Raw_MobileNet_V1-Copy1_75%-80epoch_91.81.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>MobileNet - Pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 1: Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MobileNet-Pytorch\n",
    "import argparse \n",
    "import torch\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from sklearn.metrics import accuracy_score\n",
    "#from mobilenets import mobilenet\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "use_cudause_cud  = torch.cuda.is_available()\n",
    "dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Train, Validate, Test. Heavily inspired by Kevinzakka https://github.com/kevinzakka/DenseNet/blob/master/data_loader.py\n",
    "\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "\n",
    "valid_size=0.1\n",
    "\n",
    "# define transforms\n",
    "valid_transform = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        normalize\n",
    "])\n",
    "\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])\n",
    "\n",
    "\n",
    "# load the dataset\n",
    "train_dataset = datasets.CIFAR10(root=\"data\", train=True, \n",
    "            download=True, transform=train_transform)\n",
    "\n",
    "valid_dataset = datasets.CIFAR10(root=\"data\", train=True, \n",
    "            download=True, transform=valid_transform)\n",
    "\n",
    "num_train = len(train_dataset)\n",
    "indices = list(range(num_train))\n",
    "split = int(np.floor(valid_size * num_train)) #5w张图片的10%用来当做验证集\n",
    "\n",
    "\n",
    "np.random.seed(42)# 42\n",
    "np.random.shuffle(indices) # 随机乱序[0,1,...,49999]\n",
    "\n",
    "train_idx, valid_idx = indices[split:], indices[:split]\n",
    "\n",
    "\n",
    "train_sampler = SubsetRandomSampler(train_idx) # 这个很有意思\n",
    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "###################################################################################\n",
    "# ------------------------- 使用不同的批次大小 ------------------------------------\n",
    "###################################################################################\n",
    "\n",
    "show_step=2  # 批次大，show_step就小点\n",
    "max_epoch=350  # 训练最大epoch数目\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, \n",
    "                batch_size=512, sampler=train_sampler)\n",
    "\n",
    "valid_loader = torch.utils.data.DataLoader(valid_dataset, \n",
    "                batch_size=256, sampler=valid_sampler)\n",
    "\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(), normalize\n",
    "])\n",
    "\n",
    "test_dataset = datasets.CIFAR10(root=\"data\", \n",
    "                                train=False, \n",
    "                                download=True,transform=test_transform)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, \n",
    "                                          batch_size=256, \n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Model Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 32  缩放5次到 1x1@1024 \n",
    "# From https://github.com/kuangliu/pytorch-cifar \n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "     \n",
    "\n",
    "\n",
    "class Block(nn.Module):\n",
    "    '''Depthwise conv + Pointwise conv'''\n",
    "    def __init__(self, in_planes, out_planes, stride=1):\n",
    "        super(Block, self).__init__()\n",
    "        \n",
    "        # 分组卷积数=输入通道数\n",
    "        self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm2d(in_planes)\n",
    "        \n",
    "        self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = F.relu(self.bn2(self.conv2(out)))\n",
    "        return out\n",
    "\n",
    "\n",
    "class MobileNet(nn.Module):\n",
    "    # (128,2) means conv planes=128, conv stride=2, by default conv stride=1\n",
    "    # cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), 1024]\n",
    "    # cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), (1024,1)]\n",
    "    #cfg = [48, (96,2), 96, (192,2), 192, (384,2), 384, 384, 384, 384, 384, (768,2), (1024,1)]\n",
    "    #cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512,1024,1024]\n",
    "    cfg = [48, (96,2), 96, 192, 192, (384,2), 384, 384, 384, 384, 384,768,768]\n",
    "    \n",
    "    def __init__(self, num_classes=10):\n",
    "        super(MobileNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.layers = self._make_layers(in_planes=32) # 自动化构建层\n",
    "        self.linear = nn.Linear(768, num_classes)\n",
    "\n",
    "    def _make_layers(self, in_planes):\n",
    "        layers = []\n",
    "        for x in self.cfg:\n",
    "            if isinstance(x, int):\n",
    "                out_planes = x\n",
    "                stride = 1 \n",
    "                layers.append(Block(in_planes, out_planes, stride))\n",
    "            elif isinstance(x, tuple):\n",
    "                out_planes = x[0]\n",
    "                stride = x[1]\n",
    "                layers.append(Block(in_planes, out_planes, stride))\n",
    "            # AC层通过list存放设置参数\n",
    "            elif isinstance(x, list):\n",
    "                out_planes= x[0]\n",
    "                stride = x[1] if len(x)==2 else 1\n",
    "                layers.append(Block_Attention(in_planes, out_planes, stride))   \n",
    "            else:\n",
    "                pass\n",
    "            \n",
    "            in_planes = out_planes\n",
    "            \n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layers(out)\n",
    "        #print(\"X\",out.shape)\n",
    "        out = F.avg_pool2d(out, 8)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# From https://github.com/Z0m6ie/CIFAR-10_PyTorch\n",
    "#model = mobilenet(num_classes=10, large_img=False)\n",
    "\n",
    "# From https://github.com/kuangliu/pytorch-cifar \n",
    "if torch.cuda.is_available():\n",
    "    model=MobileNet(10).cuda()\n",
    "else:\n",
    "    model=MobileNet(10)\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)\n",
    "#scheduler = StepLR(optimizer, step_size=70, gamma=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50,70,75,80], gamma=0.1)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Implement validation\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    #writer = SummaryWriter()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        correct = 0\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).sum()\n",
    "        \n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        accuracy = 100. * (correct.cpu().numpy()/ len(output))\n",
    "        optimizer.step()\n",
    "        if batch_idx % 5*show_step == 0:\n",
    "#             if batch_idx % 2*show_step == 0:\n",
    "#                 print(model.layers[1].conv1D.weight.shape)\n",
    "#                 print(model.layers[1].conv1D.weight[0:2][0:2])\n",
    "            \n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item(), accuracy))\n",
    "            f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "            f1.write(\"\\n\"+'Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item(), accuracy))\n",
    "            f1.close()\n",
    "            \n",
    "            #writer.add_scalar('Loss/Loss', loss.item(), epoch)\n",
    "            #writer.add_scalar('Accuracy/Accuracy', accuracy, epoch)\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validate(epoch):\n",
    "    model.eval()\n",
    "    #writer = SummaryWriter()\n",
    "    valid_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in valid_loader:\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        output = model(data)\n",
    "        valid_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).sum()\n",
    "\n",
    "    valid_loss /= len(valid_idx)\n",
    "    accuracy = 100. * correct.cpu().numpy() / len(valid_idx)\n",
    "    print('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        valid_loss, correct, len(valid_idx),\n",
    "        100. * correct / len(valid_idx)))\n",
    "    \n",
    "    f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "    f1.write('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        valid_loss, correct, len(valid_idx),\n",
    "        100. * correct / len(valid_idx)))\n",
    "    f1.close()\n",
    "    #writer.add_scalar('Loss/Validation_Loss', valid_loss, epoch)\n",
    "    #writer.add_scalar('Accuracy/Validation_Accuracy', accuracy, epoch)\n",
    "    return valid_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fix best model\n",
    "\n",
    "def test(epoch):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        output = model(data)\n",
    "        test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct.cpu().numpy() / len(test_loader.dataset)))\n",
    "    \n",
    "    f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "    f1.write('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct.cpu().numpy() / len(test_loader.dataset)))\n",
    "    f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_best(loss, accuracy, best_loss, best_acc):\n",
    "    if best_loss == None:\n",
    "        best_loss = loss\n",
    "        best_acc = accuracy\n",
    "        file = 'saved_models/best_save_model.p'\n",
    "        torch.save(model.state_dict(), file)\n",
    "        \n",
    "    elif loss < best_loss and accuracy > best_acc:\n",
    "        best_loss = loss\n",
    "        best_acc = accuracy\n",
    "        file = 'saved_models/best_save_model.p'\n",
    "        torch.save(model.state_dict(), file)\n",
    "    return best_loss, best_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/50000 (0%)]\tLoss: 2.320540, Accuracy: 10.94\n",
      "Train Epoch: 0 [2560/50000 (6%)]\tLoss: 2.280775, Accuracy: 14.84\n",
      "Train Epoch: 0 [5120/50000 (11%)]\tLoss: 2.126061, Accuracy: 20.70\n",
      "Train Epoch: 0 [7680/50000 (17%)]\tLoss: 1.926232, Accuracy: 24.80\n",
      "Train Epoch: 0 [10240/50000 (23%)]\tLoss: 1.914714, Accuracy: 26.76\n",
      "Train Epoch: 0 [12800/50000 (28%)]\tLoss: 1.835051, Accuracy: 31.05\n",
      "Train Epoch: 0 [15360/50000 (34%)]\tLoss: 1.837966, Accuracy: 29.69\n",
      "Train Epoch: 0 [17920/50000 (40%)]\tLoss: 1.793274, Accuracy: 33.40\n",
      "Train Epoch: 0 [20480/50000 (45%)]\tLoss: 1.700804, Accuracy: 36.33\n",
      "Train Epoch: 0 [23040/50000 (51%)]\tLoss: 1.689474, Accuracy: 38.09\n",
      "Train Epoch: 0 [25600/50000 (57%)]\tLoss: 1.707492, Accuracy: 34.57\n",
      "Train Epoch: 0 [28160/50000 (62%)]\tLoss: 1.652916, Accuracy: 34.18\n",
      "Train Epoch: 0 [30720/50000 (68%)]\tLoss: 1.667323, Accuracy: 35.74\n",
      "Train Epoch: 0 [33280/50000 (74%)]\tLoss: 1.670337, Accuracy: 35.35\n",
      "Train Epoch: 0 [35840/50000 (80%)]\tLoss: 1.587429, Accuracy: 40.43\n",
      "Train Epoch: 0 [38400/50000 (85%)]\tLoss: 1.570593, Accuracy: 44.34\n",
      "Train Epoch: 0 [40960/50000 (91%)]\tLoss: 1.452824, Accuracy: 48.24\n",
      "Train Epoch: 0 [43520/50000 (97%)]\tLoss: 1.457189, Accuracy: 45.90\n",
      "\n",
      "Validation set: Average loss: 1.6775, Accuracy: 1991/5000 (39.00%)\n",
      "\n",
      "the time of this epoch:[26.214950799942017 s]\n",
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 1.412795, Accuracy: 46.88\n",
      "Train Epoch: 1 [2560/50000 (6%)]\tLoss: 1.406076, Accuracy: 50.20\n",
      "Train Epoch: 1 [5120/50000 (11%)]\tLoss: 1.480186, Accuracy: 45.12\n",
      "Train Epoch: 1 [7680/50000 (17%)]\tLoss: 1.493258, Accuracy: 45.12\n",
      "Train Epoch: 1 [10240/50000 (23%)]\tLoss: 1.433507, Accuracy: 47.07\n",
      "Train Epoch: 1 [12800/50000 (28%)]\tLoss: 1.468902, Accuracy: 43.75\n",
      "Train Epoch: 1 [15360/50000 (34%)]\tLoss: 1.320029, Accuracy: 51.56\n",
      "Train Epoch: 1 [17920/50000 (40%)]\tLoss: 1.334651, Accuracy: 51.95\n",
      "Train Epoch: 1 [20480/50000 (45%)]\tLoss: 1.311732, Accuracy: 52.54\n",
      "Train Epoch: 1 [23040/50000 (51%)]\tLoss: 1.352187, Accuracy: 51.17\n",
      "Train Epoch: 1 [25600/50000 (57%)]\tLoss: 1.213653, Accuracy: 55.47\n",
      "Train Epoch: 1 [28160/50000 (62%)]\tLoss: 1.310560, Accuracy: 50.98\n",
      "Train Epoch: 1 [30720/50000 (68%)]\tLoss: 1.221311, Accuracy: 56.64\n",
      "Train Epoch: 1 [33280/50000 (74%)]\tLoss: 1.207177, Accuracy: 57.62\n",
      "Train Epoch: 1 [35840/50000 (80%)]\tLoss: 1.263428, Accuracy: 54.30\n",
      "Train Epoch: 1 [38400/50000 (85%)]\tLoss: 1.228597, Accuracy: 55.08\n",
      "Train Epoch: 1 [40960/50000 (91%)]\tLoss: 1.224944, Accuracy: 56.45\n",
      "Train Epoch: 1 [43520/50000 (97%)]\tLoss: 1.240943, Accuracy: 56.45\n",
      "\n",
      "Validation set: Average loss: 1.3318, Accuracy: 2709/5000 (54.00%)\n",
      "\n",
      "the time of this epoch:[26.13287377357483 s]\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.114556, Accuracy: 60.94\n",
      "Train Epoch: 2 [2560/50000 (6%)]\tLoss: 1.204834, Accuracy: 58.98\n",
      "Train Epoch: 2 [5120/50000 (11%)]\tLoss: 1.176634, Accuracy: 58.40\n",
      "Train Epoch: 2 [7680/50000 (17%)]\tLoss: 1.203647, Accuracy: 57.03\n",
      "Train Epoch: 2 [10240/50000 (23%)]\tLoss: 1.086015, Accuracy: 60.94\n",
      "Train Epoch: 2 [12800/50000 (28%)]\tLoss: 1.086472, Accuracy: 61.52\n",
      "Train Epoch: 2 [15360/50000 (34%)]\tLoss: 1.076961, Accuracy: 62.70\n",
      "Train Epoch: 2 [17920/50000 (40%)]\tLoss: 1.093811, Accuracy: 63.09\n",
      "Train Epoch: 2 [20480/50000 (45%)]\tLoss: 1.067842, Accuracy: 62.89\n",
      "Train Epoch: 2 [23040/50000 (51%)]\tLoss: 1.099586, Accuracy: 60.35\n",
      "Train Epoch: 2 [25600/50000 (57%)]\tLoss: 1.105007, Accuracy: 62.50\n",
      "Train Epoch: 2 [28160/50000 (62%)]\tLoss: 1.074413, Accuracy: 60.74\n",
      "Train Epoch: 2 [30720/50000 (68%)]\tLoss: 1.090635, Accuracy: 58.79\n",
      "Train Epoch: 2 [33280/50000 (74%)]\tLoss: 1.021698, Accuracy: 62.30\n",
      "Train Epoch: 2 [35840/50000 (80%)]\tLoss: 1.016963, Accuracy: 62.50\n",
      "Train Epoch: 2 [38400/50000 (85%)]\tLoss: 0.981796, Accuracy: 64.45\n",
      "Train Epoch: 2 [40960/50000 (91%)]\tLoss: 0.984674, Accuracy: 68.55\n",
      "Train Epoch: 2 [43520/50000 (97%)]\tLoss: 0.911410, Accuracy: 67.77\n",
      "\n",
      "Validation set: Average loss: 1.2157, Accuracy: 2900/5000 (58.00%)\n",
      "\n",
      "the time of this epoch:[26.09618306159973 s]\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 0.998346, Accuracy: 66.60\n",
      "Train Epoch: 3 [2560/50000 (6%)]\tLoss: 0.964865, Accuracy: 65.82\n",
      "Train Epoch: 3 [5120/50000 (11%)]\tLoss: 0.909887, Accuracy: 68.36\n",
      "Train Epoch: 3 [7680/50000 (17%)]\tLoss: 0.934052, Accuracy: 68.16\n",
      "Train Epoch: 3 [10240/50000 (23%)]\tLoss: 0.867124, Accuracy: 68.75\n",
      "Train Epoch: 3 [12800/50000 (28%)]\tLoss: 0.880401, Accuracy: 69.53\n",
      "Train Epoch: 3 [15360/50000 (34%)]\tLoss: 0.944588, Accuracy: 67.38\n",
      "Train Epoch: 3 [17920/50000 (40%)]\tLoss: 0.828766, Accuracy: 71.09\n",
      "Train Epoch: 3 [20480/50000 (45%)]\tLoss: 0.931437, Accuracy: 66.60\n",
      "Train Epoch: 3 [23040/50000 (51%)]\tLoss: 0.896780, Accuracy: 69.92\n",
      "Train Epoch: 3 [25600/50000 (57%)]\tLoss: 0.923730, Accuracy: 68.55\n",
      "Train Epoch: 3 [28160/50000 (62%)]\tLoss: 0.866032, Accuracy: 70.12\n",
      "Train Epoch: 3 [30720/50000 (68%)]\tLoss: 0.868874, Accuracy: 69.73\n",
      "Train Epoch: 3 [33280/50000 (74%)]\tLoss: 0.936812, Accuracy: 66.99\n",
      "Train Epoch: 3 [35840/50000 (80%)]\tLoss: 0.815978, Accuracy: 72.85\n",
      "Train Epoch: 3 [38400/50000 (85%)]\tLoss: 0.862052, Accuracy: 69.53\n",
      "Train Epoch: 3 [40960/50000 (91%)]\tLoss: 0.900489, Accuracy: 69.34\n",
      "Train Epoch: 3 [43520/50000 (97%)]\tLoss: 0.931229, Accuracy: 67.97\n",
      "\n",
      "Validation set: Average loss: 1.0194, Accuracy: 3251/5000 (65.00%)\n",
      "\n",
      "the time of this epoch:[26.075051069259644 s]\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 0.727393, Accuracy: 73.63\n",
      "Train Epoch: 4 [2560/50000 (6%)]\tLoss: 0.845884, Accuracy: 70.12\n",
      "Train Epoch: 4 [5120/50000 (11%)]\tLoss: 0.810469, Accuracy: 71.68\n",
      "Train Epoch: 4 [7680/50000 (17%)]\tLoss: 0.828638, Accuracy: 71.29\n",
      "Train Epoch: 4 [10240/50000 (23%)]\tLoss: 0.794442, Accuracy: 72.27\n",
      "Train Epoch: 4 [12800/50000 (28%)]\tLoss: 0.789883, Accuracy: 72.27\n",
      "Train Epoch: 4 [15360/50000 (34%)]\tLoss: 0.891224, Accuracy: 68.95\n",
      "Train Epoch: 4 [17920/50000 (40%)]\tLoss: 0.827291, Accuracy: 70.51\n",
      "Train Epoch: 4 [20480/50000 (45%)]\tLoss: 0.715297, Accuracy: 73.44\n",
      "Train Epoch: 4 [23040/50000 (51%)]\tLoss: 0.773382, Accuracy: 74.61\n",
      "Train Epoch: 4 [25600/50000 (57%)]\tLoss: 0.837342, Accuracy: 72.07\n",
      "Train Epoch: 4 [28160/50000 (62%)]\tLoss: 0.730213, Accuracy: 75.98\n",
      "Train Epoch: 4 [30720/50000 (68%)]\tLoss: 0.787956, Accuracy: 72.07\n",
      "Train Epoch: 4 [33280/50000 (74%)]\tLoss: 0.728924, Accuracy: 74.61\n",
      "Train Epoch: 4 [35840/50000 (80%)]\tLoss: 0.721162, Accuracy: 75.59\n",
      "Train Epoch: 4 [38400/50000 (85%)]\tLoss: 0.786173, Accuracy: 74.02\n",
      "Train Epoch: 4 [40960/50000 (91%)]\tLoss: 0.773776, Accuracy: 74.02\n",
      "Train Epoch: 4 [43520/50000 (97%)]\tLoss: 0.672541, Accuracy: 77.15\n",
      "\n",
      "Validation set: Average loss: 0.8446, Accuracy: 3544/5000 (70.00%)\n",
      "\n",
      "the time of this epoch:[26.084761381149292 s]\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.613761, Accuracy: 79.10\n",
      "Train Epoch: 5 [2560/50000 (6%)]\tLoss: 0.697802, Accuracy: 77.54\n",
      "Train Epoch: 5 [5120/50000 (11%)]\tLoss: 0.655157, Accuracy: 75.59\n",
      "Train Epoch: 5 [7680/50000 (17%)]\tLoss: 0.708528, Accuracy: 74.80\n",
      "Train Epoch: 5 [10240/50000 (23%)]\tLoss: 0.678940, Accuracy: 77.54\n",
      "Train Epoch: 5 [12800/50000 (28%)]\tLoss: 0.677148, Accuracy: 76.56\n",
      "Train Epoch: 5 [15360/50000 (34%)]\tLoss: 0.724032, Accuracy: 74.02\n",
      "Train Epoch: 5 [17920/50000 (40%)]\tLoss: 0.735698, Accuracy: 73.05\n",
      "Train Epoch: 5 [20480/50000 (45%)]\tLoss: 0.693181, Accuracy: 75.00\n",
      "Train Epoch: 5 [23040/50000 (51%)]\tLoss: 0.697310, Accuracy: 75.00\n",
      "Train Epoch: 5 [25600/50000 (57%)]\tLoss: 0.662396, Accuracy: 76.76\n",
      "Train Epoch: 5 [28160/50000 (62%)]\tLoss: 0.642166, Accuracy: 78.71\n",
      "Train Epoch: 5 [30720/50000 (68%)]\tLoss: 0.676162, Accuracy: 75.59\n",
      "Train Epoch: 5 [33280/50000 (74%)]\tLoss: 0.763105, Accuracy: 73.24\n",
      "Train Epoch: 5 [35840/50000 (80%)]\tLoss: 0.666190, Accuracy: 75.78\n",
      "Train Epoch: 5 [38400/50000 (85%)]\tLoss: 0.704380, Accuracy: 74.22\n",
      "Train Epoch: 5 [40960/50000 (91%)]\tLoss: 0.762346, Accuracy: 72.46\n",
      "Train Epoch: 5 [43520/50000 (97%)]\tLoss: 0.704842, Accuracy: 78.12\n",
      "\n",
      "Validation set: Average loss: 1.4044, Accuracy: 2987/5000 (59.00%)\n",
      "\n",
      "the time of this epoch:[26.286314964294434 s]\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.581617, Accuracy: 79.49\n",
      "Train Epoch: 6 [2560/50000 (6%)]\tLoss: 0.627460, Accuracy: 78.52\n",
      "Train Epoch: 6 [5120/50000 (11%)]\tLoss: 0.556379, Accuracy: 81.05\n",
      "Train Epoch: 6 [7680/50000 (17%)]\tLoss: 0.708618, Accuracy: 75.98\n",
      "Train Epoch: 6 [10240/50000 (23%)]\tLoss: 0.685291, Accuracy: 76.76\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [12800/50000 (28%)]\tLoss: 0.668533, Accuracy: 75.59\n",
      "Train Epoch: 6 [15360/50000 (34%)]\tLoss: 0.663161, Accuracy: 77.93\n",
      "Train Epoch: 6 [17920/50000 (40%)]\tLoss: 0.607729, Accuracy: 78.91\n",
      "Train Epoch: 6 [20480/50000 (45%)]\tLoss: 0.591236, Accuracy: 79.49\n",
      "Train Epoch: 6 [23040/50000 (51%)]\tLoss: 0.635040, Accuracy: 77.73\n",
      "Train Epoch: 6 [25600/50000 (57%)]\tLoss: 0.614128, Accuracy: 79.10\n",
      "Train Epoch: 6 [28160/50000 (62%)]\tLoss: 0.611652, Accuracy: 77.73\n",
      "Train Epoch: 6 [30720/50000 (68%)]\tLoss: 0.664279, Accuracy: 75.39\n",
      "Train Epoch: 6 [33280/50000 (74%)]\tLoss: 0.563895, Accuracy: 79.10\n",
      "Train Epoch: 6 [35840/50000 (80%)]\tLoss: 0.562376, Accuracy: 80.66\n",
      "Train Epoch: 6 [38400/50000 (85%)]\tLoss: 0.578421, Accuracy: 78.52\n",
      "Train Epoch: 6 [40960/50000 (91%)]\tLoss: 0.575584, Accuracy: 79.10\n",
      "Train Epoch: 6 [43520/50000 (97%)]\tLoss: 0.616315, Accuracy: 77.15\n",
      "\n",
      "Validation set: Average loss: 0.8642, Accuracy: 3606/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[26.16749358177185 s]\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.579126, Accuracy: 79.30\n",
      "Train Epoch: 7 [2560/50000 (6%)]\tLoss: 0.589677, Accuracy: 78.91\n",
      "Train Epoch: 7 [5120/50000 (11%)]\tLoss: 0.536370, Accuracy: 82.23\n",
      "Train Epoch: 7 [7680/50000 (17%)]\tLoss: 0.568017, Accuracy: 80.47\n",
      "Train Epoch: 7 [10240/50000 (23%)]\tLoss: 0.626665, Accuracy: 80.08\n",
      "Train Epoch: 7 [12800/50000 (28%)]\tLoss: 0.603724, Accuracy: 80.66\n",
      "Train Epoch: 7 [15360/50000 (34%)]\tLoss: 0.646516, Accuracy: 77.93\n",
      "Train Epoch: 7 [17920/50000 (40%)]\tLoss: 0.574849, Accuracy: 80.66\n",
      "Train Epoch: 7 [20480/50000 (45%)]\tLoss: 0.527852, Accuracy: 81.84\n",
