torchvision的用法實例.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据下载(train和val是在不同文件夹中的)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 代码来自：\n",
    "# https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-10-18 01:49:53--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
      "Resolving download.pytorch.org (download.pytorch.org)... 99.86.230.63, 99.86.230.13, 99.86.230.94, ...\n",
      "Connecting to download.pytorch.org (download.pytorch.org)|99.86.230.63|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 47286322 (45M) [application/zip]\n",
      "Saving to: ‘hymenoptera_data.zip’\n",
      "\n",
      "hymenoptera_data.zi 100%[===================>]  45.10M  54.9MB/s    in 0.8s    \n",
      "\n",
      "2019-10-18 01:49:54 (54.9 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 数据下载\n",
    "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  hymenoptera_data.zip\n",
      "   creating: hymenoptera_data/\n",
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     ]
    }
   ],
   "source": [
    "!unzip hymenoptera_data.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working/hymenoptera_data\r\n"
     ]
    }
   ],
   "source": [
    "!cd hymenoptera_data/;pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 179 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据强化设置（注释完毕）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n",
      "Wall time: 48.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "\n",
    "# 下面是数据强化\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "#     三個通道\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "# data_dir = 'data/hymenoptera_data'\n",
    "data_dir='/kaggle/working/hymenoptera_data'\n",
    "\n",
    "\n",
    "\n",
    "#读取图片数据\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}# 既有训练集又有训练集\n",
    "# 注:这里的train和val其实就是文件夹的名字\n",
    "# 因为确定了文件夹,所以train和val的比例也是确定的\n",
    "\n",
    "\n",
    "# 读取类别标签数据\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "\n",
    "\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}#数据长度\n",
    "class_names = image_datasets['train'].classes#获取类别\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': Dataset ImageFolder\n",
       "     Number of datapoints: 244\n",
       "     Root location: /kaggle/working/hymenoptera_data/train\n",
       "     StandardTransform\n",
       " Transform: Compose(\n",
       "                RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BILINEAR)\n",
       "                RandomHorizontalFlip(p=0.5)\n",
       "                ToTensor()\n",
       "                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "            ), 'val': Dataset ImageFolder\n",
       "     Number of datapoints: 153\n",
       "     Root location: /kaggle/working/hymenoptera_data/val\n",
       "     StandardTransform\n",
       " Transform: Compose(\n",
       "                Resize(size=256, interpolation=PIL.Image.BILINEAR)\n",
       "                CenterCrop(size=(224, 224))\n",
       "                ToTensor()\n",
       "                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "            )}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图片显示(大部分註釋完)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize a few images\n",
    "# Let’s visualize a few training images so as to understand the data augmentations.\n",
    "\n",
    "\n",
    "# 显示图片\n",
    "# inp输入的是什么?\n",
    "# ｍｅａｎ和ｓｔｄ后面的是什么意思？\n",
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))#更換張量中每個元素的位置,詳細可以參考:\n",
    "#     https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html\n",
    "    mean = np.array([0.485, 0.456, 0.406])#RGB的mean的設置\n",
    "    std = np.array([0.229, 0.224, 0.225])#RGB的std的設置\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)#clip函數中是最小值和最大值\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "#☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)#网格\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])#图像名称绘制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练函数定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 21.2 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):#遍历epoch\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:# 每次epoch包含train和val两个结果\n",
    "            if phase == 'train':\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)#输入数据放入GPU\n",
    "                labels = labels.to(device)#输入数据放入GPU\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()#梯度初始化为0\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()#误差反向传播\n",
    "                        optimizer.step()#梯度计算\n",
    "\n",
    "                # statistics\n",
