training_markdown.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1d1fd313650>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from preprocessing import data_loader\n",
    "from model import CNN\n",
    "from model_new import CNN2\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "from preprocessing import AlexEmbed\n",
    "from torch.utils.data import DataLoader\n",
    "alexembed = AlexEmbed()\n",
    "\n",
    "from train import performance_per_class\n",
    "from train import train\n",
    "\n",
    "torch.manual_seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def new_dataset(batch_size=1, dire = \"alex_embedding_set\"):\n",
    "    # Fixed PyTorch random seed for reproducible result\n",
    "    torch.manual_seed(0)\n",
    "\n",
    "    train_loader, val_loader = data_loader(batch_size=batch_size, shuffle=False)\n",
    "    id=0\n",
    "    for batch in tqdm(train_loader):\n",
    "        imgs, labels = batch.values()\n",
    "        labels = torch.argmax(labels, dim=1)\n",
    "        np.save(f\"{dire}/embed_{id}\", imgs.numpy())\n",
    "        np.save(f\"{dire}/label_{id}\", labels.numpy())\n",
    "        id += 1\n",
    "    \n",
    "    for batch in tqdm(val_loader):\n",
    "        imgs, labels = batch.values()\n",
    "        labels = torch.argmax(labels, dim=1)\n",
    "        np.save(f\"{dire}/embed_{id}\", imgs.numpy())\n",
    "        np.save(f\"{dire}/label_{id}\", labels.numpy())\n",
    "        id += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 70560/70560 [15:56<00:00, 73.79it/s]  \n",
      "100%|██████████| 2520/2520 [00:33<00:00, 75.49it/s] \n"
     ]
    }
   ],
   "source": [
    "new_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(0.7 * len(alexembed))\n",
    "val_size = len(alexembed) - train_size\n",
    "train_set, val_set = torch.utils.data.random_split(alexembed, [train_size, val_size])\n",
    "\n",
    "train_loader = DataLoader(train_set, batch_size=256, shuffle=True)\n",
    "val_loader = DataLoader(val_set, batch_size=1024, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train import train\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempt 1: BS = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CNN2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\Public\\Documents\\Wondershare\\CreatorTemp\\ipykernel_24932\\3182294334.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCNN2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtrain_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_loss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#error_rate, wrong_guess_rate, guesses = performance_per_class(net)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'CNN2' is not defined"
     ]
    }
   ],
   "source": [
    "net = CNN2()\n",
    "train_err, train_loss, val_err, val_loss = train(net, train_loader, val_loader)\n",
    "#error_rate, wrong_guess_rate, guesses = performance_per_class(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 30\n",
    "epochs = np.linspace(0, 29, num=30)\n",
    "train_acc = np.full(30, 1) - train_err\n",
    "val_acc = np.full(30, 1) - val_err\n",
    "\n",
    "print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Device:\", device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = CNN2()\n",
    "net.load_state_dict(torch.load(\"./bs64_lr0.001_epoch21\", map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "The error rate is:{0: 0.5654761904761905, 1: 0.39880952380952384, 2: 0.48214285714285715, 3: 0.4107142857142857, 4: 0.5178571428571429, 5: 0.5535714285714286, 6: 0.3392857142857143, 7: 0.13690476190476192, 8: 0.6904761904761905, 9: 0.375, 10: 0.21428571428571427, 11: 0.26785714285714285, 12: 0.5595238095238095, 13: 0.30357142857142855, 14: 0.6904761904761905}\n",
      "{0: 0.5067567567567568, 1: 0.5429864253393665, 2: 0.43137254901960786, 3: 0.23255813953488372, 4: 0.6267281105990783, 5: 0.43609022556390975, 6: 0.37988826815642457, 7: 0.19444444444444445, 8: 0.6645161290322581, 9: 0.3674698795180723, 10: 0.23255813953488372, 11: 0.33513513513513515, 12: 0.5515151515151515, 13: 0.3641304347826087, 14: 0.6090225563909775}\n",
      "{0: 148, 1: 221, 2: 153, 3: 129, 4: 217, 5: 133, 6: 179, 7: 180, 8: 155, 9: 166, 10: 172, 11: 185, 12: 165, 13: 184, 14: 133}\n"
     ]
    }
   ],
   "source": [
    "error_rate, wrong_guess_rate, guesses = performance_per_class(net)\n",
    "print(\"The error rate is:\"+ str(error_rate))\n",
    "print(wrong_guess_rate)\n",
    "print(guesses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "  3%|▎         | 1/30 [00:56<27:17, 56.47s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.7472870100421121 Val err: 0.5962301587301587 Train loss: 2.2597159530214697 Val loss: 1.896952504203433\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  7%|▋         | 2/30 [01:53<26:31, 56.83s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.6412374473598963 Val err: 0.5565003779289494 Train loss: 1.9637209343786683 Val loss: 1.76057197366442\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 10%|█         | 3/30 [02:53<26:11, 58.22s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.597424684159378 Val err: 0.5378401360544217 Train loss: 1.842548832992198 Val loss: 1.6818625870205106\n"
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    },
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      " 13%|█▎        | 4/30 [04:02<27:00, 62.32s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.5690192743764172 Val err: 0.5295256991685563 Train loss: 1.7585512807332173 Val loss: 1.6505598624547322\n"
     ]
    },
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      " 17%|█▋        | 5/30 [05:04<26:02, 62.51s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.5445213799805636 Val err: 0.5086923658352229 Train loss: 1.6916248829253597 Val loss: 1.591367244720459\n"
     ]
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    {
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      " 20%|██        | 6/30 [06:06<24:54, 62.25s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.5286078717201166 Val err: 0.5035430839002267 Train loss: 1.6445979577889713 Val loss: 1.5753364676520938\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 23%|██▎       | 7/30 [07:07<23:40, 61.75s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.512532393909945 Val err: 0.5005668934240363 Train loss: 1.5966306066265996 Val loss: 1.5636806885401409\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 27%|██▋       | 8/30 [08:07<22:24, 61.11s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.5044136702299967 Val err: 0.4985355253212396 Train loss: 1.5598295704688432 Val loss: 1.562485002336048\n"
     ]
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    {
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     "output_type": "stream",
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      " 30%|███       | 9/30 [09:08<21:26, 61.28s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.487325882734046 Val err: 0.4938586545729403 Train loss: 1.5230856164131759 Val loss: 1.5335029306865873\n"
     ]
    },
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      " 33%|███▎      | 10/30 [10:09<20:20, 61.00s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.48236556527372854 Val err: 0.4882369614512472 Train loss: 1.494008649816167 Val loss: 1.5297890901565552\n"
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      " 37%|███▋      | 11/30 [11:11<19:26, 61.38s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.4735989633948818 Val err: 0.4885204081632653 Train loss: 1.4665129332962432 Val loss: 1.5249191976728893\n"
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      " 40%|████      | 12/30 [12:13<18:29, 61.65s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.46422497570456756 Val err: 0.4808201058201058 Train loss: 1.4415825506566102 Val loss: 1.5069047609965007\n"
     ]
    },
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      " 43%|████▎     | 13/30 [13:14<17:23, 61.41s/it]"
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     "text": [
      "Epoch 12: Train err: 0.45754373177842567 Val err: 0.47874149659863946 Train loss: 1.4180787937628792 Val loss: 1.5016895475841703\n"
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     "text": [
      "Epoch 13: Train err: 0.44916180758017493 Val err: 0.4818121693121693 Train loss: 1.3892865977756719 Val loss: 1.511051785378229\n"
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      "Epoch 14: Train err: 0.4415694849368319 Val err: 0.47926114890400606 Train loss: 1.3705595453786108 Val loss: 1.501935879389445\n"
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     "text": [
      "Epoch 15: Train err: 0.43867427923550373 Val err: 0.4755291005291005 Train loss: 1.3557568819411678 Val loss: 1.4978791248230707\n"
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      "Epoch 16: Train err: 0.4325599287333981 Val err: 0.47642668178382463 Train loss: 1.3378347226375125 Val loss: 1.4933095205397833\n"