      "Train Epoch: 7 [23040/50000 (51%)]\tLoss: 0.527987, Accuracy: 83.40\n",
      "Train Epoch: 7 [25600/50000 (57%)]\tLoss: 0.520497, Accuracy: 81.84\n",
      "Train Epoch: 7 [28160/50000 (62%)]\tLoss: 0.592766, Accuracy: 77.34\n",
      "Train Epoch: 7 [30720/50000 (68%)]\tLoss: 0.563806, Accuracy: 80.08\n",
      "Train Epoch: 7 [33280/50000 (74%)]\tLoss: 0.585973, Accuracy: 81.05\n",
      "Train Epoch: 7 [35840/50000 (80%)]\tLoss: 0.544922, Accuracy: 79.69\n",
      "Train Epoch: 7 [38400/50000 (85%)]\tLoss: 0.526118, Accuracy: 81.64\n",
      "Train Epoch: 7 [40960/50000 (91%)]\tLoss: 0.603151, Accuracy: 81.05\n",
      "Train Epoch: 7 [43520/50000 (97%)]\tLoss: 0.549647, Accuracy: 80.08\n",
      "\n",
      "Validation set: Average loss: 0.8899, Accuracy: 3495/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[26.131977796554565 s]\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 0.508521, Accuracy: 81.45\n",
      "Train Epoch: 8 [2560/50000 (6%)]\tLoss: 0.529044, Accuracy: 82.62\n",
      "Train Epoch: 8 [5120/50000 (11%)]\tLoss: 0.564970, Accuracy: 80.66\n",
      "Train Epoch: 8 [7680/50000 (17%)]\tLoss: 0.537635, Accuracy: 81.45\n",
      "Train Epoch: 8 [10240/50000 (23%)]\tLoss: 0.515001, Accuracy: 81.05\n",
      "Train Epoch: 8 [12800/50000 (28%)]\tLoss: 0.625176, Accuracy: 79.10\n",
      "Train Epoch: 8 [15360/50000 (34%)]\tLoss: 0.506334, Accuracy: 80.27\n",
      "Train Epoch: 8 [17920/50000 (40%)]\tLoss: 0.512354, Accuracy: 82.42\n",
      "Train Epoch: 8 [20480/50000 (45%)]\tLoss: 0.508944, Accuracy: 82.23\n",
      "Train Epoch: 8 [23040/50000 (51%)]\tLoss: 0.539258, Accuracy: 80.86\n",
      "Train Epoch: 8 [25600/50000 (57%)]\tLoss: 0.455675, Accuracy: 83.20\n",
      "Train Epoch: 8 [28160/50000 (62%)]\tLoss: 0.506671, Accuracy: 82.42\n",
      "Train Epoch: 8 [30720/50000 (68%)]\tLoss: 0.488501, Accuracy: 83.01\n",
      "Train Epoch: 8 [33280/50000 (74%)]\tLoss: 0.594668, Accuracy: 79.10\n",
      "Train Epoch: 8 [35840/50000 (80%)]\tLoss: 0.575923, Accuracy: 81.05\n",
      "Train Epoch: 8 [38400/50000 (85%)]\tLoss: 0.548076, Accuracy: 80.08\n",
      "Train Epoch: 8 [40960/50000 (91%)]\tLoss: 0.543169, Accuracy: 81.64\n",
      "Train Epoch: 8 [43520/50000 (97%)]\tLoss: 0.561625, Accuracy: 80.08\n",
      "\n",
      "Validation set: Average loss: 0.7079, Accuracy: 3811/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[26.397221565246582 s]\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 0.518718, Accuracy: 82.81\n",
      "Train Epoch: 9 [2560/50000 (6%)]\tLoss: 0.461620, Accuracy: 84.77\n",
      "Train Epoch: 9 [5120/50000 (11%)]\tLoss: 0.459146, Accuracy: 84.57\n",
      "Train Epoch: 9 [7680/50000 (17%)]\tLoss: 0.517355, Accuracy: 82.42\n",
      "Train Epoch: 9 [10240/50000 (23%)]\tLoss: 0.407585, Accuracy: 87.11\n",
      "Train Epoch: 9 [12800/50000 (28%)]\tLoss: 0.525852, Accuracy: 79.10\n",
      "Train Epoch: 9 [15360/50000 (34%)]\tLoss: 0.491280, Accuracy: 83.98\n",
      "Train Epoch: 9 [17920/50000 (40%)]\tLoss: 0.394539, Accuracy: 85.94\n",
      "Train Epoch: 9 [20480/50000 (45%)]\tLoss: 0.502874, Accuracy: 83.20\n",
      "Train Epoch: 9 [23040/50000 (51%)]\tLoss: 0.465275, Accuracy: 84.18\n",
      "Train Epoch: 9 [25600/50000 (57%)]\tLoss: 0.565264, Accuracy: 82.03\n",
      "Train Epoch: 9 [28160/50000 (62%)]\tLoss: 0.522699, Accuracy: 83.01\n",
      "Train Epoch: 9 [30720/50000 (68%)]\tLoss: 0.523792, Accuracy: 81.05\n",
      "Train Epoch: 9 [33280/50000 (74%)]\tLoss: 0.501014, Accuracy: 82.81\n",
      "Train Epoch: 9 [35840/50000 (80%)]\tLoss: 0.587199, Accuracy: 80.27\n",
      "Train Epoch: 9 [38400/50000 (85%)]\tLoss: 0.478784, Accuracy: 84.77\n",
      "Train Epoch: 9 [40960/50000 (91%)]\tLoss: 0.483180, Accuracy: 83.79\n",
      "Train Epoch: 9 [43520/50000 (97%)]\tLoss: 0.505470, Accuracy: 82.62\n",
      "\n",
      "Validation set: Average loss: 0.7223, Accuracy: 3758/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[26.130029678344727 s]\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 0.436166, Accuracy: 85.94\n",
      "Train Epoch: 10 [2560/50000 (6%)]\tLoss: 0.419661, Accuracy: 86.33\n",
      "Train Epoch: 10 [5120/50000 (11%)]\tLoss: 0.446864, Accuracy: 83.98\n",
      "Train Epoch: 10 [7680/50000 (17%)]\tLoss: 0.428719, Accuracy: 87.11\n",
      "Train Epoch: 10 [10240/50000 (23%)]\tLoss: 0.480514, Accuracy: 84.18\n",
      "Train Epoch: 10 [12800/50000 (28%)]\tLoss: 0.553534, Accuracy: 82.23\n",
      "Train Epoch: 10 [15360/50000 (34%)]\tLoss: 0.393180, Accuracy: 87.30\n",
      "Train Epoch: 10 [17920/50000 (40%)]\tLoss: 0.499260, Accuracy: 83.98\n",
      "Train Epoch: 10 [20480/50000 (45%)]\tLoss: 0.469991, Accuracy: 85.35\n",
      "Train Epoch: 10 [23040/50000 (51%)]\tLoss: 0.430152, Accuracy: 83.40\n",
      "Train Epoch: 10 [25600/50000 (57%)]\tLoss: 0.455469, Accuracy: 84.57\n",
      "Train Epoch: 10 [28160/50000 (62%)]\tLoss: 0.444512, Accuracy: 83.79\n",
      "Train Epoch: 10 [30720/50000 (68%)]\tLoss: 0.452943, Accuracy: 84.18\n",
      "Train Epoch: 10 [33280/50000 (74%)]\tLoss: 0.493032, Accuracy: 82.23\n",
      "Train Epoch: 10 [35840/50000 (80%)]\tLoss: 0.509029, Accuracy: 82.62\n",
      "Train Epoch: 10 [38400/50000 (85%)]\tLoss: 0.531460, Accuracy: 82.03\n",
      "Train Epoch: 10 [40960/50000 (91%)]\tLoss: 0.579513, Accuracy: 80.27\n",
      "Train Epoch: 10 [43520/50000 (97%)]\tLoss: 0.499972, Accuracy: 81.45\n",
      "\n",
      "Validation set: Average loss: 0.7547, Accuracy: 3731/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[26.058083057403564 s]\n",
      "\n",
      "Test set: Average loss: 0.7707, Accuracy: 7388/10000 (73.88%)\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 0.433066, Accuracy: 85.74\n",
      "Train Epoch: 11 [2560/50000 (6%)]\tLoss: 0.429425, Accuracy: 84.96\n",
      "Train Epoch: 11 [5120/50000 (11%)]\tLoss: 0.486103, Accuracy: 83.79\n",
      "Train Epoch: 11 [7680/50000 (17%)]\tLoss: 0.462290, Accuracy: 85.55\n",
      "Train Epoch: 11 [10240/50000 (23%)]\tLoss: 0.439756, Accuracy: 85.35\n",
      "Train Epoch: 11 [12800/50000 (28%)]\tLoss: 0.547711, Accuracy: 81.64\n",
      "Train Epoch: 11 [15360/50000 (34%)]\tLoss: 0.423519, Accuracy: 83.79\n",
      "Train Epoch: 11 [17920/50000 (40%)]\tLoss: 0.389531, Accuracy: 87.50\n",
      "Train Epoch: 11 [20480/50000 (45%)]\tLoss: 0.414843, Accuracy: 86.33\n",
      "Train Epoch: 11 [23040/50000 (51%)]\tLoss: 0.417126, Accuracy: 83.01\n",
      "Train Epoch: 11 [25600/50000 (57%)]\tLoss: 0.487249, Accuracy: 83.01\n",
      "Train Epoch: 11 [28160/50000 (62%)]\tLoss: 0.424948, Accuracy: 85.35\n",
      "Train Epoch: 11 [30720/50000 (68%)]\tLoss: 0.461886, Accuracy: 83.79\n",
      "Train Epoch: 11 [33280/50000 (74%)]\tLoss: 0.456072, Accuracy: 81.64\n",
      "Train Epoch: 11 [35840/50000 (80%)]\tLoss: 0.461620, Accuracy: 84.18\n",
      "Train Epoch: 11 [38400/50000 (85%)]\tLoss: 0.471882, Accuracy: 83.79\n",
      "Train Epoch: 11 [40960/50000 (91%)]\tLoss: 0.510064, Accuracy: 85.35\n",
      "Train Epoch: 11 [43520/50000 (97%)]\tLoss: 0.424177, Accuracy: 84.57\n",
      "\n",
      "Validation set: Average loss: 0.5070, Accuracy: 4125/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.511013507843018 s]\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 0.381031, Accuracy: 86.72\n",
      "Train Epoch: 12 [2560/50000 (6%)]\tLoss: 0.404288, Accuracy: 86.33\n",
      "Train Epoch: 12 [5120/50000 (11%)]\tLoss: 0.417646, Accuracy: 85.16\n",
      "Train Epoch: 12 [7680/50000 (17%)]\tLoss: 0.415313, Accuracy: 85.16\n",
      "Train Epoch: 12 [10240/50000 (23%)]\tLoss: 0.355409, Accuracy: 87.50\n",
      "Train Epoch: 12 [12800/50000 (28%)]\tLoss: 0.399461, Accuracy: 85.16\n",
      "Train Epoch: 12 [15360/50000 (34%)]\tLoss: 0.439676, Accuracy: 83.59\n",
      "Train Epoch: 12 [17920/50000 (40%)]\tLoss: 0.474273, Accuracy: 84.77\n",
      "Train Epoch: 12 [20480/50000 (45%)]\tLoss: 0.378044, Accuracy: 87.89\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 12 [23040/50000 (51%)]\tLoss: 0.433716, Accuracy: 84.57\n",
      "Train Epoch: 12 [25600/50000 (57%)]\tLoss: 0.426053, Accuracy: 85.55\n",
      "Train Epoch: 12 [28160/50000 (62%)]\tLoss: 0.384438, Accuracy: 86.13\n",
      "Train Epoch: 12 [30720/50000 (68%)]\tLoss: 0.411371, Accuracy: 85.35\n",
      "Train Epoch: 12 [33280/50000 (74%)]\tLoss: 0.374838, Accuracy: 87.89\n",
      "Train Epoch: 12 [35840/50000 (80%)]\tLoss: 0.453261, Accuracy: 85.55\n",
      "Train Epoch: 12 [38400/50000 (85%)]\tLoss: 0.389578, Accuracy: 85.55\n",
      "Train Epoch: 12 [40960/50000 (91%)]\tLoss: 0.406190, Accuracy: 86.13\n",
      "Train Epoch: 12 [43520/50000 (97%)]\tLoss: 0.412077, Accuracy: 87.11\n",
      "\n",
      "Validation set: Average loss: 0.5482, Accuracy: 4062/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[26.086731433868408 s]\n",
      "\n",
      "Test set: Average loss: 0.5517, Accuracy: 8206/10000 (82.06%)\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 0.348192, Accuracy: 88.67\n",
      "Train Epoch: 13 [2560/50000 (6%)]\tLoss: 0.371063, Accuracy: 87.30\n",
      "Train Epoch: 13 [5120/50000 (11%)]\tLoss: 0.423601, Accuracy: 85.74\n",
      "Train Epoch: 13 [7680/50000 (17%)]\tLoss: 0.381169, Accuracy: 86.72\n",
      "Train Epoch: 13 [10240/50000 (23%)]\tLoss: 0.331046, Accuracy: 89.65\n",
      "Train Epoch: 13 [12800/50000 (28%)]\tLoss: 0.395062, Accuracy: 85.94\n",
      "Train Epoch: 13 [15360/50000 (34%)]\tLoss: 0.347979, Accuracy: 87.70\n",
      "Train Epoch: 13 [17920/50000 (40%)]\tLoss: 0.362743, Accuracy: 86.13\n",
      "Train Epoch: 13 [20480/50000 (45%)]\tLoss: 0.430071, Accuracy: 83.20\n",
      "Train Epoch: 13 [23040/50000 (51%)]\tLoss: 0.384221, Accuracy: 87.50\n",
      "Train Epoch: 13 [25600/50000 (57%)]\tLoss: 0.351392, Accuracy: 88.67\n",
      "Train Epoch: 13 [28160/50000 (62%)]\tLoss: 0.434887, Accuracy: 85.16\n",
      "Train Epoch: 13 [30720/50000 (68%)]\tLoss: 0.467466, Accuracy: 84.38\n",
      "Train Epoch: 13 [33280/50000 (74%)]\tLoss: 0.425341, Accuracy: 86.52\n",
      "Train Epoch: 13 [35840/50000 (80%)]\tLoss: 0.378994, Accuracy: 87.30\n",
      "Train Epoch: 13 [38400/50000 (85%)]\tLoss: 0.420937, Accuracy: 84.96\n",
      "Train Epoch: 13 [40960/50000 (91%)]\tLoss: 0.430753, Accuracy: 85.35\n",
      "Train Epoch: 13 [43520/50000 (97%)]\tLoss: 0.353871, Accuracy: 87.30\n",
      "\n",
      "Validation set: Average loss: 0.5554, Accuracy: 4066/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[28.46344256401062 s]\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 0.377121, Accuracy: 86.52\n",
      "Train Epoch: 14 [2560/50000 (6%)]\tLoss: 0.346016, Accuracy: 88.28\n",
      "Train Epoch: 14 [5120/50000 (11%)]\tLoss: 0.302523, Accuracy: 91.02\n",
      "Train Epoch: 14 [7680/50000 (17%)]\tLoss: 0.439584, Accuracy: 84.57\n",
      "Train Epoch: 14 [10240/50000 (23%)]\tLoss: 0.388914, Accuracy: 87.30\n",
      "Train Epoch: 14 [12800/50000 (28%)]\tLoss: 0.410375, Accuracy: 84.77\n",
      "Train Epoch: 14 [15360/50000 (34%)]\tLoss: 0.428145, Accuracy: 84.96\n",
      "Train Epoch: 14 [17920/50000 (40%)]\tLoss: 0.385913, Accuracy: 86.72\n",
      "Train Epoch: 14 [20480/50000 (45%)]\tLoss: 0.344906, Accuracy: 87.50\n",
      "Train Epoch: 14 [23040/50000 (51%)]\tLoss: 0.391179, Accuracy: 86.13\n",
      "Train Epoch: 14 [25600/50000 (57%)]\tLoss: 0.399653, Accuracy: 86.52\n",
      "Train Epoch: 14 [28160/50000 (62%)]\tLoss: 0.376099, Accuracy: 86.52\n",
      "Train Epoch: 14 [30720/50000 (68%)]\tLoss: 0.387775, Accuracy: 87.70\n",
      "Train Epoch: 14 [33280/50000 (74%)]\tLoss: 0.422966, Accuracy: 84.57\n",
      "Train Epoch: 14 [35840/50000 (80%)]\tLoss: 0.384059, Accuracy: 86.72\n",
      "Train Epoch: 14 [38400/50000 (85%)]\tLoss: 0.325525, Accuracy: 89.06\n",
      "Train Epoch: 14 [40960/50000 (91%)]\tLoss: 0.363797, Accuracy: 87.11\n",
      "Train Epoch: 14 [43520/50000 (97%)]\tLoss: 0.436441, Accuracy: 85.35\n",
      "\n",
      "Validation set: Average loss: 0.6106, Accuracy: 4001/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[26.06699275970459 s]\n",
      "\n",
      "Test set: Average loss: 0.5984, Accuracy: 8030/10000 (80.30%)\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 0.329603, Accuracy: 89.06\n",
      "Train Epoch: 15 [2560/50000 (6%)]\tLoss: 0.398584, Accuracy: 85.94\n",
      "Train Epoch: 15 [5120/50000 (11%)]\tLoss: 0.397092, Accuracy: 85.16\n",
      "Train Epoch: 15 [7680/50000 (17%)]\tLoss: 0.307820, Accuracy: 88.67\n",
      "Train Epoch: 15 [10240/50000 (23%)]\tLoss: 0.296251, Accuracy: 90.82\n",
      "Train Epoch: 15 [12800/50000 (28%)]\tLoss: 0.366152, Accuracy: 86.72\n",
      "Train Epoch: 15 [15360/50000 (34%)]\tLoss: 0.416715, Accuracy: 87.70\n",
      "Train Epoch: 15 [17920/50000 (40%)]\tLoss: 0.346812, Accuracy: 88.87\n",
      "Train Epoch: 15 [20480/50000 (45%)]\tLoss: 0.411569, Accuracy: 85.55\n",
      "Train Epoch: 15 [23040/50000 (51%)]\tLoss: 0.381110, Accuracy: 86.91\n",
      "Train Epoch: 15 [25600/50000 (57%)]\tLoss: 0.427191, Accuracy: 85.16\n",
      "Train Epoch: 15 [28160/50000 (62%)]\tLoss: 0.403176, Accuracy: 86.91\n",
      "Train Epoch: 15 [30720/50000 (68%)]\tLoss: 0.351659, Accuracy: 88.09\n",
      "Train Epoch: 15 [33280/50000 (74%)]\tLoss: 0.355041, Accuracy: 86.52\n",
      "Train Epoch: 15 [35840/50000 (80%)]\tLoss: 0.372280, Accuracy: 88.09\n",
      "Train Epoch: 15 [38400/50000 (85%)]\tLoss: 0.393690, Accuracy: 86.52\n",
      "Train Epoch: 15 [40960/50000 (91%)]\tLoss: 0.404033, Accuracy: 87.50\n",
      "Train Epoch: 15 [43520/50000 (97%)]\tLoss: 0.408039, Accuracy: 85.74\n",
      "\n",
      "Validation set: Average loss: 0.6489, Accuracy: 3908/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[28.471410989761353 s]\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 0.338489, Accuracy: 87.89\n",
      "Train Epoch: 16 [2560/50000 (6%)]\tLoss: 0.379218, Accuracy: 87.50\n",
      "Train Epoch: 16 [5120/50000 (11%)]\tLoss: 0.306067, Accuracy: 90.23\n",
      "Train Epoch: 16 [7680/50000 (17%)]\tLoss: 0.366572, Accuracy: 88.48\n",
      "Train Epoch: 16 [10240/50000 (23%)]\tLoss: 0.390976, Accuracy: 87.11\n",
      "Train Epoch: 16 [12800/50000 (28%)]\tLoss: 0.314779, Accuracy: 88.87\n",
      "Train Epoch: 16 [15360/50000 (34%)]\tLoss: 0.348564, Accuracy: 88.67\n",
      "Train Epoch: 16 [17920/50000 (40%)]\tLoss: 0.385620, Accuracy: 86.91\n",
      "Train Epoch: 16 [20480/50000 (45%)]\tLoss: 0.398560, Accuracy: 87.30\n",
      "Train Epoch: 16 [23040/50000 (51%)]\tLoss: 0.337853, Accuracy: 88.09\n",
      "Train Epoch: 16 [25600/50000 (57%)]\tLoss: 0.386823, Accuracy: 87.89\n",
      "Train Epoch: 16 [28160/50000 (62%)]\tLoss: 0.299888, Accuracy: 89.65\n",
      "Train Epoch: 16 [30720/50000 (68%)]\tLoss: 0.385800, Accuracy: 86.72\n",
      "Train Epoch: 16 [33280/50000 (74%)]\tLoss: 0.309116, Accuracy: 88.28\n",
      "Train Epoch: 16 [35840/50000 (80%)]\tLoss: 0.355432, Accuracy: 87.70\n",
      "Train Epoch: 16 [38400/50000 (85%)]\tLoss: 0.374129, Accuracy: 87.30\n",
      "Train Epoch: 16 [40960/50000 (91%)]\tLoss: 0.383269, Accuracy: 86.52\n",
      "Train Epoch: 16 [43520/50000 (97%)]\tLoss: 0.359481, Accuracy: 86.33\n",
      "\n",
      "Validation set: Average loss: 0.7611, Accuracy: 3762/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[26.107715845108032 s]\n",
      "\n",
      "Test set: Average loss: 0.7863, Accuracy: 7493/10000 (74.93%)\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 0.366098, Accuracy: 88.28\n",
      "Train Epoch: 17 [2560/50000 (6%)]\tLoss: 0.337477, Accuracy: 90.82\n",
      "Train Epoch: 17 [5120/50000 (11%)]\tLoss: 0.290606, Accuracy: 90.04\n",
      "Train Epoch: 17 [7680/50000 (17%)]\tLoss: 0.323087, Accuracy: 88.09\n",
      "Train Epoch: 17 [10240/50000 (23%)]\tLoss: 0.342537, Accuracy: 88.09\n",
      "Train Epoch: 17 [12800/50000 (28%)]\tLoss: 0.349970, Accuracy: 88.48\n",
      "Train Epoch: 17 [15360/50000 (34%)]\tLoss: 0.393700, Accuracy: 86.13\n",
      "Train Epoch: 17 [17920/50000 (40%)]\tLoss: 0.290741, Accuracy: 91.02\n",
      "Train Epoch: 17 [20480/50000 (45%)]\tLoss: 0.412901, Accuracy: 84.57\n",
      "Train Epoch: 17 [23040/50000 (51%)]\tLoss: 0.350330, Accuracy: 87.89\n",
      "Train Epoch: 17 [25600/50000 (57%)]\tLoss: 0.335972, Accuracy: 89.26\n",
      "Train Epoch: 17 [28160/50000 (62%)]\tLoss: 0.394106, Accuracy: 86.72\n",
      "Train Epoch: 17 [30720/50000 (68%)]\tLoss: 0.379033, Accuracy: 87.89\n",
      "Train Epoch: 17 [33280/50000 (74%)]\tLoss: 0.334990, Accuracy: 87.70\n",
      "Train Epoch: 17 [35840/50000 (80%)]\tLoss: 0.394669, Accuracy: 87.50\n",
      "Train Epoch: 17 [38400/50000 (85%)]\tLoss: 0.393223, Accuracy: 87.50\n",
      "Train Epoch: 17 [40960/50000 (91%)]\tLoss: 0.420706, Accuracy: 85.55\n",
      "Train Epoch: 17 [43520/50000 (97%)]\tLoss: 0.405547, Accuracy: 86.13\n",
      "\n",
      "Validation set: Average loss: 0.5218, Accuracy: 4124/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.49174952507019 s]\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.370739, Accuracy: 86.33\n",
      "Train Epoch: 18 [2560/50000 (6%)]\tLoss: 0.297997, Accuracy: 89.65\n",
      "Train Epoch: 18 [5120/50000 (11%)]\tLoss: 0.371400, Accuracy: 86.52\n",
      "Train Epoch: 18 [7680/50000 (17%)]\tLoss: 0.349745, Accuracy: 87.50\n",
      "Train Epoch: 18 [10240/50000 (23%)]\tLoss: 0.290248, Accuracy: 90.04\n",
      "Train Epoch: 18 [12800/50000 (28%)]\tLoss: 0.286152, Accuracy: 90.04\n",
      "Train Epoch: 18 [15360/50000 (34%)]\tLoss: 0.346108, Accuracy: 88.28\n",
      "Train Epoch: 18 [17920/50000 (40%)]\tLoss: 0.392209, Accuracy: 86.13\n",
      "Train Epoch: 18 [20480/50000 (45%)]\tLoss: 0.382258, Accuracy: 86.52\n",
      "Train Epoch: 18 [23040/50000 (51%)]\tLoss: 0.409427, Accuracy: 86.13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 18 [25600/50000 (57%)]\tLoss: 0.339683, Accuracy: 87.11\n",
      "Train Epoch: 18 [28160/50000 (62%)]\tLoss: 0.303571, Accuracy: 89.26\n",
      "Train Epoch: 18 [30720/50000 (68%)]\tLoss: 0.336795, Accuracy: 88.48\n",
      "Train Epoch: 18 [33280/50000 (74%)]\tLoss: 0.330799, Accuracy: 89.26\n",
      "Train Epoch: 18 [35840/50000 (80%)]\tLoss: 0.313879, Accuracy: 89.06\n",
      "Train Epoch: 18 [38400/50000 (85%)]\tLoss: 0.361262, Accuracy: 88.28\n",
      "Train Epoch: 18 [40960/50000 (91%)]\tLoss: 0.382246, Accuracy: 89.26\n",
      "Train Epoch: 18 [43520/50000 (97%)]\tLoss: 0.289037, Accuracy: 87.89\n",
      "\n",
      "Validation set: Average loss: 0.6454, Accuracy: 3946/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[26.100104331970215 s]\n",
      "\n",
      "Test set: Average loss: 0.6682, Accuracy: 7892/10000 (78.92%)\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 0.329498, Accuracy: 88.87\n",
      "Train Epoch: 19 [2560/50000 (6%)]\tLoss: 0.352383, Accuracy: 87.89\n",
      "Train Epoch: 19 [5120/50000 (11%)]\tLoss: 0.333722, Accuracy: 88.28\n",
      "Train Epoch: 19 [7680/50000 (17%)]\tLoss: 0.348966, Accuracy: 88.09\n",
      "Train Epoch: 19 [10240/50000 (23%)]\tLoss: 0.310286, Accuracy: 87.89\n",
      "Train Epoch: 19 [12800/50000 (28%)]\tLoss: 0.372956, Accuracy: 88.28\n",
      "Train Epoch: 19 [15360/50000 (34%)]\tLoss: 0.310329, Accuracy: 89.84\n",
      "Train Epoch: 19 [17920/50000 (40%)]\tLoss: 0.352371, Accuracy: 88.87\n",
      "Train Epoch: 19 [20480/50000 (45%)]\tLoss: 0.355299, Accuracy: 88.67\n",
      "Train Epoch: 19 [23040/50000 (51%)]\tLoss: 0.318917, Accuracy: 87.30\n",
      "Train Epoch: 19 [25600/50000 (57%)]\tLoss: 0.320676, Accuracy: 88.48\n",
      "Train Epoch: 19 [28160/50000 (62%)]\tLoss: 0.337984, Accuracy: 89.26\n",
      "Train Epoch: 19 [30720/50000 (68%)]\tLoss: 0.393037, Accuracy: 87.70\n",
      "Train Epoch: 19 [33280/50000 (74%)]\tLoss: 0.337316, Accuracy: 88.87\n",
      "Train Epoch: 19 [35840/50000 (80%)]\tLoss: 0.337468, Accuracy: 89.26\n",
      "Train Epoch: 19 [38400/50000 (85%)]\tLoss: 0.370999, Accuracy: 86.33\n",
      "Train Epoch: 19 [40960/50000 (91%)]\tLoss: 0.302478, Accuracy: 88.67\n",
      "Train Epoch: 19 [43520/50000 (97%)]\tLoss: 0.373368, Accuracy: 87.89\n",
      "\n",
      "Validation set: Average loss: 0.4656, Accuracy: 4206/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[28.523446321487427 s]\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 0.313498, Accuracy: 88.09\n",
      "Train Epoch: 20 [2560/50000 (6%)]\tLoss: 0.290700, Accuracy: 89.45\n",
      "Train Epoch: 20 [5120/50000 (11%)]\tLoss: 0.297651, Accuracy: 90.04\n",
      "Train Epoch: 20 [7680/50000 (17%)]\tLoss: 0.316395, Accuracy: 87.89\n",
      "Train Epoch: 20 [10240/50000 (23%)]\tLoss: 0.313562, Accuracy: 89.45\n",
      "Train Epoch: 20 [12800/50000 (28%)]\tLoss: 0.286755, Accuracy: 89.84\n",
      "Train Epoch: 20 [15360/50000 (34%)]\tLoss: 0.318445, Accuracy: 89.06\n",
      "Train Epoch: 20 [17920/50000 (40%)]\tLoss: 0.340730, Accuracy: 88.67\n",
      "Train Epoch: 20 [20480/50000 (45%)]\tLoss: 0.304936, Accuracy: 89.65\n",
      "Train Epoch: 20 [23040/50000 (51%)]\tLoss: 0.271328, Accuracy: 90.82\n",
      "Train Epoch: 20 [25600/50000 (57%)]\tLoss: 0.359433, Accuracy: 87.30\n",
      "Train Epoch: 20 [28160/50000 (62%)]\tLoss: 0.307149, Accuracy: 89.26\n",