    "                #误差统计                 \n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "#------------------外层for循环-------------------------------------------------------------------------------                \n",
    "            if phase == 'train':\n",
    "                scheduler.step()#周期学习率   \n",
    "# 下面是损失计算                \n",
    "#-------------------------------------------------------------------------------------------------\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "#-------------------------------------------------------------------------------------------------\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())#这里的ｓｔａｔｅ_dict不是模型的所有數據\n",
    "#可以改成保存整個模型的數據\n",
    "# https://blog.csdn.net/strive_for_future/article/details/83240081\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)#这个是从内存中读取的最佳模型\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 15 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Visualizing the model predictions\n",
    "# Generic function to display predictions for a few images\n",
    "\n",
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型设置(註釋完畢)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /tmp/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
      "100%|██████████| 44.7M/44.7M [00:01<00:00, 27.7MB/s]\n"
     ]
    }
   ],
   "source": [
    "# Finetuning the convnet\n",
    "# Load a pretrained model and reset final fully connected layer.\n",
    "\n",
    "model_ft = models.resnet18(pretrained=True)#预训练模型\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "# Here the size of each output sample is set to 2.\n",
    "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)#对预训练模型添加输出层．\n",
    "\n",
    "model_ft = model_ft.to(device)#导入GPU\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()#交叉熵\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)#设定优化器\n",
    "\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)#周期学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练(不凍結預訓練模型的權重)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7049\n",
      "val Loss: 0.2117 Acc: 0.9020\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.4684 Acc: 0.7869\n",
      "val Loss: 0.2180 Acc: 0.9085\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.5668 Acc: 0.7705\n",
      "val Loss: 0.2431 Acc: 0.9150\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7992\n",
      "val Loss: 0.2236 Acc: 0.9412\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.3875 Acc: 0.8402\n",
      "val Loss: 0.5563 Acc: 0.8301\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.5957 Acc: 0.8074\n",
      "val Loss: 0.4598 Acc: 0.8954\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.8033\n",
      "val Loss: 0.3413 Acc: 0.8824\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.3279 Acc: 0.8648\n",
      "val Loss: 0.2939 Acc: 0.8954\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.3121 Acc: 0.8730\n",
      "val Loss: 0.2726 Acc: 0.8889\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.2859 Acc: 0.8730\n",
      "val Loss: 0.2592 Acc: 0.9216\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.2776 Acc: 0.8852\n",
      "val Loss: 0.2477 Acc: 0.9085\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.2551 Acc: 0.9098\n",
      "val Loss: 0.2277 Acc: 0.9346\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.3693 Acc: 0.8525\n",
      "val Loss: 0.2441 Acc: 0.9216\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.2643 Acc: 0.8811\n",
      "val Loss: 0.2039 Acc: 0.9346\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.3287 Acc: 0.8607\n",
      "val Loss: 0.2148 Acc: 0.9281\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.2259 Acc: 0.9098\n",
      "val Loss: 0.2122 Acc: 0.9346\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.3350 Acc: 0.8443\n",
      "val Loss: 0.2144 Acc: 0.9281\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.2912 Acc: 0.8770\n",
      "val Loss: 0.2209 Acc: 0.9216\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.3049 Acc: 0.8934\n",
      "val Loss: 0.2433 Acc: 0.9150\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.2770 Acc: 0.8770\n",
      "val Loss: 0.2348 Acc: 0.9216\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.3133 Acc: 0.8320\n",
      "val Loss: 0.2142 Acc: 0.9281\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.3126 Acc: 0.8566\n",
      "val Loss: 0.2258 Acc: 0.9281\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2654 Acc: 0.8730\n",
      "val Loss: 0.2079 Acc: 0.9346\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.2447 Acc: 0.9098\n",
      "val Loss: 0.2084 Acc: 0.9281\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.2814 Acc: 0.8852\n",
      "val Loss: 0.2029 Acc: 0.9346\n",
      "Training complete in 1m 41s\n",