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     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.42557499190152254 Val err: 0.47458427815570675 Train loss: 1.3178135391344061 Val loss: 1.4845374765850248\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 19/30 [19:15<11:04, 60.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.4232061872367995 Val err: 0.47023809523809523 Train loss: 1.308595489343831 Val loss: 1.4878826141357422\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 20/30 [20:15<10:02, 60.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.41697035957240036 Val err: 0.4676398337112623 Train loss: 1.2886603212109502 Val loss: 1.4797728118442355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 21/30 [21:14<09:00, 60.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20: Train err: 0.41573534175574994 Val err: 0.47250566893424034 Train loss: 1.2784416459384977 Val loss: 1.4924384752909343\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 22/30 [22:15<08:01, 60.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.4093375445416262 Val err: 0.46877362055933486 Train loss: 1.267092283525615 Val loss: 1.488460273969741\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 23/30 [23:11<06:52, 58.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.4048428895367671 Val err: 0.4681594860166289 Train loss: 1.258891497868948 Val loss: 1.4813104300271898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 24/30 [24:05<05:43, 57.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23: Train err: 0.40387107223841917 Val err: 0.4643801965230537 Train loss: 1.239833946672746 Val loss: 1.47672856989361\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 25/30 [25:03<04:47, 57.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24: Train err: 0.394881762228701 Val err: 0.46400226757369617 Train loss: 1.2259793825100123 Val loss: 1.4810936905088878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 26/30 [26:00<03:49, 57.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.39156138645934563 Val err: 0.4666950113378685 Train loss: 1.2155572367455676 Val loss: 1.4810442129770915\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 27/30 [26:57<02:51, 57.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.3940314220926466 Val err: 0.4680177626606198 Train loss: 1.2125279279570504 Val loss: 1.488182788803464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 28/30 [27:53<01:53, 56.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.3895165208940719 Val err: 0.46381330309901736 Train loss: 1.199781627852682 Val loss: 1.492828329404195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 29/30 [28:50<00:57, 57.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.38656057661159704 Val err: 0.46390778533635674 Train loss: 1.1858722697267878 Val loss: 1.477362144561041\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [29:47<00:00, 59.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.3801425332037577 Val err: 0.46343537414965985 Train loss: 1.1766471062917165 Val loss: 1.4787816660744804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net = CNN2()\n",
    "train_err, train_loss, val_err, val_loss = train(net, train_loader, val_loader, batch_size = 256, lr=0.0005, num_epochs = 30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saved as bs256_lr0.0001_epoch29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 1/30 [01:00<29:25, 60.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.7590298023971493 Val err: 0.6127173091458806 Train loss: 2.3379492765881236 Val loss: 1.9806979440507435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 2/30 [02:01<28:23, 60.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.6433430515063168 Val err: 0.5640589569160998 Train loss: 1.9964069473928738 Val loss: 1.8096825111479986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 3/30 [02:58<26:29, 58.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.5973234531908002 Val err: 0.544359410430839 Train loss: 1.8593884675613956 Val loss: 1.7116454442342122\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 4/30 [03:54<25:02, 57.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.5653749595076125 Val err: 0.529572940287226 Train loss: 1.763913068128991 Val loss: 1.6600658950351534\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 5/30 [04:50<23:53, 57.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.5425574991901523 Val err: 0.51828231292517 Train loss: 1.6928667299488047 Val loss: 1.6142648458480835\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 6/30 [05:47<22:47, 56.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.5257531584062196 Val err: 0.5070861678004536 Train loss: 1.6378286088686533 Val loss: 1.581022063891093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 7/30 [06:43<21:48, 56.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.5051627793974732 Val err: 0.5 Train loss: 1.5800312738962123 Val loss: 1.5524935722351074\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 8/30 [07:41<20:57, 57.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.49283284742468414 Val err: 0.49551209372637944 Train loss: 1.5364044855295684 Val loss: 1.532399853070577\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 9/30 [08:38<19:58, 57.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.4797942986718497 Val err: 0.48733938019652306 Train loss: 1.4927353055983628 Val loss: 1.506477849824088\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 10/30 [09:35<18:58, 56.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.4699748947197927 Val err: 0.48261526832955404 Train loss: 1.454860745934007 Val loss: 1.4931722992942447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 11/30 [10:30<17:55, 56.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.45503320375769357 Val err: 0.47869425547996974 Train loss: 1.4152198091072121 Val loss: 1.4768889915375483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 12/30 [11:26<16:55, 56.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.4430879494655005 Val err: 0.476379440665155 Train loss: 1.3772535083207442 Val loss: 1.468260679926191\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████▎     | 13/30 [12:23<15:57, 56.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12: Train err: 0.4314868804664723 Val err: 0.468442932728647 Train loss: 1.3479454257945322 Val loss: 1.449897811526344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 14/30 [13:21<15:13, 57.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13: Train err: 0.42025024295432456 Val err: 0.46499433106575966 Train loss: 1.3056376048320315 Val loss: 1.4340171019236247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 15/30 [14:24<14:41, 58.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14: Train err: 0.412151765468092 Val err: 0.4651360544217687 Train loss: 1.279608470170609 Val loss: 1.4363467523029871\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 16/30 [15:21<13:35, 58.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15: Train err: 0.40314220926465827 Val err: 0.4623015873015873 Train loss: 1.2549388068945297 Val loss: 1.4246364718391782\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|█████▋    | 17/30 [16:17<12:28, 57.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16: Train err: 0.39340379008746357 Val err: 0.4560657596371882 Train loss: 1.2194692205270954 Val loss: 1.4083849078132993\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 18/30 [17:14<11:27, 57.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.3819039520570133 Val err: 0.4557823129251701 Train loss: 1.188081829041397 Val loss: 1.3990869181496757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 19/30 [18:10<10:26, 56.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.374372367994817 Val err: 0.4514833711262283 Train loss: 1.1630107476303615 Val loss: 1.392504334449768\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 20/30 [19:06<09:27, 56.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.3650186264982183 Val err: 0.4470899470899471 Train loss: 1.1382626468035841 Val loss: 1.3805857272375197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 21/30 [20:02<08:28, 56.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20: Train err: 0.36040249433106575 Val err: 0.4451530612244898 Train loss: 1.1106432701021896 Val loss: 1.3744568540936424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 22/30 [20:58<07:31, 56.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.349408811143505 Val err: 0.4426965230536659 Train loss: 1.0857719216939699 Val loss: 1.3729025352568853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 23/30 [21:55<06:34, 56.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.3421201814058957 Val err: 0.44212962962962965 Train loss: 1.0556415288559513 Val loss: 1.3741322755813599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 24/30 [22:51<05:37, 56.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23: Train err: 0.3324425008098478 Val err: 0.43806689342403626 Train loss: 1.0326927467949032 Val loss: 1.3593127330144246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 25/30 [23:47<04:41, 56.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24: Train err: 0.328271784904438 Val err: 0.43490173847316704 Train loss: 1.0118624283859767 Val loss: 1.3523525113151187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 26/30 [24:43<03:44, 56.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.3218942338840298 Val err: 0.43287037037037035 Train loss: 0.9911969195993453 Val loss: 1.3530619144439697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 27/30 [25:40<02:49, 56.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.3119938451571105 Val err: 0.4332482993197279 Train loss: 0.9636288992481529 Val loss: 1.3442214784168063\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 28/30 [26:37<01:53, 56.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.30369290573372204 Val err: 0.4313586545729403 Train loss: 0.942049803202634 Val loss: 1.3476159004938035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 29/30 [27:33<00:56, 56.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.2959993521218011 Val err: 0.4286659108087679 Train loss: 0.9155420173017472 Val loss: 1.342955129487174\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [28:28<00:00, 56.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.29045189504373176 Val err: 0.4263983371126228 Train loss: 0.9026751249565361 Val loss: 1.3434587376458305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net = CNN2(fc1_out = 256, fc2_out = 64, dropout = 0.3)\n",
    "train_err, train_loss, val_err, val_loss = train(net, train_loader, val_loader, batch_size = 128, lr=0.0001, num_epochs = 30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 256, 6, 6])\n",
      "torch.Size([64, 1, 256, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "dataloader = DataLoader(alexembed, batch_size=64, shuffle=True)\n",
    "for sample_batched in dataloader:\n",
    "    print(sample_batched['image'].shape)\n",