      "Train Epoch: 20 [30720/50000 (68%)]\tLoss: 0.362396, Accuracy: 85.16\n",
      "Train Epoch: 20 [33280/50000 (74%)]\tLoss: 0.368672, Accuracy: 88.09\n",
      "Train Epoch: 20 [35840/50000 (80%)]\tLoss: 0.357640, Accuracy: 87.11\n",
      "Train Epoch: 20 [38400/50000 (85%)]\tLoss: 0.264400, Accuracy: 90.23\n",
      "Train Epoch: 20 [40960/50000 (91%)]\tLoss: 0.392076, Accuracy: 87.50\n",
      "Train Epoch: 20 [43520/50000 (97%)]\tLoss: 0.340844, Accuracy: 88.28\n",
      "\n",
      "Validation set: Average loss: 0.5229, Accuracy: 4143/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[26.131993770599365 s]\n",
      "\n",
      "Test set: Average loss: 0.5215, Accuracy: 8301/10000 (83.01%)\n",
      "\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 0.253230, Accuracy: 91.02\n",
      "Train Epoch: 21 [2560/50000 (6%)]\tLoss: 0.306965, Accuracy: 90.43\n",
      "Train Epoch: 21 [5120/50000 (11%)]\tLoss: 0.312476, Accuracy: 90.43\n",
      "Train Epoch: 21 [7680/50000 (17%)]\tLoss: 0.289510, Accuracy: 90.43\n",
      "Train Epoch: 21 [10240/50000 (23%)]\tLoss: 0.269665, Accuracy: 90.62\n",
      "Train Epoch: 21 [12800/50000 (28%)]\tLoss: 0.356895, Accuracy: 88.09\n",
      "Train Epoch: 21 [15360/50000 (34%)]\tLoss: 0.343032, Accuracy: 88.87\n",
      "Train Epoch: 21 [17920/50000 (40%)]\tLoss: 0.316782, Accuracy: 89.65\n",
      "Train Epoch: 21 [20480/50000 (45%)]\tLoss: 0.296225, Accuracy: 89.26\n",
      "Train Epoch: 21 [23040/50000 (51%)]\tLoss: 0.347219, Accuracy: 86.72\n",
      "Train Epoch: 21 [25600/50000 (57%)]\tLoss: 0.360227, Accuracy: 87.89\n",
      "Train Epoch: 21 [28160/50000 (62%)]\tLoss: 0.351313, Accuracy: 87.30\n",
      "Train Epoch: 21 [30720/50000 (68%)]\tLoss: 0.317032, Accuracy: 89.06\n",
      "Train Epoch: 21 [33280/50000 (74%)]\tLoss: 0.346600, Accuracy: 87.70\n",
      "Train Epoch: 21 [35840/50000 (80%)]\tLoss: 0.335780, Accuracy: 87.70\n",
      "Train Epoch: 21 [38400/50000 (85%)]\tLoss: 0.312475, Accuracy: 88.67\n",
      "Train Epoch: 21 [40960/50000 (91%)]\tLoss: 0.358882, Accuracy: 88.28\n",
      "Train Epoch: 21 [43520/50000 (97%)]\tLoss: 0.357924, Accuracy: 87.30\n",
      "\n",
      "Validation set: Average loss: 0.5461, Accuracy: 4099/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[28.480819702148438 s]\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 0.303139, Accuracy: 89.26\n",
      "Train Epoch: 22 [2560/50000 (6%)]\tLoss: 0.262134, Accuracy: 91.21\n",
      "Train Epoch: 22 [5120/50000 (11%)]\tLoss: 0.355354, Accuracy: 88.67\n",
      "Train Epoch: 22 [7680/50000 (17%)]\tLoss: 0.308624, Accuracy: 90.43\n",
      "Train Epoch: 22 [10240/50000 (23%)]\tLoss: 0.233572, Accuracy: 91.60\n",
      "Train Epoch: 22 [12800/50000 (28%)]\tLoss: 0.306027, Accuracy: 88.48\n",
      "Train Epoch: 22 [15360/50000 (34%)]\tLoss: 0.287478, Accuracy: 90.62\n",
      "Train Epoch: 22 [17920/50000 (40%)]\tLoss: 0.326517, Accuracy: 89.26\n",
      "Train Epoch: 22 [20480/50000 (45%)]\tLoss: 0.321732, Accuracy: 88.87\n",
      "Train Epoch: 22 [23040/50000 (51%)]\tLoss: 0.323657, Accuracy: 89.45\n",
      "Train Epoch: 22 [25600/50000 (57%)]\tLoss: 0.321897, Accuracy: 89.06\n",
      "Train Epoch: 22 [28160/50000 (62%)]\tLoss: 0.307535, Accuracy: 88.28\n",
      "Train Epoch: 22 [30720/50000 (68%)]\tLoss: 0.375220, Accuracy: 87.11\n",
      "Train Epoch: 22 [33280/50000 (74%)]\tLoss: 0.307690, Accuracy: 90.23\n",
      "Train Epoch: 22 [35840/50000 (80%)]\tLoss: 0.334439, Accuracy: 88.67\n",
      "Train Epoch: 22 [38400/50000 (85%)]\tLoss: 0.315765, Accuracy: 87.89\n",
      "Train Epoch: 22 [40960/50000 (91%)]\tLoss: 0.402160, Accuracy: 85.16\n",
      "Train Epoch: 22 [43520/50000 (97%)]\tLoss: 0.305534, Accuracy: 91.60\n",
      "\n",
      "Validation set: Average loss: 0.4552, Accuracy: 4224/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[26.086594343185425 s]\n",
      "\n",
      "Test set: Average loss: 0.4697, Accuracy: 8483/10000 (84.83%)\n",
      "\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.283103, Accuracy: 89.65\n",
      "Train Epoch: 23 [2560/50000 (6%)]\tLoss: 0.284671, Accuracy: 90.23\n",
      "Train Epoch: 23 [5120/50000 (11%)]\tLoss: 0.267800, Accuracy: 92.58\n",
      "Train Epoch: 23 [7680/50000 (17%)]\tLoss: 0.309074, Accuracy: 89.65\n",
      "Train Epoch: 23 [10240/50000 (23%)]\tLoss: 0.281497, Accuracy: 90.04\n",
      "Train Epoch: 23 [12800/50000 (28%)]\tLoss: 0.298810, Accuracy: 89.65\n",
      "Train Epoch: 23 [15360/50000 (34%)]\tLoss: 0.387407, Accuracy: 85.74\n",
      "Train Epoch: 23 [17920/50000 (40%)]\tLoss: 0.318832, Accuracy: 88.09\n",
      "Train Epoch: 23 [20480/50000 (45%)]\tLoss: 0.257003, Accuracy: 92.19\n",
      "Train Epoch: 23 [23040/50000 (51%)]\tLoss: 0.242471, Accuracy: 91.02\n",
      "Train Epoch: 23 [25600/50000 (57%)]\tLoss: 0.298909, Accuracy: 88.48\n",
      "Train Epoch: 23 [28160/50000 (62%)]\tLoss: 0.309375, Accuracy: 88.28\n",
      "Train Epoch: 23 [30720/50000 (68%)]\tLoss: 0.326776, Accuracy: 89.26\n",
      "Train Epoch: 23 [33280/50000 (74%)]\tLoss: 0.300987, Accuracy: 89.06\n",
      "Train Epoch: 23 [35840/50000 (80%)]\tLoss: 0.329676, Accuracy: 89.06\n",
      "Train Epoch: 23 [38400/50000 (85%)]\tLoss: 0.291779, Accuracy: 88.67\n",
      "Train Epoch: 23 [40960/50000 (91%)]\tLoss: 0.335551, Accuracy: 88.28\n",
      "Train Epoch: 23 [43520/50000 (97%)]\tLoss: 0.300736, Accuracy: 89.45\n",
      "\n",
      "Validation set: Average loss: 0.5959, Accuracy: 4016/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[28.473003387451172 s]\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.246411, Accuracy: 91.02\n",
      "Train Epoch: 24 [2560/50000 (6%)]\tLoss: 0.258381, Accuracy: 91.41\n",
      "Train Epoch: 24 [5120/50000 (11%)]\tLoss: 0.330513, Accuracy: 88.87\n",
      "Train Epoch: 24 [7680/50000 (17%)]\tLoss: 0.284013, Accuracy: 89.65\n",
      "Train Epoch: 24 [10240/50000 (23%)]\tLoss: 0.357321, Accuracy: 87.30\n",
      "Train Epoch: 24 [12800/50000 (28%)]\tLoss: 0.304373, Accuracy: 88.67\n",
      "Train Epoch: 24 [15360/50000 (34%)]\tLoss: 0.271414, Accuracy: 89.65\n",
      "Train Epoch: 24 [17920/50000 (40%)]\tLoss: 0.321469, Accuracy: 89.84\n",
      "Train Epoch: 24 [20480/50000 (45%)]\tLoss: 0.334603, Accuracy: 87.70\n",
      "Train Epoch: 24 [23040/50000 (51%)]\tLoss: 0.284577, Accuracy: 91.02\n",
      "Train Epoch: 24 [25600/50000 (57%)]\tLoss: 0.316861, Accuracy: 88.87\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 24 [28160/50000 (62%)]\tLoss: 0.289761, Accuracy: 89.45\n",
      "Train Epoch: 24 [30720/50000 (68%)]\tLoss: 0.329044, Accuracy: 89.06\n",
      "Train Epoch: 24 [33280/50000 (74%)]\tLoss: 0.284844, Accuracy: 89.84\n",
      "Train Epoch: 24 [35840/50000 (80%)]\tLoss: 0.330215, Accuracy: 89.45\n",
      "Train Epoch: 24 [38400/50000 (85%)]\tLoss: 0.345916, Accuracy: 89.45\n",
      "Train Epoch: 24 [40960/50000 (91%)]\tLoss: 0.249509, Accuracy: 91.99\n",
      "Train Epoch: 24 [43520/50000 (97%)]\tLoss: 0.363359, Accuracy: 87.30\n",
      "\n",
      "Validation set: Average loss: 0.5120, Accuracy: 4155/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[26.011828422546387 s]\n",
      "\n",
      "Test set: Average loss: 0.5123, Accuracy: 8338/10000 (83.38%)\n",
      "\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.317702, Accuracy: 89.45\n",
      "Train Epoch: 25 [2560/50000 (6%)]\tLoss: 0.247908, Accuracy: 91.41\n",
      "Train Epoch: 25 [5120/50000 (11%)]\tLoss: 0.260408, Accuracy: 91.02\n",
      "Train Epoch: 25 [7680/50000 (17%)]\tLoss: 0.251280, Accuracy: 91.80\n",
      "Train Epoch: 25 [10240/50000 (23%)]\tLoss: 0.241497, Accuracy: 91.80\n",
      "Train Epoch: 25 [12800/50000 (28%)]\tLoss: 0.260714, Accuracy: 91.99\n",
      "Train Epoch: 25 [15360/50000 (34%)]\tLoss: 0.312598, Accuracy: 88.28\n",
      "Train Epoch: 25 [17920/50000 (40%)]\tLoss: 0.323860, Accuracy: 87.50\n",
      "Train Epoch: 25 [20480/50000 (45%)]\tLoss: 0.267212, Accuracy: 91.02\n",
      "Train Epoch: 25 [23040/50000 (51%)]\tLoss: 0.330179, Accuracy: 88.09\n",
      "Train Epoch: 25 [25600/50000 (57%)]\tLoss: 0.321968, Accuracy: 89.65\n",
      "Train Epoch: 25 [28160/50000 (62%)]\tLoss: 0.327841, Accuracy: 89.26\n",
      "Train Epoch: 25 [30720/50000 (68%)]\tLoss: 0.304072, Accuracy: 88.48\n",
      "Train Epoch: 25 [33280/50000 (74%)]\tLoss: 0.350700, Accuracy: 87.89\n",
      "Train Epoch: 25 [35840/50000 (80%)]\tLoss: 0.297630, Accuracy: 88.87\n",
      "Train Epoch: 25 [38400/50000 (85%)]\tLoss: 0.302171, Accuracy: 89.84\n",
      "Train Epoch: 25 [40960/50000 (91%)]\tLoss: 0.324078, Accuracy: 89.06\n",
      "Train Epoch: 25 [43520/50000 (97%)]\tLoss: 0.353526, Accuracy: 87.89\n",
      "\n",
      "Validation set: Average loss: 0.5619, Accuracy: 4085/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[28.442550897598267 s]\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 0.264418, Accuracy: 92.38\n",
      "Train Epoch: 26 [2560/50000 (6%)]\tLoss: 0.310105, Accuracy: 89.06\n",
      "Train Epoch: 26 [5120/50000 (11%)]\tLoss: 0.317547, Accuracy: 87.89\n",
      "Train Epoch: 26 [7680/50000 (17%)]\tLoss: 0.242979, Accuracy: 91.99\n",
      "Train Epoch: 26 [10240/50000 (23%)]\tLoss: 0.247857, Accuracy: 91.02\n",
      "Train Epoch: 26 [12800/50000 (28%)]\tLoss: 0.284817, Accuracy: 90.23\n",
      "Train Epoch: 26 [15360/50000 (34%)]\tLoss: 0.317936, Accuracy: 87.70\n",
      "Train Epoch: 26 [17920/50000 (40%)]\tLoss: 0.360110, Accuracy: 88.48\n",
      "Train Epoch: 26 [20480/50000 (45%)]\tLoss: 0.319867, Accuracy: 88.48\n",
      "Train Epoch: 26 [23040/50000 (51%)]\tLoss: 0.263878, Accuracy: 91.21\n",
      "Train Epoch: 26 [25600/50000 (57%)]\tLoss: 0.270233, Accuracy: 90.04\n",
      "Train Epoch: 26 [28160/50000 (62%)]\tLoss: 0.317608, Accuracy: 89.26\n",
      "Train Epoch: 26 [30720/50000 (68%)]\tLoss: 0.340749, Accuracy: 86.91\n",
      "Train Epoch: 26 [33280/50000 (74%)]\tLoss: 0.242528, Accuracy: 91.99\n",
      "Train Epoch: 26 [35840/50000 (80%)]\tLoss: 0.286907, Accuracy: 89.45\n",
      "Train Epoch: 26 [38400/50000 (85%)]\tLoss: 0.325647, Accuracy: 89.84\n",
      "Train Epoch: 26 [40960/50000 (91%)]\tLoss: 0.323110, Accuracy: 87.70\n",
      "Train Epoch: 26 [43520/50000 (97%)]\tLoss: 0.322609, Accuracy: 87.30\n",
      "\n",
      "Validation set: Average loss: 0.6028, Accuracy: 4069/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[26.050013303756714 s]\n",
      "\n",
      "Test set: Average loss: 0.6281, Accuracy: 8051/10000 (80.51%)\n",
      "\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.258450, Accuracy: 90.82\n",
      "Train Epoch: 27 [2560/50000 (6%)]\tLoss: 0.244018, Accuracy: 91.41\n",
      "Train Epoch: 27 [5120/50000 (11%)]\tLoss: 0.250889, Accuracy: 91.80\n",
      "Train Epoch: 27 [7680/50000 (17%)]\tLoss: 0.261544, Accuracy: 92.19\n",
      "Train Epoch: 27 [10240/50000 (23%)]\tLoss: 0.216066, Accuracy: 93.55\n",
      "Train Epoch: 27 [12800/50000 (28%)]\tLoss: 0.294579, Accuracy: 89.84\n",
      "Train Epoch: 27 [15360/50000 (34%)]\tLoss: 0.276481, Accuracy: 91.21\n",
      "Train Epoch: 27 [17920/50000 (40%)]\tLoss: 0.244145, Accuracy: 91.21\n",
      "Train Epoch: 27 [20480/50000 (45%)]\tLoss: 0.274550, Accuracy: 90.43\n",
      "Train Epoch: 27 [23040/50000 (51%)]\tLoss: 0.322966, Accuracy: 89.84\n",
      "Train Epoch: 27 [25600/50000 (57%)]\tLoss: 0.250509, Accuracy: 91.41\n",
      "Train Epoch: 27 [28160/50000 (62%)]\tLoss: 0.347077, Accuracy: 86.52\n",
      "Train Epoch: 27 [30720/50000 (68%)]\tLoss: 0.310243, Accuracy: 90.23\n",
      "Train Epoch: 27 [33280/50000 (74%)]\tLoss: 0.294868, Accuracy: 89.45\n",
      "Train Epoch: 27 [35840/50000 (80%)]\tLoss: 0.299893, Accuracy: 89.65\n",
      "Train Epoch: 27 [38400/50000 (85%)]\tLoss: 0.296902, Accuracy: 88.87\n",
      "Train Epoch: 27 [40960/50000 (91%)]\tLoss: 0.312858, Accuracy: 90.43\n",
      "Train Epoch: 27 [43520/50000 (97%)]\tLoss: 0.288766, Accuracy: 90.23\n",
      "\n",
      "Validation set: Average loss: 0.5782, Accuracy: 4116/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.50130271911621 s]\n",
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 0.248826, Accuracy: 92.19\n",
      "Train Epoch: 28 [2560/50000 (6%)]\tLoss: 0.287822, Accuracy: 89.65\n",
      "Train Epoch: 28 [5120/50000 (11%)]\tLoss: 0.268168, Accuracy: 90.23\n",
      "Train Epoch: 28 [7680/50000 (17%)]\tLoss: 0.332712, Accuracy: 87.30\n",
      "Train Epoch: 28 [10240/50000 (23%)]\tLoss: 0.287775, Accuracy: 90.82\n",
      "Train Epoch: 28 [12800/50000 (28%)]\tLoss: 0.240834, Accuracy: 91.99\n",
      "Train Epoch: 28 [15360/50000 (34%)]\tLoss: 0.226146, Accuracy: 92.38\n",
      "Train Epoch: 28 [17920/50000 (40%)]\tLoss: 0.319043, Accuracy: 91.02\n",
      "Train Epoch: 28 [20480/50000 (45%)]\tLoss: 0.217654, Accuracy: 91.80\n",
      "Train Epoch: 28 [23040/50000 (51%)]\tLoss: 0.344636, Accuracy: 87.70\n",
      "Train Epoch: 28 [25600/50000 (57%)]\tLoss: 0.314426, Accuracy: 89.45\n",
      "Train Epoch: 28 [28160/50000 (62%)]\tLoss: 0.326956, Accuracy: 87.50\n",
      "Train Epoch: 28 [30720/50000 (68%)]\tLoss: 0.272992, Accuracy: 91.21\n",
      "Train Epoch: 28 [33280/50000 (74%)]\tLoss: 0.277807, Accuracy: 90.23\n",
      "Train Epoch: 28 [35840/50000 (80%)]\tLoss: 0.299563, Accuracy: 89.06\n",
      "Train Epoch: 28 [38400/50000 (85%)]\tLoss: 0.296995, Accuracy: 88.48\n",
      "Train Epoch: 28 [40960/50000 (91%)]\tLoss: 0.311923, Accuracy: 88.87\n",
      "Train Epoch: 28 [43520/50000 (97%)]\tLoss: 0.298726, Accuracy: 90.23\n",
      "\n",
      "Validation set: Average loss: 0.7400, Accuracy: 3838/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[26.318681955337524 s]\n",
      "\n",
      "Test set: Average loss: 0.7418, Accuracy: 7656/10000 (76.56%)\n",
      "\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 0.269687, Accuracy: 89.84\n",
      "Train Epoch: 29 [2560/50000 (6%)]\tLoss: 0.261287, Accuracy: 90.82\n",
      "Train Epoch: 29 [5120/50000 (11%)]\tLoss: 0.302900, Accuracy: 89.45\n",
      "Train Epoch: 29 [7680/50000 (17%)]\tLoss: 0.308996, Accuracy: 89.06\n",
      "Train Epoch: 29 [10240/50000 (23%)]\tLoss: 0.326095, Accuracy: 89.26\n",
      "Train Epoch: 29 [12800/50000 (28%)]\tLoss: 0.291325, Accuracy: 89.84\n",
      "Train Epoch: 29 [15360/50000 (34%)]\tLoss: 0.296696, Accuracy: 91.02\n",
      "Train Epoch: 29 [17920/50000 (40%)]\tLoss: 0.255447, Accuracy: 89.84\n",
      "Train Epoch: 29 [20480/50000 (45%)]\tLoss: 0.230577, Accuracy: 92.19\n",
      "Train Epoch: 29 [23040/50000 (51%)]\tLoss: 0.223246, Accuracy: 93.55\n",
      "Train Epoch: 29 [25600/50000 (57%)]\tLoss: 0.261295, Accuracy: 91.60\n",
      "Train Epoch: 29 [28160/50000 (62%)]\tLoss: 0.209167, Accuracy: 92.58\n",
      "Train Epoch: 29 [30720/50000 (68%)]\tLoss: 0.279439, Accuracy: 89.84\n",
      "Train Epoch: 29 [33280/50000 (74%)]\tLoss: 0.252570, Accuracy: 91.80\n",
      "Train Epoch: 29 [35840/50000 (80%)]\tLoss: 0.246570, Accuracy: 91.80\n",
      "Train Epoch: 29 [38400/50000 (85%)]\tLoss: 0.336127, Accuracy: 88.67\n",
      "Train Epoch: 29 [40960/50000 (91%)]\tLoss: 0.350602, Accuracy: 86.13\n",
      "Train Epoch: 29 [43520/50000 (97%)]\tLoss: 0.288417, Accuracy: 90.43\n",
      "\n",
      "Validation set: Average loss: 0.5188, Accuracy: 4173/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[28.80600595474243 s]\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 0.233182, Accuracy: 92.38\n",
      "Train Epoch: 30 [2560/50000 (6%)]\tLoss: 0.218802, Accuracy: 92.38\n",
      "Train Epoch: 30 [5120/50000 (11%)]\tLoss: 0.253295, Accuracy: 91.60\n",
      "Train Epoch: 30 [7680/50000 (17%)]\tLoss: 0.230895, Accuracy: 93.36\n",
      "Train Epoch: 30 [10240/50000 (23%)]\tLoss: 0.227447, Accuracy: 91.99\n",
      "Train Epoch: 30 [12800/50000 (28%)]\tLoss: 0.240029, Accuracy: 92.58\n",
      "Train Epoch: 30 [15360/50000 (34%)]\tLoss: 0.344179, Accuracy: 88.28\n",
      "Train Epoch: 30 [17920/50000 (40%)]\tLoss: 0.278126, Accuracy: 90.04\n",
      "Train Epoch: 30 [20480/50000 (45%)]\tLoss: 0.337324, Accuracy: 89.65\n",
      "Train Epoch: 30 [23040/50000 (51%)]\tLoss: 0.287686, Accuracy: 90.23\n",
      "Train Epoch: 30 [25600/50000 (57%)]\tLoss: 0.233723, Accuracy: 91.99\n",
      "Train Epoch: 30 [28160/50000 (62%)]\tLoss: 0.280975, Accuracy: 89.65\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 30 [30720/50000 (68%)]\tLoss: 0.323584, Accuracy: 88.09\n",
      "Train Epoch: 30 [33280/50000 (74%)]\tLoss: 0.312949, Accuracy: 87.89\n",
      "Train Epoch: 30 [35840/50000 (80%)]\tLoss: 0.319275, Accuracy: 89.26\n",
      "Train Epoch: 30 [38400/50000 (85%)]\tLoss: 0.279250, Accuracy: 90.23\n",
      "Train Epoch: 30 [40960/50000 (91%)]\tLoss: 0.256239, Accuracy: 92.58\n",
      "Train Epoch: 30 [43520/50000 (97%)]\tLoss: 0.288439, Accuracy: 90.62\n",
      "\n",
      "Validation set: Average loss: 0.8114, Accuracy: 3838/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[26.074071407318115 s]\n",
      "\n",
      "Test set: Average loss: 0.8225, Accuracy: 7610/10000 (76.10%)\n",
      "\n",
      "Train Epoch: 31 [0/50000 (0%)]\tLoss: 0.319432, Accuracy: 88.67\n",
      "Train Epoch: 31 [2560/50000 (6%)]\tLoss: 0.240216, Accuracy: 91.02\n",
      "Train Epoch: 31 [5120/50000 (11%)]\tLoss: 0.212941, Accuracy: 92.97\n",
      "Train Epoch: 31 [7680/50000 (17%)]\tLoss: 0.233598, Accuracy: 91.41\n",
      "Train Epoch: 31 [10240/50000 (23%)]\tLoss: 0.225329, Accuracy: 91.99\n",
      "Train Epoch: 31 [12800/50000 (28%)]\tLoss: 0.278316, Accuracy: 90.43\n",
      "Train Epoch: 31 [15360/50000 (34%)]\tLoss: 0.275420, Accuracy: 90.23\n",
      "Train Epoch: 31 [17920/50000 (40%)]\tLoss: 0.219928, Accuracy: 91.99\n",
      "Train Epoch: 31 [20480/50000 (45%)]\tLoss: 0.224323, Accuracy: 92.19\n",
      "Train Epoch: 31 [23040/50000 (51%)]\tLoss: 0.244365, Accuracy: 89.84\n",
      "Train Epoch: 31 [25600/50000 (57%)]\tLoss: 0.248585, Accuracy: 91.02\n",
      "Train Epoch: 31 [28160/50000 (62%)]\tLoss: 0.275000, Accuracy: 91.21\n",
      "Train Epoch: 31 [30720/50000 (68%)]\tLoss: 0.316363, Accuracy: 89.26\n",
      "Train Epoch: 31 [33280/50000 (74%)]\tLoss: 0.290191, Accuracy: 89.65\n",
      "Train Epoch: 31 [35840/50000 (80%)]\tLoss: 0.279188, Accuracy: 89.65\n",
      "Train Epoch: 31 [38400/50000 (85%)]\tLoss: 0.349739, Accuracy: 87.50\n",
      "Train Epoch: 31 [40960/50000 (91%)]\tLoss: 0.256596, Accuracy: 91.21\n",
      "Train Epoch: 31 [43520/50000 (97%)]\tLoss: 0.280777, Accuracy: 90.23\n",
      "\n",
      "Validation set: Average loss: 0.4715, Accuracy: 4249/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[28.458681344985962 s]\n",
      "Train Epoch: 32 [0/50000 (0%)]\tLoss: 0.243439, Accuracy: 91.41\n",
      "Train Epoch: 32 [2560/50000 (6%)]\tLoss: 0.283262, Accuracy: 90.04\n",
      "Train Epoch: 32 [5120/50000 (11%)]\tLoss: 0.229233, Accuracy: 92.38\n",
      "Train Epoch: 32 [7680/50000 (17%)]\tLoss: 0.239678, Accuracy: 91.99\n",
      "Train Epoch: 32 [10240/50000 (23%)]\tLoss: 0.226638, Accuracy: 92.97\n",
      "Train Epoch: 32 [12800/50000 (28%)]\tLoss: 0.289861, Accuracy: 89.65\n",
      "Train Epoch: 32 [15360/50000 (34%)]\tLoss: 0.276422, Accuracy: 90.62\n",
      "Train Epoch: 32 [17920/50000 (40%)]\tLoss: 0.227949, Accuracy: 93.36\n",
      "Train Epoch: 32 [20480/50000 (45%)]\tLoss: 0.281059, Accuracy: 90.82\n",
      "Train Epoch: 32 [23040/50000 (51%)]\tLoss: 0.334369, Accuracy: 87.89\n",
      "Train Epoch: 32 [25600/50000 (57%)]\tLoss: 0.349571, Accuracy: 87.70\n",
      "Train Epoch: 32 [28160/50000 (62%)]\tLoss: 0.267859, Accuracy: 90.62\n",
      "Train Epoch: 32 [30720/50000 (68%)]\tLoss: 0.285842, Accuracy: 91.21\n",
      "Train Epoch: 32 [33280/50000 (74%)]\tLoss: 0.278815, Accuracy: 90.04\n",
      "Train Epoch: 32 [35840/50000 (80%)]\tLoss: 0.208427, Accuracy: 93.16\n",
      "Train Epoch: 32 [38400/50000 (85%)]\tLoss: 0.276228, Accuracy: 91.21\n",
      "Train Epoch: 32 [40960/50000 (91%)]\tLoss: 0.238063, Accuracy: 91.99\n",
      "Train Epoch: 32 [43520/50000 (97%)]\tLoss: 0.291419, Accuracy: 89.26\n",
      "\n",
      "Validation set: Average loss: 0.5683, Accuracy: 4115/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[26.039079189300537 s]\n",
      "\n",
      "Test set: Average loss: 0.6265, Accuracy: 8107/10000 (81.07%)\n",
      "\n",
      "Train Epoch: 33 [0/50000 (0%)]\tLoss: 0.253120, Accuracy: 89.84\n",
      "Train Epoch: 33 [2560/50000 (6%)]\tLoss: 0.287937, Accuracy: 90.04\n",
      "Train Epoch: 33 [5120/50000 (11%)]\tLoss: 0.254432, Accuracy: 91.99\n",
      "Train Epoch: 33 [7680/50000 (17%)]\tLoss: 0.236905, Accuracy: 90.23\n",
      "Train Epoch: 33 [10240/50000 (23%)]\tLoss: 0.228986, Accuracy: 91.21\n",
      "Train Epoch: 33 [12800/50000 (28%)]\tLoss: 0.218671, Accuracy: 92.58\n",
      "Train Epoch: 33 [15360/50000 (34%)]\tLoss: 0.270247, Accuracy: 90.43\n",
      "Train Epoch: 33 [17920/50000 (40%)]\tLoss: 0.251869, Accuracy: 91.02\n",
      "Train Epoch: 33 [20480/50000 (45%)]\tLoss: 0.250977, Accuracy: 92.38\n",
      "Train Epoch: 33 [23040/50000 (51%)]\tLoss: 0.294634, Accuracy: 89.45\n",
      "Train Epoch: 33 [25600/50000 (57%)]\tLoss: 0.314284, Accuracy: 88.09\n",
      "Train Epoch: 33 [28160/50000 (62%)]\tLoss: 0.227145, Accuracy: 92.77\n",
