Cifar10有验证集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4383ebd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.datasets import CIFAR10\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# from rich import print\n",
    "import time \n",
    "# 全局取消证书验证\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71429a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import getDataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e3095e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeSince(since: float) -> str:\n",
    "    s = time.time() - since\n",
    "    return f\"{int(s/60):2}m {int(s%60):02}s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c35f93",
   "metadata": {},
   "source": [
    "## 加载数据集并归一化增强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "862ec8b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_mean = np.array([0.4915, 0.4823, 0.4468])\n",
    "dataset_std = np.array([0.2470, 0.2435, 0.2616])\n",
    "\n",
    "transform = {\n",
    "    'train': transforms.Compose([\n",
    "#         transforms.RandomCrop(32, padding=4),\n",
    "        transforms.RandomHorizontalFlip(p=0.9),\n",
    "        transforms.ToTensor(),\n",
    "#         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        transforms.Normalize(dataset_mean, dataset_std)\n",
    "     ]), \n",
    "    'test':  transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(dataset_mean, dataset_std)\n",
    "     ])\n",
    "}\n",
    "    \n",
    "\n",
    "batch_size = 512\n",
    "\n",
    "kwargs = {'num_workers': 1, 'pin_memory': True, 'batch_size':batch_size}\n",
    "\n",
    "# trainset = CIFAR10(root='~/data', train=True, transform=transform['train'])\n",
    "# K-Fold\n",
    "train_dataset_index = [1, 2, 3, 4]\n",
    "val_dataset_index = 5\n",
    "# batch_size大小\n",
    "train_batch_size = 512\n",
    "val_batch_size = 512\n",
    "test_batch_size = 512\n",
    "trainloader = getDataLoader(train_dataIndex=train_dataset_index,\n",
    "                             is_train_dataset=True,\n",
    "                             batch_size=train_batch_size,\n",
    "                             shuffle=True)\n",
    "valloader = getDataLoader(train_dataIndex=val_dataset_index,\n",
    "                           is_train_dataset=True,\n",
    "                           batch_size=val_batch_size,\n",
    "                           shuffle=False)\n",
    "testloader = getDataLoader(is_train_dataset=False,\n",
    "                            batch_size=test_batch_size,\n",
    "                            shuffle=False)\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "#testset = CIFAR10(root='~/data', train=False, transform=transform['test'])\n",
    "#testloader = DataLoader(testset, shuffle=False, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d69baf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68afb9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "# Defining the model\n",
    "from coatnet import coatnet_0\n",
    "from coatnet import coatnet_1\n",
    "from coatnet import coatnet_2\n",
    "from coatnet import coatnet_3\n",
    "from coatnet import coatnet_4\n",
    "from coatnet import coatnet_5\n",
    "\n",
    "model = coatnet_0()\n",
    "print(model(torch.randn(1,3,32,32,)).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b62f051",
   "metadata": {},
   "source": [
    "## 模型参数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86fd995a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1          [512, 64, 16, 16]           1,728\n",
      "       BatchNorm2d-2          [512, 64, 16, 16]             128\n",
      "              GELU-3          [512, 64, 16, 16]               0\n",
      "            Conv2d-4          [512, 64, 16, 16]          36,864\n",
      "       BatchNorm2d-5          [512, 64, 16, 16]             128\n",
      "              GELU-6          [512, 64, 16, 16]               0\n",
      "         MaxPool2d-7            [512, 64, 8, 8]               0\n",
      "            Conv2d-8            [512, 96, 8, 8]           6,144\n",
      "       BatchNorm2d-9          [512, 64, 16, 16]             128\n",
      "           Conv2d-10           [512, 256, 8, 8]          16,384\n",
      "      BatchNorm2d-11           [512, 256, 8, 8]             512\n",
      "             GELU-12           [512, 256, 8, 8]               0\n",
      "           Conv2d-13           [512, 256, 8, 8]           2,304\n",
      "      BatchNorm2d-14           [512, 256, 8, 8]             512\n",
      "             GELU-15           [512, 256, 8, 8]               0\n",
      "AdaptiveAvgPool2d-16           [512, 256, 1, 1]               0\n",
      "           Linear-17                  [512, 16]           4,096\n",
      "             GELU-18                  [512, 16]               0\n",
      "           Linear-19                 [512, 256]           4,096\n",
      "          Sigmoid-20                 [512, 256]               0\n",
      "               SE-21           [512, 256, 8, 8]               0\n",
      "           Conv2d-22            [512, 96, 8, 8]          24,576\n",
      "      BatchNorm2d-23            [512, 96, 8, 8]             192\n",
      "          PreNorm-24            [512, 96, 8, 8]               0\n",
      "           MBConv-25            [512, 96, 8, 8]               0\n",
      "      BatchNorm2d-26            [512, 96, 8, 8]             192\n",
      "           Conv2d-27           [512, 384, 8, 8]          36,864\n",
      "      BatchNorm2d-28           [512, 384, 8, 8]             768\n",
      "             GELU-29           [512, 384, 8, 8]               0\n",
      "           Conv2d-30           [512, 384, 8, 8]           3,456\n",
      "      BatchNorm2d-31           [512, 384, 8, 8]             768\n",
      "             GELU-32           [512, 384, 8, 8]               0\n",
      "AdaptiveAvgPool2d-33           [512, 384, 1, 1]               0\n",
      "           Linear-34                  [512, 24]           9,216\n",
      "             GELU-35                  [512, 24]               0\n",
      "           Linear-36                 [512, 384]           9,216\n",
      "          Sigmoid-37                 [512, 384]               0\n",
      "               SE-38           [512, 384, 8, 8]               0\n",
      "           Conv2d-39            [512, 96, 8, 8]          36,864\n",
      "      BatchNorm2d-40            [512, 96, 8, 8]             192\n",
      "          PreNorm-41            [512, 96, 8, 8]               0\n",
      "           MBConv-42            [512, 96, 8, 8]               0\n",
      "        MaxPool2d-43            [512, 96, 4, 4]               0\n",
      "           Conv2d-44           [512, 192, 4, 4]          18,432\n",
      "      BatchNorm2d-45            [512, 96, 8, 8]             192\n",
      "           Conv2d-46           [512, 384, 4, 4]          36,864\n",
      "      BatchNorm2d-47           [512, 384, 4, 4]             768\n",
      "             GELU-48           [512, 384, 4, 4]               0\n",
      "           Conv2d-49           [512, 384, 4, 4]           3,456\n",
      "      BatchNorm2d-50           [512, 384, 4, 4]             768\n",
      "             GELU-51           [512, 384, 4, 4]               0\n",
      "AdaptiveAvgPool2d-52           [512, 384, 1, 1]               0\n",
      "           Linear-53                  [512, 24]           9,216\n",
      "             GELU-54                  [512, 24]               0\n",
      "           Linear-55                 [512, 384]           9,216\n",
      "          Sigmoid-56                 [512, 384]               0\n",
      "               SE-57           [512, 384, 4, 4]               0\n",
      "           Conv2d-58           [512, 192, 4, 4]          73,728\n",
      "      BatchNorm2d-59           [512, 192, 4, 4]             384\n",
      "          PreNorm-60           [512, 192, 4, 4]               0\n",
      "           MBConv-61           [512, 192, 4, 4]               0\n",
      "      BatchNorm2d-62           [512, 192, 4, 4]             384\n",
      "           Conv2d-63           [512, 768, 4, 4]         147,456\n",
      "      BatchNorm2d-64           [512, 768, 4, 4]           1,536\n",
      "             GELU-65           [512, 768, 4, 4]               0\n",
      "           Conv2d-66           [512, 768, 4, 4]           6,912\n",
      "      BatchNorm2d-67           [512, 768, 4, 4]           1,536\n",
      "             GELU-68           [512, 768, 4, 4]               0\n",
      "AdaptiveAvgPool2d-69           [512, 768, 1, 1]               0\n",
      "           Linear-70                  [512, 48]          36,864\n",
      "             GELU-71                  [512, 48]               0\n",
      "           Linear-72                 [512, 768]          36,864\n",
      "          Sigmoid-73                 [512, 768]               0\n",
      "               SE-74           [512, 768, 4, 4]               0\n",
      "           Conv2d-75           [512, 192, 4, 4]         147,456\n",
      "      BatchNorm2d-76           [512, 192, 4, 4]             384\n",
      "          PreNorm-77           [512, 192, 4, 4]               0\n",
      "           MBConv-78           [512, 192, 4, 4]               0\n",
      "      BatchNorm2d-79           [512, 192, 4, 4]             384\n",
