M_accelerate_6lastlayer-8fold_91.41-Copy3.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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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=80  # 训练最大epoch数目\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, \n",
    "                batch_size=256, 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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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",
    "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",
    "        \n",
    "        #self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        one_conv_kernel_size = 3\n",
    "        self.conv1D= nn.Conv1d(1, out_planes, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1,bias=False) # 在__init__初始化        \n",
    "        \n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        \n",
    "        # -------------------------- Attention -----------------------\n",
    "        w = F.avg_pool2d(x,x.shape[-1])  #最好在初始化层定义好\n",
    "        #print(w.shape)\n",
    "        # [bs,in_Channel,1,1]\n",
    "        w = w.view(w.shape[0],1,w.shape[1])\n",
    "        # [bs,1,in_Channel]\n",
    "        # one_conv_filter = nn.Conv1d(1, out_channel, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1) # 在__init__初始化\n",
    "        # [bs,out_channel,in_Channel]\n",
    "        w = self.conv1D(w)\n",
    "        w = 0.5*F.tanh(w) # [-0.5,+0.5]\n",
    "        # -------------- softmax ---------------------------\n",
    "        #print(w.shape)\n",
    "        w = w.view(w.shape[0],w.shape[1],w.shape[2],1,1)\n",
    "        #print(w.shape)\n",
    "        \n",
    "        # ------------------------- fusion --------------------------\n",
    "        out=out.view(out.shape[0],1,out.shape[1],out.shape[2],out.shape[3])\n",
    "        #print(\"x size:\",out.shape)\n",
    "        \n",
    "        out=out*w\n",
    "        #print(\"after fusion x size:\",out.shape)\n",
    "        out=out.sum(dim=2)\n",
    "        \n",
    "        out = F.relu(self.bn2(out))\n",
    "        \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",
    "\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(1024, num_classes)\n",
    "\n",
    "    def _make_layers(self, in_planes):\n",
    "        layers = []\n",
    "        for x in self.cfg:\n",
    "            out_planes = x if isinstance(x, int) else x[0]\n",
    "            stride = 1 if isinstance(x, int) else x[1]\n",
    "            layers.append(Block(in_planes, out_planes, stride))\n",
    "            in_planes = out_planes\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",
    "        out = F.avg_pool2d(out, 2)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "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",
    "class Block_Attention_HALF(nn.Module):\n",
    "    '''Depthwise conv + Pointwise conv'''\n",
    "    def __init__(self, in_planes, out_planes, stride=1):\n",
    "        super(Block_Attention_HALF, 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",
    "        #------------------------ 一半 ------------------------------\n",
    "        self.conv2 = nn.Conv2d(in_planes, int(out_planes*0.125), kernel_size=1, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        #------------------------ 另一半 ----------------------------\n",
    "        one_conv_kernel_size = 17 # [3,7,9]\n",
    "        self.conv1D= nn.Conv1d(1, int(out_planes*0.875), one_conv_kernel_size, stride=1,padding=8,groups=1,dilation=1,bias=False) # 在__init__初始化        \n",
    "        \n",
    "        #------------------------------------------------------------\n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        out = F.relu6(self.bn1(self.conv1(x)))\n",
    "        \n",
    "        # -------------------------- Attention -----------------------\n",
    "        w = F.avg_pool2d(x,x.shape[-1])  #最好在初始化层定义好\n",
    "        #print(w.shape)\n",
    "        # [bs,in_Channel,1,1]\n",
    "        in_channel=w.shape[1]\n",
    "        #w = w.view(w.shape[0],1,w.shape[1])\n",
    "        # [bs,1,in_Channel]\n",
    "        # 对这批数据取平均 且保留第0维\n",
    "        \n",
    "        #w= w.mean(dim=0,keepdim=True)\n",
    "        \n",
    "        \n",
    "#         MAX=w.shape[0]\n",
    "#         NUM=torch.floor(MAX*torch.rand(1)).long()\n",
    "#         if NUM>=0 and NUM<MAX:\n",
    "#             w=w[NUM]\n",
    "#         else:\n",
    "#             w=w[0]\n",
    "        #w=w[0]-torch.mean(w[0])\n",
    "        w=torch.randn(w[0].shape).cuda()*1\n",
    "        a=torch.randn(1).cuda()*0.1\n",
    "        if a>0.37:\n",
    "            print(w.shape)\n",
    "            print(w)\n",
    "        \n",
    "        w=w.view(1,1,in_channel)\n",
    "        # [bs=1,1,in_Channel]\n",
    "        # one_conv_filter = nn.Conv1d(1, out_channel, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1) # 在__init__初始化\n",
    "        # [bs=1,out_channel//2,in_Channel]\n",
    "        w = self.conv1D(w)\n",
    "        # [bs=1,out_channel//2,in_Channel]\n",
    "        \n",
    "        #-------------------------------------\n",
    "        w = 0.1*F.tanh(w) # [-0.5,+0.5]\n",
    "        \n",
    "        if a>0.37:\n",
    "            print(w.shape)\n",
    "            print(w)\n",
    "            \n",
    "        # [bs=1,out_channel//2,in_Channel]\n",
    "        w=w.view(w.shape[1],w.shape[2],1,1)\n",
    "        # [out_channel//2,in_Channel,1,1]\n",
    "        \n",
    "        # -------------- softmax ---------------------------\n",
    "        #print(w.shape)\n",
    "        \n",
    "        # ------------------------- fusion --------------------------\n",
    "       \n",
    "        # conv 1x1\n",
    "        out_1=self.conv2(out)\n",
    "        out_2=F.conv2d(out,w,bias=None,stride=1,groups=1,dilation=1)\n",
    "        out=torch.cat([out_1,out_2],1)\n",
    "        \n",
    "        # ----------------------- 试一试不要用relu -------------------------------\n",
    "        out = F.relu6(self.bn2(out))\n",
    "        \n",
    "        return out\n",
    "\n",
    "class Block_Attention(nn.Module):\n",
    "    '''Depthwise conv + Pointwise conv'''\n",
    "    def __init__(self, in_planes, out_planes, stride=1):\n",
    "        super(Block_Attention, 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",
    "        \n",
    "        #self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        one_conv_kernel_size = 17 # [3,7,9]\n",
    "        self.conv1D= nn.Conv1d(1, out_planes, one_conv_kernel_size, stride=1,padding=8,groups=1,dilation=1,bias=False) # 在__init__初始化        \n",
    "        \n",
    "        \n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        \n",
    "        # -------------------------- Attention -----------------------\n",
    "        w = F.avg_pool2d(x,x.shape[-1])  #最好在初始化层定义好\n",
    "        #print(w.shape)\n",
    "        # [bs,in_Channel,1,1]\n",
    "        in_channel=w.shape[1]\n",
    "        #w = w.view(w.shape[0],1,w.shape[1])\n",
    "        # [bs,1,in_Channel]\n",
    "        # 对这批数据取平均 且保留第0维\n",
    "        \n",
    "        #w= w.mean(dim=0,keepdim=True)\n",
    "        \n",
    "        \n",
    "#         MAX=w.shape[0]\n",
    "#         NUM=torch.floor(MAX*torch.rand(1)).long()\n",
    "#         if NUM>=0 and NUM<MAX:\n",
    "#             w=w[NUM]\n",
    "#         else:\n",
    "#             w=w[0]\n",
    "        \n",
    "        w=w[0]\n",
    "        \n",
    "        w=w.view(1,1,in_channel)\n",
    "        # [bs=1,1,in_Channel]\n",
    "        # one_conv_filter = nn.Conv1d(1, out_channel, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1) # 在__init__初始化\n",
    "        # [bs=1,out_channel,in_Channel]\n",
    "        w = self.conv1D(w)\n",
    "        # [bs=1,out_channel,in_Channel]\n",
    "        w = 0.5*F.tanh(w) # [-0.5,+0.5]\n",
    "         # [bs=1,out_channel,in_Channel]\n",
    "        w=w.view(w.shape[1],w.shape[2],1,1)\n",
    "        # [out_channel,in_Channel,1,1]\n",
    "        \n",
    "        # -------------- softmax ---------------------------\n",
    "        #print(w.shape)\n",
    "        \n",
    "        # ------------------------- fusion --------------------------\n",
    "       \n",
    "        # conv 1x1\n",
    "        out=F.conv2d(out,w,bias=None,stride=1,groups=1,dilation=1)\n",
    "\n",
    "        out = F.relu(self.bn2(out))\n",
    "        \n",
    "        return out\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",
    "    \n",
    "    #cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), [1024,1]]\n",
    "    cfg = [64, (128,2), 128, 256, 256, (512,2), 512, [512,1], [512,1],[512,1], [512,1], [1024,1], [1024,1]]\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(1024, 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_HALF(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",
    "        out = F.avg_pool2d(out, 8)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "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": 5,
   "metadata": {
    "collapsed": true
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   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/50000 (0%)]\tLoss: 2.299163, Accuracy: 12.50\n",
      "Train Epoch: 0 [1280/50000 (3%)]\tLoss: 2.363844, Accuracy: 6.64\n",
      "Train Epoch: 0 [2560/50000 (6%)]\tLoss: 2.315773, Accuracy: 10.16\n",
      "Train Epoch: 0 [3840/50000 (9%)]\tLoss: 2.287831, Accuracy: 14.45\n",
      "Train Epoch: 0 [5120/50000 (11%)]\tLoss: 2.342692, Accuracy: 14.06\n",
      "Train Epoch: 0 [6400/50000 (14%)]\tLoss: 2.319940, Accuracy: 12.50\n",
      "Train Epoch: 0 [7680/50000 (17%)]\tLoss: 2.221014, Accuracy: 16.02\n",
      "Train Epoch: 0 [8960/50000 (20%)]\tLoss: 2.307380, Accuracy: 12.11\n",
      "Train Epoch: 0 [10240/50000 (23%)]\tLoss: 2.325997, Accuracy: 12.50\n",
      "Train Epoch: 0 [11520/50000 (26%)]\tLoss: 2.314395, Accuracy: 14.45\n",
      "Train Epoch: 0 [12800/50000 (28%)]\tLoss: 2.398648, Accuracy: 11.33\n",
      "Train Epoch: 0 [14080/50000 (31%)]\tLoss: 2.213932, Accuracy: 14.45\n",
      "Train Epoch: 0 [15360/50000 (34%)]\tLoss: 2.167650, Accuracy: 14.06\n",
      "Train Epoch: 0 [16640/50000 (37%)]\tLoss: 2.194702, Accuracy: 19.53\n",
      "Train Epoch: 0 [17920/50000 (40%)]\tLoss: 2.205210, Accuracy: 16.41\n",
      "Train Epoch: 0 [19200/50000 (43%)]\tLoss: 2.200907, Accuracy: 15.62\n",
      "Train Epoch: 0 [20480/50000 (45%)]\tLoss: 2.178798, Accuracy: 16.41\n",
      "Train Epoch: 0 [21760/50000 (48%)]\tLoss: 2.125535, Accuracy: 19.92\n",
      "Train Epoch: 0 [23040/50000 (51%)]\tLoss: 2.181942, Accuracy: 17.97\n",
      "Train Epoch: 0 [24320/50000 (54%)]\tLoss: 2.090106, Accuracy: 17.58\n",
      "Train Epoch: 0 [25600/50000 (57%)]\tLoss: 2.180779, Accuracy: 18.36\n",
      "Train Epoch: 0 [26880/50000 (60%)]\tLoss: 2.198870, Accuracy: 16.41\n",
      "Train Epoch: 0 [28160/50000 (62%)]\tLoss: 2.129070, Accuracy: 17.58\n",
      "Train Epoch: 0 [29440/50000 (65%)]\tLoss: 2.133813, Accuracy: 16.02\n",
      "Train Epoch: 0 [30720/50000 (68%)]\tLoss: 2.192158, Accuracy: 19.14\n",
      "Train Epoch: 0 [32000/50000 (71%)]\tLoss: 2.149213, Accuracy: 19.92\n",
      "Train Epoch: 0 [33280/50000 (74%)]\tLoss: 2.092402, Accuracy: 18.36\n",
      "Train Epoch: 0 [34560/50000 (77%)]\tLoss: 2.091425, Accuracy: 15.62\n",
      "Train Epoch: 0 [35840/50000 (80%)]\tLoss: 2.036795, Accuracy: 24.61\n",
      "Train Epoch: 0 [37120/50000 (82%)]\tLoss: 2.095965, Accuracy: 18.75\n",
      "Train Epoch: 0 [38400/50000 (85%)]\tLoss: 2.116307, Accuracy: 16.80\n",
      "Train Epoch: 0 [39680/50000 (88%)]\tLoss: 1.949261, Accuracy: 23.05\n",
      "Train Epoch: 0 [40960/50000 (91%)]\tLoss: 2.026497, Accuracy: 24.22\n",
      "Train Epoch: 0 [42240/50000 (94%)]\tLoss: 1.976111, Accuracy: 20.31\n",
      "Train Epoch: 0 [43520/50000 (97%)]\tLoss: 2.083636, Accuracy: 20.70\n",
      "Train Epoch: 0 [35000/50000 (99%)]\tLoss: 2.101996, Accuracy: 15.00\n",
      "\n",
      "Validation set: Average loss: 2.8013, Accuracy: 910/5000 (18.00%)\n",
      "\n",
      "the time of this epoch:[35.79983735084534 s]\n",
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 2.055466, Accuracy: 19.53\n",
      "Train Epoch: 1 [1280/50000 (3%)]\tLoss: 2.102582, Accuracy: 11.72\n",
      "Train Epoch: 1 [2560/50000 (6%)]\tLoss: 2.107438, Accuracy: 18.36\n",
      "Train Epoch: 1 [3840/50000 (9%)]\tLoss: 2.014188, Accuracy: 21.88\n",
      "Train Epoch: 1 [5120/50000 (11%)]\tLoss: 2.103794, Accuracy: 16.41\n",
      "Train Epoch: 1 [6400/50000 (14%)]\tLoss: 2.014919, Accuracy: 23.44\n",
      "Train Epoch: 1 [7680/50000 (17%)]\tLoss: 1.994719, Accuracy: 21.88\n",
      "Train Epoch: 1 [8960/50000 (20%)]\tLoss: 1.989221, Accuracy: 22.27\n",
      "Train Epoch: 1 [10240/50000 (23%)]\tLoss: 1.982024, Accuracy: 23.05\n",
      "Train Epoch: 1 [11520/50000 (26%)]\tLoss: 2.032757, Accuracy: 25.39\n",
      "Train Epoch: 1 [12800/50000 (28%)]\tLoss: 2.059769, Accuracy: 15.23\n",
      "Train Epoch: 1 [14080/50000 (31%)]\tLoss: 1.986414, Accuracy: 17.97\n",
      "Train Epoch: 1 [15360/50000 (34%)]\tLoss: 2.027579, Accuracy: 17.58\n",
      "Train Epoch: 1 [16640/50000 (37%)]\tLoss: 1.920955, Accuracy: 25.78\n",
      "Train Epoch: 1 [17920/50000 (40%)]\tLoss: 2.012389, Accuracy: 20.31\n",
      "Train Epoch: 1 [19200/50000 (43%)]\tLoss: 1.987772, Accuracy: 21.09\n",
      "Train Epoch: 1 [20480/50000 (45%)]\tLoss: 1.916398, Accuracy: 25.00\n",
      "Train Epoch: 1 [21760/50000 (48%)]\tLoss: 1.882261, Accuracy: 27.73\n",
      "Train Epoch: 1 [23040/50000 (51%)]\tLoss: 1.908473, Accuracy: 21.48\n",
      "Train Epoch: 1 [24320/50000 (54%)]\tLoss: 1.911971, Accuracy: 27.73\n",
      "Train Epoch: 1 [25600/50000 (57%)]\tLoss: 1.965969, Accuracy: 25.39\n",
      "Train Epoch: 1 [26880/50000 (60%)]\tLoss: 1.916425, Accuracy: 21.09\n",
      "Train Epoch: 1 [28160/50000 (62%)]\tLoss: 2.008017, Accuracy: 23.44\n",
      "Train Epoch: 1 [29440/50000 (65%)]\tLoss: 1.849778, Accuracy: 23.05\n",
      "Train Epoch: 1 [30720/50000 (68%)]\tLoss: 1.919482, Accuracy: 19.53\n",
      "Train Epoch: 1 [32000/50000 (71%)]\tLoss: 1.943514, Accuracy: 21.48\n",
      "Train Epoch: 1 [33280/50000 (74%)]\tLoss: 1.911271, Accuracy: 19.53\n",
      "Train Epoch: 1 [34560/50000 (77%)]\tLoss: 1.869518, Accuracy: 26.56\n",
      "Train Epoch: 1 [35840/50000 (80%)]\tLoss: 1.839879, Accuracy: 24.61\n",
      "Train Epoch: 1 [37120/50000 (82%)]\tLoss: 1.845641, Accuracy: 28.52\n",
      "Train Epoch: 1 [38400/50000 (85%)]\tLoss: 1.816284, Accuracy: 23.44\n",
      "Train Epoch: 1 [39680/50000 (88%)]\tLoss: 1.881824, Accuracy: 22.66\n",
      "Train Epoch: 1 [40960/50000 (91%)]\tLoss: 1.929816, Accuracy: 22.66\n",
      "Train Epoch: 1 [42240/50000 (94%)]\tLoss: 1.847005, Accuracy: 24.61\n",
      "Train Epoch: 1 [43520/50000 (97%)]\tLoss: 1.891947, Accuracy: 25.78\n",
      "Train Epoch: 1 [35000/50000 (99%)]\tLoss: 1.871776, Accuracy: 24.00\n",
      "\n",
      "Validation set: Average loss: 2.5494, Accuracy: 1017/5000 (20.00%)\n",
      "\n",
      "the time of this epoch:[35.84187150001526 s]\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.800807, Accuracy: 25.00\n",
      "Train Epoch: 2 [1280/50000 (3%)]\tLoss: 1.856245, Accuracy: 27.73\n",
      "Train Epoch: 2 [2560/50000 (6%)]\tLoss: 1.829139, Accuracy: 26.17\n",
      "Train Epoch: 2 [3840/50000 (9%)]\tLoss: 1.839266, Accuracy: 28.91\n",
      "Train Epoch: 2 [5120/50000 (11%)]\tLoss: 1.770644, Accuracy: 30.47\n",
      "Train Epoch: 2 [6400/50000 (14%)]\tLoss: 1.793242, Accuracy: 25.39\n",
      "Train Epoch: 2 [7680/50000 (17%)]\tLoss: 1.776109, Accuracy: 26.56\n",
      "Train Epoch: 2 [8960/50000 (20%)]\tLoss: 1.889587, Accuracy: 27.73\n",
      "Train Epoch: 2 [10240/50000 (23%)]\tLoss: 1.784342, Accuracy: 29.69\n",
      "Train Epoch: 2 [11520/50000 (26%)]\tLoss: 1.724650, Accuracy: 29.30\n",
      "Train Epoch: 2 [12800/50000 (28%)]\tLoss: 1.769199, Accuracy: 27.34\n",
      "Train Epoch: 2 [14080/50000 (31%)]\tLoss: 1.760664, Accuracy: 33.59\n",
      "Train Epoch: 2 [15360/50000 (34%)]\tLoss: 1.795730, Accuracy: 31.64\n",
      "Train Epoch: 2 [16640/50000 (37%)]\tLoss: 1.815325, Accuracy: 29.69\n",
      "Train Epoch: 2 [17920/50000 (40%)]\tLoss: 1.773997, Accuracy: 27.73\n",
      "Train Epoch: 2 [19200/50000 (43%)]\tLoss: 1.779367, Accuracy: 28.91\n",
      "Train Epoch: 2 [20480/50000 (45%)]\tLoss: 1.696214, Accuracy: 33.59\n",
      "Train Epoch: 2 [21760/50000 (48%)]\tLoss: 1.790305, Accuracy: 30.08\n",
      "Train Epoch: 2 [23040/50000 (51%)]\tLoss: 1.742937, Accuracy: 30.08\n",
      "Train Epoch: 2 [24320/50000 (54%)]\tLoss: 1.674271, Accuracy: 33.20\n",
      "Train Epoch: 2 [25600/50000 (57%)]\tLoss: 1.757753, Accuracy: 29.30\n",
      "Train Epoch: 2 [26880/50000 (60%)]\tLoss: 1.673637, Accuracy: 33.59\n",
      "Train Epoch: 2 [28160/50000 (62%)]\tLoss: 1.698039, Accuracy: 35.55\n",
      "Train Epoch: 2 [29440/50000 (65%)]\tLoss: 1.650065, Accuracy: 37.89\n",