      "Train Epoch: 33 [30720/50000 (68%)]\tLoss: 0.273191, Accuracy: 90.43\n",
      "Train Epoch: 33 [33280/50000 (74%)]\tLoss: 0.268407, Accuracy: 90.62\n",
      "Train Epoch: 33 [35840/50000 (80%)]\tLoss: 0.285302, Accuracy: 90.23\n",
      "Train Epoch: 33 [38400/50000 (85%)]\tLoss: 0.293020, Accuracy: 89.06\n",
      "Train Epoch: 33 [40960/50000 (91%)]\tLoss: 0.298358, Accuracy: 89.26\n",
      "Train Epoch: 33 [43520/50000 (97%)]\tLoss: 0.300185, Accuracy: 89.45\n",
      "\n",
      "Validation set: Average loss: 0.6734, Accuracy: 3995/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[28.44477677345276 s]\n",
      "Train Epoch: 34 [0/50000 (0%)]\tLoss: 0.253777, Accuracy: 91.02\n",
      "Train Epoch: 34 [2560/50000 (6%)]\tLoss: 0.240701, Accuracy: 91.80\n",
      "Train Epoch: 34 [5120/50000 (11%)]\tLoss: 0.256662, Accuracy: 90.62\n",
      "Train Epoch: 34 [7680/50000 (17%)]\tLoss: 0.187035, Accuracy: 94.14\n",
      "Train Epoch: 34 [10240/50000 (23%)]\tLoss: 0.222275, Accuracy: 90.62\n",
      "Train Epoch: 34 [12800/50000 (28%)]\tLoss: 0.284133, Accuracy: 91.02\n",
      "Train Epoch: 34 [15360/50000 (34%)]\tLoss: 0.223181, Accuracy: 91.60\n",
      "Train Epoch: 34 [17920/50000 (40%)]\tLoss: 0.208168, Accuracy: 92.38\n",
      "Train Epoch: 34 [20480/50000 (45%)]\tLoss: 0.219225, Accuracy: 92.77\n",
      "Train Epoch: 34 [23040/50000 (51%)]\tLoss: 0.216380, Accuracy: 92.58\n",
      "Train Epoch: 34 [25600/50000 (57%)]\tLoss: 0.203538, Accuracy: 92.38\n",
      "Train Epoch: 34 [28160/50000 (62%)]\tLoss: 0.308224, Accuracy: 90.43\n",
      "Train Epoch: 34 [30720/50000 (68%)]\tLoss: 0.276637, Accuracy: 89.65\n",
      "Train Epoch: 34 [33280/50000 (74%)]\tLoss: 0.281391, Accuracy: 90.04\n",
      "Train Epoch: 34 [35840/50000 (80%)]\tLoss: 0.265581, Accuracy: 90.82\n",
      "Train Epoch: 34 [38400/50000 (85%)]\tLoss: 0.339647, Accuracy: 88.48\n",
      "Train Epoch: 34 [40960/50000 (91%)]\tLoss: 0.280871, Accuracy: 91.41\n",
      "Train Epoch: 34 [43520/50000 (97%)]\tLoss: 0.304024, Accuracy: 89.26\n",
      "\n",
      "Validation set: Average loss: 0.4382, Accuracy: 4256/5000 (85.00%)\n",
      "\n",
      "the time of this epoch:[26.080177068710327 s]\n",
      "\n",
      "Test set: Average loss: 0.4700, Accuracy: 8504/10000 (85.04%)\n",
      "\n",
      "Train Epoch: 35 [0/50000 (0%)]\tLoss: 0.207288, Accuracy: 92.58\n",
      "Train Epoch: 35 [2560/50000 (6%)]\tLoss: 0.229949, Accuracy: 91.60\n",
      "Train Epoch: 35 [5120/50000 (11%)]\tLoss: 0.249935, Accuracy: 89.84\n",
      "Train Epoch: 35 [7680/50000 (17%)]\tLoss: 0.215591, Accuracy: 92.58\n",
      "Train Epoch: 35 [10240/50000 (23%)]\tLoss: 0.201376, Accuracy: 91.99\n",
      "Train Epoch: 35 [12800/50000 (28%)]\tLoss: 0.225362, Accuracy: 92.19\n",
      "Train Epoch: 35 [15360/50000 (34%)]\tLoss: 0.261360, Accuracy: 89.84\n",
      "Train Epoch: 35 [17920/50000 (40%)]\tLoss: 0.231136, Accuracy: 92.77\n",
      "Train Epoch: 35 [20480/50000 (45%)]\tLoss: 0.250620, Accuracy: 90.62\n",
      "Train Epoch: 35 [23040/50000 (51%)]\tLoss: 0.231813, Accuracy: 91.99\n",
      "Train Epoch: 35 [25600/50000 (57%)]\tLoss: 0.226649, Accuracy: 92.19\n",
      "Train Epoch: 35 [28160/50000 (62%)]\tLoss: 0.210265, Accuracy: 93.36\n",
      "Train Epoch: 35 [30720/50000 (68%)]\tLoss: 0.245310, Accuracy: 91.41\n",
      "Train Epoch: 35 [33280/50000 (74%)]\tLoss: 0.278167, Accuracy: 91.02\n",
      "Train Epoch: 35 [35840/50000 (80%)]\tLoss: 0.290448, Accuracy: 88.67\n",
      "Train Epoch: 35 [38400/50000 (85%)]\tLoss: 0.253052, Accuracy: 91.41\n",
      "Train Epoch: 35 [40960/50000 (91%)]\tLoss: 0.271617, Accuracy: 90.04\n",
      "Train Epoch: 35 [43520/50000 (97%)]\tLoss: 0.244334, Accuracy: 90.62\n",
      "\n",
      "Validation set: Average loss: 0.5038, Accuracy: 4148/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.442163467407227 s]\n",
      "Train Epoch: 36 [0/50000 (0%)]\tLoss: 0.226064, Accuracy: 92.19\n",
      "Train Epoch: 36 [2560/50000 (6%)]\tLoss: 0.225403, Accuracy: 91.41\n",
      "Train Epoch: 36 [5120/50000 (11%)]\tLoss: 0.232147, Accuracy: 91.02\n",
      "Train Epoch: 36 [7680/50000 (17%)]\tLoss: 0.291756, Accuracy: 89.45\n",
      "Train Epoch: 36 [10240/50000 (23%)]\tLoss: 0.223928, Accuracy: 92.58\n",
      "Train Epoch: 36 [12800/50000 (28%)]\tLoss: 0.213087, Accuracy: 92.77\n",
      "Train Epoch: 36 [15360/50000 (34%)]\tLoss: 0.277148, Accuracy: 89.45\n",
      "Train Epoch: 36 [17920/50000 (40%)]\tLoss: 0.259429, Accuracy: 90.04\n",
      "Train Epoch: 36 [20480/50000 (45%)]\tLoss: 0.235555, Accuracy: 91.41\n",
      "Train Epoch: 36 [23040/50000 (51%)]\tLoss: 0.225741, Accuracy: 91.99\n",
      "Train Epoch: 36 [25600/50000 (57%)]\tLoss: 0.210738, Accuracy: 92.58\n",
      "Train Epoch: 36 [28160/50000 (62%)]\tLoss: 0.225884, Accuracy: 91.41\n",
      "Train Epoch: 36 [30720/50000 (68%)]\tLoss: 0.186672, Accuracy: 93.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 36 [33280/50000 (74%)]\tLoss: 0.249589, Accuracy: 91.80\n",
      "Train Epoch: 36 [35840/50000 (80%)]\tLoss: 0.284752, Accuracy: 90.04\n",
      "Train Epoch: 36 [38400/50000 (85%)]\tLoss: 0.282904, Accuracy: 91.02\n",
      "Train Epoch: 36 [40960/50000 (91%)]\tLoss: 0.278897, Accuracy: 91.02\n",
      "Train Epoch: 36 [43520/50000 (97%)]\tLoss: 0.271443, Accuracy: 90.23\n",
      "\n",
      "Validation set: Average loss: 0.5910, Accuracy: 4094/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[26.025617837905884 s]\n",
      "\n",
      "Test set: Average loss: 0.6180, Accuracy: 8099/10000 (80.99%)\n",
      "\n",
      "Train Epoch: 37 [0/50000 (0%)]\tLoss: 0.188527, Accuracy: 92.77\n",
      "Train Epoch: 37 [2560/50000 (6%)]\tLoss: 0.179892, Accuracy: 93.95\n",
      "Train Epoch: 37 [5120/50000 (11%)]\tLoss: 0.223200, Accuracy: 92.19\n",
      "Train Epoch: 37 [7680/50000 (17%)]\tLoss: 0.184907, Accuracy: 93.75\n",
      "Train Epoch: 37 [10240/50000 (23%)]\tLoss: 0.265002, Accuracy: 91.41\n",
      "Train Epoch: 37 [12800/50000 (28%)]\tLoss: 0.250627, Accuracy: 91.41\n",
      "Train Epoch: 37 [15360/50000 (34%)]\tLoss: 0.245652, Accuracy: 91.21\n",
      "Train Epoch: 37 [17920/50000 (40%)]\tLoss: 0.242869, Accuracy: 91.21\n",
      "Train Epoch: 37 [20480/50000 (45%)]\tLoss: 0.268954, Accuracy: 89.84\n",
      "Train Epoch: 37 [23040/50000 (51%)]\tLoss: 0.242817, Accuracy: 90.82\n",
      "Train Epoch: 37 [25600/50000 (57%)]\tLoss: 0.308798, Accuracy: 88.87\n",
      "Train Epoch: 37 [28160/50000 (62%)]\tLoss: 0.185097, Accuracy: 92.97\n",
      "Train Epoch: 37 [30720/50000 (68%)]\tLoss: 0.229690, Accuracy: 91.99\n",
      "Train Epoch: 37 [33280/50000 (74%)]\tLoss: 0.211244, Accuracy: 92.19\n",
      "Train Epoch: 37 [35840/50000 (80%)]\tLoss: 0.216905, Accuracy: 93.16\n",
      "Train Epoch: 37 [38400/50000 (85%)]\tLoss: 0.227335, Accuracy: 92.38\n",
      "Train Epoch: 37 [40960/50000 (91%)]\tLoss: 0.278402, Accuracy: 91.02\n",
      "Train Epoch: 37 [43520/50000 (97%)]\tLoss: 0.281521, Accuracy: 89.84\n",
      "\n",
      "Validation set: Average loss: 0.5868, Accuracy: 4110/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.430438995361328 s]\n",
      "Train Epoch: 38 [0/50000 (0%)]\tLoss: 0.233860, Accuracy: 91.99\n",
      "Train Epoch: 38 [2560/50000 (6%)]\tLoss: 0.280820, Accuracy: 89.26\n",
      "Train Epoch: 38 [5120/50000 (11%)]\tLoss: 0.252909, Accuracy: 91.02\n",
      "Train Epoch: 38 [7680/50000 (17%)]\tLoss: 0.254519, Accuracy: 91.02\n",
      "Train Epoch: 38 [10240/50000 (23%)]\tLoss: 0.236712, Accuracy: 91.60\n",
      "Train Epoch: 38 [12800/50000 (28%)]\tLoss: 0.250617, Accuracy: 92.38\n",
      "Train Epoch: 38 [15360/50000 (34%)]\tLoss: 0.309399, Accuracy: 89.84\n",
      "Train Epoch: 38 [17920/50000 (40%)]\tLoss: 0.251069, Accuracy: 92.38\n",
      "Train Epoch: 38 [20480/50000 (45%)]\tLoss: 0.269826, Accuracy: 90.43\n",
      "Train Epoch: 38 [23040/50000 (51%)]\tLoss: 0.247732, Accuracy: 91.99\n",
      "Train Epoch: 38 [25600/50000 (57%)]\tLoss: 0.195079, Accuracy: 93.36\n",
      "Train Epoch: 38 [28160/50000 (62%)]\tLoss: 0.258060, Accuracy: 91.21\n",
      "Train Epoch: 38 [30720/50000 (68%)]\tLoss: 0.246340, Accuracy: 91.99\n",
      "Train Epoch: 38 [33280/50000 (74%)]\tLoss: 0.252917, Accuracy: 92.19\n",
      "Train Epoch: 38 [35840/50000 (80%)]\tLoss: 0.206464, Accuracy: 92.58\n",
      "Train Epoch: 38 [38400/50000 (85%)]\tLoss: 0.320920, Accuracy: 88.28\n",
      "Train Epoch: 38 [40960/50000 (91%)]\tLoss: 0.254440, Accuracy: 91.60\n",
      "Train Epoch: 38 [43520/50000 (97%)]\tLoss: 0.240517, Accuracy: 91.60\n",
      "\n",
      "Validation set: Average loss: 0.5597, Accuracy: 4125/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[25.990684032440186 s]\n",
      "\n",
      "Test set: Average loss: 0.5873, Accuracy: 8202/10000 (82.02%)\n",
      "\n",
      "Train Epoch: 39 [0/50000 (0%)]\tLoss: 0.193485, Accuracy: 93.36\n",
      "Train Epoch: 39 [2560/50000 (6%)]\tLoss: 0.222188, Accuracy: 93.16\n",
      "Train Epoch: 39 [5120/50000 (11%)]\tLoss: 0.262378, Accuracy: 90.04\n",
      "Train Epoch: 39 [7680/50000 (17%)]\tLoss: 0.197727, Accuracy: 93.36\n",
      "Train Epoch: 39 [10240/50000 (23%)]\tLoss: 0.188316, Accuracy: 94.14\n",
      "Train Epoch: 39 [12800/50000 (28%)]\tLoss: 0.207054, Accuracy: 92.77\n",
      "Train Epoch: 39 [15360/50000 (34%)]\tLoss: 0.260914, Accuracy: 91.02\n",
      "Train Epoch: 39 [17920/50000 (40%)]\tLoss: 0.280524, Accuracy: 91.21\n",
      "Train Epoch: 39 [20480/50000 (45%)]\tLoss: 0.206180, Accuracy: 92.58\n",
      "Train Epoch: 39 [23040/50000 (51%)]\tLoss: 0.209811, Accuracy: 93.36\n",
      "Train Epoch: 39 [25600/50000 (57%)]\tLoss: 0.246628, Accuracy: 91.60\n",
      "Train Epoch: 39 [28160/50000 (62%)]\tLoss: 0.291698, Accuracy: 91.02\n",
      "Train Epoch: 39 [30720/50000 (68%)]\tLoss: 0.257923, Accuracy: 90.43\n",
      "Train Epoch: 39 [33280/50000 (74%)]\tLoss: 0.257891, Accuracy: 91.60\n",
      "Train Epoch: 39 [35840/50000 (80%)]\tLoss: 0.245326, Accuracy: 92.97\n",
      "Train Epoch: 39 [38400/50000 (85%)]\tLoss: 0.292804, Accuracy: 90.04\n",
      "Train Epoch: 39 [40960/50000 (91%)]\tLoss: 0.277509, Accuracy: 90.04\n",
      "Train Epoch: 39 [43520/50000 (97%)]\tLoss: 0.255610, Accuracy: 91.02\n",
      "\n",
      "Validation set: Average loss: 0.4862, Accuracy: 4195/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[28.47910451889038 s]\n",
      "Train Epoch: 40 [0/50000 (0%)]\tLoss: 0.265699, Accuracy: 92.38\n",
      "Train Epoch: 40 [2560/50000 (6%)]\tLoss: 0.191068, Accuracy: 93.75\n",
      "Train Epoch: 40 [5120/50000 (11%)]\tLoss: 0.187686, Accuracy: 92.97\n",
      "Train Epoch: 40 [7680/50000 (17%)]\tLoss: 0.317244, Accuracy: 89.26\n",
      "Train Epoch: 40 [10240/50000 (23%)]\tLoss: 0.263981, Accuracy: 90.43\n",
      "Train Epoch: 40 [12800/50000 (28%)]\tLoss: 0.196622, Accuracy: 92.77\n",
      "Train Epoch: 40 [15360/50000 (34%)]\tLoss: 0.270858, Accuracy: 91.21\n",
      "Train Epoch: 40 [17920/50000 (40%)]\tLoss: 0.207280, Accuracy: 92.19\n",
      "Train Epoch: 40 [20480/50000 (45%)]\tLoss: 0.260527, Accuracy: 91.80\n",
      "Train Epoch: 40 [23040/50000 (51%)]\tLoss: 0.280574, Accuracy: 90.82\n",
      "Train Epoch: 40 [25600/50000 (57%)]\tLoss: 0.334802, Accuracy: 90.23\n",
      "Train Epoch: 40 [28160/50000 (62%)]\tLoss: 0.252453, Accuracy: 90.43\n",
      "Train Epoch: 40 [30720/50000 (68%)]\tLoss: 0.256671, Accuracy: 90.43\n",
      "Train Epoch: 40 [33280/50000 (74%)]\tLoss: 0.286515, Accuracy: 88.28\n",
      "Train Epoch: 40 [35840/50000 (80%)]\tLoss: 0.294943, Accuracy: 88.87\n",
      "Train Epoch: 40 [38400/50000 (85%)]\tLoss: 0.318916, Accuracy: 88.48\n",
      "Train Epoch: 40 [40960/50000 (91%)]\tLoss: 0.336048, Accuracy: 88.67\n",
      "Train Epoch: 40 [43520/50000 (97%)]\tLoss: 0.291141, Accuracy: 90.43\n",
      "\n",
      "Validation set: Average loss: 0.4708, Accuracy: 4218/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[26.062405109405518 s]\n",
      "\n",
      "Test set: Average loss: 0.5054, Accuracy: 8379/10000 (83.79%)\n",
      "\n",
      "Train Epoch: 41 [0/50000 (0%)]\tLoss: 0.233971, Accuracy: 92.77\n",
      "Train Epoch: 41 [2560/50000 (6%)]\tLoss: 0.200913, Accuracy: 93.16\n",
      "Train Epoch: 41 [5120/50000 (11%)]\tLoss: 0.183820, Accuracy: 94.53\n",
      "Train Epoch: 41 [7680/50000 (17%)]\tLoss: 0.220191, Accuracy: 92.97\n",
      "Train Epoch: 41 [10240/50000 (23%)]\tLoss: 0.239619, Accuracy: 91.41\n",
      "Train Epoch: 41 [12800/50000 (28%)]\tLoss: 0.323325, Accuracy: 90.23\n",
      "Train Epoch: 41 [15360/50000 (34%)]\tLoss: 0.211839, Accuracy: 92.77\n",
      "Train Epoch: 41 [17920/50000 (40%)]\tLoss: 0.272738, Accuracy: 91.21\n",
      "Train Epoch: 41 [20480/50000 (45%)]\tLoss: 0.229963, Accuracy: 91.41\n",
      "Train Epoch: 41 [23040/50000 (51%)]\tLoss: 0.196716, Accuracy: 92.97\n",
      "Train Epoch: 41 [25600/50000 (57%)]\tLoss: 0.231068, Accuracy: 92.97\n",
      "Train Epoch: 41 [28160/50000 (62%)]\tLoss: 0.221044, Accuracy: 92.58\n",
      "Train Epoch: 41 [30720/50000 (68%)]\tLoss: 0.278292, Accuracy: 90.43\n",
      "Train Epoch: 41 [33280/50000 (74%)]\tLoss: 0.269272, Accuracy: 91.80\n",
      "Train Epoch: 41 [35840/50000 (80%)]\tLoss: 0.239352, Accuracy: 91.80\n",
      "Train Epoch: 41 [38400/50000 (85%)]\tLoss: 0.191702, Accuracy: 93.36\n",
      "Train Epoch: 41 [40960/50000 (91%)]\tLoss: 0.275491, Accuracy: 89.84\n",
      "Train Epoch: 41 [43520/50000 (97%)]\tLoss: 0.246770, Accuracy: 91.21\n",
      "\n",
      "Validation set: Average loss: 0.6125, Accuracy: 4030/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[28.513256549835205 s]\n",
      "Train Epoch: 42 [0/50000 (0%)]\tLoss: 0.230105, Accuracy: 91.80\n",
      "Train Epoch: 42 [2560/50000 (6%)]\tLoss: 0.231854, Accuracy: 91.41\n",
      "Train Epoch: 42 [5120/50000 (11%)]\tLoss: 0.271340, Accuracy: 91.41\n",
      "Train Epoch: 42 [7680/50000 (17%)]\tLoss: 0.264464, Accuracy: 90.04\n",
      "Train Epoch: 42 [10240/50000 (23%)]\tLoss: 0.231480, Accuracy: 91.99\n",
      "Train Epoch: 42 [12800/50000 (28%)]\tLoss: 0.233392, Accuracy: 92.77\n",
      "Train Epoch: 42 [15360/50000 (34%)]\tLoss: 0.214621, Accuracy: 92.58\n",
      "Train Epoch: 42 [17920/50000 (40%)]\tLoss: 0.264848, Accuracy: 91.60\n",
      "Train Epoch: 42 [20480/50000 (45%)]\tLoss: 0.218906, Accuracy: 92.77\n",
      "Train Epoch: 42 [23040/50000 (51%)]\tLoss: 0.252854, Accuracy: 91.21\n",
      "Train Epoch: 42 [25600/50000 (57%)]\tLoss: 0.228658, Accuracy: 92.19\n",
      "Train Epoch: 42 [28160/50000 (62%)]\tLoss: 0.251985, Accuracy: 90.04\n",
      "Train Epoch: 42 [30720/50000 (68%)]\tLoss: 0.246693, Accuracy: 89.65\n",
      "Train Epoch: 42 [33280/50000 (74%)]\tLoss: 0.263759, Accuracy: 92.19\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 42 [35840/50000 (80%)]\tLoss: 0.300894, Accuracy: 88.87\n",
      "Train Epoch: 42 [38400/50000 (85%)]\tLoss: 0.253970, Accuracy: 91.99\n",
      "Train Epoch: 42 [40960/50000 (91%)]\tLoss: 0.300693, Accuracy: 89.65\n",
      "Train Epoch: 42 [43520/50000 (97%)]\tLoss: 0.192415, Accuracy: 93.55\n",
      "\n",
      "Validation set: Average loss: 0.4926, Accuracy: 4235/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[26.055849075317383 s]\n",
      "\n",
      "Test set: Average loss: 0.5074, Accuracy: 8385/10000 (83.85%)\n",
      "\n",
      "Train Epoch: 43 [0/50000 (0%)]\tLoss: 0.228716, Accuracy: 91.80\n",
      "Train Epoch: 43 [2560/50000 (6%)]\tLoss: 0.154202, Accuracy: 94.92\n",
      "Train Epoch: 43 [5120/50000 (11%)]\tLoss: 0.256743, Accuracy: 91.21\n",
      "Train Epoch: 43 [7680/50000 (17%)]\tLoss: 0.202763, Accuracy: 92.58\n",
      "Train Epoch: 43 [10240/50000 (23%)]\tLoss: 0.238300, Accuracy: 91.80\n",
      "Train Epoch: 43 [12800/50000 (28%)]\tLoss: 0.202646, Accuracy: 93.36\n",
      "Train Epoch: 43 [15360/50000 (34%)]\tLoss: 0.207093, Accuracy: 92.38\n",
      "Train Epoch: 43 [17920/50000 (40%)]\tLoss: 0.249943, Accuracy: 91.41\n",
      "Train Epoch: 43 [20480/50000 (45%)]\tLoss: 0.235667, Accuracy: 91.80\n",
      "Train Epoch: 43 [23040/50000 (51%)]\tLoss: 0.195158, Accuracy: 93.36\n",
      "Train Epoch: 43 [25600/50000 (57%)]\tLoss: 0.213144, Accuracy: 91.99\n",
      "Train Epoch: 43 [28160/50000 (62%)]\tLoss: 0.230772, Accuracy: 91.60\n",
      "Train Epoch: 43 [30720/50000 (68%)]\tLoss: 0.213600, Accuracy: 93.36\n",
      "Train Epoch: 43 [33280/50000 (74%)]\tLoss: 0.268432, Accuracy: 91.60\n",
      "Train Epoch: 43 [35840/50000 (80%)]\tLoss: 0.213255, Accuracy: 92.58\n",
      "Train Epoch: 43 [38400/50000 (85%)]\tLoss: 0.249003, Accuracy: 92.19\n",
      "Train Epoch: 43 [40960/50000 (91%)]\tLoss: 0.234342, Accuracy: 92.58\n",
      "Train Epoch: 43 [43520/50000 (97%)]\tLoss: 0.227268, Accuracy: 93.95\n",
      "\n",
      "Validation set: Average loss: 0.6526, Accuracy: 4095/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[28.457558631896973 s]\n",
      "Train Epoch: 44 [0/50000 (0%)]\tLoss: 0.215822, Accuracy: 91.99\n",
      "Train Epoch: 44 [2560/50000 (6%)]\tLoss: 0.268942, Accuracy: 89.84\n",
      "Train Epoch: 44 [5120/50000 (11%)]\tLoss: 0.243866, Accuracy: 91.80\n",
      "Train Epoch: 44 [7680/50000 (17%)]\tLoss: 0.257632, Accuracy: 91.60\n",
      "Train Epoch: 44 [10240/50000 (23%)]\tLoss: 0.258975, Accuracy: 91.99\n",
      "Train Epoch: 44 [12800/50000 (28%)]\tLoss: 0.236391, Accuracy: 92.38\n",
      "Train Epoch: 44 [15360/50000 (34%)]\tLoss: 0.237524, Accuracy: 91.21\n",
      "Train Epoch: 44 [17920/50000 (40%)]\tLoss: 0.238080, Accuracy: 92.58\n",
      "Train Epoch: 44 [20480/50000 (45%)]\tLoss: 0.224826, Accuracy: 91.80\n",
      "Train Epoch: 44 [23040/50000 (51%)]\tLoss: 0.254925, Accuracy: 91.41\n",
      "Train Epoch: 44 [25600/50000 (57%)]\tLoss: 0.208354, Accuracy: 92.58\n",
      "Train Epoch: 44 [28160/50000 (62%)]\tLoss: 0.248700, Accuracy: 91.21\n",
      "Train Epoch: 44 [30720/50000 (68%)]\tLoss: 0.246375, Accuracy: 90.23\n",
      "Train Epoch: 44 [33280/50000 (74%)]\tLoss: 0.287062, Accuracy: 89.65\n",
      "Train Epoch: 44 [35840/50000 (80%)]\tLoss: 0.222238, Accuracy: 93.16\n",
      "Train Epoch: 44 [38400/50000 (85%)]\tLoss: 0.251592, Accuracy: 92.38\n",
      "Train Epoch: 44 [40960/50000 (91%)]\tLoss: 0.223666, Accuracy: 91.99\n",
      "Train Epoch: 44 [43520/50000 (97%)]\tLoss: 0.261702, Accuracy: 90.43\n",
      "\n",
      "Validation set: Average loss: 0.6411, Accuracy: 4043/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[26.070399284362793 s]\n",
      "\n",
      "Test set: Average loss: 0.6939, Accuracy: 7950/10000 (79.50%)\n",
      "\n",
      "Train Epoch: 45 [0/50000 (0%)]\tLoss: 0.203126, Accuracy: 93.95\n",
      "Train Epoch: 45 [2560/50000 (6%)]\tLoss: 0.200343, Accuracy: 92.97\n",
      "Train Epoch: 45 [5120/50000 (11%)]\tLoss: 0.213247, Accuracy: 94.34\n",
      "Train Epoch: 45 [7680/50000 (17%)]\tLoss: 0.284939, Accuracy: 89.45\n",
      "Train Epoch: 45 [10240/50000 (23%)]\tLoss: 0.178056, Accuracy: 92.97\n",
      "Train Epoch: 45 [12800/50000 (28%)]\tLoss: 0.226790, Accuracy: 91.60\n",
      "Train Epoch: 45 [15360/50000 (34%)]\tLoss: 0.222629, Accuracy: 92.58\n",
      "Train Epoch: 45 [17920/50000 (40%)]\tLoss: 0.266459, Accuracy: 89.84\n",
      "Train Epoch: 45 [20480/50000 (45%)]\tLoss: 0.192681, Accuracy: 93.55\n",
      "Train Epoch: 45 [23040/50000 (51%)]\tLoss: 0.208545, Accuracy: 93.16\n",
      "Train Epoch: 45 [25600/50000 (57%)]\tLoss: 0.195620, Accuracy: 92.97\n",
      "Train Epoch: 45 [28160/50000 (62%)]\tLoss: 0.237671, Accuracy: 91.80\n",
      "Train Epoch: 45 [30720/50000 (68%)]\tLoss: 0.248832, Accuracy: 90.43\n",
      "Train Epoch: 45 [33280/50000 (74%)]\tLoss: 0.258910, Accuracy: 90.04\n",
      "Train Epoch: 45 [35840/50000 (80%)]\tLoss: 0.280596, Accuracy: 90.04\n",
      "Train Epoch: 45 [38400/50000 (85%)]\tLoss: 0.280903, Accuracy: 90.62\n",
      "Train Epoch: 45 [40960/50000 (91%)]\tLoss: 0.271927, Accuracy: 89.84\n",
      "Train Epoch: 45 [43520/50000 (97%)]\tLoss: 0.227514, Accuracy: 92.58\n",
      "\n",
      "Validation set: Average loss: 0.7301, Accuracy: 3869/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[28.46656060218811 s]\n",
      "Train Epoch: 46 [0/50000 (0%)]\tLoss: 0.188158, Accuracy: 93.16\n",
      "Train Epoch: 46 [2560/50000 (6%)]\tLoss: 0.225637, Accuracy: 91.41\n",
      "Train Epoch: 46 [5120/50000 (11%)]\tLoss: 0.253511, Accuracy: 91.60\n",
      "Train Epoch: 46 [7680/50000 (17%)]\tLoss: 0.192608, Accuracy: 94.14\n",
      "Train Epoch: 46 [10240/50000 (23%)]\tLoss: 0.224352, Accuracy: 91.41\n",
      "Train Epoch: 46 [12800/50000 (28%)]\tLoss: 0.216520, Accuracy: 92.97\n",
      "Train Epoch: 46 [15360/50000 (34%)]\tLoss: 0.280269, Accuracy: 89.45\n",
      "Train Epoch: 46 [17920/50000 (40%)]\tLoss: 0.244037, Accuracy: 91.80\n",
      "Train Epoch: 46 [20480/50000 (45%)]\tLoss: 0.245655, Accuracy: 91.80\n",
      "Train Epoch: 46 [23040/50000 (51%)]\tLoss: 0.220614, Accuracy: 92.38\n",
      "Train Epoch: 46 [25600/50000 (57%)]\tLoss: 0.208965, Accuracy: 93.16\n",
      "Train Epoch: 46 [28160/50000 (62%)]\tLoss: 0.168776, Accuracy: 93.36\n",
      "Train Epoch: 46 [30720/50000 (68%)]\tLoss: 0.234641, Accuracy: 91.60\n",