      "           Conv2d-80           [512, 768, 4, 4]         147,456\n",
      "      BatchNorm2d-81           [512, 768, 4, 4]           1,536\n",
      "             GELU-82           [512, 768, 4, 4]               0\n",
      "           Conv2d-83           [512, 768, 4, 4]           6,912\n",
      "      BatchNorm2d-84           [512, 768, 4, 4]           1,536\n",
      "             GELU-85           [512, 768, 4, 4]               0\n",
      "AdaptiveAvgPool2d-86           [512, 768, 1, 1]               0\n",
      "           Linear-87                  [512, 48]          36,864\n",
      "             GELU-88                  [512, 48]               0\n",
      "           Linear-89                 [512, 768]          36,864\n",
      "          Sigmoid-90                 [512, 768]               0\n",
      "               SE-91           [512, 768, 4, 4]               0\n",
      "           Conv2d-92           [512, 192, 4, 4]         147,456\n",
      "      BatchNorm2d-93           [512, 192, 4, 4]             384\n",
      "          PreNorm-94           [512, 192, 4, 4]               0\n",
      "           MBConv-95           [512, 192, 4, 4]               0\n",
      "        MaxPool2d-96           [512, 192, 2, 2]               0\n",
      "           Conv2d-97           [512, 384, 2, 2]          73,728\n",
      "        MaxPool2d-98           [512, 192, 2, 2]               0\n",
      "        Rearrange-99              [512, 4, 192]               0\n",
      "       LayerNorm-100              [512, 4, 192]             384\n",
      "          Linear-101              [512, 4, 768]         147,456\n",
      "         Softmax-102             [512, 8, 4, 4]               0\n",
      "          Linear-103              [512, 4, 384]          98,688\n",
      "         Dropout-104              [512, 4, 384]               0\n",
      "       Attention-105              [512, 4, 384]               0\n",
      "         PreNorm-106              [512, 4, 384]               0\n",
      "       Rearrange-107           [512, 384, 2, 2]               0\n",
      "       Rearrange-108              [512, 4, 384]               0\n",
      "       LayerNorm-109              [512, 4, 384]             768\n",
      "          Linear-110              [512, 4, 768]         295,680\n",
      "            GELU-111              [512, 4, 768]               0\n",
      "         Dropout-112              [512, 4, 768]               0\n",
      "          Linear-113              [512, 4, 384]         295,296\n",
      "         Dropout-114              [512, 4, 384]               0\n",
      "     FeedForward-115              [512, 4, 384]               0\n",
      "         PreNorm-116              [512, 4, 384]               0\n",
      "       Rearrange-117           [512, 384, 2, 2]               0\n",
      "     Transformer-118           [512, 384, 2, 2]               0\n",
      "       Rearrange-119              [512, 4, 384]               0\n",
      "       LayerNorm-120              [512, 4, 384]             768\n",
      "          Linear-121              [512, 4, 768]         294,912\n",
      "         Softmax-122             [512, 8, 4, 4]               0\n",
      "          Linear-123              [512, 4, 384]          98,688\n",
      "         Dropout-124              [512, 4, 384]               0\n",
      "       Attention-125              [512, 4, 384]               0\n",
      "         PreNorm-126              [512, 4, 384]               0\n",
      "       Rearrange-127           [512, 384, 2, 2]               0\n",
      "       Rearrange-128              [512, 4, 384]               0\n",
      "       LayerNorm-129              [512, 4, 384]             768\n",
      "          Linear-130             [512, 4, 1536]         591,360\n",
      "            GELU-131             [512, 4, 1536]               0\n",
      "         Dropout-132             [512, 4, 1536]               0\n",
      "          Linear-133              [512, 4, 384]         590,208\n",
      "         Dropout-134              [512, 4, 384]               0\n",
      "     FeedForward-135              [512, 4, 384]               0\n",
      "         PreNorm-136              [512, 4, 384]               0\n",
      "       Rearrange-137           [512, 384, 2, 2]               0\n",
      "     Transformer-138           [512, 384, 2, 2]               0\n",
      "       Rearrange-139              [512, 4, 384]               0\n",
      "       LayerNorm-140              [512, 4, 384]             768\n",
      "          Linear-141              [512, 4, 768]         294,912\n",
      "         Softmax-142             [512, 8, 4, 4]               0\n",
      "          Linear-143              [512, 4, 384]          98,688\n",
      "         Dropout-144              [512, 4, 384]               0\n",
      "       Attention-145              [512, 4, 384]               0\n",
      "         PreNorm-146              [512, 4, 384]               0\n",
      "       Rearrange-147           [512, 384, 2, 2]               0\n",
      "       Rearrange-148              [512, 4, 384]               0\n",
      "       LayerNorm-149              [512, 4, 384]             768\n",
      "          Linear-150             [512, 4, 1536]         591,360\n",
      "            GELU-151             [512, 4, 1536]               0\n",
      "         Dropout-152             [512, 4, 1536]               0\n",
      "          Linear-153              [512, 4, 384]         590,208\n",
      "         Dropout-154              [512, 4, 384]               0\n",
      "     FeedForward-155              [512, 4, 384]               0\n",
      "         PreNorm-156              [512, 4, 384]               0\n",
      "       Rearrange-157           [512, 384, 2, 2]               0\n",
      "     Transformer-158           [512, 384, 2, 2]               0\n",
      "       Rearrange-159              [512, 4, 384]               0\n",
      "       LayerNorm-160              [512, 4, 384]             768\n",
      "          Linear-161              [512, 4, 768]         294,912\n",
      "         Softmax-162             [512, 8, 4, 4]               0\n",
      "          Linear-163              [512, 4, 384]          98,688\n",
      "         Dropout-164              [512, 4, 384]               0\n",
      "       Attention-165              [512, 4, 384]               0\n",
      "         PreNorm-166              [512, 4, 384]               0\n",
      "       Rearrange-167           [512, 384, 2, 2]               0\n",
      "       Rearrange-168              [512, 4, 384]               0\n",
      "       LayerNorm-169              [512, 4, 384]             768\n",
      "          Linear-170             [512, 4, 1536]         591,360\n",
      "            GELU-171             [512, 4, 1536]               0\n",
      "         Dropout-172             [512, 4, 1536]               0\n",
      "          Linear-173              [512, 4, 384]         590,208\n",
      "         Dropout-174              [512, 4, 384]               0\n",
      "     FeedForward-175              [512, 4, 384]               0\n",
      "         PreNorm-176              [512, 4, 384]               0\n",
      "       Rearrange-177           [512, 384, 2, 2]               0\n",
      "     Transformer-178           [512, 384, 2, 2]               0\n",
      "       Rearrange-179              [512, 4, 384]               0\n",
      "       LayerNorm-180              [512, 4, 384]             768\n",
      "          Linear-181              [512, 4, 768]         294,912\n",
      "         Softmax-182             [512, 8, 4, 4]               0\n",
      "          Linear-183              [512, 4, 384]          98,688\n",
      "         Dropout-184              [512, 4, 384]               0\n",
      "       Attention-185              [512, 4, 384]               0\n",
      "         PreNorm-186              [512, 4, 384]               0\n",
      "       Rearrange-187           [512, 384, 2, 2]               0\n",
      "       Rearrange-188              [512, 4, 384]               0\n",
      "       LayerNorm-189              [512, 4, 384]             768\n",
      "          Linear-190             [512, 4, 1536]         591,360\n",
      "            GELU-191             [512, 4, 1536]               0\n",
      "         Dropout-192             [512, 4, 1536]               0\n",
      "          Linear-193              [512, 4, 384]         590,208\n",
      "         Dropout-194              [512, 4, 384]               0\n",
      "     FeedForward-195              [512, 4, 384]               0\n",
      "         PreNorm-196              [512, 4, 384]               0\n",
      "       Rearrange-197           [512, 384, 2, 2]               0\n",
      "     Transformer-198           [512, 384, 2, 2]               0\n",
      "       MaxPool2d-199           [512, 384, 1, 1]               0\n",
      "          Conv2d-200           [512, 768, 1, 1]         294,912\n",
      "       MaxPool2d-201           [512, 384, 1, 1]               0\n",
      "       Rearrange-202              [512, 1, 384]               0\n",
      "       LayerNorm-203              [512, 1, 384]             768\n",
      "          Linear-204              [512, 1, 768]         294,912\n",
      "         Softmax-205             [512, 8, 1, 1]               0\n",