    "    print(sample_batched['label'].shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F9j59+uDAgQPV2sfixYvx2GOPISAgwBglmrVmzZphw4YNOHDgABo1aoR58+YhNTWVIYeIyMyl5RbitxgNfj2ZjFPXsvXtNpYqPB7qicGP+KBbc3dYqur/AGzZQk5GRga0Wi08PT0rtHt6eiI1NfWBr9doNNi6dSt++umn+25X3tekXE5OTu0KNjMffvghEhIS8MQTT8DW1hYTJkzA4MGDkZ2d/eAXExFRvZJbWIJtp1Px68kUHLicgfLBUCqlAt2au2Fwu8Z4PNQTdmrzunIh+7u5+/SXEKJap8SWLVsGZ2dnDB48+L7bzZ49GzNnznyYEuuVMWPGYMyYMfrHgYGBEEJU2s7FxQW//PLLffe1a9euCo+vXLlSaZuTJ0/WvEgiIjK64lIddsWl4deTKfj73PUKnYfb+ztjULvGGNDGG272ahmrNC7ZQo6bmxtUKlWlszZpaWmVzu7cTQiBJUuWYNSoUbCysrrvttOnT8fUqVP1j3NycuDn51f7womIiEyUEAInrmZh/bFr2HJKg+xbJfrnmrrbYXC7xhjUrjH8XW1lrLLuyBZyrKysEB4ejujoaDz99NP69ujoaAwaNOi+r929ezcuXbqEcePGPfA4arUaarX5plQiIqL03CJsOnENPx+9hktpefp2Dwc1Brb1weBHGqOVj2O97Dz8MGS9XDV16lSMGjUKERERiIqKwqJFi5CUlISJEycCkM7CJCcnY8WKFRVet3jxYkRGRiIsLEyOsomIiGRXotVhV1w6fj56FTvPp6G0rKONtaUS/Vt7Y0h7X3Rq4gqVsmEFmzvJGnKGDx+OzMxMzJo1CxqNBmFhYfjjjz/0o6U0Gg2SkpIqvCY7OxsbNmzAf/7zHzlKJiIiktWltFysO3oNG44nIyPv9sCaR/ydMSzCDwPaeMPR+sGLMDcEClFVr1QzlpOTAycnJ2RnZ8PRseIKqYWFhUhISEBQUBCsrU1jSmoyDP63JaL6LLewBFtOafDz0as4npSlb3ezt8Iz7X3xbLgvmns6yFdgHbjf3+97kX10FREREVUmhMDhhBtYd/Qa/ojV6BeLVikV6BnsgWERvugZ4mEW89kYC0MOERGRCckpLMGaf5Kw6nASEu+YhbiJux2GR/jh6faNTWYBTFPHkENERGQCNNm3sHT/Ffx0OAl5RaUAADsrFZ5s44NhHfzQ3t+5wY2OelgMOURERDI6p8nB93visTkmRT9CqpmHPcZ3DcJTbX3MbhbiusQLeQQA6NGjB6ZMmaJ/HBgYiPnz59/3NQqF4oGzJleHofZDRFRfCCGw/1IGXlzyD/r9Zy82nkhGqU4gMsgFi0dH4K8p3TGioz8DzkPip2cGnnrqKdy6dQt///13pecOHjyIzp0749ixY2jfvn2193nkyBHY2dkZskzMmDEDv/zyS6WlIDQaDRo1amTQYxERmaJSrQ5bYjVYtCceZ1KktRSVCqBfmDde7t4E7fyc5S3QzDDkmIFx48bhmWeeQWJiYqUV2ZcsWYJ27drVKOAAgLu7uyFLvC8vL686OxYRkRzyi0qx9shVLN6XgOSsWwCkSfuGRfhhXNcgBLga9h+VJOHlKjPw5JNPwsPDA8uWLavQXlBQgLVr12Lw4MF47rnn4OvrC1tbW7Ru3RqrV6++7z7vvlx18eJFdO/eHdbW1ggNDUV0dHSl17z77rto0aIFbG1t0aRJE3z44YcoKZHWTVm2bBlmzpyJmJgYKBQKKBQKfb13X66KjY1Fr169YGNjA1dXV0yYMAF5ebenKR8zZgwGDx6ML774At7e3nB1dcXrr7+uPxYRkalIyy3E59vOI2r2dsz6/SySs27B1c4KUx9vgQPTemPWoDAGHCPimZwHEQIoKXjwdsZgaQtUoye9hYUFXnzxRSxbtgz//ve/9b3v161bh+LiYowfPx6rV6/Gu+++C0dHR2zZsgWjRo1CkyZNEBkZ+cD963Q6PPPMM3Bzc8OhQ4eQk5NTof9OOQcHByxbtgw+Pj6IjY3Fyy+/DAcHB7zzzjsYPnw4Tp8+jW3btukvqzk5OVXaR0FBAfr27YtOnTrhyJEjSEtLw/jx4/HGG29UCHE7d+6Et7c3du7ciUuXLmH48OFo164dXn755Qe+HyIiYyos0eJQfCb+iNXglxMpKNZKq38HudlhfLcgDGnvC2tLlcxVNgwMOQ9SUgB86iPPsd9LAayql/DHjh2LuXPnYteuXejZsycA6VLVM888g8aNG+Ptt9/Wbztp0iRs27YN69atq1bI+fvvv3Hu3DlcuXIFvr6+AIBPP/0U/fr1q7DdBx98oL8fGBiIt956C2vXrsU777wDGxsb2Nvbw8LC4r6Xp1atWoVbt25hxYoV+j5BX3/9NZ566il89tln+hXqGzVqhK+//hoqlQohISEYMGAAtm/fzpBDRLJIzS7EjvNp2HE+DfsvZegn7gOA9v7OmNC9KR4P9WzQ60jJgSHHTISEhKBz585YsmQJevbsicuXL2Pv3r3466+/oNVqMWfOHKxduxbJyckoKipCUVFRtTsWnzt3Dv7+/vqAAwBRUVGVtlu/fj3mz5+PS5cuIS8vD6WlpdWeevvOY7Vt27ZCbV26dIFOp0NcXJw+5LRq1Qoq1e1/CXl7eyM2NrZGxyIiqi2tTuDk1SzsLAs2ZzU5FZ73dFSjV4gHhrT3RUSgi0xVEkPOg1jaSmdU5Dp2DYwbNw5vvPEGvvnmGyxduhQBAQHo3bs35s6di6+++grz589H69atYWdnhylTpqC4uLha+61qebO7J6Q6dOgQRowYgZkzZ+KJJ56Ak5MT1qxZgy+//LJG70EIcc/Jru5st7S0rPScTqer0bGIiGoi+1YJ9lxIx87zadh1IR038m//DlUogHZ+zugV7IGeIR5o5ePIiftMAEPOgygU1b5kJLdhw4Zh8uTJ+Omnn7B8+XK8/PLLUCgU2Lt3LwYNGoSRI0cCkPrYXLx4ES1btqzWfkNDQ5GUlISUlBT4+EiX7g4ePFhhm/379yMgIADvv/++vi0xMbHCNlZWVtBqtbif0NBQLF++HPn5+fqzOfv374dSqUSLFi2qVS8RkSEIIXApLU9/Gepo4k1odbf/0edgbYHuLdzRK9gDPYLd4WqvlrFaqgpDjhmxt7fH8OHD8d577yE7OxtjxowBADRr1gwbNmzAgQMH0KhRI8ybNw+pqanVDjmPPfYYgoOD8eKLL+LLL79ETk5OhTBTfoykpCSsWbMGHTp0wJYtW7Bp06YK2wQGBiIhIQEnT56Er68vHBwcoFZX/KXwwgsv4KOPPsLo0aMxY8YMpKenY9KkSRg1apT+UhURkaEJIZB0owBnUnJwJiUbp5NzcCYlBxl5RRW2a+Zhj14hHugV4oHwgEZcHNPEMeSYmXHjxmHx4sXo06cP/P39AQAffvghEhIS8MQTT8DW1hYTJkzA4MGDkZ2dXa19KpVKbNq0CePGjUPHjh0RGBiI//73v+jbt69+m0GDBuH//u//8MYbb6CoqAgDBgzAhx9+iBkzZui3GTJkCDZu3IiePXsiKysLS5cu1Qexcra2tvjzzz8xefJkdOjQAba2thgyZAjmzZv30J8NEREgTch3KT0PZ8qCzOmUbJxLyUFu2XpRd7JSKdGpqSt6lwUbP5eadSMgeSlEVR0uzFhOTg6cnJyQnZ1dqVNsYWEhEhISEBQUBGtrrvBqTvjflqhhKizR4pwmp+wMTQ7OpmTjXGouiksr9+GzUikR7OWAsMaOCPVxQisfR7T0coSNFYd7m4L7/f2+F57JISIis3M6ORtL9iXgt1MpKNFW/re8vdoCod6OCPVxRFhjKdA087Dn5Sczw5BDRERmQacT2BmXhh/2JuBgfKa+3c3eSn9mJqzsp7+LLZScs8bsMeQQEVG9dqtYiw3Hr2HJvgTEZ+QDAFRKBZ5s441xXYPQurETh3M3UAw5RERUL6XlFmLlwUT8eCgRNwuktescrC3wfKQ/RkcFwsfZRuYKSW4MOVVoYH2xGwT+NyUyH+dTc7B4bwJ+PXl7XSg/FxuM7RKEZyP8YK/mnzaS8Jtwh/JZdAsKCmBjw38BmJOCAmmR1btnSiai+kEIgd0X0rF4XwL2XszQt4cHNML4rkHo08qL60JRJQw5d1CpVHB2dkZaWhoAac4WXset34QQKCgoQFpaGpydnSusd0VEpq+wRItfTybjh70JuJiWBwBQKoB+raX+Nu39G8lcIZkyhpy7lK+QXR50yDw4Ozvfd/VzIjIdxaU67L+cgS2nNPjrTCpyCqVJ+uzVFhjewQ9jOgdyUj6qFoacuygUCnh7e8PDwwMlJSVyl0MGYGlpyTM4RCauRKvDgcuZ2HIqBX+euY7sW7d//zZ2tsGYzoEY3tEPjta85EzVx5BzDyqVin8YiYiMqFSrw6H4G9gSm4Jtp1P1I6QAwM1ejf6tvTCgtTciAl3Y34ZqhSGHiIjqTKlWh38SbuD3WA22nU7Fjfxi/XOudlbo19oLA1r7oGMQgw09PIYcIiIyKq1O4J+E22dsMvJuBxsXOyv0DfPCk6290THIBRZcVoEMiCGHiIiMIq+oFMv2J2D5wUSk5xbp251tLdEvTDpj06kJgw0ZD0MOEREZVH5RKZYfvIJFe+KRVdbPxsnGEn1beWFAG29ENXXlQphUJxhyiIjIIG4Va7Hy0BV8uzte39emibsdJvdujn5h3rCyYLChusWQQ0RED6WwRIsfDyXi293xyMiTLksFutpi8mPNMbBtY3YgJtkw5BARUa0Ulmix5p8kLNh1GWllfW78XGzwZq/mePqRxuxrQ7JjyCEiohopKtXi5yNX8c3Oy0jNKQQgTdj3Zu9meKa9L/vbkMlgyCEiomopLtVh/bFr+HrHRaRkS+HG28kab/RqhmfD/djnhkwOQw4REd1XiVaHTceT8d8dF3Ht5i0AgKejGq/3bIbhHfygtuDs8GSaGHKIiKiC3MISxF7LxomrWYi5moXjSTf1E/i52avxWo+meD7SH9aWDDdk2hhyiIgasBKtDnGpufpAc/JqFi6n50GIitu52lnh1R5N8UJkAGysGG6ofmDIISJqIIQQuHrjFk5ey8LJpCzEXMvC6eRsFJXqKm3b2NkG7fyd0c7XGe38ndG6sRPP3FC9w5BDRGTGCku0+P2UBn/EanDyalaFBTHLOVpboK2fM9qV3dr4OsPdQS1DtUSGxZBDRGSGrucUYtWhRKw6nITMO4KNpUqBUB8ntPN10gebQFc7KDlhH5khhhwiIjNyIukmlu6/gj9iNSjVSR1rfJys8UKnAHRp5oaW3g4cDUUNBkMOEVE9V1yqw9bTGizZfwUxV7P07R0DXTCmSyD6hHpy9mFqkBhyiIjqqYy8Ivx0OAk/HkrUL6tgpVJiYDsfjOkciLDGTjJXSCQvhhwionrmdHI2lu6/gt9iUlCslUZGeTioMbJTAJ6P9IebPTsNEwEMOURE9UKpVoc/z1zHsgMJOHLlpr69nZ8zXuoSiH5h3lxWgeguDDlERCZKCIHTyTnYHJOM32I0+sUwLZQKDGjjjTGdA/GIfyOZqyQyXQw5REQm5lJaLjbHaPBbTAoSMvL17a52Vngh0h8vdAqAp6O1jBUS1Q8MOUREJuDazQL8FqPB5pgUnNPk6NutLZXo3dITA9v6oEewO4d/E9UAQw4RkUzSc4vwR6wUbI4l3u5nY6FUoHsLdwxs64PHQz1hp+avaqLa4P85RER1KPtWCf48k4rfYlKw/1IGyubrg0IBdApyxcB2PugX5gVnWyt5CyUyAww5RERGJoTAvksZWHkwEbvi0vXDvgGgrZ8zBrb1wZNtvNnPhsjAGHKIiIzoRNJNfL4tDgfjM/VtLTztMbCtD55q64MAVzsZqyMybww5RERGcPF6Lub+GYe/zl4HIM1E/HykP0Z09EOIl6PM1RE1DAw5REQGdO1mAeb/fREbj1+DTgBKBTCkvS+mPN4CjZ1t5C6PqEFhyCEiMoDMvCJ8s/MyfjyUqO9z80QrT7zdJxjNPR1kro6oYWLIISJ6CLmFJfhhbwJ+2BuP/GItACCqiSve6RvM2YiJZMaQQ0RUC4UlWqw6nIRvdl7CjfxiAEDrxk54p28wujZzg0KhkLlCImLIISKqgVKtDhtPJGN+9AWkZEtrSTVxs8PbTwSjX5gXww2RCWHIISKqBp1O4K+zqfjirwu4lJYHAPB2ssaUx5pjSHtfWKi4AjiRqWHIISK6j8ISLTYcv4bF+xIQny4tlulsa4nXezTDqKgAWFtyLSkiUyX7Pz0WLFiAoKAgWFtbIzw8HHv37r3v9kVFRXj//fcREBAAtVqNpk2bYsmSJXVULRE1FBl5RZgXfQGd5+zA+5tOIz49Hw7WFpjUqxn2vNMTL3dvwoBDZOJkPZOzdu1aTJkyBQsWLECXLl3w3XffoV+/fjh79iz8/f2rfM2wYcNw/fp1LF68GM2aNUNaWhpKS0vruHIiMleX0nLxw94EbDyRjOJSaSi4byMbjO0ShGEd/GDPxTKJ6g2FEELIdfDIyEi0b98eCxcu1Le1bNkSgwcPxuzZsyttv23bNowYMQLx8fFwcXGp1TFzcnLg5OSE7OxsODpy1lEiktaWOng5E9/vjcfOuHR9ezs/Z7zcrQmeaOXJPjdEMqvN32/Z/klSXFyMY8eOYdq0aRXa+/TpgwMHDlT5ms2bNyMiIgKff/45Vq5cCTs7OwwcOBAff/wxbGyqnkm0qKgIRUVF+sc5OTmGexNEVK+VaHX4/VQKftibgDMp0u8GhQLoE+qJl7s1QXhAI46WIqrHZAs5GRkZ0Gq18PT0rNDu6emJ1NTUKl8THx+Pffv2wdraGps2bUJGRgZee+013Lhx4579cmbPno2ZM2cavH4iqr+yb5Vg9T9JWLb/ClJzpGHgNpYqPBvhi7FdghDoxkUzicyB7BeX7/5XkhDinv9y0ul0UCgUWLVqFZycnAAA8+bNw9ChQ/HNN99UeTZn+vTpmDp1qv5xTk4O/Pz8DPgOiKi+uHqjAEv3X8HaI0n62YndHdQY0zkQL0T6w9nWSuYKiciQZAs5bm5uUKlUlc7apKWlVTq7U87b2xuNGzfWBxxA6sMjhMC1a9fQvHnzSq9Rq9VQq9WGLZ6I6g2tTmDPhXSsPJSInXFpKO+FGOzpgPHdgjCwnQ/UFhwlRWSOZAs5VlZWCA8PR3R0NJ5++ml9e3R0NAYNGlTla7p06YJ169YhLy8P9vb2AIALFy5AqVTC19e3TuomovrhRn4x1h29ih8PJ+LqjVv69u4t3DG+axC6NefSC0TmTtbLVVOnTsWoUaMQERGBqKgoLFq0CElJSZg4cSIA6VJTcnIyVqxYAQB4/vnn8fHHH+Oll17CzJkzkZGRgX/9618YO3bsPTseE1HDIYRAzLVsrDh4Bb+f0uiHgDvZWOLZcF+80CkAQexvQ9RgyBpyhg8fjszMTMyaNQsajQZhYWH4448/EBAQAADQaDRISkrSb29vb4/o6GhMmjQJERERcHV1xbBhw/DJJ5/I9RaIyATcKtbit5gUrDyUiNjkbH1768ZOGBUVgKfa+MDGipekiBoaWefJkQPnySEyHwkZ+Vh1KBHrjl1D9q0SAICVhRJPtvHGi1GBaOvrxEtSRGaiXs2TQ0RUG1qdwI7zaVh5KBF7LtyeuM/PxQYjIwPwbIQfXOw4SoqIGHKIqJ5IzMzHhuPJ2HDsGpKzpI7ECgXQM9gDozoFoHsLd6iUPGtDRLcx5BCRycotLMEfsRpsOJaMf67c0Lc3srXEsA5+eKFjAPxdbWWskIhMGUMOEZkUrU5aR2rD8WvYelqDwhJphJRSAXRt7o4h7RvjiVZeXAGciB6IIYeITEJ8eh42HL+GTceTkZJdqG9v6m6HoeF+ePqRxvByspaxQiKqbxhyiEg22bdKsOWUBhuOX8OxxJv6dkdrCwxs54Mh7X3Rzs+ZI6SIqFYYcoioTml1AvsuZWD9sWv460wqikpvX456tIU7hoT74rGWnrwcRUQPjSGHiOqEVifw68lk/Gf7RSRmFujbW3jaY2i4Lwa3awwPR16OIiLDYcghIqPS6QS2nUnFvOgLuJSWBwBwtrXEoLY+GBLui9aNOWEfERkHQw4RGYUQArvi0vHFX3E4k5IDQFpD6pVHm2BM50DYWvHXDxEZF3/LEJHBHbicgS//uqDvTGxnpcK4bk0wvlsQHK0tZa6OiBoKhhwiMpjjSTfx5V9x2H8pEwCgtlBidOdATHy0KZdaIDI3Oh1QkAnkpgC5qUBOCpCrkW4qNTDgC7krZMghood3JiUb8/66gO3n0wAAlioFnuvojzd6NmNnYqL6qLhACis5ZQEmNwXI0dwRaMrCjK6k6tfbujHkEFH9diktD1/9fQFbTmkASMPAh4b7YlKv5vBz4XILRCarKBfISrr37daNB+8DAKAA7NwBR2/Aoezm6CPdTABDDhHV2NUbBZj/90VsOnENOiG1PdXWB//3WHM0cbeXtziihk4IoDALyL4GZF29I7wkSj+zrwK3bj5wN7C0uyu8eAMOPhXbHLwAlen2s2PIIaJqS88twv92XMTqf5JQopXSzeOhnpj6eAu09HaUuToiM1ZaDBRkAPnpQF669DM/HchPA/Izbj8uf+5el5HuZNMIcPYvuwUATn63Hzv5AtZOQD2f3oEhh4geKLewBIv2xGPxvgQUFGsBAN2au+GtPsFo5+csb3FEdUkIoDgPyEsD8q6X3aq6nyaFDSEApYV0tkOpApSWVT9WWgCqsp/Ksud02ttBpjC75rXaut4RYvwBpzvuO/sBagfDfz4mhiGHiO6pqFSLlQcT8c3OS7hZIP3LsK2fM6b1DUFUU1eZqyMygpJbwI0E4MZlIPOSdMnn7vBSUvDg/dxJqwW0RQ9fm0Il9X+xcwfs3AB7j9v37Twqttu6AZbs9M+QQ0SVaHUCm04k46voC0jOugUAaOJuh3eeCMYTrbw4QzHVb6XFUv+UzMu3w0zmZeBGvBRqIB68DysHKUzYe1bx0/N2AFGqAG0JoCu9fbvzsf5+iXTm5s7nFMrbocbeA7B2BpRKY386ZoUhh4j0hBDYfi4Nc/+MQ9z1XACAl6M1pjzWHEPDfWGh4i9YqkdyrwPXT1cOM1lJgNDe+3XWToBLU8C1GdAo4I7gUh5kPAAru7p7H1RrDDlEBAA4euUG5mw9j6NlsxQ7WlvgtZ7NMKZzIFcEJ9Om00rhJfUUkBp7+5afdu/XWNqWBZnyW7Pbj21d632HW5Iw5BA1cHGpuZj753n8fU76g6C2UOKlLkF49dGmcLI13aGh1EAV5wPXz1YMNNfPAKW3qthYIYUWt2DAtUnFIOPgzSDTADDkEDVQ124W4Kvoi9h44hqEAFRKBYZF+GJy7xbwcmKHRTICnU7qgKstlvrF3H2/tFh6XOF+sXR5qTzQZF5ClX1mLG0Bz1aAZxjg1RrwagN4hvKyUgPHkEPUwGQVFOPrHZew4mAiirU6AEC/MC+81ScYzTw4kR/VQmE2kJ0M5CRLHXdzksseX5N+5l0HSgulzrSGYO9ZFmRa3w40Lk2kTr5Ed2DIIWogCku0WH7gCr7ZeQk5hdIfm6gmrni3XwjnuqGqCSHNjJufLq1hdHd4KX9cnFu7/SstAQs1oLKSbhZW0sKOd963sJJGF5UHGs/WgIOnYd8nmS2GHCIzp9MJbI5Jwdw/4/TDwUO8HDCtXwgebeHO4eANjU4rrRydn357wrq8NKmTbl75DLo1nDkXkIY3O/kCjo0Bp8ZlP8seO3gDVrZ3hBl12YR3HK1HxsWQQ2TGDlzOwOw/ziM2WZot1dNRjbf6BGNIe1+olAw3Zq0oF9CcAlJOSLf089Jlo4JMQOhqti+1k7RG0d3hxakx4OgrLcao5qVOMj0MOURm6OL1XMzZeh7bz0sjpuzVFpj4aBOM69oENlbst2B2ivOlTrnlgSblBJBxEfee1E4B2LpIs+Tal8+gW37f446ZdMtunDmX6imGHCIzkpZTiK/+voi1R5KgKxsx9XxHf0x+rDnc7NVyl0eGUHJLGjJ9Z6BJP1/12RlHX8CnnXTzaiNdNiqf8l/FX/9k/vgtJzID+UWlWLQnHt/vjdcvoPlEK0+80zcETd15GaHeKg80mpNAyknpZ9q5qkcp2XsCPu0Bn0fKbu2kQEPUgDHkENVjpVodfj56DV/9fQHpudICgO38nPH+gJboEOgic3VUI8UF0hIEKScBTcztQFPV8gO2bkDjskDj3U766ehdxwUTmT6GHKJ6SAiBHefTMGfreVxMywMA+LvY4t2+Iejfmgtomjx9H5qTUpjRxNz7kpOtm3RWxrutFGgat5c6/fK/MdEDMeQQ1TMZeUWYvjEW0WevAwCcbS0xqVdzjOzkD7UFOxUbTcENIHE/UJgjXS4SWmk4tk5bdr/0jvtVtOu0wK0bUrDJuIAqOwXbeZQFmnZSqPFpx0BD9BAYcojqkb/OpGL6xlhk5hfDUqXA2C5BeK1nMzjZcI0pg9PppPWRLkYDl6KBa0dqPvT6fhy8b5+dKQ82Dl4MNEQGxJBDVA/kFJZg1m9nsf7YNQDSZH5fDW+Hlt6OMldmZm5lAfE7y4LN39K8MndyDwGc/AClhbSEgEJ5+77SAlCopAnu9PfL28varOykWXu923HWXqI6wJBDZOIOXs7E2+tikJx1CwoF8Er3pvi/x5vz0pQhCCF19r0YLd2uHq7Y0dfSDmjSA2j+GNDsccDZT7ZSiajmGHKITFRhiRZz/4zD4n0JAKSOxV8Oa8tRUw+rMAeI3yVdgrr4N5CbUvF5txZA8z5As8eAgM7SEgREVC8x5BCZoNPJ2fi/tSf1I6ee6+iP9we0hL2a/8tWS3EBkJUEZCUCN68AN8t+ZiVKo5junGfGwgYI6g40f1y6NQqUqWgiMjT+xiQyIaVaHRbuuoz/bL+IUp2Am70anw9tjV4h7L9RgU4rrYpdVYi5eaVyX5q7uTS9HWoCunLZAiIzxZBDZCLi0/Pwfz/HIOZqFgCgf2svfDK4NVzsrOQtrLaEkFaxTo8DMuKktZTS46Th0wU3pM64CsXtnyi/f2d72Q13bCcEkKt58OrYakfAOQBoFCCdnWkUKD12aw64BBn//ROR7BhyiGQmhMDKQ4n49I9zKCzRwcHaAh8PCsOgdj71Y1I/nbbsMtCFsjBz4fb9wmzjHVdpATj7lwWZwNthpvyxTSMOxyZq4BhyiGSkyb6Fd9afwt6LGQCArs3c8PnQNvBxtpG5svtIjQXO/wGkn5PCTOYlQFt0j40VUvhwCwbcW0g/3VpI88FASPPOCFF2093RprvdVqG9bAI9e0/A0Ucaok1EdA8MOUQy0OoEfjmRjJm/nUFOYSmsLZWY3q8lRnUKgFJpgmcfclOB2HVAzBppyPXdVGrpMpBbC+lWHmhcm7G/CxHJhiGHqA7dKtZi/bGr+GFfAhIzCwAAbf2cMW9YW9NbLby4ADi/BYhZLU2QVz7br8pKGmLtFwm4B0vhxjmAZ1WIyOQw5BDVgfTcIqw8eAUrDyXiZoHUYdbJxhITujfBK92bwEKllLnCMjodkLhPOmNz9legOO/2c36RQNsRQKunpf4uREQmjiGHyIgupeVh8b54bDiejOJS6UyIn4sNxndtgmcjfGFrZSL/C6ZfkM7YnPoZyLl2u905AGj7HNBmGODaVL76iIhqwUR+wxKZDyEEDifcwPd74rH9fJq+vZ2fMyZ0b4InWnlBZQr9bvIzgdMbpHCTcvx2u9oJCHsaaDMC8O/EEUpEVG8x5BAZSKlWh62nU/H93nicuiYNnVYogMdbemJC9yYID2hUt0PCtSXSpHg5KdItVwPkJAM5ZT+vHbk9869CJU2M13YE0KIfOwsTkVlgyCF6SHlFpfj5yFUs3peA5KxbAAC1hRJDw30xrmsQmhijQ7G2RJrZ987QkqupeD8vDYC4/36820mXo8KGAPbuhq+TiEhGDDlEtZSRV4TF+xKw6lAicgqlMyIudlZ4MSoAozoFwNXeQAs75qVLw7avnyn7eVqaOVhb/ODXKi0AB29pTpnyn+X3vdpIQ72JiMwUQw5RDZVqdVh1OAlf/BWH3LJwE+Rmh/HdgjCkvS+sLWs5lLq0WJot+PoZ4Hps2c8z916HydIWcPItCy0+gKP3XfcbA7ZugNJERm4REdUxhhyiGjiWeAMf/nIGZzU5AIBWPo6Y3Ls5HmvpWbNJ/AqzgeTjQOqp22EmPe4e6zEppLWWPMPKbq2km3MAAwwR0X0w5BBVQ0ZeET7beh7rjknDqx2tLfCvviF4vqP/g0dK6bRA2jkg+ajU2ffaUSnQVNVfRu10O8R4tgK8WgPuIYDaxCYKJCKqBxhyiO5DqxP46XAi5v4Zp+93MzzCD+/0Db53n5vc6xUDTcqJipPqlXMOAHweAbzuOEPj5Mch20REBlLjkBMYGIixY8dizJgx8Pf3N0ZNRCbheNJN/PvX0zidfPvS1KxBYQgPuGO239IiQHOqLNAckcJNVlLlnVk5AI0fAXw7SLfGERzNRERkZDUOOW+99RaWLVuGWbNmoWfPnhg3bhyefvppqNUGGklCJLPMvCJ8vi0Oa49eBVB2aeqJYDwfGSBdmiotBi5sA07+BFzeXsUoJwXg0RJoHH471LgHc20nIqI6phBCPGAijarFxMRgyZIlWL16NUpLS/H8889j7NixaN++vaFrNKicnBw4OTkhOzsbjo6OcpdDJkSrE1j9TxLm/hmH7FtSB+Bnw33xbr8QuNmrAU2MFGxO/QzcunH7hbZuZWGmLNT4tAes+d0iIjKk2vz9rnXIKVdSUoIFCxbg3XffRUlJCcLCwjB58mS89NJLdTu7azUx5FBVTl7Nwoe/nEZssjRTcai3Iz4e3Arhbjop1Jz8SRrWXc7eS5oduO0IqWOwCX7XiYjMSW3+fte643FJSQk2bdqEpUuXIjo6Gp06dcK4ceOQkpKC999/H3///Td++umn2u6eqE7cyC/G3D/PY82RqxACcLC2wL8ea4IXXOKgOjhJuixVvvSBygoIGQC0ewFo0hNQsd8+EZEpq/Fv6ePHj2Pp0qVYvXo1VCoVRo0aha+++gohISH6bfr06YPu3bsbtFAiQyrR6rDqUCLmb7+IrALp0tQbrYrwmvMe2B58HchPv72xzyNSsAkbAti6yFQxERHVVI1DTocOHfD4449j4cKFGDx4MCwtLSttExoaihEjRhikQCJDEkJgV1w6PtlyFpfT8+GMXPyr0XGMsT0Au8t3XI6ycwfaDJfCjWeofAUTEVGt1bhPTmJiIgICAgxWwIIFCzB37lxoNBq0atUK8+fPR7du3arcdteuXejZs2el9nPnzlU4k3Q/7JPTcF1MTsdPm7dAe+042irj8YgqAUFIhqJ8Uj6lJRDcF2g3EmjWG1BVDvBERCSPOumTk5aWhtTUVERGRlZoP3z4MFQqFSIiIqq9r7Vr12LKlClYsGABunTpgu+++w79+vXD2bNn7zsHT1xcXIU36O7O+UboLtpSIP0ckHwchYlHcOPiYQQWXMZHCi1wd3bxai0Fm9bPAnauspRLRESGV+MzOR07dsQ777yDoUOHVmjfuHEjPvvsMxw+fLja+4qMjET79u2xcOFCfVvLli0xePBgzJ49u9L25Wdybt68CWdn55qUrcczOWZIpwNuxAMpx6X1oFKOSxP0ld6qtGmO0hkWfuGwDewANG4v9bex95ChaCIiqok6OZNz9uzZKufCeeSRR3D27Nlq76e4uBjHjh3DtGnTKrT36dMHBw4cuO9rH3nkERQWFiI0NBQffPBBlZewyhUVFaGoqEj/OCcnp9o1kgkrzAEu/gWc/QVI2CMteHmXfNggRhuEU6Ipbji3whOPD0B4m9Yc7k1E1EDUOOSo1Wpcv34dTZo0qdCu0WhgYVH93WVkZECr1cLT07NCu6enJ1JTU6t8jbe3NxYtWoTw8HAUFRVh5cqV6N27N3bt2nXP0VyzZ8/GzJkzq10XmbBbN4G4bcDZXyvPNKxSA95tkOkUhjXJbth43QPxwhtuDjb4V59gvBzu++CFNImIyKzUOOQ8/vjjmD59On799Vc4OTkBALKysvDee+/h8ccfr3EBd08YKIS45ySCwcHBCA4O1j+OiorC1atX8cUXX9wz5EyfPh1Tp07VP87JyYGfn1+N6ySZ5GcCcVukYBO/G9CV3H7OtRkQOggIHoB0+2B8uT0ea49K891YWSjxWrcgvNqjGezVnM+GiKghqvFv/y+//BLdu3dHQEAAHnnkEQDAyZMn4enpiZUrV1Z7P25ublCpVJXO2qSlpVU6u3M/nTp1wo8//njP59VqNdfVqm/y0oBzv0nB5so+QGhvP+cRCrQcKIUbj5YoLNVh6f4r+GbnfuQVSZP2PdnGG+/2DYGfi61Mb4CIiExBjUNO48aNcerUKaxatQoxMTGwsbHBSy+9hOeee67KOXPuxcrKCuHh4YiOjsbTTz+tb4+OjsagQYOqvZ8TJ07A29u7Ru+BTFBOyu1gk3gAwB394b3aSKEmdBDg1lzffDO/GKOX/oNT16T+OG19nfDhk6GICOSEfUREVMtlHezs7DBhwoSHPvjUqVMxatQoREREICoqCosWLUJSUhImTpwIQLrUlJycjBUrVgAA5s+fj8DAQLRq1QrFxcX48ccfsWHDBmzYsOGhayEZCAFc+BPY9xVw9VDF5xqHS6Gm5UDAJajSS9NyCjFy8WFcuJ4HFzsrfDCgJQa3awwl+90QEVGZWndWOHv2LJKSklBcXFyhfeDAgdXex/Dhw5GZmYlZs2ZBo9EgLCwMf/zxh36yQY1Gg6SkJP32xcXFePvtt5GcnAwbGxu0atUKW7ZsQf/+/Wv7NkgOQgDxu4AdnwDJR2+3+3UqCzZPAc737jd17WYBXvjhMBIzC+DlaI0fx0eimYe98esmIqJ6pcbz5MTHx+Ppp59GbGwsFAoFyl9e3llYq9Xe7+Wy4zw5Mks8KIWbxH3SYwsbIPIV6ebo88CXx6fn4YUfDkOTXQg/Fxv8NL4T+94QETUAtfn7razpQSZPnoygoCBcv34dtra2OHPmDPbs2YOIiAjs2rWrprujhiL5OPDjEGBpXyngqKyAyFeByTHA4zOrFXDOaXIw7LtD0GQXoqm7Hda90pkBh4iI7qnGl6sOHjyIHTt2wN3dHUqlEkqlEl27dsXs2bPx5ptv4sSJE8aok+qr62eAnZ8C53+XHistgEdGAd3fBpx8q72bk1ezMHrJP8i+VYJQb0esHNcRrvYcNUdERPdW45Cj1Wphby/1f3Bzc0NKSgqCg4MREBCAuLg4gxdI9VTGJWDXbOD0BgACUCilVb0ffbfKjsT3cyg+E+OWHUF+sRbt/Z2x9KWOcLLh4plERHR/NQ45YWFhOHXqFJo0aYLIyEh8/vnnsLKywqJFiyrNgkwN0M1EYPfnQMxPgNBJba2eBnpMB9yD7//aKuyKS8MrK4+hqFSHzk1d8f2LEbDj5H5ERFQNNf5r8cEHHyA/Px8A8Mknn+DJJ59Et27d4OrqirVr1xq8QKoncjTAnrnA8RW3ZyVu0Q/o+R7g3aZWu9waq8Gba06gRCvQO8QD37zQHtaWKgMWTURE5qzGo6uqcuPGDTRq1OieyzGYEo6uMrD8DGmemyM/AKWFUluTnkCvDwDfiFrvdsOxa/jX+hjohDSD8VfD28FSVeN+8kREZCaMvgp5aWkprK2tcfLkSYSFhenbXVw4w2yDcysLOPg1cGghUJwntflHSeEmsOtD7XrlwSv48NczAIBhEb6Y/UwbLq5JREQ1VqOQY2FhgYCAAJOfC4eMqDgfOPwdsP8/QGGW1ObdDuj9IdC0N/CQZ/O+3X0Zc7aeBwCM6RyIfz8ZylmMiYioVmrVJ2f69On48ccfeQanISktAo4tA/Z8AeSnSW3uIdKZm5AnHzrcCCEwL/oC/rfjEgDgjZ7N8FafFvXiEigREZmmGoec//73v7h06RJ8fHwQEBAAOzu7Cs8fP37cYMWRCdCWSiOldn8OZF+V2hoFAj3eA1oPBZQP3xFYCIFZv5/F0v1XAADv9A3Gaz2aPfR+iYioYatxyBk8eLARyiCTo9MBZzZKE/nduCy1OfgAj74DPDISUBlmnppSrQ7vbzqNtUelADVrUCu8GBVokH0TEVHDZpDRVfUJR1c9gBDAhW3S+lLXT0tttq5At7eAiHGApbXBDpWRV4Q3V5/AgcuZUCqAz4a0wbMR916Yk4iIGi6jj64iMxe/C9j+8e2VwdVOQOdJQKeJgNrBoIc6lngDr606jus5RbC1UmHesLboG+Zt0GMQEVHDVuOQo1Qq79sZlCOv6qG088DWfwEJe6THlrZA5EQp4NgatnO5EALLD1zBJ1vOoVQn0NTdDt+ODEdzT8OGKCIiohqHnE2bNlV4XFJSghMnTmD58uWYOXOmwQqjOqDTAocWSGdvtEXSyuARY6VLU/YeBj9cflEppm+MxeaYFADAgNbe+GxoG9hzmQYiIjICg/XJ+emnn7B27Vr8+uuvhtid0bBPTpkbCcAvrwFJB6THzR4HnpwHOPsb5XCX0vLw6o/HcDEtDxZKBab3b4mxXQI5RJyIiKpF1j45kZGRePnllw21OzIWIYBjS4E/PwBK8gEre+CJT4H2Lz70XDf38kesBv9aF4P8Yi08HNT45oX26BDIOZaIiMi4DBJybt26hf/973/w9fU1xO7IWHJSgF/fAC5vlx4HdAUGLwAaBRjlcCVaHT7beh4/7EsAAEQGueB/zz8CDwfDjdAiIiK6lxqHnLsX4hRCIDc3F7a2tvjxxx8NWhwZiBDAqZ+lzsWF2YCFNfDYDKDjK4DSOItepuUU4o2fTuCfKzcAAK882gT/6hMMCy6ySUREdaTGIeerr76qEHKUSiXc3d0RGRmJRo0aGbQ4MoD8DOD3KcC536THjcOBwd8C7i2MdsjD8Zl4/acTyMgrgr3aAl882xZ9w7yMdjwiIqKq1DjkjBkzxghlkFGc+x34bTJQkAEoLYAe04Au/weojDOaSQiB7/fG47NtcdDqBII9HbBwZHs0cbc3yvGIiIjup8Z/7ZYuXQp7e3s8++yzFdrXrVuHgoICjB492mDFUS3dygK2vgucWiM99mgFPP0t4N3GaIfMLSzBO+tPYevpVADA0480xv97Ogy2VhweTkRE8qhxB4k5c+bAzc2tUruHhwc+/fRTgxRFD+HSdmBBlBRwFEqg6/8BE3YaNeBcuJ6LQV/vx9bTqbBUKfDx4DDMG9aWAYeIiGRV479CiYmJCAoKqtQeEBCApKQkgxRFtVCUB0T/Gzi6WHrs0lQ6e+PX0aiHPafJwYhFh5B9qwTeTtZY8EJ7POLPvllERCS/GoccDw8PnDp1CoGBgRXaY2Ji4Orqaqi6qCZuJgI/DgEyL0qPO06QRk9Z2Rn1sJfT8zBq8WFk3yrBI/7O+OHFCLjaq416TCIiouqqccgZMWIE3nzzTTg4OKB79+4AgN27d2Py5MkYMWKEwQukB7h+Blj5DJCXCjj4AE8vBJr0MPphr94owMgfDiMjrxitfByx7KWOcLKxNPpxiYiIqqvGIeeTTz5BYmIievfuDQsL6eU6nQ4vvvgi++TUtcQDwE8jgKJswCMUGLkBcPQx+mGv5xTihR8OQ5NdiGYe9lgxlgGHiIhMT63Xrrp48SJOnjwJGxsbtG7dGgEBxpk119DMZu2q81uA9WOB0kLArxPw/BrAxvh9YW7kF2P4dwdxMS0P/i62+PmVKHg5cQZjIiIyrjpdu6p58+Zo3rx5bV9OD+PYcmmCP6EDgvsDQ5cAljZGP2z2rRKMWnwYF9Py4OVojVXjIxlwiIjIZNV4CPnQoUMxZ86cSu1z586tNHcOGZgQwJ4vgN/elALOIyOBYSvrJOAUFJdi7LIjOJOSA1c7K/w4PhJ+LrZGPy4REVFt1Tjk7N69GwMGDKjU3rdvX+zZs8cgRVEVdDpg2zRgx8fS465TgYFfG2324jsVlmjx8oqjOJZ4E47WFlg5LhLNPDiLMRERmbYa/4XMy8uDlZVVpXZLS0vk5OQYpCi6S2kx8MtE4PQG6XHfOUCnV+vk0CVaHd746Tj2X8qEnZUKy8d2RKhPPe7LREREDUaNz+SEhYVh7dq1ldrXrFmD0NBQgxRFdyjKBX56Vgo4SktgyOI6CzhancDUn2Pw97k0qC2U+GF0B070R0RE9UaNz+R8+OGHGDJkCC5fvoxevXoBALZv346ffvoJ69evN3iBDVpeuhRwUk4AlnbA8JVAs951cmidTuC9jbH4LSYFlioFvh0ZjqimnOyRiIjqjxqHnIEDB+KXX37Bp59+ivXr18PGxgZt27bFjh076veQbFNz84o0yd+Ny4CtK/DCOqBxeJ0cWgiBj7ecxdqjV6FUAP8d8Qh6hnjUybGJiIgMpdbz5JTLysrCqlWrsHjxYsTExECr1RqqNqOoF/PkpJ6WlmnISwWc/IFRGwG3uhuu/+VfcfjfjksAgC+ebYuh4b51dmwiIqKq1Obvd4375JTbsWMHRo4cCR8fH3z99dfo378/jh49WtvdUbkr+4Gl/aWA49EKGPdXnQacb3df1gecjwe1YsAhIqJ6q0aXq65du4Zly5ZhyZIlyM/Px7Bhw1BSUoINGzaw07EhnPtdmsVYWwT4dwaeWw3YONfZ4VcevII5W88DAKb1C8GoqMA6OzYREZGhVftMTv/+/REaGoqzZ8/if//7H1JSUvC///3PmLU1LHHbgJ9HSQEneIB0iaoOA876Y9fw4a9nAACTejXDxEeb1tmxiYiIjKHaZ3L++usvvPnmm3j11Ve5nIOhCSFN8id0QJsRwKBv6mSSv3K74tLwzvoYAMDYLkGY+niLOjs2ERGRsVT7TM7evXuRm5uLiIgIREZG4uuvv0Z6eroxa2s4EnYD108DlrZAvzl1GnBu5hfjX+tPQSeAZ8N98eGTLaFQKOrs+ERERMZS7ZATFRWF77//HhqNBq+88grWrFmDxo0bQ6fTITo6Grm5ucas07wd/Eb6+cjIOllJ/E4fbT6D9NwiNHW3w8eDwxhwiIjIbNR4dJWtrS3Gjh2Lffv2ITY2Fm+99RbmzJkDDw8PDBw40Bg1mrf0OODiXwAUQOTEOj30H7EabI5JgUqpwJfD2sHaUlWnxyciIjKmWg8hB4Dg4GB8/vnnuHbtGlavXm2omhqWQwuknyEDANe66+ybnluED345DQB49dGmaOfnXGfHJiIiqgsPFXLKqVQqDB48GJs3bzbE7hqO/AwgZo10P+qNOjusEALvb4rFjfxihHg54M3e7EhORETmxyAhh2rp6BKgtBDwaQ/4d6qzw246kYy/zl6HpUqBecPawcqCXwMiIjI//Osml5JC4J9F0v2o14E66vCryb6FjzZL8+FM7t0coT4murQFERHRQ2LIkcvp9UB+OuDoC4QOqpNDCiHw7oZY5BaWoq2vEyf8IyIis8aQIwchbg8bj3wFUFnWyWFX/3MVey6kQ22hxJfD2sFCxf/8RERkvvhXTg6XdwBpZwEre6D9i3VyyKs3CvDJlrMAgH89EYxmHvZ1clwiIiK5MOTIQT/536g6WZ9KpxN4e10MCoq16BjogrFdgox+TCIiIrkx5NS162eBy9sBhVK6VFUHlh24gsMJN2BrpcLcZ9tAqeSsxkREZP4YcuqafvK/JwEX459RuZyeh8+2nQcAvNe/JQJc7Yx+TCIiIlPAkFOX8tKAUz9L9+tg8r9SrQ5vr4tBUakO3Zq74YVIf6Mfk4iIyFQw5NSlI4sBbRHQOALw62j0wy3aG48TSVlwUFvgsyFtuPgmERE1KAw5daXkFnDkB+l+HUz+dz41B19FXwAAfDSwFXycbYx6PCIiIlPDkFNXTv0MFGQATv5AS+Ou1l5cqsPUtTEo0Qo81tITQ9o3NurxiIiITBFDTl24c/K/ThMBlYVRD/f1zks4q8lBI1tLfPpMGC9TERFRg8SQUxcubQcy4gArB2luHCM6dS0L3+y8BAD4eHAYPBysjXo8IiIiU8WQUxcOfi39DB8NWBtvQczCEi2m/hwDrU7gyTbeeLKNj9GORUREZOoYcozt+hkgfmedTP43L/oCLqXlwc1ejY8HhRn1WERERKZO9pCzYMECBAUFwdraGuHh4di7d2+1Xrd//35YWFigXbt2xi3wYZX3xQkdBDgbb56ao1du4Pu98QCAOc+0RiM7K6Mdi4iIqD6QNeSsXbsWU6ZMwfvvv48TJ06gW7du6NevH5KSku77uuzsbLz44ovo3bt3HVVaS7mpdTL5X4lWh3+tPwUhgKHhvngs1NNoxyIiIqovZA058+bNw7hx4zB+/Hi0bNkS8+fPh5+fHxYuXHjf173yyit4/vnnERUVVUeV1tKRHwBdCeAXCfhGGO0wv8WkICEjH652Vvj3U6FGOw4REVF9IlvIKS4uxrFjx9CnT58K7X369MGBAwfu+bqlS5fi8uXL+Oijj6p1nKKiIuTk5FS41YniAmmGY0Ca/M9IdDqBb3dfBgCM7RoER2tLox2LiIioPpEt5GRkZECr1cLTs+KlFU9PT6Smplb5mosXL2LatGlYtWoVLCyqN9fM7Nmz4eTkpL/5+fk9dO3VcmoNcOsG4BwgLcZpJNvPp+HC9TzYqy0wslOA0Y5DRERU38je8fjuieqEEFVOXqfVavH8889j5syZaNGiRbX3P336dGRnZ+tvV69efeiaH0inAw6WrTbe6VVAqTLKYYQQWLBLmhNnZKcAONnwLA4REVE54069ex9ubm5QqVSVztqkpaVVOrsDALm5uTh69ChOnDiBN96QOvHqdDoIIWBhYYG//voLvXr1qvQ6tVoNtVptnDdxL5eigcyLgNoReGSk0Q5zOOEGTiRlwcpCibFdA412HCIiovpItjM5VlZWCA8PR3R0dIX26OhodO7cudL2jo6OiI2NxcmTJ/W3iRMnIjg4GCdPnkRkZGRdlf5gd07+p3Yw2mEW7JL64jwb7suZjYmIiO4i25kcAJg6dSpGjRqFiIgIREVFYdGiRUhKSsLEiRMBSJeakpOTsWLFCiiVSoSFVZzgzsPDA9bW1pXaZaU5BSTsARQqoKPxJv87nZyNPRfSoVQAr3RvarTjEBER1Veyhpzhw4cjMzMTs2bNgkajQVhYGP744w8EBEgdaDUazQPnzDE5h8r64rQaDDgbr5PzwrIRVU+28YG/q63RjkNERFRfKYQQQu4i6lJOTg6cnJyQnZ0NR0cDryOVowHmt5bmxnl5J9C4vWH3XyYhIx+9v9wFnQC2Tu6Glt7GWw+LiIjIFNTm77fso6vMypHvpYDj39loAQcAFu25DJ0AeoV4MOAQERHdA0OOoRTnA0eXSPeNOPnf9ZxCbDiWDAB4tQf74hAREd0LQ46hpJ+XVhpvFAQE9zPaYRbvS0CxVocOgY3QIdDFaMchIiKq72TteGxWGocD/3cGuJlotMn/sgtKsOpQIgDgtR7NjHIMIiIic8EzOYZkaQN4hBht9ysOXkF+sRYhXg7oEexutOMQERGZA4aceuJWsRZLD1wBIPXFqWrpCyIiIrqNIaeeWHMkCTfyi+HvYosBrb3lLoeIiMjkMeTUAyVaHb7fEw8AmNC9CSxU/M9GRET0IPxrWQ/8ejIFKdmFcLNXY2i4r9zlEBER1QsMOSZOpxP4tmwJh3Fdg2BtaZyRW0REROaGIcfERZ+7jktpeXCwtsDITv5yl0NERFRvMOSYMCEEFuySzuK8GBUAB2tLmSsiIiKqPxhyTNjB+EzEXM2C2kKJl7oEyV0OERFRvcKQY8IWlp3FGd7BD272apmrISIiql8YckxU7LVs7L2YAZVSgZe7NZG7HCIionqHIcdELdx9CQAwsK0P/FxsZa6GiIio/mHIMUGX0/Ow9XQqAGDio01lroaIiKh+YsgxQYt2x0MI4LGWHgj2cpC7HCIionqJIcfEaLJvYeOJawCAV3s0k7kaIiKi+oshx8T8sDcBJVqByCAXhAc0krscIiKieoshx4TczC/G6n+SAACv9mBfHCIioofBkGNClh+8goJiLUK9HfFoC3e5yyEiIqrXGHJMREFxKZYduAJAOoujUCjkLYiIiKieY8gxEeuPXUNWQQkCXW3Rv7W33OUQERHVeww5JuJw/A0AwLAOflApeRaHiIjoYTHkmIizmhwAQJvGzvIWQkREZCYYckxAXlEprmTmAwBaenPyPyIiIkNgyDEBcak5EALwcrSGK1cbJyIiMgiGHBNwNkW6VBXq4yhzJUREROaDIccElPfHCfVmyCEiIjIUhhwTUH4mpyVDDhERkcEw5MisVKvD+dRcALxcRUREZEgMOTJLyMhHUakOtlYqBLjYyl0OERGR2WDIkVl5f5yW3o5QchJAIiIig2HIkZl+ZBX74xARERkUQ47M9COr2B+HiIjIoBhyZCSE4JkcIiIiI2HIkVFabhEy84uhVADBXlzOgYiIyJAYcmRUfhanqbs9rC1VMldDRERkXhhyZMT+OERERMbDkCMj9schIiIyHoYcGfFMDhERkfEw5Mgkr6gUVzLzAXDNKiIiImNgyJFJXGoOhAA8HdVws1fLXQ4REZHZYciRCfvjEBERGRdDjkzYH4eIiMi4GHJkcvtMjpPMlRAREZknhhwZlGp1OJ+aC4BncoiIiIyFIUcGCRn5KCrVwdZKhQAXW7nLISIiMksMOTIo74/T0tsRSqVC5mqIiIjME0OODDiyioiIyPgYcmTAkVVERETGx5BTx4QQPJNDRERUBxhy6lhabhEy84uhVADBXg5yl0NERGS2GHLqWPlZnKbu9rC2VMlcDRERkfliyKlj7I9DRERUNxhy6pg+5LA/DhERkVEx5NSxcyk8k0NERFQXGHLqUH5RKRIy8wFIEwESERGR8TDk1KHzqbkQAvB0VMPNXi13OURERGZN9pCzYMECBAUFwdraGuHh4di7d+89t923bx+6dOkCV1dX2NjYICQkBF999VUdVvtw2B+HiIio7ljIefC1a9diypQpWLBgAbp06YLvvvsO/fr1w9mzZ+Hv719pezs7O7zxxhto06YN7OzssG/fPrzyyiuws7PDhAkTZHgHNXOW/XGIiIjqjEIIIeQ6eGRkJNq3b4+FCxfq21q2bInBgwdj9uzZ1drHM888Azs7O6xcubJa2+fk5MDJyQnZ2dlwdKzbsDHom/2IuZqFb55vjwFtvOv02ERERPVZbf5+y3a5qri4GMeOHUOfPn0qtPfp0wcHDhyo1j5OnDiBAwcO4NFHHzVGiQZVqtXhPOfIISIiqjOyXa7KyMiAVquFp6dnhXZPT0+kpqbe97W+vr5IT09HaWkpZsyYgfHjx99z26KiIhQVFekf5+TkPFzhtXQlMx9FpTrYWqkQ4GIrSw1EREQNiewdjxUKRYXHQohKbXfbu3cvjh49im+//Rbz58/H6tWr77nt7Nmz4eTkpL/5+fkZpO6aOlPWH6eltyOUyvu/PyIiInp4sp3JcXNzg0qlqnTWJi0trdLZnbsFBQUBAFq3bo3r169jxowZeO6556rcdvr06Zg6dar+cU5OjixBhyOriIiI6pZsZ3KsrKwQHh6O6OjoCu3R0dHo3LlztfcjhKhwOepuarUajo6OFW5y4MgqIiKiuiXrEPKpU6di1KhRiIiIQFRUFBYtWoSkpCRMnDgRgHQWJjk5GStWrAAAfPPNN/D390dISAgAad6cL774ApMmTZLtPVSHEOJ2yOGZHCIiojoha8gZPnw4MjMzMWvWLGg0GoSFheGPP/5AQEAAAECj0SApKUm/vU6nw/Tp05GQkAALCws0bdoUc+bMwSuvvCLXW6iW9NwiZOYXQ6kAgr0c5C6HiIioQZB1nhw5yDFPzs64NLy09Aiae9gjeqrpD3cnIiIyNfVqnpyGhP1xiIiI6h5DTh3gyCoiIqK6x5BTB87xTA4REVGdY8gxsvyiUiRk5gOQJgIkIiKiusGQY2TnU3MhBODpqIabvVrucoiIiBoMhhwjY38cIiIieTDkGBlHVhEREcmDIcfIbp/JcZK5EiIiooaFIceISrU6nNfwTA4REZEcGHKM6EpmPopKdbC1UiHAxVbucoiIiBoUhhwjOlPWHyfEywFKpULmaoiIiBoWhhwjOstLVURERLJhyDEi/cgqdjomIiKqcww5RiKE4PBxIiIiGTHkGEl6bhEy84uhVADBng5yl0NERNTgMOQYyZmy/jhN3O1hY6WSuRoiIqKGhyHHSG73x+GlKiIiIjkw5BgJR1YRERHJiyHHSM7xTA4REZGsGHKMIL+oFAmZ+QCAlgw5REREsmDIMYLzqbkQAvBwUMPdQS13OURERA0SQ44RsD8OERGR/BhyjIAjq4iIiOTHkGMEPJNDREQkP4YcAyvV6nBewzM5REREcmPIMbArmfkoKtXB1kqFAFc7ucshIiJqsBhyDOxMWX+cEC8HqJQKmashIiJquBhyDIz9cYiIiEwDQ46B3R5Z5SRzJURERA0bQ46BndPkAuCZHCIiIrkx5BhQWm4hMvKKoFQAwZ4OcpdDRETUoDHkGFD5paom7vawsVLJXA0REVHDxpBjQGc5Pw4REZHJYMgxIH2nY/bHISIikh1DjgHxTA4REZHpYMgxkILiUiRk5AMAWjLkEBERyc5C7gLMRUpWITwc1BACcHdQy10OERFRg8eQYyDNPOxx+L3HkFtYIncpREREBF6uMjgHa0u5SyAiIiIw5BAREZGZYsghIiIis8SQQ0RERGaJIYeIiIjMEkMOERERmSWGHCIiIjJLDDlERERklhhyiIiIyCwx5BAREZFZYsghIiIis8SQQ0RERGaJIYeIiIjMEkMOERERmSULuQuoa0IIAEBOTo7MlRAREVF1lf/dLv87Xh0NLuTk5uYCAPz8/GSuhIiIiGoqNzcXTk5O1dpWIWoSicyATqdDSkoKHBwcoFAoDLrvnJwc+Pn54erVq3B0dDTovs0ZP7ea42dWO/zcaoefW+3wc6u5+31mQgjk5ubCx8cHSmX1ets0uDM5SqUSvr6+Rj2Go6Mjv9C1wM+t5viZ1Q4/t9rh51Y7/Nxq7l6fWXXP4JRjx2MiIiIySww5REREZJYYcgxIrVbjo48+glqtlruUeoWfW83xM6sdfm61w8+tdvi51ZyhP7MG1/GYiIiIGgaeySEiIiKzxJBDREREZokhh4iIiMwSQw4RERGZJYYcA1mwYAGCgoJgbW2N8PBw7N27V+6STNqMGTOgUCgq3Ly8vOQuy+Ts2bMHTz31FHx8fKBQKPDLL79UeF4IgRkzZsDHxwc2Njbo0aMHzpw5I0+xJuRBn9uYMWMqff86deokT7EmYvbs2ejQoQMcHBzg4eGBwYMHIy4ursI2/L5VVp3Pjd+3yhYuXIg2bdroJ/2LiorC1q1b9c8b6rvGkGMAa9euxZQpU/D+++/jxIkT6NatG/r164ekpCS5SzNprVq1gkaj0d9iY2PlLsnk5Ofno23btvj666+rfP7zzz/HvHnz8PXXX+PIkSPw8vLC448/rl+jraF60OcGAH379q3w/fvjjz/qsELTs3v3brz++us4dOgQoqOjUVpaij59+iA/P1+/Db9vlVXncwP4fbubr68v5syZg6NHj+Lo0aPo1asXBg0apA8yBvuuCXpoHTt2FBMnTqzQFhISIqZNmyZTRabvo48+Em3btpW7jHoFgNi0aZP+sU6nE15eXmLOnDn6tsLCQuHk5CS+/fZbGSo0TXd/bkIIMXr0aDFo0CBZ6qkv0tLSBACxe/duIQS/b9V19+cmBL9v1dWoUSPxww8/GPS7xjM5D6m4uBjHjh1Dnz59KrT36dMHBw4ckKmq+uHixYvw8fFBUFAQRowYgfj4eLlLqlcSEhKQmppa4bunVqvx6KOP8rtXDbt27YKHhwdatGiBl19+GWlpaXKXZFKys7MBAC4uLgD4fauuuz+3cvy+3ZtWq8WaNWuQn5+PqKgog37XGHIeUkZGBrRaLTw9PSu0e3p6IjU1VaaqTF9kZCRWrFiBP//8E99//z1SU1PRuXNnZGZmyl1avVH+/eJ3r+b69euHVatWYceOHfjyyy9x5MgR9OrVC0VFRXKXZhKEEJg6dSq6du2KsLAwAPy+VUdVnxvA79u9xMbGwt7eHmq1GhMnTsSmTZsQGhpq0O9ag1uF3FgUCkWFx0KISm10W79+/fT3W7dujaioKDRt2hTLly/H1KlTZays/uF3r+aGDx+uvx8WFoaIiAgEBARgy5YteOaZZ2SszDS88cYbOHXqFPbt21fpOX7f7u1enxu/b1ULDg7GyZMnkZWVhQ0bNmD06NHYvXu3/nlDfNd4Juchubm5QaVSVUqXaWlplVIo3ZudnR1at26Nixcvyl1KvVE+Go3fvYfn7e2NgIAAfv8ATJo0CZs3b8bOnTvh6+urb+f37f7u9blVhd83iZWVFZo1a4aIiAjMnj0bbdu2xX/+8x+DftcYch6SlZUVwsPDER0dXaE9OjoanTt3lqmq+qeoqAjnzp2Dt7e33KXUG0FBQfDy8qrw3SsuLsbu3bv53auhzMxMXL16tUF//4QQeOONN7Bx40bs2LEDQUFBFZ7n961qD/rcqsLvW9WEECgqKjLsd81AnaIbtDVr1ghLS0uxePFicfbsWTFlyhRhZ2cnrly5IndpJuutt94Su3btEvHx8eLQoUPiySefFA4ODvzM7pKbmytOnDghTpw4IQCIefPmiRMnTojExEQhhBBz5swRTk5OYuPGjSI2NlY899xzwtvbW+Tk5Mhcubzu97nl5uaKt956Sxw4cEAkJCSInTt3iqioKNG4ceMG/bm9+uqrwsnJSezatUtoNBr9raCgQL8Nv2+VPehz4/etatOnTxd79uwRCQkJ4tSpU+K9994TSqVS/PXXX0IIw33XGHIM5JtvvhEBAQHCyspKtG/fvsLwQaps+PDhwtvbW1haWgofHx/xzDPPiDNnzshdlsnZuXOnAFDpNnr0aCGENKz3o48+El5eXkKtVovu3buL2NhYeYs2Aff73AoKCkSfPn2Eu7u7sLS0FP7+/mL06NEiKSlJ7rJlVdXnBUAsXbpUvw2/b5U96HPj961qY8eO1f/NdHd3F71799YHHCEM911TCCFELc8sEREREZks9skhIiIis8SQQ0RERGaJIYeIiIjMEkMOERERmSWGHCIiIjJLDDlERERklhhyiIiIyCwx5BARQVoM8JdffpG7DCIyIIYcIpLdmDFjoFAoKt369u0rd2lEVI9ZyF0AEREA9O3bF0uXLq3QplarZaqGiMwBz+QQkUlQq9Xw8vKqcGvUqBEA6VLSwoUL0a9fP9jY2CAoKAjr1q2r8PrY2Fj06tULNjY2cHV1xYQJE5CXl1dhmyVLlqBVq1ZQq9Xw9vbGG2+8UeH5jIwMPP3007C1tUXz5s2xefNm475pIjIqhhwiqhc+/PBDDBkyBDExMRg5ciSee+45nDt3DgBQUFCAvn37olGjRjhy5AjWrVuHv//+u0KIWbhwIV5//XVMmDABsbGx2Lx5M5o1a1bhGDNnzsSwYcNw6tQp9O/fHy+88AJu3LhRp++TiAzIcGuKEhHVzujRo4VKpRJ2dnYVbrNmzRJCSCs9T5w4scJrIiMjxauvviqEEGLRokWiUaNGIi8vT//8li1bhFKpFKmpqUIIIXx8fMT7779/zxoAiA8++ED/OC8vTygUCrF161aDvU8iqlvsk0NEJqFnz55YuHBhhTYXFxf9/aioqArPRUVF4eTJkwCAc+fOoW3btrCzs9M/36VLF+h0OsTFxUGhUCAlJQW9e/e+bw1t2rTR37ezs4ODgwPS0tJq+5aISGYMOURkEuzs7CpdPnoQhUIBABBC6O9XtY2NjU219mdpaVnptTqdrkY1EZHpYJ8cIqoXDh06VOlxSEgIACA0NBQnT55Efn6+/vn9+/dDqVSiRYsWcHBwQGBgILZv316nNRORvHgmh4hMQlFREVJTUyu0WVhYwM3NDQCwbt06REREoGvXrli1ahX++ecfLF68GADwwgsv4KOPPsLo0aMxY8YMpKenY9KkSRg1ahQ8PT0BADNmzMDEiRPh4eGBfv36ITc3F/v378ekSZPq9o0SUZ1hyCEik7Bt2zZ4e3tXaAsODsb58+cBSCOf1qxZg9deew1eXl5YtWoVQkNDAQC2trb4888/MXnyZHTo0AG2trYYMmQI5s2bp9/X6NGjUVhYiK+++gpvv/023NzcMHTo0Lp7g0RU5xRCCCF3EURE96NQKLBp0yYMHjxY7lKIqB5hnxwiIiIySww5REREZJbYJ4eITB6vqhNRbfBMDhEREZklhhwiIiIySww5REREZJYYcoiIiMgsMeQQERGRWWLIISIiIrPEkENERERmiSGHiIiIzBJDDhEREZml/w+S5bPpfUjydgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 30\n",
    "epochs = np.linspace(0, 29, num=30)\n",
    "train_acc = np.full(30, 1) - train_err\n",
    "val_acc = np.full(30, 1) - val_err\n",
    "\n",
    "#print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 1/30 [00:55<27:00, 55.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.7916261742792355 Val err: 0.627739984882842 Train loss: 2.4108923420387227 Val loss: 2.052453290848505\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 2/30 [01:49<25:22, 54.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.685515873015873 Val err: 0.5868291761148904 Train loss: 2.109592969553458 Val loss: 1.8865423599878948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 3/30 [02:42<24:10, 53.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.6387269193391643 Val err: 0.5644368858654573 Train loss: 1.9744548204649297 Val loss: 1.7988247644333613\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 4/30 [03:35<23:13, 53.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.6088030450275348 Val err: 0.5480442176870748 Train loss: 1.8891956342934326 Val loss: 1.7319830826350622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 5/30 [04:30<22:32, 54.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.5871598639455783 Val err: 0.5332577475434618 Train loss: 1.8231752215271786 Val loss: 1.6752800941467285\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 6/30 [05:24<21:34, 53.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.5668326854551344 Val err: 0.5250377928949358 Train loss: 1.7677823218656945 Val loss: 1.6442287479128157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 7/30 [06:18<20:40, 53.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.5525388726919339 Val err: 0.521494708994709 Train loss: 1.7216586041326967 Val loss: 1.6225193057741438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 8/30 [07:12<19:51, 54.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.5405936183997408 Val err: 0.5095427059712774 Train loss: 1.6835602472483184 Val loss: 1.600076471056257\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 9/30 [08:08<19:07, 54.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.5291747651441528 Val err: 0.5010393046107332 Train loss: 1.6445267107820263 Val loss: 1.567852264358884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 10/30 [09:04<18:23, 55.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.5171080336896663 Val err: 0.49839380196523053 Train loss: 1.6159049562839647 Val loss: 1.553632140159607\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 11/30 [10:02<17:44, 56.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.5071873987690314 Val err: 0.4937641723356009 Train loss: 1.5817736887561225 Val loss: 1.5368135145732336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 12/30 [10:58<16:49, 56.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.49908892128279886 Val err: 0.491307634164777 Train loss: 1.5520864595403325 Val loss: 1.5240006730670022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████▎     | 13/30 [11:54<15:50, 55.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12: Train err: 0.4852202785876255 Val err: 0.4834656084656085 Train loss: 1.5230041896741007 Val loss: 1.5064897083100819\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 14/30 [12:51<14:58, 56.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13: Train err: 0.47746598639455784 Val err: 0.47831632653061223 Train loss: 1.4943311121797314 Val loss: 1.4910373403912498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 15/30 [13:46<13:59, 55.