      "Train Epoch: 46 [33280/50000 (74%)]\tLoss: 0.226151, Accuracy: 91.60\n",
      "Train Epoch: 46 [35840/50000 (80%)]\tLoss: 0.249084, Accuracy: 90.23\n",
      "Train Epoch: 46 [38400/50000 (85%)]\tLoss: 0.214596, Accuracy: 91.99\n",
      "Train Epoch: 46 [40960/50000 (91%)]\tLoss: 0.307277, Accuracy: 89.26\n",
      "Train Epoch: 46 [43520/50000 (97%)]\tLoss: 0.241048, Accuracy: 91.21\n",
      "\n",
      "Validation set: Average loss: 0.4933, Accuracy: 4213/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[26.011860132217407 s]\n",
      "\n",
      "Test set: Average loss: 0.5424, Accuracy: 8271/10000 (82.71%)\n",
      "\n",
      "Train Epoch: 47 [0/50000 (0%)]\tLoss: 0.186866, Accuracy: 94.34\n",
      "Train Epoch: 47 [2560/50000 (6%)]\tLoss: 0.267909, Accuracy: 91.02\n",
      "Train Epoch: 47 [5120/50000 (11%)]\tLoss: 0.208825, Accuracy: 94.14\n",
      "Train Epoch: 47 [7680/50000 (17%)]\tLoss: 0.164334, Accuracy: 93.75\n",
      "Train Epoch: 47 [10240/50000 (23%)]\tLoss: 0.189572, Accuracy: 93.55\n",
      "Train Epoch: 47 [12800/50000 (28%)]\tLoss: 0.213057, Accuracy: 92.77\n",
      "Train Epoch: 47 [15360/50000 (34%)]\tLoss: 0.196441, Accuracy: 93.75\n",
      "Train Epoch: 47 [17920/50000 (40%)]\tLoss: 0.211483, Accuracy: 93.36\n",
      "Train Epoch: 47 [20480/50000 (45%)]\tLoss: 0.225345, Accuracy: 92.58\n",
      "Train Epoch: 47 [23040/50000 (51%)]\tLoss: 0.228814, Accuracy: 93.36\n",
      "Train Epoch: 47 [25600/50000 (57%)]\tLoss: 0.191201, Accuracy: 92.97\n",
      "Train Epoch: 47 [28160/50000 (62%)]\tLoss: 0.287767, Accuracy: 89.84\n",
      "Train Epoch: 47 [30720/50000 (68%)]\tLoss: 0.223434, Accuracy: 91.60\n",
      "Train Epoch: 47 [33280/50000 (74%)]\tLoss: 0.209589, Accuracy: 91.80\n",
      "Train Epoch: 47 [35840/50000 (80%)]\tLoss: 0.244520, Accuracy: 89.84\n",
      "Train Epoch: 47 [38400/50000 (85%)]\tLoss: 0.284190, Accuracy: 90.23\n",
      "Train Epoch: 47 [40960/50000 (91%)]\tLoss: 0.253487, Accuracy: 92.19\n",
      "Train Epoch: 47 [43520/50000 (97%)]\tLoss: 0.257565, Accuracy: 91.02\n",
      "\n",
      "Validation set: Average loss: 0.4878, Accuracy: 4233/5000 (84.00%)\n",
      "\n",
      "the time of this epoch:[28.41965889930725 s]\n",
      "Train Epoch: 48 [0/50000 (0%)]\tLoss: 0.218092, Accuracy: 91.60\n",
      "Train Epoch: 48 [2560/50000 (6%)]\tLoss: 0.216943, Accuracy: 92.19\n",
      "Train Epoch: 48 [5120/50000 (11%)]\tLoss: 0.200707, Accuracy: 92.19\n",
      "Train Epoch: 48 [7680/50000 (17%)]\tLoss: 0.240615, Accuracy: 92.77\n",
      "Train Epoch: 48 [10240/50000 (23%)]\tLoss: 0.147957, Accuracy: 94.34\n",
      "Train Epoch: 48 [12800/50000 (28%)]\tLoss: 0.206523, Accuracy: 92.38\n",
      "Train Epoch: 48 [15360/50000 (34%)]\tLoss: 0.237325, Accuracy: 91.41\n",
      "Train Epoch: 48 [17920/50000 (40%)]\tLoss: 0.162540, Accuracy: 92.58\n",
      "Train Epoch: 48 [20480/50000 (45%)]\tLoss: 0.207934, Accuracy: 92.19\n",
      "Train Epoch: 48 [23040/50000 (51%)]\tLoss: 0.251121, Accuracy: 91.60\n",
      "Train Epoch: 48 [25600/50000 (57%)]\tLoss: 0.211718, Accuracy: 91.41\n",
      "Train Epoch: 48 [28160/50000 (62%)]\tLoss: 0.282371, Accuracy: 91.41\n",
      "Train Epoch: 48 [30720/50000 (68%)]\tLoss: 0.229786, Accuracy: 92.58\n",
      "Train Epoch: 48 [33280/50000 (74%)]\tLoss: 0.171324, Accuracy: 94.53\n",
      "Train Epoch: 48 [35840/50000 (80%)]\tLoss: 0.271915, Accuracy: 89.84\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 48 [38400/50000 (85%)]\tLoss: 0.311512, Accuracy: 90.62\n",
      "Train Epoch: 48 [40960/50000 (91%)]\tLoss: 0.268307, Accuracy: 90.62\n",
      "Train Epoch: 48 [43520/50000 (97%)]\tLoss: 0.240898, Accuracy: 91.02\n",
      "\n",
      "Validation set: Average loss: 0.5580, Accuracy: 4138/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[26.038015127182007 s]\n",
      "\n",
      "Test set: Average loss: 0.5879, Accuracy: 8178/10000 (81.78%)\n",
      "\n",
      "Train Epoch: 49 [0/50000 (0%)]\tLoss: 0.201243, Accuracy: 92.97\n",
      "Train Epoch: 49 [2560/50000 (6%)]\tLoss: 0.260706, Accuracy: 90.23\n",
      "Train Epoch: 49 [5120/50000 (11%)]\tLoss: 0.255721, Accuracy: 91.80\n",
      "Train Epoch: 49 [7680/50000 (17%)]\tLoss: 0.203216, Accuracy: 94.14\n",
      "Train Epoch: 49 [10240/50000 (23%)]\tLoss: 0.204721, Accuracy: 92.97\n",
      "Train Epoch: 49 [12800/50000 (28%)]\tLoss: 0.252771, Accuracy: 89.65\n",
      "Train Epoch: 49 [15360/50000 (34%)]\tLoss: 0.265880, Accuracy: 90.62\n",
      "Train Epoch: 49 [17920/50000 (40%)]\tLoss: 0.204768, Accuracy: 92.19\n",
      "Train Epoch: 49 [20480/50000 (45%)]\tLoss: 0.194031, Accuracy: 92.97\n",
      "Train Epoch: 49 [23040/50000 (51%)]\tLoss: 0.287867, Accuracy: 90.82\n",
      "Train Epoch: 49 [25600/50000 (57%)]\tLoss: 0.224608, Accuracy: 93.16\n",
      "Train Epoch: 49 [28160/50000 (62%)]\tLoss: 0.232906, Accuracy: 91.80\n",
      "Train Epoch: 49 [30720/50000 (68%)]\tLoss: 0.224767, Accuracy: 91.99\n",
      "Train Epoch: 49 [33280/50000 (74%)]\tLoss: 0.238470, Accuracy: 91.60\n",
      "Train Epoch: 49 [35840/50000 (80%)]\tLoss: 0.269318, Accuracy: 90.62\n",
      "Train Epoch: 49 [38400/50000 (85%)]\tLoss: 0.256915, Accuracy: 91.60\n",
      "Train Epoch: 49 [40960/50000 (91%)]\tLoss: 0.210908, Accuracy: 91.41\n",
      "Train Epoch: 49 [43520/50000 (97%)]\tLoss: 0.246845, Accuracy: 92.58\n",
      "\n",
      "Validation set: Average loss: 0.5586, Accuracy: 4122/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[28.41494655609131 s]\n",
      "Train Epoch: 50 [0/50000 (0%)]\tLoss: 0.198357, Accuracy: 92.77\n",
      "Train Epoch: 50 [2560/50000 (6%)]\tLoss: 0.164373, Accuracy: 94.73\n",
      "Train Epoch: 50 [5120/50000 (11%)]\tLoss: 0.279397, Accuracy: 91.02\n",
      "Train Epoch: 50 [7680/50000 (17%)]\tLoss: 0.193556, Accuracy: 92.58\n",
      "Train Epoch: 50 [10240/50000 (23%)]\tLoss: 0.242573, Accuracy: 90.23\n",
      "Train Epoch: 50 [12800/50000 (28%)]\tLoss: 0.281576, Accuracy: 90.82\n",
      "Train Epoch: 50 [15360/50000 (34%)]\tLoss: 0.222475, Accuracy: 92.38\n",
      "Train Epoch: 50 [17920/50000 (40%)]\tLoss: 0.212012, Accuracy: 92.38\n",
      "Train Epoch: 50 [20480/50000 (45%)]\tLoss: 0.221273, Accuracy: 91.99\n",
      "Train Epoch: 50 [23040/50000 (51%)]\tLoss: 0.195275, Accuracy: 94.34\n",
      "Train Epoch: 50 [25600/50000 (57%)]\tLoss: 0.262685, Accuracy: 90.62\n",
      "Train Epoch: 50 [28160/50000 (62%)]\tLoss: 0.224261, Accuracy: 91.99\n",
      "Train Epoch: 50 [30720/50000 (68%)]\tLoss: 0.200984, Accuracy: 92.38\n",
      "Train Epoch: 50 [33280/50000 (74%)]\tLoss: 0.274727, Accuracy: 90.62\n",
      "Train Epoch: 50 [35840/50000 (80%)]\tLoss: 0.263363, Accuracy: 91.02\n",
      "Train Epoch: 50 [38400/50000 (85%)]\tLoss: 0.254165, Accuracy: 90.43\n",
      "Train Epoch: 50 [40960/50000 (91%)]\tLoss: 0.271338, Accuracy: 89.84\n",
      "Train Epoch: 50 [43520/50000 (97%)]\tLoss: 0.212006, Accuracy: 92.97\n",
      "\n",
      "Validation set: Average loss: 0.5303, Accuracy: 4176/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[25.981784343719482 s]\n",
      "\n",
      "Test set: Average loss: 0.5409, Accuracy: 8313/10000 (83.13%)\n",
      "\n",
      "Train Epoch: 51 [0/50000 (0%)]\tLoss: 0.189313, Accuracy: 93.16\n",
      "Train Epoch: 51 [2560/50000 (6%)]\tLoss: 0.193697, Accuracy: 94.34\n",
      "Train Epoch: 51 [5120/50000 (11%)]\tLoss: 0.189445, Accuracy: 93.95\n",
      "Train Epoch: 51 [7680/50000 (17%)]\tLoss: 0.140796, Accuracy: 95.12\n",
      "Train Epoch: 51 [10240/50000 (23%)]\tLoss: 0.161919, Accuracy: 94.92\n",
      "Train Epoch: 51 [12800/50000 (28%)]\tLoss: 0.167897, Accuracy: 95.12\n",
      "Train Epoch: 51 [15360/50000 (34%)]\tLoss: 0.149443, Accuracy: 95.51\n",
      "Train Epoch: 51 [17920/50000 (40%)]\tLoss: 0.157188, Accuracy: 94.73\n",
      "Train Epoch: 51 [20480/50000 (45%)]\tLoss: 0.139870, Accuracy: 95.31\n",
      "Train Epoch: 51 [23040/50000 (51%)]\tLoss: 0.123462, Accuracy: 96.09\n",
      "Train Epoch: 51 [25600/50000 (57%)]\tLoss: 0.142910, Accuracy: 95.31\n",
      "Train Epoch: 51 [28160/50000 (62%)]\tLoss: 0.119451, Accuracy: 95.90\n",
      "Train Epoch: 51 [30720/50000 (68%)]\tLoss: 0.125747, Accuracy: 96.09\n",
      "Train Epoch: 51 [33280/50000 (74%)]\tLoss: 0.101721, Accuracy: 96.88\n",
      "Train Epoch: 51 [35840/50000 (80%)]\tLoss: 0.111884, Accuracy: 96.09\n",
      "Train Epoch: 51 [38400/50000 (85%)]\tLoss: 0.085471, Accuracy: 98.05\n",
      "Train Epoch: 51 [40960/50000 (91%)]\tLoss: 0.122100, Accuracy: 96.09\n",
      "Train Epoch: 51 [43520/50000 (97%)]\tLoss: 0.121717, Accuracy: 96.48\n",
      "\n",
      "Validation set: Average loss: 0.2726, Accuracy: 4589/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[28.440663814544678 s]\n",
      "Train Epoch: 52 [0/50000 (0%)]\tLoss: 0.128099, Accuracy: 96.48\n",
      "Train Epoch: 52 [2560/50000 (6%)]\tLoss: 0.089269, Accuracy: 97.27\n",
      "Train Epoch: 52 [5120/50000 (11%)]\tLoss: 0.116685, Accuracy: 96.68\n",
      "Train Epoch: 52 [7680/50000 (17%)]\tLoss: 0.100235, Accuracy: 96.48\n",
      "Train Epoch: 52 [10240/50000 (23%)]\tLoss: 0.098873, Accuracy: 97.27\n",
      "Train Epoch: 52 [12800/50000 (28%)]\tLoss: 0.084597, Accuracy: 97.27\n",
      "Train Epoch: 52 [15360/50000 (34%)]\tLoss: 0.145304, Accuracy: 94.34\n",
      "Train Epoch: 52 [17920/50000 (40%)]\tLoss: 0.090695, Accuracy: 96.88\n",
      "Train Epoch: 52 [20480/50000 (45%)]\tLoss: 0.114750, Accuracy: 96.68\n",
      "Train Epoch: 52 [23040/50000 (51%)]\tLoss: 0.075898, Accuracy: 98.05\n",
      "Train Epoch: 52 [25600/50000 (57%)]\tLoss: 0.086837, Accuracy: 96.68\n",
      "Train Epoch: 52 [28160/50000 (62%)]\tLoss: 0.060267, Accuracy: 98.63\n",
      "Train Epoch: 52 [30720/50000 (68%)]\tLoss: 0.117440, Accuracy: 96.48\n",
      "Train Epoch: 52 [33280/50000 (74%)]\tLoss: 0.060402, Accuracy: 98.63\n",
      "Train Epoch: 52 [35840/50000 (80%)]\tLoss: 0.104944, Accuracy: 96.29\n",
      "Train Epoch: 52 [38400/50000 (85%)]\tLoss: 0.095041, Accuracy: 97.27\n",
      "Train Epoch: 52 [40960/50000 (91%)]\tLoss: 0.074788, Accuracy: 97.66\n",
      "Train Epoch: 52 [43520/50000 (97%)]\tLoss: 0.098969, Accuracy: 96.88\n",
      "\n",
      "Validation set: Average loss: 0.2653, Accuracy: 4592/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[26.078484535217285 s]\n",
      "\n",
      "Test set: Average loss: 0.2810, Accuracy: 9135/10000 (91.35%)\n",
      "\n",
      "Train Epoch: 53 [0/50000 (0%)]\tLoss: 0.077315, Accuracy: 97.85\n",
      "Train Epoch: 53 [2560/50000 (6%)]\tLoss: 0.060482, Accuracy: 98.63\n",
      "Train Epoch: 53 [5120/50000 (11%)]\tLoss: 0.061646, Accuracy: 98.63\n",
      "Train Epoch: 53 [7680/50000 (17%)]\tLoss: 0.103199, Accuracy: 97.07\n",
      "Train Epoch: 53 [10240/50000 (23%)]\tLoss: 0.097679, Accuracy: 97.07\n",
      "Train Epoch: 53 [12800/50000 (28%)]\tLoss: 0.075600, Accuracy: 97.46\n",
      "Train Epoch: 53 [15360/50000 (34%)]\tLoss: 0.101146, Accuracy: 97.27\n",
      "Train Epoch: 53 [17920/50000 (40%)]\tLoss: 0.083135, Accuracy: 97.85\n",
      "Train Epoch: 53 [20480/50000 (45%)]\tLoss: 0.063346, Accuracy: 98.44\n",
      "Train Epoch: 53 [23040/50000 (51%)]\tLoss: 0.075124, Accuracy: 97.46\n",
      "Train Epoch: 53 [25600/50000 (57%)]\tLoss: 0.061886, Accuracy: 97.27\n",
      "Train Epoch: 53 [28160/50000 (62%)]\tLoss: 0.070502, Accuracy: 97.85\n",
      "Train Epoch: 53 [30720/50000 (68%)]\tLoss: 0.085098, Accuracy: 97.27\n",
      "Train Epoch: 53 [33280/50000 (74%)]\tLoss: 0.058184, Accuracy: 98.24\n",
      "Train Epoch: 53 [35840/50000 (80%)]\tLoss: 0.061787, Accuracy: 98.44\n",
      "Train Epoch: 53 [38400/50000 (85%)]\tLoss: 0.071712, Accuracy: 97.46\n",
      "Train Epoch: 53 [40960/50000 (91%)]\tLoss: 0.081191, Accuracy: 97.66\n",
      "Train Epoch: 53 [43520/50000 (97%)]\tLoss: 0.095865, Accuracy: 96.09\n",
      "\n",
      "Validation set: Average loss: 0.2706, Accuracy: 4592/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[28.407047271728516 s]\n",
      "Train Epoch: 54 [0/50000 (0%)]\tLoss: 0.059517, Accuracy: 98.63\n",
      "Train Epoch: 54 [2560/50000 (6%)]\tLoss: 0.054650, Accuracy: 98.24\n",
      "Train Epoch: 54 [5120/50000 (11%)]\tLoss: 0.062032, Accuracy: 98.24\n",
      "Train Epoch: 54 [7680/50000 (17%)]\tLoss: 0.067119, Accuracy: 97.85\n",
      "Train Epoch: 54 [10240/50000 (23%)]\tLoss: 0.039068, Accuracy: 98.83\n",
      "Train Epoch: 54 [12800/50000 (28%)]\tLoss: 0.074925, Accuracy: 97.66\n",
      "Train Epoch: 54 [15360/50000 (34%)]\tLoss: 0.077727, Accuracy: 97.85\n",
      "Train Epoch: 54 [17920/50000 (40%)]\tLoss: 0.074706, Accuracy: 98.05\n",
      "Train Epoch: 54 [20480/50000 (45%)]\tLoss: 0.079790, Accuracy: 97.27\n",
      "Train Epoch: 54 [23040/50000 (51%)]\tLoss: 0.052231, Accuracy: 98.63\n",
      "Train Epoch: 54 [25600/50000 (57%)]\tLoss: 0.076859, Accuracy: 97.85\n",
      "Train Epoch: 54 [28160/50000 (62%)]\tLoss: 0.059143, Accuracy: 97.85\n",
      "Train Epoch: 54 [30720/50000 (68%)]\tLoss: 0.080906, Accuracy: 96.68\n",
      "Train Epoch: 54 [33280/50000 (74%)]\tLoss: 0.080067, Accuracy: 97.27\n",
      "Train Epoch: 54 [35840/50000 (80%)]\tLoss: 0.063371, Accuracy: 98.44\n",
      "Train Epoch: 54 [38400/50000 (85%)]\tLoss: 0.045810, Accuracy: 98.24\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 54 [40960/50000 (91%)]\tLoss: 0.066113, Accuracy: 98.24\n",
      "Train Epoch: 54 [43520/50000 (97%)]\tLoss: 0.094573, Accuracy: 96.68\n",
      "\n",
      "Validation set: Average loss: 0.2745, Accuracy: 4598/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[26.01299786567688 s]\n",
      "\n",
      "Test set: Average loss: 0.2846, Accuracy: 9172/10000 (91.72%)\n",
      "\n",
      "Train Epoch: 55 [0/50000 (0%)]\tLoss: 0.056188, Accuracy: 98.63\n",
      "Train Epoch: 55 [2560/50000 (6%)]\tLoss: 0.069962, Accuracy: 97.66\n",
      "Train Epoch: 55 [5120/50000 (11%)]\tLoss: 0.058482, Accuracy: 97.85\n",
      "Train Epoch: 55 [7680/50000 (17%)]\tLoss: 0.087752, Accuracy: 97.46\n",
      "Train Epoch: 55 [10240/50000 (23%)]\tLoss: 0.067529, Accuracy: 98.24\n",
      "Train Epoch: 55 [12800/50000 (28%)]\tLoss: 0.058376, Accuracy: 97.85\n",
      "Train Epoch: 55 [15360/50000 (34%)]\tLoss: 0.043992, Accuracy: 99.02\n",
      "Train Epoch: 55 [17920/50000 (40%)]\tLoss: 0.089281, Accuracy: 97.46\n",
      "Train Epoch: 55 [20480/50000 (45%)]\tLoss: 0.063598, Accuracy: 97.66\n",
      "Train Epoch: 55 [23040/50000 (51%)]\tLoss: 0.063177, Accuracy: 97.66\n",
      "Train Epoch: 55 [25600/50000 (57%)]\tLoss: 0.045618, Accuracy: 98.83\n",
      "Train Epoch: 55 [28160/50000 (62%)]\tLoss: 0.063227, Accuracy: 98.05\n",
      "Train Epoch: 55 [30720/50000 (68%)]\tLoss: 0.037110, Accuracy: 99.02\n",
      "Train Epoch: 55 [33280/50000 (74%)]\tLoss: 0.051562, Accuracy: 98.63\n",
      "Train Epoch: 55 [35840/50000 (80%)]\tLoss: 0.058584, Accuracy: 98.05\n",
      "Train Epoch: 55 [38400/50000 (85%)]\tLoss: 0.056567, Accuracy: 98.05\n",
      "Train Epoch: 55 [40960/50000 (91%)]\tLoss: 0.060354, Accuracy: 98.05\n",
      "Train Epoch: 55 [43520/50000 (97%)]\tLoss: 0.047061, Accuracy: 99.02\n",
      "\n",
      "Validation set: Average loss: 0.2671, Accuracy: 4628/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.41944718360901 s]\n",
      "Train Epoch: 56 [0/50000 (0%)]\tLoss: 0.072986, Accuracy: 97.46\n",
      "Train Epoch: 56 [2560/50000 (6%)]\tLoss: 0.050024, Accuracy: 98.63\n",
      "Train Epoch: 56 [5120/50000 (11%)]\tLoss: 0.066973, Accuracy: 98.05\n",
      "Train Epoch: 56 [7680/50000 (17%)]\tLoss: 0.049551, Accuracy: 98.24\n",
      "Train Epoch: 56 [10240/50000 (23%)]\tLoss: 0.047723, Accuracy: 97.85\n",
      "Train Epoch: 56 [12800/50000 (28%)]\tLoss: 0.054400, Accuracy: 98.24\n",
      "Train Epoch: 56 [15360/50000 (34%)]\tLoss: 0.054403, Accuracy: 98.24\n",
      "Train Epoch: 56 [17920/50000 (40%)]\tLoss: 0.069435, Accuracy: 97.66\n",
      "Train Epoch: 56 [20480/50000 (45%)]\tLoss: 0.065911, Accuracy: 97.27\n",
      "Train Epoch: 56 [23040/50000 (51%)]\tLoss: 0.059164, Accuracy: 98.05\n",
      "Train Epoch: 56 [25600/50000 (57%)]\tLoss: 0.041953, Accuracy: 98.83\n",
      "Train Epoch: 56 [28160/50000 (62%)]\tLoss: 0.043040, Accuracy: 98.63\n",
      "Train Epoch: 56 [30720/50000 (68%)]\tLoss: 0.079306, Accuracy: 97.66\n",
      "Train Epoch: 56 [33280/50000 (74%)]\tLoss: 0.061705, Accuracy: 98.44\n",
      "Train Epoch: 56 [35840/50000 (80%)]\tLoss: 0.067837, Accuracy: 98.05\n",
      "Train Epoch: 56 [38400/50000 (85%)]\tLoss: 0.041547, Accuracy: 99.02\n",
      "Train Epoch: 56 [40960/50000 (91%)]\tLoss: 0.071994, Accuracy: 97.85\n",
      "Train Epoch: 56 [43520/50000 (97%)]\tLoss: 0.081676, Accuracy: 98.05\n",
      "\n",
      "Validation set: Average loss: 0.2724, Accuracy: 4609/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.048551082611084 s]\n",
      "\n",
      "Test set: Average loss: 0.2911, Accuracy: 9175/10000 (91.75%)\n",
      "\n",
      "Train Epoch: 57 [0/50000 (0%)]\tLoss: 0.047714, Accuracy: 98.83\n",
      "Train Epoch: 57 [2560/50000 (6%)]\tLoss: 0.042521, Accuracy: 98.83\n",
      "Train Epoch: 57 [5120/50000 (11%)]\tLoss: 0.038085, Accuracy: 99.02\n",
      "Train Epoch: 57 [7680/50000 (17%)]\tLoss: 0.038044, Accuracy: 98.63\n",
      "Train Epoch: 57 [10240/50000 (23%)]\tLoss: 0.045975, Accuracy: 98.44\n",
      "Train Epoch: 57 [12800/50000 (28%)]\tLoss: 0.031355, Accuracy: 99.41\n",
      "Train Epoch: 57 [15360/50000 (34%)]\tLoss: 0.055812, Accuracy: 98.63\n",
      "Train Epoch: 57 [17920/50000 (40%)]\tLoss: 0.042389, Accuracy: 98.83\n",
      "Train Epoch: 57 [20480/50000 (45%)]\tLoss: 0.045661, Accuracy: 98.63\n",
      "Train Epoch: 57 [23040/50000 (51%)]\tLoss: 0.062218, Accuracy: 98.44\n",
      "Train Epoch: 57 [25600/50000 (57%)]\tLoss: 0.053290, Accuracy: 98.24\n",
      "Train Epoch: 57 [28160/50000 (62%)]\tLoss: 0.060447, Accuracy: 98.63\n",
      "Train Epoch: 57 [30720/50000 (68%)]\tLoss: 0.061132, Accuracy: 97.85\n",
      "Train Epoch: 57 [33280/50000 (74%)]\tLoss: 0.034296, Accuracy: 98.63\n",
      "Train Epoch: 57 [35840/50000 (80%)]\tLoss: 0.050898, Accuracy: 98.24\n",
      "Train Epoch: 57 [38400/50000 (85%)]\tLoss: 0.047469, Accuracy: 98.24\n",
      "Train Epoch: 57 [40960/50000 (91%)]\tLoss: 0.044486, Accuracy: 98.83\n",
      "Train Epoch: 57 [43520/50000 (97%)]\tLoss: 0.053234, Accuracy: 98.05\n",
      "\n",
      "Validation set: Average loss: 0.2775, Accuracy: 4602/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.445203065872192 s]\n",
      "Train Epoch: 58 [0/50000 (0%)]\tLoss: 0.039143, Accuracy: 99.22\n",
      "Train Epoch: 58 [2560/50000 (6%)]\tLoss: 0.052335, Accuracy: 98.44\n",
      "Train Epoch: 58 [5120/50000 (11%)]\tLoss: 0.058949, Accuracy: 98.44\n",
      "Train Epoch: 58 [7680/50000 (17%)]\tLoss: 0.049483, Accuracy: 98.24\n",
      "Train Epoch: 58 [10240/50000 (23%)]\tLoss: 0.054527, Accuracy: 98.24\n",
      "Train Epoch: 58 [12800/50000 (28%)]\tLoss: 0.043521, Accuracy: 98.63\n",
      "Train Epoch: 58 [15360/50000 (34%)]\tLoss: 0.037946, Accuracy: 99.22\n",
      "Train Epoch: 58 [17920/50000 (40%)]\tLoss: 0.035828, Accuracy: 98.83\n",
      "Train Epoch: 58 [20480/50000 (45%)]\tLoss: 0.070372, Accuracy: 98.44\n",
      "Train Epoch: 58 [23040/50000 (51%)]\tLoss: 0.033978, Accuracy: 98.83\n",
      "Train Epoch: 58 [25600/50000 (57%)]\tLoss: 0.033353, Accuracy: 99.02\n",
      "Train Epoch: 58 [28160/50000 (62%)]\tLoss: 0.036836, Accuracy: 99.02\n",
      "Train Epoch: 58 [30720/50000 (68%)]\tLoss: 0.048746, Accuracy: 98.44\n",
      "Train Epoch: 58 [33280/50000 (74%)]\tLoss: 0.050757, Accuracy: 98.63\n",
      "Train Epoch: 58 [35840/50000 (80%)]\tLoss: 0.031368, Accuracy: 99.22\n",
      "Train Epoch: 58 [38400/50000 (85%)]\tLoss: 0.037692, Accuracy: 99.02\n",
      "Train Epoch: 58 [40960/50000 (91%)]\tLoss: 0.033056, Accuracy: 99.22\n",
      "Train Epoch: 58 [43520/50000 (97%)]\tLoss: 0.045319, Accuracy: 98.63\n",
      "\n",
      "Validation set: Average loss: 0.2783, Accuracy: 4630/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.013028383255005 s]\n",
      "\n",
      "Test set: Average loss: 0.2981, Accuracy: 9169/10000 (91.69%)\n",
      "\n",
      "Train Epoch: 59 [0/50000 (0%)]\tLoss: 0.029855, Accuracy: 99.22\n",
      "Train Epoch: 59 [2560/50000 (6%)]\tLoss: 0.043354, Accuracy: 99.02\n",
      "Train Epoch: 59 [5120/50000 (11%)]\tLoss: 0.043175, Accuracy: 98.63\n",
      "Train Epoch: 59 [7680/50000 (17%)]\tLoss: 0.038252, Accuracy: 99.22\n",
      "Train Epoch: 59 [10240/50000 (23%)]\tLoss: 0.040425, Accuracy: 99.22\n",
      "Train Epoch: 59 [12800/50000 (28%)]\tLoss: 0.033906, Accuracy: 99.41\n",
      "Train Epoch: 59 [15360/50000 (34%)]\tLoss: 0.043391, Accuracy: 98.44\n",
      "Train Epoch: 59 [17920/50000 (40%)]\tLoss: 0.052454, Accuracy: 98.63\n",
      "Train Epoch: 59 [20480/50000 (45%)]\tLoss: 0.016661, Accuracy: 100.00\n",
      "Train Epoch: 59 [23040/50000 (51%)]\tLoss: 0.050120, Accuracy: 98.05\n",
      "Train Epoch: 59 [25600/50000 (57%)]\tLoss: 0.052129, Accuracy: 98.24\n",
      "Train Epoch: 59 [28160/50000 (62%)]\tLoss: 0.053041, Accuracy: 97.66\n",
      "Train Epoch: 59 [30720/50000 (68%)]\tLoss: 0.042070, Accuracy: 98.44\n",