      "          Linear-206              [512, 1, 768]         197,376\n",
      "         Dropout-207              [512, 1, 768]               0\n",
      "       Attention-208              [512, 1, 768]               0\n",
      "         PreNorm-209              [512, 1, 768]               0\n",
      "       Rearrange-210           [512, 768, 1, 1]               0\n",
      "       Rearrange-211              [512, 1, 768]               0\n",
      "       LayerNorm-212              [512, 1, 768]           1,536\n",
      "          Linear-213             [512, 1, 1536]       1,181,184\n",
      "            GELU-214             [512, 1, 1536]               0\n",
      "         Dropout-215             [512, 1, 1536]               0\n",
      "          Linear-216              [512, 1, 768]       1,180,416\n",
      "         Dropout-217              [512, 1, 768]               0\n",
      "     FeedForward-218              [512, 1, 768]               0\n",
      "         PreNorm-219              [512, 1, 768]               0\n",
      "       Rearrange-220           [512, 768, 1, 1]               0\n",
      "     Transformer-221           [512, 768, 1, 1]               0\n",
      "       Rearrange-222              [512, 1, 768]               0\n",
      "       LayerNorm-223              [512, 1, 768]           1,536\n",
      "          Linear-224              [512, 1, 768]         589,824\n",
      "         Softmax-225             [512, 8, 1, 1]               0\n",
      "          Linear-226              [512, 1, 768]         197,376\n",
      "         Dropout-227              [512, 1, 768]               0\n",
      "       Attention-228              [512, 1, 768]               0\n",
      "         PreNorm-229              [512, 1, 768]               0\n",
      "       Rearrange-230           [512, 768, 1, 1]               0\n",
      "       Rearrange-231              [512, 1, 768]               0\n",
      "       LayerNorm-232              [512, 1, 768]           1,536\n",
      "          Linear-233             [512, 1, 3072]       2,362,368\n",
      "            GELU-234             [512, 1, 3072]               0\n",
      "         Dropout-235             [512, 1, 3072]               0\n",
      "          Linear-236              [512, 1, 768]       2,360,064\n",
      "         Dropout-237              [512, 1, 768]               0\n",
      "     FeedForward-238              [512, 1, 768]               0\n",
      "         PreNorm-239              [512, 1, 768]               0\n",
      "       Rearrange-240           [512, 768, 1, 1]               0\n",
      "     Transformer-241           [512, 768, 1, 1]               0\n",
      "       AvgPool2d-242           [512, 768, 1, 1]               0\n",
      "          Linear-243                [512, 1000]         768,000\n",
      "          Linear-244                  [512, 10]          10,000\n",
      "================================================================\n",
      "Total params: 17,767,760\n",
      "Trainable params: 17,767,760\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 6.00\n",
      "Forward/backward pass size (MB): 3907.76\n",
      "Params size (MB): 67.78\n",
      "Estimated Total Size (MB): 3981.54\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "summary(model.to(device), input_size=(3, 32, 32), batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3681c80",
   "metadata": {},
   "source": [
    "## 训练网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44e9cd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "def fwd_pass(net, loader, loss_function, optimizer, train=False):\n",
    "    if train: net.train()\n",
    "    else:     net.eval()\n",
    "\n",
    "    total_loss = 0\n",
    "    y_pred, y = [], []\n",
    "    for _, X, labels in loader: #加载\n",
    "        X, labels = X.to(device), labels.to(device)\n",
    "        # ===================前=====================\n",
    "        outputs = model(X)\n",
    "        loss = loss_function(outputs, labels) \n",
    "        total_loss += loss.item()\n",
    "        # ===================后====================\n",
    "        if train:\n",
    "            optimizer.zero_grad()# zero the parameter gradients\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        # ===============预测===============\n",
    "        with torch.no_grad():\n",
    "            y_hat = outputs.argmax(dim=1)\n",
    "            y_pred.extend(y_hat.cpu().numpy().tolist())\n",
    "            y.extend(labels.cpu().numpy().tolist())\n",
    "\n",
    "    # =================return=====================\n",
    "    with torch.no_grad():\n",
    "        total_loss /= len(loader.dataset)\n",
    "        acc = accuracy_score(y, y_pred)*100\n",
    "        f1 = f1_score(y, y_pred, average=\"weighted\")*100\n",
    "\n",
    "    return total_loss, acc, f1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "logs = dict( trainlosses=list(), testlosses=list(), \n",
    "            trainaccs=list(), testaccs=list() )\n",
    "    \n",
    "\n",
    "#训练\n",
    "def train(model, train_loader, test_loader,\n",
    "          optimizer, loss_function, epoch_num=1, device=device):\n",
    "    model.to(device)\n",
    "    torch.manual_seed(14)\n",
    "    \n",
    "    start = time.time()\n",
    "    print(f\"Training Process Starts at {datetime.now().strftime('%H:%M:%S')} ... \")\n",
    "    \n",
    "    epoch = 1\n",
    "    test_acc1 = 0\n",
    "    train_acc1 = 0\n",
    "    times = 0\n",
    "    # loop over the dataset multiple times\n",
    "    \n",
    "    while epoch<=epoch_num:\n",
    "        # Training\n",
    "        model = torch.load('./cifar10-1.h5')\n",
    "        \n",
    "        train_loss, train_acc, train_f1 = fwd_pass(model, trainloader, loss_function, \n",
    "                                                       optimizer, train=True)\n",
    "            \n",
    "        # Testing\n",
    "        with torch.no_grad():\n",
    "            test_loss, test_acc, test_f1 = fwd_pass(model, valloader, loss_function, \n",
    "                                                    optimizer, train=False)\n",
    "        \n",
    "        if test_acc-test_acc1 >=0:\n",
    "            reset='\\n' if epoch <= 5 or epoch%5==0 else '\\r'\n",
    "            print(f\"Epoch[{epoch:2d}]>>>\",\n",
    "                f\"Train/Test loss: {train_loss:.7f}/{test_loss:.7f},\",\n",
    "                f\"Acc.: {train_acc:.2f}/{test_acc:.2f}\",\n",
    "                f\"[[{timeSince(start)}]]\", end=reset)\n",
    "            \n",
    "            # ===================log========================\n",
    "            logs['trainlosses'].append(train_loss)\n",
    "            logs['trainaccs'].append(train_acc)\n",
    "            logs['testlosses'].append(test_loss)\n",
    "            logs['testaccs'].append(test_acc)\n",
    "            \n",
    "            train_acc1=train_acc\n",
    "            test_acc1=test_acc\n",
    "            epoch += 1\n",
    "            \n",
    "            model_name = f'cifar10-1.h5'\n",
    "            torch.save(model, model_name)\n",
    "        else:\n",
    "            times+=1\n",
    "            if times>=100:\n",
    "                break\n",
    "            continue\n",
    "\n",
    "    print(f'测试集F1: {test_f1}')\n",
    "    print(f'Finished in {timeSince(start)}')\n",
    "    return logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea48ad3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Process Starts at 14:42:19 ... \n",
      "Epoch[ 1]>>> Train/Test loss: 0.0045417/0.0029356, Acc.: 33.49/45.83 [[ 0m 10s]]\n",
      "Epoch[ 2]>>> Train/Test loss: 0.0024360/0.0023242, Acc.: 55.14/57.85 [[ 0m 20s]]\n",
      "Epoch[ 3]>>> Train/Test loss: 0.0019319/0.0020901, Acc.: 65.03/63.70 [[ 0m 31s]]\n",
      "Epoch[ 4]>>> Train/Test loss: 0.0014942/0.0019615, Acc.: 73.38/66.31 [[ 0m 42s]]\n",
      "Epoch[ 5]>>> Train/Test loss: 0.0011707/0.0020659, Acc.: 78.88/66.55 [[ 0m 52s]]\n",
      "Epoch[10]>>> Train/Test loss: 0.0002203/0.0030502, Acc.: 96.19/70.07 [[ 2m 26s]]\n",
      "Epoch[15]>>> Train/Test loss: 0.0000907/0.0030668, Acc.: 98.58/72.93 [[ 8m 49s]]\n",
      "Epoch[20]>>> Train/Test loss: 0.0000447/0.0032342, Acc.: 99.28/73.53 [[11m 36s]]\n",
      "测试集F1: 73.63209567310618\n",
      "Finished in 11m 36s\n"
     ]
    }
   ],
   "source": [
    "model = coatnet_0()\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "learning_rate = 1e-3\n",
    "optimizer = optim.Adam( model.parameters(), lr=learning_rate, weight_decay=1e-5,)\n",
    "\n",
    "logs = train(model, train_loader=trainloader, test_loader=valloader,\n",
    "             optimizer=optimizer, loss_function=criterion, \n",
    "             epoch_num=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c2df15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Process Starts at 14:53:56 ... \n",
      "Epoch[ 1]>>> Train/Test loss: 0.0000333/0.0032342, Acc.: 99.45/73.53 [[ 0m 10s]]\n",
      "Epoch[ 2]>>> Train/Test loss: 0.0000317/0.0032342, Acc.: 99.46/73.53 [[ 0m 20s]]\n",
      "Epoch[ 3]>>> Train/Test loss: 0.0000318/0.0032342, Acc.: 99.47/73.53 [[ 0m 31s]]\n",
      "Epoch[ 4]>>> Train/Test loss: 0.0000311/0.0032342, Acc.: 99.48/73.53 [[ 0m 41s]]\n",
      "Epoch[ 5]>>> Train/Test loss: 0.0000321/0.0032342, Acc.: 99.45/73.53 [[ 0m 52s]]\n",