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14: Train err: 0.47023809523809523 Val err: 0.47845804988662133 Train loss: 1.4703172414414005 Val loss: 1.4860186633609591\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 16/30 [14:42<13:01, 55.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15: Train err: 0.4632531584062196 Val err: 0.47467876039304613 Train loss: 1.4506644646738478 Val loss: 1.4719285056704567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|█████▋    | 17/30 [15:38<12:08, 56.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16: Train err: 0.4574020084224166 Val err: 0.46589191232048377 Train loss: 1.429945264455568 Val loss: 1.4608158611115956\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 18/30 [16:35<11:15, 56.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.45070051830255914 Val err: 0.46589191232048377 Train loss: 1.4067059267370194 Val loss: 1.450236235346113\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 19/30 [17:31<10:18, 56.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.44446469063816 Val err: 0.4627267573696145 Train loss: 1.3922498559704717 Val loss: 1.4423476457595825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 20/30 [18:27<09:20, 56.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.43798590864917397 Val err: 0.4570578231292517 Train loss: 1.364297168242499 Val loss: 1.4302502813793363\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 21/30 [19:23<08:24, 56.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20: Train err: 0.42960398445092324 Val err: 0.4544123204837491 Train loss: 1.3467805861191429 Val loss: 1.424878858384632\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 22/30 [20:19<07:27, 55.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.4236111111111111 Val err: 0.4561130007558579 Train loss: 1.3265436208309904 Val loss: 1.425883168265933\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 23/30 [21:15<06:31, 55.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.41735503725299644 Val err: 0.45247543461829176 Train loss: 1.3068470368113543 Val loss: 1.4077407802854265\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 24/30 [22:10<05:35, 55.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23: Train err: 0.4080822805312601 Val err: 0.44982993197278914 Train loss: 1.2833741748888876 Val loss: 1.3999509868167697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 25/30 [23:06<04:39, 55.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24: Train err: 0.4035066407515387 Val err: 0.4489795918367347 Train loss: 1.266661707601399 Val loss: 1.4004171802884056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 26/30 [24:02<03:44, 56.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.4015225137674117 Val err: 0.44401927437641725 Train loss: 1.2557357574373946 Val loss: 1.3935267073767525\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 27/30 [24:59<02:48, 56.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.3915208940719145 Val err: 0.4399092970521542 Train loss: 1.2282658914210265 Val loss: 1.3825307403291975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 28/30 [25:55<01:52, 56.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.38423226433430513 Val err: 0.43905895691609975 Train loss: 1.2086919678925232 Val loss: 1.377817261786688\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 29/30 [26:51<00:56, 56.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.3875323939099449 Val err: 0.44057067271352984 Train loss: 1.2019888267615917 Val loss: 1.3804207415807814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [27:47<00:00, 55.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.3790694849368319 Val err: 0.43376795162509446 Train loss: 1.1828085153214054 Val loss: 1.3671921945753551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net = CNN2(fc1_out = 256, fc2_out = 64, dropout = 0.4)\n",
    "train_err, train_loss, val_err, val_loss = train(net, train_loader, val_loader, batch_size = 256, lr=0.0001, num_epochs = 30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 30\n",
    "epochs = np.linspace(0, 29, num=30)\n",
    "train_acc = np.full(30, 1) - train_err\n",
    "val_acc = np.full(30, 1) - val_err\n",
    "\n",
    "#print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 3: bs = 128, lr = 0.0001, epoch = 40, drop out = 0.4\n",
    "bs128_lr0.0001_epoch39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_set, batch_size=128, shuffle=True)\n",
    "val_loader = DataLoader(val_set, batch_size=1024, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▎         | 1/40 [00:59<38:45, 59.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.770752348558471 Val err: 0.609599395313681 Train loss: 2.3438905072953418 Val loss: 1.9841085956210183\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 2/40 [01:57<37:01, 58.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.6616456106252024 Val err: 0.5676965230536659 Train loss: 2.030874324277275 Val loss: 1.8169589269728887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 3/40 [02:55<35:58, 58.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.6158487204405572 Val err: 0.5495086923658352 Train loss: 1.904380224838158 Val loss: 1.7276963903790428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 4/40 [03:51<34:23, 57.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.5905814706835115 Val err: 0.532785336356765 Train loss: 1.8266343678217478 Val loss: 1.670900747889564\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▎        | 5/40 [04:47<33:12, 56.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.5643221574344023 Val err: 0.520880574452003 Train loss: 1.7544315929857561 Val loss: 1.6280884402138847\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.5450275348234532 Val err: 0.5109599395313681 Train loss: 1.701167638746568 Val loss: 1.6006890762419927\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.5328393262066732 Val err: 0.5014644746787604 Train loss: 1.658633724086643 Val loss: 1.5764076652980985\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.5170472951085195 Val err: 0.4967403628117914 Train loss: 1.6170075461654465 Val loss: 1.5526267687479656\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.5047983479105929 Val err: 0.48516628873771733 Train loss: 1.5692917108535767 Val loss: 1.5202708811987014\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.49501943634596696 Val err: 0.4856386999244142 Train loss: 1.5456408027540216 Val loss: 1.5213313556852794\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.4826085195983155 Val err: 0.47557634164777024 Train loss: 1.5107561322073861 Val loss: 1.4928491910298665\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.47339650145772594 Val err: 0.4742063492063492 Train loss: 1.4807185055060708 Val loss: 1.4800809110913957\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 12: Train err: 0.4639212827988338 Val err: 0.4700963718820862 Train loss: 1.454448371662377 Val loss: 1.4724089361372448\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 13: Train err: 0.45855604146420476 Val err: 0.46674225245653816 Train loss: 1.4257483229118308 Val loss: 1.4641455525443667\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 14: Train err: 0.4492225461613217 Val err: 0.46130952380952384 Train loss: 1.4039089327649132 Val loss: 1.4467656839461553\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 15: Train err: 0.44021298995788793 Val err: 0.45686885865457294 Train loss: 1.3748111663087044 Val loss: 1.4342082738876343\n"
     ]
    },
    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 16: Train err: 0.4327218982831228 Val err: 0.4584278155706727 Train loss: 1.349614080060949 Val loss: 1.4266428493318104\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.4232669258179462 Val err: 0.4544123204837491 Train loss: 1.3269174960610781 Val loss: 1.4214958179564703\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.4179624230644639 Val err: 0.4494992441421013 Train loss: 1.307597347182931 Val loss: 1.4108192977451144\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.4104713313896987 Val err: 0.446192365835223 Train loss: 1.2787147917895738 Val loss: 1.4015719095865886\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 20: Train err: 0.404316488500162 Val err: 0.44576719576719576 Train loss: 1.2632346712245843 Val loss: 1.3933251414980208\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.39692662779397475 Val err: 0.44288548752834467 Train loss: 1.2399400179250253 Val loss: 1.3884231646855671\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.3913791707159054 Val err: 0.43868102796674224 Train loss: 1.2189953652997092 Val loss: 1.3798240139370872\n"
     ]
    },
    {
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      " 60%|██████    | 24/40 [22:37<14:59, 56.23s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 23: Train err: 0.38818027210884354 Val err: 0.43934240362811794 Train loss: 1.2019903656732234 Val loss: 1.379436833517892\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 24: Train err: 0.37769274376417233 Val err: 0.4357520786092215 Train loss: 1.1785766547208005 Val loss: 1.3704347156343006\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 65%|██████▌   | 26/40 [24:30<13:06, 56.19s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.3710924846128928 Val err: 0.4347127739984883 Train loss: 1.1636322997703454 Val loss: 1.364889491172064\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.3664965986394558 Val err: 0.43211451247165533 Train loss: 1.1450228864047194 Val loss: 1.3581679718835014\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.36214366699060574 Val err: 0.4317838246409675 Train loss: 1.1252560322148812 Val loss: 1.362279006413051\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.3551384839650146 Val err: 0.4285241874527589 Train loss: 1.1071933301001633 Val loss: 1.350228746732076\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.349651765468092 Val err: 0.42545351473922904 Train loss: 1.0878308404603771 Val loss: 1.3438490345364524\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 30: Train err: 0.3477486232588273 Val err: 0.42644557823129253 Train loss: 1.0817597755496366 Val loss: 1.3493849947339012\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 31: Train err: 0.34193796566245543 Val err: 0.4252173091458806 Train loss: 1.0606382913848897 Val loss: 1.3402619191578455\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 