      "Train Epoch: 59 [33280/50000 (74%)]\tLoss: 0.036403, Accuracy: 98.63\n",
      "Train Epoch: 59 [35840/50000 (80%)]\tLoss: 0.036366, Accuracy: 98.44\n",
      "Train Epoch: 59 [38400/50000 (85%)]\tLoss: 0.036361, Accuracy: 99.22\n",
      "Train Epoch: 59 [40960/50000 (91%)]\tLoss: 0.035778, Accuracy: 99.02\n",
      "Train Epoch: 59 [43520/50000 (97%)]\tLoss: 0.057701, Accuracy: 98.05\n",
      "\n",
      "Validation set: Average loss: 0.2875, Accuracy: 4624/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.490272283554077 s]\n",
      "Train Epoch: 60 [0/50000 (0%)]\tLoss: 0.029217, Accuracy: 99.41\n",
      "Train Epoch: 60 [2560/50000 (6%)]\tLoss: 0.026304, Accuracy: 99.22\n",
      "Train Epoch: 60 [5120/50000 (11%)]\tLoss: 0.031599, Accuracy: 99.22\n",
      "Train Epoch: 60 [7680/50000 (17%)]\tLoss: 0.028724, Accuracy: 99.22\n",
      "Train Epoch: 60 [10240/50000 (23%)]\tLoss: 0.045908, Accuracy: 98.44\n",
      "Train Epoch: 60 [12800/50000 (28%)]\tLoss: 0.024831, Accuracy: 99.80\n",
      "Train Epoch: 60 [15360/50000 (34%)]\tLoss: 0.034350, Accuracy: 98.63\n",
      "Train Epoch: 60 [17920/50000 (40%)]\tLoss: 0.021360, Accuracy: 99.80\n",
      "Train Epoch: 60 [20480/50000 (45%)]\tLoss: 0.026077, Accuracy: 99.22\n",
      "Train Epoch: 60 [23040/50000 (51%)]\tLoss: 0.033406, Accuracy: 99.22\n",
      "Train Epoch: 60 [25600/50000 (57%)]\tLoss: 0.029294, Accuracy: 99.22\n",
      "Train Epoch: 60 [28160/50000 (62%)]\tLoss: 0.033030, Accuracy: 98.63\n",
      "Train Epoch: 60 [30720/50000 (68%)]\tLoss: 0.034514, Accuracy: 99.22\n",
      "Train Epoch: 60 [33280/50000 (74%)]\tLoss: 0.052592, Accuracy: 98.63\n",
      "Train Epoch: 60 [35840/50000 (80%)]\tLoss: 0.044787, Accuracy: 98.83\n",
      "Train Epoch: 60 [38400/50000 (85%)]\tLoss: 0.045400, Accuracy: 98.24\n",
      "Train Epoch: 60 [40960/50000 (91%)]\tLoss: 0.048028, Accuracy: 98.24\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 60 [43520/50000 (97%)]\tLoss: 0.027011, Accuracy: 99.41\n",
      "\n",
      "Validation set: Average loss: 0.2861, Accuracy: 4624/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.022326231002808 s]\n",
      "\n",
      "Test set: Average loss: 0.3055, Accuracy: 9175/10000 (91.75%)\n",
      "\n",
      "Train Epoch: 61 [0/50000 (0%)]\tLoss: 0.032371, Accuracy: 99.22\n",
      "Train Epoch: 61 [2560/50000 (6%)]\tLoss: 0.032200, Accuracy: 99.02\n",
      "Train Epoch: 61 [5120/50000 (11%)]\tLoss: 0.038648, Accuracy: 98.83\n",
      "Train Epoch: 61 [7680/50000 (17%)]\tLoss: 0.033850, Accuracy: 98.83\n",
      "Train Epoch: 61 [10240/50000 (23%)]\tLoss: 0.038532, Accuracy: 98.83\n",
      "Train Epoch: 61 [12800/50000 (28%)]\tLoss: 0.027826, Accuracy: 99.02\n",
      "Train Epoch: 61 [15360/50000 (34%)]\tLoss: 0.041470, Accuracy: 99.02\n",
      "Train Epoch: 61 [17920/50000 (40%)]\tLoss: 0.052601, Accuracy: 98.44\n",
      "Train Epoch: 61 [20480/50000 (45%)]\tLoss: 0.025772, Accuracy: 99.41\n",
      "Train Epoch: 61 [23040/50000 (51%)]\tLoss: 0.021952, Accuracy: 99.61\n",
      "Train Epoch: 61 [25600/50000 (57%)]\tLoss: 0.027254, Accuracy: 99.22\n",
      "Train Epoch: 61 [28160/50000 (62%)]\tLoss: 0.021972, Accuracy: 99.02\n",
      "Train Epoch: 61 [30720/50000 (68%)]\tLoss: 0.044308, Accuracy: 98.24\n",
      "Train Epoch: 61 [33280/50000 (74%)]\tLoss: 0.018492, Accuracy: 99.41\n",
      "Train Epoch: 61 [35840/50000 (80%)]\tLoss: 0.023568, Accuracy: 99.61\n",
      "Train Epoch: 61 [38400/50000 (85%)]\tLoss: 0.029802, Accuracy: 99.02\n",
      "Train Epoch: 61 [40960/50000 (91%)]\tLoss: 0.022798, Accuracy: 99.61\n",
      "Train Epoch: 61 [43520/50000 (97%)]\tLoss: 0.023901, Accuracy: 99.22\n",
      "\n",
      "Validation set: Average loss: 0.2889, Accuracy: 4611/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.47516369819641 s]\n",
      "Train Epoch: 62 [0/50000 (0%)]\tLoss: 0.023588, Accuracy: 99.41\n",
      "Train Epoch: 62 [2560/50000 (6%)]\tLoss: 0.042843, Accuracy: 99.02\n",
      "Train Epoch: 62 [5120/50000 (11%)]\tLoss: 0.031291, Accuracy: 99.02\n",
      "Train Epoch: 62 [7680/50000 (17%)]\tLoss: 0.028092, Accuracy: 98.83\n",
      "Train Epoch: 62 [10240/50000 (23%)]\tLoss: 0.044754, Accuracy: 98.24\n",
      "Train Epoch: 62 [12800/50000 (28%)]\tLoss: 0.028105, Accuracy: 99.02\n",
      "Train Epoch: 62 [15360/50000 (34%)]\tLoss: 0.058367, Accuracy: 97.85\n",
      "Train Epoch: 62 [17920/50000 (40%)]\tLoss: 0.034631, Accuracy: 99.22\n",
      "Train Epoch: 62 [20480/50000 (45%)]\tLoss: 0.037554, Accuracy: 98.63\n",
      "Train Epoch: 62 [23040/50000 (51%)]\tLoss: 0.031041, Accuracy: 99.41\n",
      "Train Epoch: 62 [25600/50000 (57%)]\tLoss: 0.027633, Accuracy: 99.22\n",
      "Train Epoch: 62 [28160/50000 (62%)]\tLoss: 0.029861, Accuracy: 99.41\n",
      "Train Epoch: 62 [30720/50000 (68%)]\tLoss: 0.032671, Accuracy: 98.83\n",
      "Train Epoch: 62 [33280/50000 (74%)]\tLoss: 0.022069, Accuracy: 99.61\n",
      "Train Epoch: 62 [35840/50000 (80%)]\tLoss: 0.067259, Accuracy: 97.46\n",
      "Train Epoch: 62 [38400/50000 (85%)]\tLoss: 0.034773, Accuracy: 99.02\n",
      "Train Epoch: 62 [40960/50000 (91%)]\tLoss: 0.016118, Accuracy: 99.61\n",
      "Train Epoch: 62 [43520/50000 (97%)]\tLoss: 0.030874, Accuracy: 99.02\n",
      "\n",
      "Validation set: Average loss: 0.2891, Accuracy: 4621/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.146162033081055 s]\n",
      "\n",
      "Test set: Average loss: 0.3140, Accuracy: 9157/10000 (91.57%)\n",
      "\n",
      "Train Epoch: 63 [0/50000 (0%)]\tLoss: 0.024631, Accuracy: 99.61\n",
      "Train Epoch: 63 [2560/50000 (6%)]\tLoss: 0.034820, Accuracy: 99.22\n",
      "Train Epoch: 63 [5120/50000 (11%)]\tLoss: 0.032958, Accuracy: 99.02\n",
      "Train Epoch: 63 [7680/50000 (17%)]\tLoss: 0.026860, Accuracy: 99.02\n",
      "Train Epoch: 63 [10240/50000 (23%)]\tLoss: 0.032726, Accuracy: 98.63\n",
      "Train Epoch: 63 [12800/50000 (28%)]\tLoss: 0.038291, Accuracy: 98.83\n",
      "Train Epoch: 63 [15360/50000 (34%)]\tLoss: 0.031946, Accuracy: 99.02\n",
      "Train Epoch: 63 [17920/50000 (40%)]\tLoss: 0.025033, Accuracy: 99.61\n",
      "Train Epoch: 63 [20480/50000 (45%)]\tLoss: 0.025579, Accuracy: 99.02\n",
      "Train Epoch: 63 [23040/50000 (51%)]\tLoss: 0.017972, Accuracy: 99.61\n",
      "Train Epoch: 63 [25600/50000 (57%)]\tLoss: 0.023810, Accuracy: 99.02\n",
      "Train Epoch: 63 [28160/50000 (62%)]\tLoss: 0.015235, Accuracy: 99.80\n",
      "Train Epoch: 63 [30720/50000 (68%)]\tLoss: 0.020544, Accuracy: 99.61\n",
      "Train Epoch: 63 [33280/50000 (74%)]\tLoss: 0.021960, Accuracy: 99.61\n",
      "Train Epoch: 63 [35840/50000 (80%)]\tLoss: 0.025561, Accuracy: 99.80\n",
      "Train Epoch: 63 [38400/50000 (85%)]\tLoss: 0.015396, Accuracy: 99.80\n",
      "Train Epoch: 63 [40960/50000 (91%)]\tLoss: 0.021579, Accuracy: 99.61\n",
      "Train Epoch: 63 [43520/50000 (97%)]\tLoss: 0.033890, Accuracy: 98.83\n",
      "\n",
      "Validation set: Average loss: 0.2977, Accuracy: 4609/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.50713348388672 s]\n",
      "Train Epoch: 64 [0/50000 (0%)]\tLoss: 0.019256, Accuracy: 99.61\n",
      "Train Epoch: 64 [2560/50000 (6%)]\tLoss: 0.030716, Accuracy: 99.41\n",
      "Train Epoch: 64 [5120/50000 (11%)]\tLoss: 0.024345, Accuracy: 99.61\n",
      "Train Epoch: 64 [7680/50000 (17%)]\tLoss: 0.034949, Accuracy: 99.02\n",
      "Train Epoch: 64 [10240/50000 (23%)]\tLoss: 0.026424, Accuracy: 99.41\n",
      "Train Epoch: 64 [12800/50000 (28%)]\tLoss: 0.014735, Accuracy: 99.80\n",
      "Train Epoch: 64 [15360/50000 (34%)]\tLoss: 0.021314, Accuracy: 99.61\n",
      "Train Epoch: 64 [17920/50000 (40%)]\tLoss: 0.027045, Accuracy: 99.02\n",
      "Train Epoch: 64 [20480/50000 (45%)]\tLoss: 0.017470, Accuracy: 99.80\n",
      "Train Epoch: 64 [23040/50000 (51%)]\tLoss: 0.028551, Accuracy: 99.22\n",
      "Train Epoch: 64 [25600/50000 (57%)]\tLoss: 0.037878, Accuracy: 99.02\n",
      "Train Epoch: 64 [28160/50000 (62%)]\tLoss: 0.021621, Accuracy: 99.61\n",
      "Train Epoch: 64 [30720/50000 (68%)]\tLoss: 0.018162, Accuracy: 99.41\n",
      "Train Epoch: 64 [33280/50000 (74%)]\tLoss: 0.024519, Accuracy: 99.02\n",
      "Train Epoch: 64 [35840/50000 (80%)]\tLoss: 0.030391, Accuracy: 99.02\n",
      "Train Epoch: 64 [38400/50000 (85%)]\tLoss: 0.028061, Accuracy: 99.41\n",
      "Train Epoch: 64 [40960/50000 (91%)]\tLoss: 0.035716, Accuracy: 98.83\n",
      "Train Epoch: 64 [43520/50000 (97%)]\tLoss: 0.029248, Accuracy: 99.02\n",
      "\n",
      "Validation set: Average loss: 0.2984, Accuracy: 4624/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.078391551971436 s]\n",
      "\n",
      "Test set: Average loss: 0.3177, Accuracy: 9158/10000 (91.58%)\n",
      "\n",
      "Train Epoch: 65 [0/50000 (0%)]\tLoss: 0.025586, Accuracy: 99.41\n",
      "Train Epoch: 65 [2560/50000 (6%)]\tLoss: 0.030884, Accuracy: 99.61\n",
      "Train Epoch: 65 [5120/50000 (11%)]\tLoss: 0.010811, Accuracy: 100.00\n",
      "Train Epoch: 65 [7680/50000 (17%)]\tLoss: 0.029976, Accuracy: 99.02\n",
      "Train Epoch: 65 [10240/50000 (23%)]\tLoss: 0.021759, Accuracy: 99.22\n",
      "Train Epoch: 65 [12800/50000 (28%)]\tLoss: 0.019159, Accuracy: 99.61\n",
      "Train Epoch: 65 [15360/50000 (34%)]\tLoss: 0.029737, Accuracy: 99.22\n",
      "Train Epoch: 65 [17920/50000 (40%)]\tLoss: 0.024232, Accuracy: 99.80\n",
      "Train Epoch: 65 [20480/50000 (45%)]\tLoss: 0.026229, Accuracy: 99.02\n",
      "Train Epoch: 65 [23040/50000 (51%)]\tLoss: 0.022880, Accuracy: 99.61\n",
      "Train Epoch: 65 [25600/50000 (57%)]\tLoss: 0.023260, Accuracy: 99.41\n",
      "Train Epoch: 65 [28160/50000 (62%)]\tLoss: 0.028184, Accuracy: 99.41\n",
      "Train Epoch: 65 [30720/50000 (68%)]\tLoss: 0.022844, Accuracy: 99.61\n",
      "Train Epoch: 65 [33280/50000 (74%)]\tLoss: 0.032747, Accuracy: 99.02\n",
      "Train Epoch: 65 [35840/50000 (80%)]\tLoss: 0.017130, Accuracy: 99.61\n",
      "Train Epoch: 65 [38400/50000 (85%)]\tLoss: 0.034068, Accuracy: 99.41\n",
      "Train Epoch: 65 [40960/50000 (91%)]\tLoss: 0.027919, Accuracy: 99.22\n",
      "Train Epoch: 65 [43520/50000 (97%)]\tLoss: 0.034609, Accuracy: 99.02\n",
      "\n",
      "Validation set: Average loss: 0.3089, Accuracy: 4605/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.46236753463745 s]\n",
      "Train Epoch: 66 [0/50000 (0%)]\tLoss: 0.027971, Accuracy: 99.02\n",
      "Train Epoch: 66 [2560/50000 (6%)]\tLoss: 0.018697, Accuracy: 99.41\n",
      "Train Epoch: 66 [5120/50000 (11%)]\tLoss: 0.032038, Accuracy: 99.22\n",
      "Train Epoch: 66 [7680/50000 (17%)]\tLoss: 0.024972, Accuracy: 99.22\n",
      "Train Epoch: 66 [10240/50000 (23%)]\tLoss: 0.033434, Accuracy: 99.02\n",
      "Train Epoch: 66 [12800/50000 (28%)]\tLoss: 0.021980, Accuracy: 99.41\n",
      "Train Epoch: 66 [15360/50000 (34%)]\tLoss: 0.021506, Accuracy: 99.61\n",
      "Train Epoch: 66 [17920/50000 (40%)]\tLoss: 0.032486, Accuracy: 99.02\n",
      "Train Epoch: 66 [20480/50000 (45%)]\tLoss: 0.023375, Accuracy: 99.80\n",
      "Train Epoch: 66 [23040/50000 (51%)]\tLoss: 0.019068, Accuracy: 99.41\n",
      "Train Epoch: 66 [25600/50000 (57%)]\tLoss: 0.023941, Accuracy: 99.22\n",
      "Train Epoch: 66 [28160/50000 (62%)]\tLoss: 0.027209, Accuracy: 99.22\n",
      "Train Epoch: 66 [30720/50000 (68%)]\tLoss: 0.024108, Accuracy: 99.02\n",
      "Train Epoch: 66 [33280/50000 (74%)]\tLoss: 0.017218, Accuracy: 99.41\n",
      "Train Epoch: 66 [35840/50000 (80%)]\tLoss: 0.029746, Accuracy: 99.22\n",
      "Train Epoch: 66 [38400/50000 (85%)]\tLoss: 0.033366, Accuracy: 98.83\n",
      "Train Epoch: 66 [40960/50000 (91%)]\tLoss: 0.030238, Accuracy: 99.02\n",
      "Train Epoch: 66 [43520/50000 (97%)]\tLoss: 0.018264, Accuracy: 99.80\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 0.3126, Accuracy: 4613/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.074843406677246 s]\n",
      "\n",
      "Test set: Average loss: 0.3363, Accuracy: 9137/10000 (91.37%)\n",
      "\n",
      "Train Epoch: 67 [0/50000 (0%)]\tLoss: 0.017953, Accuracy: 99.41\n",
      "Train Epoch: 67 [2560/50000 (6%)]\tLoss: 0.021347, Accuracy: 99.22\n",
      "Train Epoch: 67 [5120/50000 (11%)]\tLoss: 0.020279, Accuracy: 99.41\n",
      "Train Epoch: 67 [7680/50000 (17%)]\tLoss: 0.024853, Accuracy: 99.61\n",
      "Train Epoch: 67 [10240/50000 (23%)]\tLoss: 0.017354, Accuracy: 99.80\n",
      "Train Epoch: 67 [12800/50000 (28%)]\tLoss: 0.019187, Accuracy: 99.41\n",
      "Train Epoch: 67 [15360/50000 (34%)]\tLoss: 0.035342, Accuracy: 99.02\n",
      "Train Epoch: 67 [17920/50000 (40%)]\tLoss: 0.018321, Accuracy: 99.41\n",
      "Train Epoch: 67 [20480/50000 (45%)]\tLoss: 0.013465, Accuracy: 99.61\n",
      "Train Epoch: 67 [23040/50000 (51%)]\tLoss: 0.014903, Accuracy: 99.80\n",
      "Train Epoch: 67 [25600/50000 (57%)]\tLoss: 0.023698, Accuracy: 99.22\n",
      "Train Epoch: 67 [28160/50000 (62%)]\tLoss: 0.016196, Accuracy: 99.80\n",
      "Train Epoch: 67 [30720/50000 (68%)]\tLoss: 0.019468, Accuracy: 99.41\n",
      "Train Epoch: 67 [33280/50000 (74%)]\tLoss: 0.016925, Accuracy: 99.61\n",
      "Train Epoch: 67 [35840/50000 (80%)]\tLoss: 0.022267, Accuracy: 99.41\n",
      "Train Epoch: 67 [38400/50000 (85%)]\tLoss: 0.013049, Accuracy: 99.80\n",
      "Train Epoch: 67 [40960/50000 (91%)]\tLoss: 0.022214, Accuracy: 99.22\n",
      "Train Epoch: 67 [43520/50000 (97%)]\tLoss: 0.028472, Accuracy: 98.83\n",
      "\n",
      "Validation set: Average loss: 0.3077, Accuracy: 4616/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.416858196258545 s]\n",
      "Train Epoch: 68 [0/50000 (0%)]\tLoss: 0.017779, Accuracy: 99.80\n",
      "Train Epoch: 68 [2560/50000 (6%)]\tLoss: 0.015425, Accuracy: 99.61\n",
      "Train Epoch: 68 [5120/50000 (11%)]\tLoss: 0.018763, Accuracy: 99.41\n",
      "Train Epoch: 68 [7680/50000 (17%)]\tLoss: 0.016497, Accuracy: 99.61\n",
      "Train Epoch: 68 [10240/50000 (23%)]\tLoss: 0.026127, Accuracy: 99.02\n",
      "Train Epoch: 68 [12800/50000 (28%)]\tLoss: 0.022616, Accuracy: 99.22\n",
      "Train Epoch: 68 [15360/50000 (34%)]\tLoss: 0.018597, Accuracy: 99.22\n",
      "Train Epoch: 68 [17920/50000 (40%)]\tLoss: 0.022313, Accuracy: 99.22\n",
      "Train Epoch: 68 [20480/50000 (45%)]\tLoss: 0.016186, Accuracy: 99.80\n",
      "Train Epoch: 68 [23040/50000 (51%)]\tLoss: 0.015237, Accuracy: 99.80\n",
      "Train Epoch: 68 [25600/50000 (57%)]\tLoss: 0.020694, Accuracy: 99.61\n",
      "Train Epoch: 68 [28160/50000 (62%)]\tLoss: 0.018594, Accuracy: 99.22\n",
      "Train Epoch: 68 [30720/50000 (68%)]\tLoss: 0.020440, Accuracy: 99.80\n",
      "Train Epoch: 68 [33280/50000 (74%)]\tLoss: 0.018236, Accuracy: 99.41\n",
      "Train Epoch: 68 [35840/50000 (80%)]\tLoss: 0.016815, Accuracy: 99.80\n",
      "Train Epoch: 68 [38400/50000 (85%)]\tLoss: 0.017325, Accuracy: 99.80\n",
      "Train Epoch: 68 [40960/50000 (91%)]\tLoss: 0.038324, Accuracy: 98.83\n",
      "Train Epoch: 68 [43520/50000 (97%)]\tLoss: 0.007889, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.3136, Accuracy: 4609/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.003952264785767 s]\n",
      "\n",
      "Test set: Average loss: 0.3369, Accuracy: 9147/10000 (91.47%)\n",
      "\n",
      "Train Epoch: 69 [0/50000 (0%)]\tLoss: 0.028885, Accuracy: 99.22\n",
      "Train Epoch: 69 [2560/50000 (6%)]\tLoss: 0.017580, Accuracy: 99.61\n",
      "Train Epoch: 69 [5120/50000 (11%)]\tLoss: 0.017292, Accuracy: 99.61\n",
      "Train Epoch: 69 [7680/50000 (17%)]\tLoss: 0.027289, Accuracy: 99.22\n",
      "Train Epoch: 69 [10240/50000 (23%)]\tLoss: 0.015633, Accuracy: 99.61\n",
      "Train Epoch: 69 [12800/50000 (28%)]\tLoss: 0.026136, Accuracy: 99.41\n",
      "Train Epoch: 69 [15360/50000 (34%)]\tLoss: 0.034548, Accuracy: 98.44\n",
      "Train Epoch: 69 [17920/50000 (40%)]\tLoss: 0.017270, Accuracy: 100.00\n",
      "Train Epoch: 69 [20480/50000 (45%)]\tLoss: 0.022892, Accuracy: 99.41\n",
      "Train Epoch: 69 [23040/50000 (51%)]\tLoss: 0.024721, Accuracy: 99.41\n",
      "Train Epoch: 69 [25600/50000 (57%)]\tLoss: 0.011992, Accuracy: 99.61\n",
      "Train Epoch: 69 [28160/50000 (62%)]\tLoss: 0.010705, Accuracy: 99.61\n",
      "Train Epoch: 69 [30720/50000 (68%)]\tLoss: 0.015988, Accuracy: 99.61\n",
      "Train Epoch: 69 [33280/50000 (74%)]\tLoss: 0.033418, Accuracy: 99.02\n",
      "Train Epoch: 69 [35840/50000 (80%)]\tLoss: 0.010838, Accuracy: 100.00\n",
      "Train Epoch: 69 [38400/50000 (85%)]\tLoss: 0.021065, Accuracy: 99.41\n",
      "Train Epoch: 69 [40960/50000 (91%)]\tLoss: 0.010729, Accuracy: 100.00\n",
      "Train Epoch: 69 [43520/50000 (97%)]\tLoss: 0.026639, Accuracy: 99.02\n",
      "\n",
      "Validation set: Average loss: 0.3222, Accuracy: 4614/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.458689212799072 s]\n",
      "Train Epoch: 70 [0/50000 (0%)]\tLoss: 0.012840, Accuracy: 99.61\n",
      "Train Epoch: 70 [2560/50000 (6%)]\tLoss: 0.022369, Accuracy: 99.02\n",
      "Train Epoch: 70 [5120/50000 (11%)]\tLoss: 0.028853, Accuracy: 99.22\n",
      "Train Epoch: 70 [7680/50000 (17%)]\tLoss: 0.020952, Accuracy: 99.02\n",
      "Train Epoch: 70 [10240/50000 (23%)]\tLoss: 0.009906, Accuracy: 99.80\n",
      "Train Epoch: 70 [12800/50000 (28%)]\tLoss: 0.012899, Accuracy: 99.80\n",
      "Train Epoch: 70 [15360/50000 (34%)]\tLoss: 0.017438, Accuracy: 99.61\n",
      "Train Epoch: 70 [17920/50000 (40%)]\tLoss: 0.012194, Accuracy: 100.00\n",
      "Train Epoch: 70 [20480/50000 (45%)]\tLoss: 0.035706, Accuracy: 98.44\n",
      "Train Epoch: 70 [23040/50000 (51%)]\tLoss: 0.012754, Accuracy: 100.00\n",
      "Train Epoch: 70 [25600/50000 (57%)]\tLoss: 0.020972, Accuracy: 99.22\n",
      "Train Epoch: 70 [28160/50000 (62%)]\tLoss: 0.025793, Accuracy: 99.22\n",
      "Train Epoch: 70 [30720/50000 (68%)]\tLoss: 0.022703, Accuracy: 99.22\n",
      "Train Epoch: 70 [33280/50000 (74%)]\tLoss: 0.018285, Accuracy: 99.61\n",
      "Train Epoch: 70 [35840/50000 (80%)]\tLoss: 0.025926, Accuracy: 99.41\n",
      "Train Epoch: 70 [38400/50000 (85%)]\tLoss: 0.018295, Accuracy: 99.61\n",
      "Train Epoch: 70 [40960/50000 (91%)]\tLoss: 0.019853, Accuracy: 99.61\n",
      "Train Epoch: 70 [43520/50000 (97%)]\tLoss: 0.016071, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3188, Accuracy: 4614/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[25.968007564544678 s]\n",
      "\n",
      "Test set: Average loss: 0.3394, Accuracy: 9158/10000 (91.58%)\n",
      "\n",
      "Train Epoch: 71 [0/50000 (0%)]\tLoss: 0.013776, Accuracy: 99.61\n",
      "Train Epoch: 71 [2560/50000 (6%)]\tLoss: 0.024009, Accuracy: 99.41\n",
      "Train Epoch: 71 [5120/50000 (11%)]\tLoss: 0.031028, Accuracy: 98.83\n",
      "Train Epoch: 71 [7680/50000 (17%)]\tLoss: 0.018407, Accuracy: 99.61\n",
      "Train Epoch: 71 [10240/50000 (23%)]\tLoss: 0.020724, Accuracy: 99.22\n",
      "Train Epoch: 71 [12800/50000 (28%)]\tLoss: 0.018889, Accuracy: 99.41\n",
      "Train Epoch: 71 [15360/50000 (34%)]\tLoss: 0.019548, Accuracy: 99.61\n",
      "Train Epoch: 71 [17920/50000 (40%)]\tLoss: 0.020498, Accuracy: 99.02\n",
      "Train Epoch: 71 [20480/50000 (45%)]\tLoss: 0.013387, Accuracy: 99.61\n",
      "Train Epoch: 71 [23040/50000 (51%)]\tLoss: 0.018806, Accuracy: 99.41\n",
      "Train Epoch: 71 [25600/50000 (57%)]\tLoss: 0.014083, Accuracy: 99.80\n",
      "Train Epoch: 71 [28160/50000 (62%)]\tLoss: 0.017375, Accuracy: 99.61\n",
      "Train Epoch: 71 [30720/50000 (68%)]\tLoss: 0.013031, Accuracy: 99.80\n",
      "Train Epoch: 71 [33280/50000 (74%)]\tLoss: 0.021358, Accuracy: 99.41\n",
      "Train Epoch: 71 [35840/50000 (80%)]\tLoss: 0.011524, Accuracy: 100.00\n",
      "Train Epoch: 71 [38400/50000 (85%)]\tLoss: 0.013374, Accuracy: 99.80\n",
      "Train Epoch: 71 [40960/50000 (91%)]\tLoss: 0.010137, Accuracy: 99.80\n",
      "Train Epoch: 71 [43520/50000 (97%)]\tLoss: 0.014440, Accuracy: 99.61\n",
      "\n",
      "Validation set: Average loss: 0.3120, Accuracy: 4636/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.364124059677124 s]\n",
      "Train Epoch: 72 [0/50000 (0%)]\tLoss: 0.015319, Accuracy: 99.61\n",
      "Train Epoch: 72 [2560/50000 (6%)]\tLoss: 0.015046, Accuracy: 99.61\n",
      "Train Epoch: 72 [5120/50000 (11%)]\tLoss: 0.018166, Accuracy: 99.61\n",
      "Train Epoch: 72 [7680/50000 (17%)]\tLoss: 0.010166, Accuracy: 99.80\n",
      "Train Epoch: 72 [10240/50000 (23%)]\tLoss: 0.015699, Accuracy: 99.61\n",
      "Train Epoch: 72 [12800/50000 (28%)]\tLoss: 0.010474, Accuracy: 99.61\n",
      "Train Epoch: 72 [15360/50000 (34%)]\tLoss: 0.007901, Accuracy: 100.00\n",
      "Train Epoch: 72 [17920/50000 (40%)]\tLoss: 0.014639, Accuracy: 99.41\n",
      "Train Epoch: 72 [20480/50000 (45%)]\tLoss: 0.015829, Accuracy: 99.61\n",
      "Train Epoch: 72 [23040/50000 (51%)]\tLoss: 0.018196, Accuracy: 99.41\n",
      "Train Epoch: 72 [25600/50000 (57%)]\tLoss: 0.013296, Accuracy: 99.61\n",
      "Train Epoch: 72 [28160/50000 (62%)]\tLoss: 0.006830, Accuracy: 100.00\n",
      "Train Epoch: 72 [30720/50000 (68%)]\tLoss: 0.017370, Accuracy: 99.41\n",
      "Train Epoch: 72 [33280/50000 (74%)]\tLoss: 0.010420, Accuracy: 100.00\n",
      "Train Epoch: 72 [35840/50000 (80%)]\tLoss: 0.037000, Accuracy: 98.83\n",
      "Train Epoch: 72 [38400/50000 (85%)]\tLoss: 0.009624, Accuracy: 99.80\n",
