LeNet5_MNIST_LARS_LR_Scheduler_PyTorch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "718df939-4e9e-4ea6-b593-dbdde154d5c6",
   "metadata": {},
   "source": [
    "# LeNet-5 CNN: LARS optimizer + LR Scheduler - MNIST & PyTorch\n",
    "\n",
    "\n",
    "LARS optimizer was introduced by Yang You et al. in [Large Batch Training of Convolutional Networks](https://arxiv.org/abs/1708.03888)\n",
    "\n",
    "Layer-wise Adaptive Rate Scaling, or LARS, is a large batch optimization technique. There are two notable differences between LARS and other adaptive algorithms such as Adam or RMSProp:\n",
    "1. first, LARS uses a separate learning rate for each layer and not for each weight.\n",
    "2. And second, the magnitude of the update is controlled with respect to the weight norm for better control of training speed.\n",
    "\n",
    "_Learning Rate Scheduler:_\n",
    "\n",
    "- linear warmup\n",
    "- cosine decay (without warm restart(s))\n",
    "\n",
    "\n",
    "References\n",
    "\n",
    "- [apex LARS optimizer](https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py)\n",
    "- [SwAV official GitHub repo](https://github.com/facebookresearch/swav/blob/main/main_swav.py)\n",
    "- [LARS explained](https://paperswithcode.com/method/lars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "976bda11-3ad4-482e-9a8d-1889dc16eaae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify GPU to be used-\n",
    "# %env CUDA_DEVICE_ORDER = PCI_BUS_ID\n",
    "'''\n",
    "Specify GPU(s) to be used here. Say you have 6 GPUs and you want to train Model A on 2 of them\n",
    "and Model B on 4 of them. You can do that as follows-\n",
    "CUDA_VISIBLE_DEVICES=0,1 python model_A.py\n",
    "CUDA_VISIBLE_DEVICES=2,3,4,5 python model_B.py\n",
    "'''\n",
    "# %env CUDA_VISIBLE_DEVICES = 0, 1, 2, 3\n",
    "%env CUDA_VISIBLE_DEVICES = 0, 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "617ce6f4-d442-4e23-b7f7-ca87839fd697",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from LARC import LARC\n",
    "\n",
    "from models.LeNet5_CNN_torch import LeNet5, init_weights\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm import trange\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bce6cce-206d-48b8-8a09-c458daf7b3f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d487520-cea0-42d9-a283-61d8cda39675",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d5a8c84-bd03-4120-a8c6-bf56a83cce6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 2.1.1+cpu\n",
      "Number of GPU(s) available = 0\n",
      "PyTorch does not have access to GPU\n",
      "Available device is cpu\n"
     ]
    }
   ],
   "source": [
    "print(f\"torch version: {torch.__version__}\")\n",
    "\n",
    "# Check if there are multiple devices (i.e., GPU cards)-\n",
    "print(f\"Number of GPU(s) available = {torch.cuda.device_count()}\")\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    print(f\"Current GPU: {torch.cuda.current_device()}\")\n",
    "    print(f\"Current GPU name: {torch.cuda.get_device_name(torch.cuda.current_device())}\")\n",
    "else:\n",
    "    print(\"PyTorch does not have access to GPU\")\n",
    "\n",
    "# Device configuration-\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Available device is {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233b7a31-c604-46f8-9c28-dff2f5a57883",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae00c43-c539-4c3c-8b34-d19b1e7b541f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c17e54db-04d1-438e-8aa4-88b6b8c93578",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters-\n",
    "num_train_epochs = 50\n",
    "warmup_epochs = 10\n",
    "batch_size = 1024\n",
    "\n",
    "# learning_rate = 0.001\n",
    "base_lr = 0.6\n",
    "final_lr = 0.0006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60c2ad87-a4df-4151-85ee-284da537badf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af56c142-48d4-4e82-a59e-17d70e9ba04d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f751311e-d8a5-46b1-a04f-04615b1a088b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define transformations for MNIST dataset-\n",
    "# MNIST dataset statistics-\n",
    "mean = np.array([0.1307])\n",
    "std_dev = np.array([0.3081])\n",
    "\n",
    "transforms_apply = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean = mean, std = std_dev)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73088cc6-71de-45e5-a629-bdf91607e95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load MNIST dataset-\n",
    "train_dataset = torchvision.datasets.MNIST(\n",
    "    root = '/home/amajumdar/Downloads/.data/', train = True,\n",
    "    transform = transforms_apply, download = True\n",
    ")\n",
    "\n",
    "test_dataset = torchvision.datasets.MNIST(\n",
    "    root = \"/home/amajumdar/Downloads/.data/\", train = False,\n",
    "    transform = transforms_apply\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b25906b4-c3d2-44e0-b442-0682e9754513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 60000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset.targets), len(train_dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "edcad2f2-c41c-4573-aa85-a19500297b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_dataset.targets), len(test_dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c46f4c8-9f95-4554-bf06-772584f20cda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset length/shape: [60000, 28, 28]\n"
     ]
    }
   ],
   "source": [
    "print(f\"training dataset length/shape: {list(train_dataset.data.size())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1dcd603-1621-402a-bcad-e84e1fa8f621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 0.1307 &std dev = 0.3081\n"
     ]
    }
   ],
   "source": [
    "print(f\"mean = {train_dataset.data.float().mean() / 255:.4f} &\"\n",
    "      f\"std dev = {train_dataset.data.float().std() / 255:.4f}\"\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "765abc95-7c2d-4957-bdea-c3471b0ee282",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_size = len(train_dataset.data)\n",
    "\n",
    "# number of steps per epoch-\n",
    "num_steps_epoch = int(np.round(train_dataset_size / batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69a6d695-91a6-47fc-9124-6c4c12d05c72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset size = 60000 & # of steps per epoch = 59\n"
     ]
    }
   ],
   "source": [
    "print(f\"train dataset size = {train_dataset_size} & # of steps per epoch = {num_steps_epoch}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ad40fae-0edd-4507-ab77-3d95a7a8ee79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed37e26a-1ec3-4bd7-8661-64e7e2500a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and testing dataloaders-\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset = train_dataset, batch_size = batch_size,\n",
    "    shuffle = True\n",
    ")\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    dataset = test_dataset, batch_size = batch_size,\n",
    "    shuffle = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77fc6dc6-a236-495b-a305-5ebccdc66767",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "451afc55-963d-4be8-86a9-5ff7b93009a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images.shape: torch.Size([256, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "# Get some random batch of training images & labels-\n",
    "images, labels = next(iter(train_loader))\n",
    "print(f\"images.shape: {images.shape}\")\n",
    "\n",
    "# De-normalize the batch of images-\n",
    "images *= 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0aaaa0f7-ef6b-4cbb-91b7-2525a9587972",
   "metadata": {},
   "outputs": [
    {
     "data": {
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//jluvvlmjB49GiEhIZg1a5acFsNb0zbYVb16dXTp0gWvvfYaatWqhUaNGiE9PR1z5szBJZdcUqK2N2rUCOPHj8fo0aPx+++/49Zbb0WNGjVw6NAh/PDDDwgNDcW4ceOwdetWDB06FPfeey+aNGmCypUrY8WKFdi6dat21ovIX3ghOBnnpZdeQo0aNfDGG2/gzjvvRPfu3fHWW2+ha9eu+OGHH+Qf15o1a+KLL75A1apV8eCDD+Kxxx5DtWrVfDpr+Jw5c5CXl4f7778fTz/9NK677jqsWrWqyOuiVLNnz8a0adOwevVq3H///bj99tvx8ssv49SpU2jXrp3P2luUoKAgTJo0ydax1atXx7hx43Do0CG8+OKLiI+Pxz333IOlS5ciISEBa9eu1S46Lo1+/frhiSeewIwZM9ChQwe0bdsWBw4cKPY5N910k7wovkePHhg9ejR69+7tqD/ArVq1QlpaGkJCQvDwww/jqaeeQosWLWRHOzw8vMzb9MEHH+DGG2/EiBEj0KtXL2zcuBFpaWmF2uJJ20eNGoVPP/0Uv/zyCx555BF069YNI0aMwL59+9ClSxcAF67/u/zyyzFjxgz07t0bd911Fz7//HNMmTIF48ePL7sPgMiNIFFWt7YQBbD33nsPjz76KDZs2CAvTicqjfj4eOzduxe//PKLv5viMZPbTlQcDs8REfnZ8OHDce2116J+/fo4evQo5s+fj7S0tCLXEnQak9tO5Cl2moiI/Oz8+fN4+eWXkZmZiaCgIDRv3hzvv/8+HnzwQX837aJMbjuRpzg8R0RERGQDLwQnIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIb2GkiIiIissFnnaYZM2YgJiYGVapUQZs2bbBmzRpfvRX5APMzG/MzG/MzHzMMTBV98aILFixAQkICZsyYgRtuuAGzZ89G9+7dsXPnTjRo0KDY5+bn5+PAgQMICwtDUFCQL5pHRRBC4MSJE4iOjsYnn3xS4vwAZugPzM9szM983sqQ+fmHml9wcDHnk4QPtGvXTgwcOFB7rGnTpmLkyJEXfW5GRoYAwC8/fWVkZJQqP2bI/PjF/MrzV2kzZH7+z684Xh+ey8vLw6ZNmxAfH689Hh8fj3Xr1hU6Pjc3Fzk5OfJLCOHtJpEHXC6XR/kBzNBJmJ/ZmJ/5PM2Q+TlLWFhYsfu93mk6fPgwzp8/j8jISO3xyMhIZGZmFjo+OTkZ4eHh8svO6WfynaNHj3qUH8AMnYT5mY35mc/TDJmfs1xsSNRnF4Jb31gIUWRjRo0ahezsbPmVkZHhqyaRB+zmBzBDJ2J+ZmN+5uPfwMDk9QvBa9WqhQoVKhTqUWdlZRXqeQMXTmW6XC5vN4NKqGbNmh7lBzBDJ2F+ZmN+5vM0Q+ZnFq+faapcuTLatGmDtLQ07fG0tDR07NjR229HXsb8zMb8zMb8zMcMA5yt2zE89NFHH4lKlSqJOXPmiJ07d4qEhAQRGhoq9u7de9HnZmdn+/3q+fL8lZ2dXar8mCHz4xfzK89fpc2Q+fk/v+L4pNMkhBDTp08XDRs2FJUrVxatW7cW6enptp7HbxhnfMOUND9myPz4xfzK81dpM2R+zsjPnSAhnHV/Y05ODsLDw/3djHIrOzsb1atXL9VrMEP/YX5mY37mK22GzM+/LpYf154jIiIisoGdJiIiIiIb2GkiIiIisoGdJiIiIiIbvD65Jf3NuoaNuu7QSy+9JOvFixeXWZuIiIioZHimiYiIiMgGdpqIiIiIbGCniYiIiMgGXtPkQ6Ghodp2s2bNZD1w4EBZ85omIipPatSooW0vX75c1ps3b5b1gAEDyqxNZJ/1b9uTTz4p64iICFnfcMMN2nE33nijrLdu3Srr119/XTvu66+/lvWff/5ZusZ6Gc80EREREdnAThMRERGRDRye86HevXu73bdw4cIybElgCwkJkXXdunW1fU888YSsa9eubev1goKCZH3y5Elt3/vvvy/ryMhIWa9YsUI77tSpU7bei5zviiuukPU111yj7YuKipJ1nz59tH133323rLOysnzTOEM98sgj2nabNm1kfemll8raugZYTk6ObxtGmvbt28taHSpt1aqVdpz156KA+rsU0IfarrrqKlnPmzdPO27btm2y7tq1q6wPHz5so9W+xTNNRERERDaw00RERERkA4fnvKxFixayHjNmjLbv2LFjsl6/fn2ZtSnQqXclpqena/usd3nYoZ5SFkJo+4YMGVLkc9auXattq7O/jx8/XtanT5/2uD3ke3fccYe2fdttt8laHXa75JJLtOPU7w/1ri8AqFy5shdbGFisPy+qBg0ayLp+/fravh07dvisTeWV+j39j3/8Q9s3atQoWavf60eOHNGOU3/HrVy5UtYHDhzQjjtx4oSs1TsmW7ZsqR2nDt1169ZN1vPnzy/6H1GGeKaJiIiIyAZ2moiIiIhsYKeJiIiIyAZe0+Rl3bt3l3WtWrW0fT/99JOst2/fXmZtCnTqtSRPPfWUtu/tt9+WdUmub7KrU6dObrfVWXGff/557bjvv//eZ20ioGrVqtr2iBEjZD127FhZ5+fnu30N9bqMkSNHavveeecdt88LCwuz3c7y5scff9S2P/nkE1nfd999sm7btq12HK9p8j712tuEhAS3x507d07WL7/8srZv1qxZHr+vet3g5MmTtX3qdB3q73TrVD1nz571+H1Li2eaiIiIiGxgp4mIiIjIBg7PeVlsbKysg4P1PunUqVPLujnlzocffqht//zzz7KuVq2aV99LnRF85syZ2j51aFYdnrMuzty8eXNZHz161KvtK6/UGbyTkpK0fT179pS1emp/6dKl2nELFiyQ9RdffCHrM2fO2G7HZ599Jmt1VmMCzp8/r23/9ttvslZvbY+JiSmzNpVXb731lqz/+usvbZ86nL1p0yZZl2Q4zuqPP/6Q9aOPPqrt27Bhg6zVv5u33367dpw/Vtbw+EzT6tWr0aNHD0RHRyMoKAiLFi3S9gshkJiYiOjoaISEhCAuLo7j0AZhfmZjfmZjfuZjhoHN407TqVOn0KpVK0ybNq3I/ZMnT8bUqVMxbdo0bNiwAVFRUbjlllu0Sa3IuVJSUpifwZif2Zif+ZhhYPO409S9e3dMmDABvXr1KrRPCIGUlBSMHj0avXr1QsuWLTFv3jycPn0aH3zwgVcaTL41c+ZM5mcw5mc25mc+ZhjYvHpN0549e5CZmYn4+Hj5mMvlQmxsLNatW6etklwgNzcXubm5ctv0VazV1bqPHz+u7Stu6QCnOHTokEf5Ac7OcMuWLT577euvv17W6vg8UHi6iQLqdQFA8be6l0Sg5eeOddkTdakFddmTiIgI7Tj1+qRx48bJ2roEil01a9aU9ezZs7V9+/fv9/j1ykt+Vuq1fuq0Dq1bt/ZHc0rF0wz9nd/Bgwdlfdlll2n71CWlpkyZ4rM25OXladvqZT9vvPGGrKOjo33WBru8evdcZmYmAP0C2YLtgn1WycnJCA8Pl1/WtYao7HmSH8AMnYb5mY35mY9/AwOXT6YcUHunwIVhO+tjBUaNGoXs7Gz5lZGR4YsmkQc8yQ9ghk7D/MzG/MzHv4GBy6vDc1FRUQAunHGqU6eOfDwrK6tQz7uAy+WCy+XyZjPKnHrKUF3Z3Hp78t69e8uqSaXiSX5AYGRoV/Xq1WX9z3/+U9atWrVy+xw1d+uttdYhXG8I1Pz69u0ra+uNKOpK7eoM3uoQKqDPRG297d0O6yzf6mzK6nQGAPDQQw95/PpA4OZXHHV49Pfff5d1x44dtePq1q0ra+uQuJOY9DfwH//4h6zvuecebV9aWpqs1Sk0fE39nfnxxx/L+pFHHtGOU2fkL6vZwb16pikmJgZRUVHaB52Xl4f09PRC3/zkTJGRkczPYMzPbMzPfMwwsHl8punkyZP49ddf5faePXuwZcsWREREoEGDBkhISEBSUhKaNGmCJk2aICkpCVWrVkW/fv282nDyjUGDBjE/gzE/szE/8zHDwOZxp2njxo248cYb5fbw4cMBXDht9t5772HEiBE4c+YMBg8ejGPHjqF9+/ZYvnx5QC9eefPNN8taHSbIysryQ2tKJyEhAUKIcpVfcayL/KqnqNU7JYujzhbu6+8J0/NTf34AfQFRdSjs5MmT2nHqwsyDBg3ySduAwncQPf7447KeO3euts86O70dpudXUupM1OrCsDVq1NCOU+9KderwnNMzbNSokbb94osvuj1WvdvX23f62qXeWXfttddq++rVqydr9WSOL3ncaYqLi9OmubcKCgpCYmIiEhMTS9Mu8hPmZzbmZzbmZz5mGNi4YC8RERGRDew0EREREdng1SkHyit317Z4YyVoKnvqbL7WVbSt1zi5o56aV1fppsLUKTtef/11bd99990n623btsl6zpw52nHq9A/epv58q9cwAcDu3btlrc5cTN6hzpQNlN1t5YGsZcuW2naVKlXcHrt9+3ZfN6dUrrzySlmX1TVNPNNEREREZAM7TUREREQ2cHjOC+Li4mStTpV/+vRpP7SG7FBnbgeA0aNHy1odnqtWrZrb11BvwX3llVe0fePHjy9tEwOWdW0tdRHdFi1aaPvU4VF1aOzEiRM+at0F6jQin3/+uay//fZb7bi7775b1keOHPFpm8oj6yoK6nAolcx1113ndt+ePXu07ZUrV/q6OaXi698DReGZJiIiIiIb2GkiIiIisoHDc16gTvZZ3MSfVPZCQkJk3aVLF1lbZ8Ht3LmzrNUh1uLyfO2112TNieyKp96B9tVXX2n7IiIiZG0d5pw0aZKsrQtge5P1Dlh1Nm/1DiJ1VnKAQ3JkPvX33XfffaftUxe/9pcbbrhB1mpbAf+0j2eaiIiIiGxgp4mIiIjIBnaaiIiIiGzgNU0+ZL1dlkouMjJS1k2aNNH2PfbYY26fp67orU4N4Q2///67V18v0DRt2lTWP/zwg9vjGjZsKOv//e9/Pm2TutL8lClTZG2d6funn36StTr9gD9ucSbyJvUaQsCZ1+ROnDhR1urv8I0bN2rHHT58uKyaJPFMExEREZEN7DQRERER2cDhOR/69NNP/d0Eo6jTAwDAvHnzZN22bVtZq8M5/qSeQu7atavb49TbZGfMmKHtW7t2raz/+usvL7bO/5588klZq6f9U1NTteN8edu+dXFS9fNXb2W2zjQ9ePBgWXNIjgKJdRFkJ1AvvwD0mfZV06dP17aPHz/uqya5xTNNRERERDaw00RERERkA4fnyDGWLFmibat3LTnRJZdcIuvevXvbes4999yjbasLYk6YMMHtPhOpdzmqQ3DW2di9MdP3fffdJ+uoqChZWxdOVmcQnjt3rqzfeOMN7bgdO3aUuk1ETnTq1Cl/N6GQd999V9tu3LixrNWf0/nz55dZm9zhmSYiIiIiG9hpIiIiIrKBnSYiIiIiGzy6pik5ORmpqanYtWsXQkJC0LFjR0yaNAlXXnmlPEYIgXHjxuHtt9/GsWPH0L59e0yfPh0tWrTweuP9JT4+Xtu++uqr/dQS7xNCIDEx0S/5WW/b98bstJmZmbJWpzD44osvbD3fmnWbNm2KPM46y2779u1tvf6NN95YZA0AwcGe/5/Gn/lZHTx4UNbqTNwzZ87Ujvvyyy9lffLkSbevoV4j9cQTT2jHqbOPq983H374oXbcqFGjZO3r2cdLqjz8Dg1kTvoZdLIHH3xQ1ur0H1YrVqyQ9blz53zaJjs8+q2cnp6OIUOGYP369UhLS8O5c+cQHx+vXVg2efJkTJ06FdOmTcOGDRsQFRWFW265hXOdGCIlJYX5GYz5mY+/Q83Gn8HA5lGnadmyZejfvz9atGiBVq1aYe7cudi/fz82bdoE4EIPOyUlBaNHj0avXr3QsmVLzJs3D6dPn8YHH3zgk38AedfMmTOZn8GYn9n4O9R8/BkMbKWaciA7OxvA30MTe/bsQWZmpjak4XK5EBsbi3Xr1mHAgAGleTvHUocD1KEGEx06dMhv+e3atUvbVod9VdZZYNPS0ty+5tSpU2X9/fffe9wmdcbu4lSrVk3bVm+ZVReDrV27ttvXWLdunYetK8yf+Vk9//zzsm7Xrp2sb7rpJu04dVudPR3Qf7ZOnz4t66NHj2rHqX+Q1KHXjz76yNNm+9XevXvL5e9Qd3799Vd/N8FjTvoZtEP9mbP+/JXWpZdeqm3feuutsp42bZqs1eF7APjkk09k/e2333q1TaVV4k6TEALDhw9Hp06d5FIFBdePWKdEj4yMxL59+4p8ndzcXG1a95ycnJI2ibzEk/wAZug0zM9cWVlZAPg71HTML3CV+O65oUOHYuvWrYUutASK/t+iux5scnIywsPD5Vf9+vVL2iTyEk/yA5ih0zA/8/F3qNmYX+Aq0ZmmYcOGYcmSJVi9ejXq1asnHy+YiTczMxN16tSRj2dlZRXqeRcYNWoUhg8fLrdzcnIc/01jndVZ9fPPP5dhS3zDk/wA72XYqVMnbbtGjRpFHmddcDIjI8Pj9/I2611fW7ZskfWwYcPKtC3+ys9KvfB10KBBsrbeJXjbbbfJ+s0333T7eurdbuvXry91+5yoYPg20H+H2rV161Z/N6FEnJzfxo0btW11CNy6ssGzzz4r68OHD7t9zYoV/+5KqH8f3377be0462UMBRYuXKhtjxgxQtbFnSX3B4/ONAkhMHToUKSmpmLFihWIiYnR9sfExCAqKkq7xiQvLw/p6eno2LFjka/pcrlQvXp17Yv8JzIy0qP8AGboJMzPbI0aNeLvUMN5+jPI/Mzi0ZmmIUOG4IMPPsDixYsRFhYmr2EKDw9HSEgIgoKCkJCQgKSkJDRp0gRNmjRBUlISqlatin79+vnkH0DeNWjQIOZnMOZnNv4ONR9/BgObR52mgknp4uLitMfnzp2L/v37A7hwWu3MmTMYPHiwnNhr+fLlha6OJ2dKSEiAEIL5GYr5mY+/Q83Gn8HAFiS8Me2yF+Xk5CA8PNzfzSjWsmXLtG11JuspU6bI+oUXXiizNnlLdnZ2qU8Pm5BhoGJ+ZmN++nUwffv21fZde+21snbqdASlzdDX+dWsWVPbXrJkiayvuuoqbd9///tfWT/88MOyvu6667TjxowZI2v1sp3iphD5/PPPZT1kyBDtuD/++MP9P8DHLpYf154jIiIisoGdJiIiIiIbSjUjeHllvT2yUqVKslaH54iIyDMHDhyQdWhoqLbv9ddfl/X48eNlvXnzZt83LEAcOXJE21YXy01NTdX29ezZU9Z2p39QpxqxzoC+Z8+eIl/v7Nmztl7bCXimiYiIiMgGdpqIiIiIbGCniYiIiMgGXtNUAu+8806x20REVDKJiYmyPnbsmNt9ffr0kTWvafKOoUOHatvbt2+XtbrESnR0tHbcJ598IusXX3xR1n/++ae3m+h3PNNEREREZAM7TUREREQ2cEZw0nBGYrMxP7MxP/M5fUZwKh5nBCciIiLyAnaaiIiIiGxgp4mIiIjIBnaaiIiIiGxgp4mIiIjIBnaaiIiIiGxgp4mIiIjIBnaaiIiIiGxwXKfJYXNtljve+PyZof8wP7MxP/OV9vNnfv51sc/fcZ2mEydO+LsJ5Zo3Pn9m6D/Mz2zMz3yl/fyZn39d7PN33DIq+fn5OHDgAIQQaNCgATIyMkq9rIDpcnJyUL9+fZ9+FkIInDhxAtHR0QgOLl1fOj8/H7t370bz5s2ZH8zMjz+Df2N+5jMpQ+ZXmJPyq+iTdy+F4OBg1KtXDzk5OQCA6tWrl/tvmAK+/iy8td5RcHAw6tatC4D5qUzKjz+DhTE/85mQIfNzzwn5OW54joiIiMiJ2GkiIiIissGxnSaXy4WxY8fC5XL5uyl+Z+JnYWKbfcXUz8LUdnubqZ+Dqe32BRM/CxPb7CtO+iwcdyE4ERERkRM59kwTERERkZOw00RERERkAztNRERERDaw00RERERkg2M7TTNmzEBMTAyqVKmCNm3aYM2aNf5ukk8lJyejbdu2CAsLQ+3atdGzZ0/s3r1bO0YIgcTERERHRyMkJARxcXHYsWOHn1pcPObH/EwTSBkyP7PzA8pfhsbkJxzoo48+EpUqVRLvvPOO2Llzp3jmmWdEaGio2Ldvn7+b5jPdunUTc+fOFdu3bxdbtmwRt99+u2jQoIE4efKkPGbixIkiLCxMLFy4UGzbtk306dNH1KlTR+Tk5Pix5YUxP+ZnokDJkPmZnZ8Q5TNDU/JzZKepXbt2YuDAgdpjTZs2FSNHjvRTi8peVlaWACDS09OFEELk5+eLqKgoMXHiRHnM2bNnRXh4uJg1a5a/mlkk5sf8AoGpGTK/C0zNTwhmKIRz83Pc8FxeXh42bdqE+Ph47fH4+HisW7fOT60qe9nZ2QCAiIgIAMCePXuQmZmpfS4ulwuxsbGO+lyY3wXMz3wmZsj8/mZifgAzLODU/BzXaTp8+DDOnz+PyMhI7fHIyEhkZmb6qVVlSwiB4cOHo1OnTmjZsiUAyH+70z8X5sf8AoGpGTK/C0zND2CGgLPzq1hm7+ShoKAgbVsIUeixQDV06FBs3boVa9euLbTPlM/FlHb6AvMzn+kZmtBGXzI9P8CcdvqCk/Nz3JmmWrVqoUKFCoV6jllZWYV6mIFo2LBhWLJkCVauXIl69erJx6OiogDA8Z8L82N+pjM5Q+Zndn4AM3R6fo7rNFWuXBlt2rRBWlqa9nhaWho6duzop1b5nhACQ4cORWpqKlasWIGYmBhtf0xMDKKiorTPJS8vD+np6Y76XJgf8zNVIGTI/MzODyi/GRqTX5ldcu6Bgtst58yZI3bu3CkSEhJEaGio2Lt3r7+b5jODBg0S4eHhYtWqVeLgwYPy6/Tp0/KYiRMnivDwcJGamiq2bdsm+vbt6+jbZZkf8zNJoGTI/MzOT4jymaEp+Tmy0ySEENOnTxcNGzYUlStXFq1bt5a3HQYqAEV+zZ07Vx6Tn58vxo4dK6KiooTL5RJdunQR27Zt81+ji8H8mJ9pAilD5md2fkKUvwxNyS/o/zeWiIiIiIrhuGuaiIiIiJyInSYiIiIiG3zWaSpviw0GGuZnNuZnNuZnPmYYmHzSaVqwYAESEhIwevRo/Pjjj+jcuTO6d++O/fv3++LtyMuYn9mYn9mYn/mYYeDyyYXg7du3R+vWrTFz5kz5WLNmzdCzZ08kJycX+9z8/HwcOHAAYWFh5Wb2UycQQuDEiROIjo5Ghw4dSpwfwAz9gfmZjfmZz1sZMj//UPMLDnZ/Psnry6gULDY4cuRI7XF3iw3m5uYiNzdXbv/xxx9o3ry5t5tFNv32228e5QcwQydhfmZjfubzNEPm5ywZGRnaTORWXh+e83SxweTkZISHh8svfrP4V15enseLRTJD52B+ZmN+5vM0Q+bnLGFhYcXu99mF4HYX1Rs1ahSys7PlV0ZGhq+aRDYUZOTJoojM0DmYn9mYn/k8zZD5OcvFhkS9Pjzn6WKDLpcLLpfL282gEqpZs6bHi0UyQ+dgfmZjfubzNEPmZxavn2kqr4sNBgrmZzbmZzbmZz5mGOB8sTZLaRYbzM7OdrsGDb98/5WdnV3qxSKZIfPjF/Mrr1+lzZD5+T+/4vhswd6SLjbIbxhnfMOUZrFIZsj8+MX8yutXaTNkfs7Izx3HLdibk5OD8PBwfzej3MrOzkb16tVL9RrM0H+Yn9mYn/lKmyHz86+L5ce154iIiIhsYKeJiIiIyAZ2moiIiIhsYKeJiIiIyAavT25JRFRSFSvqv5JCQkJkPWPGDFlfeeWV2nG33nqrrE+dOiXrypUra8edPXtW1n/99VfpGktEAIDvv/9e1gcPHpT1G2+8oR2nXuDeokULWVvX5Nu7d6+s9+3b561megXPNBERERHZwE4TERERkQ3sNBERERHZwGuaiKhMVapUSdu+9tprZf3iiy9q+3r06GHrNf/8809Zb968WdatW7fWjvvmm29kPWLECFlv2bLF1vuQdwQH6/9fb9y4sawfe+yxIh8HgHvuuUfW6rzMPXv21I5bu3atrNVr3HJzc0vWYNJYrym86qqrZN2uXTtZ33XXXW5f4+TJk7K2fj8cOXJE1ur1ijt37vS8sV7GM01ERERENrDTRERERGQDh+dKwOVyadvDhw+XdWxsrKxvueWWUr/Xm2++WeT7UNmoW7eutt2rVy9ZX3fddUU+DgChoaFFvp46VGC1Zs0abXv8+PGyXr9+/cUba4guXbpo28uXL7f1vJycHFlnZWW5Pa5GjRpu9918882yfuaZZ2T96KOP2moDldyll14q66lTp2r7+vXrZ+s18vPzi3z8s88+c/ucoUOHynrmzJm23ocKU6fveO2117R96tQgKnWYFAAmTZok619//VXW9evX145btGiRrP/zn//IOjo62n6DfYRnmoiIiIhsYKeJiIiIyAYOzxVDPc0/cOBAWb/wwgvacdWqVZN1UFCQrNW7O0qqT58+sp49e7a2b/fu3aV+fSpMHWL9+OOPtX21atWStd2s1X3WYTt1n3qXCAB069ZN1urdKU64g6Q0wsLCtG31zrXffvtN26d+/uoswRs3brT1+sePH3d73NVXX32RlpKnatasqW3fe++9sh4yZIismzdvXur3Ur8HrHdJqndjqcOwK1eu1I7btWtXqdsRyNTfVxMmTJC19a5WdYj9kUcekbV1GN3d8Ko1B3X49qWXXvKgxb7HM01ERERENrDTRERERGQDO01ERERENvCaJkV8fLy2rV67pF7nUpYiIyNlrc60CvCapqJYV7VPSkqS9eeffy7r9PR07Th1aoGlS5fKWr1eDdBnsVWnAVi4cKF2nLoy988//yxrdeZqq2bNmmnb6nUfxT3PNOrtxEVtl5b19mV3MjIyvPq+BPzrX//Stu+4445SvZ71+16d7V318ssva9vqtYdVq1aVtXq9DQCMGjWqVO0LdA899JCsExISZG2dWf3++++X9bFjx3zeLn/imSYiIiIiG9hpIiIiIrKh3A/PPf7447J+4403tH3qad3i7NixQ9bqLKfjxo3TjrO7WKR6e/lHH30ka/X0KAC8//77tl6vPFFn6QaAZ599Vtbq9A3WW5TVW//VIYFly5Zpx6mn89WsvcF6G31xt9WT7vLLL5e1NTN3/u///s9XzSm3Dh48WOrXUKegUBfoBfRpJ4qjDtOrv8eLm4KCgE6dOmnb06ZNk/Xp06dl3blzZ+24QB+SU/FMExEREZENHneaVq9ejR49eiA6OhpBQUGFLuIUQiAxMRHR0dEICQlBXFycdiaGnI35mY35mY35mY8ZBjaPO02nTp1Cq1attNN2qsmTJ2Pq1KmYNm0aNmzYgKioKNxyyy04ceJEqRtLvpeSksL8DMb8zMb8zMcMA5vH1zR1794d3bt3L3KfEAIpKSkYPXq0XPV93rx5iIyMxAcffIABAwaUrrVesGDBAm1bXbqiuGuY1OuRvvnmG22f+u/yxpi+9XqbAupttL4yc+ZMR+d3MeoUA544cOCArC+77DJvNafMmZ6fXY0bN9a209LSZF2vXj1ZW5e3UZd7UFdPdwrT86tUqVKJnvfFF1/I+ssvv5S13WuYrPLy8oqsy4LJGfbv31/bVv+ePfjgg7J2N/VDeeDVa5r27NmDzMxMbb4jl8uF2NhYrFu3rsjn5ObmIicnR/si/zl06JBH+QHM0EmYn9mYn/k8zZD5mcWrnabMzEwA+oSMBdsF+6ySk5MRHh4uv+xOTEe+40l+ADN0GuZnNuZnPv4NDFw+mXJAXf0duHCK3PpYgVGjRmH48OFyOycnx+vfNOqwlvWWdOuq86rExERZq6tjf/vtt95rXBEeffTRIh+3znat/mAeOnTIa+/vSX5A2WRoV5cuXbRtdXhGnbIhkGbYtjI5P3VoDQDGjh0r66uvvlrWjRo10o5zN3RtzVmddsKp/6M3Ob8333xT246KipK1eimE1e233y5rdcqV7Oxs7Th1Chbr0KuTOO1voF1qOwDg0ksvlfVvv/3ms/e1/m1Tf9adxqudpoIfkMzMTNSpU0c+npWVVajnXcDlcsHlcnmzGVRKnuQHMEOnYX5mY37m49/AwOXV4bmYmBhERUVpF2Xm5eUhPT0dHTt29OZbkY9ERkYyP4MxP7MxP/Mxw8Dm8ZmmkydPajMh79mzB1u2bEFERAQaNGiAhIQEJCUloUmTJmjSpAmSkpJQtWpV9OvXz6sNv5gaNWrIWr1jrmHDhtpxp06dkrU6HAfop4K9cVecO127dtW21dPTqsOHD2vbISEhXm/LoEGDHJFfSRU3jKHOaBuoTMzv+uuvl7V6FxUAXHLJJaV67Zo1a2rb3333nazvuusuWXt7dveSMjE/1datW7XtF198UdYtW7aUdUREhHaceudygwYNZG2dtV1dWFtdweH8+fMlbLH3mZyhdci6rIawrZdV3HnnnbK2LgLtbx53mjZu3Igbb7xRbheMgT7yyCN47733MGLECJw5cwaDBw/GsWPH0L59eyxfvhxhYWHeazX5TEJCAoQQzM9QzM9szM98zDCwedxpiouLK/YCvKCgICQmJhY6a0NmYH5mY35mY37mY4aBjWvPEREREdngkykHnCA1NVXW1hWZVc8//7ys33nnHZ+2SdWmTRtZz58/X9unXodx7tw5WVv/51LS2XIDjXpbrPUsqLpdFjOqk+cqVvz711Bpr2GyCg7W/1/YtGlTWQ8aNEjWzz33nFffly746aefZK1eT2qd+kX9PayuOFGtWjXtuEmTJsn6yJEjsp47d27pG0tlqnbt2rJWr08D9GxffvnlMmuTHTzTRERERGQDO01ERERENgTs8Ny1115b5OPq6WIAWLx4sVffV52kzDph2QsvvCBrdcZT623Rqv/+97+yXrhwoTeaGHDUWZ/VW5wBfQHf+++/X9bWhZvLelFP+ps6hKoujA0AVapUkfX69etl/fnnn2vHTZkyRdbqfDjWIW311mZ19mPrcFFsbKydplMJbdy4UdtWfzbvvvtuWY8fP147rnnz5rIePXq0rNUVG4DAunTh8ccf17b3798va3U+KH9SJ/JUfwcXN4v4Qw89JOtmzZpp+15//XVZ//HHH95ootfwTBMRERGRDew0EREREdkQsMNz7uzZs0fbzsrK8vg11CEDABgzZoysO3ToIGvrLKfke6tXr9a21aEfNQ/r6WDrsC2VHXUB7GuuuUbbN2vWLFn3799f1uoQhdWqVatkfd9992n71OECdRbqyy67TDvu8ssvL/I55HufffaZrNPT07V96rBeTEyMrL/88kvtOHUYz3TWIWb1TkGnDM+pdz8OHTrU4+d/88032vbIkSNL3SZf4ZkmIiIiIhvYaSIiIiKygZ0mIiIiIhsC9pomdbV7tVZvZwWKXx1bXWG7RYsWsnY3nQGgz0Ccn59vr7HFUK+Xootbt26dtt2nTx9Zf/zxx7JOSUnRjlMXoaaydcUVV8j6n//8p7ZPvfVfnRn4iSeesPXa1msW1eui1O+H6Oho7binn35a1iNGjJC1dUoE8q2jR49q22fOnCnyuCZNmmjbffv2lfWHH37o/YaVoYkTJ2rb6vfj22+/Lev//e9/ZdYmqzVr1sh68ODBsrZe/6s6fvy4rK35NWrUSNZOmz6CZ5qIiIiIbGCniYiIiMiGIGFd4dTPcnJyEB4eXurX6dq1q6z/8Y9/yPrmm28u9WsXRx0KLOlHq55mjY+Pl/Uvv/xS8obZlJ2djerVq5fqNbyVoTeoeaizgN9zzz3acTfddJOsrbc5m8TE/Dp16iTr4j579VZru8NzVmFhYbJetmyZrK+//nq3z1EX196yZUuJ3tcuE/MrSzt27JC1uviy1a5du2StXlpRFkqboTW/xo0ba/t/+OEHWat/Y9TfYYD/plFRV75o0KCBtk+dMiIzM1PW1hn+1d/VDzzwgLebWKyL5cczTUREREQ2sNNEREREZEPA3j33n//8R9bqHVU//vijdpw686+3We/amDBhgqzVRUXfeecd7Tj1joNq1ar5qHXlg3r6Wh3eueuuu7TjPvnkE1mrd3Opd3iQf0VGRsq6cuXK2j67Cy6fOHFC1v682yiQqMNf6izdVkuXLrX1ehUqVJD1VVddpe279NJLbb1GpUqVbB1ngl9//VXbvvXWW2X91Vdfydo6q7Y6hL1o0SLfNK4IkyZN8vg51gXU1ZU1nIZnmoiIiIhsYKeJiIiIyAZ2moiIiIhsCNhrmlSnT5+Wdbdu3bR9LpfL7fPUWx3r1asna7tjtv/973+1bXWG8OJW4f79999lvXnzZlvvRRenjv+rNQDceeedslZvZV6/fr3vG1bObdu2TdbWay969uwp69tuu03W1qkJ1Nut3c0abTV//nxZ9+7d29ZzSJ+tGdCnbrDOrD5jxgxZF3dNk3oNkvqzqM7aXhzr9C7q9aOBRp1yQJ1Je9OmTdpxCxculLU6E776fQ94Z+WKkoiIiJD1Nddco+3Lzs4u49bY59GZpuTkZLRt2xZhYWGoXbs2evbsid27d2vHCCGQmJiI6OhohISEIC4uTptbg5yN+ZmN+ZmPGZqN+QU2jzpN6enpGDJkCNavX4+0tDScO3cO8fHxOHXqlDxm8uTJmDp1KqZNm4YNGzYgKioKt9xyi3bXCjlXSkoK8zMY8zMff4eajT+Dga1UM4L/+eefqF27NtLT09GlSxcIIRAdHY2EhAQ5K2hubi4iIyMxadIkDBgw4KKvGciz2dapU0fW6mln6+2V6lQFDz30kO8bpoiMjMSzzz5b4vwAMzK0Dseoeaj/K7Te8ux0pudnnYn3+++/l7U6FYSVOsSnLqo7ZcoUt89RF3VVh4SsynJG8OPHj6Np06aO/h3arl07bfu7775ze6w6PDds2DBZ161bVztO3aeu4GCXekkDUHgB2LJU2p/BkuanznYP6MNzt9xyi6zffPNN7Th1Gh7r0N25c+c8bkdx1Jn3hwwZImvrrN/333+/rO0O0XqLT2cELxh3LBib3LNnDzIzM7WlP1wuF2JjYwutPl8gNzcXOTk52hf5z6FDhzzKD2CGTsL8zLZ3717+DjWcpz+DzM8sJe40CSEwfPhwdOrUCS1btgTw91oy6iR0BdvqOjOq5ORkhIeHy6/69euXtEnkJZ7kBzBDp2F+5srKygLA36GmY36Bq8R3zw0dOhRbt27F2rVrC+1TF0kFLnSwrI8VGDVqFIYPHy63c3JyAvabRh12U4fkrHfIPf3002XWpqJ4kh/grAytdyXu3LmzyOM+/fRTbVs9ld2rVy9Zx8bGaseZsJivyflZ/5et/o+9X79+sk5KStKOU4dR1X+rdVZ+Uzj5d6g1I3XW/EsuuUTbpw7DvfTSS7J+/PHHteOsC7vakZGRIevu3bt7/Hxf8kd+1mum1FUPnnvuOVlbhwifeeYZWb/yyivaPvVu1tTUVFmvWrXKbTvU4dsePXpo+55//nlZHz16VNYDBw7UjivrITlPlKjTNGzYMCxZsgSrV6/WbsWPiooCcOGMk3r9TlZWVqGedwGXy1Xsbf9U9jzJD2CGTsP8zFW7dm0A/B1qOuYXuDwanhNCYOjQoUhNTcWKFSsKrTMUExODqKgopKWlycfy8vKQnp6urbVGzhUZGcn8DMb8zNaoUSP+DjUcfwYDm0dnmoYMGYIPPvgAixcvRlhYmByjDQ8PR0hICIKCgpCQkICkpCQ0adIETZo0QVJSEqpWraqdWifnGjRoEPMzGPMzG3+Hmo8/g4HNoykH3I3Jzp07V844KoTAuHHjMHv2bBw7dgzt27fH9OnT5cXiF2PC7ep21ahRQ9tWb2UvOA0PFF6d2jpreVk6fvw43njjjRLnB/g3w5EjR2rb6md53333yfrPP//UjlNnOVYnbLXOHK7OUO1EpudXnJCQEFlb26deu6Reh1bSGVX27dsn686dO8v6jz/+KNHr2ZWdnY2wsDCjfoeqs03PmTPHp++l5jJu3DhZz5s3z6fv64nS/gz6Oj/r36X27dvL2joVi/r7U70+rbifA3UY8tixY9o+9ZrQMWPGyNo6SbY/XWzKAY/ONNn5BRQUFITExEQkJiZ68tLkEMzPbMzPfMzQbMwvsHHBXiIiIiIbysWCvf5iHcNWh+RU1tvfqeRmzZqlbaszzaqngFevXq0dp572V5cF6tKli3acOqWBu+kMyDfUhXiti/Kqwwjq7Mddu3bVjlOn8zh06JCsx48frx333nvvyfrs2bMla3A5od4erk4RAQB9+vTx+PXUXJYsWaLtS0lJkfWuXbs8fm0qPGSmLris1gBQrVo1Wbdu3drta6p/677++mtZW6cksl4WYSKeaSIiIiKygZ0mIiIiIhvYaSIiIiKygdc0+Ym6evSmTZv82JLAoi7pAFxY7qfAa6+9JuviVrUvTuPGjWXNa5qcIy8vT9ZffPFFkTUAPPvss2XWpvLi9OnTslanHwCAbdu2yVq91iw0NFQ7Tp0oWb2+zHqNIpWtkydPytp6HaiquH2BhmeaiIiIiGxgp4mIiIjIBg7P+cnSpUtlvXnzZj+2JLCpM9B26tRJ1tZhGnWV8Vq1aslaHXooapuI/qYOkwJAcnJykTWRqXimiYiIiMgGdpqIiIiIbPBowd6y4NTFQsuLiy1WaAcz9B/mZzbmZ77SZsj8/Oti+fFMExEREZEN7DQRERER2cBOExEREZEN7DQRERER2cBOExEREZENjus0OexmvnLHG58/M/Qf5mc25me+0n7+zM+/Lvb5O67TdOLECX83oVzzxufPDP2H+ZmN+ZmvtJ8/8/Ovi33+jpunKT8/HwcOHIAQAg0aNEBGRkap5y0xXU5ODurXr+/Tz0IIgRMnTiA6OhrBwaXrS+fn52P37t1o3rw584OZ+fFn8G/Mz3wmZcj8CnNSfo5bey44OBj16tVDTk4OAKB69erl/humgK8/C29NqBYcHIy6desCYH4qk/Ljz2BhzM98JmTI/NxzQn6OG54jIiIiciJ2moiIiIhscGynyeVyYezYsXC5XP5uit+Z+FmY2GZfMfWzMLXd3mbq52Bqu33BxM/CxDb7ipM+C8ddCE5ERETkRI4900RERETkJOw0EREREdnAThMRERGRDew0EREREdng2E7TjBkzEBMTgypVqqBNmzZYs2aNv5vkU8nJyWjbti3CwsJQu3Zt9OzZE7t379aOEUIgMTER0dHRCAkJQVxcHHbs2OGnFheP+TE/0wRShszP7PyA8pehMfkJB/roo49EpUqVxDvvvCN27twpnnnmGREaGir27dvn76b5TLdu3cTcuXPF9u3bxZYtW8Ttt98uGjRoIE6ePCmPmThxoggLCxMLFy4U27ZtE3369BF16tQROTk5fmx5YcyP+ZkoUDJkfmbnJ0T5zNCU/BzZaWrXrp0YOHCg9ljTpk3FyJEj/dSispeVlSUAiPT0dCGEEPn5+SIqKkpMnDhRHnP27FkRHh4uZs2a5a9mFon5Mb9AYGqGzO8CU/MTghkK4dz8HDc8l5eXh02bNiE+Pl57PD4+HuvWrfNTq8pednY2ACAiIgIAsGfPHmRmZmqfi8vlQmxsrKM+F+Z3AfMzn4kZMr+/mZgfwAwLODU/x3WaDh8+jPPnzyMyMlJ7PDIyEpmZmX5qVdkSQmD48OHo1KkTWrZsCQDy3+70z4X5Mb9AYGqGzO8CU/MDmCHg7Pwqltk7eSgoKEjbFkIUeixQDR06FFu3bsXatWsL7TPlczGlnb7A/MxneoYmtNGXTM8PMKedvuDk/Bx3pqlWrVqoUKFCoZ5jVlZWoR5mIBo2bBiWLFmClStXol69evLxqKgoAHD858L8mJ/pTM6Q+ZmdH8AMnZ6f4zpNlStXRps2bZCWlqY9npaWho4dO/qpVb4nhMDQoUORmpqKFStWICYmRtsfExODqKgo7XPJy8tDenq6oz4X5sf8TBUIGTI/s/MDym+GxuRXZpece6Dgdss5c+aInTt3ioSEBBEaGir27t3r76b5zKBBg0R4eLhYtWqVOHjwoPw6ffq0PGbixIkiPDxcpKamim3btom+ffs6+nZZ5sf8TBIoGTI/s/MTonxmaEp+juw0CSHE9OnTRcOGDUXlypVF69at5W2HgQpAkV9z586Vx+Tn54uxY8eKqKgo4XK5RJcuXcS2bdv81+hiMD/mZ5pAypD5mZ2fEOUvQ1PyC/r/jSUiIiKiYjjumiYiIiIiJ2KniYiIiMgGdpqIiIiIbPBZp6m8rdAcaJif2Zif2Zif+ZhhYPLJjOALFixAQkICZsyYgRtuuAGzZ89G9+7dsXPnTjRo0KDY5+bn5+PAgQMICwsrN7OfOoEQAidOnEB0dDQ++eSTEucHMEN/YH5mY37m81aGzM8/1PyCg4s5n+SLW/JKs0JzRkaG21sP+eX7r4yMjFKvsM0MmR+/mF95/SpthszP//kVx+vDc56u0Jybm4ucnBz5JTgDgl+5XC6PV9hmhs7B/MzG/MznaYbMz1nCwsKK3e/1TpOnKzQnJycjPDxcftk5/Uy+c/ToUY9X2GaGzsH8zMb8zOdphszPWS42JOqzC8HtrkQ8atQoZGdny6+MjAxfNYk84MlK0szQeZif2Zif+fg3MDB5/UJwT1dodrlccLlc3m4GlVDNmjU9XmGbGToH8zMb8zOfpxkyP7N4/UxTeV2hOVAwP7MxP7MxP/MxwwBn63YMD5Vmhebs7Gy/Xz1fnr+ys7NLvcI2M2R+/GJ+5fWrtBkyP//nVxyfdJqEKPkKzfyGccY3TGlW2GaGzI9fzK+8fpU2Q+bnjPzcCRLCWfc35uTkIDw83N/NKLeys7NRvXr1Ur0GM/Qf5mc25me+0mbI/PzrYvlx7TkiIiIiG3yyjAoRERGRO40aNZL1o48+qu176aWXZD1t2jRZP/PMMz5v18XwTBMRERGRDew0EREREdnA4TkiQJuFt169erKeMGGCdtyYMWPKrE3k3rPPPqttjx49WtY1a9aU9dixY7Xjpk+fLusjR474qHVEBBT+ffnQQw/JOiQkRNZ16tTRjlPvT2vYsKGsq1atqh13+vRpr7TTEzzTRERERGQDO01ERERENrDTRERERGQDr2kqRtOmTWWtjqvee++92nGPPfaYrN2tRA4A27Ztk/Vnn30m66SkJO243NxczxtLXpOfny/rSy+91I8toYiICFkvWLBA1jfffLPb56jXQyQmJmr7mjVrJuu+fft6oYVE1Lx5c1mrP3Nt2rTRjlOnGbA7r7Y60WelSpVK1kAv4pkmIiIiIhvYaSIiIiKyoVwMz1WoUEHW1jV9HnzwQVk/8MAD2j71lGNoaKit91JPOZ4/f17b17JlyyLr6Oho7bgnn3zS1ntRyam3vgJA7dq1Zb1u3TpZP/fcc2XWJipMzUUdkvvll1+049SpBHr37i3rzp07a8fVqFHD200kKnesl6Hcc889slaH5NTLWkqqS5cusq5Vq5a2Lzs7u9Sv7ymeaSIiIiKygZ0mIiIiIhsCdnhOHZJ7+umnZT1lypQSvd7Bgwdl/fzzz9t6zm+//aZtz5gxQ9atW7eW9W233VaiNlHJqXdlAUDFin//KJw6darImsre77//LusmTZrI+s8//9SOU4fC+/fv7/N2kXepC7a+/PLLsrb+DlW1atVK1jfeeKO2b/jw4bLesGGDrGfOnFmqdtIFH3/8sbZ99913e/waL774oqxTU1O1fbt27SryOepQHVD894ev8EwTERERkQ3sNBERERHZwE4TERERkQ0Be01T+/btZT1x4kRZf/vtt9px6ur2e/fu1fYtW7ZM1nv27CnyOZ5QZwFXr2kiZ1Gvlbj66qu1fVu3bi3r5pRreXl5slavX3j44Ye149RrWNTMzp07px33/vvve7uJVALfffedtt2uXTtZq7ezW29ZV/ep07v8+OOPbo9TrxnlNU0lp07l0atXL22fu9m9J0yYoG2rP3/79++XtXXlhX379slanUV8wIAB2nFLly6VtfU6R1/hmSYiIiIiG9hpIiIiIrIhYIfn1JlD1WkG1NscveXyyy+XtXqa8vjx49pxAwcOLPL5JR3uI99QZ6Fu0KCBto/Dc/7Tp08fWb/33nu2nrNz505t+9dff/Vmk+giNm/eLGt1qDQqKko7zt1C54sWLdK2f/jhB1mrt6Xff//92nH33XefrNXpZ8gz6qUK6rQQVhs3bpT12LFjZW2dOkAddlMdOHBA2/78889lPWzYMLfva3fRX2/y+EzT6tWr0aNHD0RHRyMoKKjQN7UQAomJiYiOjkZISAji4uKwY8cOb7WXfIz5mY35mY35mY8ZBjaPO02nTp1Cq1atMG3atCL3T548GVOnTsW0adOwYcMGREVF4ZZbbsGJEydK3VjyvZSUFOZnMOZnNuZnPmYY2DzuNHXv3h0TJkwodPU8cKGHnZKSgtGjR6NXr15o2bIl5s2bh9OnT+ODDz7wSoPJt2bOnMn8DMb8zMb8zMcMA5tXr2nas2cPMjMzER8fLx9zuVyIjY3FunXrCt0uCAC5ubnIzc2V2zk5OV5py5IlS4qsfeH06dOyVm+xbN68uXZc3bp1Za3ePt2zZ0/fNc5Dhw4d8ig/wHcZOsG4ceO0bfUWVycK5PzWrFkja+syDuo1Mert69YpI9T81GWN1Osw/Mn0/NRriQAgKytL1ur1gdZrmNTpWNRlT6ZOnaodp05B0bhxY1kX9zu0atWqsr7++uu1fevXr3f7vJLyNEMn5WeVlJQk627durk9bvbs2bL++uuvfdYe67QChw8f9tl7uePVu+cyMzMBAJGRkdrjkZGRcp9VcnIywsPD5Vf9+vW92SQqAU/yA5ih0zA/szE/8/FvYODyyZQD1v9FCCHc3h0xatQoZGdnyy/eSeZ/nuQHMEOnYX5mY37m49/AwOXV4bmC20gzMzNRp04d+XhWVlahnncBl8sFl8vlzWaUubNnz8r6iiuukLU6KzkA/PXXX7IeNWqUrIv7X6Q/eJIfEBgZBpJAzU+9Ldl6i7lKnUE4MTFR26euxq7OIv7hhx9qx7lbZb0sOD2/Bx98UNvu0KGDrPv166ftCw8Pl7U6bPPYY49pxx05ckTW6hCcVUREhKzffvttWVeuXNntc9TLJ3wxHFcUU/8GJicna9u33nprkcfddNNN2nZ6erpX26F2MIODnTWdpFdbExMTg6ioKKSlpcnH8vLykJ6ejo4dO3rzrchHIiMjmZ/BmJ/ZmJ/5mGFg8/hM08mTJ7UJ4vbs2YMtW7YgIiICDRo0QEJCApKSktCkSRM0adIESUlJqFq1aqH/gZAzDRo0iPkZjPmZjfmZjxkGNo87TRs3bsSNN94otwtOcT/yyCN47733MGLECJw5cwaDBw/GsWPH0L59eyxfvhxhYWHea7UfqMNuTz31lLZP3a5WrZrb11AXDv7000+92DrvSUhIgBAi4PIrKeuCk07H/PSFt/v376/tU++kuvfee2VtHYbw1/CcCfmlpKRo2+qQWXG+//57WR88eNDWc26++WZtW13RIS4uztZrlDUTMlSpi18nJCRo+9QZt9XZ9b09Wad1wd7OnTvLOj8/X9apqalefd+S8LjTFBcXV+zU5UFBQUhMTCx0LQGZgfmZjfmZjfmZjxkGNmddYUVERETkUOw0EREREdng1SkHAo167da///1vWasze3tCnRFXvS1avQaDyoZ1SQN1Cgh1fN26GjuZ7V//+pese/fuLes777xTO05dW/PcuXO+b5jDqdeGXXLJJbaf1717d1mvWrXK7XHqtUvXXnutrK0ztYeGhtp+7wLbt2/3+DmBbNCgQdr24MGDZV2pUiVt31tvvSXrN998U9b+mIkbKDwjuD/wTBMRERGRDew0EREREdnA4bliqLOcfvXVV7Ju3bq1dpx1251HHnlE1g899JCs3333Xe241157Tdbqwr7kPdbTvOps7aonnnhC2545c6bP2kT+Exsbq22rM1mrs1WXV+qQXHEzNKuzrAP6kJw6C7h1iCgmJkbWxQ3B/fDDD7JWL5Mo7pKJBQsWuN1XXqiXHDz55JPavmbNmslanVYAADZv3izrffv2+ah1+lC51erVq2WtLtrtLzzTRERERGQDO01ERERENnB4rhjqTKTWWcBV11xzTZGPW2esveeee2StrkNkfe0+ffrIukmTJrL21x0LRIFCHSJSFwVdtGiRdhyH5HTq0Ix1KFu94+qll17S9j3wwAOybtGihayrVKni9r3UITjr7OPLli2Ttbpgr3V4Jzc3V9YZGRlu36u8UO8ObdWqlbbvp59+knV8fLy2r6z+5lgXaVbbuG3bNllb7+7zB55pIiIiIrKBnSYiIiIiG9hpIiIiIrKh3F/T1Lx5c1mfPHlS27d//35br7FlyxZbj6vj8+pY/6uvvqodp84c/vvvv8u6Xbt22nH+WomdyFPqNSznz5/X9rmb7sHX1IXH1esmqLDPPvtM1idOnND2RUREFFkXtV1g48aN2rZ6Xc3zzz8v6+zsbO24xo0by1pdscFqw4YNsv7iiy/cHhfI1Gk0atWqJWv1+x4AZs+eLeuyvG5WnQZBbR+gt3HWrFmydsJ1vTzTRERERGQDO01ERERENpSL4bmKFf/+Z6qLDgJA3759Za0uSgnYH54rifnz58vaeqpaHSqoVq2arK23g3J4jkyRkJAg659//lnbt3jxYp+9r/UWZXUBZvU25/I6hFMSPXv21Lavv/56Wd91113aPnXBV3Voxfo7zzrk5446e3XNmjXdHjd37lxbrxfIunXrJusRI0bI2roagjo8523qEBygz7Sv/i1W2wrobXTCkJyKZ5qIiIiIbGCniYiIiMiGcjE817lzZ1lbF4p87rnnZL1kyZIya5Nq9+7d2vYLL7wg6ylTpsjaeup7+vTpsrbekUTeYb2rQ71759dffy3r5hjrzJkzsrYOgy9dulTW3v4+7tSpk9tt9c5UdRZqKt7atWvdbr/++us+fW/rAtrknroChco6PO4N6l3o6goXXbp00Y5TZ/pW75CzDhmqw3Vbt271Wju9gWeaiIiIiGxgp4mIiIjIBnaaiIiIiGzw6Jqm5ORkpKamYteuXQgJCUHHjh0xadIkXHnllfIYIQTGjRuHt99+G8eOHUP79u0xffp0bYXrsqbeWmy9ZuLBBx+UtToNAABkZWX5tmFuqFMJqOO+N910k3Zc5cqVZa1eM1IaQggkJiY6Kj9/Uqd8APRb1p14TZNT8/vggw9k/cYbb2j71J/BefPmlfq91FvRk5KS3B5n/Xl3Cif+DiX7nPIzGBQUVGStzhQOACtXrpR1amqqx68NAKNHj5a19TpQVXDw3+dp8vPzZf35559rxzntOiaVR2ea0tPTMWTIEKxfvx5paWk4d+4c4uPjcerUKXnM5MmTMXXqVEybNg0bNmxAVFQUbrnlFtvzcJB/paSkMD+DMT/z8Xeo2fgzGNg86jQtW7YM/fv3R4sWLdCqVSvMnTsX+/fvx6ZNmwBc6GGnpKRg9OjR6NWrF1q2bIl58+bh9OnT2v80yblmzpzJ/AzG/MzG36Hm489gYCvVlAMFiykWLMq4Z88eZGZmajNXu1wuxMbGYt26dRgwYEBp3q7Evv32W1m///772j719uc1a9Zo+0aOHClrdcFKXzt27Jisz507J+tDhw5px6mnN73l0KFDjsvPn9QhUACoWrWqn1pij1PzU89GW295njx5sqy3b9+u7Sv4D9nF1KhRQ9bLli2TdZs2bbTj1GFsJ84avXfvXkf+DnU666LPOTk5fmqJc34GX3nlFVkX972uTsmj1sWxDs+pl5FYFwRWqX+z1MtfrFMOOFmJO01CCAwfPhydOnVCy5YtAQCZmZkAgMjISO3YyMhI7Nu3r8jXyc3NRW5urtz25zc7XeBJfgAzdBrmZ66CPyT8HWo25he4Snz33NChQ7F161Z8+OGHhfYV1Qu1PlYgOTkZ4eHh8qt+/folbRJ5iSf5AczQaZif+fg71GzML3CV6EzTsGHDsGTJEqxevRr16tWTjxfcWZSZmYk6derIx7Oysgr1vAuMGjUKw4cPl9s5OTk+/aaxziir3qmmzsQNQOsQpqWlyXrixInacevWrZN1cacm3VE/QwB45513ZK0OD1kXFVX/d+JNnuQHlH2GZck6HHf11VfLWl109OjRo2XWpotxYn6nT5+W9VdffaXtU9/7yy+/1PYNHDhQ1uoQubrQNnDhd1IBdUjOOmwzfvx4We/du9dO08tU7dq1ATj7d2hZcrlc2ra74fGUlBRte+HChb5qki1OyE9dcF49y9WwYcNSv7aVOvx+4MABWX/66afaceqlMuqQnN1heCfw6EyTEAJDhw5FamoqVqxYgZiYGG1/TEwMoqKitA5GXl4e0tPT0bFjxyJf0+VyoXr16toX+U9kZKRH+QHM0EmYn9kaNWrE36GG8/RnkPmZxaMzTUOGDMEHH3yAxYsXIywsTF7DFB4ejpCQEAQFBSEhIQFJSUlo0qQJmjRpgqSkJFStWhX9+vXzyT+AvGvQoEHMz2DMz2z8HWo+/gwGNo86TTNnzgQAxMXFaY/PnTtX3oU2YsQInDlzBoMHD5YTey1fvhxhYWFeaTD5VkJCAoQQzM9QzM98/B1qNv4MBrYgUZKLcHwoJycH4eHhfnnv3r17a9tvvfWWrNWZoK1Wr14ta3VGVXX1dgDajLA9e/aU9Y033qgd16hRI1mrFw9aj1u1apXbNpVUdnZ2qU8P+zPDkpoyZYqsExISbD1HPe6f//ynl1tUMibkV6FCBW172rRpsn7ggQe0faGhobJWr+GrUqWK29f/3//+V+RrA/r0Bk5kQn5l6eabb9a2//Of/8j64MGDsrbeKv/bb7/5tmHFKG2GvsivcePGslav4y0p6+9I9Zop6+zeprlYflx7joiIiMgGdpqIiIiIbODwXDHU212fffZZWVtvd77qqquKfL71dueKFf++hMzdYorA3zOtA/o0CO+++652nHXxYW8or8MDTZs2lbV650t0dLR2nJpN165dZb1582Yfts4+0/Pr3r27tj1p0iRZF0yiW5RFixbJWh2CW79+vfcaVwZMz8/b1NndAaBbt26yVnO2ThfjT04cniP7ODxHRERE5AXsNBERERHZwE4TERERkQ0lXrC3PFBvcVaXTrEuo6Jeh3H33XfLOjY2Vjvu7Nmzsv7+++9l/eOPP2rHqdfU/Prrr542m0pAvQ03UJagMJF1iRXrNpVv6nWc3333nR9bQuUVzzQRERER2cBOExEREZENHJ7zAnUIgcMJRETe8fXXX2vbl156qazVaSaIygrPNBERERHZwE4TERERkQ0cniMiIkc6cuSItm1djYGorPFMExEREZEN7DQRERER2cBOExEREZENvKaJiIgc6d///re/m0Ck4ZkmIiIiIhsc12kSQvi7CeWaNz5/Zug/zM9szM98pf38mZ9/Xezzd1yn6cSJE/5uQrnmjc+fGfoP8zMb8zNfaT9/5udfF/v8g4TDurX5+fk4cOAAhBBo0KABMjIyUL16dX83y69ycnJQv359n34WQgicOHEC0dHRCA4uXV86Pz8fu3fvRvPmzZkfzMyPP4N/Y37mMylD5leYk/Jz3IXgwcHBqFevHnJycgAA1atXL/ffMAV8/VmEh4d75XWCg4NRt25dAMxPZVJ+/BksjPmZz4QMmZ97TsjPccNzRERERE7EThMRERGRDY7tNLlcLowdOxYul8vfTfE7Ez8LE9vsK6Z+Fqa229tM/RxMbbcvmPhZmNhmX3HSZ+G4C8GJiIiInMixZ5qIiIiInISdJiIiIiIb2GkiIiIisoGdJiIiIiIbHNtpmjFjBmJiYlClShW0adMGa9as8XeTfCo5ORlt27ZFWFgYateujZ49e2L37t3aMUIIJCYmIjo6GiEhIYiLi8OOHTv81OLiMT/mZ5pAypD5mZ0fUP4yNCY/4UAfffSRqFSpknjnnXfEzp07xTPPPCNCQ0PFvn37/N00n+nWrZuYO3eu2L59u9iyZYu4/fbbRYMGDcTJkyflMRMnThRhYWFi4cKFYtu2baJPnz6iTp06Iicnx48tL4z5MT8TBUqGzM/s/IQonxmakp8jO03t2rUTAwcO1B5r2rSpGDlypJ9aVPaysrIEAJGeni6EECI/P19ERUWJiRMnymPOnj0rwsPDxaxZs/zVzCIxP+YXCEzNkPldYGp+QjBDIZybn+OG5/Ly8rBp0ybEx8drj8fHx2PdunV+alXZy87OBgBEREQAAPbs2YPMzEztc3G5XIiNjXXU58L8LmB+5jMxQ+b3NxPzA5hhAafm57hO0+HDh3H+/HlERkZqj0dGRiIzM9NPrSpbQggMHz4cnTp1QsuWLQFA/tud/rkwP+YXCEzNkPldYGp+ADMEnJ1fxTJ7Jw8FBQVp20KIQo8FqqFDh2Lr1q1Yu3ZtoX2mfC6mtNMXmJ/5TM/QhDb6kun5Aea00xecnJ/jzjTVqlULFSpUKNRzzMrKKtTDDETDhg3DkiVLsHLlStSrV08+HhUVBQCO/1yYH/MznckZMj+z8wOYodPzc1ynqXLlymjTpg3S0tK0x9PS0tCxY0c/tcr3hBAYOnQoUlNTsWLFCsTExGj7Y2JiEBUVpX0ueXl5SE9Pd9TnwvyYn6kCIUPmZ3Z+QPnN0Jj8yuyScw8U3G45Z84csXPnTpGQkCBCQ0PF3r17/d00nxk0aJAIDw8Xq1atEgcPHpRfp0+flsdMnDhRhIeHi9TUVLFt2zbRt29fR98uy/yYn0kCJUPmZ3Z+QpTPDE3Jz5GdJiGEmD59umjYsKGoXLmyaN26tbztMFABKPJr7ty58pj8/HwxduxYERUVJVwul+jSpYvYtm2b/xpdDObH/EwTSBkyP7PzE6L8ZWhKfkH/v7FEREREVAzHXdNERERE5ETsNBERERHZ4LNOU3lbbDDQMD+zMT+zMT/zMcPA5JNO04IFC5CQkIDRo0fjxx9/ROfOndG9e3fs37/fF29HXsb8zMb8zMb8zMcMA5dPLgRv3749WrdujZkzZ8rHmjVrhp49eyI5ObnY5+bn5+PAgQMICwsrN7OfOoEQAidOnEB0dDQ6dOhQ4vwAZugPzM9szM983sqQ+fmHml9wsPvzSV5fRqVgscGRI0dqj7tbbDA3Nxe5ubly+48//kDz5s293Syy6bfffvMoP4AZOgnzMxvzM5+nGTI/Z8nIyNBmIrfy+vCcp4sNJicnIzw8XH7xm8W/8vLyPF4skhk6B/MzG/Mzn6cZMj9nCQsLK3a/zy4Et7uo3qhRo5CdnS2/MjIyfNUksqEgI08WRWSGzsH8zMb8zOdphszPWS42JOr14TlPFxt0uVxwuVzebgaVUM2aNT1eLJIZOgfzMxvzM5+nGTI/s3j9TFN5XWwwUDA/szE/szE/8zHDAOeLtVlKs9hgdna22zVo+OX7r+zs7FIvFskMmR+/mF95/SpthszP//kVx2cL9pZ0scFA/oa54YYb5NeaNWvk13XXXad9OeEbpjSLRQZyhk7/Yn5mfzE/879KmyHzc0Z+7jhuwd6cnByEh4f7uxk+ccMNN8h64sSJsn722We14zZu3FhmbbLKzs5G9erVS/UagZyh0zE/szE/85U2Q+bnXxfLj2vPEREREdng9bvn6G8VK+of7yeffCLrqKgoWdevX187zp9nmoiIiKhoPNNEREREZAM7TUREREQ2cHjOhx544AFte/v27bI+fvy4rD/77LOyahIRERGVEM80EREREdnAThMRERGRDew0EREREdnAa5q87LrrrpP17NmztX1ZWVmyvvHGG8usTeVJ//79te2aNWvKevLkybZeIzj47/9L5Ofnuz3u4YcflvX8+fNttpCIqHxq2rSprNVJna1zbA8cOLDM2uQpnmkiIiIisoGdJiIiIiIbODznBeqM3h999JGsK1WqpB03duxYWf/222++b5jh1BnV69WrJ+vXXntNO04dgouNjdX2qcNrxQ21uVPcc958801ZHz58WNv39ddfe/xegaxTp06ynjlzpravSZMmslZnzQeA9evXy3ratGmyPnLkiHZcWlqarHfs2CHrDRs2aMcxF2dSh9Vr167t9rgePXrIWv2esrr88stl/fvvv5euceQ177//vqxbt24t6507d/qjOSXCM01ERERENrDTRERERGQDh+dKoFGjRtq2Okxz2WWXyfo///mPdtzcuXN92q5AM3ToUFlbh+TsUmde/+c//+nx89U75ACgYcOGsg4PD5d1ZGSk540LcJdccoms3S1WbfXggw8Wu11AHZIFgPvvv99Wm06ePCnrtWvXynr06NHacZs3b7b1emRf8+bNZd2+fXttX2JioqytC5i7o95xtXv3bm3fiRMnStBC8rZXXnlF21aH5NS7lNXa6cxpKREREZEfsdNEREREZAM7TUREREQ28JqmEpg6daq2rd4G++uvv8q6d+/eZdamQHTnnXfaOm7Xrl2yVmddB4AJEybIeuXKlR63IS4uTttWr2mi4qmz3hd3HVNGRoas33rrLW2f+j2g3oq+ePFi7bgrrrhC1mpGl156qXacOnXFrbfeKuubbrpJO069Rka9HmvKlCnacadPnwbZ8/TTT8v6qaee8upr33HHHdr2n3/+6dXXJ/vUWb9ffPFFbZ96HZo6ncuVV16pHadeY/jqq696u4mlwjNNRERERDaw00RERERkA4fnbEpOTpZ1z5493R6nnsrPycnxZZMC3pw5c2R99OhRWS9ZskQ77ttvv5W1N2ZaV2caVmerJs+ow1+q/fv3a9vdunWTtTrUCgBvv/22rNUpHtQhveLUqFFD21anJrj33ntlbR2Gveqqq4qs09PTteNWr15tqx2BpFq1atp2y5YtZd2rVy9tnzq1QHEzeJeWdVieylZoaKis1eG0oKAgt88ZNGiQrEeNGqXtU//GcniOiIiIyEAed5pWr16NHj16IDo6GkFBQVi0aJG2XwiBxMREREdHIyQkBHFxcdpaUORszM9szM9szM98zDCwedxpOnXqFFq1aqUtnqmaPHkypk6dimnTpmHDhg2IiorCLbfcwhlaDZGSksL8DMb8zMb8zMcMA1uQUO8B9PTJQUH47LPP5PijEALR0dFISEjACy+8AADIzc1FZGQkJk2ahAEDBlz0NXNycrRrF/xJHYNXr6NRl4cA9Nukn332WVmX4qP1m8jISDz77LMlzg9wVoZ2qVmvWbNG1uptsVbqdAZjx471TcM85KT8Zs+eLWv1FnPr0govv/xyqd+rtK677jpte8iQIbLu37+/rLdt26Ydd/XVV3u1HU7Kz53p06dr2+q1Kf5i/ff6s4NS2gxN/P2pLnf03nvvydp6TdPOnTtlrV4r+O9//1s7rnPnzrJu27atrA8fPlzqtl5MdnY2qlev7na/V69p2rNnDzIzMxEfHy8fc7lciI2Nxbp164p8Tm5uLnJycrQv8p9Dhw55lB/ADJ2E+ZmN+ZnP0wyZn1m82mnKzMwEUHjx0sjISLnPKjk5GeHh4fLL7mKN5Due5AcwQ6dhfmZjfubj38DA5ZMpB6yn5IQQbm89HDVqFIYPHy63c3Jy/PZNYz0lmpqaKmt1SO5f//qXdlxJhuQqVKgg6+eee07b16hRI1kvX75c1kuXLtWOO3funK338pQn+QHOytCuKlWqaNvqbMXqkFxxw3PW7wOncEp+58+fL/Jx9XvfKTZu3KhtJyQkyFqdEsH6uagznRfXsfGEU/JTqcOQd999t1dfOxCZ+jfQri5dumjb6szf6r/TOmO+Os2Hau3atdq2Otyn1ikpKR631du82mkq+AWSmZmJOnXqyMezsrIK9bwLuFwuuFwubzaDSsmT/ABm6DTMz2zMz3z8Gxi4vDo8FxMTg6ioKKSlpcnH8vLykJ6ejo4dO3rzrchHIiMjmZ/BmJ/ZmJ/5mGFg8/hM08mTJ7VFaffs2YMtW7YgIiICDRo0QEJCApKSktCkSRM0adIESUlJqFq1Kvr16+fVhntLSEiIrNXhOACoVauWrNU75NTZwQH3Q3KVKlXStp944glZqzMTq3cKWA0cOFDWX331lbZPXSi4uGEkTwwaNMio/Dyhzvr88MMPa/vsDjmoM0IfP37cG83yKiflp05L8uijj8paHQoFgLlz58pa/d3iTxUr/v2rMTj47/9bWu+cVRcK9sbwnJPyUzVo0EDWxS2+TM7N0JusM3irC+6qfw+tv2etM/67o76GdTFff/O407Rx40Zt9fKCsdhHHnkE7733HkaMGIEzZ85g8ODBOHbsGNq3b4/ly5cjLCzMe60mn0lISIAQgvkZivmZjfmZjxkGNo87TXFxccVe7BwUFITExEQkJiaWpl3kJ8zPbMzPbMzPfMwwsHHtOSIiIiIbfDLlgEkuv/xyWavDjsCFSccK/N///Z+sDx065Pb11BXAP/zwQ23f7bffLus//vhD1uPHj9eO27Nnj6z79u0r6+7du2vHxcbGynrlypVu21SeXXPNNbJWx9cfeughW8+3zkD7z3/+U9bXXnutrdfYsmWLrJ14HZSvqLP//vjjj7K2DlNcf/31srZOBbF9+/YiX7tGjRradvv27W21Sb0desyYMbJu166ddpz6M6ne9bRv3z7tuOjoaFvvS8WbP3++rNXb2Z1+632ga9q0qawfeOABWauTdwL6z5W6osJnn