32: Train err: 0.3360058309037901 Val err: 0.42243008314436886 Train loss: 1.0485110151644197 Val loss: 1.339216050647554\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 33: Train err: 0.3321590540978296 Val err: 0.42167422524565384 Train loss: 1.033217173475058 Val loss: 1.3362049772625877\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34: Train err: 0.3302964042759961 Val err: 0.42266628873771733 Train loss: 1.0270248548045677 Val loss: 1.333941312063308\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 90%|█████████ | 36/40 [34:02<03:48, 57.19s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35: Train err: 0.3248704243602203 Val err: 0.4212018140589569 Train loss: 1.0090648399115845 Val loss: 1.342031234786624\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36: Train err: 0.3222181729834791 Val err: 0.41770597127739983 Train loss: 0.998328246292055 Val loss: 1.3336564813341414\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37: Train err: 0.3159013605442177 Val err: 0.4166666666666667 Train loss: 0.9818913124385893 Val loss: 1.3241847923823766\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 39/40 [36:54<00:57, 57.21s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38: Train err: 0.3111839974084872 Val err: 0.41463529856387 Train loss: 0.9674866041988907 Val loss: 1.3224246899286907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [37:51<00:00, 56.79s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39: Train err: 0.3095440557175251 Val err: 0.41761148904006046 Train loss: 0.9611894151707387 Val loss: 1.326066596167428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net3 = CNN2(fc1_out = 256, fc2_out = 64, dropout = 0.4)\n",
    "train_err, train_loss, val_err, val_loss = train(net3, train_loader, val_loader, batch_size = 128, lr=0.0001, num_epochs = 40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 40\n",
    "epochs = np.linspace(0, epoch_size, epoch_size)\n",
    "train_acc = np.full(epoch_size, 1) - train_err\n",
    "val_acc = np.full(epoch_size, 1) - val_err\n",
    "\n",
    "#print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/43 [00:58<40:56, 58.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.7954122125040493 Val err: 0.6344009826152683 Train loss: 2.423706950918998 Val loss: 2.0790639491308305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▍         | 2/43 [01:57<40:05, 58.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.6938977972141237 Val err: 0.5999149659863946 Train loss: 2.1351596450558596 Val loss: 1.9224014111927576\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 3/43 [02:53<38:20, 57.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.6519476838354389 Val err: 0.5753968253968254 Train loss: 2.0075651885931975 Val loss: 1.8284124249503726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 4/43 [03:48<36:38, 56.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.6251619695497247 Val err: 0.5601851851851852 Train loss: 1.9326310117627672 Val loss: 1.7666958570480347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 5/43 [04:42<35:22, 55.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.6016156462585034 Val err: 0.5436507936507936 Train loss: 1.8660598712263947 Val loss: 1.7160368703660511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 6/43 [05:37<34:07, 55.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.5829891480401684 Val err: 0.5316043083900227 Train loss: 1.816421017745616 Val loss: 1.6830974079313732\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.5710236475542598 Val err: 0.5249433106575964 Train loss: 1.7763290680134234 Val loss: 1.6577571233113606\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.5578838678328474 Val err: 0.5153533635676493 Train loss: 1.7357212545958207 Val loss: 1.6363725718997775\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.5448048266925818 Val err: 0.5083144368858654 Train loss: 1.6967822356545246 Val loss: 1.6040255285444713\n"
     ]
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    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.5347222222222222 Val err: 0.5091175359032502 Train loss: 1.6667905863702606 Val loss: 1.59261573496319\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.5264010366051183 Val err: 0.5034486016628874 Train loss: 1.6471238908372394 Val loss: 1.580599960826692\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.5199829931972789 Val err: 0.4970710506424792 Train loss: 1.6136408238213298 Val loss: 1.5576874698911394\n"
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    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 12: Train err: 0.5110139293812763 Val err: 0.4917800453514739 Train loss: 1.5939272608164061 Val loss: 1.5470502376556396\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 13: Train err: 0.5026522513767412 Val err: 0.48894557823129253 Train loss: 1.570421854757892 Val loss: 1.5340673355829149\n"
     ]
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    {
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     "text": [
      "Epoch 14: Train err: 0.4944120505344995 Val err: 0.4843631897203326 Train loss: 1.551195622725808 Val loss: 1.5272061086836315\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 15: Train err: 0.48756883705863296 Val err: 0.48011148904006046 Train loss: 1.5227894560660722 Val loss: 1.506768402599153\n"
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    {
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     "text": [
      "Epoch 16: Train err: 0.47681810819565923 Val err: 0.4769935752078609 Train loss: 1.497974344485782 Val loss: 1.4955093065897624\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.4727081308713962 Val err: 0.4732142857142857 Train loss: 1.4801489361827238 Val loss: 1.484957950455802\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.4709467120181406 Val err: 0.4750094482237339 Train loss: 1.4667527048698978 Val loss: 1.4806907971700032\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.4589204729510852 Val err: 0.4691987906273621 Train loss: 1.43908925748242 Val loss: 1.472100399789356\n"
     ]
    },
    {
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    {
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     "text": [
      "Epoch 20: Train err: 0.4506397797214124 Val err: 0.4656084656084656 Train loss: 1.4157645717803677 Val loss: 1.4557052793956937\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.4487366375121477 Val err: 0.4657501889644747 Train loss: 1.4047768792340174 Val loss: 1.4509875093187605\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.4442824748947198 Val err: 0.46381330309901736 Train loss: 1.3897040841492965 Val loss: 1.4427076067243303\n"
     ]
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    {
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     "text": [
      "Epoch 23: Train err: 0.4366091674765144 Val err: 0.4603174603174603 Train loss: 1.369575105182865 Val loss: 1.4313897291819255\n"
     ]
    },
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    {
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     "text": [
      "Epoch 24: Train err: 0.4309402332361516 Val err: 0.45762471655328796 Train loss: 1.354610034530027 Val loss: 1.4320628643035889\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.42705296404275994 Val err: 0.4555933484504913 Train loss: 1.333899097739106 Val loss: 1.4244469971883864\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.4225178166504697 Val err: 0.4541761148904006 Train loss: 1.3171749541178885 Val loss: 1.417424582299732\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.4176789763524457 Val err: 0.44968820861678005 Train loss: 1.304964492049242 Val loss: 1.4134901478177024\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.4078595724003887 Val err: 0.4468537414965986 Train loss: 1.280543928010476 Val loss: 1.404855972244626\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.4078595724003887 Val err: 0.44590891912320485 Train loss: 1.2705377411348215 Val loss: 1.3953042995362055\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 30: Train err: 0.4013605442176871 Val err: 0.4456727135298564 Train loss: 1.257112518030127 Val loss: 1.3978997185116722\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 31: Train err: 0.39591431810819566 Val err: 0.4446806500377929 Train loss: 1.2433523690144632 Val loss: 1.3849407377697172\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 32: Train err: 0.39502348558471007 Val err: 0.4379724111866969 Train loss: 1.2321707946031204 Val loss: 1.3771775960922241\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 33: Train err: 0.38903061224489793 Val err: 0.43820861678004536 Train loss: 1.212623700113494 Val loss: 1.3759391080765497\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 34: Train err: 0.3805474570780693 Val err: 0.4398148148148148 Train loss: 1.2023581607662952 Val loss: 1.3744707107543945\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 35: Train err: 0.37965662455458377 Val err: 0.43934240362811794 Train loss: 1.1851014423864492 Val loss: 1.3739550454275948\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 36: Train err: 0.376498218334953 Val err: 0.43542139077853365 Train loss: 1.1728855637071047 Val loss: 1.3680180935632615\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 37: Train err: 0.3691893424036281 Val err: 0.43428760393046106 Train loss: 1.1570855416164496 Val loss: 1.3590558369954426\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38: Train err: 0.36872367994816974 Val err: 0.4335789871504157 Train loss: 1.150718051380444 Val loss: 1.3542043084190005\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39: Train err: 0.3623866213151927 Val err: 0.42975245653817085 Train loss: 1.1320940932461634 Val loss: 1.351747745559329\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40: Train err: 0.3598558471007451 Val err: 0.4277683295540438 Train loss: 1.1173540342965893 Val loss: 1.3463595935276575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 42/43 [39:00<00:54, 54.95s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41: Train err: 0.35574586977648204 Val err: 0.4309334845049131 Train loss: 1.1052409680704995 Val loss: 1.3429983683994837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 43/43 [39:56<00:00, 55.73s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42: Train err: 0.351149983803045 Val err: 0.4261621315192744 Train loss: 1.0933742845923171 Val loss: 1.336508313814799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net4 = CNN2(fc1_out = 256, fc2_out = 64, dropout = 0.4)\n",
