qKCY4sAe1uKDJdrtLGpr8LWUHQgghhOhreryGFuDgwYMsXLgQh8NBSEgIf/vb30hPT/e5bUVFBdHR0U0ui46OxuVyUV1dTWxsbIvbOBwOHI7GQE1RFEJDQ41/dwf9cbrr8WJCrQyICiKvys4vtiAmjxyH+vNPsPk7lDPOA6DK5uRQtR2AAZFB5FXbKap1MDwhtFv2sa/p7mMouoYcx/5BjmP/IMexf+gNx7FXBLRpaWk89thj1NbWsn79ep555hnuu+8+v0Ft8xdMr/v090J++OGHvPfee8bfmZmZLF68mMTExE56BoFLSUnptsc6elAFedvyya4zMWv2ryj/+SfMW9eTcuWNAOzfXwLAwNgwRqdEkrerkHpTKKmpqd22j31Rdx5D0XXkOPYPchz7BzmO/UNPHsdeEdBaLBbjRRg6dCj79u1j+fLlXH311S22jYmJoaKiosllVVVVmM1mIiIifN7/vHnzOOuss4y/9cC3uLi428oUFEUhJSWFgoKCbpt4NdjzcmzKKua8E8aCYsKxdxeHfvoBJTGF73/ROksMi7USZdZeh735peTnW7tl//qanjiGovPJcewf5Dj2D3Ic+4euOo4WiyXg5GOvCGibU1W1SYmAt+HDh7N58+Yml23dupUhQ4Zgsfh+OlarFavVd5DW3R8gVVW77TFHe0oHfiltwB4agXnUeNi1FffGbzGdcR67i+sAGBEfgtmkBfmFNXb5UmlDdx5D0XXkOPYPchz7BzmO/UNPHscenxT21ltvsWvXLoqKijh48CBvv/02O3bsYMaMGcb1Tz/9tLH9nDlzKCkpMfrQfv3113z99dfMnTu3p55Cr5UaaSU62IzTrbKvtAFlyjQA1A2rcasqv3gWVBiZEEpSuBbwy6QwIYQQQvQ1PZ6hrays5Omnn6a8vJywsDAGDRrEwoULjU4G5eXllJSUGNsnJSWxYMECXnvtNT7//HNiY2O54oorOO6443rqKfRaiqIwOimUdTk17CquZ9TR01DffhFys8j9eR91DjfBZoVBMcEU1WqBbFGt1otWCvSFEEII0Vf0eEB73XXXtXr9DTfc0OKyMWPGsHjx4q7apX5lTGIY63Jq2Flcz7lj41EmHYe6cQ0/b9kOjGK4p9wgIcyKSQG7S6WiwUVsaI+/NYQQQgghAtLjJQeia+n9aH8uqcetqijTZwOwu0irnx3pqbO1mhXiPEGslB0IIYQQoi+RgLafGxIbQpBZodrmIq/KDqMmQFwiv4RprblGevWcTY7Q6mj18gMhhBBCiL5AAtp+zmpWGBEfAsCu4noUk4n6E04lJzwZgBE+AtrCGnv376gQQgghRAdJQHsEGJ0YBsAuT5uuvaOmoyomkurLiKktNbaTTgdCCCGE6IskoD0CjEnSsrA7i+oB+MWhZWxHVB1E/e5rY7vkiCBASg6EEEII0bdIQHsEGJkQigIU1Dgor3eyu1gLbEdUZaN+9xWq2w1AsmRohRBCCNEHSUB7BAgPMjMoJhiAncV17NYXVLAXQWkR/PwTAEmeGtqSOgcut6zYIoQQQoi+QQLaI8RoT/uulfsrqba5sJoUMseOAEBd+yUAcaEWLCZwuqGs3tlj+yqEEEII0R4S0B4h9IB2Y14tAEPiQgiafgoA6pbvUWtrjAUWAIqk7EAIIYQQfYQEtEeIMUlhTf4emRACA4dC+mBwOlA3rAa8WnfJxDAhhBBC9BES0B4hEsOtxIc1Lmc7MiEURVFQpp8KgPrtF4B36y7pRSuEEEKIvkEC2iPImMTGRRT0FcKUqTPBYoGD+1AP7pfVwoQQQgjR50hAewTRF1iIC7WQ4MnWKhFRKBOmAqCu/NToRSutu4QQQgjRV0hAewSZNiiSEfEhzBsTh6IoxuXKqecAoH6/kiSXNmlMAlohhBBC9BWWtjfxLTs7m9raWsaMGQNAQ0MDb7zxBllZWRx11FHMnz+/SdAkel5MiIXHTh/c4nJl6CgYOR52byNx4wpgKqV1ThwuFatZjqEQQgghercOZ2j/85//sGXLFuPvt99+m6+++gqn08lHH33E//73v07ZQdE9TGecD0D0mv8SZFZQ0RZYEEIIIYTo7Toc0B48eJARIzyN+VWVb7/9lt/85jcsXryYc845h5UrV3baTopuMGYiDBqGYreR5K4DpOxACCGEEH1DhwPauro6oqKiAK38oKamhhNOOAGAcePGUVhY2Dl7KLqFoihGljapPBeQTgdCCCGE6Bs6HNBGRERQUlICwPbt24mJiSElJQUAp1OWTe2TJh0HKekk1RYDkqEVQgghRN/Q4Ulho0eP5t1336W6uppPP/2USZMmGdcVFBQQHx/fKTsouo9iMqGcfh5JX/0AQGF1Qw/vkRBCCCFE2zqcof3tb3+Loij8+9//xmq1cv755xvXff/99wwfPrxTdlB0L2XqTJLNLgAK80sDvl12hY17v85hf5kEwUIIIYToXh3O0CYlJfHkk09SU1NDREREk+t+//vfExMTc7j7JnqAYrGQfPRkKIWiOheqy4ViNrd5uze2FvNDfi1xoRb+eHxqN+ypEEIIIYTmsBdWaB7M2u12Bg4caEwYE31P8rRpAFRYw2lYv7rN7asanGzOqwEgt8repfsmhBBCCNFchwPa7777js8//9z4u6CggL/85S9ccskl3H333dTU1HTKDoruFxkRShha2UHRyq9R3e5Wt197sBqXqv07t8qGqqpdvYtCCCGEEIYOB7TLli3DZrMZf7/++uvU1tZy5plnkpeXx4cfftgpOyi6n6IoJEWFAFBYZYOtG1rdflVWlfHvWrubigZXl+6fEEIIIYS3Dge0hYWFZGRkAFqZwdatW7n44ou57LLLuPDCC9m4cWOn7aTofslRwQAUhcTi/nSp36xrQbWdn0vqMSkQFazV2uZW2XxuK4QQQgjRFToc0NpsNoKDtaBn7969OBwOo3VXeno6ZWVlnbOHokckRVgBKApPhOy9sGOLz+2+OaBlZ49KCWdEvJbVza2UOlohhBBCdJ8OB7SxsbEcOHAAgB9//JG0tDRjIlhtba0R7Iq+KTncE9CmjwLwmaVVVZVVWZUAnDQ4ivRo7ZjnyMQwIYQQQnSjDrftOvbYY3nnnXfYuXMnP/74I+ecc45xXXZ2NsnJyZ2yg6JnGBnaqBSwWGHvLvhlB4wcZ2yzp7SBQ9UOgs0Kx2VE4vIEvHmVUnIghBBCiO7T4QzthRdeyPTp0ykoKGD69OlNAtotW7Ywfvz4TtlB0TOMDG29G2X6bADcny5pss0qT7nB1IxIQq0mBkQFAZKhFUIIIUT36nCGNigoiKuvvtrndQ899FDA9/Phhx+yYcMG8vLyCAoKYsSIEfzud78jLS3N72127NjBfffd1+Lyf/zjHwwYMCDgxxb+6Rnaarub+lPmEbJmBezairrvZ5Sho3C6Vb71BLQnDdZKTTI8E8lK65zUOVyEWdtekEEIIYQQ4nB1OKD1dujQIWpqaoiMjCQ1tX2rRO3cuZPTTjuNoUOH4nK5eOedd3jwwQd54oknCAkJafW2Tz75JGFhYcbfsphD5wmzmokPtVBa7+SF/W5uPO4kTGu/wr38Xcw33cWP+bVU2lxEB5uZmBoOQESwmZgQMxUNLvKq7AyPD+3hZyGEEEKII8FhBbTff/89r7/+OqWlpcZl8fHxXHrppRx33HEB3cfChQub/H399dfzhz/8gf379zNmzJhWbxsdHU14eHj7d1wE5A9Tknjs20OsyqrClf4r/qh8g/mnjagH9/HNQS1YnTE4CrNJMW6THhVERUM9uZUS0AohhBCie3Q4oN2yZQtPPvkkGRkZnH766cTGxlJWVsaaNWt48sknue2224w2Xu1RV1cHtFxS15dbb70Vh8NBeno65557LuPGjfO5ncPhwOFwGH8rikJoaKjx7+6gP053PV5nmDYoGkVReGxNHmsKnTin3cRf1v4fzuUfsS7mLABOHhLd5DmlRwezvaievGp7lz/XOocLBYVQ62Gv4ByQvngMRUtyHPsHOY79gxzH/qE3HEdF7eA6pXfddRehoaHcfvvtmEyNAYWqqjz88MM0NDTwwAMPtOs+VVXl0Ucfpba2lvvvv9/vdocOHWLnzp0MGTIEp9PJ6tWr+eKLL7jnnnt8ZnWXLl3Ke++9Z/ydmZnJ4sWL27VvR7LVe0u4/ZNtOFwqx5Zs5+jS3Tw38jwGxYXx7pVTm7yB396cwxNf7+Gk4Yk89uuumxjocLn59QvfYzErfPCH45tkiYUQQghxZOlwhvbAgQP86U9/ahLMghadn3baafzf//1fu+/z5Zdf5uDBg60GswBpaWlNJo2NGDGCkpISli1b5jOgnTdvHmeddVaTfQQoLi7G6XS2ez87QlEUUlJSKCgo8LvqVm81PBzumJnOw6ty2ZAwjk3xowGYnhFOQUFBk22jaQBgb2El+fn5XbZP2RUNFNVo7cF27M8h0dOVoSv15WMoGslx7B/kOPYPchz7h646jhaLhcTExMC27eiDmEwmv8Gg0+lsEei25ZVXXmHz5s3cd999xMfHt3t/RowYwZo1a3xeZ7VasVp9Bzzd/QFSVbVPfmgnp4Zz50npPLQqB7tb614wvXwnqjqjyXZ66678ajsOlxuLj8yp3eXmwVW5JEdYuWFq+yYR6vK8ViPLr7aRENYp8xsD0lePoWhKjmP/IMexf5Dj2D/05HHscPHh0KFD+eSTT7Dbm/YcdTgcLFu2jGHDhgV0P6qq8vLLL7N+/XruvvtukpKSOrQ/WVlZxMTEdOi2IjATU8O5Z9ZAInByXPE2kt/5J2rewSbbJIRZCLEouFQoqPbdj3Zrfh1bC+pYsbeSA+UNHdqXPK/7LqxxtLKlEEIIIfq7Dge08+fP58CBA9x444288sorfPDBB7zyyivceOONHDhwgPnz5wd0Py+//DJr1qzhT3/6E6GhoVRUVFBRUdEkUH7rrbd4+umnjb8//fRTNmzYQH5+Pjk5Obz11lusX7+e008/vaNPRwRoXHIY/75gFLe4fwK7Dfe/FqE21BnXK4rCAE8/2lw/CyxsPlRj/PvLfZUd2o88r/suqJaAVgghhDiSdXicdtSoUdx55528+eabfP7554AWzAwfPpw//elPAZcNrFixAoB77723yeXXX389J510EgDl5eWUlJQY1zmdTl5//XXKysoICgoiIyOD22+/ncmTJ3f06Yh2sFosqFf9Dff9f4aCPNT/PANX/c2oTc6ICmJfWQO5lXbIaHpbVVXZkl9r/L0qq5LLJiViNbfv3OpQlWRohRBCCKE5rMLDMWPG8NBDD2Gz2aitrSU8PJzg4GDWrVvHfffdx5IlS9q8j6VLl7a5zQ033NDk73POOafJUrui+ymR0ZiuuQX34wtRN66BYaNRZmkT7wZE60vg2lrcLq/aTmGNA4tJITLYTHm9k/W5NUwf1L5FMbxLDgpqZKldIYQQ4kjWKQ08g4ODiYuLIzg4uDPuTvQRyrAxKOddDoC69BXU/buBxiVw83yUHGw5pGVnxySFMntINABftLPsoMrmotrmMv6WDK0QQghxZOuejvSi31Jmnw2TjweXE/fzi1Frqkj3ZGhzK+0tZjtu9gS0R6eFM3uoFtBuza+lqB1BqV5uEBGkvX0rbS7qHK7WbiKEEEKIfkwCWnFYFEXBdNkfISkVykpQ33qelIggTArUO92U1je2drM53ewo1CaQTU6LICUyiPHJYajA1/sDz9Ie8pQbDIkLIdIT1LYnIPa2Ka+GVVkdm5gmhBBCiN5BAlpx2JSwcExX3wKKCXXjGiy//ERqZGOWVretsA6HWyUxzEKGp1+tnqX9an8F7gB71+mlDAMig0iO0O6noAMBrcOlsnhNHv/4Lp/SOilbEEIIIfqqdk0K279/f0DbFRUVdWhnRN+lDBqGctIZqCs/xf3mv0iffQd5VXZyq2xMTA0HGtt1TU6LMDoiHJ8RyQvWQopqnfxUUGds2xojoI0KosrmYm9ZQ4fqaA9V27G7tCA6v9pBfFjXrzYmhBBCiM7XroB2wYIFXbUfoh9Qfn0x6ua1UJjHgJIsIKlJhnaLV/2sLthi4sTBUXy2p4Iv9lUEFNDqNbRpkUGUe0oaOtLp4GBFYxeGwho745LD2n0fQgghhOh57Qpor7vuuq7aD9EPKGERKPN/j/rS3xmwbTWMON9YXOFQlZ2CGgcWE4xPaRo4njoshs/2VLAup4Yqm4uoYLPfx3CrqlFDOyAqyKjR7UiG9mClV0BbKyUHQgghRF/VroBWX+hACH+UY09EXbOC9EOHAMj1BI16ucGYxDDCrE0D1qFxIWTGBpNVbuObrErmjorze//FtQ4cbhWLSSEx3EpyhFYmcNgBraw2JoQQQvRZMilMdCpFUTBdfB0DbOUAlDe4qLG7jHKDyWm+SwpOHRoDaEvhNm/15e2QJ/BMjbRiNimkeAW0gU4q0zUpOejGDK3L3b79FEIIIUTrJKAVnU5JTSd89pnE2SoA2F9YxfYirV3X0WkRPm8zc3AUVpPCgQobe8sa/N53nmf1sTRPF4WEMCsmBRxu1ainDYTN6W7SGaG7FmfYkFvNhUt/kVZhQgghRCeSgFZ0CeXM+aTbKwD437c7sbtUEsIsZHgWXWguItjM8RmRAKxspSftIa8OBwBmk0JSuJalbU/rrrwqO24Vgsxat4Wyeid2lzvg23fUprxa7C6VjXk1Xf5YQgghxJFCAlrRJZTgYNIHpQHwvSsW0LKzersuX6YP1gJafTUxX/KaBbRAh+posz3lBsPiQgixeBZn6Iayg0JPN4YCqdkVQgghOo0EtKLLZAzNAMCtaG+zSVGtZ0DHJ4dhVrRMa0G17zZceocDveQAIMVYXCHw1l36hLBBMcFGQNzR1cbaQ88i51e3XBZYCCGEEB0jAa3oMuleWVSL28n4D/4P1Wbzu32Y1czIhFAAfixomaW1Od0U12p1sj4ztO3IeuoTwgZ6BbRdXUfrcqsUe7LAtQ431fauL3EQQgghjgQS0Ioukx4dbPx7VE0OoQd2ob72z1Yzk5M8Cyv8mN8yoM2vtqMC4UGmJr1q9U4H7amhPehZ8GFgdDDJAdbgrsup5sznvmWnZ4Jbe5XUOXB5PfV8P1loIYQQoiftKKzjuk/2sbkPzfeQgFZ0mdgQM+FW7S02ecxAMJtRN65B/XSp39voK4X9VFDXor2VsaBCZFCTWtxkT8lBYYAlB/UOt1EvOzA6KOAM7Zf7KiiusfO/PeUBPU5zzQNmCWiFEEL0RmsPVnGo2sGa7Kqe3pWASUAruoyiKEzNiCA8yMT0o4ejXKytNKd+/Cbqlu983mZoXAgRQSZqHe4W7bv0CWFpUU07JegZ2vIGFzZn28P4OZ762ZgQM1EhFpL0Gtra1gNMfSLZtsK6DtW/Ng+YZWKYEEKI3khP+nRXS8vOIAGt6FJ/PC6V/5w3nOSIIEwz5qCcMhcA98v/QD24v8X2ZpPCUSlalvaHZmUH3kveeosINhMepL2VA/nw6RPCBsZoJREpRobX/23rHC7j+tI6Z7vKG3T6RDeTJ7ksGVohhBC9kf5715Hfup4iAa3oUoqiYDE1lgcov7kSxkwCuw33P+9Dzd7X4jb+6miNll2RLXvZNtbRth0kGhPCPDW+eh/bGrubWrvL521yKpve77bC9tfR6l8MI+K1iW/57ejKIIQQQnQHVVWNDG1ZvTOgkc/eQAJa0a0UsxnTNbdA+mCoLMf92ALUbZubbDMhJQyA3SX1TQLM5osqeEsOIMuqy/YEp4M8GdpQq4lozyQzf7fPrmjanWF7BwJa/b4neZb/zZeSAyGEEL1Mtc1Fg7OxrK47erR3BgloRbdTwiIw3bIIRk8AWwPupx/Avfpz4/rkiCDSIq241cbAsarBabS5Sm01Q9v2By+nWYYWMOpoC/18cA94bjMySVu6d3sH6mj1SWsTPSUVVTYXNX4ywkIIIURPaP472FfqaCWgFT1CCQvH9Me7UY6fBW436uvP4P7wdSNI1Lsd6HW0eZ5608QwC8GWlm/bxk4FrQ/j19hclNZrvWy9l+Ft6/Z6hnbehAFYTAql9e2ro62xu4yAfFBMMLEhWkZYJoYJIYToTZovMtSeRYt6kgS0oscoFivKFX9COetCANTl76K+/ASq02EEtPoCC/46HOiSjdXCWg8Q9QlhCWEWwoMae9nqvWh9nYmqqmoEtGNToxiREAK0r45Wv9/oEDOhVpORZZaJYUIIIXqT5hnavjIxTAJa0aMURcF0zm9RLrsJTCbU9d/gfvHvjEsMwaxodaaFNfZW62ehseSgsMbRaimA0eHAq9wAWq/BLW9wUW1zYVJgcFwY45O1YLs9dbT6Ga6+nyl6QNtHznyFEEIcGfQMbVQbc0t6GwloRa9gmn4qppvuArMFtnxH6OfvNi6Dm19nlByk+aifBUgIs2JSwO5SKW/wX5fqveStt9YWV9Czs6mRQYRYzYxL1iattaeOVl+WVw+cUyO1x5OJYUIIIXoTfRLYeM9vXXuWle9JEtCKXkMZdzTK7zyLLyx7hwnuEkCro20rQ2s1KySEWQAobGUYv3HJ26b3k2wsrtAyw5tdoS3woHdFGJUQatTRBhqQ6kM2eoY2VS+R6CMlB/nVdrLKG9reUBy2LYdqeG9HaYcW7xBCiMOlJ3aO8nQcKqix94nvIwloRa9imn4qyuyzAZiw8nUAfiqsNQJHfwEtBFZH6y9D653hrWiW4c2u0ILOwZ7bBFtMjPTU0W4vCqzsoLBFyYGeoe39Aa1bVVn4xUFu/TybigZnT+9Ov/fM+gJe/7GY3SVyAiGE6F7ePWjHJoVhUsDmUqlsZeSzt5CAVvQ6yvlXwNhJDC3LItzZQK3djcOtYjUpJIRZ/d4uuY3WWxUNTiptLhQgo1kNrdWsEB/qyfA2C4j1koNBXkGwXnYQ6MSwxgytp+TA8//yBhf1jt7dtLqoxkFpvRO7S2V/mQRZXane4aakTjtp0Ou9hRDt53Cp/GPtIT77pbynd6VPqWxwYXepKGi/V/rIZ1+Y7yEBreh1FLMZ09W3YE5OY3z5L8blqZFWzF6rjjWX0kbrLT07mxxhJaSV1l/eLUpcbpWcSj2gDTEuH5cUeB2ty61S7Amykz2Z2YhgM5Gegvv2tkQpq3fy6Jo8dhW3f3GHjsj2Cqyar5gmOpd3xj5HAlohOmxbYS2rDlTx2g/FuNy9f7i8t9ATQnFhFqxmpV2LFvU0S0/vwIcffsiGDRvIy8sjKCiIESNG8Lvf/Y60tLRWb7dz505ee+01cnNziY2N5eyzz2bOnDndtNeiqylhEZhuXMjEF99mXeJRgP+WXTqj5MBPXasejDUvN9AlRQRBUX2THnwFNQ7sLpUgs2IEvAAjPXW0ZZ462tb2raTOgUsFi0khLrTxI5caYaXa5iK/2k5mbIjf2zf335/LWHuwmjqHm3tnhQV8u47yXiWt+YpponN5B7R6qzrRu9U73ORX2xkSF/hnWHQ9/fu+3ukmp9LG4HZ8xx7J9N8/vZVlcoSVbYV9o3VXj2dod+7cyWmnncZDDz3EnXfeidvt5sEHH6Shwf/QZlFREYsWLWL06NEsXryYefPm8eqrr7Ju3bpu3HPR1ZSUdCb96lTj77SfN6CWFPrdPqWVTgXQGIw1b9ml81WyoE8IGxgd3CQ73J46Wv2LIDnCiklpvI/GXrTt+6L4yVPmsL+soVsK9Q96BbF9aRh8X1kDv136C8v70JDjoSYZWglo+4JXthTyl88OsOVQTU/vivCSW9X4XfVzSX0P7knfov/+6atntjXy2Zv0eEC7cOFCTjrpJDIyMhg8eDDXX389JSUl7N+/3+9tVqxYQUJCApdffjnp6emccsopnHzyySxbtqwb91x0h5TJE0m1aB+wtJxtuO+5EfeXH6O6Wxao6x88rd6zZV1qjtGD1s/iDD4WV/BVP6sLtI62sFmHA11qByaG1dpd7PPUsVbaXJTVd/0kLe+sbE5l35jtCvDdwWpqHW5WZVX19K4E7JDXyU1xrQObs3fXVwvYWqB9/ncUdV/QtD6nWkpS2pDrdUK4WwLagOkZ2iQjQ9v6yGdv0uMlB83V1WlfDhEREX632bNnD0cddVSTyyZOnMjKlStxOp1YLE2flsPhwOFoPBiKohAaGmr8uzvoj9Ndj9efXD1jCGt+LuCEfCcU2FCXvIy6fjXmy25Cycg0tosKsRBqNVHvcFNc6yTdKxOrqqpRCzooNsTncdAXOyiscRjXGx0OYoNbHMPxyeEs2VZqLLDg79gaE8Iig5pskxoZbFwf6PtiZ3E93uVgWeU2EsJbL8U4HA6X2mTou8HppqTOZZy992b6D/7BShsqGNnx3vxZ9D65UdECXBnK9q0zjmO9w01lg9P47LdXjd1lnLDmVdm75T21p7Seh1fnMSgmmKfOGtLlj9fVuurzmOP1vfVzcUOv/Lz3RnqHA/33Sh9JLKxt/XeqN3yv9qqAVlVVXnvtNUaNGsXAgQP9bldRUUF0dHSTy6Kjo3G5XFRXVxMbG9vkug8//JD33nvP+DszM5PFixeTmJjYuU8gACkpKd3+mH3dWalw1pThqL+dRu3nH1Hxyv+hHtiD68G/EPWbK4i66A8onpOY9Jhc9hTXYA+KIDU1wbiP4hobtXY3ZkXh6BEDCbaYWzyOOcIGZFNa5yQxORmLyURezQEAJg1JIyUlDmg8hrEJLqwrcyird+IIiWFQnO961sqNpQCMSIsnNTXVuHy8GgbfHaKwztXk8tbs29V0WLPQYQ34th2xt7gGlwrhQWaSo0LYX1JLtSmMCV6vbW91yHPs6h1uCIslNSa0yfW98bNYWLsX0F7vWruLWlMYqanJPbxXvdvhHMeFy7bz5e4iXrvkGEYlR7b79ltyGstZCtrxOT4cawtyAW3kJDIukYjgXvUz3mGd+XmsqLNTbWscxTtUbSc0Op6YsK47+e8vymzZAIwemEJqaiyh0XbgAKV1TuISk3z+dnrrye/VXvVJePnllzl48CD3339/m9s2PwvQh0F9nR3MmzePs846q8Vti4uLcTq7p6+moiikpKRQUFDQZ4Zse6WJx2O6fwTuN59H/eF7qt55iap132C+6m8oyWnEhyjsAXYeLGRIaGNW/gdPfVtqpJWy4iKfd+1WtdZgDrfKtn05xIZYyCnXhqoi3bUUFNhbHMMR8aHsKKrj6+0HOH14rM/7PVCsDXmHuhvIz883Lg+yae+9omobB3LyCPbReaG57/dp+z4qIZSfS+rZml1MfmbXZfA2ZVUCkBEdRGKYmf0l8GNWAUPDevfwk83pJq+ycZhxwy85HJehBSy99bNYZ3dRVqe9rhNTwlh7sJpt2YWMj5GyA1864ziuyyrFrcIXPx0gelz7T9I27S0z/p1TXkdO3iEsrXRi6QzbDhYb/17/czZjkrp+YmhnKKyxsymvhtOHxzaZj9Ce4+hwqWRXNDA0zvcom26HZ15DUrgVq1khr8rO6h0HOCa9/Scth8vlVrn7q4NYTAr3zMpoMo+it3GrKocqtO9Ns62K/HxtnoY+8vnT3pwmI5/euup71WKxBJx87DUB7SuvvMLmzZu57777iI+Pb3XbmJgYKioqmlxWVVWF2Wz2WapgtVqxWn0PkXb3D5qqqr3qR7RPio7DdP0C1E3f4n79WTiwB9f9f0K58CqSw7VSlJ+L6xkRX4fVpGAxKcYXXEZ0sN/XX0ErhM+rslNQbaeqwYkKRAebiQkxG7fzPobjkrWAdntBHacNi/F5v3pbruRwS5PHjggyEW41UetwU1Bj9ztZTVfV4OSAp5717NGx/Lymvssnhhk1xNHBxHv6ER6s6J7JaIcjr8rWpDQju7yBqelNvxsC+Sy63Cr/3V3OpNRwv90xOote2hEdbGZEQghrD2p1kr39te5pHf1OrWxwUuXJ4u3r4OfIe/U8pxvyq22kR3Xt+8R7Yua+snpGJ4a2snXv8fLmQtblaImFM0e0PPkP5Dgu3V7Mkm2l/PG4FE4ZGuN3uxzP91ZGdBAxIRbyquzsKq5nygD/pYxdZV9ZvTHPoqDabgzh+7P8l3Le2VbCwpnpxvLv3aW83onDrWJSICG08fcqJcJKVrmN/Gp7q4sbQc/GOD0+KUxVVV5++WXWr1/P3XffTVJSUpu3GT58OD/99FOTy7Zu3cqQIUNa1M+K/kuZMh3TPf8HI8eDrQH1tadI+mEVAKsPVPG3/2Xzp+UHuOG/WSzdrg37D4ppo/WX18Sw1iaE6fS1rrcV+e5HW2NzUWPXMmx6cb2x/4pi1O4FMjFsmycoHxQdzKTUcACK6xp/lLuC92ugB9wH+8Ds++YdAg50sN3YyqxKXtlSxHMbCjpjt1qldzhIjQwygqLcI6x119rsqm7rpOH9HtnXwQVDmi8HndcNn40cr/dyVnnfmRi2p1R7rbYW1Hb4PrYc0m77Q37r95Hj6XCQHhXEKE/A31OdDn4ubnxc/TVozZf7KqhscPHG1uI2t+1s+oSwhDBLkyx6chsdhHqLHg9oX375ZdasWcOf/vQnQkNDqaiooKKiAru98Yvhrbfe4umnnzb+njNnDiUlJUYf2q+//pqvv/6auXPn9sRTED1IiUvEdPP9KOddBmYzU39YxpiaHFKCVRLCLMSEmIkIMhFiUYgNMXN8RutDTt4f3EAC2pEJoVhNCuX1Tp+Bnt4CJTrETKi15cdN73QQyAzSbZ7Z1ONTwgizmo3bduXqXU0CWs/rkFNpw93Ls4Z6qzF9pm5HA9pNedoP596yhi5vzq6f1KRFWUn3ZEEOVdmPmKbwO4rqePTbQzz+7aFueTzvLgEFNQ5q2nli6HSrxqRRPZOW08UnIJWe1Q51zQPq3qra5qLUswLejsK6Dn1/OFwqBzwB/N42vvP0Dgfp0cGM8hybPSX1PfJZ2uUV0LZ14mRzuo3n+FNBXYe/211ulQPl7R910Ftz6d+buhRjWfnefYLd4+nMFStWAHDvvfc2ufz666/npJNOAqC8vJySkhLjuqSkJBYsWMBrr73G559/TmxsLFdccQXHHXdcd+226EUUkxnl9PNQR08g7sW/8+CmpyA4BNOf70MZNrpd96XP3i+qcVDpqXFtLaANMpuYmBrOxrwaVmVVctmkpiMM+hdA85ZdOn0J3EAytHr/2aM8WeEhsSHkVzvYX9bARE/GNlA1dhfvbS/l+IGRfoe16hwuY8brwJhgwq0mrCYFu0ulsMbR5tBZT9IzNNMHRfLBzjLyq+3YnO6A6pR1TrdqZJPsLpW86rbLQg6Hd4Y2MdxKkFl7rYtqe/dr3Vl2eN7f2RU26hwuwqytTz45XM3bXu0vb+ColMA/R7mVNpxulTCriaPTwtldUk9eVddmTPXsdYhFocGpBdROt9rldbuHy7v1X7VdC9ra270jp9KGwxOQ5lc7qLG7iAjy/R7JrWzM0KZHBxmlXQcqbAztxq4hqqo2CWj3lraeJc4qt+HyikE/3FXGX6e1vsiUL+/vKOXNn0q4cWoKp/ophfOlyOhB2/T7pq9kaHs8oF26dGmb29xwww0tLhszZgyLFy/uil0SfZQyaBimu/6B+9mHYeePuP95H6abH0AZPDzg+2hcXMFufHhbC2gBZg+NZmNeDV/vr+TiCYlNflwKq/UetL4DkkB70ZbWOcirsmNSYKwe0MZpdZb72pmlUVWV//s+nw25NWwtqOUfZ2b63E4fko0NtRDlWaY3PTqIrHIbByttvTrI0vd9Qko4X+6rpMrmIqfSzrD4wH/Mfimpp87ROCFrX2lDlwa0RoY2MgizSWFAlPZa51a2XXfXH/zi9WOfVW5jbBdPdtJHVCwmrf51X1n7Alp9uH9wTDAZnt7WXb0YxkFPRnh8chg7irT3Z24fWAXrQEXT76hthXXtDmibZ2X3+zle9Q43xZ5scHp0MCZFYURCKD/k1/JzcX23BrRFtY4mvcL3lmmjW/4mhu3xfAZSI63kVzv4NruKSycmkhjevjaJG/K0WuVvs6s6FNAmt8jQ6svC9+6AtsdLDoToTEpwCKbrF8KIsVBfh/sf96DmZgV8+2RPT9cD5TYqGlwo+F8qVzdlQATRIWYqGlwtVgvyXiXMF2O1sDa+KPRJBUNiQ4yshP7FvL+sfVmh/+4uZ0Outp/7y22U+1mcoXFCWGMwleEJ6HIqeu/Qk8PlNoLDjOgg44Qku6J9gb9er6dr74lDe+mLKqR53hP65IvcLs769QaqqrK7pPH17coyGp2eodUnCrW3jlYf7s+MDTZqnvOqunbhkcbFYYLJjNUec38fqKPVh9EjgrSQo63FaHzZ26z+tPnfOn2kIzrYbJyI62UH3V1Hq2dnh8YFE2xWaHC6OdRKWcovnud0cmY0RyWH4Vbhk5/L/G7vi83pNj4/O4vr27U4i57Ead5nXJ//UVjTuxfWkYBW9DtKcDCmm+6CzBFQV4P7ibtR83MDuq0eeNo84z7JEVZC2himtpgUTs7U+iJ/ua+yyXWFbZUceIKX4loHDpf/Lwr9B0CfhAYwxPODdqjaTp0jsPq/PaX1/PsHrfVXsFnLEvibYOGrhniQMTGs/T+iaw9W8dZPxV1ef5tXZcetQrjVRFyohcGe/W9vHe0Wz+uil3h0ZZBVY3MZfTNTPFn7jMOYGOZyq31qlbGCGkeTyY0dnaQVqCqbi4oG7fFmDo7yPGb73h9ZnvdTZmwIqZFWTArUOdxdunqfsXx3TDCZnqzs/j5QR6t/9uZ4soU7i+raXc+6t0wLDod7Rln81dHqQX+614n4SM/EsO5eMUyfEDYmKczISLdW/6tnaIfHhzBvjNb3fMXeSmrsgdd37y1tMMoW7K6mJQ9tMUoOmmVok8ItKECDU21Sw93bSEAr+iUlJAzTn++FgUOguhL3E3eiFuW3ebuIIBNhXpO32io30J0yRAtoN+XVUOH1g2asEuan5CAmxEyIRcGtNn6Z+PKTZ0LYUSmNAW10iMVopRXIbOdau4vHvz2E0w3HZURwzmjtC3OznzXoD3r9eOoyPF0i2hvQ1thdPPldPku2lfJjGzOUD5c+7JsRra3uNqgDAW1Fg9MIqs4bq7UR3F/WdZPh9KxSbIjZqB01MrQdGMZ+dkMBl7y3p89kd3/xBBqec6wuD2j1GsvEMItR2tCeE0NVVY3PXGZsCFazyThpzeuiiWGqqjbJ0OontL2904HLrRqB+MmZ0YR56lnbs992l9u4D709or/A0JgQ5tU+bUR8CApaBtLfiFRX0IPJ0YmhDNMDWj+Z5Wqbi3zPKM3w+FAmpYYzKDqYBqebz/dUBP6YzYL2QL9v3apKca3vEUWr2WT81vTmOloJaEW/pYRFYPrz/TBgEFSU4f77nag/rEN1+f/RUhSlyYc50IB2YEwwI+JDcKmw6oCWpXW5vb4gIn1naBVFMYJdf3W0hTV2imodmBUYndi0rrCx7KD1AEBVVZ5ZX0BBjYOkcAs3TU1lcppWf/Zjfq3PbImvDK1eQ5pb2b7Z92sOVGH3pA02H+ragFYPtvW6xsGxeslB4D+g+o9AZmww45PDCDIr1Dvdxg9OZ/OeEKbT9z+3qn29aOsdblZlVWFzqazNru7cHe0iuz0/8vriF7lV9i7NMHuf9ESHWEjQTwwDzNKW1juptrkwKTDQc5KnN5zvqjraigYX1XY3JkU72dEztFkdmM3enQpqHNhdKkFmrS58bJKWLd1WGPj3QHaFDacbIoMbO9UU1jiarAam00/ivDO04UFm47urPWUHLrfKk98d4s0OtNCqtbuM75zRiWFG/b6/QNy7fjYy2IyiKPzak6Vdtrschyuwz4OeFdYD6B8DbJNWVu/E6dZOKuNCW06vMupoA5jA3FMkoBX9mhIZhekv90PyACgrxv3sw7hv/z3uT95CLSvxeRvvgHZwO5rpz/Y0+v5yXyWqqlJS58ClgtWk+PyC0LU1MUzPzo5ICG3R+muIUUfXekC7Ym8law9WY1bgb9MHEBFsZkR8KOFBJmrs7iYTckDLUFbaPDXEXhOhkiO02fcOt9quCQJfeJVi+MsIdxbvYAW0/VeAygZXk+x5a37wBN2TU8MxmxTjfdBVmcPGll2NP8KpkUEoQI3d3a5hvp8KanF6Tja6OhveWfQM7XEZkcSGmHGrHW+1Fgj9pEcffRgawHCwNz3wzYgKJsisfSb1VmutdTqoc7j4x9pDfPZLebuDUH2fkyOsBFtMZEQHYzFBrd3d6uhOT9MnhA2MDsZsUhifrJ1It6eOVs9qDosLISLYbARXvj6POUaGtumomN6Pdnc7huB3FdezMquKpdtL2z0qtbukHhXteMWFWowAc7+fFoB6j9rh8Y1dZ2YMiiIu1EJ5vZPVB6rafExVVY2A/YLx2shSVrmNioa2v/eMHrTh1iY9aHWNdbS9970mAa3o95ToWEy3PYJy+nkQGQ0VZajL3sF9+x9wPfMQ6s9NF+nwnuEZaIYWtBZRQWaFnEo7v5Q2NJkQ1tpyh21NDPNVP6sbEsDEsAPlDby0uRCA301INNp0mU0KEz2zhJtPgNIzCymR1iatrkyKYgSKgX7BZ5U3sK+sAYtJm1GeX+1odWLE4cppFqwEW0zGSUMgQZJbVY264slp2oQhPeDpqoBWnxDmnaENtpiMk6v2lB1s8jph2F1SH9AwemGNPeAMUGezu9zGBKuRCSHGe7oryw5ymmXxAx3p0HlPCNPpAVRrvWhXH6hi1YEq/rWxkIe+yaMqgEBDZ5QAeT5/VnPjZ7G9ZQduVeXfW4raNZTti8vd9qpQ+oQwfaRE/x7bWRR4X1j9REMPCo1sZ7Phe6db9ZoQ2vS7e2SCdpv21NFuL2oMuj/7pTzg24FXuYHn+zYtKogQiwmbS/VZF69naEd4dWKxmhXmjtRWVftoV1mbr/Whai1rbTUpTEqNMN6fWwM4sfXX4UDXFzodSEArjghKZDSm8y7DtPgVlKv+BiPGgeqGH9fj/vuduP/9T9R67ctLPxO1mpR2tUsKDzJzwkBtOOyrfZXGmay/Dgc6/TF8DeWoqmr0n/UZ0HqGHQ9W2rD7CEicbpXH1x7C7lKZnBpuDGHpjk7zHdA2//H0NtATBBwMMIOmZ2ePTY801p3vqiytw6U26XCgGxSjvU6BlB3sL7NRaXMRajEZwX97A572amzZ1fS90t5OB6qqstmzGIRZAZfadiZsfW41V3+8nxv+m8WmvI4dF5vTzQubCvmpA6tA7S/ThpOjQ8wkhVu7/OQBWmbx25uh3d8sSIPGkoPWVgv7Mb/xWGzMq+FPyw8EPPTefJ+BDk8M21FUx4e7ynh+Y0HAdcPNZVfYuH7Zfm75PLvV2nL9M6ePcgyODSYiyES90x3wMTYytJ5gb5if41VQY8elan169TIS3ShPudae0oZWJ+B62+H12fl6f1W7Xit96F/PDJsUhWFxwZ7n0zSoVlXV6HDgnaEFOG14DKEWEwcr7S2+p1s+Zp3nPkKwmhsTFj8WtJ0NL/LT4UDX2ItWSg6E6BUUqxXTsSdivuVhTPc/gzLzdFAU1LVf4r7vj6i/bDcCoSFxwT6HXloze6g2OWz1garGLGcbAa1+va+Sg7wqO+X1Tqwmxfhi9JYQpvWIdau+g7VvsirJqbQTFWzmzyektsgU6xnIvWUNTYalDrSyStrAdmRo7S4332RpAe2pQ6ONALqr6mjzq7UftDCriXivMg898GjeD9OXLflaUHdUShhWzywlI8jqYL1idoXNZ70faD9mh7x60HrL8KpZDkRWuY3SeifBZoWTPJ032srOrPBk6QprHDywKpdFq3ON2u9Afb2/kk93l/PshoJ2vz56xmxEfCiKoniNOnRNQFtjdxmdCPSsqn5886rsNARQu6u/jzK9+r/qJx+l9U6fgY/LrRrB63XHJjMgKoiyeid3fZnDm1uL28xWGmUSXidqHZ0YpgfWLhW2d6CF1o7COhasyKagxsGe0obWR4iafZeYFMWYiBdI2YHN6TaeuxHQxusnPU0DQ/1zMiBKmxDqLc1Tm+pwqwGtsOZwNQ7fRwWbaXC6Wbm/7WF/0I61XsY12ut7e5gnWG0eiBfXOqlscGFWmmb9QUuUzBmmfZY/3NV6Cy99f/XfCn3Bna35tW1+Lgv9dDjQ6cu0S4ZWiF5ISc3A9LvrMf3tIYhPgtIi3I8vZOzqpdx0TCI3HZfa7vscmxRGSoSVeqfbaOGV7KfDgU7P0BbWOFr8qOnZ2dGJoUatXpPn0CQAaPqj4nKrvLujFIB5Y+KIDmlZxxsbajF+FH/wCjIPthbQ6kvgBtCLdl1ODTV2NwlhFiakhHO0J4DeXlgXUODgrarByXvbS3lpc6HPbDQ0HUr2/kEzOh0E8MNv1M+mNTZt1+oVFWrt7nbXkH13sIo/fprFw9/4bh1XbXNRa9eeT/MRgXQjQxtYQKuXGxyVEs6x6dpr/UO+/6Chxu4yJo3MGhKNSdGO2Y3/3c+HO0uNWty26Cuq5Vc72j3LX//h14eEh3qNOrS3DMLhcrPs57JWa4f190h8qIVwT0/n2FALcaEWVCCrjUC6ztE4G907+IgIMhMbot2fr9dgf3kDNXY3YVYTpw6N4YkzBjN7aDQqsHR7KQu/PNjqSU/zul9oHKFpa5+b2+qVSffXts+f7w9Wc8/XOdQ63OifMP0ksLk6h8v4vHgv/qCPNgUS0GaV23CrWkcY/SRVf95FtU4qvU7E9YA2I6rld66iKIyMD7zsYG9ZPXaXSlSwmQvHJwCwPMDa56xyGw1OlXCrqcnxGuqn04FebjA4NtjnaoZzR8VhVrTXq7WRMSMr7BlZGpMUSpBZobTe2eayzEVtjCjql5fWOXusPKktEtCKI54yYhyme/6JMv1UUFX44kNOfute0isC613rzaQoRgsvPWBrK0MbH2bBalJwqbBibwX7yxqMGd76hLDxKS3LDXR6QNp8+G5NdhX51Q4ig82cMTzW7+31LK0+nOX2+vH0FdDqGey8alubAc+X+yoALVgymxTSo4JICrficKtsC2AYDLQWS8+uL+D3H+3j9a3FLPu53G+mpLHDQdP91oc7c9rozlBrdxlZjkleywlbzY3tv9qzwEJpnYNn1xcAWpNzX2Ulev1sfKilxY+ZEdAGWK+8yVNuMGVAOOOTwzApWgeFIj9B+IbcGpxu7Zj+6fhU/nHGYEYnhtLgVPn3D8X89bMDbdZ5utyNZTHQuEpRoPQJYXp5R2K4hcggE043ZLdjAY+DlTZu+TyblzYXsXhNnt8fXWPovtl7e2hcYMc3u7wxIG5+kthap4OtBY2lQ2aTQojFxE3HpfLXaWmEWkzsKq7ng52lPh+zrN5JrVeHA50+8lBc52zSx7c1NTZXk++K9gS0n/1Srr22bpWp6RFcebS21Le/oXB91Cjea7VBaAxodxXXtfkdovefHRYXYpykhgeZjdEM7+eiL3k9INp3EkHPXAbSm3VHobbN2KRQTh4SRYjFRG6Vvcl73Z+fS7RtRiaENhkV03voZpU3/e70V26gSwy3GguArMyq9LlNjd1lrH6n990NMpsY4/l3WyM1/nrQ6qKDtRaTKtqJRG8kAa0QgBIahumymzDdsFCbOJaXjfuhv+J+50WjtjZQJw+Jxnuwq60aWpOiGC1m/rWxkL98doD5S37hqo/2GrWmRyX7X5LTqO/0+iF2uVWWbtd+HH89Kq5FdwRveibyh4Jao9VYg1NbH95XDXFiuLbYhNPd2G7Kl8IaO1sL6lBoLMVQFMWr7KD1wGdrQS33r8zhhv9m8fneCuwulWjPj+I3B3x/qTfWGbZcizzEonVnaG2ffyqow61qQUPzzLoR8PjpI9mcqqo8ta6AantjYLX2YMs2WkbLLh9ZpQHRjQFLvaP1rEhVg9MIDo9OiyA8SOtkAf5b96zN1k4Mpg/UFhcYHBvCw6cO5KbjUogMNnOgwsZnbUwc2lvWYGSYATbmBh7Qltc7Kap1otA4jKwois/3tD+qqvLp7nL++tkBY+i9zuFme5HvoKX5hDBdoLW7+43+sy1P9ho7HfgKaLVjMKHZcq0nDo7ixuNSAPg2u9pnBlB/X6dEBDUZqQkPapzxfyDAE62fCmtxq1rgYla0rHpbrZhUVeXNrcX8a2MhKlov2NtmDGCqZxRgd0k9NT4C6uYTwnQDY4KJDDbT4FRb1JM217x+VuerDVZjhtb3ZF79pCmQDK0+IWxsUhhhVjOzhmifkU93tz05zLv/rLeUCCvhVhMOt9ok0+q9oII/+uI9q7KqfJ6U65/91EgrMV4nWhNSG9sz+uPdYtJfDa3W0tL/fI/eQAJaIbwoE6diuu9plGNmgOpG/WoZ7ruux71xTcC1gYnhVqN2CRprj1pz/bEpzB4azejEUCI9y0MW1Tqxu1Qig0wtvsy96cNvB7zO+tcerCavyk5kkIkzR8a0+tijEkIJt5qotrnYW9Zg1LxlRAdh8VFDrHU68MzobmX466v9WtB5VEpYk+BQLzvYfKjG72v66e5y7v4qh82HalGAqekRPDx7IH8/YzAKsKOo3medp3fj+eb7rF/W2sQwfeh0cmrLE4jGOtrAsqXLf6ngh/xagswKZ4/SMuRrD7bMLPubEAZa7Z4exLcWiGv7XouKlo3W136f1MqPmXe5wQmDIo3LTYrC7KExXDYxEdAmjbVGD9T0iTo/l9QHnC3Uf4QHRgcbC0oAAXc6KKmxcf/KHF7YVIjdpTIpNZzjMrT310Y/+60Hh83fI42P2frxbexw0PIzqZ+Y5jTLqNucbnZ5AuwJPkZbjhkQQbBZoajW4XNiWmO5QcvvkswAW/fp9PrZY9MjjIxlW1nat34qMU6QLxqfwHXHJmM2aQFOelQQbhW2+pjc5quXNWjvsXGeOtq2Mp6NHQ6aBofNFypQ1cbuAel+MrTD40MxKVBS56Skzn/pkMvduMLWOE82+YwR2md4Y15NmzXmu5pNCNMpisLQZoG4y60az2GEnwwtaKMukUEmyuqdPl+zXc3KDXT6d8D2ojq/k+HK6p24VK0LTWstJnt7pwMJaIVoRomMxnT1LZj+ch8kpUFlGeoLj+F+4m4cedkB3YeekYwNtbS5dC5oPWZvOi6VR+YM4o3fjOD184axyJMpu/+UgT4DS11KpJVQi3bWn1uprWa1dLvWY/fsUXFNAgVfzCbFOIvfcqim1Q4HurYmhrncqlFDrPfn1Y1PCcNqUiiqdfqsDa2yuYxG5qcMiea5s4dwx8x0xiaHkRhuNRqzr2nWl9HplX1tXnIAXhPD/Pzwq6pqDJ1618/qvDsdtHVyk1tpM5YYvmxSIuePjcekaMFS8+yGr0UVvPkLkprTOxToQ5MAE1I9QUNBy8UzvMsNfB3rY9IjUND2ubUfcH0oc/bQaAbFBONWtfdRIPRM2fCEpsFhINnSH/NruejfG9h8qBarSeEPRydx98npRsnPxjzfJ0xGhrZZRlwPkHIqba0u6mCsEBbnK0Pr6XTQ7H29q7geh1slPtTSpGRAF2wxcYwn2+lrMYzWPpPGAgsBLgqhn4BMTAk3TrxbC2jrHW4++VnLSl49JZkLj0poUp8+2U+nFGicEOarn7dRR9tK6VG9w21kXYc2z9A263RQUuekwenGrPj/LIVaTca+tNaPdn95Aw1ON+FBJiMYHxgdzFHJYbhV+F8roxbFtQ5K65yYFO17vbnmgXhOpQ2bSyXEYvL53tBZzSZmeJZp/np/yxGq5hPCdINigokO0bLh/jLTeklSYnjrLSZ7e6cDCWiF8EMZMwnTvU+hnHMxWINQd/1IwXXzcT70V9xv/gv3t1+g5mShOlvWEx2fEcmF4+O57pjkDj12VIiFMUlhzB4aY2SO/DEpCkPi9CyNje9zqsmptBNuNfGrkf5rZ715dx/wXi/en4HGEri+v9i2FtRSWuckIshkZMx0IRaTkfXwVXawdFsJtQ43mbHB3DA1pcWP04mDtYDlm2YBbX61Hadbu//mLXug7YlhOVV2SuqcBJkbZ2E3v71Z0QLukjr/NWROt8o/vsvH7lKZmBrOmSNiiQ6xGD/gzcsO8v10OND5C5K8udwqW/Ib62d1I+JDCbOaqLa7W2TwmpcbNBcTYjGGTDf4KSOod7iNH9KJqeEc4wmm/W3fnF47OLLZD78e0B4o912nbXO6eezbPCrqHQyOCebvZwxm7qg4TIrChJRwgszaCVPzbHydo/HYNT/piQu1EN3Gog4ud2N9eWaM/wyt9l5s3G+j3CA1rMXse51+HNYerGoRiB/00bJLZ0wMC2DkoLDGTkGNturg2ORQI3v3U4H/Wta1B6tocLpJiwzizBExLa73rsH33m9VVb1KDlq+Vt51tHY/JxD7yxtQ0Wpwm2cOM+O0BVNK65xU1DuNz0dqpO+RJZ0e8LW2gpbe+WFMYliTAO9Mz/fpir0Vfiem6pnSIbEhPpMZzUsl9niVVLTVVWeW52RtXU51k04aLrfKLyXa/TTP0OqfCfBfdtBWhwOdvqqlZGiF6IMUqxXTWRdoZQjjjgaXC7J+QV21HPW1p3Df/yfcf7wQ1//dh1rVWFtlNilcdFQiUzMiW7n3zqP/qO0ta2DpNm1ocO6oWGMWd1v0H7a9pQ3s9Hwht7ZKmpGh9fPDr/eenZkZ7bM7gxFA5zX9gj1UZWe5p4H55ZOSfH7BnzAwEotJCzq8AxZ/HQ50gz0BiL9gRe9uMDYpzOdM4yBz44zl1jKHS7aVsLesgYggE388LsX4QZzmFbDoVFXlUJX24+A3oDUytP4D2p9L6qm1u4kMNjcZttRWZtICB+8fM3/lBs3pnRLW+Rm+31lUh9Ot/RCmRFi9OivUttnr0+VWjdrB5gFtcoSVMGvjqENz3xyootrmIi06hMfPGNxkSDvYYjJ+wJtPUNNfw9hQCxHBTT8biqIY2TN/xzev2o7dpRJiUUjxUSIS7xmRcalN6wz1CWHN62e9TU4LJ8SiBeJ7vOq0VVX1KqXxUXLgOZnNqWo9swyN5QYjE0IJs5oZGhdCVLCZeqfbb/aucaQl2ufnaqxnJn1ZfdMTiKJaB/VONxYTPjOPGdFBRIeYsbtUdhT4nuSpHwdfJVdhVrNxv3vLGozXyF+5gU7vFb76gP++sjs89bPjkpu+L48dEEFCmIUqm8vvstK7ihs70/iiv8eyKxpwuNzGsR7RSlmZ923To4KwN1vWOrvCRoNT66Dh66THKD3yE8QbPWjbCGgbM7QS0ArRZymJKZj/fC+pL32E6epbUebMg5HjITQcHHbYvhn3o3eglrV/ze/OoGdxv9pXyYEKG6EWE3NHxrVxq0bxYVYyY4NR0TIe0EbJgSeAOFTdcoWpygYnGzwB0Kme0ovm9DrancV1TX5U/vNjMS5VC3gn+qhjBW09d/323stB+mo8700PeopqHdTYWmZY9QznJD+PC40nDv4Cnp+L63nP0yrt+mNTiA9r/IE4LiOiRdlBZYOLeqfW/shXgASBLamqlxvoS/V6a/wx8+pE0Ea5gW5quvbjv6Owzueknx+NiU5a5nF4fAjRIWbqHG52FrdeG5lTqbU2CrGYWixTalIUv907VFVl2c9aP87fTEr3ecKkB9bNM8X+JoTp2jq+enuswTEhPodmFUVp0WqtyuYyeuoe1UpAG2wxGRlu7yy+1te2ZYcDXXyohUhPL+q2ekMbx8vznjApjc33f/BRMpBbaWNXcT0mRZvs6kuQ2WScNHmXHTTW4gf7zJgqXnW0mw/6nmjlveStL95lB0b9rJ8JYbpxSWGkRwXR4FR9dktxuVV2FukdDpqO1JhNCqcPjwHgUz8rh/mbEKZLCtf64Trd2mv0SwATwnSKohjHwbvbgT5KMiIh1GcSQK/b3lva4LM1nJ6hbWsCs3cNbUf6cXc1CWiFaAdLajqmY2dg+s0VmP/2EKYn38R01z+0PraFebgfXYBalN/t+6X/+Outws4aGdsiA9UW70Au1GIiMdz/5ID4UAthVhNutelQeGWDkxc2FeJ0az82vibOgLYMZGqkFae7sTXZrqI6vs+pxqTAZZOSWt3XEz21ZKsPVBqrFB1sI1iJDG7sY7mvuGmgs6u4ju2F/utnda3VdjY43Tz5/SHcKpyUGcW0QU2H8qNDLEaphR6w6OUGCWEWn4EZNP5AH6r233Jss9GuK6LFdfqJwc/FdUanhLbKDXRpUUEMjA7CpTZdUle31ZPxm+gVIE1J0ydltV528ItXZsrXj7C/SXg/FdZxsNJOiEXhnPG+e0Xrr8Oe0gbK6xtPXto66Rka30ZA20qHA11jqzXtsbYVaJP1BkYHtTrhBjDeM99mN5Yd6KMgaZFBWP31og5ggQWXW2Wb1wmIblKa/zpafWLn0WkRre67UUfrdR96e7PWlg/XA+HNOX4C2lYytN6X7y1tMDL5zU+OmlMUhTM8pRP/29Oyr2x2hY1ah5tQi8k4wfF26rAYLCaFPaUNRt9XXZ3DZWSpfS2Eoz++HojvLKo3tvfXsqu5kzKjjImxei3rz82W2W0uPsxKRnQQKlqXi+baatmlS4qwoqB91wU68bM7SUArxGFQTCaUgUMx3bpIm0BWWqQFtYcOdut+ZEQHE+RZ1SrEonD26MCzszo96wlaBtZfrR9oX8oZxsQwrV5w2c9lXLdsP996hsLmjWl9H5p3O3hlizaJSp9c1JpjBkQQajFRVOs0vsz9zV73pk8M21vilUkqb+CBVbk43dr9tvaD2NoSuK//WEx+tYOEMAtXT/FdO91YJ6m9Rq217NIlhFsINis43b6H+oprHWRX2jApvrPLKRFWkiO0k4cdRXUBlxvojvVkadc3C1DL651kV9pQgKO8lmXWJzf5m5Sl2+2VVfLF34phyzwTlGYNiSEyxPcPcFyoxch4bfQqO/A3IUxnLOpQ4XtRB73DQWt17fqQt75ccSDlBrrJqeGEWEyU1DmNgL+tIBy8lsBtpRRmf3kD1Z6FHbzLUvTgdl9ZQ5Oew063akw+8jfS0rjf2jHf5TXi0tqEMJ0e0P6UV9VismSdw2WcLA8NIEOb00aHA28nZ0YTbFY4WGk3srE6vdxgdKLvbGdMiIXpnrKFO77I5v6VOazKqqTe4eaXkgajJZr36Iy//V6xtwK3CrEhZp91/74khFmNY6ZnmP1NCPPWuGpYy5GTIk9g7K9lly7IbCLOs5+9sY5WAlohOoESl6gFtQMGQWUZ7sfuQD24r9se32xSjB+OM0fENmliHqhRidoEImj9R0g3yDMx7JusSv70aRYvbS6i1q5N5np49kCmD2o9++ddR/ttdjW/lDYQYtFqj9sSbDFx/MDGsgOXWzV+/PxlaLV91p7XHk+GtqDazr1f51BrdzMqIZRbpqe1GshnxgZjUqC8wUWpV9uf7YV1/NfTn/LG41L91i43lh00UFBtNxZV8Fc/C1rWUx9qzvFRdqCXG4xKCCXSx3FXvIaVf8yvNcoNBrZRbuC9z6B1LvCeCKNPdBoSF0yUV9/LiSnhWEwKBTWOVlcnagxofQcrehCTVd5gZKbzq+3G8z2rjQmP+vC9r4DW34RHfVEHl4+JYaqqBpih1a7Th8CNrgKtlLLogi0mo1ziW08WvbWWXbpAMrR6IKMv7KCLD7MyKEYrN/IuS9mcV0NFg4uYEDNH+8j8e0uLCiLFc9Kkr/5lBLR+RmlAK6EYGheC3eVm4ZfZTWbP61nypPCWC1joMuNCtM9jvbZ0LLRdcgBa/96Zmdr302d7mmaHjf6zyWEtbqf73cRERsSH4FK1ibT/+C6fy97fwwubCgH/5QY6PbOsv0eGJ4S2+r3TnHfZQVm9k8IaBwr+P0tAY2lJfg25VTaqGpy43Cout2pMlGxrVUvQTpAjgkw+S5B6mgS0QnQSJTpWW0Z30DCoqcL9+J24N6xGLSvplnqjK49O4tej4/jNuPgO3d5iahwubu2LUacHQ5sO1ZJbZSc62MwNU1P4++mDW/0x0I1NCjOWZfzXRm01rXlj4tscltXN9HQ7+PZgNblVWpY42KwYPVh90QPavcU1lNc7uefrHMobXAyKCeauk9J9TgbzFuxV76kvNVzvcPPUOq3M5LRhMa3W4DYvO2irw4FOX4Eqz8fEMD3Aay3omOhp3/VjQa1RbjCtjXID3dC4EOJDLTQ4VaM8BPwvFBBqNRkZW39lB7V2lzEkP9LPUGtaZBDBZoUGp2q8Tv/dXY6KdjKU3kYwrgeGP+bXYnO6qXe4jRWO/GU7myzq0KwNVnmDi0qbC5PS+iiAkaGttFNQ3dhVYExSYEPKevbvu4PV2qp9AbTRy9S7QlQ0+C1L+dHP8YLGzL532cGXnuzsyZnRrXYN0Hm377I53cYxa+3kWFEU7jopnYGxYRTXOrnzyxyjRZxePzs0zv/r1rz+OiHM0uoiMt701RO/z6mmwlOWoqoqOzwZ23E+Op3oEsOtPHb6YJ6Zm8mF4+NJjbRiczWeVAca0OoCqZ/1dlxGJCEWEwU1Dj70rC43KCa41RaN45LDsJi0/uY3LMvikvf3ct7bu7nkvT24VbCaFGJC2k6E3Dsrgzd/M6LNk5yeIAGtEJ1IiYjC9NcHYdgYqK9FffFx3LddifvmS3D9427c7/1bW6ShtvVm9R0xOjGMKyYntdl3tjVXH5PMrdPTjFVpWqPXfJkVOGdULM+ePYQ5w2LabD2jC7Y0Bj41djexoRZ+3Y5SifHJYcSEmKm2ufjEM0koPTq41T6Kg70C2nu+OkhBjYPkCCv3zsoIuObYaMDvGX7+z49FFNQ4SAyzcPnktrPL3mUHjT1oWx/qSzcytE0DWpvTbTRZn9JK7e9RyeGYFG34Wg9aAik3AC1DrAeH+iILqqoaM+Z9ZR69yw582VOqtWNKCrcS4+cExmxSjKH0fWUN1DlcfOWZcd9Wdha0Y50