31WovdVX2/37t0leg1f4ZkmIiIiIhvYaSIiIiKyodwNz6m3zgLAokWL3B47depUWaszBqvTFABA7969Za1e/KdObgYAd911l6y///57WWdlZbltg3oaNDs7W9uXkZHh9nnllbrwLqAPkZbkVml12gkA+PTTT209T71NfdmyZbJ+++23teMWL17scZtMpP78DBs2TNv3/vvvy/rnn3/W9qnDzuo0HQcOHNCOa9mypa12qFNGqIv0WmcpHzduXJHPb9iwobZdFguIOsF//vMfWb/66qvaPnXYu7jhNHWYxTrFx2+//SZrdT4jXnZQtqwzfc+bN0/W6t9O681g6s+BOrt5cS699FJZW39vq68/cuRIWTthRnCeaSIiIiKygZ0mIiIiIhvK3fCcerccAFx22WVuj1VnNFapMxgDwH333Sdr9c4B6+KEmzdvttVG9c4g9Y4+61CAU2ZPLmvWYbaPP/5Y1tbZ1UsyU7o6tFZS6mvceuutRdaAPrSh3kEUaH744QdbxzVr1qzY7QIRERElaod6x6nq5MmT2vbChQtlrc68b33fvLy8ErXDNGfPnpW1etchoF/ikJSU5PY1nnzySVnv37/f7XGPPfaYx+1TF1gGgFdeecXj1yiv1GGyVatWafvUYTL1jjbr3yJ15Qq7f+dU1t/b6nsdOXLE49fzJZ5pIiIiIrKBnSYiIiIiG9hpIiIiIrKh3F3T1Lp1a7f7/vvf/2rb6nUO6nVL1muV1q9fL+t//OMfsrY7tutyubTt6dOny1q9fsfu7e6BQl1pvl69erK23rbfoUMHWVuvYXJ3TZN6jQagz/KujqcXt85icdRZr4u7/ubRRx+VdSBf06ReI2Sd2V7dts707UtnzpyR9T333KPtU6cmUK8rLOm1VIFs06ZNsu7WrVupX4+zrJetUaNGydr6+87d7z/rigrLly/3+H3VaQasU7uoU4+0bdvW49f2JZ5pIiIiIrKBnSYiIiIiGzg8p/jll1+07RMnTsj68ccfl/WpU6e049RbZHft2uVxm6y36aozHx88eFDW7mYpDhTq4rqAfju+dRZpu9QFctXFdvfu3asd9+9//7tEr+9O//79Zf3OO+949bVNpA6FqQt/AvpwzM0336ztU4fC1Vv/7bIOL6hDSeoQg/XnVv15t64iQM5T3KoKpGvTpo22/cwzz8havTTBSl35wu5wXGhoqLatzu49evRoWa9evVo7Tp3C4PTp07beq6zwTBMRERGRDew0EREREdlQ7obncnJytG31dGTTpk21feodUKrk5GRt292QnPVUpzob+YIFC2TdokUL7bg//vhD1k8//bSsd+zYUeT7BArr8FxJhuQmTJigbat32qmfq69Nnjy5zN7LdOriu+rivdZtdfHq4oYRVNYZu+0uAGv9GXenJDPOU/HOnTvn8XOsvzvIvuLuEFb3qbN2q7OIF0cd+gOAK6+8UtZ//vmnrK2L/JbkMpeywjNNRERERDaw00RERERkAztNRERERDaUu2uaPv74Y217wIABslavOQKATz75RNZ16tSRtXUVbnV2afXW1+bNm2vHqTOvqtauXattDx48WNbbt28v8jmByPo5qOPa6vVm1vHu1157TdbenjqgOGqbRowYoe2rWbOmrHndi3eUZNZhu2rXrq1tW2fpLzBlyhRt2/o9S6Wn/p70xgzjprvuuuu0bfU6I3X6G7vXAanTbgD6tB6zZ8/W9qm/xxISEopsA+B+FYWMjAztuDFjxsja7nWDTuPRmabk5GS0bdsWYWFhqF27Nnr27Indu3drxwghkJiYiOjoaISEhCAuLi7gL2AOJMzPbMzPfMzQbMwvsHnUaUpPT8eQIUOwfv16pKWl4dy5c4iPj9d6u5MnT8bUqVMxbdo0bNiwAVFRUbjlllu0iSLJuVJSUpifwZif+fg71Gz8GQxsQaKkK5Liwi2DtWvXRnp6Orp06QIhBKKjo5GQkIAXXngBAJCbm4vIyEhMmjRJGwpzJycnB+Hh4SVt0kVVqFBB2x40aJCs33rrLa++19GjR7VtdQHf1NRUWVtnjD5//rxX2+GJyMhIPPvssyXOD/B9hmWpWrVq2rY6K646rYB6GhsAgoP//v+IOs2FunAtoC/wfOTIkdI1FsyvpLp27aptp6WlFXnctddeq21v2bLFq+04fvw4mjZt6ujfob6mTh9gd9Fz67CS+nu9rJX2Z9Ca3yuvvKLtV4fh1KE2b9ymb539/sknn5S1+vtJnTrA6rPPPpO1Nb/Dhw+Xtok+l52djerVq7vdX6oLwbOzswH8vfL3nj17kJmZqc2n4nK5EBsbi3Xr1hX5Grm5ucjJydG+yH8OHTrkUX4AM3QS5me2vXv38neo4Tz9GWR+Zilxp0kIgeHDh6NTp05o2bIlACAzMxPAhZ62KjIyUu6zSk5ORnh4uPyqX79+SZtEXuJJfgAzdBrmZ66CG0n4O9RszC9wlfjuuaFDh2Lr1q1F3j1ina1XCOF2Bt9Ro0Zps4Hm5OT49JvGOvSlntbdsGGDtq9fv36yVk/Lt2/fXjtOXfxVHX5RZ6O2HudknuQHlH2GJXXJJZcUWQP6Aq2dOnWSdeXKlbXj2rVrV+RrW++QO3nypKx//PFHWVvvvPSFQM3Pl9RhCCv1jlh19nJfcvLvULo4b+an3nHma/v379e2y/K9TVGiTtOwYcOwZMkSrF69GvXq1ZOPR0VFAbhwxkm9RT8rK6tQz7uAy+Vye3sv+Ycn+QHM0GmYn7kKpj7g71CzMb/A5dHwnBACQ4cORWpqKlasWIGYmBhtf0xMDKKiorSLKPPy8pCeno6OHTt6p8XkU5GRkczPYMzPbI0aNeLvUMPxZzCweXSmaciQIfjggw+wePFihIWFyTHa8PBwhISEICgoCAkJCUhKSkKTJk3QpEkTJCUloWrVqtpQFznXoEGDmJ/BmJ/Z+DvUfPwZDGwedZpmzpwJAIiLi9Menzt3Lvr37w/gwszIZ86cweDBg3Hs2DG0b98ey5cvR1hYmFca7G1//fWXrL///nttn3W7PEhISIAQwpj8rKyrnd91112y7tKlS5G1lTpdQEln83744YdlvXjx4hK9RkmYnl9ZUj+Tnj17uj1OvRZRvb7JV0z7HeoEd999t7Y9YcIEWf/xxx9l2hb+DAa2Us3T5AumzzFiuovNUWGHPzN0SqdJnc+pLDtNpudXltQ/Ytb5Y9QbAH744QdZW28C8TbmV7J5mqyd2TZt2si6rDtNpc3Q9PxM59N5moiIiIjKi3K3YC8FNuuZppdeeqlUr/fTTz9p28ePH5e1ukivdTbvffv2lep9yTfUFQGWLl0qa+vUEip1QWhyJuuCy+ot+2V9pokCG880EREREdnAThMRERGRDew0EREREdnAa5oooFiX9VEXyZwzZ46t11CXO1i2bJm279ChQ6VoHflbixYtZF3cHZR//vmnrNPT033aJtKdO3dO1kePHtX2FSwOfzEffvihrHv06CHr7du3l7J1VN7xTBMRERGRDew0EREREdnA4TkKKL/++qu2HRsb66eWkBNYZ2H+5ptvbD1v3LhxslaH6sj31CE06/qmAwYMkHXXrl1lHR8frx2Xk5Mj65YtWxb52kQlwTNNRERERDaw00RERERkA4fniChgnThxQtu+9NJL/dQSKglrfq+//nqRNVFZ4ZkmIiIiIhvYaSIiIiKygZ0mIiIiIhvYaSIiIiKygZ0mIiIiIhsc12kSQvi7CeWaNz5/Zug/zM9szM98pf38mZ9/Xezzd1ynyXqLKZUtb3z+zNB/mJ/ZmJ/5Svv5Mz//utjnHyQc1q3Nz8/HgQMHIIRAgwYNkJGRgerVq/u7WX6Vk5OD+vXr+/SzEELgxIkTiI6ORnBw6frS+fn52L17N5o3b878YGZ+/Bn8G/Mzn0kZMr/CnJSf4ya3DA4ORr169eTaQdWrVy/33zAFfP1ZhIeHe+V1goODUbduXQDMT2VSfvwZLIz5mc+EDJmfe07Iz3HDc0REREROxE4TERERkQ2O7TS5XC6MHTsWLpfL303xOxM/CxPb7CumfhamttvbTP0cTG23L5j4WZjYZl9x0mfhuAvBiYiIiJzIsWeaiIiIiJyEnSYiIiIiG9hpIiIiIrKBnSYiIiIiGxzbaZoxYwZiYmJQpUoVtGnTBmvWrPF3k3wqOTkZbdu2RVhYGGrXro2ePXti9+7d2jFCCCQmJiI6OhohISGIi4vDjh07/NTi4jE/5meaQMqQ+ZmdH1D+MjQmP+FAH330kahUqZJ45513xM6dO8UzzzwjQkNDxb59+/zdNJ/p1q2bmDt3rti+fbvYsmWLuP3220WDBg3EyZMn5TETJ04UYWFhYuHChWLbtm2iT58+ok6dOiInJ8ePLS+M+TE/EwVKhszP7PyEKJ8ZmpKfIztN7dq1EwMHDtQea9q0qRg5cqSfWlT2srKyBACRnp4uhBAiPz9fREVFiYkTJ8pjzp49K8LDw8WsWbP81cwiMT/mFwhMzZD5XWBqfkIwQyGcm5/jhufy8vKwadMmxMfHa4/Hx8dj3bp1fmpV2cvOzgYAREREAAD27NmDzMxM7XNxuVyIjY111OfC/C5gfuYzMUPm9zcT8wOYYQGn5ue4TtPhw4dx/vx5REZGao9HRkYiMzPTT60qW0IIDB8+HJ06dULLli0BQP7bnf65MD/mFwhMzZD5XWBqfgAzBJydX8UyeycPBQUFadtCiEKPBaqhQ4di69atWLt2baF9pnwuprTTF5if+UzP0IQ2+pLp+QHmtNMXnJyf48401apVCxUqVCjUc8zKyirUwwxEw4YNw5IlS7By5UrUq1dPPh4VFQUAjv9cmB/zM53JGTI/s/MDmKHT83Ncp6ly5cpo06YN0tLStMfT0tLQsWNHP7XK94QQGDp0KFJTU7FixQrExMRo+2NiYhAVFaV9Lnl5eUhPT3fU58L8mJ+pAiFD5md2fkD5zdCY/MrsknMPFNxuOWfOHLFz506RkJAgQkNDxd69e/3dNJ8ZNGiQCA8PF6tWrRIHDx6UX6dPn5bHTJw4UYSHh4vU1FSxbds20bdvX0ffLsv8mJ9JAiVD5md2fkKUzwxNyc+RnSYhhJg+fbpo2LChqFy5smjdurW87TBQASjya+7cufKY/Px8MXbsWBEVFSVcLpfo0qWL2LZtm/8aXQzmx/xME0gZMj+z8xOi/GVoSn5B/7+xRERERFQMx13TRERERORE7DQRERER2cBOExEREZEN7DQRERER2cBOExEREZEN7DQRERER2cBOExEREZEN7DQRERER2cBOExEREZEN7DQRERER2cBOExEREZEN7DQRERER2fD/APPfWx4LTPTUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x600 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize 20 images from training set-\n",
    "plt.figure(figsize = (7, 6))\n",
    "for i in range(20):\n",
    "    # 4 rows & 5 columns-\n",
    "    plt.subplot(4, 5, i + 1)\n",
    "    # plt.imshow(images[i], cmap = 'gray')\n",
    "    # 0 to access first channel-\n",
    "    plt.imshow(images[i][0], cmap = 'gray')\n",
    "    \n",
    "plt.suptitle(\"Sample MNIST training images\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ed9b2a9-0306-47fd-91fc-c0a13762c19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "del images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47eaf4da-49d5-4a0d-a750-872d37de86aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3aa740c-6877-4696-82cb-9633b77240db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d85275d6-59a2-4b1e-ac91-fb4caa4fe879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeNet5(\n",
       "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), bias=False)\n",
       "  (bn1): BatchNorm2d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1), bias=False)\n",
       "  (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc1): Linear(in_features=256, out_features=120, bias=True)\n",
       "  (bn3): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (bn4): BatchNorm1d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize an instance of LeNet-5 CNN architecture-\n",
    "model = LeNet5(beta = 1.0)\n",
    "\n",
    "# Apply weights initialization-\n",
    "model.apply(init_weights)\n",
    "\n",
    "# Save randomly inialize parameters-\n",
    "# torch.save(model.state_dict(), \"LeNet5_random_params.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c8f2fc0-885f-4c10-986c-ab53a31dc511",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Push to GPU (if available)-\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8641c2b1-0378-4266-8db0-d8fb7a286c44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28a102ec-8eb6-4266-9444-6a9d4347ebc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15bd5001-b670-48ea-bbae-87e40b394940",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define cost function-\n",
    "loss_fn = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e694cc3-17dd-4a01-a18a-db06174c3a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimizer-\n",
    "optimizer = torch.optim.SGD(\n",
    "    params = model.parameters(),\n",
    "    lr = 0.0, momentum = 0.9,\n",
    "    weight_decay = 5e-4,\n",
    ")\n",
    "\n",
    "optimizer = LARC(\n",
    "    optimizer = optimizer, trust_coefficient = 0.001,\n",
    "    clip = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d985af2b-53d5-4765-aed6-8ad154768a58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11610cb8-298a-4a4b-9415-e4aabf447553",
   "metadata": {},
   "source": [
    "### Learning Rate Scheduler\n",
    "\n",
    "- linear learning rate warmup\n",
    "- cosine annealing without warm restart(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36f5d770-06bc-4673-87d4-896143fae9ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear lr warmup-\n",
    "warmup_lr_schedule = np.linspace(start = 0, stop = base_lr, num = num_steps_epoch * warmup_epochs, endpoint = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a2b9906-2f7c-4014-98fb-9d6dededfd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_annealing(t, eta, T):\n",
    "    return 0.5 * (1 + np.cos((t * np.pi) / T)) * eta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "282443a2-9fea-4976-a36a-eecc251035c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_lr_scheduler = [cosine_annealing(t = t, eta = base_lr, T = (num_train_epochs - warmup_epochs) * num_steps_epoch) for t in range(1, ((num_train_epochs - warmup_epochs) * num_steps_epoch) + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1517d6a7-e720-4f86-a716-b41247b9d9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = np.concatenate((warmup_lr_schedule, cosine_lr_scheduler))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "72a4af10-8319-4a66-802d-9d094bee4d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize lr scheduler-\n",
    "plt.figure(figsize = (10, 8))\n",
    "plt.plot(lr_scheduler)\n",