      "Train Epoch: 72 [40960/50000 (91%)]\tLoss: 0.017838, Accuracy: 99.80\n",
      "Train Epoch: 72 [43520/50000 (97%)]\tLoss: 0.011164, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3109, Accuracy: 4633/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[25.93765091896057 s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.3361, Accuracy: 9164/10000 (91.64%)\n",
      "\n",
      "Train Epoch: 73 [0/50000 (0%)]\tLoss: 0.010579, Accuracy: 99.80\n",
      "Train Epoch: 73 [2560/50000 (6%)]\tLoss: 0.018528, Accuracy: 99.61\n",
      "Train Epoch: 73 [5120/50000 (11%)]\tLoss: 0.020130, Accuracy: 99.22\n",
      "Train Epoch: 73 [7680/50000 (17%)]\tLoss: 0.014045, Accuracy: 99.41\n",
      "Train Epoch: 73 [10240/50000 (23%)]\tLoss: 0.014418, Accuracy: 99.61\n",
      "Train Epoch: 73 [12800/50000 (28%)]\tLoss: 0.016134, Accuracy: 99.41\n",
      "Train Epoch: 73 [15360/50000 (34%)]\tLoss: 0.021123, Accuracy: 99.41\n",
      "Train Epoch: 73 [17920/50000 (40%)]\tLoss: 0.016164, Accuracy: 99.41\n",
      "Train Epoch: 73 [20480/50000 (45%)]\tLoss: 0.014222, Accuracy: 99.80\n",
      "Train Epoch: 73 [23040/50000 (51%)]\tLoss: 0.016022, Accuracy: 99.61\n",
      "Train Epoch: 73 [25600/50000 (57%)]\tLoss: 0.019077, Accuracy: 99.41\n",
      "Train Epoch: 73 [28160/50000 (62%)]\tLoss: 0.014567, Accuracy: 99.41\n",
      "Train Epoch: 73 [30720/50000 (68%)]\tLoss: 0.018107, Accuracy: 99.41\n",
      "Train Epoch: 73 [33280/50000 (74%)]\tLoss: 0.010080, Accuracy: 99.80\n",
      "Train Epoch: 73 [35840/50000 (80%)]\tLoss: 0.013223, Accuracy: 99.61\n",
      "Train Epoch: 73 [38400/50000 (85%)]\tLoss: 0.020175, Accuracy: 99.41\n",
      "Train Epoch: 73 [40960/50000 (91%)]\tLoss: 0.013979, Accuracy: 99.61\n",
      "Train Epoch: 73 [43520/50000 (97%)]\tLoss: 0.009326, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.3127, Accuracy: 4628/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.45813798904419 s]\n",
      "Train Epoch: 74 [0/50000 (0%)]\tLoss: 0.012496, Accuracy: 99.80\n",
      "Train Epoch: 74 [2560/50000 (6%)]\tLoss: 0.014013, Accuracy: 99.61\n",
      "Train Epoch: 74 [5120/50000 (11%)]\tLoss: 0.013054, Accuracy: 99.80\n",
      "Train Epoch: 74 [7680/50000 (17%)]\tLoss: 0.012593, Accuracy: 99.61\n",
      "Train Epoch: 74 [10240/50000 (23%)]\tLoss: 0.013229, Accuracy: 99.80\n",
      "Train Epoch: 74 [12800/50000 (28%)]\tLoss: 0.010079, Accuracy: 99.80\n",
      "Train Epoch: 74 [15360/50000 (34%)]\tLoss: 0.007693, Accuracy: 100.00\n",
      "Train Epoch: 74 [17920/50000 (40%)]\tLoss: 0.011417, Accuracy: 100.00\n",
      "Train Epoch: 74 [20480/50000 (45%)]\tLoss: 0.014492, Accuracy: 99.61\n",
      "Train Epoch: 74 [23040/50000 (51%)]\tLoss: 0.019804, Accuracy: 99.61\n",
      "Train Epoch: 74 [25600/50000 (57%)]\tLoss: 0.012659, Accuracy: 99.80\n",
      "Train Epoch: 74 [28160/50000 (62%)]\tLoss: 0.011250, Accuracy: 99.80\n",
      "Train Epoch: 74 [30720/50000 (68%)]\tLoss: 0.010648, Accuracy: 99.80\n",
      "Train Epoch: 74 [33280/50000 (74%)]\tLoss: 0.010881, Accuracy: 99.61\n",
      "Train Epoch: 74 [35840/50000 (80%)]\tLoss: 0.015756, Accuracy: 99.80\n",
      "Train Epoch: 74 [38400/50000 (85%)]\tLoss: 0.010483, Accuracy: 99.80\n",
      "Train Epoch: 74 [40960/50000 (91%)]\tLoss: 0.007873, Accuracy: 100.00\n",
      "Train Epoch: 74 [43520/50000 (97%)]\tLoss: 0.010828, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.3135, Accuracy: 4635/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.011320114135742 s]\n",
      "\n",
      "Test set: Average loss: 0.3345, Accuracy: 9181/10000 (91.81%)\n",
      "\n",
      "Train Epoch: 75 [0/50000 (0%)]\tLoss: 0.014990, Accuracy: 99.61\n",
      "Train Epoch: 75 [2560/50000 (6%)]\tLoss: 0.010315, Accuracy: 99.80\n",
      "Train Epoch: 75 [5120/50000 (11%)]\tLoss: 0.010980, Accuracy: 99.61\n",
      "Train Epoch: 75 [7680/50000 (17%)]\tLoss: 0.007198, Accuracy: 99.80\n",
      "Train Epoch: 75 [10240/50000 (23%)]\tLoss: 0.014351, Accuracy: 99.80\n",
      "Train Epoch: 75 [12800/50000 (28%)]\tLoss: 0.010430, Accuracy: 100.00\n",
      "Train Epoch: 75 [15360/50000 (34%)]\tLoss: 0.011765, Accuracy: 99.80\n",
      "Train Epoch: 75 [17920/50000 (40%)]\tLoss: 0.019893, Accuracy: 99.22\n",
      "Train Epoch: 75 [20480/50000 (45%)]\tLoss: 0.020303, Accuracy: 99.22\n",
      "Train Epoch: 75 [23040/50000 (51%)]\tLoss: 0.015397, Accuracy: 99.41\n",
      "Train Epoch: 75 [25600/50000 (57%)]\tLoss: 0.011982, Accuracy: 100.00\n",
      "Train Epoch: 75 [28160/50000 (62%)]\tLoss: 0.013492, Accuracy: 99.61\n",
      "Train Epoch: 75 [30720/50000 (68%)]\tLoss: 0.004886, Accuracy: 100.00\n",
      "Train Epoch: 75 [33280/50000 (74%)]\tLoss: 0.007714, Accuracy: 100.00\n",
      "Train Epoch: 75 [35840/50000 (80%)]\tLoss: 0.016048, Accuracy: 99.22\n",
      "Train Epoch: 75 [38400/50000 (85%)]\tLoss: 0.024319, Accuracy: 99.41\n",
      "Train Epoch: 75 [40960/50000 (91%)]\tLoss: 0.010319, Accuracy: 99.80\n",
      "Train Epoch: 75 [43520/50000 (97%)]\tLoss: 0.012955, Accuracy: 99.61\n",
      "\n",
      "Validation set: Average loss: 0.3108, Accuracy: 4639/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.4342839717865 s]\n",
      "Train Epoch: 76 [0/50000 (0%)]\tLoss: 0.009784, Accuracy: 99.61\n",
      "Train Epoch: 76 [2560/50000 (6%)]\tLoss: 0.014305, Accuracy: 99.61\n",
      "Train Epoch: 76 [5120/50000 (11%)]\tLoss: 0.011167, Accuracy: 99.61\n",
      "Train Epoch: 76 [7680/50000 (17%)]\tLoss: 0.015079, Accuracy: 99.61\n",
      "Train Epoch: 76 [10240/50000 (23%)]\tLoss: 0.010131, Accuracy: 99.80\n",
      "Train Epoch: 76 [12800/50000 (28%)]\tLoss: 0.012683, Accuracy: 99.80\n",
      "Train Epoch: 76 [15360/50000 (34%)]\tLoss: 0.007590, Accuracy: 99.80\n",
      "Train Epoch: 76 [17920/50000 (40%)]\tLoss: 0.008019, Accuracy: 100.00\n",
      "Train Epoch: 76 [20480/50000 (45%)]\tLoss: 0.015477, Accuracy: 99.41\n",
      "Train Epoch: 76 [23040/50000 (51%)]\tLoss: 0.017742, Accuracy: 99.61\n",
      "Train Epoch: 76 [25600/50000 (57%)]\tLoss: 0.015044, Accuracy: 99.80\n",
      "Train Epoch: 76 [28160/50000 (62%)]\tLoss: 0.011071, Accuracy: 99.80\n",
      "Train Epoch: 76 [30720/50000 (68%)]\tLoss: 0.020686, Accuracy: 99.41\n",
      "Train Epoch: 76 [33280/50000 (74%)]\tLoss: 0.008842, Accuracy: 99.80\n",
      "Train Epoch: 76 [35840/50000 (80%)]\tLoss: 0.018386, Accuracy: 99.61\n",
      "Train Epoch: 76 [38400/50000 (85%)]\tLoss: 0.010636, Accuracy: 99.80\n",
      "Train Epoch: 76 [40960/50000 (91%)]\tLoss: 0.016490, Accuracy: 99.41\n",
      "Train Epoch: 76 [43520/50000 (97%)]\tLoss: 0.011713, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3107, Accuracy: 4637/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.03160524368286 s]\n",
      "\n",
      "Test set: Average loss: 0.3343, Accuracy: 9177/10000 (91.77%)\n",
      "\n",
      "Train Epoch: 77 [0/50000 (0%)]\tLoss: 0.013620, Accuracy: 99.41\n",
      "Train Epoch: 77 [2560/50000 (6%)]\tLoss: 0.005883, Accuracy: 100.00\n",
      "Train Epoch: 77 [5120/50000 (11%)]\tLoss: 0.007912, Accuracy: 100.00\n",
      "Train Epoch: 77 [7680/50000 (17%)]\tLoss: 0.023483, Accuracy: 99.41\n",
      "Train Epoch: 77 [10240/50000 (23%)]\tLoss: 0.012889, Accuracy: 99.80\n",
      "Train Epoch: 77 [12800/50000 (28%)]\tLoss: 0.009984, Accuracy: 99.80\n",
      "Train Epoch: 77 [15360/50000 (34%)]\tLoss: 0.016925, Accuracy: 99.41\n",
      "Train Epoch: 77 [17920/50000 (40%)]\tLoss: 0.016745, Accuracy: 99.80\n",
      "Train Epoch: 77 [20480/50000 (45%)]\tLoss: 0.016334, Accuracy: 99.80\n",
      "Train Epoch: 77 [23040/50000 (51%)]\tLoss: 0.013489, Accuracy: 99.80\n",
      "Train Epoch: 77 [25600/50000 (57%)]\tLoss: 0.007817, Accuracy: 99.80\n",
      "Train Epoch: 77 [28160/50000 (62%)]\tLoss: 0.007506, Accuracy: 99.80\n",
      "Train Epoch: 77 [30720/50000 (68%)]\tLoss: 0.012872, Accuracy: 99.80\n",
      "Train Epoch: 77 [33280/50000 (74%)]\tLoss: 0.014673, Accuracy: 99.61\n",
      "Train Epoch: 77 [35840/50000 (80%)]\tLoss: 0.006057, Accuracy: 100.00\n",
      "Train Epoch: 77 [38400/50000 (85%)]\tLoss: 0.008231, Accuracy: 99.80\n",
      "Train Epoch: 77 [40960/50000 (91%)]\tLoss: 0.009400, Accuracy: 100.00\n",
      "Train Epoch: 77 [43520/50000 (97%)]\tLoss: 0.022379, Accuracy: 99.22\n",
      "\n",
      "Validation set: Average loss: 0.3139, Accuracy: 4640/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.436412811279297 s]\n",
      "Train Epoch: 78 [0/50000 (0%)]\tLoss: 0.016550, Accuracy: 99.41\n",
      "Train Epoch: 78 [2560/50000 (6%)]\tLoss: 0.019496, Accuracy: 99.22\n",
      "Train Epoch: 78 [5120/50000 (11%)]\tLoss: 0.010502, Accuracy: 100.00\n",
      "Train Epoch: 78 [7680/50000 (17%)]\tLoss: 0.012705, Accuracy: 100.00\n",
      "Train Epoch: 78 [10240/50000 (23%)]\tLoss: 0.019438, Accuracy: 99.41\n",
      "Train Epoch: 78 [12800/50000 (28%)]\tLoss: 0.018112, Accuracy: 99.41\n",
      "Train Epoch: 78 [15360/50000 (34%)]\tLoss: 0.015836, Accuracy: 99.61\n",
      "Train Epoch: 78 [17920/50000 (40%)]\tLoss: 0.007301, Accuracy: 100.00\n",
      "Train Epoch: 78 [20480/50000 (45%)]\tLoss: 0.012171, Accuracy: 99.61\n",
      "Train Epoch: 78 [23040/50000 (51%)]\tLoss: 0.006338, Accuracy: 100.00\n",
      "Train Epoch: 78 [25600/50000 (57%)]\tLoss: 0.009038, Accuracy: 100.00\n",
      "Train Epoch: 78 [28160/50000 (62%)]\tLoss: 0.013532, Accuracy: 99.61\n",
      "Train Epoch: 78 [30720/50000 (68%)]\tLoss: 0.007532, Accuracy: 99.80\n",
      "Train Epoch: 78 [33280/50000 (74%)]\tLoss: 0.014052, Accuracy: 99.61\n",
      "Train Epoch: 78 [35840/50000 (80%)]\tLoss: 0.016262, Accuracy: 99.41\n",
      "Train Epoch: 78 [38400/50000 (85%)]\tLoss: 0.024091, Accuracy: 99.41\n",
      "Train Epoch: 78 [40960/50000 (91%)]\tLoss: 0.011255, Accuracy: 99.61\n",
      "Train Epoch: 78 [43520/50000 (97%)]\tLoss: 0.010220, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3099, Accuracy: 4637/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[25.980618238449097 s]\n",
      "\n",
      "Test set: Average loss: 0.3329, Accuracy: 9167/10000 (91.67%)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 79 [0/50000 (0%)]\tLoss: 0.016751, Accuracy: 99.41\n",
      "Train Epoch: 79 [2560/50000 (6%)]\tLoss: 0.007020, Accuracy: 99.80\n",
      "Train Epoch: 79 [5120/50000 (11%)]\tLoss: 0.019029, Accuracy: 99.41\n",
      "Train Epoch: 79 [7680/50000 (17%)]\tLoss: 0.011693, Accuracy: 99.61\n",
      "Train Epoch: 79 [10240/50000 (23%)]\tLoss: 0.014358, Accuracy: 99.80\n",
      "Train Epoch: 79 [12800/50000 (28%)]\tLoss: 0.015164, Accuracy: 99.61\n",
      "Train Epoch: 79 [15360/50000 (34%)]\tLoss: 0.016165, Accuracy: 99.22\n",
      "Train Epoch: 79 [17920/50000 (40%)]\tLoss: 0.013933, Accuracy: 99.61\n",
      "Train Epoch: 79 [20480/50000 (45%)]\tLoss: 0.008072, Accuracy: 100.00\n",
      "Train Epoch: 79 [23040/50000 (51%)]\tLoss: 0.017469, Accuracy: 99.61\n",
      "Train Epoch: 79 [25600/50000 (57%)]\tLoss: 0.009456, Accuracy: 100.00\n",
      "Train Epoch: 79 [28160/50000 (62%)]\tLoss: 0.010745, Accuracy: 99.80\n",
      "Train Epoch: 79 [30720/50000 (68%)]\tLoss: 0.014357, Accuracy: 99.61\n",
      "Train Epoch: 79 [33280/50000 (74%)]\tLoss: 0.015147, Accuracy: 99.61\n",
      "Train Epoch: 79 [35840/50000 (80%)]\tLoss: 0.009989, Accuracy: 99.80\n",
      "Train Epoch: 79 [38400/50000 (85%)]\tLoss: 0.024236, Accuracy: 99.02\n",
      "Train Epoch: 79 [40960/50000 (91%)]\tLoss: 0.013215, Accuracy: 99.61\n",
      "Train Epoch: 79 [43520/50000 (97%)]\tLoss: 0.009999, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.3115, Accuracy: 4638/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.4018452167511 s]\n",
      "Train Epoch: 80 [0/50000 (0%)]\tLoss: 0.008972, Accuracy: 99.80\n",
      "Train Epoch: 80 [2560/50000 (6%)]\tLoss: 0.010492, Accuracy: 99.80\n",
      "Train Epoch: 80 [5120/50000 (11%)]\tLoss: 0.011809, Accuracy: 100.00\n",
      "Train Epoch: 80 [7680/50000 (17%)]\tLoss: 0.009439, Accuracy: 99.61\n",
      "Train Epoch: 80 [10240/50000 (23%)]\tLoss: 0.009811, Accuracy: 99.80\n",
      "Train Epoch: 80 [12800/50000 (28%)]\tLoss: 0.009279, Accuracy: 99.80\n",
      "Train Epoch: 80 [15360/50000 (34%)]\tLoss: 0.022415, Accuracy: 99.22\n",
      "Train Epoch: 80 [17920/50000 (40%)]\tLoss: 0.014109, Accuracy: 99.61\n",
      "Train Epoch: 80 [20480/50000 (45%)]\tLoss: 0.013923, Accuracy: 99.61\n",
      "Train Epoch: 80 [23040/50000 (51%)]\tLoss: 0.018273, Accuracy: 99.61\n",
      "Train Epoch: 80 [25600/50000 (57%)]\tLoss: 0.019872, Accuracy: 99.22\n",
      "Train Epoch: 80 [28160/50000 (62%)]\tLoss: 0.011589, Accuracy: 99.80\n",
      "Train Epoch: 80 [30720/50000 (68%)]\tLoss: 0.016235, Accuracy: 99.80\n",
      "Train Epoch: 80 [33280/50000 (74%)]\tLoss: 0.010669, Accuracy: 100.00\n",
      "Train Epoch: 80 [35840/50000 (80%)]\tLoss: 0.013132, Accuracy: 99.80\n",
      "Train Epoch: 80 [38400/50000 (85%)]\tLoss: 0.008799, Accuracy: 99.80\n",
      "Train Epoch: 80 [40960/50000 (91%)]\tLoss: 0.010489, Accuracy: 100.00\n",
      "Train Epoch: 80 [43520/50000 (97%)]\tLoss: 0.007703, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.3130, Accuracy: 4630/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.07977056503296 s]\n",
      "\n",
      "Test set: Average loss: 0.3357, Accuracy: 9176/10000 (91.76%)\n",
      "\n",
      "Train Epoch: 81 [0/50000 (0%)]\tLoss: 0.014760, Accuracy: 99.80\n",
      "Train Epoch: 81 [2560/50000 (6%)]\tLoss: 0.013651, Accuracy: 99.41\n",
      "Train Epoch: 81 [5120/50000 (11%)]\tLoss: 0.010376, Accuracy: 99.61\n",
      "Train Epoch: 81 [7680/50000 (17%)]\tLoss: 0.006643, Accuracy: 100.00\n",
      "Train Epoch: 81 [10240/50000 (23%)]\tLoss: 0.024558, Accuracy: 99.41\n",
      "Train Epoch: 81 [12800/50000 (28%)]\tLoss: 0.010915, Accuracy: 99.80\n",
      "Train Epoch: 81 [15360/50000 (34%)]\tLoss: 0.010670, Accuracy: 99.80\n",
      "Train Epoch: 81 [17920/50000 (40%)]\tLoss: 0.009828, Accuracy: 99.80\n",
      "Train Epoch: 81 [20480/50000 (45%)]\tLoss: 0.007922, Accuracy: 99.80\n",
      "Train Epoch: 81 [23040/50000 (51%)]\tLoss: 0.020547, Accuracy: 99.61\n",
      "Train Epoch: 81 [25600/50000 (57%)]\tLoss: 0.013833, Accuracy: 99.61\n",
      "Train Epoch: 81 [28160/50000 (62%)]\tLoss: 0.017317, Accuracy: 99.02\n",
      "Train Epoch: 81 [30720/50000 (68%)]\tLoss: 0.015193, Accuracy: 99.80\n",
      "Train Epoch: 81 [33280/50000 (74%)]\tLoss: 0.023897, Accuracy: 99.41\n",
      "Train Epoch: 81 [35840/50000 (80%)]\tLoss: 0.016849, Accuracy: 99.61\n",
      "Train Epoch: 81 [38400/50000 (85%)]\tLoss: 0.008353, Accuracy: 100.00\n",
      "Train Epoch: 81 [40960/50000 (91%)]\tLoss: 0.010857, Accuracy: 99.80\n",
      "Train Epoch: 81 [43520/50000 (97%)]\tLoss: 0.007491, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3127, Accuracy: 4634/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.397704362869263 s]\n",
      "Train Epoch: 82 [0/50000 (0%)]\tLoss: 0.010426, Accuracy: 99.80\n",
      "Train Epoch: 82 [2560/50000 (6%)]\tLoss: 0.011387, Accuracy: 99.61\n",
      "Train Epoch: 82 [5120/50000 (11%)]\tLoss: 0.012079, Accuracy: 99.61\n",
      "Train Epoch: 82 [7680/50000 (17%)]\tLoss: 0.007733, Accuracy: 99.80\n",
      "Train Epoch: 82 [10240/50000 (23%)]\tLoss: 0.009356, Accuracy: 99.80\n",
      "Train Epoch: 82 [12800/50000 (28%)]\tLoss: 0.009080, Accuracy: 100.00\n",
      "Train Epoch: 82 [15360/50000 (34%)]\tLoss: 0.022853, Accuracy: 99.02\n",
      "Train Epoch: 82 [17920/50000 (40%)]\tLoss: 0.014458, Accuracy: 99.41\n",
      "Train Epoch: 82 [20480/50000 (45%)]\tLoss: 0.008303, Accuracy: 99.80\n",
      "Train Epoch: 82 [23040/50000 (51%)]\tLoss: 0.021972, Accuracy: 99.02\n",
      "Train Epoch: 82 [25600/50000 (57%)]\tLoss: 0.005125, Accuracy: 100.00\n",
      "Train Epoch: 82 [28160/50000 (62%)]\tLoss: 0.020559, Accuracy: 99.02\n",
      "Train Epoch: 82 [30720/50000 (68%)]\tLoss: 0.010393, Accuracy: 99.80\n",
      "Train Epoch: 82 [33280/50000 (74%)]\tLoss: 0.014855, Accuracy: 99.80\n",
      "Train Epoch: 82 [35840/50000 (80%)]\tLoss: 0.014069, Accuracy: 99.80\n",
      "Train Epoch: 82 [38400/50000 (85%)]\tLoss: 0.018292, Accuracy: 99.22\n",
      "Train Epoch: 82 [40960/50000 (91%)]\tLoss: 0.011539, Accuracy: 99.80\n",
      "Train Epoch: 82 [43520/50000 (97%)]\tLoss: 0.023143, Accuracy: 99.22\n",
      "\n",
      "Validation set: Average loss: 0.3100, Accuracy: 4636/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.055184364318848 s]\n",
      "\n",
      "Test set: Average loss: 0.3345, Accuracy: 9181/10000 (91.81%)\n",
      "\n",
      "Train Epoch: 83 [0/50000 (0%)]\tLoss: 0.012961, Accuracy: 99.80\n",
      "Train Epoch: 83 [2560/50000 (6%)]\tLoss: 0.015712, Accuracy: 99.80\n",
      "Train Epoch: 83 [5120/50000 (11%)]\tLoss: 0.010089, Accuracy: 99.80\n",
      "Train Epoch: 83 [7680/50000 (17%)]\tLoss: 0.012688, Accuracy: 99.80\n",
      "Train Epoch: 83 [10240/50000 (23%)]\tLoss: 0.015701, Accuracy: 99.41\n",
      "Train Epoch: 83 [12800/50000 (28%)]\tLoss: 0.018031, Accuracy: 99.61\n",
      "Train Epoch: 83 [15360/50000 (34%)]\tLoss: 0.010388, Accuracy: 100.00\n",
      "Train Epoch: 83 [17920/50000 (40%)]\tLoss: 0.010982, Accuracy: 99.61\n",
      "Train Epoch: 83 [20480/50000 (45%)]\tLoss: 0.010284, Accuracy: 99.80\n",
      "Train Epoch: 83 [23040/50000 (51%)]\tLoss: 0.013298, Accuracy: 99.61\n",
      "Train Epoch: 83 [25600/50000 (57%)]\tLoss: 0.009432, Accuracy: 100.00\n",
      "Train Epoch: 83 [28160/50000 (62%)]\tLoss: 0.010518, Accuracy: 99.80\n",
      "Train Epoch: 83 [30720/50000 (68%)]\tLoss: 0.018747, Accuracy: 99.80\n",
      "Train Epoch: 83 [33280/50000 (74%)]\tLoss: 0.020847, Accuracy: 99.61\n",
      "Train Epoch: 83 [35840/50000 (80%)]\tLoss: 0.014283, Accuracy: 99.80\n",
      "Train Epoch: 83 [38400/50000 (85%)]\tLoss: 0.016435, Accuracy: 99.61\n",
      "Train Epoch: 83 [40960/50000 (91%)]\tLoss: 0.005711, Accuracy: 100.00\n",
      "Train Epoch: 83 [43520/50000 (97%)]\tLoss: 0.007994, Accuracy: 99.80\n",
      "\n",
      "Validation set: Average loss: 0.3114, Accuracy: 4640/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.40025019645691 s]\n",
      "Train Epoch: 84 [0/50000 (0%)]\tLoss: 0.012733, Accuracy: 99.80\n",
      "Train Epoch: 84 [2560/50000 (6%)]\tLoss: 0.009382, Accuracy: 99.80\n",
      "Train Epoch: 84 [5120/50000 (11%)]\tLoss: 0.013191, Accuracy: 99.61\n",
      "Train Epoch: 84 [7680/50000 (17%)]\tLoss: 0.016756, Accuracy: 99.80\n",
      "Train Epoch: 84 [10240/50000 (23%)]\tLoss: 0.010185, Accuracy: 99.80\n",
      "Train Epoch: 84 [12800/50000 (28%)]\tLoss: 0.009679, Accuracy: 100.00\n",
      "Train Epoch: 84 [15360/50000 (34%)]\tLoss: 0.007325, Accuracy: 100.00\n",
      "Train Epoch: 84 [17920/50000 (40%)]\tLoss: 0.020144, Accuracy: 99.61\n",
      "Train Epoch: 84 [20480/50000 (45%)]\tLoss: 0.008036, Accuracy: 99.80\n",
      "Train Epoch: 84 [23040/50000 (51%)]\tLoss: 0.009060, Accuracy: 100.00\n",
      "Train Epoch: 84 [25600/50000 (57%)]\tLoss: 0.011693, Accuracy: 99.80\n",
      "Train Epoch: 84 [28160/50000 (62%)]\tLoss: 0.010777, Accuracy: 99.80\n",
      "Train Epoch: 84 [30720/50000 (68%)]\tLoss: 0.013547, Accuracy: 99.80\n",
      "Train Epoch: 84 [33280/50000 (74%)]\tLoss: 0.010022, Accuracy: 99.80\n",
      "Train Epoch: 84 [35840/50000 (80%)]\tLoss: 0.018084, Accuracy: 99.80\n",
      "Train Epoch: 84 [38400/50000 (85%)]\tLoss: 0.019086, Accuracy: 99.41\n",
      "Train Epoch: 84 [40960/50000 (91%)]\tLoss: 0.022864, Accuracy: 99.02\n",
      "Train Epoch: 84 [43520/50000 (97%)]\tLoss: 0.018731, Accuracy: 99.61\n",
      "\n",
      "Validation set: Average loss: 0.3124, Accuracy: 4636/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[26.008315086364746 s]\n",
      "\n",
      "Test set: Average loss: 0.3354, Accuracy: 9187/10000 (91.87%)\n",
      "\n",
      "Train Epoch: 85 [0/50000 (0%)]\tLoss: 0.006808, Accuracy: 100.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 85 [2560/50000 (6%)]\tLoss: 0.018632, Accuracy: 99.41\n",
      "Train Epoch: 85 [5120/50000 (11%)]\tLoss: 0.014826, Accuracy: 99.61\n",
      "Train Epoch: 85 [7680/50000 (17%)]\tLoss: 0.017707, Accuracy: 99.41\n",
      "Train Epoch: 85 [10240/50000 (23%)]\tLoss: 0.011870, Accuracy: 99.80\n",
      "Train Epoch: 85 [12800/50000 (28%)]\tLoss: 0.013299, Accuracy: 99.80\n",
      "Train Epoch: 85 [15360/50000 (34%)]\tLoss: 0.006207, Accuracy: 100.00\n",
      "Train Epoch: 85 [17920/50000 (40%)]\tLoss: 0.013784, Accuracy: 99.80\n",
      "Train Epoch: 85 [20480/50000 (45%)]\tLoss: 0.009144, Accuracy: 100.00\n",
      "Train Epoch: 85 [23040/50000 (51%)]\tLoss: 0.016359, Accuracy: 99.61\n",
      "Train Epoch: 85 [25600/50000 (57%)]\tLoss: 0.011910, Accuracy: 100.00\n",
      "Train Epoch: 85 [28160/50000 (62%)]\tLoss: 0.021529, Accuracy: 99.41\n",
      "Train Epoch: 85 [30720/50000 (68%)]\tLoss: 0.007625, Accuracy: 100.00\n",
      "Train Epoch: 85 [33280/50000 (74%)]\tLoss: 0.006658, Accuracy: 100.00\n",
      "Train Epoch: 85 [35840/50000 (80%)]\tLoss: 0.023965, Accuracy: 99.41\n",
      "Train Epoch: 85 [38400/50000 (85%)]\tLoss: 0.019329, Accuracy: 99.41\n",
      "Train Epoch: 85 [40960/50000 (91%)]\tLoss: 0.010832, Accuracy: 99.80\n",
      "Train Epoch: 85 [43520/50000 (97%)]\tLoss: 0.013603, Accuracy: 99.61\n",
      "\n",