      "Train Epoch: 2 [30720/50000 (68%)]\tLoss: 1.749747, Accuracy: 26.17\n",
      "Train Epoch: 2 [32000/50000 (71%)]\tLoss: 1.734256, Accuracy: 33.20\n",
      "Train Epoch: 2 [33280/50000 (74%)]\tLoss: 1.646685, Accuracy: 36.33\n",
      "Train Epoch: 2 [34560/50000 (77%)]\tLoss: 1.710954, Accuracy: 32.42\n",
      "Train Epoch: 2 [35840/50000 (80%)]\tLoss: 1.690595, Accuracy: 32.81\n",
      "Train Epoch: 2 [37120/50000 (82%)]\tLoss: 1.675727, Accuracy: 34.38\n",
      "Train Epoch: 2 [38400/50000 (85%)]\tLoss: 1.686774, Accuracy: 36.33\n",
      "Train Epoch: 2 [39680/50000 (88%)]\tLoss: 1.619381, Accuracy: 32.81\n",
      "Train Epoch: 2 [40960/50000 (91%)]\tLoss: 1.624344, Accuracy: 33.98\n",
      "Train Epoch: 2 [42240/50000 (94%)]\tLoss: 1.684472, Accuracy: 30.47\n",
      "Train Epoch: 2 [43520/50000 (97%)]\tLoss: 1.636148, Accuracy: 35.55\n",
      "Train Epoch: 2 [35000/50000 (99%)]\tLoss: 1.669717, Accuracy: 39.50\n",
      "\n",
      "Validation set: Average loss: 2.0242, Accuracy: 1389/5000 (27.00%)\n",
      "\n",
      "the time of this epoch:[35.90063548088074 s]\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.630099, Accuracy: 37.50\n",
      "Train Epoch: 3 [1280/50000 (3%)]\tLoss: 1.656229, Accuracy: 35.94\n",
      "Train Epoch: 3 [2560/50000 (6%)]\tLoss: 1.623990, Accuracy: 36.72\n",
      "Train Epoch: 3 [3840/50000 (9%)]\tLoss: 1.675895, Accuracy: 35.55\n",
      "Train Epoch: 3 [5120/50000 (11%)]\tLoss: 1.630797, Accuracy: 37.89\n",
      "Train Epoch: 3 [6400/50000 (14%)]\tLoss: 1.555175, Accuracy: 43.75\n",
      "Train Epoch: 3 [7680/50000 (17%)]\tLoss: 1.562819, Accuracy: 41.02\n",
      "Train Epoch: 3 [8960/50000 (20%)]\tLoss: 1.597125, Accuracy: 31.25\n",
      "Train Epoch: 3 [10240/50000 (23%)]\tLoss: 1.595677, Accuracy: 38.67\n",
      "Train Epoch: 3 [11520/50000 (26%)]\tLoss: 1.532367, Accuracy: 39.06\n",
      "Train Epoch: 3 [12800/50000 (28%)]\tLoss: 1.563517, Accuracy: 41.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [14080/50000 (31%)]\tLoss: 1.488087, Accuracy: 43.75\n",
      "Train Epoch: 3 [15360/50000 (34%)]\tLoss: 1.520939, Accuracy: 36.72\n",
      "Train Epoch: 3 [16640/50000 (37%)]\tLoss: 1.541944, Accuracy: 40.62\n",
      "Train Epoch: 3 [17920/50000 (40%)]\tLoss: 1.590273, Accuracy: 37.89\n",
      "Train Epoch: 3 [19200/50000 (43%)]\tLoss: 1.548575, Accuracy: 41.80\n",
      "Train Epoch: 3 [20480/50000 (45%)]\tLoss: 1.504360, Accuracy: 39.45\n",
      "Train Epoch: 3 [21760/50000 (48%)]\tLoss: 1.508790, Accuracy: 39.45\n",
      "Train Epoch: 3 [23040/50000 (51%)]\tLoss: 1.529989, Accuracy: 40.62\n",
      "Train Epoch: 3 [24320/50000 (54%)]\tLoss: 1.468130, Accuracy: 43.75\n",
      "Train Epoch: 3 [25600/50000 (57%)]\tLoss: 1.485421, Accuracy: 46.88\n",
      "Train Epoch: 3 [26880/50000 (60%)]\tLoss: 1.514884, Accuracy: 44.92\n",
      "Train Epoch: 3 [28160/50000 (62%)]\tLoss: 1.482904, Accuracy: 46.88\n",
      "Train Epoch: 3 [29440/50000 (65%)]\tLoss: 1.367887, Accuracy: 48.05\n",
      "Train Epoch: 3 [30720/50000 (68%)]\tLoss: 1.351625, Accuracy: 50.00\n",
      "Train Epoch: 3 [32000/50000 (71%)]\tLoss: 1.372648, Accuracy: 51.17\n",
      "Train Epoch: 3 [33280/50000 (74%)]\tLoss: 1.385412, Accuracy: 50.00\n",
      "Train Epoch: 3 [34560/50000 (77%)]\tLoss: 1.399687, Accuracy: 48.05\n",
      "Train Epoch: 3 [35840/50000 (80%)]\tLoss: 1.345515, Accuracy: 48.83\n",
      "Train Epoch: 3 [37120/50000 (82%)]\tLoss: 1.461803, Accuracy: 47.66\n",
      "Train Epoch: 3 [38400/50000 (85%)]\tLoss: 1.527436, Accuracy: 46.88\n",
      "Train Epoch: 3 [39680/50000 (88%)]\tLoss: 1.463666, Accuracy: 45.31\n",
      "Train Epoch: 3 [40960/50000 (91%)]\tLoss: 1.375313, Accuracy: 50.78\n",
      "Train Epoch: 3 [42240/50000 (94%)]\tLoss: 1.546502, Accuracy: 43.75\n",
      "Train Epoch: 3 [43520/50000 (97%)]\tLoss: 1.425747, Accuracy: 45.31\n",
      "Train Epoch: 3 [35000/50000 (99%)]\tLoss: 1.456649, Accuracy: 46.50\n",
      "\n",
      "Validation set: Average loss: 1.8381, Accuracy: 1916/5000 (38.00%)\n",
      "\n",
      "the time of this epoch:[35.7467257976532 s]\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 1.406856, Accuracy: 52.34\n",
      "Train Epoch: 4 [1280/50000 (3%)]\tLoss: 1.324180, Accuracy: 50.00\n",
      "Train Epoch: 4 [2560/50000 (6%)]\tLoss: 1.522692, Accuracy: 44.14\n",
      "Train Epoch: 4 [3840/50000 (9%)]\tLoss: 1.364211, Accuracy: 53.12\n",
      "Train Epoch: 4 [5120/50000 (11%)]\tLoss: 1.311437, Accuracy: 51.56\n",
      "Train Epoch: 4 [6400/50000 (14%)]\tLoss: 1.335423, Accuracy: 50.39\n",
      "Train Epoch: 4 [7680/50000 (17%)]\tLoss: 1.453301, Accuracy: 47.66\n",
      "Train Epoch: 4 [8960/50000 (20%)]\tLoss: 1.264633, Accuracy: 55.08\n",
      "Train Epoch: 4 [10240/50000 (23%)]\tLoss: 1.360193, Accuracy: 48.05\n",
      "Train Epoch: 4 [11520/50000 (26%)]\tLoss: 1.344229, Accuracy: 49.61\n",
      "Train Epoch: 4 [12800/50000 (28%)]\tLoss: 1.363353, Accuracy: 48.05\n",
      "Train Epoch: 4 [14080/50000 (31%)]\tLoss: 1.323350, Accuracy: 51.95\n",
      "Train Epoch: 4 [15360/50000 (34%)]\tLoss: 1.294120, Accuracy: 50.39\n",
      "Train Epoch: 4 [16640/50000 (37%)]\tLoss: 1.257265, Accuracy: 55.47\n",
      "Train Epoch: 4 [17920/50000 (40%)]\tLoss: 1.362796, Accuracy: 50.78\n",
      "Train Epoch: 4 [19200/50000 (43%)]\tLoss: 1.322956, Accuracy: 50.00\n",
      "Train Epoch: 4 [20480/50000 (45%)]\tLoss: 1.277201, Accuracy: 55.86\n",
      "Train Epoch: 4 [21760/50000 (48%)]\tLoss: 1.322775, Accuracy: 55.08\n",
      "Train Epoch: 4 [23040/50000 (51%)]\tLoss: 1.221199, Accuracy: 54.69\n",
      "Train Epoch: 4 [24320/50000 (54%)]\tLoss: 1.247497, Accuracy: 49.61\n",
      "Train Epoch: 4 [25600/50000 (57%)]\tLoss: 1.235443, Accuracy: 58.59\n",
      "Train Epoch: 4 [26880/50000 (60%)]\tLoss: 1.263954, Accuracy: 58.98\n",
      "Train Epoch: 4 [28160/50000 (62%)]\tLoss: 1.178662, Accuracy: 58.98\n",
      "Train Epoch: 4 [29440/50000 (65%)]\tLoss: 1.226373, Accuracy: 56.25\n",
      "Train Epoch: 4 [30720/50000 (68%)]\tLoss: 1.189901, Accuracy: 57.42\n",
      "Train Epoch: 4 [32000/50000 (71%)]\tLoss: 1.250179, Accuracy: 51.17\n",
      "Train Epoch: 4 [33280/50000 (74%)]\tLoss: 1.218908, Accuracy: 55.08\n",
      "Train Epoch: 4 [34560/50000 (77%)]\tLoss: 1.245543, Accuracy: 55.47\n",
      "Train Epoch: 4 [35840/50000 (80%)]\tLoss: 1.198799, Accuracy: 58.20\n",
      "Train Epoch: 4 [37120/50000 (82%)]\tLoss: 1.234748, Accuracy: 52.73\n",
      "Train Epoch: 4 [38400/50000 (85%)]\tLoss: 1.150840, Accuracy: 57.81\n",
      "Train Epoch: 4 [39680/50000 (88%)]\tLoss: 1.223862, Accuracy: 52.73\n",
      "Train Epoch: 4 [40960/50000 (91%)]\tLoss: 1.189586, Accuracy: 55.47\n",
      "Train Epoch: 4 [42240/50000 (94%)]\tLoss: 1.248918, Accuracy: 50.39\n",
      "Train Epoch: 4 [43520/50000 (97%)]\tLoss: 1.224329, Accuracy: 54.69\n",
      "Train Epoch: 4 [35000/50000 (99%)]\tLoss: 1.077227, Accuracy: 62.50\n",
      "\n",
      "Validation set: Average loss: 1.5293, Accuracy: 2382/5000 (47.00%)\n",
      "\n",
      "the time of this epoch:[35.918463468551636 s]\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 1.128077, Accuracy: 60.94\n",
      "Train Epoch: 5 [1280/50000 (3%)]\tLoss: 1.158317, Accuracy: 57.03\n",
      "Train Epoch: 5 [2560/50000 (6%)]\tLoss: 1.065774, Accuracy: 62.11\n",
      "Train Epoch: 5 [3840/50000 (9%)]\tLoss: 1.156934, Accuracy: 58.20\n",
      "Train Epoch: 5 [5120/50000 (11%)]\tLoss: 1.101551, Accuracy: 57.81\n",
      "Train Epoch: 5 [6400/50000 (14%)]\tLoss: 1.092010, Accuracy: 58.98\n",
      "Train Epoch: 5 [7680/50000 (17%)]\tLoss: 1.183486, Accuracy: 56.64\n",
      "Train Epoch: 5 [8960/50000 (20%)]\tLoss: 1.072843, Accuracy: 60.94\n",
      "Train Epoch: 5 [10240/50000 (23%)]\tLoss: 1.141242, Accuracy: 56.64\n",
      "Train Epoch: 5 [11520/50000 (26%)]\tLoss: 1.254682, Accuracy: 54.30\n",
      "Train Epoch: 5 [12800/50000 (28%)]\tLoss: 1.161165, Accuracy: 61.33\n",
      "Train Epoch: 5 [14080/50000 (31%)]\tLoss: 1.169902, Accuracy: 57.03\n",
      "Train Epoch: 5 [15360/50000 (34%)]\tLoss: 1.106395, Accuracy: 60.94\n",
      "Train Epoch: 5 [16640/50000 (37%)]\tLoss: 1.137993, Accuracy: 55.47\n",
      "Train Epoch: 5 [17920/50000 (40%)]\tLoss: 1.099708, Accuracy: 60.16\n",
      "Train Epoch: 5 [19200/50000 (43%)]\tLoss: 0.946901, Accuracy: 66.80\n",
      "Train Epoch: 5 [20480/50000 (45%)]\tLoss: 1.097171, Accuracy: 58.98\n",
      "Train Epoch: 5 [21760/50000 (48%)]\tLoss: 1.172080, Accuracy: 57.42\n",
      "Train Epoch: 5 [23040/50000 (51%)]\tLoss: 1.071399, Accuracy: 58.98\n",
      "Train Epoch: 5 [24320/50000 (54%)]\tLoss: 0.989871, Accuracy: 65.23\n",
      "Train Epoch: 5 [25600/50000 (57%)]\tLoss: 1.101448, Accuracy: 59.77\n",
      "Train Epoch: 5 [26880/50000 (60%)]\tLoss: 0.904571, Accuracy: 67.97\n",
      "Train Epoch: 5 [28160/50000 (62%)]\tLoss: 1.022656, Accuracy: 61.33\n",
      "Train Epoch: 5 [29440/50000 (65%)]\tLoss: 0.982715, Accuracy: 62.50\n",
      "Train Epoch: 5 [30720/50000 (68%)]\tLoss: 1.086889, Accuracy: 61.33\n",
      "Train Epoch: 5 [32000/50000 (71%)]\tLoss: 1.090633, Accuracy: 60.94\n",
      "Train Epoch: 5 [33280/50000 (74%)]\tLoss: 1.023831, Accuracy: 64.06\n",
      "Train Epoch: 5 [34560/50000 (77%)]\tLoss: 1.207474, Accuracy: 56.64\n",
      "Train Epoch: 5 [35840/50000 (80%)]\tLoss: 0.967573, Accuracy: 65.23\n",
      "Train Epoch: 5 [37120/50000 (82%)]\tLoss: 1.016835, Accuracy: 60.55\n",
      "Train Epoch: 5 [38400/50000 (85%)]\tLoss: 0.961382, Accuracy: 60.55\n",
      "Train Epoch: 5 [39680/50000 (88%)]\tLoss: 1.057094, Accuracy: 65.23\n",
      "Train Epoch: 5 [40960/50000 (91%)]\tLoss: 1.086867, Accuracy: 60.16\n",
      "Train Epoch: 5 [42240/50000 (94%)]\tLoss: 1.040059, Accuracy: 64.84\n",
      "Train Epoch: 5 [43520/50000 (97%)]\tLoss: 0.944201, Accuracy: 64.06\n",
      "Train Epoch: 5 [35000/50000 (99%)]\tLoss: 1.061335, Accuracy: 60.50\n",
      "\n",
      "Validation set: Average loss: 1.3867, Accuracy: 2717/5000 (54.00%)\n",
      "\n",
      "the time of this epoch:[35.758758783340454 s]\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 1.027696, Accuracy: 63.28\n",
      "Train Epoch: 6 [1280/50000 (3%)]\tLoss: 1.111102, Accuracy: 61.33\n",
      "Train Epoch: 6 [2560/50000 (6%)]\tLoss: 1.016189, Accuracy: 67.19\n",
      "Train Epoch: 6 [3840/50000 (9%)]\tLoss: 0.955408, Accuracy: 65.23\n",
      "Train Epoch: 6 [5120/50000 (11%)]\tLoss: 0.982681, Accuracy: 66.02\n",
      "Train Epoch: 6 [6400/50000 (14%)]\tLoss: 1.062888, Accuracy: 63.67\n",
      "Train Epoch: 6 [7680/50000 (17%)]\tLoss: 1.028975, Accuracy: 64.06\n",
      "Train Epoch: 6 [8960/50000 (20%)]\tLoss: 1.082533, Accuracy: 58.20\n",
      "Train Epoch: 6 [10240/50000 (23%)]\tLoss: 1.138235, Accuracy: 55.86\n",
      "Train Epoch: 6 [11520/50000 (26%)]\tLoss: 0.926988, Accuracy: 68.75\n",
      "Train Epoch: 6 [12800/50000 (28%)]\tLoss: 0.882864, Accuracy: 66.41\n",
      "Train Epoch: 6 [14080/50000 (31%)]\tLoss: 1.051187, Accuracy: 62.11\n",
      "Train Epoch: 6 [15360/50000 (34%)]\tLoss: 0.996685, Accuracy: 66.41\n",
      "Train Epoch: 6 [16640/50000 (37%)]\tLoss: 1.026188, Accuracy: 63.28\n",
      "Train Epoch: 6 [17920/50000 (40%)]\tLoss: 1.054323, Accuracy: 64.06\n",
      "Train Epoch: 6 [19200/50000 (43%)]\tLoss: 0.915863, Accuracy: 69.92\n",
      "Train Epoch: 6 [20480/50000 (45%)]\tLoss: 1.106771, Accuracy: 62.50\n",
      "Train Epoch: 6 [21760/50000 (48%)]\tLoss: 0.988090, Accuracy: 61.72\n",
      "Train Epoch: 6 [23040/50000 (51%)]\tLoss: 0.937533, Accuracy: 67.58\n",
      "Train Epoch: 6 [24320/50000 (54%)]\tLoss: 0.945285, Accuracy: 66.41\n",
      "Train Epoch: 6 [25600/50000 (57%)]\tLoss: 1.061942, Accuracy: 61.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [26880/50000 (60%)]\tLoss: 0.881978, Accuracy: 70.70\n",
      "Train Epoch: 6 [28160/50000 (62%)]\tLoss: 0.902986, Accuracy: 69.14\n",
      "Train Epoch: 6 [29440/50000 (65%)]\tLoss: 1.042276, Accuracy: 62.50\n",
      "Train Epoch: 6 [30720/50000 (68%)]\tLoss: 1.001053, Accuracy: 64.06\n",
      "Train Epoch: 6 [32000/50000 (71%)]\tLoss: 1.009541, Accuracy: 64.45\n",
      "Train Epoch: 6 [33280/50000 (74%)]\tLoss: 0.961700, Accuracy: 62.50\n",
      "Train Epoch: 6 [34560/50000 (77%)]\tLoss: 1.061793, Accuracy: 65.23\n",
      "Train Epoch: 6 [35840/50000 (80%)]\tLoss: 1.024943, Accuracy: 63.67\n",
      "Train Epoch: 6 [37120/50000 (82%)]\tLoss: 0.974980, Accuracy: 62.89\n",
      "Train Epoch: 6 [38400/50000 (85%)]\tLoss: 0.867566, Accuracy: 69.14\n",
      "Train Epoch: 6 [39680/50000 (88%)]\tLoss: 0.992152, Accuracy: 66.41\n",
      "Train Epoch: 6 [40960/50000 (91%)]\tLoss: 0.939165, Accuracy: 67.19\n",
      "Train Epoch: 6 [42240/50000 (94%)]\tLoss: 0.907185, Accuracy: 67.58\n",
      "Train Epoch: 6 [43520/50000 (97%)]\tLoss: 0.926584, Accuracy: 66.41\n",
      "Train Epoch: 6 [35000/50000 (99%)]\tLoss: 0.844185, Accuracy: 69.00\n",
      "\n",
      "Validation set: Average loss: 1.1714, Accuracy: 3012/5000 (60.00%)\n",
      "\n",
      "the time of this epoch:[35.81236982345581 s]\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 1.016127, Accuracy: 61.72\n",
      "Train Epoch: 7 [1280/50000 (3%)]\tLoss: 0.904281, Accuracy: 70.31\n",
      "Train Epoch: 7 [2560/50000 (6%)]\tLoss: 1.008967, Accuracy: 62.50\n",
      "Train Epoch: 7 [3840/50000 (9%)]\tLoss: 0.826487, Accuracy: 71.88\n",
      "Train Epoch: 7 [5120/50000 (11%)]\tLoss: 0.879575, Accuracy: 66.41\n",
      "Train Epoch: 7 [6400/50000 (14%)]\tLoss: 0.891379, Accuracy: 68.75\n",
      "Train Epoch: 7 [7680/50000 (17%)]\tLoss: 0.945156, Accuracy: 66.80\n",
      "Train Epoch: 7 [8960/50000 (20%)]\tLoss: 1.002892, Accuracy: 65.62\n",
      "Train Epoch: 7 [10240/50000 (23%)]\tLoss: 0.856596, Accuracy: 67.19\n",
      "Train Epoch: 7 [11520/50000 (26%)]\tLoss: 0.896720, Accuracy: 67.19\n",
      "Train Epoch: 7 [12800/50000 (28%)]\tLoss: 0.892371, Accuracy: 67.19\n",
      "Train Epoch: 7 [14080/50000 (31%)]\tLoss: 0.854739, Accuracy: 70.70\n",
      "Train Epoch: 7 [15360/50000 (34%)]\tLoss: 0.892586, Accuracy: 67.97\n",
      "Train Epoch: 7 [16640/50000 (37%)]\tLoss: 0.935907, Accuracy: 64.84\n",
      "Train Epoch: 7 [17920/50000 (40%)]\tLoss: 0.869715, Accuracy: 68.75\n",
      "Train Epoch: 7 [19200/50000 (43%)]\tLoss: 0.900002, Accuracy: 69.92\n",
      "Train Epoch: 7 [20480/50000 (45%)]\tLoss: 0.907585, Accuracy: 67.19\n",
      "Train Epoch: 7 [21760/50000 (48%)]\tLoss: 0.824971, Accuracy: 68.75\n",
      "Train Epoch: 7 [23040/50000 (51%)]\tLoss: 0.840798, Accuracy: 67.58\n",
      "Train Epoch: 7 [24320/50000 (54%)]\tLoss: 0.896500, Accuracy: 69.14\n",
      "Train Epoch: 7 [25600/50000 (57%)]\tLoss: 0.832165, Accuracy: 73.05\n",
      "Train Epoch: 7 [26880/50000 (60%)]\tLoss: 0.930848, Accuracy: 67.58\n",
      "Train Epoch: 7 [28160/50000 (62%)]\tLoss: 0.890869, Accuracy: 70.31\n",
      "Train Epoch: 7 [29440/50000 (65%)]\tLoss: 0.882060, Accuracy: 71.88\n",
      "Train Epoch: 7 [30720/50000 (68%)]\tLoss: 0.807315, Accuracy: 70.31\n",
      "Train Epoch: 7 [32000/50000 (71%)]\tLoss: 0.864288, Accuracy: 68.36\n",
      "Train Epoch: 7 [33280/50000 (74%)]\tLoss: 0.787386, Accuracy: 69.92\n",
      "Train Epoch: 7 [34560/50000 (77%)]\tLoss: 0.814528, Accuracy: 70.31\n",
      "Train Epoch: 7 [35840/50000 (80%)]\tLoss: 0.883708, Accuracy: 67.97\n",
      "Train Epoch: 7 [37120/50000 (82%)]\tLoss: 0.855024, Accuracy: 70.31\n",
      "Train Epoch: 7 [38400/50000 (85%)]\tLoss: 0.888894, Accuracy: 69.53\n",
      "Train Epoch: 7 [39680/50000 (88%)]\tLoss: 0.937571, Accuracy: 64.06\n",
      "Train Epoch: 7 [40960/50000 (91%)]\tLoss: 0.769471, Accuracy: 72.27\n",
      "Train Epoch: 7 [42240/50000 (94%)]\tLoss: 0.890126, Accuracy: 70.31\n",
      "Train Epoch: 7 [43520/50000 (97%)]\tLoss: 0.913489, Accuracy: 68.75\n",
      "Train Epoch: 7 [35000/50000 (99%)]\tLoss: 0.690347, Accuracy: 75.00\n",
      "\n",
      "Validation set: Average loss: 1.0931, Accuracy: 3151/5000 (63.00%)\n",
      "\n",
      "the time of this epoch:[35.724284172058105 s]\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 0.680814, Accuracy: 75.78\n",
      "Train Epoch: 8 [1280/50000 (3%)]\tLoss: 0.774368, Accuracy: 69.14\n",
      "Train Epoch: 8 [2560/50000 (6%)]\tLoss: 0.664408, Accuracy: 75.39\n",
      "Train Epoch: 8 [3840/50000 (9%)]\tLoss: 0.900098, Accuracy: 69.92\n",
      "Train Epoch: 8 [5120/50000 (11%)]\tLoss: 0.836410, Accuracy: 71.48\n",
      "Train Epoch: 8 [6400/50000 (14%)]\tLoss: 0.834248, Accuracy: 69.53\n",
      "Train Epoch: 8 [7680/50000 (17%)]\tLoss: 0.809024, Accuracy: 74.22\n",
      "Train Epoch: 8 [8960/50000 (20%)]\tLoss: 0.758079, Accuracy: 73.83\n",
      "Train Epoch: 8 [10240/50000 (23%)]\tLoss: 0.807618, Accuracy: 71.48\n",
      "Train Epoch: 8 [11520/50000 (26%)]\tLoss: 0.802793, Accuracy: 74.22\n",
      "Train Epoch: 8 [12800/50000 (28%)]\tLoss: 0.778211, Accuracy: 74.61\n",
      "Train Epoch: 8 [14080/50000 (31%)]\tLoss: 0.919523, Accuracy: 71.88\n",
      "Train Epoch: 8 [15360/50000 (34%)]\tLoss: 0.839863, Accuracy: 69.14\n",