QZsHu0gJxvQQgOsTc6ghG80UdyuudrD5QxYue7FtaZFCrJz4pEUGYFKh3uo2JO3pXgUBMSgsn1FN2sLukPqBSmgGRQQR5gn9fQ8E2p9uYrKT3Jm7ymF4LcKiqSlm90zhRmt1GuYFOLzvYcqiWg5U23Kr2Wse2cYIaF2bluQsmkRpppajWwZ1fHqSkztFm/azO+/q2TnK8DYkLYWRCCE43fOFZojun0k61zUWQWfFb5uAtPSqYi45K5Lm5Q3j89EHMHRXLzMFRRvbXn/hQS5PgsbUFFXwJsZiY5jnx0UeGWis30G9zwfgEbbUyz+I9KlDrqasfHu97omNz/mr9e4PAUiFCiIApoWGY/nwv6oevo/78E+TnQE0V7PwRdeePAKhmM4yZhHLMDJSJU1FC285odofIYHOLyUz+jEwI4b5ZGSRHWP02Mm/L0QMi2OSZGf27CQkBLUKhM5sUZgyKYtnucqPWz1dbI28DooKxmLQMYa3dRWyImftmZQScFQat/m1VVhX7yhr4qaCW5b9UAFqpQSBBy3EZEfxroxYw6U+37Qytdv2mvBoeXJWDqmo/RvUON3aXSkKYpdW644hgM8PiQviltAGXGni5gW5qRiSf7algQ24N1x2rrchUVq/17PWVjTpmQATPbyxkd0k9VQ3OJiUJ0LhC2Mg2RgKGxgXzc0k9+8ttVNm0jhDpUUGtZsF1iqJwzIAIPttTwca8GuMHv7VaVGisb1yXU82OoroWi3+Mb2P0wWrWlozOq7LzuacBfyD1s7ogs4mp6RGsOlDFx7vKqG9jsQDQPguDYoLZU9pAVnlDi24IO4q0PrMJYRYG+LifMc1ab205pC2POyohNOAgcXxKGBaTttrZd54a8UBKlwCSIoN5aPYg7vgim4IaB3d9eRCbU8s0++twoBsWF8rXnlrStiaENXf68Fh2l+Tz+Z4Kzh0Tb5QbjEoMxWoOvARA6/ARGvDELn1imP7d19Zz9GXWkGi+2l+JnpBv3n/Wl/njEpg/LgHQJglW211U21zU2t2tlrT0FRLQCtEFlOAQlAuvAkB12CEvW6upPbgfde8uyMuGbZtQt21CtVhh/NGYjjsJJh3frlqqnqQoSkB1ga2Zmh7BGz8WkxEdHFBWuLmZmVpAq3+ptxWsWM0KA6KCya6wER5k4t5ZGe0OxvUM3p6Sep7y1CyePjwm4NdCLzvQmtmDSWm7dm1obAgKWguojXktZykfnxHZ5vtmYmq4MdEo0HID3bikMMKsJioaXPxS0mD0jx2TGOozY5MYrrWByyq3selQrdEQXveLn/6zzelZsj2l9azL0YaFzxoZG/Bn5Nh0LaDdkFdj1If7mxDW/DErbVqJgYI2mXB8chjjk8OMhQRakx6lBbTlnrpOX8vdtmbaoEhWHaji+xwtS5oWFdRmgDUkNoQ9pQ28v6OU0YmhTSYl6RPTJqaG+3ztgswmxiWFsSW/li35tUYf6UCzs6BlAMcmhbK1oM5YSSvQgBYgIdzKg7MHcscXB43acmg72PPOpLY3oJ0+KJJXNhdSXOdk86Gaxv6zrZQbdJZh8VpAmxZpbXdHGtBOQpLCrUaHgrYytM2ZTQoxIRZi/NQn90X955kI0Usp1iAYPBxl8HDjMjU/F3XjGtSNa6AgF35Yh/uHdTBmEqbLbkKJS+jBPe4+8WFWXjl3GGZFCbhUwduwuBBSI63ke34AW5sQpjtjeAyf76/mmqMTW52w4k9mrLZCkRasuEgKt3DZpLZLDbxNGxhp1KMmhlvbDFZSIoN4cPZAo0RBAfS4JMjc2L+0NRNTw1m6Xau3C7TcQGc1a/XVq7OrWJ9bbUyumtBKEH/MgAiyym1szKth1pBoVFVlW2EdH+8qMzJT/joc6PRgRa9rDA8y+e2H6sv45DBCLCbK652s8dQOt7YCHmiv9R+OTqKwxsHY5DDGJYX5nGzXmvSoINZ7/h1iMTE8gOyZt0mp4YRZTdR5hoMDyaafNSqW73Oq2V9u45b/ZXPnSenGyZe+qEZrmeJJaeFsya/lk5/LKa93EmJRmNbO98nktHC2FtQZ+93ez1diuJUHZ2ew8IuDFNc5tQlIbbz2+kRNt9r2CW1zQWYTpwyN4aNdZXz2S4XRxaI7AtppA7WT8VOGxHTo9iZF4aTMKJZuLyU6xGz0iD2SSUArRA9QUtNRzr4Ide6FkHcAdd03qF//F3b+gPvem1AuvArl+JP7TLb2cLSnzKA5RVGYOTiKdzyrowXyw3/myDh+f9JY8vPzOzRZL8xqJs2TgQO4KcBSA2/HZ0Ty/MZC3GrrQ8nexiWHGRPKOmJUQignDIwkMsjcrnID3bHpWkD73cFqKjyZx4mtBEjHDIhg6fZSfjhUy5f7Klj2c7kx811By461lX1Ljw7GalJweFLwc4bGtOv9YjWbmJQaxvc5NV5L3rb9es8d1f62d968h+nHJ4cGNKnKm9VTdrAyyxOEB3C8BkYH89hpg3hgVS65VXZuX5HNX6elMTIh1Hjdj2olU6yXceh9e6cNjGr3+3pyWgSvbmlcXKY9GVpdcoR28vbCpsI2O6WAVot/9qg4DlTY2ixh8eX04VpAq/fQtZoUhnfgftprYEwwb/1mxGHdxxkjYvkhv5bpg9oeoTkSSEArRA9SFAXSM1HOz0SdPhv3K09qS+u++iTqlu8wXXIDSnTbE2COZDMHR7N0eykRQeZWOxx0ptGJoeRV2TljeEyrqz/54112kNbGhLDOYjYp3DZjQIdvf/SAcCymxv6T0cFmo6+vL8PiQ4gNMVPe4OKpdVoXi2CzwilDo5k7Mo60AIaHLSaFwbFabahJgV8FMBmsuWPTI42he2h/Fq8jvIe+21M/6236oCgjoM0IsL4xJTKIxacN4rE1efxYUMei1XlGoJoZG9zq8HJ6VBAJYRYj8G+r96wvGV73YVIC6wnr73ncfXJGwNtfMbn1hVhakxqp1WTrkyVHJIT06olP3uJCLTx++uCe3o1eQwJaIXoJJSUd022LUVd8iPrxW7B1A+59u1BO+hVERkFoOEpYOISGQXgUpA5AMXW8o0F/kRYVxEOzBxJqNXWobKEjLpuYyKTUcI7LaN+QrLeLj0rE4SpizrCYztuxLhRmNXNUcriRyToqJazVWdEmRWHG4Cg++bmc+FALvxoZy5xhMe0evh8Wp9WGHpcR2aETlqPTwlHQJtFFBpuJ7kC9Ynt5T8rqyAkPaIFwVLCZGrvLWPAhEBFBZu4+OYMXNxXy2Z4K43i1FVjrNfFf7qtkQFRQu2sy9fuYnBbOir3affSVwPCMETFGQNt8uVvRd0hAK0QvopjNKGecjzr+aC1bm5OF+t93jOubDJCnDcQ07xKYcOwRP9w0ppt/hKJCLAENh7ZmVGIoj8wZ1El71D2OTY8IOEACuHRiIjMHRzM4Nrjdw+6688fFE2wxcU4HVr8DLRs+KjGUXcX1ZEQFdctnJTzIzB+OTqLe4W6z84Y/VrPCA6dkUGlzkdLOiYtmk8I1xySTHh3Ey5uLcKu+V5Fr7qyRsfxSUs/FExI7/DrNyozmy32VHNsL+5T6MyUtwphgdbgTXUXPUdTu6PjeSxUXF+NwdM/ybYqikJqa2uG6PdHzuvsYqk4H6jf/g9wDqPW1UF8HdZ7/l5eA3dP4fdhoTOdehjJ8TJfvU38gn8WOK61z8PsP96ECL/16aLeVePjSnuP46e5yXthUyPlj47lkYvsm8PV1O4vqyK6wcfrwmG478a1zuAixmALqa9pbPo95VXZyKm2HNepyJOuq42i1WklMDOwzKxlaIXopxWJFOWWuz+vU2hrUz99H/WoZ7N2F+9HbYfwUTOdegpKe2c17Ko4U8WFWbpmRhtOl9mgw215njohhSGywMev/SDImKazbRzAOZ3GXnjIgKqhF/17Rt/R4QLtz504++eQTsrKyKC8v529/+xvHHnus3+137NjBfffd1+Lyf/zjHwwY0PEJD0L0JUp4BMq5l6HOOgt12RLUb1fAtk24t22C0RMwzTwdJkxFsfT4R1z0M+3tYdsbKIrCaKmNFKJf6/FfO5vNxuDBgzn55JP5+9//HvDtnnzyScLCGr+goqL63pesEIdLiYlHueR61FPPQf34TdTNa2HXVty7tkJ0LMq02Sgz5qAkJPf0rgohhBBdpscD2kmTJjFp0qR23y46OprwcCneFgJASRmAcs2tqCWFqGu+0DK2leWoy99F/ew9yMiEqBiUiCjQ/4uMRhl39BGziIMQQoj+q8cD2o669dZbcTgcpKenc+655zJu3Di/2zocjiaTvxRFITQ01Ph3d9Af50ifjd6X9YVjqCSmwLmXoJ59EeqP61G/+R/qrh/h4H6gWZcEQA0KwnTmfJTT5mkrmh0B+sJxFG2T49g/yHHsH3rDcexVXQ7mz5/fZg3toUOH2LlzJ0OGDMHpdLJ69Wq++OIL7rnnHsaM8T3Le+nSpbz33nvG35mZmSxevLjT91+I3shZkIfj4H5cleW4qypwV1bgrqrAfmAvjj07ATAnDyDmqr8QetxM+WERQgjR5/S5DG1aWhppaWnG3yNGjKCkpIRly5b5DWjnzZvHWWedZfyt/2AXFxfjdDq7doe9HjMlJYWCggJpFdRH9d1jaIKMYdBs4R1VVTFtXIP73VdwFeZR+uDfUMZMxHTBHyBtYL8NbPvucRTe5Dj2D3Ic+4euOo4Wi+XIats1YsQI1qxZ4/d6q9WK1eq7xUx3f4BUVZUPbR/Xn46hcswMTOOnoH72PuqKD1B3/ojrnhtBMUFICASHQHAohIRCbDzKiHEoI8dpy/Wa+sYqQP70p+N4JJPj2D/IcewfevI49ouANisri5iYmJ7eDSH6JCUkFGXe71CnnYL73Vfgx/WgurUFHOrrGjfM3qvV5QKERcCIsSgjx6NMOh4l/shqVi+EEKJ36fGAtqGhgYKCAuPvoqIiDhw4QEREBAkJCbz11luUlZVx4403AvDpp5+SmJhIRkYGTqeTNWvWsH79ev7617/21FMQol9QklIx37AQ1dagBbK2BrDVQ4P2f/VQDurubbBnB9TVwI/rtQD3vX+jnDAL5czfSHswIYQQPaLHA9p9+/Y1WSjhP//5DwAzZ87khhtuoLy8nJKSEuN6p9PJ66+/TllZGUFBQWRkZHD77bczefLkbt93IfojJdhTatD88vFT4LR5qC4XHNyHunsb6k8bYc9O1DUrUL/7CuV4T2CbmNIDey6EEOJI1au6HHS34uLiJu28ulJvWa9adJwcQ9/UPTtxL3sbdm3VLjCZUI6ZAYmpYDaByQxmM5jMKLHxMPG4Hl3BTI5j/yDHsX+Q49g/dNVxtFqtR9akMCFEz1GGj8F88wOoe3fh/u87sOMH1PXf+NxWBUhI1rK4x8+SpXmFEEJ0Cvk1EUJ0CmXYaMx/vg9138+oW74Dux3cLnC5jP+ru7ZCSSHqf55G/XQpyq/moxx/MorFdxcSIYQQIhAS0AohOpUydBTK0FE+r1NtDajffIb6vw+gtKgxsD1hFkrGEG2J3vgknz1wVbcbaqrAbEYJj+zqpyGEEKIPkYBWCNFtlOAQlDnzUGeeqQW2n3sC22XvNC7LGxoO6YNQUjOgvg61vBTKS6CyDJxOUEwoM09H+fXFEtgKIYQAJKAVQvQAJTgYZc6vUWeegbpuJezbhZpzAPJzoL5W65zgWZa3BdWNumo56sY1KL/+HcqJc1BM5m7dfyGEEL2LBLRCiB6jBAejzDwdZp4OgOp0QEEu6sEsKDoE4ZFaZ4SYeIiNh+hY2LsL9zsvQl426pvPoa7+H6aLrkEZ7nvpayGEEP2fBLRCiF5DsVi1ZXXTM/1vNOooTHc9ibrqM9RP3oScLNyP3g6pGWAygap6/nMDirZkb1IqJKWhJKdBchpqQkK3PSchhBBdTwJaIUSfo5jNKKechXrsDNSP3kBds0IrV/ClIFfrrgBGnW5ecAicdCbK6eeiRER1z04LIYToMhLQCiH6LCUyGuWSG1BPmwclhaCYtCtMJlAUcLtRS4u18oXCQ6hFh6AoX1ve9/MPUFf/D+XUX6OcejZKSFjPPhkhhBAdJgGtEKLPU5LSICnN93U+LovN20/Jy/+EnP2on7yF+vV/Uc44D+XoaVBVCZVlqBVlWmeF6kpIG4gycSpKXGAr1gghhOheEtAKIY4oiqIQesx0zGmZqJvX4v74TSjIQ333VdR3X/V7O/XtF2DQMC2wnXScFuT66JcrhBCi+0lAK4Q4IikmE0yZjmnS8ajrVqL+dwmUFUNULMTEQUwcSnQshEVoLcT27YLsvajZe1E/fhMSU1BGT4RR41FGjkOJim3xGKqqQmU5lBRA6kCU8Ijuf6JCCHEEkIBWCHFEU8xmlGmzUU84BVRVC3R9UKvKUbduRP1hHezaCsUFqMX/g9X/0yabpWagjBqvtRgryEUtyIOCXKiv0+4gLBzlvMtRpp/q9zGEEEJ0jAS0QgiBVopAKyUESlQsyow5MGMOakM9/PwT6u5tqD9vg9wsyM9B9dVpQTFBeATUVKG+/gzq9ysxXXI9StrALnw2QghxZJGAVggh2kkJCYWJU1EmTgVAramCX7ZrwW19HaQMQElJh5R0SEoFkwl15X9RP3oT9u7Eff+ftZZhv5qPYg3S7qOhHipKobwUbA0QFgERkRAeCeERWo9eIYQQPklAK4QQh0mJiILJJ6BMPsH/NrPPQZ18Au63noetG1A/XYr6/dcQEqYFsfW1rT9IcCgMHYXp1HNg7CSZkCaEEF4koBVCiG6ixCViumEh/PA97rdfgLKSphuEhmk1uMEhUFcDtTXa/1UVbPWw8wfcO3+AAYNQ5vwa5dgTJXMrhBBIQCuEEN1KURSYfAKm0RO1yWUhoRAbDzHxKKEtF3dQ3W4te1tRhvrtl9qqaHnZqK/+H+qHr6PMPEMrTaiphtpqqKlGra3WHmvkeJSxE2HAYMnoCiH6NQlohRCiByihYTD5+La3M5k8dbSRKBf8HnXuBajffI761TItyP34Tb+3VbdtQn0PiI7VWoyNnYgyYhzEJrQa4Bo1wbU1KFOm+wy0hRCiN5GAVggh+hAlLALljPNQTz0bdcNq1E1rwWxB0SeQ6f9vqEfdtRV2b4PKctR1K2HdSq3FWGS0tkjEoKEog4ZB2kA4dFDr2rB7G+QeMB5PXfYOpouvQ5lwTE89ZSGEaJMEtEII0QcpFivKCafACaf43+jUc1AdDti3C3XnD6g7ftRajFVXwvbNqNs3awGuL2kDtW4LpUW4n34A5ZgZKBdehRIV0/lPRgghDpMEtEII0Y8pViuMOgpl1FFw7mWodhvkZKEe3OdZ+Wwf5OdAUpq24tnI8TBi3P+3d+/BUdX3/8efZ5NNSMj9fgcSCPdAAgICFRAUK/ykWuTnWH6IYOsU8NY61irgF2ql2ta249Rp5ysKhUEQFUG0gGhtARHkTgglXBK5hiTkTq6bPb8/DllcQjBiQrLh9ZjZ2ezZz57zOfue6Dsf3p/PByMoBLOmBvPDFZib1mJ+tQUzax/G/30EY9ho6+Q11VBWYiXI5aWACV7eYPMCLy/r505+1iQ2bSYhIq1ICa2IyE3E8PGFlF4YKb1cx0zTvGpNreHrizH5YcxbfoBzyWtwOgfzzT9hvvsWVFdCbW3zLhoZgzFiHMbwsRih4S11KyIiLkpoRURuct+2AoLRpTu25/+I+ckHmOvetkZlG/j4QGAIBARZo7L19ZceDuu5tNjaJviD5ZhrV0C/DGw/uBNSekL+Ocxzp+H8GWur4PxzEBKG0WegNYktsZtGdkWkWZTQiojItzK8vTF+OBlzxFgozLcmlgWFYPh2uubnzJoazN3bMLdugqNZcHAXzoO7mv7AuVOYh/djshQCAjF6DbBKJuKSIDLGWrFBS5CJyBWU0IqISLMZQaEQFNr89r6+GMNvh+G3Y+adttbS3f6ZNcobFgkxCRgx8dZ2wZGxmOfPXF6doaIcc9dW2LX18uQ1H1+rhCEyhuL4JOpraqyRYdulml0vG0TGWqO8gcGt8RWISDukhFZERG4IIyYBY/J0zPumQb0Dw+7TuE2/DBj7fzAdDsg9ipm1D/NYllWOUFQItTXWxhJnvqZi344mr2UaBiSlYPRNx+iTDik9MbztmKZp1f7WVFmT2kwn+AeAf2cMm1dr3r6ItCIltCIickMZNhvYGiezbm28vaF7b4zuvV3HTEcdXCiAgnNQcJ4Ap4Py0pLLNbvOeqirw8w9aq2l+/UxzK+PYX68Guw+YLNZCbHZxGJl/p0vb2IRFQe90zB6D8QIj2y5mxeRVqGEVkREPILhbYfoOIiOwzAMgmNjqTx3zhp1vYJZUoSZtQ+y9lrP5aWNT+jjC4bNGq0FqLxoPQryrKR457+tUoeoWIzeA6ylzzoHWgm0sx6cTuvZ5mXV90bGWKtIiMgN1+YJbVZWFuvWrSMnJ4fi4mKefvpphgwZ8q2fWbp0KadPnyY0NJR77rmHO++88wb1WERE2jsjJOxy7a7TCQV5YBjQqRP4dAIfX9cKCqbDAZUVcLECLpZDRZk1snt4P+RkW6sx5J/D/PeGb7moAaERVsIdHQ+xCRiJyZDQVdsHi7SyNk9oa2pq6Nq1K2PGjOGPf/zjt7bPz89n0aJFjB07lscee4wjR47wxhtvEBQUxLBhw25Aj0VExJMYNps1stvU+97eEBRiPRqODRwKk36CWVUJ2ZnWygvHDoOjzipdaNg8wmaDujqrxrfqIhQVQFGBlQzD5clskTHWMmTxXa2Sh7ISzLJia1mzshKorcHoN8hKwlP7abkyke+ozRPa9PR00tPTm91+06ZNREREMH36dAASEhI4fvw4H374oRJaERFpUYafPwwYgjHg2v9yaJomVJTB+bOY58/C+dOYZ0/BqRPWZLaCPKuUYc/2ps+x/TNrBYiwSIxhYzBuHWOtANFw/ppqqKq0NrWwW+v/Gr4qcRCBdpDQfldHjx4lLS3N7djAgQP517/+hcPhwNu78S3V1dVRV1fnem0YBn5+fq6fb4SG62j9RM+lGHYMimPH0N7iaBjG5VHeHn3c3jMryjBP5VhbDp/5Grzt1hq+waEQHIIRFIpZV4e589+YX22xRng/fgfz43esEoaaaiuJdTobX9jXD4KCXeczeqVhDBiCER51Q+77+2pvcZTr0x7i6HEJbUlJCcHB7msLBgcHU19fT3l5OaGhjddHXLNmDe+++67rdbdu3Xj55ZeJjLzxM1djYmJu+DWlZSmGHYPi2DF4RhxjoUfPb2825k6cNdVU7/gPFz9dT/WeL6G40L2Nlxc2/wCc1VVQd2n5sYIqa/QXrBHgFX/HnpyK39BR+A29DXu3HtQX5FF35iSOsydxnD2F49xpbEHBdBowBN+Bt+AdEd0qd95cnhFH+TZtGUePS2ih8V8ADTNcm/rL4N5772XixImNPl9QUIDD4WilXrozDIOYmBjy8vKuOiNX2j/FsGNQHDuGDh3HHv2hR3+8Souh8Dz4dQZ/f+vZxxfDMDBME6qrrPrbshLMshLIP4fzwFdw7DB1J7KpO5FN2dv/a01Wa+I7qvzsY+uH6HiM3mkYvQZgRMdBSBgEBF31/6umw2Fdt7QIOvlZm2Nc58hch47jTaS14ujt7d3swUePS2hDQkIoKSlxO1ZWVoaXlxcBAQFX/Yzdbsdut1/1vRv9C2Sapn5pPZxi2DEojh1Dh47jFRPVGrjut5Of9YiKpSGd9LrrPszyMsyDuzD374BDe62SBbsPRMVabaPiICoGLlyavJZ7DM6fsXZp+/yfly/k7Q3BYVZy6+sHDZPYKsrcE+TOgZDSC6N7b4yU3tC1+3devqxDx/Em0pZx9LiEtkePHuzevdvt2P79+0lOTr5q/ayIiMjNxAgMurxkWV2ttRRZUOjVV0649/9hVlZcWsnhgLUrW1GhlbQ6HHAh33pcycsLAkOsc18shwNfYR74ylrVoeE9/86XHgEY/p2t5NtpWruzudbxNSmOjsEZHQ9dU621fJsY7TVra6x1goNDVXMrjbR5BlhdXU1eXp7rdX5+Prm5uQQEBBAREcGKFSsoKipizpw5ANx5551s3LiRpUuXMnbsWLKzs/nss8944okn2uoWRERE2iXD7gMh4ddu4x8AA4dhDLy8UpBZV2eNyJYUQUkRZnUVRnCINWIbHGqVI9hs1u5tJ09gHjuMefwwHDtslSOUXLAeDee7xvUrvvkiIAi6pWJ07WFtYFF4HvPCeav0orTYahMVi5E+DCP9VqutljgT2kFCe/z4cRYsWOB6/Y9//AOAUaNGMXv2bIqLiyksvFwUHxUVxa9//WuWLl3Kxo0bCQ0N5eGHH9aSXSIiIi3EsNshPMp6AE2NhxredkjuiZHcE/iR9c/NxYXWzmyXdl4zKyusn6urLq3he/lhGDb8a6uoOLgHTh63RoYP7sI8uKvpzuWfw9y4BnPjGggOwxg4BKP/LdAl5Zqjt2ZZCeRkY54/YyXMKb0xvLy+1/ck7Ydh3sRFKwUFBW7LebUmwzCIjY3lXBPbNEr7pxh2DIpjx6A4dgzfjKOzthZOncDMyYavj4FvJ4iItpYgi4i2Ht7ekLkHc++XVtJbVel+wsBgSErGSEqG+K5WgnziiHXOgjz3tgGBGGlDrE00+qRrTd/vobV+H+12e8edFCYiIiIdj2H/5mjvNQweiTF4pFUWceQA5p7t1i5ueWeskeFDezEP7b36Z2MTrclxR7Ogohzzi08xv/jUmjSX0gsjIAj8Lq0o0fBcVwOlJVBWjNmws1tlBUaPvhg/uAN6DWiy7MGsr4fco1bdcGKKkuZWpIRWREREPI5ht0O/QRj9BgFg1tTAmVzMkyeskd4zX1sT0pJTrSS5aw+rXphLS48dy8LctwNz3w5r4tt/D1yz1vdK5q6tmLu2QngUxshxGMPHYYRFYBZfwDy0BzNzN2Ttt7ZEBmuyXHxXqy8NiXtUbLMmuJkFeVBbYy2RpjKJq1LJgUoOpJkUw45BcewYFMeOoT3E0TRNOJ2LeeqEVcJQdfHSc6VV+2u3WxPhgkKtGt2gEPDyxty1BfPLf19OWA0bREZD/jn3C/gHWOdomNT2TYHBkNoXI7UfRmpfiOtiTbarvAhHDlqJcda+y+USPj6Q1N2qAe7Ww3q+xsoQN4pKDkRERETakGEYkNgNI7Hbd/tcal/MyQ9j7vkCc+tmOHLQSmYNwxoN7peB0TcDuvWwkt3iQquet6GmN/eYVSKx+wvM3V9Yo8OdA61a4VMn3Lc69vKyyiKqq6yR5WNZwKXVIzr5QWwiRlwSxF16joiBilLM4gvWdYsvYBYVWqURqX0xeg2AmPg2T4RbkhJaERERketg+PhiDBsDw8Zg5p+Fc2cgpadVi3ulsEgIi8QYPBK4tDRa7lHM7EzM7ENw/PDldX3B2r2tz0CMvunQsx/4dILzZy8lw0cxc49aiW91lbV6Q062dd5r9NcE2LXVeg4Jw+iVBr3SwMcXigqszTaKCqyfK8qhSwpG3wwrOW/j7ZG/jRJaERERke/J2oEtrvnt7Xbo0QejRx+YcKmu9+RxzMLzGMk9r55AxiZgxCbA8NuBS5/JPwtnT2JeenD2lJWQBgZDaDhGaCSEhkNoBFRXYv73gLVecEkR5pefw5efN93J4kKrzhisEd2+l0adU/u1uwluSmhFRERE2pjh7d28VR6u/ExcEsQlNblWcCMTplg7yB07jPnfA9bosAFGWCSERVijyGGR0Mkf8+ghzEN74Ph/Ie8MZt4ZzE8/xJjxFMatY67rPluLEloRERGRm4hh94HeAzB6D7h2u9S+VgJceRH+ux8zcw/mob0YfQfemI5+B0poRURERKRJhn9nyBiOkTEc0zTb5WQybYAsIiIiIs3SHpNZUEIrIiIiIh5OCa2IiIiIeDQltCIiIiLi0ZTQioiIiIhHU0IrIiIiIh5NCa2IiIiIeDQltCIiIiLi0ZTQioiIiIhHU0IrIiIiIh5NCa2IiIiIeDTvtu5AW/L2vvG33xbXlJalGHYMimPHoDh2DIpjx9DScfwu5zNM0zRb9OoiIiIiIjeQSg5ukKqqKn71q19RVVXV1l2R66QYdgyKY8egOHYMimPH0B7iqIT2BjFNk5ycHDQg7rkUw45BcewYFMeOQXHsGNpDHJXQioiIiIhHU0IrIiIiIh5NCe0NYrfbmTx5Mna7va27ItdJMewYFMeOQXHsGBTHjqE9xFGrHIiIiIiIR9MIrYiIiIh4NCW0IiIiIuLRlNCKiIiIiEdTQisiIiIiHk2bJ98AGzduZN26dZSUlJCQkMD06dPp3bt3W3dLmrBmzRp27tzJmTNn8PHxITU1lalTpxIXF+dqY5omq1ev5tNPP6WiooIePXowc+ZMEhMT27Dn0pQ1a9bw9ttvc/fddzN9+nRAMfQURUVFLF+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"text/plain": [ - "{'VGG_08': {'train_acc': [0.010694736842105264,\n", - " 0.03562105263157895,\n", - " 0.0757684210526316,\n", - " 0.10734736842105265,\n", - " 0.13741052631578948,\n", - " 0.16888421052631578,\n", - " 0.1941263157894737,\n", - " 0.21861052631578948,\n", - " 0.24134736842105264,\n", - " 0.26399999999999996,\n", - " 0.27898947368421056,\n", - " 0.29532631578947366,\n", - " 0.31138947368421044,\n", - " 0.3236842105263158,\n", - " 0.33486315789473686,\n", - " 0.3462526315789474,\n", - " 0.35381052631578946,\n", - " 0.36157894736842106,\n", - " 0.36774736842105266,\n", - " 0.37753684210526317,\n", - " 0.38597894736842114,\n", - " 0.3912421052631579,\n", - " 0.39840000000000003,\n", - " 0.4036,\n", - " 0.4105263157894737,\n", - " 0.41501052631578944,\n", - " 0.4193263157894737,\n", - " 0.4211578947368421,\n", - " 0.4260842105263159,\n", - " 0.4313684210526315,\n", - " 0.4370526315789474,\n", - " 0.439642105263158,\n", - " 