      "Epoch[10]>>> Train/Test loss: 0.0000316/0.0032342, Acc.: 99.46/73.53 [[ 1m 45s]]\n",
      "测试集F1: 73.63209567310618\n",
      "Finished in  1m 45s\n"
     ]
    }
   ],
   "source": [
    "model_name = f'cifar10-1.h5'\n",
    "torch.save(model, model_name)\n",
    "\n",
    "#用保存了的模型继续跑\n",
    "model = torch.load('./cifar10-1.h5')\n",
    "learning_rate = 1e-3\n",
    "logs = train(model, train_loader=trainloader, test_loader=valloader,\n",
    "             optimizer=optimizer, loss_function=criterion, \n",
    "             epoch_num=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c10f0d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Process Starts at 14:55:42 ... \n",
      "Epoch[ 1]>>> Train/Test loss: 0.0000333/0.0032342, Acc.: 99.45/73.53 [[ 0m 10s]]\n",
      "Epoch[ 2]>>> Train/Test loss: 0.0000317/0.0032342, Acc.: 99.46/73.53 [[ 0m 20s]]\n",
      "Epoch[ 3]>>> Train/Test loss: 0.0000318/0.0032342, Acc.: 99.47/73.53 [[ 0m 31s]]\n",
      "Epoch[ 4]>>> Train/Test loss: 0.0000311/0.0032342, Acc.: 99.48/73.53 [[ 0m 41s]]\n",
      "Epoch[ 5]>>> Train/Test loss: 0.0000321/0.0032342, Acc.: 99.45/73.53 [[ 0m 52s]]\n",
      "Epoch[10]>>> Train/Test loss: 0.0000316/0.0032342, Acc.: 99.46/73.53 [[ 1m 45s]]\n",
      "测试集F1: 73.63209567310618\n",
      "Finished in  1m 45s\n"
     ]
    }
   ],
   "source": [
    "model_name = f'cifar10.h5'\n",
    "torch.save(model, model_name)\n",
    "\n",
    "#用保存了的模型继续跑\n",
    "model = torch.load('./cifar10.h5')\n",
    "learning_rate = 1e-5\n",
    "logs = train(model, train_loader=trainloader, test_loader=valloader,\n",
    "             optimizer=optimizer, loss_function=criterion, \n",
    "             epoch_num=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8634734",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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ZR44ccTj+nDlzjIYNGxru7u4GYHz33XeXndOcOXMMwKhWrZqRkZGR7/X4+HgjKirKqFq1qlGrVi1j2LBhxq+//prvvZkwYYJxcYnnzJkzxtChQ42AgACjWrVqRr9+/YyjR48agDFhwgSHscnJyca///1vIywszPDw8DCCg4ONm2++2XjvvffsY959913jxhtvNGrWrGl4eXkZjRo1Mp566ikjNTXV4VijR4826tWrZ1it1svmF5GyZTGMK7ytpoiIiIiIiIjIZezbt4+mTZvy1VdfcfPNNzt7OlIGsrKyaNCgAWPGjOGJJ55w9nRETE89fkVERERERESkzDVs2JChQ4cyefJkZ09FysjcuXPx8PDg4YcfdvZURATQil8RERERERERERGRSkYrfkVEREREREREREQqGRV+RURERERERERERCoZFX5FREREREREREREKpkqzp6Aq7JarRw+fJhq1aphsVicPR0RERERl2YYBqdOnSI0NBQ3N61NqEx03SwiIiJSeopz3azCbwkdPnyYsLAwZ09DREREpFI5ePAgV111lbOnIaVI180iIiIipa8o180q/JZQtWrVANub7O/v7+TZiIiIiLi2tLQ0wsLC7NdYUnnoullERESk9BTnulmF3xLK+5qav7+/LmBFRERESolaAVQ+um4WERERKX1FuW5WAzURERERERERERGRSkaFXxEREREREREREZFKRoVfERERERERERERkUpGPX5FREREiiA3N5ecnBxnT8NleXh44O7u7uxpiIiIiIiYhgq/IiIiIpdgGAZJSUmkpKQ4eyouLzAwkODgYN3ATURERESkHKjwKyIiInIJeUXfOnXq4Ovrq6JlCRiGwZkzZzh69CgAISEhTp6RiIiIiEjlp8KviIiISCFyc3PtRd+aNWs6ezouzcfHB4CjR49Sp04dtX0QERERESljurmbiIiISCHyevr6+vo6eSaVQ977qF7JIiIiIiJlT4VfERERkctQe4fSofdRRERERKT8qPArIiIiIiIiIiIil/XWW2/RoEEDvL29iYyMZOPGjZccv2TJEpo2bYq3tzctW7bkyy+/dHjdMAzGjx9PSEgIPj4+REVFsXv3bocxvXv3pl69enh7exMSEsL999/P4cOH7a9///333HHHHYSEhODn50dERAQLFixwOMby5ctp164dgYGB9jH/93//V+i8H374YSwWCzNmzLBv279/P0OHDiU8PBwfHx8aNWrEhAkTyM7Ovtzb5jQq/IqIiIiIiIiIiMglLVq0iJEjRzJhwgS2bNlC69atiY6Ott/A92Lr1q1jwIABDB06lK1btxITE0NMTAw7duywj5k6dSozZ85k9uzZbNiwAT8/P6Kjo8nMzLSP6d69O4sXL2bXrl0sW7aMvXv30rdvX4fztGrVimXLlvHbb78xZMgQBg4cyKpVq+xjatSowTPPPENcXJx9zJAhQ/j666/zzfvTTz9l/fr1hIaGOmzfuXMnVquVd999l99//53XX3+d2bNnM27cuBK/p2XNYhiG4exJuKK0tDQCAgJITU3F39/f2dMRERGRMpCZmUlCQgLh4eF4e3tf0bFyrQYbE05w9FQmdap50yG8Bu5urtX6oEGDBowYMYIRI0aUaP9LvZ+6tqq89NmKiIhUDpGRkbRv355Zs2YBYLVaCQsL47HHHmPMmDH5xvfv35/09HSHAmzHjh2JiIhg9uzZGIZBaGgoTz75JKNGjQIgNTWVoKAg5s2bxz333FPgPFauXElMTAxZWVl4eHgUOKZnz54EBQXx4YcfFpqnTZs29OzZkxdffNG+7dChQ0RGRvL111/Ts2fPy177Tps2jXfeeYd9+/YVOqa0FefaSit+RURERMpY7I4jdJnyLQPmrOeJT7YxYM56ukz5ltgdR8rkfBaL5ZI/EydOLNFxN23axPDhw0t3siIiIiJS4WVnZ7N582aioqLs29zc3IiKiiIuLq7AfeLi4hzGA0RHR9vHJyQkkJSU5DAmICCAyMjIQo954sQJFixYQOfOnQst+oKtgFyjRo0CXzMMg7Vr17Jr1y5uvPFG+3ar1cr999/PU089xbXXXlvosYt6noqgirMnIJdWGVYHiYiImFnsjiM88t8tXPwVq6TUTB757xbe+WcberQIKdVzHjlyvqC8aNEixo8fz65du+zbqlatan9sGAa5ublUqXL5y8LatWuX6jxFSp01FxLXwelkqBoE9TuDm7uzZ1V2zJYXzJfZbHnBfJnNlhfMl7mS5D1+/Di5ubkEBQU5bA8KCmLnzp2Og89lTjpymCDLSdvzc5mDgoJISkoCsP8u6Jh5r+UZPXo0s2bN4syZM3Ts2NFhFfHFFi9ezKZNm3j33XcdtqemplK3bl2ysrJwd3fn7bff5pZbbrG/PmXKFKpUqcLjjz9ehHcE9uzZw5tvvsmrU6dCwo8V8jNW4bcCi91xhOc/j+dI6vm+JiEB3ky4vXmp/wVRREREisYwDDJycos0NtdqMGHl7/mKvgAGYAEmrozn+sa1ivQPuz4e7lgslx8XHBxsfxwQEIDFYrFv+/777+nevTtffvklzz77LNu3b+ebb74hLCyMkSNHsn79etLT02nWrBmTJk1yWIFxcasHi8XCnDlz+OKLL/j666+pW7cu06dPp3fv3pedo0ipi18JsaMh7fzNXvAPhR5ToHkl/DNptrxgvsxmywvmy2y2vGC+zGbLC46Zrbnwv2mQu/iKMz/11FMMHTqUxMREnn/+eXsP34uvjb/77juGDBnCnDlz8q3arVatGtu2beP06dOsXbuWkSNH0rBhQ7p168bmzZt544032LJlS5Gutw8dOkSPHj24+5YODEufAfMr5meswm8F5YzVQSIiInJ5GTm5NB+f/yYQJWEASWmZtJz4TZHGx78Qja9n6Vy+jRkzhldffZWGDRtSvXp1Dh48yD/+8Q9efvllvLy8+Oijj7j99tvZtWsX9erVK/Q4zz//PFOnTmXatGm8+eab3HfffSQmJlbor7xJJRS/EhYPhIuvntOO2Lb3+6hC/OWr1JgtL5gvs9nygvkymy0vmC9zJctbq1Yt3N3dSU5OdtienJx8ftHBRZmDq1pITrc6ZL5wfN7v5ORkQkJCHI4ZERGR7/y1atXi6quvplmzZoSFhbF+/Xo6depkH/PDDz9w++238/rrrzNw4MB8Gdzc3GjcuDEAERER/PHHH0yaNIlu3brx448/cvToUYfr3tzcXJ588klmzJjB/v377dsPHz5M9+7d6XxtGO9duxHSLjpRBfqMVfitgHKtBs9/Hn/J1UHPfx7PLc2D1fZBRERESuSFF15w+GpbjRo1aN26tf35iy++yKeffsrKlSt59NFHCz3O4MGDGTBgAACvvPIKM2fOZOPGjfTo0aPsJi9yIWuubWXRpa6eY0dDw24V5muXV8SaC189jWnygvkymy0vmC+z2fKC+TJXwryeQNs217H2m1hi/mG7hrRaraxdu4ZHH3kIMtPyZe4U5s7ahFxGdDyfefXX/nTq0A6y0wmvW4fg4CDWfv0VEc2bALYbl23YsIFHhg2B7PQC52LNPA1A1ukU+5jvf/gfvfrczZSXX2D44PsK3dfhOGezyco8A9np3N//TqK6dnZ4PbpXDPffO4AhA/9pP96hQ4fpfus/aHtda+a2345bgafJyzsGmvZ06meswm8FtDHhhEN7h4sZwJHUTDYmnKBTo5rlNzERERHBx8Od+BeiizR2Y8IJBs/ddNlx84a0p0P45VfI+niU3kVju3btHJ6fPn2aiRMn8sUXX3DkyBHOnj1LRkYGBw4cuORxWrVqZX/s5+eHv78/R48eLbV5ilxW4jrHr9DmY9henxxWblNyLrPlBfNlNlteMF9ms+UF82V2zbwj6+Uw6L1faHd4Ph3qujNjfTbpf+cw5OQ0mDydgZ9mULeahUlR3gA8EelJ13lnmL4ui55XV+GT7/fxy6Zs3mvzO7yyFAsw4tosXpo4jibbXiQ80I3nvssi1CeXmJ2PwytPsOGvs2w6bKVLPXeqe1vYe9LKc99l0ai6hU4/9IefLXyXcJZeH5/hiUhP7jo4gaRxEwDwdLdQw8e2YHLSj1m0C3WnUQ03ss4afLn7LP+3Not3enrDK6HUBC6usHmknyJ4+1tcs+x9AA6lWek2/wz1Ayy8elUSxy5Y/Bxc1e2ivQ1IO2S7Tgm/ofQ/jCJS4bcCOnqq8KJvScaJiIhI6bFYLEVut3BDk9qEBHiTlJpZ4HoPCxAc4M0NTWqX+7d4/Pz8HJ6PGjWK1atX8+qrr9K4cWN8fHzo27cv2dnZlzzOxXdTtlgsWK3WUp+vSKFOJ19+jIiIiFyx/i08OHbGYPz3WSSdNogIdiP2Pl+CzhU9D6RacbOcL4B2DqvCwjt9ePa7LMZ9m0WTGm6suMeHFnXOL2Z4+npP0nMMhn+eSUqmQZd67sT+0xfvKrZrY18PC8v/yGHC91mkZxuEVLPQo1EVnu3rg9e5MfN/zeFMDkz6KZtJP52/du1a353vB9uuedNzDP71ZSZ/pVnxqQJNa7nz3z4+9G/heC17Kav3nWXPCSt7TsBVr592eM2Y4F/wTk6+TlHhtwKqU827VMeJiIiIc7i7WZhwe3Me+e8WLDh+2S+vzDvh9uYVonXTzz//zODBg+nTpw9gWwF8YS8zkQqratDlxwDct9R2l21Xl7gOFvS9/LjKkhfMl9lsecF8mc2WF8yXuRLnffTcTz6J6/ie/JnvvtaDu6+9oLh6UWYL8MIz8EIh52sJfHuZOc0bB/MuM+alcfDSZcZcbP84x+eDz/0ARf+Mi3qdUkZU+K2AOoTXKNLqoKJ8JVREREScq0eLEN75Zxue/zzeoZVTcIA3E25vXmFu1tqkSROWL1/O7bffjsVi4bnnntPKXXEN9Tvb7p6ddoSCeylabK83usll+iheUqObzJUXzJfZbHnBfJnNlhfMl9lsecF8mYua18mF/YsbUEgFkLc6CM6vBspT0VYHiYiIyOX1aBHCT6Nv4uNhHXnjngg+HtaRn0bfVGGKvgCvvfYa1atXp3Pnztx+++1ER0fTpk0bZ09L5PLc3KHHlHNPCrl67jG5cvwlE8yXF8yX2Wx5wXyZzZYXzJfZbHnBfJldJK/FMIyCytJyGWlpaQQEBJCamoq/fyF9PK5Q7I4j+VYHhVSw1UEiIiKVWWZmJgkJCYSHh+PtrRZLV+pS72d5XFuJc5TbZxu/0naH9Atv9OZf1/aXrua9y+68zmK2vGC+zGbLC+bLbLa8YL7MZssL5svshLzFubZS4beEyusCNtdq0P/dOH5JPMkD1zfgmZ5a6SsiIlJeVPgtXSr8mlO5frbWXFvPvdPJ5HjV5Jk5X7D80xXUqFGDhx9+mAceeMA+NDk5mdDQUHJzc8t2TmXJbHnBnjnn5CGeeXcly9duNEVefcaVP68+48qfV59x5c9bXp9xca6t1OqhgnN3s9CodlUAavh5qugrIiIiIlIYN3cIvwFa9uXlhT/w0f/9l4cffphbb72VkSNH8tBDDzkMd/k1MGbLC/bML6/cyUeffWuavPqMK39efcaVP68+48qft0J+xoaUSGpqqgEYqampZX6uV76IN+qPXmW8+PnvZX4uEREROS8jI8OIj483MjIynD2VSuFS72d5XltJ+XLWZ9u4cWPj888/tz/fvXu30bhxY2Pw4MGG1Wo1kpKSDDc3t3KdU1lS3sqd1zDMl9lseQ3DfJnNltcwzJfZbHkNo3wyF+faSit+XUCArwcAKRk5Tp6JiIiIiIhrOHToEC1atLA/b9y4Md9//z3r1q3j/vvvd/2vlV5EeSt3XjBfZrPlBfNlNlteMF9ms+WFipdZhV8XEOjjCUDKGRV+RURERESKIjg4mL179zpsq1u3Lt999x2bNm1i8ODBzplYGVHeyp0XzJfZbHnBfJnNlhfMl9lseaHiZVbh1wUEnlvxm5qR7eSZiIiIiIi4hptuuomFCxfm2x4aGsq3335LQkKCE2ZVdpTXprLmBfNlNlteMF9ms+UF82U2W16oeJmrlOvZpETyCr8nteJXRERERKRInnvuOXbu3Fnga3Xr1uWHH35g9erV5TyrsqO851XGvGC+zGbLC+bLbLa8YL7MZssLFS+zxTAqw+3zyl9aWhoBAQGkpqbi7+9fpueKP5zGP2b+SK2qXvzybFSZnktERETOy8zMJCEhgfDwcLy9vZ09HZd3qfezPK+tpHzpsxUREREpPcW5tlKrBxdwYasH1elFREREREquYcOG7N6929nTKDfKW/mZLbPZ8oL5MpstL5gvs9nygvMyq9WDC8gr/ObkGpzJzsXPSx+biIiIy7HmQuI6OJ0MVYOgfmdwc3f2rEQqrZkzZxa4/cCBA8ydO5fg4GAAHn/88fKcVplRXpvKmhfMl9lsecF8mc2WF8yX2Wx5oeJlVquHEirPr6wZhsE1z8WSfdbKT6O7c1V13zI9n4iIiNiUWquH+JUQOxrSDp/f5h8KPaZA895XPtGLWCyWS74+YcIEJk6cWOJjf/rpp8TExBR7X7V6MCdnfbZubm7UrVuXKlUcF00kJiYSGhqKh4cHFouFffv2lducypLy2lTWvGC+zGbLC+bLbLa8YL7MZssL5ZO5ONdWWjrqAiwWC4E+Hhw9lUXKmRyuqu7sGYmIiEiRxa+ExQOBi/6tPe2IbXu/j0q9+HvkyBH740WLFjF+/Hh27dpl31a1atVSPZ9IRTR8+HA2bNjAwoULadasmX27h4cH33zzDc2bN3fi7Eqf8tpU1rxgvsxmywvmy2y2vGC+zGbLCxUvs3r8uojzfX5znDwTERERkzMMyE4v2k9mGnz1NPmKvrYD2X7FjraNK8rxivhFreDgYPtPQEAAFovFYdsnn3xCs2bN8Pb2pmnTprz99tv2fbOzs3n00UcJCQnB29ub+vXrM2nSJAAaNGgAQJ8+fbBYLPbnIhXR7NmzGT9+PNHR0cyaNcvZ0ylzylv5mS2z2fKC+TKbLS+YL7PZ8kLFy6wVvy4i0McTgJQzKvyKiIg4Vc4ZeCW0lA5m2No/TA4r2vBxh8HT74rOuGDBAsaPH8+sWbO47rrr2Lp1K8OGDcPPz49BgwYxc+ZMVq5cyeLFi6lXrx4HDx7k4MGDAGzatIk6deowd+5cevTogbu7ehRLxdanTx86dOjAwIED+eKLL5g7d66zp1SmlLdy5wXzZTZbXjBfZrPlBfNlNlteqFiZteLXReSt+E3JyHbyTERERMSVTZgwgenTp3PnnXcSHh7OnXfeyX/+8x/effddwHbjiSZNmtClSxfq169Ply5dGDBgAAC1a9cGIDAwkODgYPtzkYqsbt26rFmzhhtvvJHrrruOyn6LE+Wt3HnBfJnNlhfMl9lsecF8mc2WFypOZq34dRH2wq9W/IqIiDiXh69t5W1RJK6DBX0vP+6+pVC/c9HOfQXS09PZu3cvQ4cOZdiwYfbtZ8+eJSAgAIDBgwdzyy23cM0119CjRw969erFrbfeekXnFXE2i8XC2LFjufXWW/npp58ICQlx9pTKlPJW7rxgvsxmywvmy2y2vGC+zGbLCxUjswq/LiLQN6/Vg1b8ioiIOJXFUvR2C41