      "Train Epoch: 8 [16640/50000 (37%)]\tLoss: 0.632941, Accuracy: 78.12\n",
      "Train Epoch: 8 [17920/50000 (40%)]\tLoss: 0.780475, Accuracy: 72.66\n",
      "Train Epoch: 8 [19200/50000 (43%)]\tLoss: 0.825620, Accuracy: 69.53\n",
      "Train Epoch: 8 [20480/50000 (45%)]\tLoss: 0.709770, Accuracy: 73.05\n",
      "Train Epoch: 8 [21760/50000 (48%)]\tLoss: 0.818739, Accuracy: 70.31\n",
      "Train Epoch: 8 [23040/50000 (51%)]\tLoss: 0.788747, Accuracy: 74.61\n",
      "Train Epoch: 8 [24320/50000 (54%)]\tLoss: 0.706787, Accuracy: 72.27\n",
      "Train Epoch: 8 [25600/50000 (57%)]\tLoss: 0.809266, Accuracy: 71.09\n",
      "Train Epoch: 8 [26880/50000 (60%)]\tLoss: 0.834326, Accuracy: 71.09\n",
      "Train Epoch: 8 [28160/50000 (62%)]\tLoss: 0.693388, Accuracy: 77.34\n",
      "Train Epoch: 8 [29440/50000 (65%)]\tLoss: 0.702311, Accuracy: 73.83\n",
      "Train Epoch: 8 [30720/50000 (68%)]\tLoss: 0.921305, Accuracy: 71.09\n",
      "Train Epoch: 8 [32000/50000 (71%)]\tLoss: 0.725327, Accuracy: 76.17\n",
      "Train Epoch: 8 [33280/50000 (74%)]\tLoss: 0.750894, Accuracy: 73.44\n",
      "Train Epoch: 8 [34560/50000 (77%)]\tLoss: 0.772531, Accuracy: 75.78\n",
      "Train Epoch: 8 [35840/50000 (80%)]\tLoss: 0.803756, Accuracy: 72.66\n",
      "Train Epoch: 8 [37120/50000 (82%)]\tLoss: 0.821746, Accuracy: 71.88\n",
      "Train Epoch: 8 [38400/50000 (85%)]\tLoss: 0.788826, Accuracy: 74.61\n",
      "Train Epoch: 8 [39680/50000 (88%)]\tLoss: 0.670134, Accuracy: 76.56\n",
      "Train Epoch: 8 [40960/50000 (91%)]\tLoss: 0.785394, Accuracy: 71.48\n",
      "Train Epoch: 8 [42240/50000 (94%)]\tLoss: 0.780113, Accuracy: 73.83\n",
      "Train Epoch: 8 [43520/50000 (97%)]\tLoss: 0.772575, Accuracy: 72.27\n",
      "Train Epoch: 8 [35000/50000 (99%)]\tLoss: 0.804339, Accuracy: 71.50\n",
      "\n",
      "Validation set: Average loss: 0.9860, Accuracy: 3334/5000 (66.00%)\n",
      "\n",
      "the time of this epoch:[35.81318378448486 s]\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 0.720364, Accuracy: 76.95\n",
      "Train Epoch: 9 [1280/50000 (3%)]\tLoss: 0.846721, Accuracy: 69.53\n",
      "Train Epoch: 9 [2560/50000 (6%)]\tLoss: 0.696827, Accuracy: 72.27\n",
      "Train Epoch: 9 [3840/50000 (9%)]\tLoss: 0.703176, Accuracy: 77.34\n",
      "Train Epoch: 9 [5120/50000 (11%)]\tLoss: 0.804326, Accuracy: 75.39\n",
      "Train Epoch: 9 [6400/50000 (14%)]\tLoss: 0.758834, Accuracy: 74.22\n",
      "Train Epoch: 9 [7680/50000 (17%)]\tLoss: 0.685312, Accuracy: 76.17\n",
      "Train Epoch: 9 [8960/50000 (20%)]\tLoss: 0.662599, Accuracy: 78.91\n",
      "Train Epoch: 9 [10240/50000 (23%)]\tLoss: 0.737498, Accuracy: 75.39\n",
      "Train Epoch: 9 [11520/50000 (26%)]\tLoss: 0.730673, Accuracy: 72.66\n",
      "Train Epoch: 9 [12800/50000 (28%)]\tLoss: 0.765988, Accuracy: 72.66\n",
      "Train Epoch: 9 [14080/50000 (31%)]\tLoss: 0.714698, Accuracy: 77.73\n",
      "Train Epoch: 9 [15360/50000 (34%)]\tLoss: 0.638869, Accuracy: 81.25\n",
      "Train Epoch: 9 [16640/50000 (37%)]\tLoss: 0.615255, Accuracy: 76.95\n",
      "Train Epoch: 9 [17920/50000 (40%)]\tLoss: 0.718234, Accuracy: 76.95\n",
      "Train Epoch: 9 [19200/50000 (43%)]\tLoss: 0.658416, Accuracy: 78.52\n",
      "Train Epoch: 9 [20480/50000 (45%)]\tLoss: 0.763219, Accuracy: 74.61\n",
      "Train Epoch: 9 [21760/50000 (48%)]\tLoss: 0.712059, Accuracy: 76.56\n",
      "Train Epoch: 9 [23040/50000 (51%)]\tLoss: 0.739493, Accuracy: 74.61\n",
      "Train Epoch: 9 [24320/50000 (54%)]\tLoss: 0.774015, Accuracy: 75.39\n",
      "Train Epoch: 9 [25600/50000 (57%)]\tLoss: 0.791455, Accuracy: 73.83\n",
      "Train Epoch: 9 [26880/50000 (60%)]\tLoss: 0.760824, Accuracy: 73.05\n",
      "Train Epoch: 9 [28160/50000 (62%)]\tLoss: 0.595619, Accuracy: 81.25\n",
      "Train Epoch: 9 [29440/50000 (65%)]\tLoss: 0.742578, Accuracy: 75.78\n",
      "Train Epoch: 9 [30720/50000 (68%)]\tLoss: 0.778333, Accuracy: 73.44\n",
      "Train Epoch: 9 [32000/50000 (71%)]\tLoss: 0.775560, Accuracy: 72.27\n",
      "Train Epoch: 9 [33280/50000 (74%)]\tLoss: 0.806220, Accuracy: 75.00\n",
      "Train Epoch: 9 [34560/50000 (77%)]\tLoss: 0.679930, Accuracy: 76.56\n",
      "Train Epoch: 9 [35840/50000 (80%)]\tLoss: 0.650108, Accuracy: 77.73\n",
      "Train Epoch: 9 [37120/50000 (82%)]\tLoss: 0.733909, Accuracy: 77.73\n",
      "Train Epoch: 9 [38400/50000 (85%)]\tLoss: 0.806510, Accuracy: 71.88\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [39680/50000 (88%)]\tLoss: 0.615576, Accuracy: 78.91\n",
      "Train Epoch: 9 [40960/50000 (91%)]\tLoss: 0.876861, Accuracy: 68.75\n",
      "Train Epoch: 9 [42240/50000 (94%)]\tLoss: 0.725457, Accuracy: 76.17\n",
      "Train Epoch: 9 [43520/50000 (97%)]\tLoss: 0.719431, Accuracy: 73.83\n",
      "Train Epoch: 9 [35000/50000 (99%)]\tLoss: 0.774504, Accuracy: 70.00\n",
      "\n",
      "Validation set: Average loss: 0.9951, Accuracy: 3385/5000 (67.00%)\n",
      "\n",
      "the time of this epoch:[35.7651903629303 s]\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 0.650134, Accuracy: 78.52\n",
      "Train Epoch: 10 [1280/50000 (3%)]\tLoss: 0.689337, Accuracy: 74.22\n",
      "Train Epoch: 10 [2560/50000 (6%)]\tLoss: 0.607822, Accuracy: 78.91\n",
      "Train Epoch: 10 [3840/50000 (9%)]\tLoss: 0.728098, Accuracy: 74.22\n",
      "Train Epoch: 10 [5120/50000 (11%)]\tLoss: 0.538873, Accuracy: 80.47\n",
      "Train Epoch: 10 [6400/50000 (14%)]\tLoss: 0.671259, Accuracy: 75.78\n",
      "Train Epoch: 10 [7680/50000 (17%)]\tLoss: 0.716126, Accuracy: 72.27\n",
      "Train Epoch: 10 [8960/50000 (20%)]\tLoss: 0.725944, Accuracy: 76.17\n",
      "Train Epoch: 10 [10240/50000 (23%)]\tLoss: 0.647554, Accuracy: 77.73\n",
      "Train Epoch: 10 [11520/50000 (26%)]\tLoss: 0.638462, Accuracy: 77.34\n",
      "Train Epoch: 10 [12800/50000 (28%)]\tLoss: 0.678472, Accuracy: 78.52\n",
      "Train Epoch: 10 [14080/50000 (31%)]\tLoss: 0.705899, Accuracy: 78.91\n",
      "Train Epoch: 10 [15360/50000 (34%)]\tLoss: 0.526528, Accuracy: 83.20\n",
      "Train Epoch: 10 [16640/50000 (37%)]\tLoss: 0.799245, Accuracy: 72.66\n",
      "Train Epoch: 10 [17920/50000 (40%)]\tLoss: 0.650809, Accuracy: 78.52\n",
      "Train Epoch: 10 [19200/50000 (43%)]\tLoss: 0.727748, Accuracy: 73.05\n",
      "Train Epoch: 10 [20480/50000 (45%)]\tLoss: 0.787268, Accuracy: 69.53\n",
      "Train Epoch: 10 [21760/50000 (48%)]\tLoss: 0.800883, Accuracy: 73.05\n",
      "Train Epoch: 10 [23040/50000 (51%)]\tLoss: 0.587965, Accuracy: 80.86\n",
      "Train Epoch: 10 [24320/50000 (54%)]\tLoss: 0.688643, Accuracy: 74.22\n",
      "Train Epoch: 10 [25600/50000 (57%)]\tLoss: 0.581606, Accuracy: 79.30\n",
      "Train Epoch: 10 [26880/50000 (60%)]\tLoss: 0.604833, Accuracy: 76.17\n",
      "Train Epoch: 10 [28160/50000 (62%)]\tLoss: 0.806769, Accuracy: 77.73\n",
      "Train Epoch: 10 [29440/50000 (65%)]\tLoss: 0.669703, Accuracy: 80.08\n",
      "Train Epoch: 10 [30720/50000 (68%)]\tLoss: 0.706935, Accuracy: 75.00\n",
      "Train Epoch: 10 [32000/50000 (71%)]\tLoss: 0.714179, Accuracy: 74.22\n",
      "Train Epoch: 10 [33280/50000 (74%)]\tLoss: 0.761316, Accuracy: 75.78\n",
      "Train Epoch: 10 [34560/50000 (77%)]\tLoss: 0.598401, Accuracy: 78.52\n",
      "Train Epoch: 10 [35840/50000 (80%)]\tLoss: 0.682144, Accuracy: 73.83\n",
      "Train Epoch: 10 [37120/50000 (82%)]\tLoss: 0.693875, Accuracy: 75.39\n",
      "Train Epoch: 10 [38400/50000 (85%)]\tLoss: 0.637806, Accuracy: 78.91\n",
      "Train Epoch: 10 [39680/50000 (88%)]\tLoss: 0.565429, Accuracy: 81.25\n",
      "Train Epoch: 10 [40960/50000 (91%)]\tLoss: 0.634236, Accuracy: 77.34\n",
      "Train Epoch: 10 [42240/50000 (94%)]\tLoss: 0.787105, Accuracy: 75.39\n",
      "Train Epoch: 10 [43520/50000 (97%)]\tLoss: 0.671458, Accuracy: 77.73\n",
      "Train Epoch: 10 [35000/50000 (99%)]\tLoss: 0.542095, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 1.0293, Accuracy: 3370/5000 (67.00%)\n",
      "\n",
      "the time of this epoch:[35.88676643371582 s]\n",
      "\n",
      "Test set: Average loss: 1.0120, Accuracy: 6808/10000 (68.08%)\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 0.614922, Accuracy: 79.30\n",
      "Train Epoch: 11 [1280/50000 (3%)]\tLoss: 0.655775, Accuracy: 77.73\n",
      "Train Epoch: 11 [2560/50000 (6%)]\tLoss: 0.661008, Accuracy: 78.12\n",
      "Train Epoch: 11 [3840/50000 (9%)]\tLoss: 0.755335, Accuracy: 72.66\n",
      "Train Epoch: 11 [5120/50000 (11%)]\tLoss: 0.602733, Accuracy: 79.30\n",
      "Train Epoch: 11 [6400/50000 (14%)]\tLoss: 0.620622, Accuracy: 78.12\n",
      "Train Epoch: 11 [7680/50000 (17%)]\tLoss: 0.669248, Accuracy: 77.73\n",
      "Train Epoch: 11 [8960/50000 (20%)]\tLoss: 0.763496, Accuracy: 74.22\n",
      "Train Epoch: 11 [10240/50000 (23%)]\tLoss: 0.565154, Accuracy: 82.81\n",
      "Train Epoch: 11 [11520/50000 (26%)]\tLoss: 0.622297, Accuracy: 78.12\n",
      "Train Epoch: 11 [12800/50000 (28%)]\tLoss: 0.637835, Accuracy: 79.69\n",
      "Train Epoch: 11 [14080/50000 (31%)]\tLoss: 0.520584, Accuracy: 82.42\n",
      "Train Epoch: 11 [15360/50000 (34%)]\tLoss: 0.629595, Accuracy: 79.30\n",
      "Train Epoch: 11 [16640/50000 (37%)]\tLoss: 0.564184, Accuracy: 82.03\n",
      "Train Epoch: 11 [17920/50000 (40%)]\tLoss: 0.557787, Accuracy: 80.47\n",
      "Train Epoch: 11 [19200/50000 (43%)]\tLoss: 0.786460, Accuracy: 74.22\n",
      "Train Epoch: 11 [20480/50000 (45%)]\tLoss: 0.616296, Accuracy: 76.95\n",
      "Train Epoch: 11 [21760/50000 (48%)]\tLoss: 0.612814, Accuracy: 80.08\n",
      "Train Epoch: 11 [23040/50000 (51%)]\tLoss: 0.592217, Accuracy: 78.12\n",
      "Train Epoch: 11 [24320/50000 (54%)]\tLoss: 0.579253, Accuracy: 79.30\n",
      "torch.Size([512, 1, 1])\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 11 [25600/50000 (57%)]\tLoss: 0.695758, Accuracy: 75.78\n",
      "Train Epoch: 11 [26880/50000 (60%)]\tLoss: 0.655846, Accuracy: 78.52\n",
      "Train Epoch: 11 [28160/50000 (62%)]\tLoss: 0.681582, Accuracy: 77.73\n",
      "Train Epoch: 11 [29440/50000 (65%)]\tLoss: 0.549185, Accuracy: 80.08\n",
      "Train Epoch: 11 [30720/50000 (68%)]\tLoss: 0.644344, Accuracy: 78.12\n",
      "Train Epoch: 11 [32000/50000 (71%)]\tLoss: 0.749969, Accuracy: 78.12\n",
      "Train Epoch: 11 [33280/50000 (74%)]\tLoss: 0.609944, Accuracy: 76.95\n",
      "Train Epoch: 11 [34560/50000 (77%)]\tLoss: 0.690347, Accuracy: 75.00\n",
      "Train Epoch: 11 [35840/50000 (80%)]\tLoss: 0.646117, Accuracy: 77.73\n",
      "Train Epoch: 11 [37120/50000 (82%)]\tLoss: 0.696785, Accuracy: 77.34\n",
      "Train Epoch: 11 [38400/50000 (85%)]\tLoss: 0.580561, Accuracy: 80.86\n",
      "Train Epoch: 11 [39680/50000 (88%)]\tLoss: 0.679817, Accuracy: 77.34\n",
      "Train Epoch: 11 [40960/50000 (91%)]\tLoss: 0.618010, Accuracy: 77.73\n",
      "Train Epoch: 11 [42240/50000 (94%)]\tLoss: 0.495297, Accuracy: 85.16\n",
      "Train Epoch: 11 [43520/50000 (97%)]\tLoss: 0.769129, Accuracy: 72.27\n",
      "Train Epoch: 11 [35000/50000 (99%)]\tLoss: 0.569330, Accuracy: 77.50\n",
      "\n",
      "Validation set: Average loss: 0.8191, Accuracy: 3619/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[39.037050008773804 s]\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 0.584094, Accuracy: 78.91\n",
      "Train Epoch: 12 [1280/50000 (3%)]\tLoss: 0.566464, Accuracy: 79.69\n",
      "Train Epoch: 12 [2560/50000 (6%)]\tLoss: 0.572778, Accuracy: 82.42\n",
      "Train Epoch: 12 [3840/50000 (9%)]\tLoss: 0.555601, Accuracy: 82.42\n",
      "Train Epoch: 12 [5120/50000 (11%)]\tLoss: 0.507008, Accuracy: 83.98\n",
      "Train Epoch: 12 [6400/50000 (14%)]\tLoss: 0.658096, Accuracy: 76.56\n",
      "Train Epoch: 12 [7680/50000 (17%)]\tLoss: 0.629722, Accuracy: 78.52\n",
      "Train Epoch: 12 [8960/50000 (20%)]\tLoss: 0.636896, Accuracy: 78.91\n",
      "Train Epoch: 12 [10240/50000 (23%)]\tLoss: 0.614893, Accuracy: 75.78\n",
      "Train Epoch: 12 [11520/50000 (26%)]\tLoss: 0.483068, Accuracy: 84.38\n",
      "Train Epoch: 12 [12800/50000 (28%)]\tLoss: 0.587792, Accuracy: 80.47\n",
      "Train Epoch: 12 [14080/50000 (31%)]\tLoss: 0.571187, Accuracy: 83.20\n",
      "Train Epoch: 12 [15360/50000 (34%)]\tLoss: 0.564642, Accuracy: 81.64\n",
      "Train Epoch: 12 [16640/50000 (37%)]\tLoss: 0.687635, Accuracy: 77.73\n",
      "Train Epoch: 12 [17920/50000 (40%)]\tLoss: 0.527004, Accuracy: 82.42\n",
      "Train Epoch: 12 [19200/50000 (43%)]\tLoss: 0.561415, Accuracy: 80.86\n",
      "Train Epoch: 12 [20480/50000 (45%)]\tLoss: 0.618770, Accuracy: 78.91\n",
      "Train Epoch: 12 [21760/50000 (48%)]\tLoss: 0.519817, Accuracy: 83.59\n",
      "Train Epoch: 12 [23040/50000 (51%)]\tLoss: 0.570304, Accuracy: 81.64\n",
      "Train Epoch: 12 [24320/50000 (54%)]\tLoss: 0.618628, Accuracy: 81.64\n",
      "Train Epoch: 12 [25600/50000 (57%)]\tLoss: 0.761470, Accuracy: 75.39\n",
      "Train Epoch: 12 [26880/50000 (60%)]\tLoss: 0.694013, Accuracy: 75.78\n",
      "Train Epoch: 12 [28160/50000 (62%)]\tLoss: 0.597117, Accuracy: 77.34\n",
      "Train Epoch: 12 [29440/50000 (65%)]\tLoss: 0.666628, Accuracy: 74.61\n",
      "Train Epoch: 12 [30720/50000 (68%)]\tLoss: 0.670457, Accuracy: 76.95\n",
      "Train Epoch: 12 [32000/50000 (71%)]\tLoss: 0.615679, Accuracy: 77.73\n",
      "Train Epoch: 12 [33280/50000 (74%)]\tLoss: 0.692888, Accuracy: 76.95\n",
      "Train Epoch: 12 [34560/50000 (77%)]\tLoss: 0.601788, Accuracy: 81.64\n",
      "Train Epoch: 12 [35840/50000 (80%)]\tLoss: 0.694881, Accuracy: 76.56\n",
      "Train Epoch: 12 [37120/50000 (82%)]\tLoss: 0.589049, Accuracy: 80.08\n",
      "Train Epoch: 12 [38400/50000 (85%)]\tLoss: 0.648658, Accuracy: 79.69\n",
      "Train Epoch: 12 [39680/50000 (88%)]\tLoss: 0.581471, Accuracy: 83.20\n",
      "Train Epoch: 12 [40960/50000 (91%)]\tLoss: 0.495098, Accuracy: 83.20\n",
      "Train Epoch: 12 [42240/50000 (94%)]\tLoss: 0.662762, Accuracy: 77.73\n",
      "Train Epoch: 12 [43520/50000 (97%)]\tLoss: 0.640184, Accuracy: 79.30\n",
      "Train Epoch: 12 [35000/50000 (99%)]\tLoss: 0.532899, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.8872, Accuracy: 3628/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[35.81188178062439 s]\n",
      "\n",
      "Test set: Average loss: 0.9867, Accuracy: 6966/10000 (69.66%)\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 0.570862, Accuracy: 79.69\n",
      "Train Epoch: 13 [1280/50000 (3%)]\tLoss: 0.529834, Accuracy: 81.64\n",
      "Train Epoch: 13 [2560/50000 (6%)]\tLoss: 0.565044, Accuracy: 82.03\n",
      "Train Epoch: 13 [3840/50000 (9%)]\tLoss: 0.620411, Accuracy: 81.64\n",
      "Train Epoch: 13 [5120/50000 (11%)]\tLoss: 0.582935, Accuracy: 81.25\n",
      "Train Epoch: 13 [6400/50000 (14%)]\tLoss: 0.669870, Accuracy: 76.17\n",
      "Train Epoch: 13 [7680/50000 (17%)]\tLoss: 0.686477, Accuracy: 75.00\n",
      "Train Epoch: 13 [8960/50000 (20%)]\tLoss: 0.610490, Accuracy: 78.91\n",
      "Train Epoch: 13 [10240/50000 (23%)]\tLoss: 0.523339, Accuracy: 82.03\n",
      "Train Epoch: 13 [11520/50000 (26%)]\tLoss: 0.503660, Accuracy: 82.81\n",
      "Train Epoch: 13 [12800/50000 (28%)]\tLoss: 0.651402, Accuracy: 76.17\n",
      "Train Epoch: 13 [14080/50000 (31%)]\tLoss: 0.626058, Accuracy: 80.08\n",
      "Train Epoch: 13 [15360/50000 (34%)]\tLoss: 0.520592, Accuracy: 82.03\n",
      "Train Epoch: 13 [16640/50000 (37%)]\tLoss: 0.613382, Accuracy: 80.86\n",
      "Train Epoch: 13 [17920/50000 (40%)]\tLoss: 0.480370, Accuracy: 83.59\n",
      "Train Epoch: 13 [19200/50000 (43%)]\tLoss: 0.560842, Accuracy: 80.47\n",
      "Train Epoch: 13 [20480/50000 (45%)]\tLoss: 0.522594, Accuracy: 82.81\n",
      "Train Epoch: 13 [21760/50000 (48%)]\tLoss: 0.674999, Accuracy: 76.56\n",
      "Train Epoch: 13 [23040/50000 (51%)]\tLoss: 0.692453, Accuracy: 76.95\n",
      "Train Epoch: 13 [24320/50000 (54%)]\tLoss: 0.551234, Accuracy: 80.86\n",
      "Train Epoch: 13 [25600/50000 (57%)]\tLoss: 0.730218, Accuracy: 76.17\n",
      "Train Epoch: 13 [26880/50000 (60%)]\tLoss: 0.617127, Accuracy: 80.08\n",
      "Train Epoch: 13 [28160/50000 (62%)]\tLoss: 0.608395, Accuracy: 79.69\n",
      "Train Epoch: 13 [29440/50000 (65%)]\tLoss: 0.510905, Accuracy: 83.98\n",
      "Train Epoch: 13 [30720/50000 (68%)]\tLoss: 0.548552, Accuracy: 82.03\n",
      "Train Epoch: 13 [32000/50000 (71%)]\tLoss: 0.722075, Accuracy: 75.00\n",
      "Train Epoch: 13 [33280/50000 (74%)]\tLoss: 0.616457, Accuracy: 78.52\n",
      "Train Epoch: 13 [34560/50000 (77%)]\tLoss: 0.662634, Accuracy: 77.73\n",
      "Train Epoch: 13 [35840/50000 (80%)]\tLoss: 0.635117, Accuracy: 80.08\n",
      "Train Epoch: 13 [37120/50000 (82%)]\tLoss: 0.541083, Accuracy: 80.86\n",
      "Train Epoch: 13 [38400/50000 (85%)]\tLoss: 0.548027, Accuracy: 81.64\n",
      "Train Epoch: 13 [39680/50000 (88%)]\tLoss: 0.634438, Accuracy: 79.30\n",
      "Train Epoch: 13 [40960/50000 (91%)]\tLoss: 0.683774, Accuracy: 75.78\n",
      "Train Epoch: 13 [42240/50000 (94%)]\tLoss: 0.523626, Accuracy: 80.47\n",
      "Train Epoch: 13 [43520/50000 (97%)]\tLoss: 0.654293, Accuracy: 78.52\n",