    "plt.xlabel(\"train steps\")\n",
    "plt.ylabel(\"lr\")\n",
    "plt.title(\"lr scheduler\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f998a2b6-ae22-4af6-b6e2-b20bfee333db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3fa9165-a3d9-4250-9b23-d24171eeb60f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9b63b5ad-46e9-44a8-973b-f1fd83a293dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fad14273-a432-45bd-8700-f0fbbcad7163",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6af38156-c6a6-46f2-8d54-87bcb9917eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model_progress(model, train_loader, train_dataset):\n",
    "    '''\n",
    "    Function to perform one epoch of training by using 'train_loader'.\n",
    "    Returns loss and number of correct predictions for this epoch.\n",
    "    '''\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0.0\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    with tqdm(train_loader, unit = 'batch') as tepoch:\n",
    "        for images, labels in tepoch:\n",
    "            tepoch.set_description(f\"Training: \")\n",
    "            \n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            \n",
    "            # Get model predictions-\n",
    "            outputs = model(images)\n",
    "            \n",
    "            # Compute loss-\n",
    "            J = loss_fn(outputs, labels)\n",
    "            \n",
    "            # Empty accumulated gradients-\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # Perform backprop-\n",
    "            J.backward()\n",
    "            \n",
    "            # Update parameters-\n",
    "            optimizer.step()\n",
    "\n",
    "            # Update LR scheduler & LARS params-\n",
    "            global step\n",
    "            # optimizer.param_groups[0]['lr'] = custom_lr_scheduler.get_lr(step)\n",
    "            for param_group in optimizer.param_groups:\n",
    "                param_group['lr'] = lr_scheduler[step]\n",
    "            step += 1\n",
    "            \n",
    "            # Compute model's performance statistics-\n",
    "            running_loss += J.item() * images.size(0)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            running_corrects += torch.sum(predicted == labels.data)\n",
    "            \n",
    "            tepoch.set_postfix(\n",
    "                loss = running_loss / len(train_dataset),\n",
    "                accuracy = (running_corrects.double().cpu().numpy() / len(train_dataset)) * 100\n",
    "            )\n",
    "    \n",
    "    train_loss = running_loss / len(train_dataset)\n",
    "    train_acc = (running_corrects.double() / len(train_dataset)) * 100\n",
    "    \n",
    "\n",
    "    # return running_loss, running_corrects\n",
    "    return train_loss, train_acc.cpu().numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b834c734-ebfe-4d47-afb8-53f9372a0be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model_progress(model, test_loader, test_dataset):\n",
    "    total = 0.0\n",
    "    correct = 0.0\n",
    "    running_loss_val = 0.0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        with tqdm(test_loader, unit = 'batch') as tepoch:\n",
    "            for images, labels in tepoch:\n",
    "                tepoch.set_description(f\"Validation: \")\n",
    "                \n",
    "                images = images.to(device)\n",
    "                labels = labels.to(device)\n",
    "                \n",
    "                # Set model to evaluation mode-\n",
    "                model.eval()\n",
    "            \n",
    "                # Predict using trained model-\n",
    "                outputs = model(images)\n",
    "                _, y_pred = torch.max(outputs, 1)\n",
    "                \n",
    "                # Compute validation loss-\n",
    "                J_val = loss_fn(outputs, labels)\n",
    "                \n",
    "                running_loss_val += J_val.item() * labels.size(0)\n",
    "    \n",
    "                # Total number of labels-\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total number of correct predictions-\n",
    "                correct += (y_pred == labels).sum()\n",
    "                \n",
    "                tepoch.set_postfix(\n",
    "                    val_loss = running_loss_val / len(test_dataset),\n",
    "                    val_acc = 100 * (correct.cpu().numpy() / total)\n",
    "                )\n",
    "            \n",
    "        \n",
    "    # return (running_loss_val, correct, total)\n",
    "    val_loss = running_loss_val / len(test_dataset)\n",
    "    val_acc = (correct / total) * 100\n",
    "\n",
    "    return val_loss, val_acc.cpu().numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a095de8-25ab-44ea-ae2a-e286ef8492ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14409ed2-2ca3-428e-875b-837671bfea71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83752618-315a-4875-b199-c9a6f733145d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python3 dict to contain training metrics-\n",
    "training_history = {}\n",
    "\n",
    "# Initialize parameters saving 'best' models-\n",
    "best_val_acc = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9444d29-f6a3-4f50-8cb1-918bf2b6620e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████████████████████████████████████████| 59/59 [00:09<00:00,  6.42batch/s, accuracy=17.6, loss=2.37]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.58batch/s, val_acc=33.2, val_loss=1.91]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 1 training loss = 2.3736, training accuracy = 17.61%, val_loss = 1.9092, val_accuracy = 33.17% & LR = 0.0591\n",
      "\n",
      "Saving model with highest val_acc = 33.1700%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.89batch/s, accuracy=57.8, loss=1.4]\n",
      "Validation: : 100%|██████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.23batch/s, val_acc=81, val_loss=0.853]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 2 training loss = 1.3951, training accuracy = 57.83%, val_loss = 0.8528, val_accuracy = 81.01% & LR = 0.1192\n",
      "\n",
      "Saving model with highest val_acc = 81.0100%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.96batch/s, accuracy=86.5, loss=0.603]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.20batch/s, val_acc=91.7, val_loss=0.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 3 training loss = 0.6033, training accuracy = 86.49%, val_loss = 0.3496, val_accuracy = 91.70% & LR = 0.1793\n",
      "\n",
      "Saving model with highest val_acc = 91.7000%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.28batch/s, accuracy=93.1, loss=0.271]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.41batch/s, val_acc=95.2, val_loss=0.181]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 4 training loss = 0.2708, training accuracy = 93.15%, val_loss = 0.1806, val_accuracy = 95.24% & LR = 0.2394\n",
      "\n",
      "Saving model with highest val_acc = 95.2400%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.93batch/s, accuracy=95.9, loss=0.151]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.11batch/s, val_acc=96.9, val_loss=0.114]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 5 training loss = 0.1509, training accuracy = 95.89%, val_loss = 0.1136, val_accuracy = 96.94% & LR = 0.2995\n",
      "\n",
      "Saving model with highest val_acc = 96.9400%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.77batch/s, accuracy=97.1, loss=0.102]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.00batch/s, val_acc=97.4, val_loss=0.0847]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 6 training loss = 0.1022, training accuracy = 97.10%, val_loss = 0.0847, val_accuracy = 97.43% & LR = 0.3596\n",
      "\n",
      "Saving model with highest val_acc = 97.4300%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.79batch/s, accuracy=97.8, loss=0.0769]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.84batch/s, val_acc=98, val_loss=0.0666]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 7 training loss = 0.0769, training accuracy = 97.81%, val_loss = 0.0666, val_accuracy = 98.02% & LR = 0.4197\n",
      "\n",
      "Saving model with highest val_acc = 98.0200%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.81batch/s, accuracy=98.2, loss=0.0617]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.11batch/s, val_acc=98.4, val_loss=0.0573]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 8 training loss = 0.0617, training accuracy = 98.20%, val_loss = 0.0573, val_accuracy = 98.40% & LR = 0.4798\n",
      "\n",
      "Saving model with highest val_acc = 98.4000%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.97batch/s, accuracy=98.6, loss=0.0514]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.88batch/s, val_acc=98.5, val_loss=0.0521]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 9 training loss = 0.0514, training accuracy = 98.56%, val_loss = 0.0521, val_accuracy = 98.46% & LR = 0.5399\n",
      "\n",
      "Saving model with highest val_acc = 98.4600%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:09<00:00,  6.29batch/s, accuracy=98.7, loss=0.0441]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.66batch/s, val_acc=98.7, val_loss=0.0452]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 10 training loss = 0.0441, training accuracy = 98.70%, val_loss = 0.0452, val_accuracy = 98.67% & LR = 0.6000\n",
      "\n",