0.4440842105263158,\n", - " 0.44696842105263157,\n", 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" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result_dict" + "plot_result_graphs('VGG38_BN_RC', result_dict, keys_to_plot=['VGG38_BN_RC'])\n", + "plt.savefig('../report/figures/VGG38_BN_RC_loss_and_acc.pdf')" ] } ], diff --git a/pytorch_mlp_framework/experiment_builder.py b/pytorch_mlp_framework/experiment_builder.py index aeceeb8..b97f5b0 100644 --- a/pytorch_mlp_framework/experiment_builder.py +++ b/pytorch_mlp_framework/experiment_builder.py @@ -79,6 +79,7 @@ class ExperimentBuilder(nn.Module): print("Total number of conv layers", num_conv_layers) print("Total number of linear layers", num_linear_layers) + print(f"Learning rate: {learning_rate}") self.optimizer = optim.Adam( self.parameters(), amsgrad=False, diff --git a/report/VGG38_BN/result_outputs/summary.csv b/report/VGG38_BN/result_outputs/summary.csv new file mode 100644 index 0000000..e2dd347 --- /dev/null +++ b/report/VGG38_BN/result_outputs/summary.csv @@ -0,0 +1,101 @@ +train_acc,train_loss,val_acc,val_loss +0.027410526315789472,4.440032,0.0368,4.238186 +0.0440842105263158,4.1909122,0.0644,4.1239405 +0.05604210526315791,4.0817885,0.0368,4.495799 +0.0685263157894737,3.984858,0.0964,3.8527937 +0.08345263157894738,3.8947835,0.09080000000000002,3.8306112 +0.09391578947368423,3.8246264,0.10399999999999998,3.7504945 +0.10189473684210527,3.760145,0.1124,3.6439042 +0.11197894736842108,3.704831,0.0992,3.962508 +0.12534736842105265,3.6408415,0.1404,3.516474 +0.1385894736842105,3.5672796,0.1444,3.5242612 +0.14873684210526317,3.5145628,0.12960000000000002,3.5745378 +0.16103157894736844,3.4476008,0.1852,3.3353982 +0.16846315789473681,3.399858,0.15600000000000003,3.453797 +0.1760210526315789,3.3611393,0.1464,3.5799885 +0.18625263157894736,3.3005812,0.196,3.201007 +0.19233684210526317,3.26565,0.17439999999999997,3.397586 +0.19625263157894737,3.2346153,0.212,3.169959 +0.20717894736842105,3.174345,0.2132,3.0981174 +0.2136,3.1425776,0.2036,3.2191591 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+0.7435368421052632,0.83374214,0.582,1.697568 diff --git a/report/VGG38_BN_RC/result_outputs/test_summary.csv b/report/VGG38_BN_RC/result_outputs/test_summary.csv new file mode 100644 index 0000000..18de21b --- /dev/null +++ b/report/VGG38_BN_RC/result_outputs/test_summary.csv @@ -0,0 +1,2 @@ +test_acc,test_loss +0.6018000000000001,1.5933747 diff --git a/report/epoch99.pdf b/report/epoch99.pdf deleted file mode 100644 index 98383ff..0000000 Binary files a/report/epoch99.pdf and /dev/null differ diff --git a/report/figures/VGG38_BN_RC_accuracy_performance.pdf b/report/figures/VGG38_BN_RC_accuracy_performance.pdf new file mode 100644 index 0000000..d6cdadf Binary files /dev/null and b/report/figures/VGG38_BN_RC_accuracy_performance.pdf differ diff --git a/report/figures/VGG38_BN_RC_loss_performance.pdf b/report/figures/VGG38_BN_RC_loss_performance.pdf new file mode 100644 index 0000000..a6ced50 Binary files /dev/null and b/report/figures/VGG38_BN_RC_loss_performance.pdf differ diff --git a/report/figures/gradplot_38.pdf b/report/figures/gradplot_38.pdf index 4a9927b..98383ff 100644 Binary files a/report/figures/gradplot_38.pdf and b/report/figures/gradplot_38.pdf differ diff --git a/report/figures/gradplot_38_bn.pdf b/report/figures/gradplot_38_bn.pdf new file mode 100644 index 0000000..499d5db Binary files /dev/null and b/report/figures/gradplot_38_bn.pdf differ diff --git a/report/figures/gradplot_38_bn_rc.pdf b/report/figures/gradplot_38_bn_rc.pdf new file mode 100644 index 0000000..99d2b72 Binary files /dev/null and b/report/figures/gradplot_38_bn_rc.pdf differ diff --git a/report/mlp-cw2-questions.tex b/report/mlp-cw2-questions.tex index 861299b..fe466ca 100644 --- a/report/mlp-cw2-questions.tex +++ b/report/mlp-cw2-questions.tex @@ -44,20 +44,25 @@ Our results suggest that the increased capacity of VGG38 does not translate into } %% Question 3: +% In this coursework, we didn't incorporate residual connections to the downsampling layers. Explain and justify what would need to be changed in order to add residual connections to the downsampling layers. Give and explain 2 ways of incorporating these changes and discuss pros and cons of each. \newcommand{\questionThree} { -\youranswer{Question 3 - In this coursework, we didn't incorporate residual connections to the downsampling layers. Explain and justify what would need to be changed in order to add residual connections to the downsampling layers. Give and explain 2 ways of incorporating these changes and discuss pros and cons of each. +\youranswer{ +Our work does not incorporate residual connections across the downsampling layers, as this creates a dimensional mismatch between the input and output feature maps due to the reduction in spatial dimensions. To add residual connections, one approach is to use a convolutional layer with a kernel size of $1\times 1$, stride, and padding matched to the downsampling operation to transform the input to the same shape as the output. Another approach would be to use average pooling or max pooling directly on the residual connection to downsample the input feature map, matching its spatial dimensions to the output, followed by a linear transformation to align the channel dimensions. + +The difference between these two methods is that the first approach using a $1\times 1$ convolution provides more flexibility by learning the transformation, which can enhance model expressiveness but increases computational cost, whereas the second approach with pooling is computationally cheaper and simpler but may lose fine-grained information due to the fixed, non-learnable nature of pooling operations. } } %% Question 4: +% Question 4 - Present and discuss the experiment results (all of the results and not just the ones you had to fill in) in Table 1 and Figures 4 and 5 (you may use any of the other Figures if you think they are relevant to your analysis). You will have to determine what data are relevant to the discussion, and what information can be extracted from it. Also, discuss what further experiments you would have ran on any combination of VGG08, VGG38, BN, RC in order to +% \begin{itemize} +% \item Improve performance of the model trained (explain why you expect your suggested experiments will help with this). +% \item Learn more about the behaviour of BN and RC (explain what you are trying to learn and how). +% \end{itemize} +% +% The average length for an answer to this question is approximately 1 of the columns in a 2-column page \newcommand{\questionFour} { -\youranswer{Question 4 - Present and discuss the experiment results (all of the results