      "Train Epoch: 13 [35000/50000 (99%)]\tLoss: 0.664204, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 1.2019, Accuracy: 3225/5000 (64.00%)\n",
      "\n",
      "the time of this epoch:[38.76267981529236 s]\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 0.623481, Accuracy: 77.34\n",
      "Train Epoch: 14 [1280/50000 (3%)]\tLoss: 0.620758, Accuracy: 79.30\n",
      "Train Epoch: 14 [2560/50000 (6%)]\tLoss: 0.499754, Accuracy: 85.55\n",
      "Train Epoch: 14 [3840/50000 (9%)]\tLoss: 0.515960, Accuracy: 85.16\n",
      "Train Epoch: 14 [5120/50000 (11%)]\tLoss: 0.383796, Accuracy: 90.62\n",
      "Train Epoch: 14 [6400/50000 (14%)]\tLoss: 0.585600, Accuracy: 79.30\n",
      "Train Epoch: 14 [7680/50000 (17%)]\tLoss: 0.632400, Accuracy: 78.91\n",
      "Train Epoch: 14 [8960/50000 (20%)]\tLoss: 0.704827, Accuracy: 74.22\n",
      "Train Epoch: 14 [10240/50000 (23%)]\tLoss: 0.508809, Accuracy: 82.03\n",
      "Train Epoch: 14 [11520/50000 (26%)]\tLoss: 0.523016, Accuracy: 82.03\n",
      "Train Epoch: 14 [12800/50000 (28%)]\tLoss: 0.585509, Accuracy: 80.86\n",
      "Train Epoch: 14 [14080/50000 (31%)]\tLoss: 0.438981, Accuracy: 85.94\n",
      "Train Epoch: 14 [15360/50000 (34%)]\tLoss: 0.497749, Accuracy: 83.59\n",
      "Train Epoch: 14 [16640/50000 (37%)]\tLoss: 0.549200, Accuracy: 82.03\n",
      "Train Epoch: 14 [17920/50000 (40%)]\tLoss: 0.626423, Accuracy: 78.91\n",
      "Train Epoch: 14 [19200/50000 (43%)]\tLoss: 0.489786, Accuracy: 85.16\n",
      "Train Epoch: 14 [20480/50000 (45%)]\tLoss: 0.678817, Accuracy: 78.52\n",
      "Train Epoch: 14 [21760/50000 (48%)]\tLoss: 0.586381, Accuracy: 78.91\n",
      "Train Epoch: 14 [23040/50000 (51%)]\tLoss: 0.569377, Accuracy: 79.30\n",
      "Train Epoch: 14 [24320/50000 (54%)]\tLoss: 0.653550, Accuracy: 78.12\n",
      "Train Epoch: 14 [25600/50000 (57%)]\tLoss: 0.625895, Accuracy: 80.47\n",
      "Train Epoch: 14 [26880/50000 (60%)]\tLoss: 0.553010, Accuracy: 81.25\n",
      "Train Epoch: 14 [28160/50000 (62%)]\tLoss: 0.587732, Accuracy: 82.03\n",
      "Train Epoch: 14 [29440/50000 (65%)]\tLoss: 0.497384, Accuracy: 83.59\n",
      "Train Epoch: 14 [30720/50000 (68%)]\tLoss: 0.521284, Accuracy: 81.64\n",
      "Train Epoch: 14 [32000/50000 (71%)]\tLoss: 0.528458, Accuracy: 82.03\n",
      "Train Epoch: 14 [33280/50000 (74%)]\tLoss: 0.429689, Accuracy: 84.38\n",
      "Train Epoch: 14 [34560/50000 (77%)]\tLoss: 0.561303, Accuracy: 79.30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 14 [35840/50000 (80%)]\tLoss: 0.504812, Accuracy: 82.81\n",
      "Train Epoch: 14 [37120/50000 (82%)]\tLoss: 0.612828, Accuracy: 78.52\n",
      "Train Epoch: 14 [38400/50000 (85%)]\tLoss: 0.544476, Accuracy: 79.69\n",
      "Train Epoch: 14 [39680/50000 (88%)]\tLoss: 0.605362, Accuracy: 79.30\n",
      "Train Epoch: 14 [40960/50000 (91%)]\tLoss: 0.596490, Accuracy: 79.69\n",
      "Train Epoch: 14 [42240/50000 (94%)]\tLoss: 0.486098, Accuracy: 84.77\n",
      "Train Epoch: 14 [43520/50000 (97%)]\tLoss: 0.505759, Accuracy: 85.94\n",
      "Train Epoch: 14 [35000/50000 (99%)]\tLoss: 0.543526, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.7035, Accuracy: 3804/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[35.77878212928772 s]\n",
      "\n",
      "Test set: Average loss: 0.6964, Accuracy: 7677/10000 (76.77%)\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 0.534181, Accuracy: 80.47\n",
      "Train Epoch: 15 [1280/50000 (3%)]\tLoss: 0.525186, Accuracy: 81.64\n",
      "Train Epoch: 15 [2560/50000 (6%)]\tLoss: 0.538213, Accuracy: 82.81\n",
      "Train Epoch: 15 [3840/50000 (9%)]\tLoss: 0.504810, Accuracy: 83.20\n",
      "Train Epoch: 15 [5120/50000 (11%)]\tLoss: 0.556091, Accuracy: 78.91\n",
      "Train Epoch: 15 [6400/50000 (14%)]\tLoss: 0.549702, Accuracy: 80.08\n",
      "Train Epoch: 15 [7680/50000 (17%)]\tLoss: 0.519258, Accuracy: 85.16\n",
      "Train Epoch: 15 [8960/50000 (20%)]\tLoss: 0.473995, Accuracy: 84.38\n",
      "Train Epoch: 15 [10240/50000 (23%)]\tLoss: 0.484838, Accuracy: 87.11\n",
      "Train Epoch: 15 [11520/50000 (26%)]\tLoss: 0.513380, Accuracy: 80.86\n",
      "Train Epoch: 15 [12800/50000 (28%)]\tLoss: 0.618200, Accuracy: 80.86\n",
      "Train Epoch: 15 [14080/50000 (31%)]\tLoss: 0.599381, Accuracy: 78.52\n",
      "Train Epoch: 15 [15360/50000 (34%)]\tLoss: 0.576169, Accuracy: 81.64\n",
      "Train Epoch: 15 [16640/50000 (37%)]\tLoss: 0.702464, Accuracy: 78.12\n",
      "Train Epoch: 15 [17920/50000 (40%)]\tLoss: 0.561253, Accuracy: 81.64\n",
      "Train Epoch: 15 [19200/50000 (43%)]\tLoss: 0.481392, Accuracy: 83.59\n",
      "Train Epoch: 15 [20480/50000 (45%)]\tLoss: 0.607834, Accuracy: 75.39\n",
      "Train Epoch: 15 [21760/50000 (48%)]\tLoss: 0.591136, Accuracy: 79.69\n",
      "Train Epoch: 15 [23040/50000 (51%)]\tLoss: 0.458166, Accuracy: 84.77\n",
      "Train Epoch: 15 [24320/50000 (54%)]\tLoss: 0.492017, Accuracy: 83.59\n",
      "Train Epoch: 15 [25600/50000 (57%)]\tLoss: 0.549871, Accuracy: 81.25\n",
      "Train Epoch: 15 [26880/50000 (60%)]\tLoss: 0.539956, Accuracy: 81.64\n",
      "Train Epoch: 15 [28160/50000 (62%)]\tLoss: 0.516339, Accuracy: 83.59\n",
      "Train Epoch: 15 [29440/50000 (65%)]\tLoss: 0.617898, Accuracy: 78.91\n",
      "Train Epoch: 15 [30720/50000 (68%)]\tLoss: 0.674637, Accuracy: 78.52\n",
      "Train Epoch: 15 [32000/50000 (71%)]\tLoss: 0.559300, Accuracy: 79.30\n",
      "Train Epoch: 15 [33280/50000 (74%)]\tLoss: 0.496566, Accuracy: 84.38\n",
      "Train Epoch: 15 [34560/50000 (77%)]\tLoss: 0.591234, Accuracy: 83.20\n",
      "Train Epoch: 15 [35840/50000 (80%)]\tLoss: 0.591112, Accuracy: 80.08\n",
      "Train Epoch: 15 [37120/50000 (82%)]\tLoss: 0.579010, Accuracy: 79.30\n",
      "Train Epoch: 15 [38400/50000 (85%)]\tLoss: 0.439274, Accuracy: 85.16\n",
      "Train Epoch: 15 [39680/50000 (88%)]\tLoss: 0.471660, Accuracy: 83.98\n",
      "Train Epoch: 15 [40960/50000 (91%)]\tLoss: 0.588916, Accuracy: 80.08\n",
      "Train Epoch: 15 [42240/50000 (94%)]\tLoss: 0.482828, Accuracy: 83.20\n",
      "Train Epoch: 15 [43520/50000 (97%)]\tLoss: 0.469595, Accuracy: 85.55\n",
      "Train Epoch: 15 [35000/50000 (99%)]\tLoss: 0.483570, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.6565, Accuracy: 3915/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[38.87792253494263 s]\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 0.443257, Accuracy: 85.16\n",
      "Train Epoch: 16 [1280/50000 (3%)]\tLoss: 0.379538, Accuracy: 85.55\n",
      "Train Epoch: 16 [2560/50000 (6%)]\tLoss: 0.489889, Accuracy: 80.47\n",
      "Train Epoch: 16 [3840/50000 (9%)]\tLoss: 0.495066, Accuracy: 81.64\n",
      "Train Epoch: 16 [5120/50000 (11%)]\tLoss: 0.457159, Accuracy: 85.55\n",
      "Train Epoch: 16 [6400/50000 (14%)]\tLoss: 0.480863, Accuracy: 83.59\n",
      "Train Epoch: 16 [7680/50000 (17%)]\tLoss: 0.566079, Accuracy: 81.64\n",
      "Train Epoch: 16 [8960/50000 (20%)]\tLoss: 0.510303, Accuracy: 83.98\n",
      "Train Epoch: 16 [10240/50000 (23%)]\tLoss: 0.499914, Accuracy: 81.25\n",
      "Train Epoch: 16 [11520/50000 (26%)]\tLoss: 0.519719, Accuracy: 80.86\n",
      "Train Epoch: 16 [12800/50000 (28%)]\tLoss: 0.478924, Accuracy: 82.42\n",
      "Train Epoch: 16 [14080/50000 (31%)]\tLoss: 0.462343, Accuracy: 85.16\n",
      "Train Epoch: 16 [15360/50000 (34%)]\tLoss: 0.625895, Accuracy: 80.86\n",
      "Train Epoch: 16 [16640/50000 (37%)]\tLoss: 0.577300, Accuracy: 81.25\n",
      "Train Epoch: 16 [17920/50000 (40%)]\tLoss: 0.572561, Accuracy: 79.69\n",
      "Train Epoch: 16 [19200/50000 (43%)]\tLoss: 0.481916, Accuracy: 83.20\n",
      "Train Epoch: 16 [20480/50000 (45%)]\tLoss: 0.477772, Accuracy: 82.42\n",
      "Train Epoch: 16 [21760/50000 (48%)]\tLoss: 0.556343, Accuracy: 78.52\n",
      "Train Epoch: 16 [23040/50000 (51%)]\tLoss: 0.523838, Accuracy: 78.91\n",
      "Train Epoch: 16 [24320/50000 (54%)]\tLoss: 0.507112, Accuracy: 82.81\n",
      "Train Epoch: 16 [25600/50000 (57%)]\tLoss: 0.588939, Accuracy: 80.47\n",
      "Train Epoch: 16 [26880/50000 (60%)]\tLoss: 0.483488, Accuracy: 83.98\n",
      "Train Epoch: 16 [28160/50000 (62%)]\tLoss: 0.526391, Accuracy: 81.64\n",
      "Train Epoch: 16 [29440/50000 (65%)]\tLoss: 0.502539, Accuracy: 83.59\n",
      "Train Epoch: 16 [30720/50000 (68%)]\tLoss: 0.501383, Accuracy: 83.98\n",
      "Train Epoch: 16 [32000/50000 (71%)]\tLoss: 0.489153, Accuracy: 84.77\n",
      "Train Epoch: 16 [33280/50000 (74%)]\tLoss: 0.543605, Accuracy: 78.52\n",
      "Train Epoch: 16 [34560/50000 (77%)]\tLoss: 0.430207, Accuracy: 86.72\n",
      "Train Epoch: 16 [35840/50000 (80%)]\tLoss: 0.497178, Accuracy: 82.81\n",
      "Train Epoch: 16 [37120/50000 (82%)]\tLoss: 0.537179, Accuracy: 83.20\n",
      "Train Epoch: 16 [38400/50000 (85%)]\tLoss: 0.484838, Accuracy: 84.77\n",
      "Train Epoch: 16 [39680/50000 (88%)]\tLoss: 0.505340, Accuracy: 83.98\n",
      "Train Epoch: 16 [40960/50000 (91%)]\tLoss: 0.485365, Accuracy: 82.42\n",
      "Train Epoch: 16 [42240/50000 (94%)]\tLoss: 0.655810, Accuracy: 78.12\n",
      "Train Epoch: 16 [43520/50000 (97%)]\tLoss: 0.548480, Accuracy: 78.12\n",
      "Train Epoch: 16 [35000/50000 (99%)]\tLoss: 0.551124, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.7486, Accuracy: 3740/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[35.87185502052307 s]\n",
      "\n",
      "Test set: Average loss: 0.7785, Accuracy: 7460/10000 (74.60%)\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 0.488764, Accuracy: 83.59\n",
      "Train Epoch: 17 [1280/50000 (3%)]\tLoss: 0.464160, Accuracy: 85.55\n",
      "Train Epoch: 17 [2560/50000 (6%)]\tLoss: 0.462434, Accuracy: 85.16\n",
      "Train Epoch: 17 [3840/50000 (9%)]\tLoss: 0.457506, Accuracy: 84.77\n",
      "Train Epoch: 17 [5120/50000 (11%)]\tLoss: 0.514120, Accuracy: 83.20\n",
      "Train Epoch: 17 [6400/50000 (14%)]\tLoss: 0.425605, Accuracy: 85.16\n",
      "Train Epoch: 17 [7680/50000 (17%)]\tLoss: 0.427497, Accuracy: 87.50\n",
      "Train Epoch: 17 [8960/50000 (20%)]\tLoss: 0.465372, Accuracy: 80.47\n",
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      "Train Epoch: 17 [11520/50000 (26%)]\tLoss: 0.477925, Accuracy: 82.81\n",
      "Train Epoch: 17 [12800/50000 (28%)]\tLoss: 0.560330, Accuracy: 80.47\n",
      "Train Epoch: 17 [14080/50000 (31%)]\tLoss: 0.474052, Accuracy: 84.38\n",
      "Train Epoch: 17 [15360/50000 (34%)]\tLoss: 0.495061, Accuracy: 85.55\n",
      "Train Epoch: 17 [16640/50000 (37%)]\tLoss: 0.468432, Accuracy: 84.77\n",
      "Train Epoch: 17 [17920/50000 (40%)]\tLoss: 0.516050, Accuracy: 83.98\n",
      "Train Epoch: 17 [19200/50000 (43%)]\tLoss: 0.483799, Accuracy: 83.98\n",
      "Train Epoch: 17 [20480/50000 (45%)]\tLoss: 0.615572, Accuracy: 78.91\n",
      "Train Epoch: 17 [21760/50000 (48%)]\tLoss: 0.435921, Accuracy: 85.94\n",
      "Train Epoch: 17 [23040/50000 (51%)]\tLoss: 0.544908, Accuracy: 83.98\n",
      "Train Epoch: 17 [24320/50000 (54%)]\tLoss: 0.533143, Accuracy: 83.59\n",
      "Train Epoch: 17 [25600/50000 (57%)]\tLoss: 0.543789, Accuracy: 82.81\n",
      "Train Epoch: 17 [26880/50000 (60%)]\tLoss: 0.413327, Accuracy: 85.55\n",
      "Train Epoch: 17 [28160/50000 (62%)]\tLoss: 0.559485, Accuracy: 81.64\n",
      "Train Epoch: 17 [29440/50000 (65%)]\tLoss: 0.548759, Accuracy: 80.47\n",
      "Train Epoch: 17 [30720/50000 (68%)]\tLoss: 0.544382, Accuracy: 82.03\n",
      "Train Epoch: 17 [32000/50000 (71%)]\tLoss: 0.536586, Accuracy: 81.25\n",
      "Train Epoch: 17 [33280/50000 (74%)]\tLoss: 0.461494, Accuracy: 84.77\n",
      "Train Epoch: 17 [34560/50000 (77%)]\tLoss: 0.469552, Accuracy: 84.38\n",
      "Train Epoch: 17 [35840/50000 (80%)]\tLoss: 0.544189, Accuracy: 81.64\n",
      "Train Epoch: 17 [37120/50000 (82%)]\tLoss: 0.485712, Accuracy: 86.33\n",
      "Train Epoch: 17 [38400/50000 (85%)]\tLoss: 0.522568, Accuracy: 80.86\n",
      "Train Epoch: 17 [39680/50000 (88%)]\tLoss: 0.488267, Accuracy: 83.98\n",
      "Train Epoch: 17 [40960/50000 (91%)]\tLoss: 0.486900, Accuracy: 85.16\n",
      "Train Epoch: 17 [42240/50000 (94%)]\tLoss: 0.470605, Accuracy: 84.77\n",
      "Train Epoch: 17 [43520/50000 (97%)]\tLoss: 0.423630, Accuracy: 84.38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 17 [35000/50000 (99%)]\tLoss: 0.622972, Accuracy: 78.50\n",
      "\n",
      "Validation set: Average loss: 1.4088, Accuracy: 3082/5000 (61.00%)\n",
      "\n",
      "the time of this epoch:[38.95738887786865 s]\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.375126, Accuracy: 87.50\n",
      "Train Epoch: 18 [1280/50000 (3%)]\tLoss: 0.453524, Accuracy: 86.33\n",
      "Train Epoch: 18 [2560/50000 (6%)]\tLoss: 0.482831, Accuracy: 83.20\n",
      "Train Epoch: 18 [3840/50000 (9%)]\tLoss: 0.357505, Accuracy: 86.72\n",
      "Train Epoch: 18 [5120/50000 (11%)]\tLoss: 0.390622, Accuracy: 88.28\n",
      "Train Epoch: 18 [6400/50000 (14%)]\tLoss: 0.415037, Accuracy: 85.16\n",
      "Train Epoch: 18 [7680/50000 (17%)]\tLoss: 0.437908, Accuracy: 85.94\n",
      "Train Epoch: 18 [8960/50000 (20%)]\tLoss: 0.510512, Accuracy: 81.64\n",
      "Train Epoch: 18 [10240/50000 (23%)]\tLoss: 0.490303, Accuracy: 83.98\n",
      "Train Epoch: 18 [11520/50000 (26%)]\tLoss: 0.483492, Accuracy: 82.03\n",
      "Train Epoch: 18 [12800/50000 (28%)]\tLoss: 0.389920, Accuracy: 87.11\n",
      "Train Epoch: 18 [14080/50000 (31%)]\tLoss: 0.351586, Accuracy: 89.06\n",
      "Train Epoch: 18 [15360/50000 (34%)]\tLoss: 0.467861, Accuracy: 83.98\n",
      "Train Epoch: 18 [16640/50000 (37%)]\tLoss: 0.536819, Accuracy: 82.42\n",
      "Train Epoch: 18 [17920/50000 (40%)]\tLoss: 0.533176, Accuracy: 80.47\n",
      "Train Epoch: 18 [19200/50000 (43%)]\tLoss: 0.554016, Accuracy: 81.64\n",
      "Train Epoch: 18 [20480/50000 (45%)]\tLoss: 0.478496, Accuracy: 83.98\n",
      "Train Epoch: 18 [21760/50000 (48%)]\tLoss: 0.491672, Accuracy: 84.77\n",
      "Train Epoch: 18 [23040/50000 (51%)]\tLoss: 0.415506, Accuracy: 86.72\n",
      "Train Epoch: 18 [24320/50000 (54%)]\tLoss: 0.490170, Accuracy: 85.55\n",
      "Train Epoch: 18 [25600/50000 (57%)]\tLoss: 0.449299, Accuracy: 85.16\n",
      "Train Epoch: 18 [26880/50000 (60%)]\tLoss: 0.529814, Accuracy: 82.81\n",
      "Train Epoch: 18 [28160/50000 (62%)]\tLoss: 0.517376, Accuracy: 82.81\n",
      "Train Epoch: 18 [29440/50000 (65%)]\tLoss: 0.604133, Accuracy: 81.25\n",
      "Train Epoch: 18 [30720/50000 (68%)]\tLoss: 0.462762, Accuracy: 87.11\n",
      "Train Epoch: 18 [32000/50000 (71%)]\tLoss: 0.496592, Accuracy: 80.08\n",
      "Train Epoch: 18 [33280/50000 (74%)]\tLoss: 0.550860, Accuracy: 82.42\n",
      "Train Epoch: 18 [34560/50000 (77%)]\tLoss: 0.489021, Accuracy: 82.81\n",
      "Train Epoch: 18 [35840/50000 (80%)]\tLoss: 0.606295, Accuracy: 79.69\n",
      "Train Epoch: 18 [37120/50000 (82%)]\tLoss: 0.475584, Accuracy: 83.20\n",
      "Train Epoch: 18 [38400/50000 (85%)]\tLoss: 0.512497, Accuracy: 81.64\n",
      "Train Epoch: 18 [39680/50000 (88%)]\tLoss: 0.535595, Accuracy: 82.81\n",
      "Train Epoch: 18 [40960/50000 (91%)]\tLoss: 0.520552, Accuracy: 82.42\n",
      "Train Epoch: 18 [42240/50000 (94%)]\tLoss: 0.425681, Accuracy: 87.11\n",
      "Train Epoch: 18 [43520/50000 (97%)]\tLoss: 0.480187, Accuracy: 85.16\n",
      "Train Epoch: 18 [35000/50000 (99%)]\tLoss: 0.521554, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 0.6029, Accuracy: 3996/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[35.93850064277649 s]\n",
      "\n",
      "Test set: Average loss: 0.6428, Accuracy: 7873/10000 (78.73%)\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 0.396641, Accuracy: 86.72\n",
      "Train Epoch: 19 [1280/50000 (3%)]\tLoss: 0.477869, Accuracy: 85.55\n",
      "Train Epoch: 19 [2560/50000 (6%)]\tLoss: 0.483225, Accuracy: 87.89\n",
      "Train Epoch: 19 [3840/50000 (9%)]\tLoss: 0.373763, Accuracy: 87.50\n",
      "Train Epoch: 19 [5120/50000 (11%)]\tLoss: 0.522826, Accuracy: 83.98\n",
      "Train Epoch: 19 [6400/50000 (14%)]\tLoss: 0.494100, Accuracy: 84.38\n",
      "Train Epoch: 19 [7680/50000 (17%)]\tLoss: 0.476989, Accuracy: 83.59\n",
      "Train Epoch: 19 [8960/50000 (20%)]\tLoss: 0.502529, Accuracy: 83.59\n",
      "Train Epoch: 19 [10240/50000 (23%)]\tLoss: 0.527058, Accuracy: 81.25\n",
      "Train Epoch: 19 [11520/50000 (26%)]\tLoss: 0.485557, Accuracy: 83.59\n",
      "Train Epoch: 19 [12800/50000 (28%)]\tLoss: 0.523241, Accuracy: 80.47\n",
      "Train Epoch: 19 [14080/50000 (31%)]\tLoss: 0.530676, Accuracy: 85.16\n",
      "Train Epoch: 19 [15360/50000 (34%)]\tLoss: 0.446896, Accuracy: 85.16\n",
      "Train Epoch: 19 [16640/50000 (37%)]\tLoss: 0.509379, Accuracy: 85.16\n",
      "Train Epoch: 19 [17920/50000 (40%)]\tLoss: 0.571199, Accuracy: 80.08\n",
      "Train Epoch: 19 [19200/50000 (43%)]\tLoss: 0.560176, Accuracy: 80.08\n",
      "Train Epoch: 19 [20480/50000 (45%)]\tLoss: 0.560568, Accuracy: 80.86\n",
      "Train Epoch: 19 [21760/50000 (48%)]\tLoss: 0.409704, Accuracy: 88.28\n",
      "Train Epoch: 19 [23040/50000 (51%)]\tLoss: 0.422006, Accuracy: 85.55\n",
      "Train Epoch: 19 [24320/50000 (54%)]\tLoss: 0.494678, Accuracy: 81.25\n",