      "Saving model with highest val_acc = 98.6700%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|██████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.63batch/s, accuracy=99, loss=0.0379]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.43batch/s, val_acc=98.7, val_loss=0.0445]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 11 training loss = 0.0379, training accuracy = 98.97%, val_loss = 0.0445, val_accuracy = 98.68% & LR = 0.5991\n",
      "\n",
      "Saving model with highest val_acc = 98.6800%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.23batch/s, accuracy=99.1, loss=0.0328]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.20batch/s, val_acc=98.8, val_loss=0.0407]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 12 training loss = 0.0328, training accuracy = 99.09%, val_loss = 0.0407, val_accuracy = 98.75% & LR = 0.5963\n",
      "\n",
      "Saving model with highest val_acc = 98.7500%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.27batch/s, accuracy=99.2, loss=0.0288]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.46batch/s, val_acc=98.5, val_loss=0.0423]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 13 training loss = 0.0288, training accuracy = 99.25%, val_loss = 0.0423, val_accuracy = 98.54% & LR = 0.5917\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.32batch/s, accuracy=99.4, loss=0.0258]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.97batch/s, val_acc=98.9, val_loss=0.036]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 14 training loss = 0.0258, training accuracy = 99.36%, val_loss = 0.0360, val_accuracy = 98.90% & LR = 0.5853\n",
      "\n",
      "Saving model with highest val_acc = 98.9000%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.28batch/s, accuracy=99.4, loss=0.0229]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.51batch/s, val_acc=98.9, val_loss=0.0341]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 15 training loss = 0.0229, training accuracy = 99.45%, val_loss = 0.0341, val_accuracy = 98.87% & LR = 0.5772\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.29batch/s, accuracy=99.5, loss=0.0212]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.42batch/s, val_acc=98.9, val_loss=0.0341]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 16 training loss = 0.0212, training accuracy = 99.49%, val_loss = 0.0341, val_accuracy = 98.93% & LR = 0.5673\n",
      "\n",
      "Saving model with highest val_acc = 98.9300%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.18batch/s, accuracy=99.5, loss=0.0203]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.38batch/s, val_acc=98.9, val_loss=0.035]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 17 training loss = 0.0203, training accuracy = 99.52%, val_loss = 0.0350, val_accuracy = 98.87% & LR = 0.5558\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.27batch/s, accuracy=99.6, loss=0.0179]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.62batch/s, val_acc=99, val_loss=0.0322]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 18 training loss = 0.0179, training accuracy = 99.62%, val_loss = 0.0322, val_accuracy = 98.95% & LR = 0.5427\n",
      "\n",
      "Saving model with highest val_acc = 98.9500%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.26batch/s, accuracy=99.6, loss=0.0167]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.49batch/s, val_acc=99.1, val_loss=0.031]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 19 training loss = 0.0167, training accuracy = 99.64%, val_loss = 0.0310, val_accuracy = 99.05% & LR = 0.5281\n",
      "\n",
      "Saving model with highest val_acc = 99.0500%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.33batch/s, accuracy=99.7, loss=0.0156]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.46batch/s, val_acc=99, val_loss=0.0316]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 20 training loss = 0.0156, training accuracy = 99.69%, val_loss = 0.0316, val_accuracy = 98.98% & LR = 0.5121\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.33batch/s, accuracy=99.7, loss=0.0147]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.40batch/s, val_acc=99.1, val_loss=0.0322]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 21 training loss = 0.0147, training accuracy = 99.70%, val_loss = 0.0322, val_accuracy = 99.05% & LR = 0.4948\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.26batch/s, accuracy=99.8, loss=0.0136]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 22 training loss = 0.0136, training accuracy = 99.76%, val_loss = 0.0321, val_accuracy = 98.97% & LR = 0.4763\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|█████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.29batch/s, accuracy=99.8, loss=0.013]\n",
      "Validation: : 100%|█████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.52batch/s, val_acc=99, val_loss=0.0311]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 23 training loss = 0.0130, training accuracy = 99.77%, val_loss = 0.0311, val_accuracy = 98.95% & LR = 0.4567\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.30batch/s, accuracy=99.8, loss=0.0125]\n",
      "Validation: : 100%|████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.08batch/s, val_acc=99.1, val_loss=0.029]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 24 training loss = 0.0125, training accuracy = 99.80%, val_loss = 0.0290, val_accuracy = 99.13% & LR = 0.4362\n",
      "\n",
      "Saving model with highest val_acc = 99.1300%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.18batch/s, accuracy=99.8, loss=0.0118]\n",
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     ]
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    {
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     "text": [
      "\n",
      "epoch: 25 training loss = 0.0118, training accuracy = 99.81%, val_loss = 0.0292, val_accuracy = 99.15% & LR = 0.4148\n",
      "\n",
      "Saving model with highest val_acc = 99.1500%\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.21batch/s, accuracy=99.8, loss=0.0112]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 26 training loss = 0.0112, training accuracy = 99.83%, val_loss = 0.0316, val_accuracy = 99.03% & LR = 0.3927\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 27 training loss = 0.0109, training accuracy = 99.84%, val_loss = 0.0295, val_accuracy = 99.13% & LR = 0.3700\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.33batch/s, accuracy=99.9, loss=0.0104]\n",
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     ]
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 28 training loss = 0.0104, training accuracy = 99.86%, val_loss = 0.0294, val_accuracy = 99.06% & LR = 0.3469\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     "text": [
      "\n",
      "epoch: 29 training loss = 0.0098, training accuracy = 99.87%, val_loss = 0.0295, val_accuracy = 99.09% & LR = 0.3235\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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     ]
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     "text": [
      "\n",
      "epoch: 30 training loss = 0.0096, training accuracy = 99.89%, val_loss = 0.0294, val_accuracy = 99.09% & LR = 0.3000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.33batch/s, accuracy=99.9, loss=0.0095]\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 31 training loss = 0.0095, training accuracy = 99.89%, val_loss = 0.0291, val_accuracy = 99.12% & LR = 0.2765\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  7.19batch/s, accuracy=99.9, loss=0.00891]\n",
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     ]
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     "text": [
      "\n",
      "epoch: 32 training loss = 0.0089, training accuracy = 99.89%, val_loss = 0.0287, val_accuracy = 99.18% & LR = 0.2531\n",
      "\n",
      "Saving model with highest val_acc = 99.1800%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 33 training loss = 0.0086, training accuracy = 99.91%, val_loss = 0.0282, val_accuracy = 99.16% & LR = 0.2300\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: : 100%|███████████████████████████████████████████████████████████████████████████| 59/59 [00:09<00:00,  6.52batch/s, accuracy=99.9, loss=0.00842]\n",
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     ]
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     "text": [
      "\n",
      "epoch: 34 training loss = 0.0084, training accuracy = 99.92%, val_loss = 0.0291, val_accuracy = 99.13% & LR = 0.2073\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "\n",
      "epoch: 35 training loss = 0.0080, training accuracy = 99.94%, val_loss = 0.0279, val_accuracy = 99.16% & LR = 0.1852\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "\n",
      "epoch: 36 training loss = 0.0080, training accuracy = 99.93%, val_loss = 0.0290, val_accuracy = 99.14% & LR = 0.1638\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "\n",
      "epoch: 37 training loss = 0.0077, training accuracy = 99.95%, val_loss = 0.0284, val_accuracy = 99.15% & LR = 0.1433\n",
      "\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 38 training loss = 0.0076, training accuracy = 99.94%, val_loss = 0.0281, val_accuracy = 99.16% & LR = 0.1237\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.85batch/s, accuracy=100, loss=0.00745]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 39 training loss = 0.0075, training accuracy = 99.95%, val_loss = 0.0278, val_accuracy = 99.15% & LR = 0.1052\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.81batch/s, accuracy=99.9, loss=0.00756]\n",
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     "text": [