uAv9Q243cCuzza7G93ugmcCv7tgmnT58GYM6cOURGRjq8lte2oU2bNiQkJPDVV1+xZs0a+vXrR1RUFEuXLi3z+YmUtbZt29K2bVtnT6PcKG/lZ7bMZssL5ststrxgvsxmywvOzaxWDy4iwEcrfkVERFyOmzv0mHLuieWiF8897zG5XIq+AEFBQYSGhrJv3z4aN27s8BMeHm4f5+/vT//+/ZkzZw6LFi1i2bJlnDhxArDdkTg3N7dc5itypVavXs2ECRP49ttvAfjf//7Hbbfdxk033VQpewwqb+XOC+bLbLa8YL7MZssL5ststrxQsTKr8Osizvf4VeFXRETEpTTvDf0+Av+LvtrlH2rb3rx3uU7n+eefZ9KkScycOZM///yT7du3M3fuXF577TUAXnvtNT7++GN27tzJn3/+yZIlSwgODiYwMBCABg0asHbtWpKSkjh58mS5zl2kOP773//yj3/8g1WrVnHHHXcwb9487rjjDq666irCw8N5+OGHK9VKduWt3HnBfJnNlhfMl9lsecF8mc2WFypgZkNKJDU11QCM1NTUcjnfF78dNuqPXmX0fefncjmfiIiIGEZGRoYRHx9vZGRkXPnBcs8axr7/GcZvS2y/c89e+TGLYO7cuUZAQIDDtgULFhgRERGGp6enUb16dePGG280li9fbhiGYbz33ntGRESE4efnZ/j7+xs333yzsWXLFvu+K1euNBo3bmxUqVLFqF+/frHmcqn3s7yvraT8OOuzjYiIMN544w3DMAxjzZo1ho+Pj/Haa6/ZX3/11VeN66+/vlznVJaUt3LnNQzzZTZbXsMwX2az5TUM82U2W17DKJ/Mxbm2shiGCW6lVwbS0tIICAggNTUVf3//Mj/fuj3Huff9DTSpU5XVI7uW+flEREQEMjMzSUhIIDw8HG9vb2dPx+Vd6v0s72srKT/O+myrVq3K9u3b7W1MPD09+eWXX2jVqhUAO3fupEuXLhw/frzc5lSWlLdy5wXzZTZbXjBfZrPlBfNlNlteKJ/Mxbm2UqsHFxGgVg8iIiIiIkXm4eFBdvb5GyN7eXlRtWpVh+cZGRnOmFqZUN7KnRfMl9lsecF8mc2WF8yX2Wx5oeJlVuHXRQT6egKQeiYHLdIWEREREbm0xo0bs3PnTvvzQ4cOOdzEcO/evVx11VXOmFqZUN7KnRfMl9lsecF8mc2WF8yX2Wx5oeJlVuHXRVQ/t+I3O9dKRo7upC0iIiJS2bz11ls0aNAAb29vIiMj2bhx4yXHL1myhKZNm+Lt7U3Lli358ssvHV43DIPx48cTEhKCj48PUVFR7N6922FM7969qVevHt7e3oSEhHD//fdz+PBhhzG//fYbN9xwA97e3oSFhTF16tTSCVzGxo0bR/Xq1e3P/f39sVgs9ue//PIL/fr1c8bUyoTyVu68YL7MZssL5ststrxgvsxmywsVL7N6/JZQefcqMwyDa56NJTvXys9jbqJuoE+Zn1NERMTs1OO3dKnHb+EWLVrEwIEDmT17NpGRkcyYMYMlS5awa9cu6tSpk2/8unXruPHGG5k0aRK9evVi4cKFTJkyhS1bttCiRQsApkyZwqRJk5g/fz7h4eE899xzbN++nfj4ePv7//rrr9OpUydCQkI4dOgQo0aNsh8fbJ/L1VdfTVRUFGPHjmX79u088MADzJgxg+HDhxcpm9k/WxEREZHSVJxrKxV+S8gZF7DtX17DsVNZfPF4F64NDSiXc4qIiJhZXqGyfv36+Pr6Ons6Lu/MmTMkJiaq8FuAyMhI2rdvz6xZswCwWq2EhYXx2GOPMWbMmHzj+/fvT3p6OqtWrbJv69ixIxEREcyePRvDMAgNDeXJJ5+0F3NTU1MJCgpi3rx53HPPPQXOY+XKlcTExJCVlYWHhwfvvPMOzzzzDElJSXh62lqPjRkzhhUrVjh8jfFCWVlZZGVl2Z+npaURFhZm2s9WREREpDTp5m6VVKCPrd1D6hnd4E1ERKQ8eHp64ubmxuHDh0lNTSUjI4PMzEz9FPMnIyOD1NRUDh8+jJubm72AKDbZ2dls3ryZqKgo+zY3NzeioqKIi4srcJ+4uDiH8QDR0dH28QkJCSQlJTmMCQgIIDIystBjnjhxggULFtC5c2c8PDzs57nxxhsdPrPo6Gh27drFyZMnCzzOpEmTCAgIsP+EhYUV4V0oG19++SUPPvggTz/9dL5C9cmTJ7npppucNLOyobznVca8YL7MZssL5ststrxgvsxmywsVK3OVcjuTXLHAc31+UzJU+BURESkPbm5uhIeHc+TIkXx9T6X4fH19qVevHm5uWntwoePHj5Obm0tQUJDD9qCgoEJX1SYlJRU4Pikpyf563rbCxuQZPXo0s2bN4syZM3Ts2NFhFXFSUpLDDUkuPGZSUpJDD7s8Y8eOZeTIkfbneSt+y9vChQsZOHAgPXr0YNeuXbz55pu8//773HfffYCt4P7DDz+U+7zKivJW7rxgvsxmywvmy2y2vGC+zGbLCxUvswq/LiTQ17bS4uSZbCfPRERExDw8PT2pV68eZ8+eJTdXN1gtKXd3d6pUqeJwcwupGJ566imGDh1KYmIizz//PAMHDmTVqlUl/qy8vLzw8vIq5VkW37Rp03jttdd4/PHHAVi8eDEPPPAAmZmZDB061MmzK33KW7nzgvkymy0vmC+z2fKC+TKbLS9UvMwq/LqQvFYPKWr1ICIiUq4sFgseHh72r7+LlKZatWrh7u5OcnKyw/bk5GSCg4ML3Cc4OPiS4/N+JycnExIS4jAmIiIi3/lr1arF1VdfTbNmzQgLC2P9+vV06tSp0PNceI6Kavfu3dx+++325/369aN27dr07t2bnJwc+vTp48TZlT7lrdx5wXyZzZYXzJfZbHnBfJnNlhcqXmYVfl1IXquHVLV6EBEREak0PD09adu2LWvXriUmJgaw3dxt7dq1PProowXu06lTJ9auXcuIESPs21avXk2nTp0ACA8PJzg4mLVr19oLvWlpaWzYsIFHHnmk0LlYrVYA+83ZOnXqxDPPPENOTo79Hz5Wr17NNddcU2Cbh4rE39+f5ORkh1YV3bt3Z9WqVfTq1Yu//vrLibMrfcpbufOC+TKbLS+YL7PZ8oL5MpstL1S8zGqw5kLyWj2kqNWDiIiISKUycuRI5syZw/z58/njjz945JFHSE9PZ8iQIQAMHDiQsWPH2sc/8cQTxMbGMn36dHbu3MnEiRP55Zdf7IVii8XCiBEjeOmll1i5ciXbt29n4MCBhIaG2ovLGzZsYNasWWzbto3ExES+/fZbBgwYQKNGjewF5HvvvRdPT0+GDh3K77//zqJFi3jjjTccevhWVB06dOCrr77Kt71r1658/vnnzJgxo/wnVYaU16ay5gXzZTZbXjBfZrPlBfNlNlteqHiZVfh1Ifabu6nVg4iIiEil0r9/f1599VXGjx9PREQE27ZtIzY21n4jtQMHDnDkyBH7+M6dO7Nw4ULee+89WrduzdKlS1mxYgUtWrSwj3n66ad57LHHGD58OO3bt+f06dPExsbi7e0N2G62t3z5cm6++WauueYahg4dSqtWrfjhhx/sPXoDAgL45ptvSEhIoG3btjz55JOMHz+e4cOHl+O7UzL/+c9/7Fkv1q1bNz7//HMGDhxYzrMqO8p7XmXMC+bLbLa8YL7MZssL5ststrxQ8TJbDMMwyu1slUhaWhoBAQGkpqbi7+9fLuf84rcj/HvhFjo0qMHihzuVyzlFREREyoMzrq2kfOizFRERESk9xbm20opfF2Jf8ZuhVg8iIiIiIkWRm5vr8HzDhg3873//Iyencn6LTnkrd14wX2az5QXzZTZbXjBfZrPlhYqTWYVfFxLgo1YPIiIiIiJFceTIEa6//nq8vLzo2rUrJ0+epFevXnTq1Ilu3brRokULh/YZrk55K3deMF9ms+UF82U2W14wX2az5YWKl1mFXxdyfsVvDurQISIiIiJSuNGjRwPw6aefEhISQq9evUhLS+PgwYPs37+f2rVr8/LLLzt5lqVHeSt3XjBfZrPlBfNlNlteMF9ms+WFCpjZkBJJTU01ACM1NbXcznk6M8eoP3qVUX/0KiM9K6fczisiIiJS1pxxbSXlw1mfbUhIiBEXF2cYhmH8/fffhsViMdasWWN/fe3atUbDhg3LdU5lSXkrd17DMF9ms+U1DPNlNltewzBfZrPlNYzyyVycayut+HUhvp7ueLhbALV7EBERERG5lJMnT1K3bl0AatSoga+vL/Xr17e/3rhx40r19VLlrdx5wXyZzZYXzJfZbHnBfJnNlhcqXmYVfl2IxWIhwMcTUOFXRERERORS6tSp4/AXq0cffZQaNWrYn588eRI/Pz9nTK1MKG/lzgvmy2y2vGC+zGbLC+bLbLa8UPEyq/DrYs73+c128kxERERERCquiIgI4uLi7M8nT57s8Bevn376iVatWjljamVCeSt3XjBfZrPlBfNlNlteMF9ms+WFipfZYhi6S1hJpKWlERAQQGpqKv7+/uV23rtnr2PT/pO8c18bbmsZUm7nFRERESlLzrq2krJXUT/bjRs34uvrS4sWLZw9lXKhvJWf2TKbLS+YL7PZ8oL5MpstL5RO5uJcW1WIFb9vvfUWDRo0wNvbm8jISDZu3HjJ8UuWLKFp06Z4e3vTsmVLvvzyS4fXDcNg/PjxhISE4OPjQ1RUFLt37y7wWFlZWURERGCxWNi2bVtpRSozea0eTqrVg4iIiIhIiXXo0MFUf9FU3srPbJnNlhfMl9lsecF8mc2WF8o/c5VyO1MhFi1axMiRI5k9ezaRkZHMmDGD6Ohodu3aRZ06dfKNX7duHQMGDGDSpEn06tWLhQsXEhMTw5YtW+xv3NSpU5k5cybz588nPDyc5557jujoaOLj4/H29nY43tNPP01oaCi//vprueS9Umr1ICIiIiJSNNnZ2axYsYK4uDiSkpIACA4OpnPnztxxxx14eno6eYalS3krd14wX2az5QXzZTZbXjBfZrPlhYqV2ekrfl977TWGDRvGkCFDaN68ObNnz8bX15cPP/ywwPFvvPEGPXr04KmnnqJZs2a8+OKLtGnThlmzZgG21b4zZszg2Wef5Y477qBVq1Z89NFHHD58mBUrVjgc66uvvuKbb77h1VdfLeuYpSbQx1b4TdWKXxERERGRQu3Zs4dmzZoxaNAgtm7ditVqxWq1snXrVgYOHMi1117Lnj17nD3NUqO8lTsvmC+z2fKC+TKbLS+YL7PZ8kLFy+zUHr/Z2dn4+vqydOlSYmJi7NsHDRpESkoKn332Wb596tWrx8iRIxkxYoR924QJE1ixYgW//vor+/bto1GjRmzdupWIiAj7mK5duxIREcEbb7wBQHJyMm3btmXFihXUqlWL8PDwfPtcKCsri6ysLPvztLQ0wsLCyr1X2axvd/PqN3/Sv10YU/pWrgbYIiIiYl4VtQ+sXDlnfba33HILfn5+fPTRR/nOm5aWxsCBA8nIyODrr78utzmVJeU9rzLmBfNlNlteMF9ms+UF82U2W14on8zFubZyaquH48ePk5ubS1BQkMP2oKAgdu7cWeA+SUlJBY7PWzqd9/tSYwzDYPDgwTz88MO0a9eO/fv3X3aukyZN4vnnny9SrrIU6JvX41etHkRERERECvPzzz+zcePGAv9C5O/vz4svvkhkZKQTZlY2lPe8ypgXzJfZbHnBfJnNlhfMl9lseaHiZXZ6qwdnePPNNzl16hRjx44t8j5jx44lNTXV/nPw4MEynGHhzvf4VasHEREREZHCBAYGXnKBx/79+wkMDCy3+ZQ15XVU2fKC+TKbLS+YL7PZ8oL5MpstL1S8zE5d8VurVi3c3d1JTk522J6cnExwcHCB+wQHB19yfN7v5ORkQkJCHMbktXH49ttviYuLw8vLy+E47dq147777mP+/Pn5zuvl5ZVvvDME+thW/KrHr4iIiIhI4R588EEGDhzIc889x80332z/RmBycjJr167lpZde4rHHHnPyLEuP8lbuvGC+zGbLC+bLbLa8YL7MZssLFTCz4WQdOnQwHn30Ufvz3Nxco27dusakSZMKHN+vXz+jV69eDts6depkPPTQQ4ZhGIbVajWCg4ONV1991f56amqq4eXlZXz88ceGYRhGYmKisX37dvvP119/bQDG0qVLjYMHDxZp3qmpqQZgpKamFivvldr+V4pRf/Qqo8PLq8v1vCIiIiJlyVnXVlL2nPnZTp482QgJCTEsFovh5uZmuLm5GRaLxQgJCTGmTJlS7vMpa8pbufMahvkymy2vYZgvs9nyGob5Mpstr2GUfebiXFs59eZuAIsWLWLQoEG8++67dOjQgRkzZrB48WJ27txJUFAQAwcOpG7dukyaNAmAdevW0bVrVyZPnkzPnj355JNPeOWVV9iyZQstWrQAYMqUKUyePJn58+cTHh7Oc889x2+//UZ8fDze3t755rB///7L3tztYs66ScVfJ8/QZcp3eFZxY9eLPbBYLOV2bhEREZGyopu7VV4V4bPdt2+f/VuDwcHBhIeHO2Ue5UV5K3deMF9ms+UF82U2W14wX2az5YWyy+wyN3cD6N+/P8eOHWP8+PEkJSURERFBbGysfSn0gQMHcHM734q4c+fOLFy4kGeffZZx48bRpEkTVqxYYS/6Ajz99NOkp6czfPhwUlJS6NKlC7GxsQUWfV1N3s3dss9aycyx4uPp7uQZiYiIiIhUbA0bNqRhw4bOnka5Ud7Kz2yZzZYXzJfZbHnBfJnNlhcqRmanr/h1Vc5auWAYBk2e+YqzVoO4sTcREuBTbucWERERKSsVYVWolA1nfrbx8fHMmjWLuLg4kpKSANuKm06dOvHoo4/SvHnzcp1PWVPeyp0XzJfZbHnBfJnNlhfMl9lseaHsM7vUil8pHovFQqCvB8dPZ5NyJkeFXxERERGRAnz11VfExMTQpk0b7rjjDoebq6xevZo2bdrw2WefER0d7eSZlg7lrdx5wXyZzZYXzJfZbHnBfJnNlhcqXmat+C0hZ65cuHn69+w9ls7HwzrSqVHNcj23iIiISFnQit/Ky1mfbevWrbnjjjt44YUXCnx94sSJLF++nN9++63c5lSWlNdRZcsL5ststrxgvsxmywvmy2y2vFA+mYtzbaXCbwk58y8nfd9Zxy+JJ3nnvjbc1jKkXM8tIiIiUhZU+K28nPXZ+vj4sG3bNq655poCX9+1axcRERFkZGSU25zKkvI6qmx5wXyZzZYXzJfZbHnBfJnNlhfKJ3Nxrq3cLvmqVEiBvh4ApGTkOHkmIiIiIiIVU4MGDfjiiy8Kff2LL76gfv365TijsqW8jipbXjBfZrPlBfNlNlteMF9ms+WFipdZPX5dUICPJwApZ1T4FREREREpyAsvvMC9997L999/T1RUlEOPvbVr1xIbG8vChQudPMvSo7yVOy+YL7PZ8oL5MpstL5gvs9nyQsXLrFYPJeTMryO+uCqeD35K4KGuDRl7W7NyPbeIiIhIWVCrh8rLmZ/tunXrmDlzZoF31X7iiSfo1KlTuc6nrClv5c4L5ststrxgvsxmywvmy2y2vFD2mdXjtxw48wJ21re7efWbP+nfLowpfVuV67lFREREyoIKv5WXPlsRERGR0qMev5VcgO+5Vg8Z2U6eiYiIiIiIiIiIiFREKvy6oECfczd3U49fEREREZESGTduHA888ICzp1FulLfyM1tms+UF82U2W14wX2az5YXyz6ybu7mgQF9b4Tc1Q4VfEREREZGS+Ouvv/jrr7+cPY1yo7yVn9kymy0vmC+z2fKC+TKbLS+Uf2b1+C0hZ/Yq2/5XKrfP+olgf2/Wj7u5XM8tIiIiUhbUB7by0mcrIiIiUnqKc22lFb8uKG/F78kz6vErIiIiIlKY48eP8+GHH+a7q3bnzp0ZPHgwtWvXdvIMS5fyVu68YL7MZssL5ststrxgvsxmywsVK7NW/JaQM1cunMrMoeXEbwDY+WIPvD3cy/X8IiIiIqVNq0IrL2d9tps2bSI6OhpfX1+ioqIICgoCIDk5mbVr13LmzBm+/vpr2rVrV25zKkvKW7nzgvkymy0vmC+z2fKC+TKbLS+UT+biXFup8FtCzvzLiWEYNH7mK3KtBuvH3kxwgHe5nl9ERESktKnwW3k567Pt2LEjrVu3Zvbs2VgsFofXDMPg4Ycf5rfffiMuLq7c5lSWlPe8ypgXzJfZbHnBfJnNlhfMl9lseaF8MqvwWw6c/ZeTti+u5u/0bGJH3EDTYP3lSERERFybs6+tpOw467P18fFh69atNG3atMDXd+7cyXXXXUdGRka5zaksKa+jypYXzJfZbHnBfJnNlhfMl9lseaF8Mhfn2sqtxGcRp7L3+U3PcfJMREREREQqnuDgYDZu3Fjo6xs3brR//bIyUF5HlS0vmC+z2fKC+TKbLS+YL7PZ8kLFy6ybu7moQF9PIJ3UDN3gTURERETkYqNGjWL48OFs3ryZm2++OV+PvTlz5vDqq686eZalR3krd14wX2az5QXzZTZbXjBfZrPlhQqY2ZASSU1NNQAjNTXVKed/YO5Go/7oVcbHGxKdcn4RERGR0uTsayspO878bD/55BMjMjLSqFKlimGxWAyLxWJUqVLFiIyMNBYtWlTu8ylrylu58xqG+TKbLa9hmC+z2fIahvkymy2vYZR95uJcW6nHbwk5uw/dyMXbWL7lEGNua8rDXRuV+/lFRERESpOzr62k7FSEzzYnJ4fjx48DUKtWLTw8PJwyj/KivJU7L5gvs9nygvkymy0vmC+z2fJC2WUuzrWVWj24qEAfTwBSzqjHr4iIiIjIpXh4eBASEuLsaZQb5a38zJbZbHnBfJnNlhfMl9lseaFiZNbN3VxU9XM3d0s5ox6/IiIiIiIiIiIi4kiFXxcVaC/8asWviIiIiIiIiIiIOFLh10UF+J5r9ZChFb8iIiIiIiIiIiLiSIVfFxXooxW/IiIiIiIiIiIiUjAVfl1UdV/d3E1EREREREREREQKpsKvi7L3+FWrBxEREREREREREbmICr8uKuBc4Tczx0pmTq6TZyMiIiIiIiIiIiIViQq/LqqaVxXc3SwApGao3YOIiIiIiIiIiIicp8Kvi7JYLAToBm8iIiIiIiIiIiJSABV+XVhen9+TZ9TnV0RERERERERERM5T4deFBWrFr4iIiIiIiIiIiBRAhV8XFujrCUBqhlb8ioiIiIiIiIiIyHkq/LowrfgVERERERERERGRgqjw68LyVvyeVOFXRERERERERERELqDCrwvLu7mbWj2IiIiIiIiIiIjIhVT4dWF5hV+1ehARERFxfW+99RYNGjTA29ubyMhINm7ceMnxS5YsoWnTpnh7e9OyZUu+/PJLh9cNw2D8+PGEhITg4+NDVFQUu3fvtr++f/9+hg4dSnh4OD4+PjRq1IgJEyaQnZ3tMMZiseT7Wb9+femGFxEREZFSp8KvCwtQj18RERGRSmHRokWMHDmSCRMmsGXLFlq3bk10dDRHjx4tcPy6desYMGAAQ4cOZevWrcTExBATE8OOHTvsY6ZOncrMmTOZPXs2GzZswM/Pj+joaDIzMwHYuXMnVquVd999l99//53XX3+d2bNnM27cuHznW7NmDUeOHLH/tG3btmzeCBEREREpNRbDMAxnT8IVpaWlERAQQGpqKv7+/k6Zw//+PMbADzfSLMSfr564wSlzEBERESkNFeHaypkiIyNp3749s2bNAsBqtRIWFsZjjz3GmDFj8o3v378/6enprFq1yr6tY8eOREREMHv2bAzDIDQ0lCeffJJRo0YBkJqaSlBQEPPmzeOee+4pcB7Tpk3jnXfeYd++fYBtxW94eDhbt24lIiKiRNnM/tmKiIiIlKbiXFtpxa8LO9/qQT1+RURERFxVdnY2mzdvJioqyr7Nzc2NqKgo4uLiCtwnLi7OYTxAdHS0fXxCQgJJSUkOYwICAoiMjCz0mGArDteoUSPf9t69e1OnTh26dOnCypUrL5knKyuLtLQ0hx8RERERKX8q/LqwQB9PQK0eRERERFzZ8ePHyc3NJSgoyGF7UFAQSUlJBe6TlJR0yfF5v4tzzD179vDmm2/y0EMP2bdVrVqV6dOns2TJEr744gu6dOlCTEzMJYu/kyZNIiAgwP4TFhZW6FgRERERKTtVnD0BKbmAcyt+M3JyyczJxdvD3ckzEhERERFXdOjQIXr06MHdd9/NsGHD7Ntr1arFyJEj7c/bt2/P4cOHmTZtGr179y7wWGPHjnXYJy0tTcVfERERESfQil8XVs2rCm4W2+O0DK36FREREXFFtWrVwt3dneTkZIftycnJBAcHF7hPcHDwJcfn/S7KMQ8fPkz37t3p3Lkz77333mXnGxkZyZ49ewp93cvLC39/f4cfERERESl/Kvy6MDc3C4G+tnYPJ9XuQURERMQleXp60rZtW9auXWvfZrVaWbt2LZ06dSpwn06dOjmMB1i9erV9fHh4OMHBwQ5j0tLS2LBhg8MxDx06RLdu3Wjbti1z587Fze3yfz3Ytm0bISEhxcooIiIiIuVPrR5cXKCPByfSs3WDNxEREREXNnLkSAYNGkS7du3o0KEDM2bMID09nSFDhgAwcOBA6taty6RJkwB44okn6Nq1K9OnT6dnz5588skn/PLLL/YVuxaLhREjRvDSSy/RpEkTwsPDee655wgNDSUmJgY4X/StX78+r776KseOHbPPJ29V8Pz58/H09OS6664DYPny5Xz44Ye8//775fXWiIiIiEgJqfDr4vL6/Kao1YOIiIiIy+rfvz/Hjh1j/PjxJCUlERERQWxsrP3mbAcOHHBYjdu5c2cWLlzIs88+y7hx42jSpAkrVqygRYsW9jFPP/006enpDB8+nJSUFLp06UJsbCze3t6AbYXwnj172LNnD1dddZXDfAzDsD9+8cUXSUxMpEqVKjRt2pRFixbRt2/fsnw7RERERKQUWIwLr+qkyNLS0ggICCA1NdWpfcuGzN3Id7uOMfWuVvRrr5tmiIiIiGuqKNdWUvr02YqIiIiUnuJcW6nHr4urfq7Hb0qGWj2IiIiIiIiIiIiIjQq/Li6v1YNu7iYiIiIiIiIiIiJ5VPh1cYE+51b8qvArIiIiIiIiIiIi56jw6+ICz634TVWrBxERERERERERETlHhV8Xl1f41YpfERERERERERERyaPCr4sLPHdzN/X4FRERERERERERkTwq/Lq4QJ9zrR7OqNWDiIiIiIiIiIiI2Kjw6+LsrR4ytOJXREREREREREREbFT4dXGBPrZWD2eyc8k6m+vk2YiIiIiIiIiIiEhFoMKvi6vmXQU3i+1xqlb9ioiIFNtbb71FgwYN8Pb2JjIyko0bN15y/JIlS2jatCne3t60bNmSL7/80uF1wzAYP348ISEh+Pj4EBUVxe7dux3G9O7dm3r16uHt7U1ISAj3338/hw8ftr/+/fffc8cddxASEoKfnx8REREsWLDA4RjLly+nXbt2BAYG2sf83//9X6Hzfvjhh7FYLMyYMcO+bf/+/QwdOpTw8HB8fHxo1KgREyZMIDtbLaRERERERFydCr8uzs3NQsC5Pr8pusGbiIhIsSxatIiRI0cyYcIEtmzZQuvWrYmOjubo0aMFjl+3bh0DBgxg6NChbN26lZiYGGJiYtixY4d9zNSpU5k5cyazZ89mw4YN+Pn5ER0dTWZmpn1M9+7dWbx4Mbt27WLZsmXs3buXvn37OpynVatWLFu2jN9++40hQ4YwcOBAVq1aZR9To0YNnnnmGeLi4uxjhgwZwtdff51v3p9++inr168nNDTUYfvOnTuxWq28++67/P7777z++uvMnj2bcePGlfg9FRERERGRisFiGIbh7Em4orS0NAICAkhNTcXf39+pc+n+6vckHE9n8UOd6BBew6lzERERcSWRkZG0b9+eWbNmAWC1WgkLC+Oxxx5jzJgx+cb379+f9PR0hwJsx44diYiIYPbs2RiGQWhoKE8++SSjRo0CIDU1laCgIObNm8c999xT4DxWrlxJTEwMWVlZeHh4FDimZ8+eBAUF8eGHHxaap02bNvTs2ZMXX3zRvu3QoUNERkby9ddf07NnT0aMGMGIESMKPca0adN455132LdvX6FjykJFuraS0qXPVkRERKT0FOfaSit+K4HzK371tUwREZGiys7OZvPmzURFRdm3ubm5ERUVRVxcXIH7xMXFOYwHiI6Oto9PSEggKSnJYUxAQACRkZGFHvPEiRMsWLCAzp07F1r0BVsBuUaNgv+B1zAM1q5dy65du7jxxhvt261WK/fffz9PPfUU1157baHHLup5RERERETEdVRx9gTkygX6niv8qseviPlYcyFxHZxOhqpBUL8zuLlrP+2n/Yqw3/Hjx8nNzSWodi1I+NG+X1Cd2uzcubPAfZKSkggKCnI4X9DZwyQdOgjWXJKSkgDOjzknKCjI/lrefqNfnM6sT77mTEYmHTt2dFhFfLHFixezadMm3n1uOGxfas+Xeuo0devWJSsrC3d3d95++21uueUW+35TJk+iSs4pHu8east4GXv27OHNN9/k1alTHd6TIn8GIiIiIiJSYajwWwlU9/UEtOJXxHTiV0LsaEg7f0Mo/EOhxxRo3lv7aT/tV5T9ABb9E2qcPP/8lyqQUfPS+1x4vm3ZkJkFM1pA0OAi7/dULStDh7qTmB3G85tS7T18LRaLwy7fffcdQwYPZM5dNbk27jGHfNVuncS2bds4ffo0a9euZeTIkTRs2JBu3bqxeclrvDF5AluG+WBZ/qBtn7QzkLSDghw6dIgePXpw9y0dGJY+A+aX4L0UEREREZEKQ60eKgHd3E3EhOJXwuKBjkUugLQjtu3xK7Wf9tN+l9mv1tE43C2QfNGN3JJPniI4O6HA/YKDg0n+da3D+ZJPWwmuaoG0IwRvfNm2LTnZ8ZjJyQR7nnHYr5avG1fXdOeWkDQ+6fYXX375JevXr3fY74cffuD2nv/g9ZvdGHh1er58bksH0zg7noiICJ588kn69u3LpEmTIH4lP37wDEdP5VLv9dNUeSGNKi+kkXjyLE9O/YAGdR1XJB8+fJju3bvT+dow3rt2Y/E/AxERERERqXBU+K0E1OpBxGSsubYVgxR0b85z22LH2MZpP+2n/Qrdz/PbZ2kb6sbafWfPbzYM1u47S6er3Avcr1PHjqz9/BOH863el2sbj0F4oIXgau6sXbPa/npaWhobNmygE1sKnaf13OasjDP2rd9//z09e/ZkSo8AhrctqPdv/nxWq5WsrEyIHc39rarw2yN+bHv4/E9oNQtPdfbi6/ur2vc5dOgQ3bp1o22bNsy94QhulgJOdan3UkREREREKiSLYRgF/Q1ELqMi3Z143s8JTPw8np4tQ3jrvjZOnYuIlIOEH2F+r8uP86wG7hcUi3JzIPuU9tN+2u+C/RbtyGHQigze7eVNh7ruzFifzeL4HHb+uypBVd0Y+FkOdQM9mdSjOgDr9p2m6/vHmXyzFz2vrsInO3J45cdstjzkR4s6th64U37KYvLP2czvV5vwGlV47puT/HYkm/h/+eJdxcKGv86y6bCVLvXcqe5tYe9JK899l0XyaSu/jwjBy8uT7/Zm0GveUZ7o7Mfj7S6I4W6hho+tMjvpxyzahbrTqIYbWRZfvtydw5jYk7zTO5AHW1sLfBsazDjFiI6ejOjoBZ7VOHTaQrf3kqhfvQrz7wzEPfe0fWxw1ULWBwxaBeE3XP49L6aKdG0lpUufrYiIiEjpKc61lXr8VgLV/Ww9fk+qx6+IOZxOvvwYKFoxTPtpP5Pv17+FB8fOGIz/Pouk0wYRwW7E3udL0Lmi54GT2bgZZyHD9u/knUNg4Z0+PPtdFuO+zaJJDTdW3ONjL/oCPH29J+k5BsOXHSMl06BLPXdi7/PGu4qtYOvrYWH5HzlM+D6L9GyDkGoWejSqwrN9ffCynoYMmL8xgzM5BpN+OM2kH87Pt2t9d74f7AdAeo7Bv77M5K80Kz5VTtO0ljv/7eND/xYFF30Lek9Wx2ez5++z7Pn7LFdNTXJ42ZhQyEVkUf8fJCIiIiIiTqUVvyVUkVYufL/rKIPnbqJ5iD9fPlH6K3BEpBisuZC4zlYYqRoE9TuDm/vl9yuOoq74jXkH6rY9//zQZljxiPbTftqvsu9X1ufSil8pJn22IiIiIqVHK35NJtDXtuI3VT1+RfIrj0JsnviVtl6jF94UyT8UekyB5r1L7zz1O9uOe/HNl+wsttdb9XfMWrMxfPui7SZNBfYZ1X7aT/tViv3K+lz1OxfwmoiIiIiIVDS6uVslEOhz7uZuavUg4ih+JcxoYVsdu2yo7feMFmVzV/r4lbY73l9cjE07Yttemud0c4d2Qwt58dxdmXpMzl/gdnO3FaEvHKf9tJ/2q3z7lfccRURERESkQlLhtxII9LUVftOzc8k+W8S+fiKVXXkWYq25tpW+Ba6QO7ctdoxtXGnIyYTfFtkee/g6vuYfCv0+KnyFcfPettf9Q7Sf9tN+lXm/8p6jiIiIiIhUOOrxW0IVqVeZ1WrQ6JkvMQzY+MzN1Knm7dT5iDidNde2svdyrRBGbC+dlWtF7blbWn0x174AP063ta54ZB0c/aP4rSxK2gJD+2k/7eda+5X3HK9ARbq2ktKlz1ZERESk9BTn2kqF3xKqaBewES98Q8qZHFb/50aaBFVz9nREnKu8C7Hbl9paSVzOXR9Ay75Xdq4jv8F73cDIhf7/hWa3X9nxREQqiIp2bSWlR5+tiIiISOkpzrWVWj1UEvY+v7rBm4hthVppjrucqkGlO64wuTnw2b9tRd/md6joKyIiIiIiIiKFUuG3kgjw9QQg5YwKv1LBWXNtK3K3L7X9Lq2+t3nOZsOeNUUbe6WF2Dz1O9taR1zuXPU7X9l51s2EpN/Apzr849UrO5aIiIiIiIiIVGpVnD0BKR3Vz93g7eSZbCfPROQS4lfaboJ2Ye9d/1DbXeRL44ZBh7bAZ4/C0d8vP9a/7pUXYvO4udvudL94YOFjrLmQfhyqlbDYfOxP+H6K7XGPyVC1TsmOIyIiIiIiIiKmoBW/lUReq4dUrfiViip+pa0wevEN19KO2LbHryz5sXMyYfUEeP9mW9HXtyZ0+jdgOfdTgB6TS/dGRV6F9NWpFmJb7XvmOCzoC1mnin9sqxVWPga5WdA4Clr1v7K5ioiIiIiIiEilpxW/lURgXquHDK34lQrImmtb6UtB95I0AAvEjoGmPQsvxhZ2h/kDG2x9b//ebRvXoi/cNgX8akFYx/wrjPME1iulcIBhwHcv2x63H2brv3vhPFMS4f1bbG0aFg+CexeBu0fRj7/pfTi4HjyrQq8ZYCmkmC0iIiIiIiIico4Kv5VEQN7N3bTit3IorMjpqhLXFVx8tTMg7RDs/Q6aROV/uaAWEdVCIKQV/PmNbf+qwdDrNVvxOE/z3rbnF76Xv8yF35fBty/BP5eWTr7dq+GvTVDFB24cBdWCHV+v0RDuWwzzesHetbDycYh5u2gF3JOJsGai7XHURAgMK505i4iIiIiIiEilpsJvJRF4rsdvSoYKvy7vSvrgVtSC8enkoo1b0BdqNYHglhDUAoJbwanDtkLpxauFTx2x/QBE3AfRL9tuenYxN3cIv+H8c/9Q+OMz2LPa9l5daZ9fw4DvXrI97vBg/qJvnrpt4e558PEA+HUhBNSFm569/LE/fwJy0qH+9dBu6JXNVURERERERERMQ4XfSqJ6XqsH3dzNteX1wb24yJnXB7ffR4UXf8v6xmlXwjuwiAMNOP6n7WfHsqLt4lsTer9Z9AJ3zUZw3f2weS6seR4eiL2y1gk7V8GRX8HDD64fcemxV0dDr9fh88fhf9Nsn0+7Bwofv20h7PsOqnjD7TPBTW3ZRURERERERKRoVEWoJAJ81erB5V22Dy62PrjW3Pwvl+WN065Uwv/gi5GXGWQB/7rwnz/gn8tsLQ1a3AX+V13++Gf+tq3cLY6uT9uKqQfXw+5virfvhaxW+O4V2+OOj9j6Cl9O20HQdbTt8RdPwq6vCh53Kgm+Hmt73G0s1Gpc8nmKiIiIiIiIiOloxW8lEagev66vqH1wF9xtuzFZFS9w97TdJGzje1zRjdPKQmYarJkAv3xoe+5TAzJO2ObjMNdzq217TIaAUNtP43N9frcvhWVFaG9Q1FYSefxDocNwWDcT1r4IjW8p2Wra35fD0XjwCoDOjxZ9v25jbZ/l1v/CkiEweBVc1c5xzJejIDMVQiKgUzGOLSIiIiIiIiKCCr+VRuC5Vg+p6vHruopavNy7tpgHPlcwTlzn2Ou2NBTWU3j3Gltv2rS/bOPaDoFbXoB93xfSjmJywe0oqgYVbR5FHXehLv+BzfMgebutgNuyb/H2zz0L30+2Pe78aMH9hQtjsUCvGbZVvXvWwMJ+MCTW9j6eTobju+GPz8GtCtwxC9z1v2oRERERERERKZ4K0erhrbfeokGDBnh7exMZGcnGjRsvOX7JkiU0bdoUb29vWrZsyZdffunwumEYjB8/npCQEHx8fIiKimL37t0OY3r37k29evXw9vYmJCSE+++/n8OHL7XasmKrfq7Vw+mss2SftTp5NlIiRS1ethkM3cbBDU/aVoI2uqlo+xV3VezlxK+EGS1gfi/bqtz5veD15jC3Fyy4y1b0rd4ABn0Ot88Ab39bcXfEDhi0Cu76wPZ7xPbCexDX72wrDFNYD95zLSJKcoM23xrQ+XHb429fgtxi/qPJ9iXw925bwTfy4eKf390D7p5vW9F75m94u+P59/KHcwXlpr1sN7oTERERERERESkmpxd+Fy1axMiRI5kwYQJbtmyhdevWREdHc/To0QLHr1u3jgEDBjB06FC2bt1KTEwMMTEx7Nixwz5m6tSpzJw5k9mzZ7Nhwwb8/PyIjo4mMzPTPqZ79+4sXryYXbt2sWzZMvbu3UvfvsVc8VeBVPP2sN+fSqt+XZS9yFmYc0XOXq9Bt9Fw83iIfhm6XK5/7jklWRVbmMJ6Cp9KgsQfbY87/gseWQfhNzqOcXO3rTxu2df2+1LtJ9zcbTenA/IXfy9oEVHSFhYdHwG/2nAyAbb+X9H3y805X5y9/glbUbskvKpC+wdtj42Cejd/5tz+zCIiIiIiIiLisiyGYRTUGLTcREZG0r59e2bNmgWA1WolLCyMxx57jDFjxuQb379/f9LT01m1apV9W8eOHYmIiGD27NkYhkFoaChPPvkko0aNAiA1NZWgoCDmzZvHPffcU+A8Vq5cSUxMDFlZWXh4eFx23mlpaQQEBJCamoq/fwmLPqWs9fPfkJqRw5qRN9K4TjVnT0dKYsv/wcqC+rmeK3L2+yj/6lhrrm3lbdoRCu7za7EVlEdsL50ev/bzXWKFvF9teHJX6fUUjl9ZQIuIuoW3iCiO9bNtx64WAo9vBQ+fy++zeZ6tlYVfbXjiV/D0K9m5L/telvJnJyJSgVXEayspHfpsRUREREpPca6tnLriNzs7m82bNxMVFWXf5ubmRlRUFHFxcQXuExcX5zAeIDo62j4+ISGBpKQkhzEBAQFERkYWeswTJ06wYMECOnfuXGjRNysri7S0NIefiibQVzd4c3kn9tl+u13059A/tOCiL1xmVSyAAbe8WHqFw8vehA5IP2YbV1qK2yKiONoNgYAwOHXk3E3yLuNsFvwwzfa4y8iSF32h6Df0K833UkRERERERERMwamF3+PHj5Obm0tQkONX0IOCgkhKSipwn6SkpEuOz/tdlGOOHj0aPz8/atasyYEDB/jss88KneukSZMICAiw/4SFhRUtZDkK9FHh16VlpsKm922P7/qweEXO5r1thWH/EMftlnP/ie//sfTmWdRewaXdU7g4LSKKo4oXdDv37YKfXrd9Dpeyeb6tf3G1EGj3wJWd21nvpYiIiIiIiIhUek7v8etMTz31FFu3buWbb77B3d2dgQMHUljni7Fjx5Kammr/OXjwYPlM0poLCT/C9qW239YC+oCeE+jrCcDJM9nlMzcpXb98CFlpULspNOtV/CJnQatiBywCLLB5Lvy2uHTmWdRewaXZU7istboHal0NGSdh3azCx+VkwI/TbY9vHAUe3ld23sr4XoqIiIiIiIhIhVDFmSevVasW7u7uJCc7rmZLTk4mODi4wH2Cg4MvOT7vd3JyMiEhIQ5jIiIi8p2/Vq1aXH311TRr1oywsDDWr19Pp06d8p3Xy8sLLy+vYme8IgX2NQ21fa2/gNWfea0edHM3F5STAXFv2x5fPwLcSvhvMnmrYi/U9Wn4YQp8PgJCWkPta65kppB5uTYn5/rS1u98ZecpT+5V4KZnbTesi3sLOgyHqrXzj9v0AZxOgoB6cN3AKz9v3g39Ltef2ZXeSxERERERERGpEJy64tfT05O2bduydu1a+zar1cratWsLLL4CdOrUyWE8wOrVq+3jw8PDCQ4OdhiTlpbGhg0bCj1m3nnB1su3QohfaStCXdz/M+2IbXv8yny7qNWDC9u2ANKP2nrNtuxbusfuOhrCu0JOuu3PTnZ6yY/1xypYMuiCDRf3FD73vMdk17sZWbPeEHqd7X3KW9V7oazT8NNrtsddn4Yqnld+zkv2Z3bh91JEREREREREnM7prR5GjhzJnDlzmD9/Pn/88QePPPII6enpDBkyBICBAwcyduxY+/gnnniC2NhYpk+fzs6dO5k4cSK//PILjz76KAAWi4URI0bw0ksvsXLlSrZv387AgQMJDQ0lJiYGgA0bNjBr1iy2bdtGYmIi3377LQMGDKBRo0aXLA6XG2uubaVvgSsAz22LHZOv7UPAuVYPKRlq9eBScs/CzzNtjzs/Bu4F32CwxNzc4a73oWowHNsJq/4DhbQ0uaT