      "Train Epoch: 19 [25600/50000 (57%)]\tLoss: 0.453219, Accuracy: 87.50\n",
      "Train Epoch: 19 [26880/50000 (60%)]\tLoss: 0.455455, Accuracy: 84.77\n",
      "Train Epoch: 19 [28160/50000 (62%)]\tLoss: 0.587407, Accuracy: 82.42\n",
      "Train Epoch: 19 [29440/50000 (65%)]\tLoss: 0.455050, Accuracy: 83.20\n",
      "Train Epoch: 19 [30720/50000 (68%)]\tLoss: 0.497181, Accuracy: 80.86\n",
      "Train Epoch: 19 [32000/50000 (71%)]\tLoss: 0.427429, Accuracy: 86.33\n",
      "Train Epoch: 19 [33280/50000 (74%)]\tLoss: 0.464149, Accuracy: 84.77\n",
      "Train Epoch: 19 [34560/50000 (77%)]\tLoss: 0.503061, Accuracy: 82.81\n",
      "Train Epoch: 19 [35840/50000 (80%)]\tLoss: 0.495823, Accuracy: 82.81\n",
      "Train Epoch: 19 [37120/50000 (82%)]\tLoss: 0.444423, Accuracy: 84.77\n",
      "Train Epoch: 19 [38400/50000 (85%)]\tLoss: 0.587800, Accuracy: 81.25\n",
      "Train Epoch: 19 [39680/50000 (88%)]\tLoss: 0.555754, Accuracy: 80.86\n",
      "Train Epoch: 19 [40960/50000 (91%)]\tLoss: 0.467440, Accuracy: 83.20\n",
      "Train Epoch: 19 [42240/50000 (94%)]\tLoss: 0.466168, Accuracy: 84.38\n",
      "Train Epoch: 19 [43520/50000 (97%)]\tLoss: 0.559211, Accuracy: 80.08\n",
      "Train Epoch: 19 [35000/50000 (99%)]\tLoss: 0.468720, Accuracy: 83.50\n",
      "\n",
      "Validation set: Average loss: 0.8214, Accuracy: 3710/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[38.911999464035034 s]\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 0.438433, Accuracy: 86.33\n",
      "Train Epoch: 20 [1280/50000 (3%)]\tLoss: 0.536375, Accuracy: 80.86\n",
      "Train Epoch: 20 [2560/50000 (6%)]\tLoss: 0.524844, Accuracy: 83.59\n",
      "Train Epoch: 20 [3840/50000 (9%)]\tLoss: 0.392575, Accuracy: 87.89\n",
      "Train Epoch: 20 [5120/50000 (11%)]\tLoss: 0.535325, Accuracy: 78.91\n",
      "Train Epoch: 20 [6400/50000 (14%)]\tLoss: 0.413692, Accuracy: 84.38\n",
      "Train Epoch: 20 [7680/50000 (17%)]\tLoss: 0.391061, Accuracy: 85.55\n",
      "Train Epoch: 20 [8960/50000 (20%)]\tLoss: 0.364706, Accuracy: 84.77\n",
      "Train Epoch: 20 [10240/50000 (23%)]\tLoss: 0.385017, Accuracy: 85.94\n",
      "Train Epoch: 20 [11520/50000 (26%)]\tLoss: 0.463457, Accuracy: 83.98\n",
      "Train Epoch: 20 [12800/50000 (28%)]\tLoss: 0.475856, Accuracy: 85.94\n",
      "Train Epoch: 20 [14080/50000 (31%)]\tLoss: 0.452919, Accuracy: 85.16\n",
      "Train Epoch: 20 [15360/50000 (34%)]\tLoss: 0.462932, Accuracy: 83.59\n",
      "Train Epoch: 20 [16640/50000 (37%)]\tLoss: 0.585895, Accuracy: 78.52\n",
      "Train Epoch: 20 [17920/50000 (40%)]\tLoss: 0.405522, Accuracy: 84.38\n",
      "Train Epoch: 20 [19200/50000 (43%)]\tLoss: 0.487947, Accuracy: 85.16\n",
      "Train Epoch: 20 [20480/50000 (45%)]\tLoss: 0.427455, Accuracy: 83.59\n",
      "Train Epoch: 20 [21760/50000 (48%)]\tLoss: 0.520714, Accuracy: 79.30\n",
      "Train Epoch: 20 [23040/50000 (51%)]\tLoss: 0.483166, Accuracy: 85.16\n",
      "Train Epoch: 20 [24320/50000 (54%)]\tLoss: 0.462873, Accuracy: 84.38\n",
      "Train Epoch: 20 [25600/50000 (57%)]\tLoss: 0.469987, Accuracy: 84.77\n",
      "Train Epoch: 20 [26880/50000 (60%)]\tLoss: 0.485182, Accuracy: 82.81\n",
      "Train Epoch: 20 [28160/50000 (62%)]\tLoss: 0.464687, Accuracy: 83.59\n",
      "Train Epoch: 20 [29440/50000 (65%)]\tLoss: 0.466139, Accuracy: 83.98\n",
      "Train Epoch: 20 [30720/50000 (68%)]\tLoss: 0.449432, Accuracy: 86.72\n",
      "Train Epoch: 20 [32000/50000 (71%)]\tLoss: 0.456024, Accuracy: 82.42\n",
      "Train Epoch: 20 [33280/50000 (74%)]\tLoss: 0.440877, Accuracy: 84.77\n",
      "Train Epoch: 20 [34560/50000 (77%)]\tLoss: 0.525033, Accuracy: 82.42\n",
      "Train Epoch: 20 [35840/50000 (80%)]\tLoss: 0.413531, Accuracy: 85.16\n",
      "Train Epoch: 20 [37120/50000 (82%)]\tLoss: 0.403975, Accuracy: 87.89\n",
      "Train Epoch: 20 [38400/50000 (85%)]\tLoss: 0.363782, Accuracy: 87.89\n",
      "Train Epoch: 20 [39680/50000 (88%)]\tLoss: 0.486890, Accuracy: 83.59\n",
      "Train Epoch: 20 [40960/50000 (91%)]\tLoss: 0.462478, Accuracy: 87.11\n",
      "Train Epoch: 20 [42240/50000 (94%)]\tLoss: 0.498864, Accuracy: 83.20\n",
      "Train Epoch: 20 [43520/50000 (97%)]\tLoss: 0.530455, Accuracy: 82.81\n",
      "Train Epoch: 20 [35000/50000 (99%)]\tLoss: 0.491784, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.6187, Accuracy: 4004/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[35.64623045921326 s]\n",
      "\n",
      "Test set: Average loss: 0.6210, Accuracy: 7954/10000 (79.54%)\n",
      "\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 0.407519, Accuracy: 85.94\n",
      "Train Epoch: 21 [1280/50000 (3%)]\tLoss: 0.438614, Accuracy: 85.16\n",
      "Train Epoch: 21 [2560/50000 (6%)]\tLoss: 0.475976, Accuracy: 82.81\n",
      "Train Epoch: 21 [3840/50000 (9%)]\tLoss: 0.400869, Accuracy: 88.28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 21 [5120/50000 (11%)]\tLoss: 0.410871, Accuracy: 86.72\n",
      "Train Epoch: 21 [6400/50000 (14%)]\tLoss: 0.469544, Accuracy: 85.55\n",
      "Train Epoch: 21 [7680/50000 (17%)]\tLoss: 0.408407, Accuracy: 86.33\n",
      "Train Epoch: 21 [8960/50000 (20%)]\tLoss: 0.596469, Accuracy: 79.30\n",
      "Train Epoch: 21 [10240/50000 (23%)]\tLoss: 0.460763, Accuracy: 85.55\n",
      "Train Epoch: 21 [11520/50000 (26%)]\tLoss: 0.445019, Accuracy: 85.94\n",
      "Train Epoch: 21 [12800/50000 (28%)]\tLoss: 0.548675, Accuracy: 80.47\n",
      "Train Epoch: 21 [14080/50000 (31%)]\tLoss: 0.463139, Accuracy: 85.55\n",
      "Train Epoch: 21 [15360/50000 (34%)]\tLoss: 0.448365, Accuracy: 85.55\n",
      "Train Epoch: 21 [16640/50000 (37%)]\tLoss: 0.452142, Accuracy: 83.98\n",
      "Train Epoch: 21 [17920/50000 (40%)]\tLoss: 0.469326, Accuracy: 83.59\n",
      "Train Epoch: 21 [19200/50000 (43%)]\tLoss: 0.448576, Accuracy: 84.38\n",
      "Train Epoch: 21 [20480/50000 (45%)]\tLoss: 0.347341, Accuracy: 87.89\n",
      "Train Epoch: 21 [21760/50000 (48%)]\tLoss: 0.631081, Accuracy: 77.34\n",
      "Train Epoch: 21 [23040/50000 (51%)]\tLoss: 0.469695, Accuracy: 84.38\n",
      "Train Epoch: 21 [24320/50000 (54%)]\tLoss: 0.429938, Accuracy: 83.98\n",
      "Train Epoch: 21 [25600/50000 (57%)]\tLoss: 0.396000, Accuracy: 85.16\n",
      "Train Epoch: 21 [26880/50000 (60%)]\tLoss: 0.427269, Accuracy: 86.33\n",
      "Train Epoch: 21 [28160/50000 (62%)]\tLoss: 0.459689, Accuracy: 85.16\n",
      "Train Epoch: 21 [29440/50000 (65%)]\tLoss: 0.456435, Accuracy: 84.77\n",
      "Train Epoch: 21 [30720/50000 (68%)]\tLoss: 0.510014, Accuracy: 82.03\n",
      "Train Epoch: 21 [32000/50000 (71%)]\tLoss: 0.484679, Accuracy: 82.42\n",
      "Train Epoch: 21 [33280/50000 (74%)]\tLoss: 0.409507, Accuracy: 87.11\n",
      "Train Epoch: 21 [34560/50000 (77%)]\tLoss: 0.360498, Accuracy: 88.28\n",
      "Train Epoch: 21 [35840/50000 (80%)]\tLoss: 0.404884, Accuracy: 87.11\n",
      "Train Epoch: 21 [37120/50000 (82%)]\tLoss: 0.464280, Accuracy: 84.77\n",
      "Train Epoch: 21 [38400/50000 (85%)]\tLoss: 0.413739, Accuracy: 83.59\n",
      "Train Epoch: 21 [39680/50000 (88%)]\tLoss: 0.529101, Accuracy: 80.47\n",
      "Train Epoch: 21 [40960/50000 (91%)]\tLoss: 0.408458, Accuracy: 87.50\n",
      "Train Epoch: 21 [42240/50000 (94%)]\tLoss: 0.444520, Accuracy: 83.59\n",
      "Train Epoch: 21 [43520/50000 (97%)]\tLoss: 0.551465, Accuracy: 81.64\n",
      "Train Epoch: 21 [35000/50000 (99%)]\tLoss: 0.408861, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6720, Accuracy: 3872/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[38.85717034339905 s]\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 0.409561, Accuracy: 86.33\n",
      "Train Epoch: 22 [1280/50000 (3%)]\tLoss: 0.436338, Accuracy: 85.94\n",
      "Train Epoch: 22 [2560/50000 (6%)]\tLoss: 0.497547, Accuracy: 82.42\n",
      "Train Epoch: 22 [3840/50000 (9%)]\tLoss: 0.455231, Accuracy: 84.38\n",
      "Train Epoch: 22 [5120/50000 (11%)]\tLoss: 0.437750, Accuracy: 85.16\n",
      "Train Epoch: 22 [6400/50000 (14%)]\tLoss: 0.429277, Accuracy: 83.20\n",
      "Train Epoch: 22 [7680/50000 (17%)]\tLoss: 0.381336, Accuracy: 86.72\n",
      "Train Epoch: 22 [8960/50000 (20%)]\tLoss: 0.413823, Accuracy: 87.11\n",
      "Train Epoch: 22 [10240/50000 (23%)]\tLoss: 0.400985, Accuracy: 86.72\n",
      "Train Epoch: 22 [11520/50000 (26%)]\tLoss: 0.494495, Accuracy: 82.81\n",
      "Train Epoch: 22 [12800/50000 (28%)]\tLoss: 0.396870, Accuracy: 88.67\n",
      "Train Epoch: 22 [14080/50000 (31%)]\tLoss: 0.540015, Accuracy: 80.08\n",
      "Train Epoch: 22 [15360/50000 (34%)]\tLoss: 0.401111, Accuracy: 88.67\n",
      "Train Epoch: 22 [16640/50000 (37%)]\tLoss: 0.488136, Accuracy: 83.98\n",
      "Train Epoch: 22 [17920/50000 (40%)]\tLoss: 0.437139, Accuracy: 84.77\n",
      "Train Epoch: 22 [19200/50000 (43%)]\tLoss: 0.543233, Accuracy: 82.81\n",
      "Train Epoch: 22 [20480/50000 (45%)]\tLoss: 0.516708, Accuracy: 81.25\n",
      "Train Epoch: 22 [21760/50000 (48%)]\tLoss: 0.401448, Accuracy: 85.94\n",
      "Train Epoch: 22 [23040/50000 (51%)]\tLoss: 0.543015, Accuracy: 82.03\n",
      "Train Epoch: 22 [24320/50000 (54%)]\tLoss: 0.432294, Accuracy: 83.20\n",
      "Train Epoch: 22 [25600/50000 (57%)]\tLoss: 0.444171, Accuracy: 85.16\n",
      "Train Epoch: 22 [26880/50000 (60%)]\tLoss: 0.366297, Accuracy: 87.50\n",
      "Train Epoch: 22 [28160/50000 (62%)]\tLoss: 0.488688, Accuracy: 83.59\n",
      "Train Epoch: 22 [29440/50000 (65%)]\tLoss: 0.433488, Accuracy: 85.16\n",
      "Train Epoch: 22 [30720/50000 (68%)]\tLoss: 0.382361, Accuracy: 87.50\n",
      "Train Epoch: 22 [32000/50000 (71%)]\tLoss: 0.478428, Accuracy: 85.55\n",
      "Train Epoch: 22 [33280/50000 (74%)]\tLoss: 0.435869, Accuracy: 84.38\n",
      "Train Epoch: 22 [34560/50000 (77%)]\tLoss: 0.473880, Accuracy: 85.16\n",
      "Train Epoch: 22 [35840/50000 (80%)]\tLoss: 0.472249, Accuracy: 82.81\n",
      "Train Epoch: 22 [37120/50000 (82%)]\tLoss: 0.442673, Accuracy: 83.98\n",
      "Train Epoch: 22 [38400/50000 (85%)]\tLoss: 0.426854, Accuracy: 86.33\n",
      "Train Epoch: 22 [39680/50000 (88%)]\tLoss: 0.407790, Accuracy: 86.72\n",
      "Train Epoch: 22 [40960/50000 (91%)]\tLoss: 0.535022, Accuracy: 81.64\n",
      "Train Epoch: 22 [42240/50000 (94%)]\tLoss: 0.471225, Accuracy: 83.20\n",
      "Train Epoch: 22 [43520/50000 (97%)]\tLoss: 0.413265, Accuracy: 84.77\n",
      "Train Epoch: 22 [35000/50000 (99%)]\tLoss: 0.507049, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.6931, Accuracy: 3877/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[35.77128553390503 s]\n",
      "\n",
      "Test set: Average loss: 0.7387, Accuracy: 7683/10000 (76.83%)\n",
      "\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.470724, Accuracy: 83.20\n",
      "Train Epoch: 23 [1280/50000 (3%)]\tLoss: 0.402162, Accuracy: 88.67\n",
      "Train Epoch: 23 [2560/50000 (6%)]\tLoss: 0.424422, Accuracy: 85.55\n",
      "Train Epoch: 23 [3840/50000 (9%)]\tLoss: 0.447638, Accuracy: 85.94\n",
      "Train Epoch: 23 [5120/50000 (11%)]\tLoss: 0.389921, Accuracy: 87.50\n",
      "Train Epoch: 23 [6400/50000 (14%)]\tLoss: 0.403435, Accuracy: 85.16\n",
      "Train Epoch: 23 [7680/50000 (17%)]\tLoss: 0.425962, Accuracy: 85.16\n",
      "Train Epoch: 23 [8960/50000 (20%)]\tLoss: 0.454521, Accuracy: 86.33\n",
      "Train Epoch: 23 [10240/50000 (23%)]\tLoss: 0.410412, Accuracy: 84.77\n",
      "Train Epoch: 23 [11520/50000 (26%)]\tLoss: 0.473033, Accuracy: 84.38\n",
      "Train Epoch: 23 [12800/50000 (28%)]\tLoss: 0.474430, Accuracy: 83.98\n",
      "Train Epoch: 23 [14080/50000 (31%)]\tLoss: 0.419194, Accuracy: 83.59\n",
      "Train Epoch: 23 [15360/50000 (34%)]\tLoss: 0.451212, Accuracy: 83.59\n",
      "Train Epoch: 23 [16640/50000 (37%)]\tLoss: 0.398862, Accuracy: 85.94\n",
      "Train Epoch: 23 [17920/50000 (40%)]\tLoss: 0.430038, Accuracy: 85.16\n",
      "Train Epoch: 23 [19200/50000 (43%)]\tLoss: 0.522455, Accuracy: 82.81\n",
      "Train Epoch: 23 [20480/50000 (45%)]\tLoss: 0.428744, Accuracy: 85.94\n",
      "Train Epoch: 23 [21760/50000 (48%)]\tLoss: 0.457418, Accuracy: 83.98\n",
      "Train Epoch: 23 [23040/50000 (51%)]\tLoss: 0.453664, Accuracy: 84.38\n",
      "Train Epoch: 23 [24320/50000 (54%)]\tLoss: 0.447705, Accuracy: 83.98\n",
      "Train Epoch: 23 [25600/50000 (57%)]\tLoss: 0.303673, Accuracy: 89.06\n",
      "Train Epoch: 23 [26880/50000 (60%)]\tLoss: 0.453204, Accuracy: 84.77\n",
      "Train Epoch: 23 [28160/50000 (62%)]\tLoss: 0.529173, Accuracy: 83.59\n",
      "Train Epoch: 23 [29440/50000 (65%)]\tLoss: 0.539218, Accuracy: 80.08\n",
      "Train Epoch: 23 [30720/50000 (68%)]\tLoss: 0.418650, Accuracy: 86.72\n",
      "Train Epoch: 23 [32000/50000 (71%)]\tLoss: 0.398899, Accuracy: 88.67\n",
      "Train Epoch: 23 [33280/50000 (74%)]\tLoss: 0.497756, Accuracy: 83.98\n",
      "Train Epoch: 23 [34560/50000 (77%)]\tLoss: 0.480398, Accuracy: 82.81\n",
      "Train Epoch: 23 [35840/50000 (80%)]\tLoss: 0.460029, Accuracy: 85.16\n",
      "Train Epoch: 23 [37120/50000 (82%)]\tLoss: 0.431694, Accuracy: 87.11\n",
      "Train Epoch: 23 [38400/50000 (85%)]\tLoss: 0.436066, Accuracy: 83.20\n",
      "Train Epoch: 23 [39680/50000 (88%)]\tLoss: 0.501731, Accuracy: 84.38\n",
      "Train Epoch: 23 [40960/50000 (91%)]\tLoss: 0.538046, Accuracy: 82.03\n",
      "Train Epoch: 23 [42240/50000 (94%)]\tLoss: 0.399736, Accuracy: 86.72\n",
      "Train Epoch: 23 [43520/50000 (97%)]\tLoss: 0.496291, Accuracy: 85.16\n",
      "Train Epoch: 23 [35000/50000 (99%)]\tLoss: 0.397542, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7235, Accuracy: 3842/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[38.948015213012695 s]\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.379143, Accuracy: 87.89\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[-0.9598]],\n",
      "\n",
      "        [[-0.5745]],\n",
      "\n",
      "        [[ 1.3494]],\n",
      "\n",
      "        [[-1.3956]],\n",
      "\n",
      "        [[-0.9635]],\n",
      "\n",
      "        [[-0.5036]],\n",
      "\n",
      "        [[-1.9124]],\n",
      "\n",
      "        [[ 0.8781]],\n",
      "\n",
      "        [[ 0.8296]],\n",
      "\n",
      "        [[-1.4861]],\n",
      "\n",
      "        [[ 1.7118]],\n",
      "\n",
      "        [[ 0.5358]],\n",
      "\n",
      "        [[-0.0214]],\n",
      "\n",
      "        [[-0.4931]],\n",
      "\n",
      "        [[ 1.1956]],\n",
      "\n",
      "        [[-0.8141]],\n",
      "\n",
      "        [[-1.8275]],\n",
      "\n",
      "        [[-0.0886]],\n",
      "\n",
      "        [[ 0.1175]],\n",
      "\n",
      "        [[-0.5751]],\n",
      "\n",
      "        [[ 0.2064]],\n",
      "\n",
      "        [[-1.5297]],\n",
      "\n",
      "        [[-2.7173]],\n",
      "\n",
      "        [[ 0.6864]],\n",
      "\n",
      "        [[-1.7698]],\n",
      "\n",
      "        [[ 1.6250]],\n",
      "\n",
      "        [[-0.6583]],\n",
      "\n",
      "        [[ 1.0893]],\n",
      "\n",
      "        [[-2.5800]],\n",
      "\n",
      "        [[-0.3145]],\n",
      "\n",
      "        [[ 0.2180]],\n",
      "\n",
      "        [[-1.1678]],\n",
      "\n",
      "        [[ 2.9146]],\n",
      "\n",
      "        [[-1.5653]],\n",
      "\n",
      "        [[-0.0194]],\n",
      "\n",
      "        [[ 0.8869]],\n",
      "\n",
      "        [[ 0.1480]],\n",
      "\n",
      "        [[ 0.2970]],\n",
      "\n",
      "        [[-0.7111]],\n",
      "\n",
      "        [[ 0.3578]],\n",
      "\n",
      "        [[ 0.6137]],\n",
      "\n",
      "        [[-0.5392]],\n",
      "\n",
      "        [[-0.9387]],\n",
      "\n",
      "        [[-0.9076]],\n",
      "\n",
      "        [[-0.4077]],\n",
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      "        [[-0.5281]],\n",
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      "        [[-2.1744]],\n",
      "\n",
      "        [[ 0.7496]],\n",
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      "        [[-0.8710]],\n",
      "\n",
      "        [[-0.3977]],\n",
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      "        [[ 0.1794]],\n",
      "\n",
      "        [[ 1.5865]],\n",
      "\n",
      "        [[-0.5418]],\n",
      "\n",
      "        [[ 0.4106]],\n",
      "\n",
      "        [[-0.8265]],\n",
      "\n",
      "        [[ 1.0262]],\n",
      "\n",
      "        [[-0.4315]],\n",
      "\n",
      "        [[-1.7480]],\n",
      "\n",
      "        [[ 1.1593]],\n",
      "\n",
      "        [[-1.2112]],\n",
      "\n",
      "        [[ 0.8694]],\n",
      "\n",
      "        [[ 0.3176]],\n",
      "\n",
      "        [[-0.9078]],\n",
      "\n",
      "        [[-0.4253]],\n",
      "\n",
      "        [[-1.1038]],\n",
      "\n",
      "        [[-2.3071]],\n",
      "\n",
      "        [[-1.5710]],\n",
      "\n",
      "        [[ 1.4579]],\n",
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      "        [[-0.3820]],\n",
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      "        [[ 0.5802]],\n",
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      "        [[ 0.1724]],\n",