      "Best val Acc: 0.941176\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate\n",
    "# It should take around 15-25 min on CPU. On GPU though, it takes less than a minute.\n",
    "\n",
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练(凍結預訓練模型的權重,效果變差):\n",
    "\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ConvNet as fixed feature extractor\n",
    "# Here, we need to freeze all the network except the final layer. We need to set requires_grad == False to \n",
    "# freeze the parameters so that the gradients are not computed in backward().\n",
    "\n",
    "# You can read more about this in the documentation here.\n",
    "\n",
    "model_conv = torchvision.models.resnet18(pretrained=True)#\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "num_ftrs = model_conv.fc.in_features\n",
    "model_conv.fc = nn.Linear(num_ftrs, 2)#由於預訓練的模型被凍結了,所以訓練僅僅針對最後一層新增的權重.\n",
    "\n",
    "model_conv = model_conv.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only parameters of final layer are being optimized as\n",
    "# opposed to before.\n",
    "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5658 Acc: 0.6926\n",
      "val Loss: 0.2156 Acc: 0.9477\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.5597 Acc: 0.7377\n",
      "val Loss: 0.1952 Acc: 0.9412\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.3994 Acc: 0.8197\n",
      "val Loss: 0.1966 Acc: 0.9281\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.4667 Acc: 0.7705\n",
      "val Loss: 0.1942 Acc: 0.9477\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.4534 Acc: 0.8279\n",
      "val Loss: 0.1930 Acc: 0.9477\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.4972 Acc: 0.7582\n",
      "val Loss: 0.1947 Acc: 0.9477\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.6185 Acc: 0.7459\n",
      "val Loss: 0.3729 Acc: 0.8693\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.3724 Acc: 0.8484\n",
      "val Loss: 0.1842 Acc: 0.9542\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.4187 Acc: 0.8115\n",
      "val Loss: 0.2145 Acc: 0.9346\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.3233 Acc: 0.8525\n",
      "val Loss: 0.1956 Acc: 0.9542\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.4171 Acc: 0.7951\n",
      "val Loss: 0.2074 Acc: 0.9216\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.3922 Acc: 0.8156\n",
      "val Loss: 0.1687 Acc: 0.9542\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.3541 Acc: 0.8607\n",
      "val Loss: 0.1681 Acc: 0.9542\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.3157 Acc: 0.8525\n",
      "val Loss: 0.2077 Acc: 0.9477\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.3682 Acc: 0.8197\n",
      "val Loss: 0.2192 Acc: 0.9281\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.3464 Acc: 0.8279\n",
      "val Loss: 0.1704 Acc: 0.9542\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.3397 Acc: 0.8566\n",
      "val Loss: 0.1873 Acc: 0.9412\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.3166 Acc: 0.8525\n",
      "val Loss: 0.1774 Acc: 0.9477\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.3877 Acc: 0.8361\n",
      "val Loss: 0.1826 Acc: 0.9477\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.3308 Acc: 0.8566\n",
      "val Loss: 0.1907 Acc: 0.9542\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.3642 Acc: 0.8484\n",
      "val Loss: 0.1861 Acc: 0.9477\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.2682 Acc: 0.8975\n",
      "val Loss: 0.1684 Acc: 0.9542\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2903 Acc: 0.8770\n",
      "val Loss: 0.1718 Acc: 0.9542\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.3946 Acc: 0.8320\n",
      "val Loss: 0.1765 Acc: 0.9542\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.3337 Acc: 0.8484\n",
      "val Loss: 0.1670 Acc: 0.9477\n",
      "Training complete in 1m 34s\n",
      "Best val Acc: 0.954248\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TfOQIHVvfYbB7ED0WIyqs+FWdZCJMLJ+hwBPA7XbT196Bv6KGfCyMjsBpk5A9PqREhuKKKtwOF/UVdfSEk2SSUVqO7WTpBZfgsHsZ6u2BVIJ0Nkdeg+rpDYx09fH571175sKdsXCVYTXLBAqCWM0SyC5S0X7IOejr3Yvi9oPqJR0ZxiRGGR2NMRoKU1IznYI5DoKeMvLDHloOv0YiPshYNIOW0rDarFSUl9N6shOnw0c+D4qIk81lKfDbWPNPX+GDn7kKTdPo2HKEPdu2UxT0suGhh3EGXFgsdiKhEOTzLFy8kJG+LgyhYdHNWPwWIokchapKMgEBi4neeJgSr4/eTBqzLmFxF0E6hceUwzAMJHSyignZ40YaT1I1u4buA63c94fnzly4VaUuw0AjGAyQHowypKUJhdLoukGh10aZz8r6Cy/iaNsh2ptb6R7NEklOfCAozTBT6TYz1zeL7vY28vksnf0pHHaJ/tEc6H/6kFCRFII+ldLiQn7yymsEgkX0DvVy2w03suWll8nlQdfhjx9MSpJA1yferpSWuJk7fzbBQIDzzzqXBect5sYb70S2Odj/1u8pmV5Bd88Y3W2jKIqOx6ui2swoVo3kYAaHRcIWcJIYzgIZiorMOAuL0AzY/HrraYE7JV756XmVooAPr5rDU+mnvtiPrk8Y7LQIFtWXUlJVxsUXfwSvp5CAZ3LmhySjmwwK3C58IseSRY14kKnwmynwOnHb5H+XjyRpTKup447HHqKkxI+MzhO3fIPDm/6ATZbR31UuCQOXRaK4xEF5hQ9foZNsepzO7nascgH+Eg/bNj6NySPjclmZPr0KRdJBh3w0D/E0NtVK7axKqmbVYNazlJZayadhsDvJ6Ogwqpo7bb5OCbgep0o8lKO4oICgU+Z9778MAAXB+csWcvWnvkj59HOID7ezYHoVM+oLMUkykkVgUhWqrUHOWnk+i6Y3smJZDQsbKhmP2yn0qEx8MQsmSVBcVsFXH/4BrmIvXYcO89IdN1FfbGXJDDeyCSbsmLioFElgURVcVoUSl5WSAg8WRUJWDULpCLfdfTd+n5l0RsdqslJY3Eh19TQAgkELjgIneUOQjhkMjQ2CAv6iEvS8xvQllUxfXI2IZQhH46fN1ykBt8ARYP2qRSh4qAjWkervQkIQcFlYt2Y9NYvOI3nsaUZH+jASUSKJCCVOMJllNBnmTaul0KngLGjA6i4hRR6fPE7XSHYyB8HZK1dxy5M/xWqyMtR2FGM0zJI16/AVzmD67DlYVAlJ+lPdzeugSRKFPjfJnExay2K1mfAUl0EqzNKzGigsrEY2DGRJJjM+TC6dBiGTSutIpizFNX5KAn6KbHaMjMbY0DjewgJUWUFVZRrOOosKX/C0+Tol4Po8gtBolKXLGzCZBb6yEnQJxlM5Smxe+o++RSZvQ3VaWLJiPkdaogymwBAaet5AT2fwFASpKCll5sJzmFZVTrDYRj6bR5IMSmc2ctvTP2bu9EZCu/cjtbVRUhogEx4jh4/a8hq8FhmL8kdDBIoEiq5jMck01BVRWlSCz2Glrrwcm2rBImu4i3w4LGasDjPV0xrxF3hw2K0YGshCRZYkJDsIDfRkiplnn0UulcOsWDCrElbSFFWU/xV3/n5NCbgz509HSoxyoq2Deauu4rWNu5EAn02laG49wlrDwgXTmDdzBVtf206Vx8S9X5iLbjYhZXQqaxsxEnl6xoco8VlZsWwuDrOEhI7d7eRbP/8XdEVhtKMDeXwIu8NNZnSMvqEkgYpp5Lz1JLI6wpDQkQEDGRmzBEYqQmSoF7PIk0lkGerpp6i4CtVdgkjGMZmBaJJ0Io4r4CVQ7CebzKPFU5jR8HldzDzrXJwVDmK97biLLAwcbyLc3YvbGcRqc5w2X6fEZzaBIj+laxYztH834RNvc6itH12HC5Y30tfRRnFpNa+8dpCzz5tLZZmLzYcMvvPTwxRXqvgCJcxsbKTQ4yByqIXheI72rkHMmRSyIvHB2z5NWtJ44O57WZaN4zbpiCIrNl+ARc5CEtYSFCOKnoOsECiSNtEkoyFkFZPVitsuU1JWT3H9HIRQ6Dv6OqpoxF9YRS6rUVJdhM9mIxuO4fQ4GLcLdEMjmcqSGI1gk3optAYRlgTjoVHmrFpJ4mQvo337KK6de9p8nRI1N5O34HMXsvKaD7P37bewCMGvf3wvi2Y1Yi2qw1JUS4nfQLFX4ve7EJKgpsDJWDzPSHiUll1v0j+YYdH5F1MdLKI84KOuupzSadWMjMbZsXUnj9/6C3LZMAV+J4psR8uEkSxWCsxpvG4ZYZXRdAOzeWKErVocqCYZu1lGFiYG+7tobzlIz9E38JXWIJsk/EEfZlmmYc77KatZjNtfzfsXTsdfGKCq1oeRTJFJRtDzYPcW0H+ii2nzL0CLjVNYVYrIyaT6/+N5XP9VTYmaW2rO4bEoZEeGOXtePW3pPh595nG++Zl1tO18AdOK91E991x2PHEPm470MS1gpTOSQTIJ4mlYsPoDhFr3c3jbEKl4F0OtB5EL3biTTrZvP8Toa7vRkTHLgmh0gALTDAwti1lPIGFGT6aQNJB1QSafBwRaNkUWiUQsRsBfQYXXQaB+Fpm8QU1jPVqkj3DXCDbFIDJ8AOusWSxvnEubyPCPBfU897v7KC4LMB5JkAoPMqbnKKqaTn/vcxSX/QOpwYNUzFuC11992nydEjX3yOGDDIy60PDidDmYVWynoaqCzRv3smDpYsoryzi8aRN1s+Zx2frl2GWNhmIrKV0mnc1w/JUHqFxwMeUBK+Q1nA4vKauP/pEI8c0H0YHKoA0DA4+vlmg8zWg4QlqTSOd1bHYbiWyWIu+fbp00wGwxISwSJ/tGONk1SNPe7US7D9C5501weCipqkCTBAND/egiyuMPPsFNP3wIu6OGxed+CNJB1GwGh8dDpK8dWY5SaFvKwd3PYgiBx1VJPvHnZpH+92hKwF22aDq66ub5p15g2qr309Q1wq+eeIZjQ3289LMHOXnkHTxlDnC4OPjqC3zl219CkzRU2SCXMSiqm8NIXwtVs5dQU16GWlHF0Gia0N4mUtk8ki7zna9cTklNNWabisMq0Vg/g/TYEOHhHlL5PKm0Qe94hrwOkgS6LqEAqbSKkUvz0s4TON0O6hesQbUIbFqaVE7DZILSwio6W45z/rrFFAZt3Hf3Z9m2+RFSmX60vCAy3E9F/SzGu9uID51k4dIrCAZnEQm3Eiyfddp8nRJwR4fHCe9/hPVXf4Q3Nh/jcNsQ6AIZlcWXrYFcnuO7D5FOJgn1Rdj61hY6h3VsHkDWwV9DSamXztZuBvpPoOkKW3Yewev845MsoKSajMNOKptGy8fp7+mhpHEGwUI7IjGKSVGYU16OxMQjSEmBZDqHKvKUVgdZe34DY4P99DVtwVNajewJMB7qR5JBUQQIiZwwkE0W8oZAxA08DoXS8nL6WyP0dTVTUb8QxWwl0bWLI6Et6JpG/9Gdp83XKQG35fAJRuIaXTs2MG+6l9GxGOga9SV2Xt10iEwOzl63nkfvvIfF6y7klZ0HEbkkkYgEmozf4UIuWkJf0w5GUykikQEyPSGSsQyXXLmadTeuJ5EbJKtrjDcfJjEaIZeOEx2Lk4rnMfQc50yrwGWOTDpiADpj+RzZFOTHhyENXe0DeAvKOHloJ+G+TmrqZuIxyQhyLF5yLul4lHBoHJsiASqhsSGS8SGq5zZSWlGH1VdD9eJVZHJOivAxkoxSPn/1afN1SgyoZp89n1hvL7PXfobnX3iECYd1DuxqYczUhzPRS+30ai78wCVERgcZi8Lx4SyiDshDODzE4BtPU71oEfY+D+MDw5hNCksWzyRwUQPTHV7Gjrfx0gMP8bWbvoDV5UM1WRjvbcZWPQ9FOUHFrBlUpzrZ1hwGBHoeyjw2MukMdlchTqcD4S3F4rGzfP4nsXlchMeGyChWiiv9GHIOv7+IZCSMkchhd2YwqzbGQjGCjjE8Lj+p0FFG9BLqFy7HG6jmzhe/xbyKv+mH6f4uTYmaGw23MBQZJzbeQcpiB0mAJNEdiiAMnYQ5wO+e2cjOpm72to0SzUJKlxCyjNUp097SzFgkjNlThJFIEx6OUXNRI41X1GISGTyjI+x8/CmWnDWNdHSIRHSMdDyE2Wqm0GnHkFQe//1Gfv5CBzDR55pk6A8nkWw6hqRSYMsyo1Cj58hWulq2gJYnnQWvx43QDPR0AkNPEQ7HyGNCT+bQk2kUDay6Fc3sQVecaJk4mVQfo4P7+P5HfsWznc2nzdcpATc9JjN/fiOt77zIVRecxZqVc0HX0WUVq55kPBnB7DezbfchDjR1kc8mKbQoBKyQyWpkeg+y6Kxp9JzYRn9fB4lYiHCBieGBPh768mPETw5z6dpFvNk0SCYjkUuEcdqsjIT6SCejqDYbejZPLp3iT6/7QMfApJtQ8gmi4Qil5dOoX3YxDlsRnc176Ti2BzAo9JSiyRHcniC6liWXy5HUNBw2C7LdwVikj/72dwgGSjApCnlDxVdYDULmj6Pz06EpAbc4aKdu9gy0uExJaSUbt+znhqsvxCbDBUsbaG5pYv+JIbJpmWlzA7hqbJjMMuO5LCQ1rE4foYFujEQCy4yZML+QbFxi630HsWYMKmoX88QzR4jFs5hdHgJ+H0ODQxQVlZOWdLLJLC6bgrfA/W/vcG1WGzazikhDImaQzyskkzGcFjfZTIRUJsqrr72JSQgkixtVspKIR5ElCbsiYxUSiVwKm00g6TIeRwmRVJhENE6Z34thyBiyxudWf/y0+Tol4D713FH6O5q57Euf5ljzEZBktu44wvUfu5y3d5zA47YyFJNIyBreumJcs4ppnFaOU7WiA3HJidBziIKlmGwaLo+XnqcPEYknWbv6PDb87lU6s3Dl2hnEBzo5efwIipRlqL+F9jd/zUDHTnwWGSMZwWpWAchqOTJ5MNl0Erk0uZFuuo4dZ6DzME53kPLyaRzY8Q66nuJkTzeyqQjD0MllICcMhC7IpQ3MVgkkhfHRARQhUVg8m87ufjKpMWLj/eRP48cSUwLuqosXoUkw0HYE1WlBz2sEXVkeffIV7vv5d5FljbVnNbB+URXbj7RSUGchUpxmLJ1BUlUGon2oFgvhZDO6nmVo12HicY18VmPjln10Nx/igpl2duxq5VDrIOaCKkhGKQiWUTJ3HVouQ0OZk3BcI5XJI0kQi6VwCQ00MAwJzWTFXeBAzydBMZFJTMw38vlLsMgmJAtomo6w6GR1ARYZ2abgcAQxyyacVgcOm4+R/mPYXS4C5fNRTDKqMJ02X6cE3KKgFYerCGGk+P3v30ACZq79CJeuv4irrv46S+aUEcsneXVPN7moillWqJ1upspvw1KsYjarZGQHg/1dRHa9Q34whclkUFHegDBSrLp0BdayeexoCbP3RA+W7DC+0jrI5chlwGLxESgOIivgt/+xDxTosiAjg1VRESYLVovA5SpEzyRJGGCSM5hNGVIjRyAZxeMpxCqZQBhgaJhllXQyhupzMZ7Jg6ZTP/ccvKUNZDIxgmWzMPt8f8ma/5KmBNxkrYKq2MloGvdv2IgkwVjTITbvfIcPfPxKEskUm3e2U+r3kO5LMNo1zPH4MB1aBmF3khQGiVgbjKfoP9ZLuG8Ml8uP1xJFIcvAsUMoyShmi4Wtx/voO9lKKjVM+dxVgEF0bBCyWVRFxe6x4vK5URQJchrkJISWxi67EbE86d5u+pp2kBsdx2IRE8+kFYVoSufEse0EiwU2oWBxqqQzWRQM9FSGwmA1FpsNXVFRVS99PZ2MDYWwW07fy/opcZ/baeRxdndy/GQEdAlkmNlYwIm2Nn55/7Pk8nEe/dcLuOOxHqqtTt7+Qw/mpTYcZht5s4yiWpCFm/NXzmSPoTH0zi5GQqOMjOm4HSpen05M1cnmDHKajMVcREnDCpLpCOl0mpHBHjRJw8hrDIayeG0G0byOySohmXTUKjtDoz2kY8O4+zpYfPmnGRqPksvLWANVNMxZSWhokOefe4lgqZnYQBI9rpFVzMhWP+u/+M80nH0OTc17kfb34rEH8M0uwOos4tkdj3Lu0sWnxdcpAbddT5JoOs7PfnsUmLjNrZu3lvz1ZiVtAAAYCklEQVTLmxkIh5neWM3CFcs5ftcjnD/LxwqtluESwbx5bt4+Nkp0KIqUtKBbkowN9CGSMfwBD/FkCgyNadNm0j6cg0SK5eeci+IwGB/twClbSQ+cIJ7OQTjGjAuq6T2eY/maBrKlFq6++oM88bnvEqxdQq6ziXOvu46sEkcusVIVKOfHa35B5O0YFquKM1jCkcPtLD1LpaewgA0Ht6JaZd7a8yiHt73Jjvt/zfQFyzlWfpSlcUHAMp2vP3YTd3z0J6fN1ynRLDssNt7u7kJFRkKnosjP848+wSWrL6Qu6MQiacTGe/FLI7ywaR+h4TCWoiz5YhXJ58A3vRpHVTGDmkZc+LB6y3CoCgurrKgmmbzhIB/uoqhAxqUMYcOG4lmAMFnB6UVoOlmTTu54iH/46nk0dw7yuc9dz3evvYWh9lH+6Xs34vC7KZpTT0nDLFJdJnyeJdjdNWRKDfakD9CT24Pigvsf7+fZ3bu5sraB1YWlbLrx5wxs3EE0PEJkbJRGWz3+wnqeaNrKL65/eOJKPk2aEnADBW68pcXkFImg3coX3reUBYvKGQr1MBTL0NM3xC23b+eXX1tCaZGPVCxLYCjDcLib/hO9GH0pOrpayFo9VKy+hLJzL6VvcID2gTi6JuN32qkOBmmoqeWdfX0MJWXyWppULEZ30w4KfG6qzzuf27Y+Qn3jEpzmGE1HjtK/u4t7Wjax/+WNzP3oh9j+/e+RGRqn6oILiCdCXN+wiqZHH6A0Uog/YWHj4WZu23A9Dzx/NcIwsMkC1W4jk00Ty8VIJEeolFfwo6ZbmF1ZCLqElv7r/vy9mhLNskXVyQfdBAtcDA/Hscsyv3lpK6nYGNOqXRxqDdM5HKekqoh0Ruei+XUsKPFwc8dOsofTOP1d5A9LtAYMyubMQphtlK1aTefrb+Bxa/T3D1AUnMPCVQrrPzGHeXMbETI0b/49FimDqJ9F+drV3H3p59nR1YeWzVAx/wD+gI/UQJhj29+h7dWXcDskVv/oXrL5NCs885kesFDg8pId2U/x/M8wlh8nsvckrz68mRk3zMPcPsZA2yCNddXUzFpJIjWO0+7hkpWNzEmu5HjbYW6//TbW7dlzWnydEnB1OY1SLJg3vZrKZXaODNuYM7uUF98cRTY5sJqTJLNZJIsN8mk2bNnHk+1WpOUquj2JvbSUw3vbCM62MqCFCa5Yz2O/vJeHH3uRt372A2Kj3SiqTu/RQWZNm0/H/ncIlAXJaBkKz78Sa3kJt196NZHEOEo6y+J1y6kqryV16RC3X/sl4i1dzK8uwlddiFX2ktiyiZwODoeOr9CPw2whVlfEvT+7iVzzGJGsRueDh7HKgsDaALYr3IQ3dbHonA8hSSaqgpXQruMz0vS0nThtvk6JZpl0mgK/k4Qlgl810dPdwp4jLQhZJTY2zIWNFfTHkrQ195PQFRIZjWpZxZJO8+HzzseeyzJ/1bmMdB1j/NARFCWLv7iET3/2KhzBUqYvvRiPr4jl5/wD5VXFVNeW4bbrONx+iucu55efuR6H04lFNfCUlHHDbd+kp62N5se20nO0g7kfvIAcMgu+ewMnm3Yx3LYHszRRM6J5GxXz1xPOJHjz6xsY6T2O3yUjyzLB0gC1sTKcG11orgRdnjcxFCv5Vi+/eu7r/O6N5yYmcp8mTQm49rSTYJkNe6kVh99HUsvzztEhBkdHuXzZDAzSLC0v4O7f7MeUSyLLMuOjoMdNdA/2kstmKGsoxWZWGAgl2PTAoySSUVSng+/86sdEYmmcwTq8xUW8/MxzbHnlCcLNOyk+70L2b3sSu5ZHF4JU3Maln76SrY88xO9/9gyJnE5pRQ1lWhU1X7oUIVtJJ7O88uqOiTe+OvT19RH3OHA19ZLRdbpGMygF9WjJHEgJRoYHESYffqWRkpEl7Dqwh3H7CdZ8vJaVi9eRT5/hMw7mus/Ga/HgrXTR2h8jGh5AUSCVy9Hd2U4EM4lEkn0tg5zTKKMImfF8llweQpKGZjGIRhOMx8fQ9AxvvryZDff+guT4MHannapFCykoqqOgqJhj+3ZRWlZIb9dxnOXFmPbvQ5YU0qEY1/3264w2NfPmG8fQ0mkiGY3I4Civv7WRRWdfwKbrbiafjfL6gZPkkUkmDcY6jzNUYJAcDoEuYUQE2bFhKoocqJIFpCzDHbsQjiDhRJI7bvw6G3cY6O3ltLreAln+q/78vZoScNWknbQmY/e72HNsFwc74+h5HRMyreMp9jad4PhADJ/bzYI5XjKGht0qY7WZ8VqDjI9289L9P0M1uekZHccQcNfNt/GpT34JlQxSWRBnUS0IlcPNLVSW+jj/pl/Q+Yc/MNAXIpfPIjvMPPGF2/nkHTdysqMdR7GDNTdciMkKK667lMeu+QqNdeUooThXfOfjSGgkNVj41S8x+MRrOPxWDAk0oTF9XZakqiCULIGiSmRfBTanl8999gY+/IVLeOuul7nuqtu54+rHmH2d9a8b9HdqSsCNJwT+XDU5kxnJ6sYkg82mYmDQMZhmPKWRykH3UIT6QhPCgHm15aybeR5WI8yMRVfw/k9ch5FOsbCxnNqAA9Vm5cTGrSRHByn0uPBWljEYz2B3WFFd5WSlNCOte9ANlZHxFAMjMfJZC3ImT/XSALGhFHFpkIZCC/PWzObYgQ5eeHUn0dEQ93z2x9z56K1YLApxnx1LaD+t+17DJAlUxURVaRmDwxnSSQf+0lI0Pc2vHnwUPQ/S4jJIa5h0A7vdzJF7k6fN1ykBNx2RIW5mNJdi0JanwA45Q6YuYGZGgYJbteB1WnH6ghQXyQgBm/edYNeB7ZBxsGDlUnZt34y/toEjrX1EYimcEritCsdefoHEeIi4nCUlp5ElGcVk5uRjvyQ5GqfzZIhcOoXVJKNIFn567Tc5/lof88+vpOvtcT714pM8/upDBHx+WiImDu1txpAEN/7jTbhnzSR8173oTjciq2GWQZN19r/Sh1UGYckhKW6wV3Jo1zvUN9QwfjxLNBwmp+ukIilk6fTdsEwJuIO97RhjYdyKi7nnVCA7bWRSKZpDSZrH8iRzWUyyjKFncfjsSBK4TDLpeIq93U28/PTjHD85Svn0ZfSPpfj91t2E4ilGYjEq/XYq7Q7Q8gSrq+gfiTPccZidu1rY35NE1yP0xZKkFYNoUuPI4Wb8wSoObunEH2zkD1ufJNMxQvTj5SxfOJeKGVUTXwEhcflPPk/G5cMRKCGdjJAzwGNRGexN4vCoZHMSiZyGSbIg6QbtJzt46Z4n0Sem8IIEgfmnb67QlIDb092CFFNxCyt1i2ex9qbLUcwy6KDndXRDIZFKUeKQsZitaDmD4ViK9nCaVFrjyJEmcuODHHj9KV7b08zYSAwJEyuXzsap9zLecQSL2UagopSrvv9ViqvnsPCCyxnp7GEgFKPQ5iSVlZFrFRyyhjnRj9nh5DsPfo/Y3Y/R/fgJCrb10zM/zO7yQYp9ZlZ+dD33Lv0EsZEQ0d5enBd9EF2CfM6gOFiBYlGRVIGkKtxz7wPkgTuev5tsOAxMGm+SmTd3wWnzdUrAzURzKIoVJW9FkzP4SwtZdu0iJARIIJElk8uSzGUR+SSGzOTM+zzCkcdbVsPl71+Oz1MAsom2wW6uv+Z8/umKs6lfeDGxkTGskkpWz3PpxZcy5vYw0HOCaTNLGR1N4XKpuPUseZsVr9OCpFp5s/MdPlBzLhu2DeMhjxqW0X7VQsf2wwS/XE/d+tnoQiYayyFM1fQ99SyrP7kat02Qi4codNmxWQoY6o2j6xo6Os6An3x+Yg6wLoGe0Whr33vafJ0ScE1uN2ZN4M+Z6RvpwZ4aoa6hgBlX1aHrBpm8QSKtEYmm0NNhdA0kBHlDRtMEJ9rbeP71FsoqfRzesZUXfvEwP31kE3f//EVSUhG1c6+gbdthDh3aj7PITaaimkDDLHpamzHZHGh6FtWu4KrxEUvpzGpo4Jvvvw4tmabSL9NYXUBNsZ1VZxdT31VI4+txtm14FusNVbx1Ik3D3Jm0t4d4+4GNSLLKjIvex47cAKqzkocffQIAl8/BwFgEi82GSVWQkJAkmbbtp29ANSUeP1o8VsoKnJgyeQrVAkJ9LTS3h4i4tYm3JpMT3g0dMhookk4eUPIGyDrRTAoRzyI3rMQdKOTY0S0gwdD4OOnQDhKBWva99RpxT5bMtDl4HA56gnbKK6uRlGF6+sZoG4pSpy7A5bVw/Ut38qHadQhDZjwNrQMx7HaZmdOrqPM6UVbdwO9XfYmyXU58H/bSnDnE4e5RMgJiOvTv3cZlyy7FLIL/VnvqZzaQGEuQz+XRjYnvsmVZRvtb/vvg36kpUXNDA4OkRQ7ZbKXaUc4xPcO/VqlEDoewznGDIpNHI60bRMc1dF2a+NUZXcesWonnZMqDxTx3961osokjveNcs66KWz9zISX155IYG6bUPU654aP9jedJREdwVNSw2V9H13A/DdVOYskMAw9v5Jpff4vr51zO+FgOWYL+mEYyreO3eBgfHsVqctLx8BNYDIGSzzJ3xyije3qwfzFA+Tw/y1euwhFs4O2X3uFoz5sgCXQJVn1gDS/9+neggSzJmC02FJMVu8N22nydEnAz41ES0Sj93ePomo7qcHPRw908fbEVxSxjXegAHWwKdI3r+CtcgEFeBkNkkYVKd38vgar1PPejhxiNZLjmsjUsXTCX/YePUDV3AbLip6X9GHOnLyObiKNYraxccRZvNGfxeN3UVQRJZQT7324BLYMqaQghsbzMTSqRZ3drDwMjMVI1AbbvO45qBl0SVNdUIDSFHZ8/RtqcpeZ9dbzw8usca+3kzacP86kbP4hFdZBW8gwdOYnH7cZpc4FhYDLJ5E4jgikBt6+/n/GETvPBo2SGszQ4q8jPsHLRLSFum5vCM8uBc3UAi5bj9b4MmXIJSVJANsgKg+qyWWiqk+v/5Ts8tflBJBPs2HeMb33/fhbMvwhJsaCNvMNMn0H4xGaSx49QYXWzbOYsis5dgKEWM3daMbcfeoDtP32GlCuAzQwOs2B/f4yhVJ6yoEo+ofHij37FkfFCUhko9Eq09o9y9Egbpjz0vR3hlqvuBGS+8fzHkEwy05x2NF3j2Gv7kR1gsipYPTaqy6so8gRwWc7wJ1Rehxc9PUJf21GIKvhMLkrLC9HLPdz1fJ6v1QS46MKFnF2jcmAwS86lsWh2A58+90I8Tg8rL13DynVXUNzoY2g8CRo8/MIxZtWVIrmd5JJR6pefQ/PhJoZbu6grLqWnqw0tnaCyrIye4UHW3XIdD37iDkbCIebOn4OCiVxew6kqWIVgb1uEjqFRHIFqxjpPIGchFMkzt2E+5uYhrimxYFdkkGTQJH5x9e+oO6uKux9/kaKSIEfeOclXfvA1VNVKwOvFpMoES4LcuenHp81X+eabbz5tif+tevW3D94cqKwkPj6O0+bE4ncSsQ+hOmQiTVG27R/jnz+2koq5pWx44gRSlcRd13+LhpmNbNl3ANuIBXPaz7yzl7P6io9yydrz2frWH1g8rYQ551yEoSdo2vwM9bNmMjbYxTOPPskoEqMuD2tWzySdLcDmc1GwfDYLF5zNL39wH3lZYdmy5Qg1TUK3UOyAQp+bDW93kDckspLBWDzHm/s7aE5rXHj31+mOtFBR7aNoRgDvEiu2GQpFZ3kpK5EpstfQuXOAz//wQ/QcH8Vlc7D6I+fQo3ezun71LafD1ylRc7Ph5MREZquT4YEoelKnwlyJ6jETLzMTDWVoOTmCz2NFTWh4xgQHWg/QMTjAsvrZfOSjH+O6m77CXbf+ErNJxuX2kkrnqZu3nKGhYfRkFndVHUf3H+DeVzoZGtUwdffiiIcpcJSw6pPr6NzWwo3/8GWC86v5zis3o2U13ty6ncRoirzsYeuJMBt29SGEQkWRD5OqYrKoyJLMhVes455r72TogIYUKyF8SMN8rBhjcymeHXOIvuOnQzuEdV0fXdsEC98/h7JpDXhnT2fvD1tOm69TAu5rh45js7spbpzLk89uJheCKoefKAksJju26T5ef3gbm7Yc57IVM5nvDNKVOEDT6C50fZAtjz+A1afy0K++wZLZlaxdfS4ne0Z58ukHGUskeObRh9hw/5PUzp7DnBklXP+VjzA2cpjdx16guKAIBzZSwQBoOv/64W9x88U389lHP4bb56a5P8xA73HyuoHDZsPrtKLLKqWBQiRF5epPXkNvIkSguByPQ0XRBXMWzGfZoqU01lTR2deOzeNmlnY+9a1LaG15ieyJca782tVsfeoV7nzxptPm65SAOxiNEUpmGRqNMhYbpW9gHLtsw11oJmPoxLOCvniK3q4wraYxWk+O8vEVS0i4JHSnFe/MuTz9+AbqZkz87I/XCmc32LjisvXY7B5cNhPnrJjJfQ+9QndzK69uO8STWwZJ5aIcbTqGUCXOOnsuldMaGOns4gtf/gK/+vjD3PXSnaguM6pqxmyxY7LYUcxmSkorsdoclJXVUHhpFdYxlfqycgJFFQwM9fLOzh1s+N1TvLHpdTwOhdn1lVy9toxiOljot7Dq01chMHHz9+/gX973vdPm65R4iCEBbd2tjA+OoqMx2DVMAyVUFRdypGoQ/cU4kQqZ7QMaWj6PnoEfH9rKvNnTKC4tY/fzO1m+4DK27toHOgylZEIdSb5gtuJx+8iEY5RW1nD1lV5e27aTnS2tXPbxVdTMKufwjk2UlxRTWFTIT56/gz80vcocTymHb9hM7mQXDz/wTY6Kbj6gTMNk0kjrMmZDorOtFdMHP0XsoR9xzhUNHG0eomMgzt6xLLOXLMbiCOL2FxDwONCzMWpnzuPVownGtX4Sra8y3mLgH1C49ee3njZfpwRckJAyGkKTsZgdjAyNY6RM1DpqOG+RTk1NA3NXzMRl8mKKGRglWUL/t51762mzAAM4/u8JCqV9gZaOlkIHjMPCoJEpm5txjukcGOa2OE28WIyRxcRk0RnITAyJV2YmeiPZYsIu5yFesKnDMDbHYRwCWyxQJ2NraQmHUkppKbS0pcULPwMJefP+PsLzT56752GRSMyNOdeK7dIZevru0najhfVYjMBMiIlhOy3f9fPzSx/wzPWMWDCGOruQwHKYco2JxgMV/Op6gv3WU6a8cc69f55gOMbMp110rQQwCAqOXGjCsfAQRbGalM2LNa+a3o5pasyHGft3GZ37OvuMVTwYuE9V6QEcjiCWmqOYyipRqzV03/uToHeNPEHGO42vcLqpgb8iN7A7R/jQ8AlOyySLrPPCDk11V/xb/ub7q9uKDBmjQ4/o/OkPtFodV6+3ERNWuem6jX84SY5Chb5YABL45qLkG9doaDqJ3xfhymvtZGoz/z+YlmeQpRNIboZoeuM8b73byJ2OVrbXkzhXVtEbdHiDaThnZzn1WT0r00vMdM9w+WY7WRoN471DdP/wI+kk2dpOsf/4qwzf7UMwGigtqSCakNHYcBytZpt4+iqqPTosqmIW/AHWluO8XHeYjVgSj/MJsrQslKE5eh8McPa9c9SUl/Lt+NesBePIZAIXD7bg9Qc501Qv3n/LX1xqw/FoiuXFEHKZjGqbjVhEgUIhoNOqsVhyCPsSLHqipEihN6g5Wvg2srASz/w8SjVkZ5vQG4uwmE2kyTMpyoY3T54g5E9we8hL59/z6DUqeka9jE/PEIvDqnOJPeYCVBo1k7/1Elv0IpfLKMrU8KJJRyQlZ3JkEMPeAiwFZizFezlxpJqK12sZFwYpM9RiXDORDENVrhVHcJoh1y+E4n1ocXGsZIHZrX7yKi3cezjCHecoEUBYqyD5WIffM4nH3b9jc90VaznfVIQsTUFubi5ZGXk8d7nwu7xYLYVcrP+IRG2CXuEx9j4ngblNcqwqFPv0TD4d4eNDX/HllWuYSvLxun0oEwr0WhVbmVaMxSY62juRK9IwG7Lod0fJzhZIV2lJJ8aSPUBd80GmlDoWxv5h8PksG5EQ4dAG4xtxykqsWEyFzPsXaP38MtHIBskCC9ENF7+3OKhrHca2X8liMEXPoEBKn87A7SWuTdyn+cJZBuw+Tp9qJl6yxWr+Jm7fGKXOem51d3HskI0JzzKV5TU7NtddsZYlO2NXrGXJzpDiipgUV8SkuCImxRUxKa6ISXFFTIorYlJcEZPiipgUV8SkuCImxRUxKa6ISXFFTIorYlJcEZPiipgUV8SkuCImxRUxKa6ISXFF7D8UmhfHev2W5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H3N+OYGouIV2WRjPpfLCP1+vHIQwwPiTSSUz0LA0VnmRYWJew8S2GRMPrjQtnWb34Er/74O8zYoXv/YH3os5hkxrNsSn8qe9k5XLJjJ/n4Scusr65zNhInYOzDUqdYWasjh2rsbm5yR8+9gdcXByw0XOo9Hnh4jpry6t845EHGRvLMNaSpcqXPvt5Fl96EdpdxIS4zGkIkmWrwB+2amF2aZqwJObDNUioGINRvV6W0uBIGROkUhwgEZNkLE6VCqVUIKq6CshFaW8u8/QzZ3n08TM8/PXnuPzCIh/5Gz9AYlrcfd+befzRh7i00ueRc2163QIFRkcbFGWPtJZx8uQctazBmRdfYm3xEmkNyGvML6wwOTbNSnuDF8+8QH/QQ3BkrRFO3XWSfcePc3FthaKqrmeOJEB01JsAbSHUVrebdgRTlZAzdcMEvAlhwDC3irAV9xkUb8KxDEPhXazHCokG5IQVoSUlk0mPD33f23jXu+5BnDJ/dZ7mWItWY4IPffg7eeL3vkiF46oIvV5Jr+xz5NA+mqkFcVRln8lGxvvuG2dmvMZISzh+8ynqzZxnzqxw35uOYo1lc/EK7/rgX+GW2RabbY+mHu+VwwcOsHDuElZthNKERL/zFajgNJZ6t5l2hKMU4rkYiCvgQ6wnMYEwzAY5GXqMIe9b4LAS7CdOY/lOqIzj67/3VerNOoXcxm0338Sdb7ibaz0l1zE+84u/xu23TXFwssa51ZLbjh3m7ILiih73nDrJ8voVpsZHWLi2yk2H38pjj7/ApaU273/n/VQra6wsdGmNJ+ybzHEaEhf37pvlrr98P09fuMLU3EnUlnhSDt58jM0rizT2TQbAGUpibFDHYv749rX/QtoRCP33//WPqQyT+aroFiAJxCmVhJSaUwUT4SgSszXDAkzlEWuxqkw3HWefPcPzL16iUuWD7/8O3vLtb+PkvXfxox/5OEvri9RrGccOTnLsxCF+9Tceo9Ws8Z3vuIuZQzNMjM6wtHCZpnWk5FQjM/zzT/5r3vnAG7iyMc/9997KqROH6JYps9MNvvTQ83SLDUZGDrJRWorKgRR4ahhVbGJx3uExpB6cFURDntt4z2d+7t/sPoioHSZto+odMtRqwCXZLZMkOA3VDSsGDYiWWJyWmEdVDkw3+d4PPsCP/Z3vJ80aPLcwy4lD9/F//v2f5Obbj1DPLK1mQjOvMTk1iseQjY5Rb7So16fw4hiZalF2HaNHjzE+Psr/+De/l8eePMNHf/B7uO2Wm+kyxbMvLPDxH/tXdFY2uPuu2/nyQ1+l5yoKETzZVp3VOeX5008H0yEC3gWTEyK2bacdIanf89GPacATeVTZqkd6PCFwCWo3wM2CxAacUPiU9WFDIMLSiy+yOH+JH/yBv0R9ZJQ77r4Dm57gb3zsQyRJwdzMDG84dZC3vOMB0myML/zK53j4hQs06TIxkvEd7/3LPPo7X+DQ7fex0A4Vl4lWA3yfzgtPcPsD7+DCYodr61cYGV1g+cWUm759gbFmi6tPTFPYQxHg6vA+5IRtrCplxlxPCw41k7DtkrojbCpGQmmDmHmJ0JFEDZWwBaROFELZORSsUU8S862iIVW4OL9Ip9PnU//2c7z97W/hypU+g/7vcvBQi7WVTe6851bmxht4Mp5/8lnW11ZoGktnw3HPG+9gVAp+6H1v55/96heZPX4/JjNstLs4U3LLiRrPPf4g6W2GY4cK9s3t54XGAgOf0umWHHvjAs89cShuwpAkCf5CSIC4CL0J2aWAQAy1xu2lncFUF+qMlTqsSUh8rJkKUb9KdDIgi6jZxAmaROC2CXCWzComqbAWpo8foTvI+Mxnf4U0y3ng/jtR77j91G3U62NcWOpx7eKLmOY4frHPO958C+/7rh/kq4/9KreeupPvf88DfG2hZPLgInMnBbIRGuYO5k8/RFnkNA7UqY167nvPWzl/9WnmLywz/40jYEPRwEYwnBeDwWNUQvJBHF5Dsd8YcK9DnLojmCoiGBWsJKBQSkA0DKstwz6aADILIK4iwPJCLOs8lRXy1NHvFvSrCpzw3NNfo9VMsKLcfffN+MGAJ586z+rGOiePHKI+WOVws8kl12Fi352oEe449W7+/Zc/zb5T09z1znmadh9ry5avP3KBzY1nuffetyI3XaWukNRu4j/8X9+kqJSbDtzHsD7u472FdgwX1ayJOeDwTJiAwHC7NaQJSAIBH+qkleoWKh6ijY0xq+P6e5UE79iKwSaQpgPqIzmDnmdzc408z3nrPSfp93sMNq9xuZ2x5L/O2JGM+cE5akeFmYHhu997gDP2DP/u4X/M9EzO9NsM9YmcxfNH+NozV/m2O9/M/NVrSD7KpbMlxbkmidZQOuybOUGpSuljzBk3Y8wshCK6C6nLFBhIKMOVMea2r4OntCMcpQ989GMa0PUQkF66FccRlohK/VYqUKJGdhHpJymkucfJOkfyKR579AX272+iSc618xeY3T9D/eQmI4ccR/Y1qWUDuoOCzmafw+dHeDJzlOLJahXtdce55zbprSQcmH0jKo2AvhAbsU5+a+MNEQxOPVmURMFhNAFxiIQSeCUOqQSs2aobi2jAARvLZ/6fn919jlLI7oX4xJkEq0Pgtm59wEiorW6BbW2UUFsh+SInjhWY0QXmF1/g4NtKjt02RXvVUL+zz+z+BDpHeOn0Vb5x+QreOwa+YnJ8hOVWxekrXQI22FH1DGefajOxf4q5mXqAmQ6B4eKooq20COqryLhQSqtQEjUBua8EBIeETjdrTej9EYPHBBsbjct2045gqnjFG0HFoOK3FhJCElytQaqQMao0HG9lBpddo3AD8B2eP1ty9NumMI0NDh8+xL13vIGvfvERJmfHWb9mOfvYE6xXBdOHJzh4eJZWa4Te6hK31A8zODjP2uVVnvraBTaXCkwKrdFRiHgpJ4pYQ+WCfU+9UIiLcFYdos8CFgmCuhXBW8Fq6JcJiI34WJUHGzHAbvtzSjuCqcaaCNLagvsFisAtjYsJAQ1vCZ1kSVKjp1dRX6PXVx57cJ3xesZjV87ztf452oOS8WbKZnaZk8ePcHh8hlT65GlGZnIYm+WJ81dJzCjPP/oEd526hdtOvpmJ8XGWVjo8cfE8KGRqGBQFFkJGSJQspipLwIrFaRWA5CbafWNJYtNXgLMIxgREhLdgUQZIwDdvM+0IphJxssBWF7kYDShDAUGv2ywN9cpB1+N0hDwfoy9rbGwUdNe6nDnfp7MxwIgnzTIWpw0/8IF3suauMb2vQUXGWH0StZtov0Y/rXPpzEv8k3/0KWyecvGlF+n01snrBqUikSza85AZckaxhNyti6V6p0HFigiVh1QV7zxqTCzUE3p+vCeLgO5CwIgHtr/tYocwNZTfDKEW6hIPVWizt0i0V4AP7yXEhqqewepR8to888unWXmmE7rMHThrEAb87U/8Vyy1NxjXOWZmJ7CSkKZgqkkqTZi97W4+8oEHwPZYm18gyRMYONJWC2vAVaHXdVgoAqFwVWguVg2bTBSvIZ0ZOigjbtkF21uIYGLbYuUVn4T+GsVvFcu3k3ZE7tcZSEVwPthL668XvV30i8Kimpg5GoY2iusr+CkOHB5j5EgNk1h8CklNGN+XMTMzS2usSZIDOiBv1hkdGyHTnMce+SL33fUe9h08QL1ep0TBOcSmJFUFZcAQeR97YAVK78mM4Y/2lQZ773xAEBbqSZQ40gAYdumJUBrFlg6ljPHrLgWeJRo8SjEmlt1iFznEFkaDw0eMr8R0GxSipA4G6wm37X8Hd890+INLf4DBUMs9vie0r51j7sBRep0pkrRPLRX6a5bN5Q7vf/cnaHcWUDvA0kKdx4ln0CtIbUaSJvQKhxUo1ZPosB4ac9TGUKoPaA3xYUyAhH5ZFaH0Q7MiEQSgWBQ1cdzBlqewvbQjJLWSiMDX2NYfa6eW4BhVMfkdmnzDcVSxLrRcJEBn1VGXJgenUiYncvIRg69VXLy2QVHMkyQ5WiSURRdfDpi/tIi1CYnNyZOUbm+Zyg0wJFT9PuvdLoOqwoijtAUJA8bHogxI6IH1sSHKa/DK4yFC6SEkF1JP0MmqWK/gJb4MXQTpbsX9Wo1lM69bDIMANJNox2wo4+ANqHPkxuJQeqpY9fS6JS9WJTMTt3KteB4xKXnD8Ftffpx3+3u4600nSL2hN1imt9DhntvejldBzBquajE+PsmVK+f54pO/zbn1p8jqFY0ZwZngfddqGV2vZKMOVyrFtVuxNBEnIC7gkCUgIR3xGbxQStAwzkTHysQuBAFbhbEB2007gqlBtYaJJd55rDF470LuV6+rYkUjvNJQ+WFVR7fyqBTQdmt0tEeNFkk6wNULvvzl59noVtx17ySpHaGs6qRJRr3WYGzmZhrSoFtUfOq3fxqXrNMcqQMFiTE06k1cv09ioD/oUs9zylTID51j/cJREhrYOKgjRSgjrFWVkLgXKIwnxZD44ayYkB40QLFbp7NkGJwJ4UwpPsxNIKADNO5+G8GiRCQ/wBCk7yKDHVBWc0zm+6EqSNKUe+88yYm3jHNxeZFHfnsVp8Lh6WPYJKGsBixcuojPlR/7qe8jybvkSUqj3mT/2AQN22Q0HSezGa4QGlkTV1kym9PptClap6m0T6k+IDQI/bRGQbwPm1HBeCjxFOIpvG7Z14LXI6DZIUxFfRiHw7CvNCDc1QhGDF6jKiZAQB0OJ4bKO8qY9VEfZj5014WUQ5BmZDLKG07dSmancNqh7dcoy4LNjTAGABXUWP7OP/swzWwGW1pGsnHSgeBcRmaEwaBHZjPm9h2m0RzHSZfVzgrdnuHwgTmS4QQeIXrJ1zeYI7z2GsDkW/EuxPE7bNni7aQdwdRy6OhKQK4P+2dUQL0PkozgJLQv2ojDT+IUMz+Ew8TAfn1JmK2/CckNjz9/hqTf5+CJcUq/ydnTHZJEEFsyMdLg//7NT9LQFodGa1ibU6/XaNY8g94GaV6jO1glE8fC0nkGgxXqWYP9+/ZzfO4e1i8dpnRpqI0ipAoglD7kfo33qAompj4THxoyrfexAz3MVdpu2hFMNQRwdhp7S6pYqcGF4RsFPmSXvGyNCxhmFK0PXXFDmItBcIVh0HbsM7eSdfaj+Sa246m1lKvrz/G7j36FRt7k537j58kqS89scmVtlSwbUPTXqbwlqyUUZY9GLgxsn1oNsiwnSVJcKdx08lbc9AsYqjhtJUgisflZCPYyUQUXRwaIEvRMHPYRw6HtX88dQFuDJL1SqN8KE0QCUCvgkarQRR5bGIchBRHIPSzbOfWoeFYWK/obQtG7jE8MppHQGskYG89gYo1f+MKnKfqhlHZo7CQjY+NYG5IeFQOMc7hEsVlKIhkiKUidqfHjKMrZi1/hwMwE4zct41BK7yk1RJ6eOKGN4P0WKAMchfcBxuLc1nihXTvx7OBowvHpCWwSUm2JCc4RmDDqRkMA71wYW5cioA4TB1LKsJs7zlFIMBh1dPsV+8f34UqwLuSP640a+FXWq2WcuYrp55TdAeO1OjZpgQxoNhp4SUhMQq83oDKORFLytEGSG2p1izCgLNskowNKvx5RGPFefbCv3nsqH8b1DLNkGrvOh87S65F82BEhzZtOzLG6fo2bJudIdROpjzJ7+FYe+8PTXJpfp1+VDOIIWFWJ5lOug7jigMnZWspYI+P80gbHD+/nDXceY239JVauzeNcjzSzFK4gbyZhRmHRYaRRo18VuE1hvCmsD2o42iQ1Ibd1stFRTO5opUcwRlhov0CapFRGSNOUxPdozRqK+XEKhgmk0Ljl40CvMN4uaB2VYDKcKikR5bHNtCOYOj1pmJ44QafbxtpxRps5zqzx7vtu57lnz7C0ME8qOdnUQZ68dJW1djtURYZtFxrQhT1f8RduvR1nnmdsss7BmeNceukC899cZ/SUJU/BGMWQkqeGqnRUxXoAsjFNMWiSSolqE1e18baDsQneGxbal6jXLYltkNsa6VhCt7dEklnqrYLBfFhMLx4jBnXB0/XO42yoryrBT6hESLyn3BofsL20I5g6MAlon3Qkp5bWGJQlSdHHlKscmcqZSBIaE3ewcPFp7mg5XM3SlxbGJBR5jZX2gHOLi/TYMrU+AAAJzklEQVT6jq9dfI7v/64PMr/2PC++9BBvONDgdx7sMurHqLDQryi8J6kpNvP0Sg8qJFWPen0aKBmpGZb6XUrvyZMGI+k+kHNUGHxV0tMBuAFim/jKMT4Ci3jSLSfO40xIRKTD+kzpsTbOr9BQjhuIZ/A6rOeOwCh95H/4uB6bG6GRJSRWmRybwYon8QZbrJNax2Dg8b023nmqfIKNtVU8DdbX5xkZa3J5Uxk4y7WNNUoGfPh9f5FDI2M8/I1HePLxJ1hLNhk7Mopzm6R1jzEJg26Fq4gtdylZ2qKeNjB5gckMhV/BOYupTVCZVSwZWjhMXqeqPLW8xZ0TdzIxMcnP/OzvBo9cQmgWJtwGGEui0EfJYzeUVwekGHF4ha9+9te3VVx3BFPf/9GPqY0pQB8B2pMjNaw6ctPjtuPHaaYDWmlKbhMSk7K+dJbNdo9+35LmTYqew/suyfQMeW0MkZTV7ln21fdTFD3+1a/9AhurjqmDY6xe6/Dm7zlKllRsdLoUXUjylJptkddriLFMNBM23SY282hXsK0MVxVoc5SqFFKrNO0Ezz7kWB2sBRuqimBjxSbkqxUbUp1hcDDiHSQG7yEVS6UVD3/us7sPeDaclxQacQ1OPYvrXVSURiNj+dlLHJ2d5MhUwr5RhycjH59kcn+Tor3BWnvAWnedfHSS0fooaeJJ8oLD+++l4wacv/ACCQVZDc49exnUcu7pNr7f4cT9DRojwuZySd90cN0OTgXPGFmWUpZ9soalLEtc6RDTQUxKZXL67R4bvQJjwgxCFYmebvDiBxLGswsOK5bECC5NQs0iopqT1yEA2RFMzVJL6TyVdwF/NGw+VqHXLlGB02evcfpFJcsthyeafNvN+zEmIRvbR8uuU9k6CcJIWlLPPBOzc2RTk5y9ukJr9AA//Fc/wU//0qeYGKuz2Sk5//hlitKxeKXN+z9+gn37DMtLXfq9Pq60rG4sMlafZnRyHGcryv4SgjDoDPBqsLUGTVuLLR8hwWlcnIsYM0jWRSfdGmrNjDQVkjQM6ApwUonBzfbSjlC/P/FPP6ndoqTbK1BXsN5ps9np0S8c6kOmJvEB0iJc7zCf29fiTScm2TeS0UgTOpvKTNJD0gb7T9xC29dY73dZ6ZUsv/QkVVnnK09/jVZths9/4fP0ug4Ejhyb4r/+e/ex0XacvXqFzlqXxFsGA0sjG6M+kjGwG6gXtAphUZaAupRLp0eoSqX0DhNHA4kqRg3eGow4mo2ErAlZNsCmg9BvaxJUHL40/NpPPbj71G8xPo+4ikP5JLmZYtCfQkmYG5nmwrV5Bt0BG5vr9AdtFjf6tPsDnIdrV9p8dmmDiZE6dx1ucveBOWpjU8zMHaHCMOgt03MjNBujzNcO0pKzfPu9b6QxdpSr157n/PmLbGw6Lp1d5p//2IMcuWk/taMNNttdTt52lCxp021v4qVJM5ni5P6TpLVxRBynV75JmpfMHV/h/DMNAJw6jMRBmDk0UrBZTpZ6JFtD8o1Y6DeYNI4WkmLb13NHMLVtVpiqz7FYXsFWdSrTp/J9rm4aktookhpmJ6ZIZI4D/ZJBDxYXrnB1ZRlfCUurHR5a69JozDG9L2ezdKRpSlcSllbXKNwq0/sO0l1eZgqPTRM++N4P4UX4rS/8Oo899iztjR6nH38RvmGxiXD0wBuZunmW5SvXqKpNOgPPb37hcdqa09CUvhahWUuaqHo0ziFuYXFpQr1uyWtKlpbYtMIlvVCD01B5gvCvmF2KJqz7Gp3uRdJ8hFLWUOPJNYkdYqsUrkOrYVkdPAP5gGzScOhwnVN2lKrryJNpVC+T2uc40xnH9hcpBhU1maE5Msdsc5I8S8gPvQevhqXL80ynhiyp+Cf/6F/y9/7Bj/B7Dz1Omlhc5bAGzj27wcUXDPvmppi5a5S1q1cYP+DRy+N0fYlxESUYkRmKJ9eUnvE0UovNekjehlqBiGLF4ZzEDgPwGiaeGfJtX88dwdQzz1Qcmz7ApnSwjQRjKpJaBtKmIeMYSjaKy2Qmo3SbqNRIM0tlHGayjZEFWrUaWdJipD6D83P4dofz577OgfIeMltnfWmVzAzoDwasr1xlZvoYBQlPPfcEH/nwj3L2/N/i0qX1UBcVgzhFjXL1aptaa5KZk/sox1aoZ+usXG6w0m0gGn8fygQojhcXy3ADHG0aySD8hEkicb4/AY9jbGy7yLbwzttJO4Kpm8vKE4tLsf0PKjVg+ngM9dEOrWZObcRTyx2aZuTJAK2BtSXNvIlkhm6/j8snwHax7gwmaVIfn2Fj4xrOW4pOn/1zR5mabdAaO0DVW0dRjt50C8uL1/iJ//kneerp0zz30lOsra1xoRfA5M54zrywSPKi4YG33gpTVyhLw/LZEoPBeRfCGJEYlhm8eqx4ChdaLVwsxjurEX4DEpMPouV/ZnX+5LQjmJoR8L0u/iqF4GM/iqVYUxZXh4N7lSytY7OM8RnD2LglGR1QHxnHSE6/V2BIUF9SDRyb7U1qfZgev4lyvQIqOu0ONqmRNxqkWYojZ7MzYGSsRmNswN133oI3LX75wa+QAJUKYgJS9dGvzlOpw6kj9YYyjFdDKkNphNSDGo/aoJCdD4NtjRe8AesMiMGaJHa9eSp2qaPkie0WsbPNaBzo4QPKnTgD36ih6CuuL7TXlUsBR0BW73PieMX4iCfJzmKsp78plP2M46Nz1E2NdSusbmwwOrGPshpQr9Wpi9LtXGF6fJS1do+J0eMsLJzBpmFj9VVI4uQCJ0rflWBCoX5AmFwmzqAmwD8dQi215HkJeYHTPqjBbP1SUkWeZhGZXoQpX7u1kzxAOwJI2sRfZhLvgzqLIwCGP18StVeAQXsQYyk6nqeeDElcL55EFF86Kq24MnmRu8uEozOHUOlTFAUT49N0uoukiaU+KLjaMVBmDLRPrTnH0toiWZJQVS46NAECqib8tsxwqIqJP7SQxh8W8r6iBPKsT1ZzFD5kmqx14B1F7IzzgLUSZi/67WfBjiiSb83UNganAZSFhB/lw4fZ+d4KpUR0u2qUXA2NwIBxFXgf+mgGoSvOSMWllR6/8fBTbPQ7uHSELGvQH3Tpri+ztrLEan8UVyhZLWV6cprxyWmmJpocnJ0NJTTxW+Pf5YYqTEJI2AtC4aD0IZuUYMmSnKzVIKuBTUNTlSYerMOaIsxYUodzuoVx3k7aERmlPdpe2hGSukfbS3tM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSHtM3YW0x9RdSP8/1bg9YxD3JNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and evaluate\n",
    "# On CPU this will take about half the time compared to previous scenario. This is expected as gradients don’t need to be computed for most of the network. However, forward does need to be computed.\n",
    "\n",
    "model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
    "                         exp_lr_scheduler, num_epochs=25)#criterion是誤差函數\n",
    "\n",
    "visualize_model(model_conv)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结束"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}