      "\n",
      "epoch: 40 training loss = 0.0076, training accuracy = 99.95%, val_loss = 0.0276, val_accuracy = 99.19% & LR = 0.0879\n",
      "\n",
      "Saving model with highest val_acc = 99.1900%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 41 training loss = 0.0073, training accuracy = 99.95%, val_loss = 0.0281, val_accuracy = 99.18% & LR = 0.0719\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|███████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.97batch/s, accuracy=99.9, loss=0.00721]\n",
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     "text": [
      "\n",
      "epoch: 42 training loss = 0.0072, training accuracy = 99.95%, val_loss = 0.0280, val_accuracy = 99.22% & LR = 0.0573\n",
      "\n",
      "Saving model with highest val_acc = 99.2200%\n",
      "\n"
     ]
    },
    {
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     "text": [
      "\n",
      "epoch: 43 training loss = 0.0071, training accuracy = 99.96%, val_loss = 0.0280, val_accuracy = 99.21% & LR = 0.0442\n",
      "\n"
     ]
    },
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     "text": [
      "\n",
      "epoch: 44 training loss = 0.0070, training accuracy = 99.95%, val_loss = 0.0279, val_accuracy = 99.21% & LR = 0.0327\n",
      "\n"
     ]
    },
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      "\n",
      "epoch: 45 training loss = 0.0069, training accuracy = 99.96%, val_loss = 0.0278, val_accuracy = 99.21% & LR = 0.0228\n",
      "\n"
     ]
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      "\n",
      "epoch: 46 training loss = 0.0069, training accuracy = 99.96%, val_loss = 0.0277, val_accuracy = 99.21% & LR = 0.0147\n",
      "\n"
     ]
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      "\n",
      "epoch: 47 training loss = 0.0069, training accuracy = 99.97%, val_loss = 0.0278, val_accuracy = 99.19% & LR = 0.0083\n",
      "\n"
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      "\n",
      "epoch: 48 training loss = 0.0068, training accuracy = 99.96%, val_loss = 0.0278, val_accuracy = 99.19% & LR = 0.0037\n",
      "\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.75batch/s, accuracy=100, loss=0.00671]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.23batch/s, val_acc=99.2, val_loss=0.0279]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 49 training loss = 0.0067, training accuracy = 99.96%, val_loss = 0.0279, val_accuracy = 99.19% & LR = 0.0009\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: : 100%|████████████████████████████████████████████████████████████████████████████| 59/59 [00:08<00:00,  6.98batch/s, accuracy=100, loss=0.00673]\n",
      "Validation: : 100%|███████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  8.90batch/s, val_acc=99.2, val_loss=0.0279]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 50 training loss = 0.0067, training accuracy = 99.97%, val_loss = 0.0279, val_accuracy = 99.20% & LR = 0.0000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(num_train_epochs):\n",
    "\n",
    "    # Train and validate model for 1 epoch-\n",
    "    train_loss, train_acc = train_model_progress(\n",
    "        model = model, train_loader = train_loader,\n",
    "        train_dataset = train_dataset\n",
    "    )\n",
    "    \n",
    "    val_loss, val_acc = test_model_progress(\n",
    "        model = model, test_loader = test_loader,\n",
    "        test_dataset = test_dataset\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nepoch: {epoch + 1} training loss = {train_loss:.4f}, \"\n",
    "          f\"training accuracy = {train_acc:.2f}%, val_loss = {val_loss:.4f}\"\n",
    "          f\", val_accuracy = {val_acc:.2f}%\"\n",
    "          f\" & LR = {optimizer.param_groups[0]['lr']:.4f}\\n\")\n",
    "    \n",
    "    training_history[epoch + 1] = {\n",
    "        'loss': train_loss, 'acc': train_acc,\n",
    "        'val_loss': val_loss, 'val_acc': val_acc,\n",
    "        'lr': optimizer.param_groups[0]['lr']\n",
    "    }\n",
    "\n",
    "    \n",
    "    # Save best weights achieved until now-\n",
    "    if (val_acc > best_val_acc):\n",
    "        # update 'best_val_loss' variable to lowest loss encountered so far-\n",
    "        best_val_acc = val_acc\n",
    "\n",
    "        print(f\"Saving model with highest val_acc = {val_acc:.4f}%\\n\")\n",
    "        \n",
    "        # Save trained model with 'best' validation accuracy-\n",
    "        torch.save(model.state_dict(), \"LeNet5_best_model.pth\")\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01039511-4834-40c6-82b1-f7aade08e2d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9b53260-11e3-4137-862a-5f281cea7526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training metrics as Python3 history for later analysis-\n",
    "with open(\"LeNet5_swish_LARS-training_history.pkl\", \"wb\") as file:\n",
    "    pickle.dump(training_history, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f725a3c-73bd-42ba-8b04-5413f0860365",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6428aaf8-d0cf-4f51-b73e-0959b38e03c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73bec9e0-081e-472d-b7fb-fd281c092258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history.keys()), [training_history[k]['acc'] for k in training_history.keys()], label = 'training acc')\n",
    "plt.plot(list(training_history.keys()), [training_history[k]['val_acc'] for k in training_history.keys()], label = 'val acc')\n",
    "plt.title(\"LeNet-5: Accuracy\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy (%)\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53cab234-d17d-4d76-983a-c18ec6a11718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history.keys()), [training_history[k]['loss'] for k in training_history.keys()], label = 'training loss')\n",
    "plt.plot(list(training_history.keys()), [training_history[k]['val_loss'] for k in training_history.keys()], label = 'val loss')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.title(\"LeNet-5: Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca778dd8-dc2f-4fae-a9d3-930c9ee92321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 7))\n",
    "plt.plot(list(training_history.keys()), [training_history[k]['lr'] for k in training_history.keys()])\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"lr\")\n",
    "plt.title(\"LeNet-5: Learning-Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df5f9b6f-7459-4938-b2a7-d2cb9cd6c38e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.01468304511145394,\n",
       " 0.008289023880697032,\n",
       " 0.003693497821458702,\n",
       " 0.0009247998800616108,\n",
       " 0.0]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 lr values-\n",
    "[training_history[k]['lr'] for k in training_history.keys()][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "09accd4b-1c8d-4144-98c4-5192ff2111a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.05908319185059423,\n",
       " 0.11918505942275041,\n",
       " 0.17928692699490661,\n",
       " 0.2393887945670628,\n",
       " 0.299490662139219]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 5 lr values-\n",
    "[training_history[k]['lr'] for k in training_history.keys()][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea78bae0-6391-4c86-b343-bb1bdfe0fe58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e18bd48-8726-409e-803a-553678c73f2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b531e1fb-0973-4306-9c42-acc48c12daa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save LARS optimizer-\n",
    "torch.save(optimizer.state_dict(), 'LeNet5_bestmodel_swish_lrscheduler_LARS_optim.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7ddca8-968d-44e7-a1ba-0790106b6ab4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b22094a-4228-410b-a4e3-054b02376223",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a52961a3-70bf-4937-ba83-d6101f00efd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['state', 'param_groups'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d9240ed1-5cee-401c-b2b7-518dd5be479c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict()['state'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cf27d6f1-6676-43b1-bb0b-ff61707eecd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['lr', 'momentum', 'dampening', 'weight_decay', 'nesterov', 'maximize', 'foreach', 'differentiable', 'params'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict()['param_groups'][0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "68870100-e11d-4859-8791-3853c494ddcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lr': 0.0,\n",
       " 'momentum': 0.9,\n",
       " 'dampening': 0,\n",
       " 'weight_decay': 0.0005,\n",
       " 'nesterov': False,\n",
       " 'maximize': False,\n",
       " 'foreach': None,\n",
       " 'differentiable': False,\n",
       " 'params': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict()['param_groups'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b0e89ce3-78af-4bc9-b899-718b2e17ce30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict()['param_groups'][0]['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4a858ed-0d64-4abd-b761-aac64bbb72c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['momentum_buffer'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer.state_dict()['state'][0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0ad1ec1-e562-44fd-b67e-5933bfa53be6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0e50cd-f193-4b4c-89a1-67a8d37371e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}