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      "        [[ 0.6335]],\n",
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      "        [[-0.1821]],\n",
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      "        [[ 0.9428]],\n",
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      "        [[ 0.5378]],\n",
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      "        [[-0.5272]],\n",
      "\n",
      "        [[ 0.2248]],\n",
      "\n",
      "        [[ 1.3113]],\n",
      "\n",
      "        [[-0.8883]],\n",
      "\n",
      "        [[ 0.5606]],\n",
      "\n",
      "        [[ 0.0718]],\n",
      "\n",
      "        [[-0.9555]],\n",
      "\n",
      "        [[ 1.4013]],\n",
      "\n",
      "        [[ 0.6503]],\n",
      "\n",
      "        [[-0.7776]],\n",
      "\n",
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      "        [[-0.3969]],\n",
      "\n",
      "        [[-0.8063]],\n",
      "\n",
      "        [[-0.1733]],\n",
      "\n",
      "        [[ 0.5946]],\n",
      "\n",
      "        [[-0.9496]],\n",
      "\n",
      "        [[-0.4563]],\n",
      "\n",
      "        [[ 2.3976]],\n",
      "\n",
      "        [[-0.1192]],\n",
      "\n",
      "        [[-0.2483]],\n",
      "\n",
      "        [[ 0.2018]],\n",
      "\n",
      "        [[-0.3859]],\n",
      "\n",
      "        [[ 0.9021]],\n",
      "\n",
      "        [[-0.0805]],\n",
      "\n",
      "        [[-0.2459]],\n",
      "\n",
      "        [[-1.7512]],\n",
      "\n",
      "        [[ 0.6119]],\n",
      "\n",
      "        [[ 0.1823]],\n",
      "\n",
      "        [[-0.7198]],\n",
      "\n",
      "        [[-1.4412]],\n",
      "\n",
      "        [[-0.1799]],\n",
      "\n",
      "        [[-0.1176]],\n",
      "\n",
      "        [[ 0.0165]],\n",
      "\n",
      "        [[ 0.1797]],\n",
      "\n",
      "        [[-0.2564]],\n",
      "\n",
      "        [[-0.7068]],\n",
      "\n",
      "        [[-2.6066]],\n",
      "\n",
      "        [[ 0.7977]],\n",
      "\n",
      "        [[-1.8183]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[-2.9698e-03,  1.3582e-02,  2.8033e-03,  ..., -1.5874e-03,\n",
      "           7.5978e-03, -3.4434e-04],\n",
      "         [-1.3871e-02,  7.0364e-03,  4.5467e-03,  ...,  1.1816e-02,\n",
      "           1.4083e-02,  5.7367e-03],\n",
      "         [ 5.6714e-03, -6.9452e-03,  7.2180e-03,  ..., -1.3733e-02,\n",
      "          -1.6438e-03,  3.8303e-04],\n",
      "         ...,\n",
      "         [-4.4669e-04, -1.4264e-03, -2.4254e-03,  ..., -1.1478e-02,\n",
      "          -7.6595e-03, -2.4677e-03],\n",
      "         [-5.3051e-03, -1.1749e-02,  3.7081e-03,  ...,  1.3891e-04,\n",
      "           3.1434e-03, -4.6308e-04],\n",
      "         [-1.1031e-02, -2.7082e-03,  5.5060e-03,  ..., -8.2862e-03,\n",
      "           8.1019e-03, -1.3808e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 24 [1280/50000 (3%)]\tLoss: 0.354077, Accuracy: 85.94\n",
      "Train Epoch: 24 [2560/50000 (6%)]\tLoss: 0.386844, Accuracy: 87.50\n",
      "Train Epoch: 24 [3840/50000 (9%)]\tLoss: 0.419282, Accuracy: 88.67\n",
      "Train Epoch: 24 [5120/50000 (11%)]\tLoss: 0.408030, Accuracy: 83.98\n",
      "Train Epoch: 24 [6400/50000 (14%)]\tLoss: 0.433054, Accuracy: 83.59\n",
      "Train Epoch: 24 [7680/50000 (17%)]\tLoss: 0.401839, Accuracy: 87.50\n",
      "Train Epoch: 24 [8960/50000 (20%)]\tLoss: 0.385073, Accuracy: 87.89\n",
      "Train Epoch: 24 [10240/50000 (23%)]\tLoss: 0.493798, Accuracy: 83.20\n",
      "Train Epoch: 24 [11520/50000 (26%)]\tLoss: 0.337925, Accuracy: 89.45\n",
      "Train Epoch: 24 [12800/50000 (28%)]\tLoss: 0.326849, Accuracy: 88.67\n",
      "Train Epoch: 24 [14080/50000 (31%)]\tLoss: 0.526659, Accuracy: 82.81\n",
      "Train Epoch: 24 [15360/50000 (34%)]\tLoss: 0.476333, Accuracy: 84.77\n",
      "Train Epoch: 24 [16640/50000 (37%)]\tLoss: 0.436090, Accuracy: 85.16\n",
      "Train Epoch: 24 [17920/50000 (40%)]\tLoss: 0.373259, Accuracy: 88.28\n",
      "Train Epoch: 24 [19200/50000 (43%)]\tLoss: 0.409881, Accuracy: 83.59\n",
      "Train Epoch: 24 [20480/50000 (45%)]\tLoss: 0.387310, Accuracy: 86.72\n",
      "Train Epoch: 24 [21760/50000 (48%)]\tLoss: 0.420566, Accuracy: 83.59\n",
      "Train Epoch: 24 [23040/50000 (51%)]\tLoss: 0.420512, Accuracy: 86.33\n",
      "Train Epoch: 24 [24320/50000 (54%)]\tLoss: 0.414252, Accuracy: 87.11\n",
      "Train Epoch: 24 [25600/50000 (57%)]\tLoss: 0.396722, Accuracy: 87.50\n",
      "Train Epoch: 24 [26880/50000 (60%)]\tLoss: 0.357377, Accuracy: 88.28\n",
      "Train Epoch: 24 [28160/50000 (62%)]\tLoss: 0.476839, Accuracy: 83.98\n",
      "Train Epoch: 24 [29440/50000 (65%)]\tLoss: 0.450144, Accuracy: 85.55\n",
      "Train Epoch: 24 [30720/50000 (68%)]\tLoss: 0.561057, Accuracy: 82.42\n",
      "Train Epoch: 24 [32000/50000 (71%)]\tLoss: 0.536265, Accuracy: 82.81\n",
      "Train Epoch: 24 [33280/50000 (74%)]\tLoss: 0.441692, Accuracy: 85.16\n",
      "Train Epoch: 24 [34560/50000 (77%)]\tLoss: 0.405307, Accuracy: 87.11\n",
      "Train Epoch: 24 [35840/50000 (80%)]\tLoss: 0.473536, Accuracy: 83.59\n",
      "Train Epoch: 24 [37120/50000 (82%)]\tLoss: 0.528198, Accuracy: 80.47\n",
      "Train Epoch: 24 [38400/50000 (85%)]\tLoss: 0.325398, Accuracy: 88.67\n",
      "Train Epoch: 24 [39680/50000 (88%)]\tLoss: 0.506349, Accuracy: 84.77\n",
      "Train Epoch: 24 [40960/50000 (91%)]\tLoss: 0.440723, Accuracy: 85.16\n",
      "Train Epoch: 24 [42240/50000 (94%)]\tLoss: 0.435301, Accuracy: 82.03\n",
      "Train Epoch: 24 [43520/50000 (97%)]\tLoss: 0.421521, Accuracy: 85.94\n",
      "Train Epoch: 24 [35000/50000 (99%)]\tLoss: 0.413482, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6567, Accuracy: 3963/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[35.79572415351868 s]\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor([[[-0.4667]],\n",
      "\n",
      "        [[ 1.1399]],\n",
      "\n",
      "        [[ 1.4631]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.9729]],\n",
      "\n",
      "        [[ 0.7510]],\n",
      "\n",
      "        [[ 0.0972]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor([[[-4.3414e-03,  5.6008e-03,  1.7774e-03,  ...,  7.3518e-03,\n",
      "          -2.1062e-05,  1.1167e-03],\n",
      "         [-5.1881e-03,  2.7546e-03,  5.7945e-03,  ...,  4.2573e-03,\n",
      "           3.6617e-04, -4.2432e-03],\n",
      "         [ 3.1112e-03,  6.9498e-04, -2.6526e-03,  ...,  4.4044e-03,\n",
      "           5.7356e-03, -6.7403e-03],\n",
      "         ...,\n",
      "         [-2.9866e-03, -3.0466e-03, -9.2557e-04,  ...,  7.5530e-03,\n",
      "          -2.0423e-03,  2.4561e-05],\n",
      "         [-3.6956e-04, -3.2565e-03, -4.2783e-03,  ..., -2.3169e-03,\n",
      "          -8.8521e-03,  5.9910e-03],\n",
      "         [-4.7540e-03, -3.9254e-03,  7.6040e-04,  ...,  2.4077e-03,\n",
      "          -6.3202e-03, -2.7568e-03]]], device='cuda:0')\n",
      "\n",
      "Test set: Average loss: 0.6775, Accuracy: 7806/10000 (78.06%)\n",
      "\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.327280, Accuracy: 89.06\n",
      "Train Epoch: 25 [1280/50000 (3%)]\tLoss: 0.366017, Accuracy: 87.89\n",
      "Train Epoch: 25 [2560/50000 (6%)]\tLoss: 0.419751, Accuracy: 84.77\n",
      "Train Epoch: 25 [3840/50000 (9%)]\tLoss: 0.381592, Accuracy: 85.55\n",
      "Train Epoch: 25 [5120/50000 (11%)]\tLoss: 0.389597, Accuracy: 87.11\n",
      "Train Epoch: 25 [6400/50000 (14%)]\tLoss: 0.491279, Accuracy: 83.98\n",
      "Train Epoch: 25 [7680/50000 (17%)]\tLoss: 0.372315, Accuracy: 87.89\n",
      "Train Epoch: 25 [8960/50000 (20%)]\tLoss: 0.442041, Accuracy: 84.77\n",
      "Train Epoch: 25 [10240/50000 (23%)]\tLoss: 0.356062, Accuracy: 87.50\n",
      "Train Epoch: 25 [11520/50000 (26%)]\tLoss: 0.462141, Accuracy: 83.20\n",
      "Train Epoch: 25 [12800/50000 (28%)]\tLoss: 0.496638, Accuracy: 81.25\n",
      "Train Epoch: 25 [14080/50000 (31%)]\tLoss: 0.499637, Accuracy: 83.59\n",
      "Train Epoch: 25 [15360/50000 (34%)]\tLoss: 0.333523, Accuracy: 88.28\n",
      "Train Epoch: 25 [16640/50000 (37%)]\tLoss: 0.458196, Accuracy: 83.20\n",
      "Train Epoch: 25 [17920/50000 (40%)]\tLoss: 0.439879, Accuracy: 86.33\n",
      "Train Epoch: 25 [19200/50000 (43%)]\tLoss: 0.541251, Accuracy: 82.03\n",
      "Train Epoch: 25 [20480/50000 (45%)]\tLoss: 0.377835, Accuracy: 87.50\n",
      "Train Epoch: 25 [21760/50000 (48%)]\tLoss: 0.467709, Accuracy: 82.81\n",
      "Train Epoch: 25 [23040/50000 (51%)]\tLoss: 0.428159, Accuracy: 87.11\n",
      "Train Epoch: 25 [24320/50000 (54%)]\tLoss: 0.392680, Accuracy: 89.45\n",
      "Train Epoch: 25 [25600/50000 (57%)]\tLoss: 0.298092, Accuracy: 92.58\n",
      "Train Epoch: 25 [26880/50000 (60%)]\tLoss: 0.475806, Accuracy: 82.81\n",
      "Train Epoch: 25 [28160/50000 (62%)]\tLoss: 0.453810, Accuracy: 85.16\n",
      "Train Epoch: 25 [29440/50000 (65%)]\tLoss: 0.383683, Accuracy: 86.33\n",
      "Train Epoch: 25 [30720/50000 (68%)]\tLoss: 0.442953, Accuracy: 88.28\n",
      "Train Epoch: 25 [32000/50000 (71%)]\tLoss: 0.393891, Accuracy: 87.89\n",
      "Train Epoch: 25 [33280/50000 (74%)]\tLoss: 0.405068, Accuracy: 85.16\n",
      "Train Epoch: 25 [34560/50000 (77%)]\tLoss: 0.421264, Accuracy: 84.38\n",
      "Train Epoch: 25 [35840/50000 (80%)]\tLoss: 0.501326, Accuracy: 84.77\n",
      "Train Epoch: 25 [37120/50000 (82%)]\tLoss: 0.495554, Accuracy: 81.64\n",
      "Train Epoch: 25 [38400/50000 (85%)]\tLoss: 0.464763, Accuracy: 85.16\n",
      "Train Epoch: 25 [39680/50000 (88%)]\tLoss: 0.515827, Accuracy: 83.59\n",
      "Train Epoch: 25 [40960/50000 (91%)]\tLoss: 0.501238, Accuracy: 83.20\n",
      "Train Epoch: 25 [42240/50000 (94%)]\tLoss: 0.434361, Accuracy: 84.77\n",
      "Train Epoch: 25 [43520/50000 (97%)]\tLoss: 0.377672, Accuracy: 87.50\n",
      "Train Epoch: 25 [35000/50000 (99%)]\tLoss: 0.414296, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.5677, Accuracy: 4070/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[38.8887255191803 s]\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 0.363259, Accuracy: 85.16\n",
      "Train Epoch: 26 [1280/50000 (3%)]\tLoss: 0.451090, Accuracy: 84.77\n",
      "Train Epoch: 26 [2560/50000 (6%)]\tLoss: 0.371782, Accuracy: 86.33\n",
      "Train Epoch: 26 [3840/50000 (9%)]\tLoss: 0.349341, Accuracy: 87.50\n",
      "Train Epoch: 26 [5120/50000 (11%)]\tLoss: 0.442867, Accuracy: 83.98\n",
      "Train Epoch: 26 [6400/50000 (14%)]\tLoss: 0.431861, Accuracy: 85.55\n",
      "Train Epoch: 26 [7680/50000 (17%)]\tLoss: 0.378141, Accuracy: 85.94\n",
      "Train Epoch: 26 [8960/50000 (20%)]\tLoss: 0.459030, Accuracy: 84.77\n",
      "Train Epoch: 26 [10240/50000 (23%)]\tLoss: 0.370106, Accuracy: 87.50\n",
      "Train Epoch: 26 [11520/50000 (26%)]\tLoss: 0.425718, Accuracy: 85.94\n",
      "Train Epoch: 26 [12800/50000 (28%)]\tLoss: 0.521005, Accuracy: 84.77\n",
      "Train Epoch: 26 [14080/50000 (31%)]\tLoss: 0.424827, Accuracy: 86.33\n",
      "Train Epoch: 26 [15360/50000 (34%)]\tLoss: 0.372788, Accuracy: 86.72\n",
      "Train Epoch: 26 [16640/50000 (37%)]\tLoss: 0.592142, Accuracy: 80.47\n",
      "Train Epoch: 26 [17920/50000 (40%)]\tLoss: 0.415495, Accuracy: 87.11\n",
      "Train Epoch: 26 [19200/50000 (43%)]\tLoss: 0.459311, Accuracy: 85.16\n",
      "Train Epoch: 26 [20480/50000 (45%)]\tLoss: 0.551442, Accuracy: 80.47\n",
      "Train Epoch: 26 [21760/50000 (48%)]\tLoss: 0.371992, Accuracy: 87.89\n",
      "Train Epoch: 26 [23040/50000 (51%)]\tLoss: 0.365742, Accuracy: 88.67\n",
      "Train Epoch: 26 [24320/50000 (54%)]\tLoss: 0.461839, Accuracy: 84.38\n",
      "Train Epoch: 26 [25600/50000 (57%)]\tLoss: 0.451307, Accuracy: 85.16\n",
      "Train Epoch: 26 [26880/50000 (60%)]\tLoss: 0.417951, Accuracy: 86.33\n",
      "Train Epoch: 26 [28160/50000 (62%)]\tLoss: 0.438500, Accuracy: 84.77\n",
      "Train Epoch: 26 [29440/50000 (65%)]\tLoss: 0.398684, Accuracy: 86.33\n",
      "Train Epoch: 26 [30720/50000 (68%)]\tLoss: 0.364335, Accuracy: 88.28\n",
      "Train Epoch: 26 [32000/50000 (71%)]\tLoss: 0.476405, Accuracy: 83.98\n",
      "Train Epoch: 26 [33280/50000 (74%)]\tLoss: 0.477599, Accuracy: 85.16\n",
      "Train Epoch: 26 [34560/50000 (77%)]\tLoss: 0.402856, Accuracy: 87.50\n",
      "Train Epoch: 26 [35840/50000 (80%)]\tLoss: 0.366535, Accuracy: 87.89\n",
      "Train Epoch: 26 [37120/50000 (82%)]\tLoss: 0.331582, Accuracy: 88.28\n",
      "Train Epoch: 26 [38400/50000 (85%)]\tLoss: 0.428377, Accuracy: 86.33\n",
      "Train Epoch: 26 [39680/50000 (88%)]\tLoss: 0.334534, Accuracy: 89.84\n",
      "Train Epoch: 26 [40960/50000 (91%)]\tLoss: 0.488567, Accuracy: 83.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 26 [42240/50000 (94%)]\tLoss: 0.368155, Accuracy: 87.11\n",
      "Train Epoch: 26 [43520/50000 (97%)]\tLoss: 0.521007, Accuracy: 83.20\n",
      "Train Epoch: 26 [35000/50000 (99%)]\tLoss: 0.442494, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.7922, Accuracy: 3760/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[56.28773379325867 s]\n",
      "\n",
      "Test set: Average loss: 0.8156, Accuracy: 7483/10000 (74.83%)\n",
      "\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.404266, Accuracy: 88.28\n",
      "Train Epoch: 27 [1280/50000 (3%)]\tLoss: 0.415141, Accuracy: 83.59\n",
      "Train Epoch: 27 [2560/50000 (6%)]\tLoss: 0.378391, Accuracy: 85.94\n",
      "Train Epoch: 27 [3840/50000 (9%)]\tLoss: 0.424956, Accuracy: 85.16\n",
      "Train Epoch: 27 [5120/50000 (11%)]\tLoss: 0.498086, Accuracy: 84.38\n",
      "Train Epoch: 27 [6400/50000 (14%)]\tLoss: 0.446457, Accuracy: 87.11\n",
      "Train Epoch: 27 [7680/50000 (17%)]\tLoss: 0.434871, Accuracy: 84.38\n",
      "Train Epoch: 27 [8960/50000 (20%)]\tLoss: 0.478264, Accuracy: 82.81\n",
      "Train Epoch: 27 [10240/50000 (23%)]\tLoss: 0.432426, Accuracy: 86.72\n",
      "Train Epoch: 27 [11520/50000 (26%)]\tLoss: 0.518880, Accuracy: 82.81\n",
      "Train Epoch: 27 [12800/50000 (28%)]\tLoss: 0.496943, Accuracy: 83.20\n",
      "Train Epoch: 27 [14080/50000 (31%)]\tLoss: 0.361197, Accuracy: 89.45\n",
      "Train Epoch: 27 [15360/50000 (34%)]\tLoss: 0.316994, Accuracy: 91.02\n",
      "Train Epoch: 27 [16640/50000 (37%)]\tLoss: 0.366076, Accuracy: 87.11\n",
      "Train Epoch: 27 [17920/50000 (40%)]\tLoss: 0.506259, Accuracy: 83.59\n",
      "Train Epoch: 27 [19200/50000 (43%)]\tLoss: 0.367315, Accuracy: 88.67\n",
      "Train Epoch: 27 [20480/50000 (45%)]\tLoss: 0.437814, Accuracy: 84.77\n",
      "Train Epoch: 27 [21760/50000 (48%)]\tLoss: 0.368050, Accuracy: 87.11\n",
      "Train Epoch: 27 [23040/50000 (51%)]\tLoss: 0.523925, Accuracy: 83.59\n",
      "Train Epoch: 27 [24320/50000 (54%)]\tLoss: 0.322294, Accuracy: 88.67\n",
      "Train Epoch: 27 [25600/50000 (57%)]\tLoss: 0.394671, Accuracy: 87.50\n",
      "Train Epoch: 27 [26880/50000 (60%)]\tLoss: 0.448982, Accuracy: 86.33\n",
      "Train Epoch: 27 [28160/50000 (62%)]\tLoss: 0.453708, Accuracy: 83.59\n",
      "Train Epoch: 27 [29440/50000 (65%)]\tLoss: 0.383206, Accuracy: 87.50\n",
      "Train Epoch: 27 [30720/50000 (68%)]\tLoss: 0.421498, Accuracy: 85.55\n",
      "Train Epoch: 27 [32000/50000 (71%)]\tLoss: 0.343806, Accuracy: 88.67\n",
      "Train Epoch: 27 [33280/50000 (74%)]\tLoss: 0.421715, Accuracy: 86.72\n",
      "Train Epoch: 27 [34560/50000 (77%)]\tLoss: 0.388544, Accuracy: 85.55\n",
      "Train Epoch: 27 [35840/50000 (80%)]\tLoss: 0.409478, Accuracy: 85.16\n",
      "Train Epoch: 27 [37120/50000 (82%)]\tLoss: 0.354470, Accuracy: 89.06\n",
      "Train Epoch: 27 [38400/50000 (85%)]\tLoss: 0.382728, Accuracy: 87.89\n",
      "Train Epoch: 27 [39680/50000 (88%)]\tLoss: 0.588295, Accuracy: 82.03\n",
      "Train Epoch: 27 [40960/50000 (91%)]\tLoss: 0.366240, Accuracy: 88.67\n",
      "Train Epoch: 27 [42240/50000 (94%)]\tLoss: 0.403370, Accuracy: 85.16\n",
      "Train Epoch: 27 [43520/50000 (97%)]\tLoss: 0.414580, Accuracy: 85.94\n",
      "Train Epoch: 27 [35000/50000 (99%)]\tLoss: 0.303591, Accuracy: 89.50\n",
      "\n",
      "Validation set: Average loss: 0.6719, Accuracy: 3923/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[38.82859826087952 s]\n",
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 0.300842, Accuracy: 91.41\n",
      "Train Epoch: 28 [1280/50000 (3%)]\tLoss: 0.409828, Accuracy: 86.33\n",
      "Train Epoch: 28 [2560/50000 (6%)]\tLoss: 0.303884, Accuracy: 89.84\n",
      "Train Epoch: 28 [3840/50000 (9%)]\tLoss: 0.400802, Accuracy: 85.55\n",
      "Train Epoch: 28 [5120/50000 (11%)]\tLoss: 0.364077, Accuracy: 87.89\n",
      "Train Epoch: 28 [6400/50000 (14%)]\tLoss: 0.416129, Accuracy: 85.94\n",
      "Train Epoch: 28 [7680/50000 (17%)]\tLoss: 0.516139, Accuracy: 82.42\n",
      "Train Epoch: 28 [8960/50000 (20%)]\tLoss: 0.391243, Accuracy: 83.98\n",
      "Train Epoch: 28 [10240/50000 (23%)]\tLoss: 0.356167, Accuracy: 90.23\n",
      "Train Epoch: 28 [11520/50000 (26%)]\tLoss: 0.388768, Accuracy: 86.72\n",
      "Train Epoch: 28 [12800/50000 (28%)]\tLoss: 0.401783, Accuracy: 86.33\n",