4z2xFX2sOXNsH+s7L31P4Ujehq+gsFrh5vO3xLx9AygHH1ze+C2f+hhoNofWA0jtvYf2ZXfm9FBERERERERGnc2qrB4D+/ftz7Ngxxo8fT1JSEhEREcTGxtpvznbgwAHcLvjae+fOnVm4cCHPPvss48aNo0mTJqxYsYIWLVrYxzz99NOkp6czfPhwUlJS6NKlC7GxsXh72/px+vr6snz5ciZMmEB6ejohISH06NGDZ599tvzbORQkcV3+lb4ODEg7ZBt3wdf6q59r9XBSK35dy++fQkoi+NaC6+4vm3NUrQN3z4V5veC3RVCvE7QbUrw5Lh0KRi606At93rW1R2je2/bn8HSyrQ9t/c6uvTq1YXdocIPtZnjfT4GYt2zbM1PPF+e7jrFlL03Ne0PTnpXrvRQRERERERERp7IYhd3NTC4pLS2NgIAAUlNT8ff3L92Db18Ky4ZeftxdHzi0Bfh061/8Z9GvdGlci/8+GFm6c5KyYRjwzvVw9Hdbj9kbnyrb8/00A9ZMAHcveHC1refv5WxfCsuH24q+re6BmLcrd0Hy4Cb4IAqw2LK6e8KeNfDrx1DrGvhXXOXOLyLiJGV6bSVOpc9WREREpPQU59rK6a0epABVg0o0LtBHrR5czp9f24q+nlWh/YNlf77Oj8PVt0FuFiweZFvJeim/LoLlw2xF34j7Kn/RFyCsPYS2AQxY8YjtH2F+/dj2WpNbKn9+EREREREREakUVPitiOp3tvX3zHezpzwW8K9rG3eBAF/d3K3MWHMh4Ufb6teEH/P1Vy6xn163/W73APhUL51jXoqbG/R5BwLrwckEWPGvwvv9blsInz4EhhXaDITes8xR9IxfCYe3FPxa3FsF3lhRRERERERERKSicXqPXymAmzv0mAKLB2Ir/l5YmDtXDO4xOV8RLtDHVvhNVeG3dMWvtN1s78K+y/6hts/oSm68lbgODq63tRLo9O8rn2dR+VSHu+fDh9GwcxWsfxsiH3bsL3tiH3z+BGBA2yHQ8zVb0biys99Y8RJix9j68ZqhCC4iIiIiIiIiLkuF34qqeW/o91H+gqNfbeg5vcCCY3VfW6uHU1lnycm14uFugkJdWYtfea4Af9Gq2LQjtu39Pip58ffH12y/I+6FasFXNM1iq9sGol+BL0fBN8/a5nLmeP5x7R+E26aZo+gLJb6xooiIiIiIiIhIRWOSao6Lat4bRuyAQasgqIVt2/VPFFpo9D+34hcgNUOrfq+YffVnQa0Qzm2LHVOytg9J22HParC42T5TZ2j/IIR1tLVyKKjoCxB+o3mKvmBb8Vya40REREREREREnMREFR0X5eZuW1nYsq/t+YG4Qoe6u1nw97Yt4laf31JQnNWfxZXX2/faPlCjYYmmd8UMK6QkXmKABWLHll4/Y1dQwhsrioiIiIiIiIhUNCr8uor6XWy/E38Gq7XQYYHn2j2kZmSXx6wqt+T4oo0r7urPv/fC75/aHnf5T/H2LU2J6+DUkUsMuILCtqsq4Y0VRUREREREREQqGhV+XUVoBHj4QsZJOLaz0GHVfW3tHk6ma8Vvgay5kPAjbF9q+13QatZDW2DpA7Y2DkVR3NWf62baVts2vgWCWxZv39Kktgb55d1YEchf/C38xooiIiIiIiIiIhWNCr+uwt0DwjrYHif+XOiwgHMrflPU4ze/+JUwowXM7wXLhtp+z2hh2261wq6vYO4/YE532LEMsIK756WP6Ve7eKs/TyXBtoW2xzeMLHGUUqG2BgXLu7Gif4jjdv/QK7uZn4iIyGW89dZbNGjQAG9vbyIjI9m4ceMlxy9ZsoSmTZvi7e1Ny5Yt+fLLLx1eNwyD8ePHExISgo+PD1FRUezevdv++v79+xk6dCjh4eH4+PjQqFEjJkyYQHa24zfHfvvtN2644Qa8vb0JCwtj6tSppRdaRERERMqMCr+uJK/dw/6fCh0SeO4Gbyln1OrBQfxKWDwwf8/etCOw+H54rTl8fI+tqO5WBVr1h4d+hLs+wLbSs5Cv/mechO1Lij6PuLcgN9t2UzVntwtQW4PCXXhjxbs+sP0esV1FXxERKTOLFi1i5MiRTJgwgS1bttC6dWuio6M5evRogePXrVvHgAEDGDp0KFu3biUmJoaYmBh27NhhHzN16lRmzpzJ7Nmz2bBhA35+fkRHR5OZmQnAzp07sVqtvPvuu/z++++8/vrrzJ49m3HjxtmPkZaWxq233kr9+vXZvHkz06ZNY+LEibz33ntl+4aIiIiIyBWzGIZhOHsSrigtLY2AgABSU1Px9/cvn5MmroO5t9lWmY7aDZb8Bbvxn+3go7hEHrupMU/eek35zKuis+baVvZe8kZtgKc/tB8CHR6CgLrnt8evhNjRjvv7h0K1EDi02fa8y0i46Tlwu8S/pWSchNdbQPZpGLAIrulR8kylJa8gDsCF/ys492dLK1xFRKScOOXaqgKJjIykffv2zJo1CwCr1UpYWBiPPfYYY8bkbz/Vv39/0tPTWbVqlX1bx44diYiIYPbs2RiGQWhoKE8++SSjRo0CIDU1laCgIObNm8c999xT4DymTZvGO++8w759+wB45513eOaZZ0hKSsLT0/ZNqDFjxrBixQp27iy4/VhWVhZZWVn252lpaYSFhZn2sxUREREpTcW5btaKX1cS2gbcvSD9GBzfXeCQ8yt+1erBLnHd5Yu+AH3fh1tecCz6QiGrP3fA0DW2gi/AT6/BkoGQnV748Te9byv61rkWro4ueZ7SpLYGIiIiTpednc3mzZuJioqyb3NzcyMqKoq4uLgC94mLi3MYDxAdHW0fn5CQQFJSksOYgIAAIiMjCz0m2IrDNWrUcDjPjTfeaC/65p1n165dnDx5ssBjTJo0iYCAAPtPWFjYJdKLiIiISFmp4uwJSDF4eMNV7SHxJ1tLgtpX5xsSeK7H70m1ejivqDcnyzpV+Gtu7hB+Q/7tUROg1tXw+ePwx+eQcgDu+Th/8Tj7DKx/x/a4y38KXK3tNM17Q9OetgL56WRbT9/6nXUDMxERkXJy/PhxcnNzCQpy7KsfFBRU6KrapKSkAscnJSXZX8/bVtiYi+3Zs4c333yTV1991eE84eHh+Y6R91r16tXzHWfs2LGMHHn+XgZ5K35FREREpHxpxa+raXC97XchN3gL9LWt+E3Vzd3OK+ubmEUMgEGfg28tOPIrzLnpfAsIay4k/AhfjIQzf0NAPbi2T8nOU5byCtst+9p+q+grIiJiKocOHaJHjx7cfffdDBs27IqO5eXlhb+/v8OPiIiIiJQ/FX5dTf1zhd/9P0MB7ZnzCr9q9XAB+03MClMKNzGr1xGGfQu1m8HpJJj7D/j6GVtv4fm94NePbeOyUmHXl5c+loiIiJhKrVq1cHd3JznZ8VtKycnJBAcHF7hPcHDwJcfn/S7KMQ8fPkz37t3p3Llzvpu2FXaeC88hIiIiIhWTCr+u5qr24OYBpw7DyYR8Lwf42Fo9pGSo1YOdmzv0mFLIi+daLvSYfOWrXKvXh6HfQJNb4WwmxM3K31s4M812M7X4lVd2LhEREak0PD09adu2LWvXrrVvs1qtrF27lk6dOhW4T6dOnRzGA6xevdo+Pjw8nODgYIcxaWlpbNiwweGYhw4dolu3brRt25a5c+fidtGNajt16sT//vc/cnLOLypYvXo111xzTYFtHkRERESk4lDh19V4+kLdNrbHievyvVw9b8Vvulb8OmhyC1Txzb+9tG9i5u0P/ReAp18hA86t0o4dY2sDISIiIgKMHDmSOXPmMH/+fP744w8eeeQR0tPTGTJkCAADBw5k7Nix9vFPPPEEsbGxTJ8+nZ07dzJx4kR++eUXHn30UQAsFgsjRozgpZdeYuXKlWzfvp2BAwcSGhpKTEwMcL7oW69ePV599VWOHTtGUlKSQw/ge++9F09PT4YOHcrvv//OokWLeOONNxx6+IqIiIhIxaSbu7mi+tfDwQ22dg/X/dPhpbybu53KOktOrhUPd9X2AYj/DM6eAf8wiHkb0o+W3U3MDm6A7PRLDDAg7ZCtcF/QDeNERETEdPr378+xY8cYP348SUlJREREEBsba7+R2oEDBxxW43bu3JmFCxfy7LPPMm7cOJo0acKKFSto0aKFfczTTz9Neno6w4cPJyUlhS5duhAbG4u3tzdgW7m7Z88e9uzZw1VXXeUwH+NcS7GAgAC++eYb/v3vf9O2bVtq1arF+PHjGT58eFm/JSIiIiJyhSyGUUCjWLmstLQ0AgICSE1NLf8bVuxZA/+9CwLrwYjtDi+dzbXS+JmvANj8bBQ1q3qV79wqqrn/sN0Qr/sz0PXpsj3X9qWwbOjlx931ge1maiIiIuLcayspU/psRUREREpPca6ttBzUFYVFgsUdUg5AykGHl6q4u1HN27aQOyVD7R4AOL7bVvS1uEHEfWV/vqpBpTtORERERERERESkmFT4dUVe1SCkte1x4s/5Xq5+rt1DyhkVfgHY8pHtd+NbIKBu2Z+vfmdb7+C8G8flYwH/urZxIiIiIiIiIiIiZUCFX1fV4Hrb7wIKv4F5N3g7k12eM6qYzmbDtoW2x20Hlc853dyhx5RzTy4u/p573mNy6fcWFhEREREREREROUeFX1dVv4vt9/78hd8An7zCr1b8sutLOHMcqgZDk+jyO2/z3tDvI/APcdzuH2rb3rx3+c1FRERERERERERMp4qzJyAlVK8jYIETe+FUElQLtr8UmNfqQT1+z7d5iLgX3Mv5j3vz3tC0JySug9PJtp6+9Ttrpa+IiIiIiIiIiJQ5FX5dlU8gBLeApO2w/ydo2df+UuC5Fb+pZm/1cDIR9n5re9zmfufMwc0dwm9wzrlFRERERERERMS01OrBleW1e0hc57C5+rkevyfN3uph2wLAgPCuUKOhs2cjIiIiIiIiIiJSblT4dWWF3OAtQK0ewJoLW/9re9xmoHPnIiIiIiIiIiIiUs5U+HVl9Trbfh/bCenH7ZsD7Td3M3Grhz1rIe0Q+FSHZrc7ezYiIiIiIiIiIiLlSoVfV+ZXE+o0tz2+YNVv4LlWD6lmXvG7Zb7td+sBUMXLuXMREREREREREREpZyr8urr651b97r+w8Gtr9XDSrCt+TyXBrq9sj9sMcu5cREREREREREREnECFX1dXP6/P7/kbvOWt+E0x683dti0EIxfCIqFOU2fPRkREREREREREpNyp8Ovq8gq/yTsg4yRwvsfvqcyznM21OmtmzmG1wpaPbI91UzcRERERERERETEpFX5dXbUgqNkEMCAxDoCAc4VfgLTMs06amJMk/gQnE8DLH67t4+zZiIiIiIiIiIiIOIUKv5VBXp/fczd4q+LuRjWvKgCkmK3P7+ZzN3Vr2Rc8/Zw7FxERERERERERESdR4bcyaNDF9jvxghu8+dlW/Z40U5/fMyfgj5W2x2rzICIiIiIiIiIiJlbF2ROQUpDX5/fIr5CZBt7+BPp4cpAMUjNMtOL3t0WQmw3BrSD0OmfPRkRERERERETElHJzc8nJMdFixFLm6emJm9uVr9dV4bcyCKgL1RvAyf1wcAM0uYVAX9uK3xSzrPg1jPNtHrTaV0RERERERESk3BmGQVJSEikpKc6eiktzc3MjPDwcT0/PKzqOCr+VRf3rbYXf/T9Bk1vsN3gzTeH3r1/g2B9QxQda9XP2bERERERERERETCev6FunTh18fX2xWCzOnpLLsVqtHD58mCNHjlCvXr0reg9V+K0s6l8P2xbY+/xW97X9i4Bpbu62ZZ7t97V9wDvAqVMRERERERERETGb3Nxce9G3Zs2azp6OS6tduzaHDx/m7NmzeHh4lPg4urlbZdHgXJ/fw1shO/18q4cME6z4zUyDHcttj9XmQURERERERESk3OX19PX19XXyTFxfXouH3NzcKzqOCr+VRWB98L8KrGfh4EaqedsWc+84lErc3r/JtRpOnmAZ2rEMcs5ArWugXkdnz0ZERERERERExLTU3uHKldZ7qMJvZWGx2Ff97tn0NW99txeALQdSGDBnPV2mfEvsjiPOnGHZ2XLBTd30PxcREREREREREREVfiuV+p0BOBH/HakXtXhISs3kkf9uqXzF3yO/2dpbuHlA63ucPRsREREREREREZEKQYXfSiS3nm3Fb2vLHrxwvKlbXqOH5z+PrxxtH6y5kPAjrB5ve960J/jVcu6cRERERERERETkiuRaDeL2/s1n2w65bPvSBg0aMGPGDGdPgyrOnoCUno2p1WlkBFLHkkKEZS8bjGYOrxvAkdRMNiacoFMjF767YvxKiB0NaYfPb9v/o217897Om5eIiIiIiIiIiJRY7I4jPP95PEdSM+3bQgK8mXB7c3q0CCn1812ul+6ECROYOHFisY+7adMm/Pz8Sjir0qMVv5XI0dNZbLA2BSDS7Y/Cx53KLPS1Ci9+JSwe6Fj0BThzwrY9fqVz5iUiIiIiIiIiIiUWu+MIj/x3i0PRF8q2femRI0fsPzNmzMDf399h26hRo+xjDcPg7NmzRTpu7dq18fX1LfX5FpcKv5VInWrebLDaVvleqvBbp5p3eU2pdFlzbSt9KWiJ/7ltsWNs40RERERERERExGkMw+BM9tki/ZzKzGHCyt8vVfFh4sp4TmXmFOl4hlG09hDBwcH2n4CAACwWi/35zp07qVatGl999RVt27bFy8uLn376ib1793LHHXcQFBRE1apVad++PWvWrHE47sWtHiwWC++//z59+vTB19eXJk2asHJl2S9eVKuHSqRDeA3e9G0NOdDGbTcenCXngo/YAgQHeNMhvIbzJnklEtflX+nrwIC0Q7Zx4TeU27RERERERERERMRRRk4uzcd/XSrHMoCktExaTvymSOPjX4jG17N0yp5jxozh1VdfpWHDhlSvXp2DBw/yj3/8g5dffhkvLy8++ugjbr/9dnbt2kW9evUKPc7zzz/P1KlTmTZtGm+++Sb33XcfiYmJ1KhRdnW6Eq34PXjwIH/99Zf9+caNGxkxYgTvvfdeqU1Mis/dzcLA3tH8bVTDx5JNS8s++2t5HUsm3N4cd7dL9y+psE4nl+44ERERERERERGRS3jhhRe45ZZbaNSoETVq1KB169Y89NBDtGjRgiZNmvDiiy/SqFGjy67gHTx4MAMGDKBx48a88sornD59mo0bN5bp3EtU+r733nsZPnw4999/P0lJSdxyyy1ce+21LFiwgKSkJMaPH1/a85Qi6tEylKT1HeHQajq6/cGW3KsBqOHnyct9WpRJI+xyUzWodMeJiIiIiIiIiEiZ8PFwJ/6F6CKN3ZhwgsFzN1123Lwh7Yv0TXYfD/cinbco2rVr5/D89OnTTJw4kS+++IIjR45w9uxZMjIyOHDgwCWP06pVK/tjPz8//P39OXr0aKnNsyAlWvG7Y8cOOnToAMDixYtp0aIF69atY8GCBcybN6805yclENwyCoDh9Y/QoYHtP4aY60Jdu+gLUL8z+IdeYoAF/OvaxomIiIiIiIiIiNNYLBZ8PasU6eeGJrUJCfCmsO+oW4CQAG9uaFK7SMezWErv2+5+fn4Oz0eNGsWnn37KK6+8wo8//si2bdto2bIl2dnZlzyOh4eHYyaLBavVWmrzLEiJCr85OTl4eXkBsGbNGnr37g1A06ZNOXKk9O+wJ8V0rvAZeHQj4676jY5u8fywM8nJkyoFbu5w6yuFvHjuP+gek23jRERERERERETEJbi7WZhwe3OAfMXfita+9Oeff2bw4MH06dOHli1bEhwczP79+509rQKVqPB77bXXMnv2bH788UdWr15Njx49ADh8+DA1a9Ys1QlKCZzYB1jgbCYRvzzNJ54v8dGpB0nesMTZM7tyef9iY7noj65/KPT7CJr3Lv85iYiIiIiIiIjIFenRIoR3/tmG4ABvh+3BAd688882Feab7E2aNGH58uVs27aNX3/9lXvvvbfMV+6WVIl6/E6ZMoU+ffowbdo0Bg0aROvWrQFYuXKlvQWEOEn8SlgyGNv9Ds8L5gSWr4ZBNS/XLo6uf8f2+4YnIbyr7UZuVYNsq5y10ldERERERERExGX1aBHCLc2D2ZhwgqOnMqlTzZsO4TUqxErfPK+99hoPPPAAnTt3platWowePZq0tDRnT6tAFsMwjMsPyy83N5e0tDSqV69u37Z//358fX2pU6dOqU2wokpLSyMgIIDU1FT8/f2dPR0bay7MaAFphwt+GXDzrwsjtrtmkfTQZphzE7h5wH9+h2q6iZuIiEhlUSGvraRU6LMVERExh8zMTBISEggPD8fb2/vyO0ihLvVeFufaqkStHjIyMsjKyrIXfRMTE5kxYwa7du0yRdG3wkpcV2jRF8592GmHbONc0frZtt8t7lLRV0RERERERERE5BJKVPi94447+OijjwBISUkhMjKS6dOnExMTwzvvvFOqE5RiOJ1cuuMqkrQj8Pty2+OODzt3LiIiIiIiIiIiIhVciQq/W7Zs4YYbbgBg6dKlBAUFkZiYyEcffcTMmTNLdYJSDFWLuAq2qOMqkk3vg/Us1OsModc5ezYiIiIiIiIiIiIVWokKv2fOnKFatWoAfPPNN9x55524ubnRsWNHEhMTS3WCUgz1O4N/KFBww2urAUnUJDesU/nO60rlZMDmubbHHR9x7lxERERERERERERcQIkKv40bN2bFihUcPHiQr7/+mltvvRWAo0eP6oYNzuTmDj2mnHuSv/hrscCE7PvZduhU+c7rSm1fAmf+hsB60LSns2cjIiIiIiIiIiJS4ZWo8Dt+/HhGjRpFgwYN6NChA5062VaQfvPNN1x3nb6G71TNe0O/j8A/JN9LViwcNOrw7U4X6vFrGLD+XN/oDsNtxW0RERERERERERG5pCol2alv37506dKFI0eO0Lp1a/v2m2++mT59+pTa5KSEmve2rYxNXGe7kVvVINj4Hu5/rGSyxxzGxDfnqeimzp5l0ST8AEfjwcMPrrvf2bMRERERERERERFxCSUq/AIEBwcTHBzMX3/9BcBVV11Fhw4dSm1icoXc3CH8hvPPa12Ndd8PtMpKoPPxJfx1MpKrqvs6b35Flbfa97r7wCfQqVMRERERERERERFxFSVq9WC1WnnhhRcICAigfv361K9fn8DAQF588UWsVmtpz1FKQ7Ug3KJfAuDJKkvYtGWLkydUBH/vhT+/tj2OfNi5cxEREREREREREXEhJVrx+8wzz/DBBx8wefJkrr/+egB++uknJk6cSGZmJi+//HKpTlJKyXX3c+h/86mb8gtNNj0HN31ru+NbRbXhXcCAJtFQs5GzZyMiIiIiIiIiImXNmuvYvrR+Z93zqYRKtOJ3/vz5vP/++zzyyCO0atWKVq1a8a9//Ys5c+Ywb968Up6ilBqLhZzbXiPT8KBF5hayNv/X2TMqXEYKbD03v46POHUqIiIiIiIiIiJSDuJXwowWML8