      "Validation set: Average loss: 0.3129, Accuracy: 4630/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[28.404757022857666 s]\n",
      "Train Epoch: 86 [0/50000 (0%)]\tLoss: 0.019929, Accuracy: 99.41\n",
      "Train Epoch: 86 [2560/50000 (6%)]\tLoss: 0.008368, Accuracy: 100.00\n",
      "Train Epoch: 86 [5120/50000 (11%)]\tLoss: 0.006135, Accuracy: 100.00\n",
      "Train Epoch: 86 [7680/50000 (17%)]\tLoss: 0.012085, Accuracy: 99.80\n",
      "Train Epoch: 86 [10240/50000 (23%)]\tLoss: 0.011577, Accuracy: 99.80\n",
      "Train Epoch: 86 [12800/50000 (28%)]\tLoss: 0.005365, Accuracy: 100.00\n",
      "Train Epoch: 86 [15360/50000 (34%)]\tLoss: 0.026482, Accuracy: 99.02\n",
      "Train Epoch: 86 [17920/50000 (40%)]\tLoss: 0.015583, Accuracy: 99.80\n",
      "Train Epoch: 86 [20480/50000 (45%)]\tLoss: 0.018598, Accuracy: 99.61\n",
      "Train Epoch: 86 [23040/50000 (51%)]\tLoss: 0.011493, Accuracy: 99.61\n",
      "Train Epoch: 86 [25600/50000 (57%)]\tLoss: 0.012803, Accuracy: 99.61\n",
      "Train Epoch: 86 [28160/50000 (62%)]\tLoss: 0.006371, Accuracy: 99.80\n",
      "Train Epoch: 86 [30720/50000 (68%)]\tLoss: 0.011129, Accuracy: 99.80\n",
      "Train Epoch: 86 [33280/50000 (74%)]\tLoss: 0.010797, Accuracy: 100.00\n",
      "Train Epoch: 86 [35840/50000 (80%)]\tLoss: 0.016635, Accuracy: 99.61\n",
      "Train Epoch: 86 [38400/50000 (85%)]\tLoss: 0.009547, Accuracy: 100.00\n",
      "Train Epoch: 86 [40960/50000 (91%)]\tLoss: 0.016662, Accuracy: 99.80\n",
      "Train Epoch: 86 [43520/50000 (97%)]\tLoss: 0.016826, Accuracy: 99.61\n",
      "\n",
      "Validation set: Average loss: 0.3114, Accuracy: 4634/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[25.981160879135132 s]\n",
      "\n",
      "Test set: Average loss: 0.3341, Accuracy: 9184/10000 (91.84%)\n",
      "\n",
      "Train Epoch: 87 [0/50000 (0%)]\tLoss: 0.026565, Accuracy: 99.41\n"
     ]
    }
   ],
   "source": [
    "# Fantastic logger for tensorboard and pytorch, \n",
    "# run tensorboard by opening a new terminal and run \"tensorboard --logdir runs\"\n",
    "# open tensorboard at http://localhost:6006/\n",
    "from tensorboardX import SummaryWriter\n",
    "best_loss = None\n",
    "best_acc = None\n",
    "\n",
    "import time \n",
    "SINCE=time.time()\n",
    "\n",
    "for epoch in range(max_epoch):\n",
    "    train(epoch)\n",
    "    \n",
    "    loss, accuracy = validate(epoch)\n",
    "    best_loss, best_acc = save_best(loss, accuracy, best_loss, best_acc)\n",
    "    \n",
    "    NOW=time.time() \n",
    "    DURINGS=NOW-SINCE\n",
    "    SINCE=NOW\n",
    "    print(\"the time of this epoch:[{} s]\".format(DURINGS))\n",
    "    \n",
    "    if epoch>=10 and (epoch-10)%2==0:\n",
    "        test(epoch)\n",
    "    \n",
    "# writer = SummaryWriter() \n",
    "# writer.export_scalars_to_json(\"./all_scalars.json\")\n",
    "\n",
    "# writer.close()\n",
    "\n",
    "#---------------------------- Test ------------------------------\n",
    "test(epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test(epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一次 scale 位于[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-7-14/70206949.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnX+MJVd157+n33QbusfE02/GMMH0\naywsL2ilNTDK4mV3hZiQkFEE+YMg2MaMgFVH7azWLFllzfYfIdK2BGSVMKvdcWgBzoh5CziEjZFj\nBTkTo91kV07aAYMd450hTE8cvLhnbMexG8nz4+wft4quqa5bdW/Vrfeq6n0/0tXrV13v1r236p13\n7rnnnCuqCkIIIe1natwNIIQQEgYKdEII6QgU6IQQ0hEo0AkhpCNQoBNCSEegQCeEkI5AgU4IIR2B\nAp0QQjoCBTohhHSEPaO82P79+3VxcXGUlySEkNbz8MMPn1fVA0XnjVSgLy4uYmNjY5SXJISQ1iMi\nmy7n0eRCCCEdgQKdEEI6AgU6IYR0BAp0QgjpCBTohBDSEQoFuojcLCLfTpTnReSjIjIvIg+IyOno\ndd8oGkxI6xgOgcVFYGrKvA6H424R6SiFAl1Vn1DVW1T1FgBvBrAN4H8AuBPAKVW9CcCp6D0hJMlw\nCCwvA5ubgKp5XV6mUCe14GtyOQzg+6q6CeDdAE5Ex08A+KWQDSOkE6yuAtvbVx/b3jbHCQmMr0B/\nH4AvRX+/UlWfAoDo9fqQDSOkE5w753eckAo4C3QRmQHwLgC/73MBEVkWkQ0R2dja2vJtHyHtZmHB\n7zghFfDR0H8BwF+p6o+i9z8SkYMAEL0+nfUhVV1X1UOqeujAgcJUBIR0i7U1YHb26mOzs+Z4W+Ei\nb2PxEejvx465BQC+DuBo9PdRAPeGahQhnWFpCVhfBwYDQMS8rq+b422Ei7yNxkmgi8gsgHcA+Fri\n8CcBvENETkf/+2T45hHSAZaWgLNngStXzGtIYT5qbXmSFnnTY3v77Y2fmYiqjuxihw4dUmZbJCQQ\nsbacFLCzs/XOAKamjGaeRsT8YHWFrLFNU/dYJxCRh1X1UNF5jBQlpK2E1JZdNX2XRd4u2NizxjZN\n3liPawxUdWTlzW9+sxJCAiGiavTlq4uIXz0nT6rOzl5dx+ysOe57rk9dTcY2tlklTQ1jAGBDHWQs\nTS6EtJXFRbMomWYwMLb6uuoZDo1meu6c0czX1nbMDqHaNG5s/UjT6wGXLrl9tsIY0ORCSJtxmbKH\ncon0DX7KW+StEkjlY6bwNWn4np81tllcvry7znEGk7mo8aEKTS6EOOBrAhkMjIlgMCg3rR8Msk0J\ng8Ho6gpp9ql6fvJztv5klbjOkOMZAUeTCwU6IU3DJhD6/erCO+sHIKTNN6suEdWVlfzPFfUZUO31\nrn7NEpjJ/vX7qnNzdgGcJ2CT9diul9eGMdnQKdAJaRquC3K+QiJP0ITQ9GNWVnb3oaitPouQRWPi\neq5t8ThrnHzrDDmeSoFOiD+Bv4Sl6/WZ5ie1zKLr1GAK2HXNlZV8DdpWh48WHKr0ertnKj5jH3os\nc6BAJ8SHutztytTroyEmNcKi64RycyzTTtt1qmjDIcv0tOrMTLU6anTPpEAnxIc6tNcq9bpqrXE9\nLtcJ3UdfbTbrOrY6ej1jAx+3oC8qaS2/JlwFOt0WCQHqczWzfX5zM999bmmpOJReZKcem8908vqh\nMz/6jM30NPDCC+4ufleuAMeOubkOjpMrV7LdN2M3SRFgzx7zOoqIURepH6pQQyeNpS4NvUjLzJum\n+2jANnNKuv0h1wlc2yey25zh6uKXPCeesQwGqnv3ul37mmvq1dCzno88M1JJswxociGtxlXwVBFQ\naRe3LBvq3r3udWa1xcVskLdY6OP9keVZsrKSLRDTni39vik+43jyZLHdeXbWPgbxNYuEnqurZVZJ\nmkSKfgT27LH/L8vGHrcz7dVTdM9KKAkU6KS9uC4kVlnIzPrs1FT2F3Bmxi0IJastroLYRhmNMelx\nYmtD0SKga+DN9PTuMUz/MPj8KPX7u4W57R5nuUdWKdPTO23P+oHL+mFZWfG/TolFaAp00k7yFgPT\ngSO+7nFJqizoJV3zej3z3qaF+ixsJsegjPtcup66XfBczVQ+i5uufahr0bTXuzrRWNHsr4y7JTV0\nMhG4TKNdtF4XDchXs4vrLKOR5bU5y7xQ1Me86X+VPvqOo4sb5MmT9pmPyzVDauA+9ytrdpM1xr51\nu8z2MqBAJ+2jSKN01Ybq1NB9NbKscPQ8W7XLGLhGdjZBQ6/q2li2D1UDlVxnf77X6ffzx9QCBTpp\nHulFwn5/Z9GuSBNztUe7BO3EQiJ9zenp7C9oUqvyFQyuCaBiwewq9MquE/iUeEHY9uPhkkrA95rx\n8zEOzdznnpbtX8kgLgp00iyyFtBcS6yVFn2B0gtqWW3IShyVFJJZPzrJOn00siJtLK89RcVn8Tft\n5eIjLHu9fPNOFQ+UURYfs09RKVpQ3rvXbt8v6QZLgU6aRdmpc9FuOGnhU8YEYcuHkmUe8bGhT02p\nHj5s/4GwtcdV4LpEm6aFbVUzTHKsQ/jP1ylYy45rlZL3o1YhNQAFOmkWZb5MWT7gJ0+6fdZnkTAv\nH0pWnWUWRpMlNuHkjUlaq85rdxZV3Chdi01A+fwglRW08Q9j1XtRR6khg2VQgQ7gOgBfBfA9AI8D\nuBXAPIAHAJyOXvcV1UOB3iJCPIwuLoZ5pderVlc6f3jRNNhFs4zNKFVd5vr94oW3Io06L5oyb0xD\nC7DYddNnBhA/U1XbE9KUUkeJ29cwgX4CwL+O/p6JBPynAdwZHbsTwKeK6qFAbwkhpouh7Kgh6ypy\n93PVFFdW6hMkSe3OZbbQJHt1XqRl1hg2pd1Fz0zoe1sCV4FeuEm0iLwCwCMAbtTEySLyBIC3qepT\nInIQwDdV9ea8urhJdEsIscmtrQ4R83i7EG/A67phrwv9PrB3784Gx0eOAPffb95PTZk9Il3a5XJe\nWaam8hNz9fvAe98L3HMPcOGCX90i5tX1HtTBzIwZvzrHMAT9vnn1HeOiOs+f9/5YyE2ibwSwBeBu\nEfmWiHxOROYAvFJVnwKA6PV671aSZhIi82Deuao7giWP5WX/6xbxzDM7GxyvrQEnTpgfC1V3AVO3\nICrKsvj888D6ejlBE+uL4+Sll5otzGdngZUV4Mc/DivMAVNfjRkXXQT6HgBvAnCXqr4RwIswJhYn\nRGRZRDZEZGNra6tkM8lIWVjwO16mjry6ej3zhTp+3P+6Pu1aXQW2t7OvL2I0ZVv7ytLv2+t15eLF\nZgvE0OTdixD0+6aImFno+rqZtWU9GyFYXa2nXgCFNhkArwJwNvH+XwD4IwBPADgYHTsI4ImiumhD\nbwkhbOhF+0pmZeqzhUWHshOnrx/a3jozk+91EXuljNsu3LYiUt9agS3xVt398QSBF0X/F4Cbo78/\nAeC3opJcFP10UT0U6C2iipeLLWAmufN7VqDR9LT9OlV9qIsiHKuWpH+5baf5+JwmR0E2sbh6/ZQt\nWYvldd6jcSfnAnALgA0A3wHwhwD2AegDOAXjtngKwHxRPRToHcY3C6ItN7Vtw4CivOV5JR3tGVIo\nZEWD2lwam+5i1+SSvIdN8uzxLdzggjQe1y+YS8bC9HQ0q+4ygrGMe6JrCZ3lcJSlTW1NmuTqMJmN\nojBSlNSOr3klfb5rkE0c6JN3Th2BL8m665i2p7X0cW9unMzVXnRu2aCvcZXkWLveR5sJbNSlZB4X\nVVUKdOKG7wJo2emub96Nuoot13XVktQcQwajsHRnrEcQKVoYWBQSBhY1EN8gojJBPr0ecN114X16\nyzIYGB/0o0fDuf/F4xUyCIpkk3w29+9vznOVR8mAopiQgUWky9iET1Ywz3BYTlhdvmwCeprCuXPA\n0pIJKgrF5qYRLhTm9bO5afzSFxfbIcwB087FxVqDigAK9MlmOLRHbKaDeYbDncjNLIoCZmZn/dtX\nF3HflpZ2wrtD0Bbh0gVUjWB3iThuCpub5js05khR0haGQ6MFxNqL7cGJz/vAB8wXI42IyXES17V/\nP/DBD9oj52ZngWPHgH377G178cXs41UjAEVMbhBXZmaMuSXm2LFm/dhMMnHIvY+QVvUX6rOzwMmT\n5rMnT/rf/7m57M/MzRV/dnt7vJGiIQsXRWvEdXHTdVHTZ9GwLndA1zI9vbMRhcu5WWPimn52lMXX\nO0PEbKgx7naXLXHgWZ3XyPLi8vV8iiNXy3ozjTtSNFShQK9Innuhy248eedV+YJUqTtUsI1PTvP4\nmskfgOT+pnUKlBBjMxg0q52hSuySWGZjaZfPlN30OqueKrncxx0pGqpQoFegSAMv2o0nJqQWnZ4B\nlHEjC6URuwQtdaW0KRjIt8R5VVyfI9/88b7fsXSJ8/VUcXtNpsBwhAK9axRp4OPS0IHdYdm+U9FQ\nAmpSQuvHHbjkU3x/sMvsCBXv/Zq8Xno/2OTsKz4nPct1eXbn5qo/Z9TQSan9MKvY0H1LOlOir6bc\nhKCjthSfnYHGWZLas8/MrWoCs6yZo+2ZH8fOT7ShE+8d6/NC+NPn5WkcIu4aSRV7uuv+n10p4zCb\njPKacZqFGJ/7GWKNwOdZHPWaBDV0YtUg0pkEy5D38PkkQUpqHlV2clftvi18lDOSdB74Io+eOD1C\nlevFJo6y+cWr5l/xeRZH+UNny/lf+BWlQO8eNhtfhc1nVdX+hev3/aahVT1e4ge+LRsIj0Mg+Bbf\nPPDJvPU+WnWvd7W9Oisf/qj77fosurq8Vn0uKihfFOhdxXXx0web/b2M2SO5QNX2UmSKCp25cTBQ\nfcMbwtYX31/fe9LvZ2vJWUnW0hr5uH35k3EJth8Y1/tcVGLf/6o7fBVAgd5VXN0TfcmyvzdZ82xC\nybsfTSjxj06ojISxhpl+Vpowo4oFeNYGKMkfnHhcQl47y8QUUJirqlKgdwXX3ONVNHTbNcctkLJK\nU1wTfQKZxlVc8s/7lKRZpcwOULZ7V1WjdzGvJNvuO1txeebSi/oU6GQXWaYQ25Q31APUhu29xu3i\nmFzYavLibZ3jVGYHqF7Prj2XfebSz35dMybfsaTJhewib7GyLm2gyRpnsv/jstNOTZXTTnu98awt\n1DlOZWYptmc3L5Q+1pDTOzFlPft1Pb9lZjsBZ80U6F3A9qAkg4mSQqLsKnrSrDNqgdOmkqV1uYxZ\n2QAbl+KakKyojrIzM9+gnLy1Hp/gOFvUZ5UZZp6rZDK1hOv3pOq6VoKgAh3AWQDfBfDtuGIA8wAe\nAHA6et1XVA8Fugd5i5Lxw5v1RfX1c22DiaWsIKtaXGZCLnuk+kTQzszs1lST4exJoRP/gOdlWIzb\nnTc7qOINk/yxcll0LNJai4LjXKM+izxufL2x4rZkuWPu3Vuurx7UIdD3p459GsCd0d93AvhUUT0U\n6Dm4fpliP+G8qXR60+I8RmFiERm/3du3uNpAXQSMz3gX3bssgZdXZ/IHYG6ueP2lzPPg6vOdJXB9\nTYYuUZ9F9yduh62u9A9S0flZ8RpNtqFbBPoTAA5Gfx8E8ERRPRToFnyn4i4atevDNApNNw4WqqPu\ndFKmEMVXyGRNw5MBOj7jXcYk4dO32JZvE6ZlngfXqEybScRH+JUZP9sPSNFMwvX8ZExCG7xcAPwA\nwF8BeBjAcnTsudQ5zxbVQ4GeoswU1+eLm3xNZ5+Lv1yjWlysK19GiOjUdH2+X06fYC9fDdPls773\nsMw1ip41V7faqjmJfMav6D7m1eVzfkiXYQuhBfpPR6/XA3gEwL90FegAlgFsANhYWFioveOtoY4F\nMp+S5f5YZwkd5JKsNybELCDLha5Ig/QJ9vI10STJa7ePpu47C6j6XPnk7S/S4F3Hz2UmUNRXl/MD\nm1Zs1OblAuATAP49TS4VaYN7YMiSXHjLC9Soom1WHdM817QyWq3tM3leGnnYxiZeeHUJQCvqS7J9\n6VzicSRmPNPLW3D01bDLaPB54+d6T4rWIGzn12RasRFMoAOYA3Bt4u//DeCdAH4rtSj66aK6KNAT\n0EXQLgxcA01CBpXEdZVJrWALAMuzVWfVUSQo8tqfVV+eF5SrYMo7L9RYuQYp+QjSvLHK+mxdKTUC\nEVKg3xiZWR4B8BiA1eh4H8CpyG3xFID5oroo0BO4aJOxJlsmXLnNJZ0bI2tHmjJBJVNT2Vpnsq6y\ndtKk4LPlE8kTmi5T+TyvjKy6bXEKPv7eeeeFGCtXzdrmeWKrP++HwWdsR2Afd4GBRU3HpkFlbSgR\nC6A2+ouXLUXmiixhkGcTtXmdpOv0FcZZ+AoHH/NAXmxC6PYVnWcT+FUSVdl8vfOeE98fdtvYjsk+\n7gIFehtIa1B5i5TpjHFdL64LilkLV7YxctVCfc0laXyn7z7n541Z6Pa5nJdlb68qGNN1Fj0rZUxv\nPu6NDYACvY24LAjWlYy/aSVLg7KNj497oKsWmo7w9Pmy2+q0LRb6aPR5C6OuhNLQq9Ttg4tQT46t\ny/eoIYudrlCgt5FxC9EmlaR5pMi9LEvbqqqFAvkbCPvaxPPc+XzqzxszV0LZ0LOoY3GxqitlVmmI\nO6IrFOhNwvXXf9w7vTSpJO20ReNSh4aePL+M1unqRpjsp8sz4tOWPPe+EF4utmv5jpULRfX7lHR6\nhYYviKqqUqA3BZ9f/ybn1h51cc0AaBvLKlpouh0htM5QmmuIfoXWPn0DdOq8Vta1i9rScJdFVVUK\n9Kbg++tvy9zWtFJ1V/Y4XNwWZJSnGSfryBMUPtplnn0+hAYXUgt06ZfLzCMURZp5aNOFy6wtee2y\nY0UNnQJ9F75h4aP2N09OwX3D8n1dzLL6n6dx+voSlzEjJH3cbb7OIWyso7bTVkkCFupadWq4Rf3z\nGVva0CnQnfHxMR6Xn3nsEuljw49NIknbpo/3TbL/NkHs6oFiG7+yJpa4fWW9XGyM0pOiCRp6nRpu\n6FkBvVwo0K2UCVQZdyRomQXZ5Bc2z1WvrPbjozmFWgStWxCNinHb0OvWcFugVYeEAn1clAlUOXly\nvMK8bHHJg101V7TrZ/PamaRs0EkbyfNyqetao9RwG65Vh4QCfVy4aIquLm1NLy4aetHCZRmyvsiu\nATeToqGTTuEq0KdAwnLuXP7x4RBYXgY2N4342NwELlwYXftCcuTIzt9ra8Ds7O5zLl82/R0Ow1wz\na/yWl811skgft7UzZnbWnENIC6FAD83CQv7x1VVge3t07amT++/f+XtpCVhfB3q93edtb5t+hyBr\n/La3s68LAIPB1e/jdg4GgAjQ75siYo6tr5tzCGkhFOihydIAk1qfTYOvg36/3OdUjYArIt2XpSXg\nyhW3c8tiq+fy5fxxT7K0BJw9a9p6/rwpV66YY77CfDgEFheBqSnzGmomQkgJKNBDk9YA01qfTYPP\nwqZ1uiBSzpQTX9OlnVnnFM1QqmKrJx5n27jXgc38Q6FOxoWLoT1UmYhF0aKVd5/w/sOHR7/QubLi\nFuBUNeS+yviOy13NNz8LIYEAvVzGQJGw8Y3GHGWgUa+3I8yzrjs3554jvG53snG5yLnej664PZLG\n4CrQxZw7Gg4dOqQbGxsju97IWVw00+40IsD8fDkTyNSU3S4dkpMnjXnC1ofBwNiY0wyHwB137PSt\n3weOHevewqJtXLKwjRUhJRGRh1X1UNF5e0bRmInBtmCnWt41cRTCHDC2X6DY7TLJcAh86EPAxYs7\nxy5cAD78YfN3l4S666Iu3R7JGOGiaEhCLfylmRrBbYpdC30WNVdXrxbmMS+9FM5NsSnYxqXfH+1C\nLCE5TLZAD+FylqzjhReAmZnAjYTR0qvUOztrTCorK/nuiOfOFbtdps/Pq6tL2Mbl2LEdF8gybo+E\nhMTF0B7Z2XsAvgXgvuj9awE8BOA0gK8AmCmqo1GLonWlRJ2etuf4rlLS+WBsHhbxtbN2qCla1Au1\ne05XPT0mKHcIaRYI7eUC4GMA/ntCoN8D4H3R378LYKWojkYJ9Do3LahrE+fkRrhZWRyTQn9ubke4\n5+264/tjls4kmZVDZWYmjLCjACVEVQMLdAA3ADgF4O0A7gMgAM4D2BP9/1YA3yiqp1ECvc5txUZV\nkgI8S8C7FldhaZuRJHcv6vfDCfMJSo9KSB6uAt3Vhv4ZAL8OIHa56AN4TlUvRe+fBPDqrA+KyLKI\nbIjIxtbWluPlaiBtL5+fzz7PZ2HTVseoiBckr1wB9u41i5G+xC52LrbfrDwqFy8C+/fviN3z58PY\nkW05W7q22EpIQAoFuoj8IoCnVfXh5OGMUzXr86q6rqqHVPXQgQMHSjazIlkh2s8/v3uh0cflbDgE\nnn02fFt9uXDBtKXMIqSvi52PS2NVRnktQjqCi4b+VgDvEpGzAL4MY3b5DIDrRCT2Y78BwA9raWEI\nbJrltdeWdzlbXa3HR9zmhZLnupjnbph3nZe/HLjtNncPn7rztIzrWoR0hEKBrqofV9UbVHURwPsA\n/KmqLgF4EMB7otOOAri3tlZWxabVPfNMeZezujRFzZjozM4Cv/Ir+W1J5iYvYnralAsXdmYsLkml\nfFwaqzLKaxHSEar4of8HAB8TkTMwNvXPh2lSDdSh7dWtKfZ6V88cjh+3p8NdWLg6N3kaEWNjj+t7\nxSt229td7NNFmSRDMsprEdIVXFZOQ5WxebnU4THhm2irTIm9V/I8WeJ++OzpmXc9QkjjALegS1CH\ntre0BNx9d/lNJFxQNWaR2DQSv2btsGObMczP714QttnpaZ8mpNVMhkAHrt6lxsdenpceYGnJuOmd\nPDmafCuAWczdu3d3P2w2Z2D3grDqbqFO+zQhrWdyBHoZXHakiTMOjiorIpC9IGubhTzzTHYdqrRP\nE9IxJjcf+nBoFgHPnTOmhrW13QLNlgO73zea+XAIHD1q33G+LnzybfvmNyeENA7XfOiTqaG77gVp\nc028cAG4/XbzmVELc8DPRZHuf4RMDJMp0F3DyvMWCdfXd9dRldhVsd/PX2zNc1FMQ/c/QiaGyRLo\n8QKnbSuxtEaep8XWoZlfvgx88YvGnHP+vN0bxTeoqeyCMCGkVUyOQE+aWWykNfKlJeNRMkqSph+G\nvxNCPJgcgZ5lZkkyPW12HEq7J15zzUia9xOSph/avwkhHkzOJtF5Zop+H/iHf9jZyDleJAXsbn91\nErc1No0UeeMQQggmyW0xz33vhRd2hHn6f0C+maYO6FJICElAt8U0NvPFkSPZwhzY2TR5lNCkQggp\nyeQIdJv7Xp4L4MKC+Vyd+VoA464I0KWQEFKJyTG52Jiays5BHjMYGC3+xInwfucxI7wHhJD2QZOL\nK0UugJubRpgfPVqPph5r54QQUhEK9LU1ewBPzPY2cM89wHPPlbvG7Cxw+HD2/17+8uxMjoQQ4gkF\n+tKSm8njwgX/6NCkrf5P/gRYWdnRyEWAPXuMh43PNnCEEGKh+wI9mc98/35T0hpx7J4YksFgd6j9\n8ePApUtGgC8smL+TuGwDRwghFrodWBSH+8eLmUn3xGTw0Noa8IEPhLvuzEyx66Et0KmuzacJIZ2n\nWxp6enehO+7I90zZ3gZuu82cF5JrrzVaed5uRyHztORdhxAyORRtOgrgZQD+AsAjAB4D8JvR8dcC\neAjAaQBfATBTVFetm0RnbQQ9rhJvzJy3MXWojavr2ACbENIo4LhJdKEfuogIgDlVfUFEpgH8GYA7\nAHwMwNdU9csi8rsAHlHVu/LqqtUPPS8t7qjJSxmQDOt32TWpCO5IREjncfVD9wosEpFZGIG+AuCP\nALxKVS+JyK0APqGqP5/3+VoFelGA0KiYnTVeLbfdlt0ekbD7j9r6Hfo6hJCxETSwSER6IvJtAE8D\neADA9wE8p6qxm8aTAF5dtrFBsNme5+aK/cxD0e/vhO6PKpc5c6YTQiKcBLqqXlbVWwDcAOBnALw+\n67Ssz4rIsohsiMjG1tZW+ZYWkZV8CwBefHF0mvtzzxnNfHHRpAuYnr76/9PT4RNvMWc6ISTCy8tF\nVZ8D8E0AbwFwnYjEbo83APih5TPrqnpIVQ8dOHCgSlvzWVoy4fnj5PLlnSChz31u9w9JHTMF7hlK\nCIkoFOgickBErov+fjmAnwXwOIAHAbwnOu0ogHvraqQzPpsn183Fi7sDh156qZ7AIe4ZSgiBW2DR\nQQAnRKQH8wNwj6reJyJ/DeDLIvKfAHwLwOdrbKcbbQjKaUMbCSGtpFCgq+p3ALwx4/jfwNjTm8PC\nQrHrYq9nIkTX1/1zs+TV6VoXFysJITXRrUjRtbXdC5FJRIwwP348rEvf8vLuhcnpaZMCIAkXKwkh\nNdItgb60BNx9tz1vuarJbT4cAvPz+XX5LGDG+dKTC5N33w184QtcrCSEjIzu7FiUjro8csRuVhkM\ngPPnjUtjmrk54Mc/9tfg5+ZMKlxCCAnMZO1YFGdV3NzccRu86y67XXtzM1uYA+Z4GXPMiy8Ct9/u\n/zlCCAlENwT66qrffp91RY6ur9dTLyGEONANge7rCliXmSmU1wwhhJSgGwK9Ka6A3PCZEDJGuiHQ\nbXlcRk28AxIhhIyBbgj0pSXg1lvrv8411xhvljS9ntkA+vjx+ttACCEWurOn6IMP1lt/r2dysbzq\nVcBnP0t/ckJI4+iGQB8O69/MIV7wTG4uTaFOCGkQ3TC51JHBMI/t7dFfkxBCCuiGQB/HXqLMmkgI\naRjdEOh1ugvagpCa4ipJCCER3RDodQb07NnDrImEkFbQDYFep4Z+8SJw7bXMmkgIaTzdEOh1h9w/\n80z+Fm/DodkYemrKvA6H9baHEEIy6IZAHwyyj/f79v/5kGcvz8r0uLxMoU4IGTndEOi20P8LF4An\nnwT27i1fd5G9PCvTI90aCSFjoBsCfWnJ2LWztPHLl8ttPOFqL7e5L9KtkRAyYroh0AEjdM+eNXbs\nqgwGdnt5Gps5hm6NhJARUyj9ROQ1IvKgiDwuIo+JyB3R8XkReUBETkev++pvbgEhUgD4uiRmmXvo\n1kgIGQMu6uwlAL+mqq8H8BYAvyoibwBwJ4BTqnoTgFPR+/HiareOsyPGJprY7bGMS2LS3EO3RkLI\nGPHeJFpE7gXwX6PyNlV9SkRrkpmcAAAJ7klEQVQOAvimqt6c99laN4kGjLnFpT8i9SfzIoSQQNSy\nSbSILAJ4I4CHALxSVZ8CgOj1estnlkVkQ0Q2tra2fC7nj6vdmvZtQkgHcRboIrIXwB8A+KiqPu/6\nOVVdV9VDqnrowIEDZdrojsvORVNTxuuFQUCEkI7hJNBFZBpGmA9V9WvR4R9FphZEr0/X00QP0u6L\nWYm1rlwx/ukMAiKEdAwXLxcB8HkAj6vqbyf+9XUAR6O/jwK4N3zzShC7L6q6mVYYBEQI6QguOxa9\nFcBtAL4rIt+Ojv1HAJ8EcI+IfATAOQC/XE8TK+Aa3MMgIEJIBygU6Kr6ZwAsScFxOGxzArOw4Lb5\nBRdJCSEdoBuRorZsh0eO2DeoiGEQECGkI7R/k+g422GcICte6PzzPwdOnLjaL10EePvbgTNnjJll\nYcEIcwYBEUI6QPsFui3b4fr67jzpqkaYnz07suYRQsioaL/Jxbagadv0ggughJCO0i6BnmUrty1o\n2ral4wIoIaSjtEeg23YGOnIkO9vh8jKzIBJCJop2CPThEDh6NNtWfv/92dkOjx9nFkRCyEThnW2x\nCqWyLaa9WNIwcyIhpOPUkm1xLGR5sSShTZwQQgC0QaDneaVMT5vMiSLAnj3mlRkUCSETSvMFuk0D\nFzHlwgXzPnZTZAZFQsiE0nyBvrYGzMzsPq4KvPRS9meYQZEQMoE0X6ADbtvKpWEAESFkwmi+QF9d\nBS5e9P8cF0sJIRNG8wV6WU37da8L2w5CCGk4zRfoZTXtb34zaDMIIaTpNF+gr60Z90RfbMm5CCGk\nozRfoAPApUv+n7El5yKEkI7SfIG+ulrOy2V5OXxbCCGkwTRfoJdZFD182CTnIoSQCaJQoIvIF0Tk\naRF5NHFsXkQeEJHT0eu+2lo4P+//mTNnwreDEEIajouG/nsA3pk6dieAU6p6E4BT0fvwDIfAs8/6\nf45BRYSQCaRQoKvq/wTwTOrwuwGciP4+AeCXArfLsLpaLjUug4oIIRNIWRv6K1X1KQCIXq8P16QE\nm5vlPsddiQghE0jti6IisiwiGyKysbW15ffhMq6H/T53JSKETCRlBfqPROQgAESvT9tOVNV1VT2k\nqocOHDjgdxXf4CAR4L3v9fsMIYR0hLIC/esAjkZ/HwVwb5jmpBgM/M5XBU6cYC50QshE4uK2+CUA\n/wfAzSLypIh8BMAnAbxDRE4DeEf0Pjxra8DsrN9nmAudEDKh7Ck6QVXfb/nX4cBt2U1sC19d9Vsg\npdsiIWQCaX6k6NIScPasn/mFbouEkAmk+QI9Zm0NmHJsLt0WCSETSHsEuqsrIt0WCSETSnsEOlAc\nNTo7Cxw7Npq2EEJIw2iXQM8LNBoMgPV1aueEkImlXQLdluN8ZcUsnFKYE0ImmEK3xUYR5zhfXzdR\npL2eEfLMfU4IIS0T6IAR3hTghBCyi3aZXAghhFihQCeEkI5AgU4IIR2BAp0QQjpC+wX6cAgsLpq0\nAIuLTJ1LCJlY2uflkmQ4NG6L29vm/ebmjq86fdIJIRNGuzX01dUdYR7DfOiEkAml3QLdlvec+dAJ\nIRNIuwW6Le8586ETQiaQdgv0rC3qZmeZD50QMpG0W6AvLZm8LoMBIMKMi4SQiabdXi6AEd4U4IQQ\nUk1DF5F3isgTInJGRO4M1ShCCCH+lBboItID8N8A/AKANwB4v4i8IVTDCCGE+FFFQ/8ZAGdU9W9U\n9SUAXwbw7jDNIoQQ4ksVgf5qAH+beP9kdIwQQsgYqCLQJeOY7jpJZFlENkRkY2trq8LlCCGE5FHF\ny+VJAK9JvL8BwA/TJ6nqOoB1ABCRLRHZLHm9/QDOl/xsG+h6/4Du97Hr/QO638em9m/gcpKo7lKq\nnRCRPQD+L4DDAP4OwF8C+Feq+lipCouvt6Gqh+qouwl0vX9A9/vY9f4B3e9j2/tXWkNX1Usi8m8A\nfANAD8AX6hLmhBBCiqkUWKSq9wO4P1BbCCGEVKBNof/r425AzXS9f0D3+9j1/gHd72Or+1fahk4I\nIaRZtElDJ4QQkkMrBHoXcsaIyGtE5EEReVxEHhORO6Lj8yLygIicjl73RcdFRP5L1OfviMibxtsD\nN0SkJyLfEpH7ovevFZGHov59RURmouPXRO/PRP9fHGe7XRGR60TkqyLyvehe3tqleygi/y56Ph8V\nkS+JyMvafg9F5Asi8rSIPJo45n3PRORodP5pETk6jr4U0XiB3qGcMZcA/Jqqvh7AWwD8atSPOwGc\nUtWbAJyK3gOmvzdFZRnAXaNvcinuAPB44v2nAPxO1L9nAXwkOv4RAM+q6usA/E50Xhs4BuCPVfUf\nAfgnMH3txD0UkVcD+LcADqnqP4bxXnsf2n8Pfw/AO1PHvO6ZiMwD+A0A/xQm7clvxD8CjUJVG10A\n3ArgG4n3Hwfw8XG3K0C/7gXwDgBPADgYHTsI4Ino788CeH/i/J+c19QCE1x2CsDbAdwHE018HsCe\n9L2EcXe9Nfp7T3SejLsPBf17BYAfpNvZlXuInXQe89E9uQ/Az3fhHgJYBPBo2XsG4P0APps4ftV5\nTSmN19DRwZwx0dT0jQAeAvBKVX0KAKLX66PT2tjvzwD4dQBXovd9AM+p6qXofbIPP+lf9P+/j85v\nMjcC2AJwd2RW+pyIzKEj91BV/w7AfwZwDsBTMPfkYXTrHsb43rNW3Ms2CHSnnDFtQUT2AvgDAB9V\n1efzTs041th+i8gvAnhaVR9OHs44VR3+11T2AHgTgLtU9Y0AXsTOVD2LVvUxMiG8G8BrAfw0gDkY\nE0SaNt/DImx9akVf2yDQnXLGtAERmYYR5kNV/Vp0+EcicjD6/0EAT0fH29bvtwJ4l4ichUml/HYY\njf26KE0EcHUfftK/6P8/BeCZUTa4BE8CeFJVH4refxVGwHflHv4sgB+o6paqXgTwNQD/DN26hzG+\n96wV97INAv0vAdwUrbTPwCzSfH3MbfJGRATA5wE8rqq/nfjX1wHEK+ZHYWzr8fEPRqvubwHw9/EU\nsYmo6sdV9QZVXYS5R3+qqksAHgTwnui0dP/ifr8nOr9xGk8SVf1/AP5WRG6ODh0G8NfoyD2EMbW8\nRURmo+c17l9n7mEC33v2DQA/JyL7opnMz0XHmsW4jfiOCxpHYBKBfR/A6rjbU7IP/xxmivYdAN+O\nyhEYm+MpAKej1/nofIHx7vk+gO/CeB6MvR+OfX0bgPuiv28E8BcAzgD4fQDXRMdfFr0/E/3/xnG3\n27FvtwDYiO7jHwLY16V7COA3AXwPwKMAvgjgmrbfQwBfglkTuAijaX+kzD0D8OGor2cAfGjc/coq\njBQlhJCO0AaTCyGEEAco0AkhpCNQoBNCSEegQCeEkI5AgU4IIR2BAp0QQjoCBTohhHQECnRCCOkI\n/x9BnCF17x72RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhLQQijshhLQQijshhLQQijshhLSQ/w9isHt8TVxEXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看训练过程的信息\n",
    "import  matplotlib.pyplot as plt\n",
    "\n",
    "def parse(in_file,flag):\n",
    "    num=-1\n",
    "    ys=list()\n",
    "    xs=list()\n",
    "    losses=list()\n",
    "    with open(in_file,\"r\") as reader:\n",
    "        for aLine in reader:\n",
    "            #print(aLine)\n",
    "\n",
    "            res=[e for e in aLine.strip('\\n').split(\" \")]\n",
    "            if res[0]==\"Train\" and flag==\"Train\":\n",
    "                num=num+1\n",
    "                ys.append(float(res[-1]))\n",
    "                xs.append(int(num))\n",
    "                losses.append(float(res[-3].split(',')[0]))\n",
    "            if res[0]==\"Validation\" and flag==\"Validation\":\n",
    "                num=num+1\n",
    "                xs.append(int(num))\n",
    "                tmp=[float(e) for e in res[-2].split('/')]\n",
    "                ys.append(100*float(tmp[0]/tmp[1]))\n",
    "                losses.append(float(res[-4].split(',')[0]))\n",
    "\n",
    "    plt.figure(1)\n",
    "    plt.plot(xs,ys,'ro')\n",
    "\n",
    "\n",
    "    plt.figure(2)\n",
    "    plt.plot(xs, losses, 'ro')\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    in_file=\"D://INFO.txt\"\n",
    "    # 显示训练阶段的正确率和Loss信息\n",
    "    parse(in_file,\"Train\") # \"Validation\"\n",
    "    # 显示验证阶段的正确率和Loss信息\n",
    "    #parse(in_file,\"Validation\") # \"Validation\"\n",
    "\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naRBY0BmGYRqE/w/U/zxNDJVCvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngFDcCSGkAaG4E0JIA0JxJ4SQBuT/AHC9SchdaANwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wPtS+GMghk59AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看训练过程的信息\n",
    "import  matplotlib.pyplot as plt\n",
    "\n",
    "def parse(in_file,flag):\n",
    "    num=-1\n",
    "    ys=list()\n",
    "    xs=list()\n",
    "    losses=list()\n",
    "    with open(in_file,\"r\") as reader:\n",
    "        for aLine in reader:\n",
    "            #print(aLine)\n",
    "\n",
    "            res=[e for e in aLine.strip('\\n').split(\" \")]\n",
    "            if res[0]==\"Train\" and flag==\"Train\":\n",
    "                num=num+1\n",
    "                ys.append(float(res[-1]))\n",
    "                xs.append(int(num))\n",
    "                losses.append(float(res[-3].split(',')[0]))\n",
    "            if res[0]==\"Validation\" and flag==\"Validation\":\n",
    "                num=num+1\n",
    "                xs.append(int(num))\n",
    "                tmp=[float(e) for e in res[-2].split('/')]\n",
    "                ys.append(100*float(tmp[0]/tmp[1]))\n",
    "                losses.append(float(res[-4].split(',')[0]))\n",
    "\n",
    "    plt.figure(1)\n",
    "    plt.plot(xs,ys,'r-')\n",
    "\n",
    "\n",
    "    plt.figure(2)\n",
    "    plt.plot(xs, losses, 'r-')\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    in_file=\"D://INFO.txt\"\n",
    "    # 显示训练阶段的正确率和Loss信息\n",
    "    parse(in_file,\"Train\") # \"Validation\"\n",
    "    # 显示验证阶段的正确率和Loss信息\n",
    "    parse(in_file,\"Validation\") # \"Validation\"\n",
    "\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}