      "Train Epoch: 28 [14080/50000 (31%)]\tLoss: 0.405423, Accuracy: 85.94\n",
      "Train Epoch: 28 [15360/50000 (34%)]\tLoss: 0.367859, Accuracy: 85.94\n",
      "Train Epoch: 28 [16640/50000 (37%)]\tLoss: 0.457916, Accuracy: 83.20\n",
      "Train Epoch: 28 [17920/50000 (40%)]\tLoss: 0.473213, Accuracy: 82.81\n",
      "Train Epoch: 28 [19200/50000 (43%)]\tLoss: 0.514706, Accuracy: 80.86\n",
      "Train Epoch: 28 [20480/50000 (45%)]\tLoss: 0.625719, Accuracy: 80.08\n",
      "Train Epoch: 28 [21760/50000 (48%)]\tLoss: 0.409253, Accuracy: 88.28\n",
      "Train Epoch: 28 [23040/50000 (51%)]\tLoss: 0.422878, Accuracy: 86.33\n",
      "Train Epoch: 28 [24320/50000 (54%)]\tLoss: 0.417171, Accuracy: 85.94\n",
      "Train Epoch: 28 [25600/50000 (57%)]\tLoss: 0.461390, Accuracy: 85.94\n",
      "Train Epoch: 28 [26880/50000 (60%)]\tLoss: 0.442087, Accuracy: 84.38\n",
      "Train Epoch: 28 [28160/50000 (62%)]\tLoss: 0.566243, Accuracy: 80.86\n",
      "Train Epoch: 28 [29440/50000 (65%)]\tLoss: 0.506419, Accuracy: 82.81\n",
      "Train Epoch: 28 [30720/50000 (68%)]\tLoss: 0.489384, Accuracy: 83.20\n",
      "Train Epoch: 28 [32000/50000 (71%)]\tLoss: 0.482070, Accuracy: 83.59\n",
      "Train Epoch: 28 [33280/50000 (74%)]\tLoss: 0.386203, Accuracy: 87.89\n",
      "Train Epoch: 28 [34560/50000 (77%)]\tLoss: 0.479738, Accuracy: 83.59\n",
      "Train Epoch: 28 [35840/50000 (80%)]\tLoss: 0.370656, Accuracy: 88.67\n",
      "Train Epoch: 28 [37120/50000 (82%)]\tLoss: 0.458055, Accuracy: 85.16\n",
      "Train Epoch: 28 [38400/50000 (85%)]\tLoss: 0.459830, Accuracy: 87.11\n",
      "Train Epoch: 28 [39680/50000 (88%)]\tLoss: 0.322529, Accuracy: 89.45\n",
      "Train Epoch: 28 [40960/50000 (91%)]\tLoss: 0.458046, Accuracy: 84.77\n",
      "Train Epoch: 28 [42240/50000 (94%)]\tLoss: 0.360082, Accuracy: 89.45\n",
      "Train Epoch: 28 [43520/50000 (97%)]\tLoss: 0.472605, Accuracy: 85.55\n",
      "Train Epoch: 28 [35000/50000 (99%)]\tLoss: 0.418350, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.5687, Accuracy: 4092/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[35.783095598220825 s]\n",
      "\n",
      "Test set: Average loss: 0.5858, Accuracy: 8110/10000 (81.10%)\n",
      "\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 0.395117, Accuracy: 88.28\n",
      "Train Epoch: 29 [1280/50000 (3%)]\tLoss: 0.352753, Accuracy: 88.67\n",
      "Train Epoch: 29 [2560/50000 (6%)]\tLoss: 0.533025, Accuracy: 82.03\n",
      "Train Epoch: 29 [3840/50000 (9%)]\tLoss: 0.467009, Accuracy: 84.38\n",
      "Train Epoch: 29 [5120/50000 (11%)]\tLoss: 0.387694, Accuracy: 87.89\n",
      "Train Epoch: 29 [6400/50000 (14%)]\tLoss: 0.330944, Accuracy: 89.06\n",
      "Train Epoch: 29 [7680/50000 (17%)]\tLoss: 0.360681, Accuracy: 87.50\n",
      "Train Epoch: 29 [8960/50000 (20%)]\tLoss: 0.484755, Accuracy: 81.64\n",
      "Train Epoch: 29 [10240/50000 (23%)]\tLoss: 0.427877, Accuracy: 83.59\n",
      "Train Epoch: 29 [11520/50000 (26%)]\tLoss: 0.429025, Accuracy: 85.94\n",
      "Train Epoch: 29 [12800/50000 (28%)]\tLoss: 0.428814, Accuracy: 86.72\n",
      "Train Epoch: 29 [14080/50000 (31%)]\tLoss: 0.469480, Accuracy: 84.38\n",
      "Train Epoch: 29 [15360/50000 (34%)]\tLoss: 0.319809, Accuracy: 87.89\n",
      "Train Epoch: 29 [16640/50000 (37%)]\tLoss: 0.380237, Accuracy: 84.38\n",
      "Train Epoch: 29 [17920/50000 (40%)]\tLoss: 0.387615, Accuracy: 87.50\n",
      "Train Epoch: 29 [19200/50000 (43%)]\tLoss: 0.313497, Accuracy: 90.23\n",
      "Train Epoch: 29 [20480/50000 (45%)]\tLoss: 0.454629, Accuracy: 86.33\n",
      "Train Epoch: 29 [21760/50000 (48%)]\tLoss: 0.398606, Accuracy: 85.94\n",
      "Train Epoch: 29 [23040/50000 (51%)]\tLoss: 0.459745, Accuracy: 86.33\n",
      "Train Epoch: 29 [24320/50000 (54%)]\tLoss: 0.401740, Accuracy: 84.77\n",
      "Train Epoch: 29 [25600/50000 (57%)]\tLoss: 0.430090, Accuracy: 84.38\n",
      "Train Epoch: 29 [26880/50000 (60%)]\tLoss: 0.504313, Accuracy: 83.98\n",
      "Train Epoch: 29 [28160/50000 (62%)]\tLoss: 0.441415, Accuracy: 83.20\n",
      "Train Epoch: 29 [29440/50000 (65%)]\tLoss: 0.423968, Accuracy: 87.50\n",
      "Train Epoch: 29 [30720/50000 (68%)]\tLoss: 0.410427, Accuracy: 86.33\n",
      "Train Epoch: 29 [32000/50000 (71%)]\tLoss: 0.351095, Accuracy: 85.55\n",
      "Train Epoch: 29 [33280/50000 (74%)]\tLoss: 0.309713, Accuracy: 89.84\n",
      "Train Epoch: 29 [34560/50000 (77%)]\tLoss: 0.410361, Accuracy: 85.55\n",
      "Train Epoch: 29 [35840/50000 (80%)]\tLoss: 0.421040, Accuracy: 86.33\n",
      "Train Epoch: 29 [37120/50000 (82%)]\tLoss: 0.410441, Accuracy: 86.72\n",
      "Train Epoch: 29 [38400/50000 (85%)]\tLoss: 0.436380, Accuracy: 84.38\n",
      "Train Epoch: 29 [39680/50000 (88%)]\tLoss: 0.471678, Accuracy: 85.94\n",
      "Train Epoch: 29 [40960/50000 (91%)]\tLoss: 0.390273, Accuracy: 85.94\n",
      "Train Epoch: 29 [42240/50000 (94%)]\tLoss: 0.575033, Accuracy: 80.86\n",
      "Train Epoch: 29 [43520/50000 (97%)]\tLoss: 0.412924, Accuracy: 85.55\n",
      "Train Epoch: 29 [35000/50000 (99%)]\tLoss: 0.396671, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.5923, Accuracy: 4056/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[38.452298402786255 s]\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 0.418418, Accuracy: 85.55\n",
      "Train Epoch: 30 [1280/50000 (3%)]\tLoss: 0.465904, Accuracy: 83.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 30 [2560/50000 (6%)]\tLoss: 0.443635, Accuracy: 83.98\n",
      "Train Epoch: 30 [3840/50000 (9%)]\tLoss: 0.400656, Accuracy: 86.33\n",
      "Train Epoch: 30 [5120/50000 (11%)]\tLoss: 0.291923, Accuracy: 89.45\n",
      "Train Epoch: 30 [6400/50000 (14%)]\tLoss: 0.380546, Accuracy: 89.06\n",
      "Train Epoch: 30 [7680/50000 (17%)]\tLoss: 0.371307, Accuracy: 86.72\n",
      "Train Epoch: 30 [8960/50000 (20%)]\tLoss: 0.413622, Accuracy: 84.77\n",
      "Train Epoch: 30 [10240/50000 (23%)]\tLoss: 0.413661, Accuracy: 85.55\n",
      "Train Epoch: 30 [11520/50000 (26%)]\tLoss: 0.378139, Accuracy: 87.11\n",
      "Train Epoch: 30 [12800/50000 (28%)]\tLoss: 0.373910, Accuracy: 89.06\n",
      "Train Epoch: 30 [14080/50000 (31%)]\tLoss: 0.450208, Accuracy: 88.28\n",
      "Train Epoch: 30 [15360/50000 (34%)]\tLoss: 0.459696, Accuracy: 85.94\n",
      "Train Epoch: 30 [16640/50000 (37%)]\tLoss: 0.433406, Accuracy: 83.98\n",
      "Train Epoch: 30 [17920/50000 (40%)]\tLoss: 0.450865, Accuracy: 83.59\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[-0.2637]],\n",
      "\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 30 [19200/50000 (43%)]\tLoss: 0.375938, Accuracy: 85.16\n",
      "Train Epoch: 30 [20480/50000 (45%)]\tLoss: 0.475424, Accuracy: 83.98\n",
      "Train Epoch: 30 [21760/50000 (48%)]\tLoss: 0.393143, Accuracy: 86.72\n",
      "Train Epoch: 30 [23040/50000 (51%)]\tLoss: 0.486105, Accuracy: 83.98\n",
      "Train Epoch: 30 [24320/50000 (54%)]\tLoss: 0.445772, Accuracy: 84.77\n",
      "Train Epoch: 30 [25600/50000 (57%)]\tLoss: 0.505426, Accuracy: 83.20\n",
      "Train Epoch: 30 [26880/50000 (60%)]\tLoss: 0.428027, Accuracy: 85.16\n",
      "Train Epoch: 30 [28160/50000 (62%)]\tLoss: 0.437345, Accuracy: 85.16\n",
      "Train Epoch: 30 [29440/50000 (65%)]\tLoss: 0.469872, Accuracy: 85.94\n",
      "Train Epoch: 30 [30720/50000 (68%)]\tLoss: 0.498191, Accuracy: 84.38\n",
      "Train Epoch: 30 [32000/50000 (71%)]\tLoss: 0.464648, Accuracy: 83.98\n",
      "Train Epoch: 30 [33280/50000 (74%)]\tLoss: 0.415944, Accuracy: 85.55\n",
      "Train Epoch: 30 [34560/50000 (77%)]\tLoss: 0.574182, Accuracy: 80.47\n",
      "Train Epoch: 30 [35840/50000 (80%)]\tLoss: 0.380671, Accuracy: 86.33\n",
      "Train Epoch: 30 [37120/50000 (82%)]\tLoss: 0.471320, Accuracy: 85.16\n",
      "Train Epoch: 30 [38400/50000 (85%)]\tLoss: 0.441307, Accuracy: 87.50\n",
      "Train Epoch: 30 [39680/50000 (88%)]\tLoss: 0.364879, Accuracy: 88.28\n",
      "Train Epoch: 30 [40960/50000 (91%)]\tLoss: 0.411803, Accuracy: 84.38\n",
      "Train Epoch: 30 [42240/50000 (94%)]\tLoss: 0.427758, Accuracy: 85.55\n",
      "Train Epoch: 30 [43520/50000 (97%)]\tLoss: 0.397235, Accuracy: 86.33\n",
      "Train Epoch: 30 [35000/50000 (99%)]\tLoss: 0.403051, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7570, Accuracy: 3803/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[35.865354776382446 s]\n",
      "\n",
      "Test set: Average loss: 0.7548, Accuracy: 7527/10000 (75.27%)\n",
      "\n",
      "Train Epoch: 31 [0/50000 (0%)]\tLoss: 0.398997, Accuracy: 87.11\n",
      "Train Epoch: 31 [1280/50000 (3%)]\tLoss: 0.384244, Accuracy: 85.16\n",
      "Train Epoch: 31 [2560/50000 (6%)]\tLoss: 0.374043, Accuracy: 89.45\n",
      "Train Epoch: 31 [3840/50000 (9%)]\tLoss: 0.379526, Accuracy: 87.89\n",
      "Train Epoch: 31 [5120/50000 (11%)]\tLoss: 0.460359, Accuracy: 85.16\n",
      "Train Epoch: 31 [6400/50000 (14%)]\tLoss: 0.394556, Accuracy: 84.38\n",
      "Train Epoch: 31 [7680/50000 (17%)]\tLoss: 0.379498, Accuracy: 87.11\n",
      "Train Epoch: 31 [8960/50000 (20%)]\tLoss: 0.346093, Accuracy: 89.45\n",
      "Train Epoch: 31 [10240/50000 (23%)]\tLoss: 0.393539, Accuracy: 86.33\n",
      "Train Epoch: 31 [11520/50000 (26%)]\tLoss: 0.530317, Accuracy: 81.25\n",
      "Train Epoch: 31 [12800/50000 (28%)]\tLoss: 0.391167, Accuracy: 87.50\n",
      "Train Epoch: 31 [14080/50000 (31%)]\tLoss: 0.380537, Accuracy: 87.11\n",
      "Train Epoch: 31 [15360/50000 (34%)]\tLoss: 0.390735, Accuracy: 85.55\n",
      "Train Epoch: 31 [16640/50000 (37%)]\tLoss: 0.436565, Accuracy: 85.16\n",
      "Train Epoch: 31 [17920/50000 (40%)]\tLoss: 0.383068, Accuracy: 86.72\n",
      "Train Epoch: 31 [19200/50000 (43%)]\tLoss: 0.584085, Accuracy: 77.73\n",
      "Train Epoch: 31 [20480/50000 (45%)]\tLoss: 0.459620, Accuracy: 86.72\n",
      "Train Epoch: 31 [21760/50000 (48%)]\tLoss: 0.444209, Accuracy: 86.33\n",
      "Train Epoch: 31 [23040/50000 (51%)]\tLoss: 0.453139, Accuracy: 85.55\n",
      "Train Epoch: 31 [24320/50000 (54%)]\tLoss: 0.438534, Accuracy: 85.16\n",
      "Train Epoch: 31 [25600/50000 (57%)]\tLoss: 0.447468, Accuracy: 82.81\n",
      "Train Epoch: 31 [26880/50000 (60%)]\tLoss: 0.302534, Accuracy: 91.02\n",
      "Train Epoch: 31 [28160/50000 (62%)]\tLoss: 0.465222, Accuracy: 84.77\n",
      "Train Epoch: 31 [29440/50000 (65%)]\tLoss: 0.441253, Accuracy: 85.55\n",
      "Train Epoch: 31 [30720/50000 (68%)]\tLoss: 0.422220, Accuracy: 85.55\n",
      "Train Epoch: 31 [32000/50000 (71%)]\tLoss: 0.372418, Accuracy: 86.72\n",
      "Train Epoch: 31 [33280/50000 (74%)]\tLoss: 0.459303, Accuracy: 82.81\n",
      "Train Epoch: 31 [34560/50000 (77%)]\tLoss: 0.470338, Accuracy: 83.59\n",
      "Train Epoch: 31 [35840/50000 (80%)]\tLoss: 0.349889, Accuracy: 86.72\n",
      "Train Epoch: 31 [37120/50000 (82%)]\tLoss: 0.406486, Accuracy: 87.11\n",
      "Train Epoch: 31 [38400/50000 (85%)]\tLoss: 0.414098, Accuracy: 85.55\n",
      "Train Epoch: 31 [39680/50000 (88%)]\tLoss: 0.358599, Accuracy: 85.55\n",
      "Train Epoch: 31 [40960/50000 (91%)]\tLoss: 0.448366, Accuracy: 85.55\n",
      "Train Epoch: 31 [42240/50000 (94%)]\tLoss: 0.504841, Accuracy: 80.08\n",
      "Train Epoch: 31 [43520/50000 (97%)]\tLoss: 0.305256, Accuracy: 90.23\n",
      "Train Epoch: 31 [35000/50000 (99%)]\tLoss: 0.329152, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.6155, Accuracy: 3972/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[38.93533134460449 s]\n",
      "Train Epoch: 32 [0/50000 (0%)]\tLoss: 0.322001, Accuracy: 87.50\n",
      "Train Epoch: 32 [1280/50000 (3%)]\tLoss: 0.332643, Accuracy: 87.89\n",
      "Train Epoch: 32 [2560/50000 (6%)]\tLoss: 0.449003, Accuracy: 83.20\n",
      "Train Epoch: 32 [3840/50000 (9%)]\tLoss: 0.500803, Accuracy: 83.20\n",
      "Train Epoch: 32 [5120/50000 (11%)]\tLoss: 0.484614, Accuracy: 83.59\n",
      "Train Epoch: 32 [6400/50000 (14%)]\tLoss: 0.453897, Accuracy: 84.38\n",
      "Train Epoch: 32 [7680/50000 (17%)]\tLoss: 0.330087, Accuracy: 91.02\n",
      "Train Epoch: 32 [8960/50000 (20%)]\tLoss: 0.390537, Accuracy: 83.98\n",
      "Train Epoch: 32 [10240/50000 (23%)]\tLoss: 0.388659, Accuracy: 89.84\n",
      "Train Epoch: 32 [11520/50000 (26%)]\tLoss: 0.384898, Accuracy: 84.77\n",
      "Train Epoch: 32 [12800/50000 (28%)]\tLoss: 0.430632, Accuracy: 86.33\n",
      "Train Epoch: 32 [14080/50000 (31%)]\tLoss: 0.448474, Accuracy: 83.98\n",
      "Train Epoch: 32 [15360/50000 (34%)]\tLoss: 0.507766, Accuracy: 83.59\n",
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      "Train Epoch: 32 [17920/50000 (40%)]\tLoss: 0.395590, Accuracy: 83.20\n",
      "Train Epoch: 32 [19200/50000 (43%)]\tLoss: 0.473661, Accuracy: 82.03\n",
      "Train Epoch: 32 [20480/50000 (45%)]\tLoss: 0.430520, Accuracy: 85.94\n",
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      "Train Epoch: 32 [26880/50000 (60%)]\tLoss: 0.441459, Accuracy: 82.42\n",
      "Train Epoch: 32 [28160/50000 (62%)]\tLoss: 0.352286, Accuracy: 89.06\n",
      "Train Epoch: 32 [29440/50000 (65%)]\tLoss: 0.351469, Accuracy: 88.28\n",
      "Train Epoch: 32 [30720/50000 (68%)]\tLoss: 0.432590, Accuracy: 85.16\n",
      "Train Epoch: 32 [32000/50000 (71%)]\tLoss: 0.373905, Accuracy: 85.94\n",
      "Train Epoch: 32 [33280/50000 (74%)]\tLoss: 0.485950, Accuracy: 82.42\n",
      "Train Epoch: 32 [34560/50000 (77%)]\tLoss: 0.407951, Accuracy: 87.11\n",
      "Train Epoch: 32 [35840/50000 (80%)]\tLoss: 0.457054, Accuracy: 83.59\n",
      "Train Epoch: 32 [37120/50000 (82%)]\tLoss: 0.373987, Accuracy: 84.77\n",
      "Train Epoch: 32 [38400/50000 (85%)]\tLoss: 0.456966, Accuracy: 82.81\n",
      "Train Epoch: 32 [39680/50000 (88%)]\tLoss: 0.292415, Accuracy: 87.11\n",
      "Train Epoch: 32 [40960/50000 (91%)]\tLoss: 0.357145, Accuracy: 87.89\n",
      "Train Epoch: 32 [42240/50000 (94%)]\tLoss: 0.432826, Accuracy: 84.77\n",
      "Train Epoch: 32 [43520/50000 (97%)]\tLoss: 0.465088, Accuracy: 83.98\n",
      "Train Epoch: 32 [35000/50000 (99%)]\tLoss: 0.461175, Accuracy: 81.00\n",
      "\n",
      "Validation set: Average loss: 0.7033, Accuracy: 3916/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[35.74781155586243 s]\n",
      "\n",
      "Test set: Average loss: 0.7089, Accuracy: 7819/10000 (78.19%)\n",
      "\n",
      "Train Epoch: 33 [0/50000 (0%)]\tLoss: 0.432923, Accuracy: 84.38\n",
      "Train Epoch: 33 [1280/50000 (3%)]\tLoss: 0.259822, Accuracy: 92.19\n",
      "Train Epoch: 33 [2560/50000 (6%)]\tLoss: 0.350759, Accuracy: 88.28\n",
      "Train Epoch: 33 [3840/50000 (9%)]\tLoss: 0.357102, Accuracy: 87.89\n",
      "Train Epoch: 33 [5120/50000 (11%)]\tLoss: 0.447230, Accuracy: 84.38\n",
      "Train Epoch: 33 [6400/50000 (14%)]\tLoss: 0.337292, Accuracy: 89.45\n",
      "Train Epoch: 33 [7680/50000 (17%)]\tLoss: 0.488875, Accuracy: 83.59\n",
      "Train Epoch: 33 [8960/50000 (20%)]\tLoss: 0.473309, Accuracy: 84.38\n",
      "Train Epoch: 33 [10240/50000 (23%)]\tLoss: 0.347951, Accuracy: 89.06\n",
      "Train Epoch: 33 [11520/50000 (26%)]\tLoss: 0.322185, Accuracy: 88.28\n",
      "Train Epoch: 33 [12800/50000 (28%)]\tLoss: 0.346955, Accuracy: 86.33\n",
      "Train Epoch: 33 [14080/50000 (31%)]\tLoss: 0.451471, Accuracy: 84.38\n",
      "Train Epoch: 33 [15360/50000 (34%)]\tLoss: 0.341545, Accuracy: 89.45\n",
      "Train Epoch: 33 [16640/50000 (37%)]\tLoss: 0.485601, Accuracy: 84.38\n",
      "Train Epoch: 33 [17920/50000 (40%)]\tLoss: 0.384938, Accuracy: 87.50\n",
      "Train Epoch: 33 [19200/50000 (43%)]\tLoss: 0.398815, Accuracy: 86.72\n",
      "Train Epoch: 33 [20480/50000 (45%)]\tLoss: 0.489537, Accuracy: 82.81\n",
      "Train Epoch: 33 [21760/50000 (48%)]\tLoss: 0.431378, Accuracy: 86.72\n",
      "Train Epoch: 33 [23040/50000 (51%)]\tLoss: 0.378453, Accuracy: 86.33\n",
      "Train Epoch: 33 [24320/50000 (54%)]\tLoss: 0.431968, Accuracy: 85.16\n",
      "Train Epoch: 33 [25600/50000 (57%)]\tLoss: 0.443910, Accuracy: 83.20\n",
      "Train Epoch: 33 [26880/50000 (60%)]\tLoss: 0.380960, Accuracy: 85.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 33 [28160/50000 (62%)]\tLoss: 0.408817, Accuracy: 85.16\n",
      "Train Epoch: 33 [29440/50000 (65%)]\tLoss: 0.413735, Accuracy: 87.11\n",
      "Train Epoch: 33 [30720/50000 (68%)]\tLoss: 0.460827, Accuracy: 84.77\n",
      "Train Epoch: 33 [32000/50000 (71%)]\tLoss: 0.398195, Accuracy: 86.72\n",
      "Train Epoch: 33 [33280/50000 (74%)]\tLoss: 0.434101, Accuracy: 85.16\n",
      "Train Epoch: 33 [34560/50000 (77%)]\tLoss: 0.477589, Accuracy: 86.33\n",
      "Train Epoch: 33 [35840/50000 (80%)]\tLoss: 0.469023, Accuracy: 82.42\n",
      "Train Epoch: 33 [37120/50000 (82%)]\tLoss: 0.415314, Accuracy: 87.11\n",
      "Train Epoch: 33 [38400/50000 (85%)]\tLoss: 0.430085, Accuracy: 83.59\n",
      "Train Epoch: 33 [39680/50000 (88%)]\tLoss: 0.443043, Accuracy: 83.98\n",
      "Train Epoch: 33 [40960/50000 (91%)]\tLoss: 0.380273, Accuracy: 88.67\n",
      "Train Epoch: 33 [42240/50000 (94%)]\tLoss: 0.381997, Accuracy: 87.50\n",
      "Train Epoch: 33 [43520/50000 (97%)]\tLoss: 0.444596, Accuracy: 83.20\n",