XLBtq+z2jhW17GbBYLJf8mThx4hUde8WKFaU215IoUeH3xIkTNG2a/+ZgTZs25cSJE1c8KSk79a9uxVzPewCwfPMsnD7m5BkVYut/IScdajeDht2cPRsRERERERERESlL8Sth8UBIO+y4Pe2IbXsZFH+PHDli/5kxYwb+/v4O20aNGlXq5yxPJSr8tm7dmlmzZuXbPmvWLFq1anXFk5KyY7FYONZiGL9b6+OZnQKxY5w9pfxyz55r84BttW9FbkchIiIiIiIiIiL5GQZkpxftJzMNvnoaMAo6kO1X7GjbuKIczyjoOPkFBwfbfwICArBYLA7bPvnkE5o1a4a3tzdNmzbl7bfftu+bnZ3No48+SkhICN7e3tSvX59JkyYB0KBBAwD69OmDxWKxPy9vJerxO3XqVHr27MmaNWvo1KkTAHFxcRw8eJAvv/yyVCcopa9bs1BGrx/GZ17jcd+xFFr1g6ujnT2t83Z9CakHwKeGbW4iIiIiIiIiIuJacs7AK6GldDDDthJ4cljRho87DJ5+V3TGBQsWMH78eGbNmsV1113H1q1bGTZsGH5+fgwaNIiZM2eycuVKFi9eTL169Th48CAHDx4EYNOmTdSpU4e5c+fSo0cP3N2d06O4RCt+u3btyp9//kmfPn1ISUkhJSWFO++8k99//53/+7//K+05SimLbFiDfR5NeP/sbbYNq0ZC1innTupCG2bbfrcbAh4+zp2LiIiIiIiIiIiYzoQJE5g+fTp33nkn4eHh3HnnnfznP//h3Xdt31I/cOAATZo0oUuXLtSvX58uXbowYMAAAGrXrg1AYGAgwcHB9uflrUQrfgFCQ0N5+eWXHbb9+uuvfPDBB7z33ntXPDEpO15V3LmhSS1e/70v9/htJSDtL1j7IvxjqrOnBoe3QeLP4FYF2j/o7NmIiIiIiIiIiEhJePjaVt4WReI6WND38uPuWwr1Oxft3FcgPT2dvXv3MnToUIYNG2bffvbsWQICAgAYPHgwt9xyC9dccw09evSgV69e3HrrrVd03tJW4sKvuLabmwXx9e/JTPf6Fy9kPQsb34OWfSGsg3Mnlrfa99o+4F9aXwcQEREREREREZFyZbEUvd1Co5tsdaC0IxTc59die73RTeBW9m0TTp8+DcCcOXOIjIx0eC2vbUObNm1ISEjgq6++Ys2aNfTr14+oqCiWLl1a5vMrqhK1ehDX1/2aOgB8dLQhGc37AwasfAzOZjtvUqeSYfu5/zgiH3HePEREREREREREpPy4uUOPKeeeWC568dzzHpPLpegLEBQURGhoKPv27aNx48YOP+Hh4fZx/v7+9O/fnzlz5rBo0SKWLVvGiRMnAPDw8CA3N7dc5lsYFX5NqnY1L1qHBQIQW/dR8K0Fx3bCj9Mh4UdbATbhR7CW4x/QXz4Aaw6ERcJVbcvvvCIiIiIVwFtvvUWDBg3w9vYmMjKSjRs3XnL8kiVLaNq0Kd7e3rRs2TLfTZYNw2D8+PGEhITg4+NDVFQUu3fvdhjz8ssv07lzZ3x9fQkMDCzwPBaLJd/PJ598ckVZRURERPJp3hv6fQT+IY7b/UNt25v3LtfpPP/880yaNImZM2fy559/sn37dubOnctrr70GwGuvvcbHH3/Mzp07+fPPP1myZAnBwcH2a6oGDRqwdu1akpKSOHnyZLnOPU+xWj3ceeedl3w9JSXlSuYi5ezmpnX49WAKX+3Nps9tU2DZUPhhMvxwwSD/UNu/uJT1f1w5mbDpA9vjyIfL9lwiIiIiFcyiRYsYOXIks2fPJjIykhkzZhAdHc2uXbuoU6dOvvHr1q1jwIABTJo0iV69erFw4UJiYmLYsmULLVq0AGDq1KnMnDmT+fPnEx4eznPPPUd0dDTx8fF4e3sDkJ2dzd13302nTp344IMPCp1f3h2p8xRWJBYRERG5Is17Q9Oetp6/p5OhapCtp285rfS90IMPPoivry/Tpk3jqaeews/Pj5YtWzJixAgAqlWrxtSpU9m9ezfu7u60b9+eL7/8Ejc32zrb6dOnM3LkSObMmUPdunXZv39/uWewGIZRUOOMAg0ZMqRI4+bOnVviCbmKtLQ0AgICSE1Nxd/f39nTKZEdh1Lp9eZP+Hq6s61vBp7LBxcw6txy+rL6lxVrru0/5h3LYPNcqFYXRvwG7mo/LSIiYiaV4drqSkRGRtK+fXtmzZoFgNVqJSwsjMcee4wxY8bkG9+/f3/S09NZtWqVfVvHjh2JiIhg9uzZGIZBaGgoTz75JKNGjQIgNTWVoKAg5s2bxz333ONwvHnz5jFixIgCF3JYLBY+/fRTYmJiSpTN7J+tiIiIWWRmZpKQkEB4eLj9H5mlZC71Xhbn2qpY1TUzFHTN5NpQf4L8vTiWloE1Nv9fKGwMwAKxY2z/4lKa/8ISvxJiR0PaBXd4zD4Fu74s9+X7IiIiIs6SnZ3N5s2bGTt2rH2bm5sbUVFRxMXFFbhPXFwcI0eOdNgWHR3NihUrAEhISCApKYmoqCj76wEBAURGRhIXF5ev8Hs5//73v3nwwQdp2LAhDz/8MEOGDMFiubj/nk1WVhZZWVn252lpacU6l4iIiIiUDvX4NTGLxcJNTYPo4LYT7zNJlxhpQNoh28rc0hK/EhYPdCz6AmSdsm2PX1l65xIRERGpwI4fP05ubi5BQUEO24OCgkhKKvgaLSkp6ZLj834X55iFeeGFF1i8eDGrV6/mrrvu4l//+hdvvvlmoeMnTZpEQECA/ScsLKxY5xMRERGR0qHv05vczU3rsPKXlKINPp1cOie15tpW+lJQl5EyXGEsIiIiIsX23HPP2R9fd911pKenM23aNB5//PECx48dO9ZhNXJaWpqKvyIiIiJOoBW/Jnd941qcdK9etMFVgy4/pigS1+Vf6eugDFYYi4iIiFRQtWrVwt3dneRkx39kT05OJjg4uMB9goODLzk+73dxjllUkZGR/PXXXw7tHC7k5eWFv7+/w4+IiIiIlD8Vfk3Ox9Mdz4ZdOGzUKHD9rZ27J9S6pnROWtSVw6W1wlhERESkAvP09KRt27asXbvWvs1qtbJ27Vo6depU4D6dOnVyGA+wevVq+/jw8HCCg4MdxqSlpbFhw4ZCj1lU27Zto3r16nh5eV3RcURERKRyslqtzp6CyzOMS1bpikytHoTuzUJ4fvdAZnvOACwU2IIhNxvmRsO9S6BW4ys74bGdRRtXWiuMRURERCq4kSNHMmjQINq1a0eHDh2YMWMG6enpDBkyBICBAwdSt25dJk2aBMATTzxB165dmT59Oj179uSTTz7hl19+4b333gNs93IYMWIEL730Ek2aNCE8PJznnnuO0NBQYmJi7Oc9cOAAJ06c4MCBA+Tm5rJt2zYAGjduTNWqVfn8889JTk6mY8eOeHt7s3r1al555RVGjRpVru+PiIiIVHyenp64ublx+PBhateujaenZ6E3g5XCGYbBsWPHsFgseHh4XNGxVPgVbmpah2dXdOBfOSOYVX0x7qcvaMPgXxc6PQrr34ET++CDKOi/ABpcX/wTZZyEL0bBjqWXGWgB/1Co37n45xARERFxQf379+fYsWOMHz+epKQkIiIiiI2Ntd+c7cCBA7i5nf+yXufOnVm4cCHPPvss48aNo0mTJqxYsYIWLVrYxzz99NOkp6czfPhwUlJS6NKlC7GxsXh7e9vHjB8/nvnz59ufX3fddQB89913dOvWDQ8PD9566y3+85//YBgGjRs35rXXXmPYsGFl/ZaIiIiIi3FzcyM8PJwjR45w+PClWnzK5VgsFq666irc3a/s3lcWo7TWDl+Bt956i2nTppGUlETr1q1588036dChQ6HjlyxZwnPPPcf+/ftp0qQJU6ZM4R//+If9dcMwmDBhAnPmzCElJYXrr7+ed955hyZNmgCwf/9+XnzxRb799luSkpIIDQ3ln//8J8888wyenp5FmnNaWhoBAQGkpqZWir5lt73xI38cSeP1fi3oU/2Arc1C1SBb8dXNHU4fhY8HwKFfwM0D7pgFre8p+gn2fgcr/gW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      "text/plain": [
       "<Figure size 1700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def res_plot(plotdata, xlabel='epoch', legend=['Train','Test'], title='Loss'):\n",
    "    fig, axes = plt.subplots(1,2, figsize = (17,6))\n",
    "    axes[0].set_ylabel(title)\n",
    "    epochs = len(plotdata[0])-1\n",
    "    for index, ax in enumerate(axes):\n",
    "        n = 10 if index==1 else 0\n",
    "        x = range(epochs-n+1, epochs+1) if index==1 else range(epochs+1)\n",
    "        title = f\"{title} ({n} last values)\" if index==1 else title\n",
    "#         ax.set_xlabel(xlabel)\n",
    "        ax.set_title(title)\n",
    "        ax.plot(x, plotdata[0][-n:], '-o', label=legend[0])\n",
    "        ax.plot(x, plotdata[1][-n:], '-o', label=legend[1])\n",
    "        p = 7 if 'loss'in title.lower() else 2\n",
    "        if index==1:\n",
    "            for i,j in list(zip(x,plotdata[1][-n:]))+list(zip(x, plotdata[0][-n:])):\n",
    "                ax.annotate(f\"{j:.{p}f}\", xy=(i,j), rotation=90, va='top')\n",
    "        ax.annotate(f\"{plotdata[0][-1]:.{p}f}\", xy=(len(plotdata[0])-1,plotdata[0][-1]),\n",
    "                   ha=\"right\")\n",
    "        ax.annotate(f\"{plotdata[1][-1]:.{p}f}\", xy=(len(plotdata[1])-1,plotdata[1][-1]),\n",
    "                   ha=\"right\")\n",
    "        ax.legend()\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "res_plot((logs['trainlosses'], logs['testlosses']), title='Loss')\n",
    "res_plot((logs['trainaccs'], logs['testaccs']), title='Accuracy' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a3e767",
   "metadata": {},
   "source": [
    "## 每类预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4dc54dfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy of plane: 76%\n",
      "Acuracy of car  : 88%\n",
      "Acuracy of bird : 59%\n",
      "Acuracy of cat  : 63%\n",
      "Acuracy of deer : 69%\n",
      "Acuracy of dog  : 57%\n",
      "Acuracy of frog : 78%\n",
      "Acuracy of horse: 75%\n",
      "Acuracy of ship : 80%\n",
      "Acuracy of truck: 79%\n"
     ]
    }
   ],
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "\n",
    "class_correct = [0.] * 10\n",
    "class_total = [0.] * 10 \n",
    "y_test, y_pred = [] , []\n",
    "X_test = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for _X, images, labels in testloader:\n",
    "        X_test.extend([_ for _ in images])\n",
    "        outputs = model(images.to(device))\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        predicted = predicted.cpu()\n",
    "        c = (predicted == labels).squeeze()\n",
    "        for i, label in enumerate(labels):\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "        y_pred.extend(predicted.numpy())\n",
    "        y_test.extend(labels.cpu().numpy())\n",
    "\n",
    "for i in range(10):\n",
    "    print(f\"Acuracy of {classes[i]:5s}: {100*class_correct[i]/class_total[i]:2.0f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d4ea182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is : 0.7237\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.75      0.76      0.76      1000\n",
      "         car       0.79      0.88      0.83      1000\n",
      "        bird       0.72      0.59      0.65      1000\n",
      "         cat       0.51      0.63      0.57      1000\n",
      "        deer       0.69      0.69      0.69      1000\n",
      "         dog       0.61      0.57      0.59      1000\n",
      "        frog       0.77      0.78      0.77      1000\n",
      "       horse       0.79      0.75      0.77      1000\n",
      "        ship       0.85      0.80      0.82      1000\n",
      "       truck       0.80      0.79      0.79      1000\n",
      "\n",
      "    accuracy                           0.72     10000\n",
      "   macro avg       0.73      0.72      0.72     10000\n",
      "weighted avg       0.73      0.72      0.72     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "ac = accuracy_score(y_test, y_pred)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cr = classification_report(y_test, y_pred, target_names=classes)\n",
    "print(\"Accuracy is :\",ac) \n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de78640d",
   "metadata": {},
   "source": [
    "### 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "442b866b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 混淆矩阵\n",
    "\n",
    "import seaborn as sns, pandas as pd\n",
    "\n",
    "labels = pd.DataFrame(cm).applymap(lambda v: f\"{v}\" if v!=0 else f\"\")\n",
    "plt.figure(figsize=(7,5))\n",
    "sns.heatmap(cm, annot=labels, fmt='s', xticklabels=classes, yticklabels=classes, linewidths=0.1 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4158f0",
   "metadata": {},
   "source": [
    "## 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a4b025a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = f'cifar10[1-78%].h5'\n",
    "torch.save(model, model_name)\n",
    "!cp {model_name} cifar10.h5\n",
    "#!ls -sh cifar10.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d715e8bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40de6ce6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "744e02f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "794d9a06",
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}