and not just the ones you had to fill in) in Table 1 and Figures 4 and 5 (you may use any of the other Figures if you think they are relevant to your analysis). You will have to determine what data are relevant to the discussion, and what information can be extracted from it. Also, discuss what further experiments you would have ran on any combination of VGG08, VGG38, BN, RC in order to -\begin{itemize} - \item Improve performance of the model trained (explain why you expect your suggested experiments will help with this). - \item Learn more about the behaviour of BN and RC (explain what you are trying to learn and how). -\end{itemize} - -The average length for an answer to this question is approximately 1 of the columns in a 2-column page +\youranswer{test1 } } @@ -80,13 +85,12 @@ The length of this question description is indicative of the average length of a %% Question Figure 3: \newcommand{\questionFigureThree} { -\youranswer{Question Figure 3 - Replace this image with a figure depicting the average gradient across layers, for the VGG38 model. - -\textit{(The provided figure is correct, and can be used in your analysis. It is partially obscured so you can get credit for producing your own copy).} -% +% Question Figure 3 - Replace this image with a figure depicting the average gradient across layers, for the VGG38 model. +%\textit{(The provided figure is correct, and can be used in your analysis. It is partially obscured so you can get credit for producing your own copy).} +\youranswer{ \begin{figure}[t] \centering - \includegraphics[width=\linewidth]{figures/gradplot_38_watermarked.pdf} + \includegraphics[width=\linewidth]{figures/gradplot_38.pdf} \caption{Gradient Flow on VGG38} \label{fig:avg_grad_flow_38} \end{figure} @@ -94,26 +98,26 @@ The length of this question description is indicative of the average length of a } %% Question Figure 4: +% Question Figure 4 - Replace this image with a figure depicting the training curves for the model with the best performance \textit{across experiments you have available (you don't need to run the experiments for the models we already give you results for)}. Edit the caption so that it clearly identifies the model and what is depicted. \newcommand{\questionFigureFour} { -\youranswer{Question Figure 4 - Replace this image with a figure depicting the training curves for the model with the best performance \textit{across experiments you have available (you don't need to run the experiments for the models we already give you results for)}. Edit the caption so that it clearly identifies the model and what is depicted. -% +\youranswer{ \begin{figure}[t] \centering - \includegraphics[width=\linewidth]{example-image-duck} - \caption{Training curves for ? ? ?} + \includegraphics[width=\linewidth]{figures/VGG38_BN_RC_accuracy_performance.pdf} + \caption{Training curves for 38 layer CNN with batch normalisation and residual connections, trained with LR of $0.01$} \label{fig:training_curves_bestModel} \end{figure} } } %% Question Figure 5: +% Question Figure 5 - Replace this image with a figure depicting the average gradient across layers, for the model with the best performance \textit{across experiments you have available (you don't need to run the experiments for the models we already give you results for)}. Edit the caption so that it clearly identifies the model and what is depicted. \newcommand{\questionFigureFive} { -\youranswer{Question Figure 5 - Replace this image with a figure depicting the average gradient across layers, for the model with the best performance \textit{across experiments you have available (you don't need to run the experiments for the models we already give you results for)}. Edit the caption so that it clearly identifies the model and what is depicted. -% +\youranswer{ \begin{figure}[t] \centering - \includegraphics[width=\linewidth]{example-image-duck} - \caption{Gradient Flow on ? ? ?} + \includegraphics[width=\linewidth]{figures/gradplot_38_bn_rc.pdf} + \caption{Gradient Flow for 38 layer CNN with batch normalisation and residual connections, trained with LR of $0.01$} \label{fig:avg_grad_flow_bestModel} \end{figure} } @@ -122,13 +126,13 @@ The length of this question description is indicative of the average length of a %% - - - - - - - - - - - - TABLES - - - - - - - - - - - - %% Question Table 1: +% Question Table 1 - Fill in Table 1 with the results from your experiments on +% \begin{enumerate} +% \item \textit{VGG38 BN (LR 1e-3)}, and +% \item \textit{VGG38 BN + RC (LR 1e-2)}. +% \end{enumerate} \newcommand{\questionTableOne} { \youranswer{ -Question Table 1 - Fill in Table 1 with the results from your experiments on -\begin{enumerate} - \item \textit{VGG38 BN (LR 1e-3)}, and - \item \textit{VGG38 BN + RC (LR 1e-2)}. -\end{enumerate} % \begin{table*}[t] \centering @@ -138,11 +142,11 @@ Question Table 1 - Fill in Table 1 with the results from your experiments on \midrule VGG08 & 1e-3 & 60 K & 1.74 & 51.59 & 1.95 & 46.84 \\ VGG38 & 1e-3 & 336 K & 4.61 & 00.01 & 4.61 & 00.01 \\ - VGG38 BN & 1e-3 & ? & ? & ? & ? & ? \\ + VGG38 BN & 1e-3 & 339 K & 1.76 & 50.62 & 1.95 & 47.68 \\ VGG38 RC & 1e-3 & 336 K & 1.33 & 61.52 & 1.84 & 52.32 \\ VGG38 BN + RC & 1e-3 & 339 K & 1.26 & 62.99 & 1.73 & 53.76 \\ VGG38 BN & 1e-2 & 339 K & 1.70 & 52.28 & 1.99 & 46.72 \\ - VGG38 BN + RC & 1e-2 & ? & ? & ? & ? & ? \\ + VGG38 BN + RC & 1e-2 & 339 K & 0.83 & 74.35 & 1.70 & 58.20 \\ \bottomrule \end{tabular} \caption{Experiment results (number of model parameters, Training and Validation loss and accuracy) for different combinations of VGG08, VGG38, Batch Normalisation (BN), and Residual Connections (RC), LR is learning rate.} diff --git a/run_vgg_38_bn.sh b/run_vgg_38_bn.sh index a206f6e..276c6d6 100644 --- a/run_vgg_38_bn.sh +++ b/run_vgg_38_bn.sh @@ -1 +1 @@ -python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 5 --experiment_name VGG_38_experiment --use_gpu True --num_classes 100 --block_type 'conv_bn' --continue_from_epoch -1 +python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 5 --experiment_name VGG38_BN --use_gpu True --num_classes 100 --block_type 'conv_bn' --continue_from_epoch -1 diff --git a/run_vgg_38_bn_rc.sh b/run_vgg_38_bn_rc.sh index 1b09df5..7584d6d 100644 --- a/run_vgg_38_bn_rc.sh +++ b/run_vgg_38_bn_rc.sh @@ -1 +1 @@ -python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 5 --experiment_name VGG_38_experiment --use_gpu True --num_classes 100 --block_type 'conv_bn_rc' --continue_from_epoch -1 --learning-rate 0.01 +python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 5 --experiment_name VGG38_BN_RC --use_gpu True --num_classes 100 --block_type 'conv_bn_rc' --continue_from_epoch -1 --learning-rate 0.01