    "train_err, train_loss, val_err, val_loss = train(net4, train_loader, val_loader, batch_size = 128, lr=0.00005, num_epochs = 43)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 43\n",
    "epochs = np.linspace(0, epoch_size, epoch_size)\n",
    "train_acc = np.full(epoch_size, 1) - train_err\n",
    "val_acc = np.full(epoch_size, 1) - val_err\n",
    "\n",
    "#print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(0.8 * len(alexembed))\n",
    "val_size = len(alexembed) - train_size\n",
    "train_set, val_set = torch.utils.data.random_split(alexembed, [train_size, val_size])\n",
    "\n",
    "train_loader = DataLoader(train_set, batch_size=128, shuffle=True)\n",
    "val_loader = DataLoader(val_set, batch_size=1024, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/43 [01:00<42:14, 60.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train err: 0.7709396258503401 Val err: 0.6152919501133787 Train loss: 2.3422223348466176 Val loss: 1.972417141710009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▍         | 2/43 [01:59<40:46, 59.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train err: 0.6623618197278912 Val err: 0.5569019274376418 Train loss: 2.03516203773265 Val loss: 1.7853908709117345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 3/43 [02:58<39:41, 59.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Train err: 0.617577239229025 Val err: 0.5357142857142857 Train loss: 1.9100452656107965 Val loss: 1.7231759003230505\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 4/43 [03:57<38:33, 59.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Train err: 0.5888959750566893 Val err: 0.5203373015873016 Train loss: 1.8272106301487168 Val loss: 1.665724984237126\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 5/43 [04:56<37:28, 59.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Train err: 0.5685586734693877 Val err: 0.5089285714285714 Train loss: 1.7636659545422682 Val loss: 1.625954508781433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 6/43 [05:56<36:36, 59.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Train err: 0.549922052154195 Val err: 0.5023384353741497 Train loss: 1.7123954215017305 Val loss: 1.5905980212347848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▋        | 7/43 [06:57<35:57, 59.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Train err: 0.5369189342403629 Val err: 0.48681972789115646 Train loss: 1.670179743074776 Val loss: 1.5592931423868452\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: Train err: 0.5236855158730159 Val err: 0.48696145124716556 Train loss: 1.628664201079042 Val loss: 1.5249453101839339\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: Train err: 0.5127728174603174 Val err: 0.4779620181405896 Train loss: 1.597191097244384 Val loss: 1.5039223177092416\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 9: Train err: 0.5004251700680272 Val err: 0.47534013605442177 Train loss: 1.5621281564911478 Val loss: 1.4900030067988805\n"
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    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 10: Train err: 0.48921130952380953 Val err: 0.46924603174603174 Train loss: 1.5333708138152315 Val loss: 1.4739890694618225\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 11: Train err: 0.4826920351473923 Val err: 0.4676162131519274 Train loss: 1.5095437824590947 Val loss: 1.4678490502493722\n"
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    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 12: Train err: 0.4763499149659864 Val err: 0.460671768707483 Train loss: 1.4795964664342451 Val loss: 1.4481805222375053\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 13: Train err: 0.4665355725623583 Val err: 0.46364795918367346 Train loss: 1.4555068740498722 Val loss: 1.4414801171847753\n"
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    {
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     "text": [
      "Epoch 14: Train err: 0.4568098072562358 Val err: 0.4494047619047619 Train loss: 1.4260916501756697 Val loss: 1.4259277156421117\n"
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    {
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     "text": [
      "Epoch 15: Train err: 0.4499716553287982 Val err: 0.4482001133786848 Train loss: 1.4027021144793417 Val loss: 1.4146214638437544\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 16: Train err: 0.443983843537415 Val err: 0.4453656462585034 Train loss: 1.3845910074489187 Val loss: 1.4128496646881104\n"
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    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 17: Train err: 0.4364547902494331 Val err: 0.44061791383219956 Train loss: 1.3646316760791943 Val loss: 1.39519168649401\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 18: Train err: 0.42860685941043086 Val err: 0.44040532879818595 Train loss: 1.3445487773877964 Val loss: 1.387623233454568\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 19: Train err: 0.42217616213151926 Val err: 0.43700396825396826 Train loss: 1.3168117094472422 Val loss: 1.3756429382732935\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 20: Train err: 0.41971371882086167 Val err: 0.4308390022675737 Train loss: 1.3062008043535713 Val loss: 1.3651319231305803\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 21: Train err: 0.4150545634920635 Val err: 0.4293509070294785 Train loss: 1.285292342136236 Val loss: 1.3635896784918649\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 22: Train err: 0.40396471088435376 Val err: 0.42885487528344673 Train loss: 1.2694668031874157 Val loss: 1.359064051083156\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 23: Train err: 0.4039824263038549 Val err: 0.4272250566893424 Train loss: 1.2587595671212592 Val loss: 1.3542856999805994\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 24: Train err: 0.3952841553287982 Val err: 0.4280045351473923 Train loss: 1.2323380618138648 Val loss: 1.3513373306819372\n"
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    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 25: Train err: 0.39101473922902497 Val err: 0.41843820861678005 Train loss: 1.222621993683363 Val loss: 1.340337029525212\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 26: Train err: 0.38885345804988664 Val err: 0.4219812925170068 Train loss: 1.208149993230426 Val loss: 1.3332774468830653\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 27: Train err: 0.3813421201814059 Val err: 0.4164540816326531 Train loss: 1.1875664821557717 Val loss: 1.3263214656284876\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 28: Train err: 0.3776218820861678 Val err: 0.4191468253968254 Train loss: 1.1692065070816178 Val loss: 1.3313233426639013\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 29: Train err: 0.37287414965986393 Val err: 0.4168792517006803 Train loss: 1.1602914730707805 Val loss: 1.3242741567747933\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 30: Train err: 0.3731221655328798 Val err: 0.4165249433106576 Train loss: 1.1504559124948757 Val loss: 1.3161066004208155\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 31: Train err: 0.3636621315192744 Val err: 0.41404478458049887 Train loss: 1.1295159268541402 Val loss: 1.3198132429804121\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 32: Train err: 0.3625637755102041 Val err: 0.4130527210884354 Train loss: 1.125296128580089 Val loss: 1.3077423317091805\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 33: Train err: 0.3562216553287982 Val err: 0.4142573696145125 Train loss: 1.106673244310885 Val loss: 1.3119569846561976\n"
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    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 34: Train err: 0.35131448412698413 Val err: 0.41220238095238093 Train loss: 1.0985272336168355 Val loss: 1.297700081552778\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 35: Train err: 0.35131448412698413 Val err: 0.41340702947845803 Train loss: 1.0889020182378168 Val loss: 1.3089483720915658\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 36: Train err: 0.3450077947845805 Val err: 0.41220238095238093 Train loss: 1.0751351987153224 Val loss: 1.3082778453826904\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 37: Train err: 0.34043721655328796 Val err: 0.40809240362811794 Train loss: 1.0604691925773275 Val loss: 1.288670437676566\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38: Train err: 0.34036635487528344 Val err: 0.4097222222222222 Train loss: 1.0516979117242118 Val loss: 1.287097351891654\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39: Train err: 0.3346619897959184 Val err: 0.40695861678004536 Train loss: 1.0378974257142635 Val loss: 1.291928265775953\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40: Train err: 0.33182752267573695 Val err: 0.4070294784580499 Train loss: 1.031771050288834 Val loss: 1.2915063841002328\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 42/43 [41:22<00:58, 58.97s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41: Train err: 0.3330676020408163 Val err: 0.40674603174603174 Train loss: 1.0241302163963144 Val loss: 1.2907732129096985\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 43/43 [42:23<00:00, 59.14s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42: Train err: 0.32895762471655327 Val err: 0.4052579365079365 Train loss: 1.0135943863937915 Val loss: 1.2819105897630965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "net5 = CNN2(fc1_out = 256, fc2_out = 64, dropout = 0.42)\n",
    "train_err, train_loss, val_err, val_loss = train(net5, train_loader, val_loader, batch_size = 128, lr=0.0001, num_epochs = 43)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_size = 43\n",
    "epochs = np.linspace(0, epoch_size, epoch_size)\n",
    "train_acc = np.full(epoch_size, 1) - train_err\n",
    "val_acc = np.full(epoch_size, 1) - val_err\n",
    "\n",
    "#print(train_acc.shape)\n",
    "\n",
    "plt.title(\"Training Curve\")\n",
    "plt.plot(train_loss, label=\"Train\")\n",
    "plt.plot(val_loss, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Accuracy Plot\")\n",
    "plt.plot(epochs, train_acc, label=\"Train\")\n",
    "plt.plot(epochs, val_acc, label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}