      "Train Epoch: 33 [35000/50000 (99%)]\tLoss: 0.423944, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.5620, Accuracy: 4120/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[38.89849877357483 s]\n",
      "Train Epoch: 34 [0/50000 (0%)]\tLoss: 0.274862, Accuracy: 91.41\n",
      "Train Epoch: 34 [1280/50000 (3%)]\tLoss: 0.419524, Accuracy: 85.94\n",
      "Train Epoch: 34 [2560/50000 (6%)]\tLoss: 0.351146, Accuracy: 89.06\n",
      "Train Epoch: 34 [3840/50000 (9%)]\tLoss: 0.396745, Accuracy: 87.89\n",
      "Train Epoch: 34 [5120/50000 (11%)]\tLoss: 0.355557, Accuracy: 87.89\n",
      "Train Epoch: 34 [6400/50000 (14%)]\tLoss: 0.376244, Accuracy: 87.11\n",
      "Train Epoch: 34 [7680/50000 (17%)]\tLoss: 0.316132, Accuracy: 90.23\n",
      "Train Epoch: 34 [8960/50000 (20%)]\tLoss: 0.343247, Accuracy: 88.28\n",
      "Train Epoch: 34 [10240/50000 (23%)]\tLoss: 0.334675, Accuracy: 88.28\n",
      "Train Epoch: 34 [11520/50000 (26%)]\tLoss: 0.480147, Accuracy: 86.33\n",
      "Train Epoch: 34 [12800/50000 (28%)]\tLoss: 0.367931, Accuracy: 88.67\n",
      "Train Epoch: 34 [14080/50000 (31%)]\tLoss: 0.449763, Accuracy: 84.77\n",
      "Train Epoch: 34 [15360/50000 (34%)]\tLoss: 0.369925, Accuracy: 87.89\n",
      "Train Epoch: 34 [16640/50000 (37%)]\tLoss: 0.456449, Accuracy: 86.33\n",
      "Train Epoch: 34 [17920/50000 (40%)]\tLoss: 0.434111, Accuracy: 85.94\n",
      "Train Epoch: 34 [19200/50000 (43%)]\tLoss: 0.479540, Accuracy: 85.16\n",
      "Train Epoch: 34 [20480/50000 (45%)]\tLoss: 0.382199, Accuracy: 87.11\n",
      "Train Epoch: 34 [21760/50000 (48%)]\tLoss: 0.491264, Accuracy: 83.20\n",
      "Train Epoch: 34 [23040/50000 (51%)]\tLoss: 0.393487, Accuracy: 87.11\n",
      "Train Epoch: 34 [24320/50000 (54%)]\tLoss: 0.425636, Accuracy: 85.94\n",
      "Train Epoch: 34 [25600/50000 (57%)]\tLoss: 0.454001, Accuracy: 85.16\n",
      "Train Epoch: 34 [26880/50000 (60%)]\tLoss: 0.448812, Accuracy: 85.55\n",
      "Train Epoch: 34 [28160/50000 (62%)]\tLoss: 0.428149, Accuracy: 86.72\n",
      "Train Epoch: 34 [29440/50000 (65%)]\tLoss: 0.455949, Accuracy: 84.77\n",
      "Train Epoch: 34 [30720/50000 (68%)]\tLoss: 0.386648, Accuracy: 88.28\n",
      "Train Epoch: 34 [32000/50000 (71%)]\tLoss: 0.413516, Accuracy: 87.11\n",
      "Train Epoch: 34 [33280/50000 (74%)]\tLoss: 0.352979, Accuracy: 88.67\n",
      "Train Epoch: 34 [34560/50000 (77%)]\tLoss: 0.537195, Accuracy: 82.81\n",
      "Train Epoch: 34 [35840/50000 (80%)]\tLoss: 0.299807, Accuracy: 91.80\n",
      "Train Epoch: 34 [37120/50000 (82%)]\tLoss: 0.391872, Accuracy: 87.89\n",
      "Train Epoch: 34 [38400/50000 (85%)]\tLoss: 0.563887, Accuracy: 82.81\n",
      "Train Epoch: 34 [39680/50000 (88%)]\tLoss: 0.288793, Accuracy: 89.84\n",
      "Train Epoch: 34 [40960/50000 (91%)]\tLoss: 0.396052, Accuracy: 86.72\n",
      "Train Epoch: 34 [42240/50000 (94%)]\tLoss: 0.380778, Accuracy: 87.11\n",
      "Train Epoch: 34 [43520/50000 (97%)]\tLoss: 0.336286, Accuracy: 89.45\n",
      "Train Epoch: 34 [35000/50000 (99%)]\tLoss: 0.406384, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.6378, Accuracy: 3942/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[35.699145555496216 s]\n",
      "\n",
      "Test set: Average loss: 0.6605, Accuracy: 7792/10000 (77.92%)\n",
      "\n",
      "Train Epoch: 35 [0/50000 (0%)]\tLoss: 0.431333, Accuracy: 85.55\n",
      "Train Epoch: 35 [1280/50000 (3%)]\tLoss: 0.398184, Accuracy: 89.06\n",
      "Train Epoch: 35 [2560/50000 (6%)]\tLoss: 0.317204, Accuracy: 88.28\n",
      "Train Epoch: 35 [3840/50000 (9%)]\tLoss: 0.373643, Accuracy: 86.72\n",
      "Train Epoch: 35 [5120/50000 (11%)]\tLoss: 0.368561, Accuracy: 87.89\n",
      "Train Epoch: 35 [6400/50000 (14%)]\tLoss: 0.364855, Accuracy: 88.67\n",
      "Train Epoch: 35 [7680/50000 (17%)]\tLoss: 0.316118, Accuracy: 87.50\n",
      "Train Epoch: 35 [8960/50000 (20%)]\tLoss: 0.288800, Accuracy: 89.45\n",
      "Train Epoch: 35 [10240/50000 (23%)]\tLoss: 0.437201, Accuracy: 85.55\n",
      "Train Epoch: 35 [11520/50000 (26%)]\tLoss: 0.360495, Accuracy: 88.67\n",
      "Train Epoch: 35 [12800/50000 (28%)]\tLoss: 0.388947, Accuracy: 85.16\n",
      "Train Epoch: 35 [14080/50000 (31%)]\tLoss: 0.359222, Accuracy: 88.67\n",
      "Train Epoch: 35 [15360/50000 (34%)]\tLoss: 0.409242, Accuracy: 87.11\n",
      "Train Epoch: 35 [16640/50000 (37%)]\tLoss: 0.438717, Accuracy: 83.98\n",
      "Train Epoch: 35 [17920/50000 (40%)]\tLoss: 0.515689, Accuracy: 82.81\n",
      "Train Epoch: 35 [19200/50000 (43%)]\tLoss: 0.350156, Accuracy: 88.28\n",
      "Train Epoch: 35 [20480/50000 (45%)]\tLoss: 0.374682, Accuracy: 88.28\n",
      "Train Epoch: 35 [21760/50000 (48%)]\tLoss: 0.406716, Accuracy: 87.11\n",
      "Train Epoch: 35 [23040/50000 (51%)]\tLoss: 0.473888, Accuracy: 84.77\n",
      "Train Epoch: 35 [24320/50000 (54%)]\tLoss: 0.525158, Accuracy: 82.03\n",
      "Train Epoch: 35 [25600/50000 (57%)]\tLoss: 0.367571, Accuracy: 87.89\n",
      "Train Epoch: 35 [26880/50000 (60%)]\tLoss: 0.464464, Accuracy: 84.38\n",
      "Train Epoch: 35 [28160/50000 (62%)]\tLoss: 0.403368, Accuracy: 83.20\n",
      "Train Epoch: 35 [29440/50000 (65%)]\tLoss: 0.474476, Accuracy: 83.59\n",
      "Train Epoch: 35 [30720/50000 (68%)]\tLoss: 0.355830, Accuracy: 87.89\n",
      "Train Epoch: 35 [32000/50000 (71%)]\tLoss: 0.332371, Accuracy: 88.67\n",
      "Train Epoch: 35 [33280/50000 (74%)]\tLoss: 0.452940, Accuracy: 85.55\n",
      "Train Epoch: 35 [34560/50000 (77%)]\tLoss: 0.418890, Accuracy: 85.94\n",
      "Train Epoch: 35 [35840/50000 (80%)]\tLoss: 0.371721, Accuracy: 86.72\n",
      "Train Epoch: 35 [37120/50000 (82%)]\tLoss: 0.464542, Accuracy: 83.20\n",
      "Train Epoch: 35 [38400/50000 (85%)]\tLoss: 0.424464, Accuracy: 85.16\n",
      "Train Epoch: 35 [39680/50000 (88%)]\tLoss: 0.474037, Accuracy: 85.55\n",
      "Train Epoch: 35 [40960/50000 (91%)]\tLoss: 0.391153, Accuracy: 87.11\n",
      "Train Epoch: 35 [42240/50000 (94%)]\tLoss: 0.374322, Accuracy: 89.06\n",
      "Train Epoch: 35 [43520/50000 (97%)]\tLoss: 0.398461, Accuracy: 86.33\n",
      "Train Epoch: 35 [35000/50000 (99%)]\tLoss: 0.399628, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6609, Accuracy: 3963/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[38.74799346923828 s]\n",
      "Train Epoch: 36 [0/50000 (0%)]\tLoss: 0.436834, Accuracy: 87.89\n",
      "Train Epoch: 36 [1280/50000 (3%)]\tLoss: 0.360296, Accuracy: 83.20\n",
      "Train Epoch: 36 [2560/50000 (6%)]\tLoss: 0.428518, Accuracy: 86.72\n",
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      "Train Epoch: 36 [6400/50000 (14%)]\tLoss: 0.381705, Accuracy: 87.89\n",
      "Train Epoch: 36 [7680/50000 (17%)]\tLoss: 0.349162, Accuracy: 88.67\n",
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      "Train Epoch: 36 [10240/50000 (23%)]\tLoss: 0.516569, Accuracy: 82.03\n",
      "Train Epoch: 36 [11520/50000 (26%)]\tLoss: 0.445818, Accuracy: 82.81\n",
      "Train Epoch: 36 [12800/50000 (28%)]\tLoss: 0.451470, Accuracy: 85.16\n",
      "Train Epoch: 36 [14080/50000 (31%)]\tLoss: 0.384085, Accuracy: 87.11\n",
      "Train Epoch: 36 [15360/50000 (34%)]\tLoss: 0.328108, Accuracy: 87.89\n",
      "Train Epoch: 36 [16640/50000 (37%)]\tLoss: 0.391485, Accuracy: 85.94\n",
      "Train Epoch: 36 [17920/50000 (40%)]\tLoss: 0.362180, Accuracy: 87.50\n",
      "Train Epoch: 36 [19200/50000 (43%)]\tLoss: 0.394857, Accuracy: 85.94\n",
      "Train Epoch: 36 [20480/50000 (45%)]\tLoss: 0.367111, Accuracy: 87.50\n",
      "Train Epoch: 36 [21760/50000 (48%)]\tLoss: 0.420071, Accuracy: 87.89\n",
      "Train Epoch: 36 [23040/50000 (51%)]\tLoss: 0.405622, Accuracy: 86.33\n",
      "Train Epoch: 36 [24320/50000 (54%)]\tLoss: 0.488437, Accuracy: 84.38\n",
      "Train Epoch: 36 [25600/50000 (57%)]\tLoss: 0.394114, Accuracy: 86.72\n",
      "Train Epoch: 36 [26880/50000 (60%)]\tLoss: 0.385273, Accuracy: 87.11\n",
      "Train Epoch: 36 [28160/50000 (62%)]\tLoss: 0.408722, Accuracy: 85.55\n",
      "Train Epoch: 36 [29440/50000 (65%)]\tLoss: 0.423881, Accuracy: 86.72\n",
      "Train Epoch: 36 [30720/50000 (68%)]\tLoss: 0.418108, Accuracy: 85.55\n",
      "Train Epoch: 36 [32000/50000 (71%)]\tLoss: 0.491673, Accuracy: 83.59\n",
      "Train Epoch: 36 [33280/50000 (74%)]\tLoss: 0.567494, Accuracy: 80.47\n",
      "Train Epoch: 36 [34560/50000 (77%)]\tLoss: 0.439697, Accuracy: 83.98\n",
      "Train Epoch: 36 [35840/50000 (80%)]\tLoss: 0.422466, Accuracy: 86.33\n",
      "Train Epoch: 36 [37120/50000 (82%)]\tLoss: 0.443815, Accuracy: 85.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 36 [38400/50000 (85%)]\tLoss: 0.502521, Accuracy: 82.42\n",
      "Train Epoch: 36 [39680/50000 (88%)]\tLoss: 0.482863, Accuracy: 83.20\n",
      "Train Epoch: 36 [40960/50000 (91%)]\tLoss: 0.403085, Accuracy: 84.77\n",
      "Train Epoch: 36 [42240/50000 (94%)]\tLoss: 0.352573, Accuracy: 89.06\n",
      "Train Epoch: 36 [43520/50000 (97%)]\tLoss: 0.460203, Accuracy: 83.59\n",
      "Train Epoch: 36 [35000/50000 (99%)]\tLoss: 0.501518, Accuracy: 83.50\n",
      "\n",
      "Validation set: Average loss: 0.6718, Accuracy: 3930/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[35.62637734413147 s]\n",
      "\n",
      "Test set: Average loss: 0.7016, Accuracy: 7777/10000 (77.77%)\n",
      "\n",
      "Train Epoch: 37 [0/50000 (0%)]\tLoss: 0.332488, Accuracy: 88.67\n",
      "Train Epoch: 37 [1280/50000 (3%)]\tLoss: 0.367603, Accuracy: 87.89\n",
      "Train Epoch: 37 [2560/50000 (6%)]\tLoss: 0.472229, Accuracy: 85.16\n",
      "Train Epoch: 37 [3840/50000 (9%)]\tLoss: 0.346960, Accuracy: 87.89\n",
      "Train Epoch: 37 [5120/50000 (11%)]\tLoss: 0.341343, Accuracy: 87.50\n",
      "Train Epoch: 37 [6400/50000 (14%)]\tLoss: 0.340633, Accuracy: 89.06\n",
      "Train Epoch: 37 [7680/50000 (17%)]\tLoss: 0.444309, Accuracy: 83.59\n",
      "Train Epoch: 37 [8960/50000 (20%)]\tLoss: 0.353956, Accuracy: 88.67\n",
      "Train Epoch: 37 [10240/50000 (23%)]\tLoss: 0.408330, Accuracy: 86.72\n",
      "Train Epoch: 37 [11520/50000 (26%)]\tLoss: 0.346333, Accuracy: 88.67\n",
      "Train Epoch: 37 [12800/50000 (28%)]\tLoss: 0.340607, Accuracy: 89.84\n",
      "Train Epoch: 37 [14080/50000 (31%)]\tLoss: 0.409077, Accuracy: 85.55\n",
      "Train Epoch: 37 [15360/50000 (34%)]\tLoss: 0.462982, Accuracy: 82.81\n",
      "Train Epoch: 37 [16640/50000 (37%)]\tLoss: 0.411553, Accuracy: 85.55\n",
      "Train Epoch: 37 [17920/50000 (40%)]\tLoss: 0.387019, Accuracy: 85.94\n",
      "Train Epoch: 37 [19200/50000 (43%)]\tLoss: 0.327283, Accuracy: 87.89\n",
      "Train Epoch: 37 [20480/50000 (45%)]\tLoss: 0.366957, Accuracy: 84.77\n",
      "Train Epoch: 37 [21760/50000 (48%)]\tLoss: 0.411654, Accuracy: 85.55\n",
      "Train Epoch: 37 [23040/50000 (51%)]\tLoss: 0.376533, Accuracy: 86.72\n",
      "Train Epoch: 37 [24320/50000 (54%)]\tLoss: 0.375378, Accuracy: 87.11\n",
      "Train Epoch: 37 [25600/50000 (57%)]\tLoss: 0.515342, Accuracy: 82.42\n",
      "Train Epoch: 37 [26880/50000 (60%)]\tLoss: 0.435561, Accuracy: 84.38\n",
      "Train Epoch: 37 [28160/50000 (62%)]\tLoss: 0.295616, Accuracy: 91.02\n",
      "Train Epoch: 37 [29440/50000 (65%)]\tLoss: 0.392120, Accuracy: 85.55\n",
      "Train Epoch: 37 [30720/50000 (68%)]\tLoss: 0.442325, Accuracy: 83.20\n",
      "Train Epoch: 37 [32000/50000 (71%)]\tLoss: 0.413178, Accuracy: 87.11\n",
      "Train Epoch: 37 [33280/50000 (74%)]\tLoss: 0.484437, Accuracy: 82.03\n",
      "Train Epoch: 37 [34560/50000 (77%)]\tLoss: 0.416653, Accuracy: 84.38\n",
      "Train Epoch: 37 [35840/50000 (80%)]\tLoss: 0.476400, Accuracy: 83.59\n",
      "Train Epoch: 37 [37120/50000 (82%)]\tLoss: 0.513159, Accuracy: 82.81\n",
      "Train Epoch: 37 [38400/50000 (85%)]\tLoss: 0.512615, Accuracy: 82.42\n",
      "Train Epoch: 37 [39680/50000 (88%)]\tLoss: 0.441628, Accuracy: 83.98\n",
      "Train Epoch: 37 [40960/50000 (91%)]\tLoss: 0.362869, Accuracy: 87.50\n",
      "Train Epoch: 37 [42240/50000 (94%)]\tLoss: 0.339956, Accuracy: 87.89\n",
      "Train Epoch: 37 [43520/50000 (97%)]\tLoss: 0.369022, Accuracy: 86.33\n",
      "Train Epoch: 37 [35000/50000 (99%)]\tLoss: 0.397419, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.5652, Accuracy: 4074/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[38.71782159805298 s]\n",
      "Train Epoch: 38 [0/50000 (0%)]\tLoss: 0.359276, Accuracy: 87.50\n",
      "Train Epoch: 38 [1280/50000 (3%)]\tLoss: 0.341830, Accuracy: 89.06\n",
      "Train Epoch: 38 [2560/50000 (6%)]\tLoss: 0.407657, Accuracy: 86.33\n",
      "Train Epoch: 38 [3840/50000 (9%)]\tLoss: 0.376493, Accuracy: 87.50\n",
      "Train Epoch: 38 [5120/50000 (11%)]\tLoss: 0.377497, Accuracy: 87.11\n",
      "Train Epoch: 38 [6400/50000 (14%)]\tLoss: 0.354446, Accuracy: 88.28\n",
      "Train Epoch: 38 [7680/50000 (17%)]\tLoss: 0.352099, Accuracy: 88.28\n",
      "Train Epoch: 38 [8960/50000 (20%)]\tLoss: 0.461070, Accuracy: 82.42\n",
      "Train Epoch: 38 [10240/50000 (23%)]\tLoss: 0.468344, Accuracy: 85.55\n",
      "Train Epoch: 38 [11520/50000 (26%)]\tLoss: 0.403520, Accuracy: 87.11\n",
      "Train Epoch: 38 [12800/50000 (28%)]\tLoss: 0.348821, Accuracy: 86.33\n",
      "Train Epoch: 38 [14080/50000 (31%)]\tLoss: 0.456196, Accuracy: 83.98\n",
      "Train Epoch: 38 [15360/50000 (34%)]\tLoss: 0.388824, Accuracy: 85.55\n",
      "Train Epoch: 38 [16640/50000 (37%)]\tLoss: 0.386815, Accuracy: 86.72\n",
      "Train Epoch: 38 [17920/50000 (40%)]\tLoss: 0.422646, Accuracy: 86.33\n",
      "Train Epoch: 38 [19200/50000 (43%)]\tLoss: 0.412604, Accuracy: 86.72\n",
      "Train Epoch: 38 [20480/50000 (45%)]\tLoss: 0.426720, Accuracy: 85.55\n",
      "Train Epoch: 38 [21760/50000 (48%)]\tLoss: 0.405104, Accuracy: 86.33\n",
      "Train Epoch: 38 [23040/50000 (51%)]\tLoss: 0.385035, Accuracy: 85.55\n",
      "Train Epoch: 38 [24320/50000 (54%)]\tLoss: 0.338288, Accuracy: 90.23\n",
      "Train Epoch: 38 [25600/50000 (57%)]\tLoss: 0.374323, Accuracy: 86.72\n",
      "Train Epoch: 38 [26880/50000 (60%)]\tLoss: 0.368628, Accuracy: 87.50\n",
      "Train Epoch: 38 [28160/50000 (62%)]\tLoss: 0.473275, Accuracy: 82.81\n",
      "Train Epoch: 38 [29440/50000 (65%)]\tLoss: 0.513312, Accuracy: 80.47\n",
      "Train Epoch: 38 [30720/50000 (68%)]\tLoss: 0.366044, Accuracy: 87.11\n",
      "Train Epoch: 38 [32000/50000 (71%)]\tLoss: 0.476519, Accuracy: 84.38\n",
      "Train Epoch: 38 [33280/50000 (74%)]\tLoss: 0.420084, Accuracy: 85.55\n",
      "Train Epoch: 38 [34560/50000 (77%)]\tLoss: 0.314543, Accuracy: 90.62\n",
      "Train Epoch: 38 [35840/50000 (80%)]\tLoss: 0.459345, Accuracy: 84.38\n",
      "Train Epoch: 38 [37120/50000 (82%)]\tLoss: 0.413699, Accuracy: 87.11\n",
      "Train Epoch: 38 [38400/50000 (85%)]\tLoss: 0.434326, Accuracy: 85.55\n",
      "Train Epoch: 38 [39680/50000 (88%)]\tLoss: 0.453283, Accuracy: 85.16\n",
      "Train Epoch: 38 [40960/50000 (91%)]\tLoss: 0.277150, Accuracy: 91.41\n",
      "Train Epoch: 38 [42240/50000 (94%)]\tLoss: 0.485446, Accuracy: 85.16\n",
      "Train Epoch: 38 [43520/50000 (97%)]\tLoss: 0.322006, Accuracy: 89.84\n",
      "Train Epoch: 38 [35000/50000 (99%)]\tLoss: 0.362086, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.5983, Accuracy: 4015/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[35.598403215408325 s]\n",
      "\n",
      "Test set: Average loss: 0.6309, Accuracy: 7948/10000 (79.48%)\n",
      "\n",
      "Train Epoch: 39 [0/50000 (0%)]\tLoss: 0.356803, Accuracy: 86.72\n",
      "Train Epoch: 39 [1280/50000 (3%)]\tLoss: 0.289477, Accuracy: 89.84\n",
      "Train Epoch: 39 [2560/50000 (6%)]\tLoss: 0.394703, Accuracy: 87.11\n",
      "Train Epoch: 39 [3840/50000 (9%)]\tLoss: 0.456520, Accuracy: 84.77\n",
      "Train Epoch: 39 [5120/50000 (11%)]\tLoss: 0.311778, Accuracy: 90.23\n",
      "Train Epoch: 39 [6400/50000 (14%)]\tLoss: 0.382243, Accuracy: 85.94\n",
      "Train Epoch: 39 [7680/50000 (17%)]\tLoss: 0.432255, Accuracy: 86.33\n",
      "Train Epoch: 39 [8960/50000 (20%)]\tLoss: 0.471411, Accuracy: 85.55\n",
      "Train Epoch: 39 [10240/50000 (23%)]\tLoss: 0.317400, Accuracy: 88.28\n",
      "Train Epoch: 39 [11520/50000 (26%)]\tLoss: 0.328196, Accuracy: 89.06\n",
      "Train Epoch: 39 [12800/50000 (28%)]\tLoss: 0.368660, Accuracy: 85.55\n",
      "Train Epoch: 39 [14080/50000 (31%)]\tLoss: 0.360553, Accuracy: 88.28\n",
      "Train Epoch: 39 [15360/50000 (34%)]\tLoss: 0.286287, Accuracy: 90.23\n",
      "Train Epoch: 39 [16640/50000 (37%)]\tLoss: 0.355626, Accuracy: 89.06\n",
      "Train Epoch: 39 [17920/50000 (40%)]\tLoss: 0.400267, Accuracy: 87.50\n",
      "Train Epoch: 39 [19200/50000 (43%)]\tLoss: 0.449166, Accuracy: 83.98\n",
      "Train Epoch: 39 [20480/50000 (45%)]\tLoss: 0.404740, Accuracy: 85.16\n",
      "Train Epoch: 39 [21760/50000 (48%)]\tLoss: 0.405607, Accuracy: 88.67\n",
      "Train Epoch: 39 [23040/50000 (51%)]\tLoss: 0.472729, Accuracy: 83.59\n",
      "Train Epoch: 39 [24320/50000 (54%)]\tLoss: 0.321065, Accuracy: 90.62\n",
      "Train Epoch: 39 [25600/50000 (57%)]\tLoss: 0.407921, Accuracy: 85.16\n",
      "Train Epoch: 39 [26880/50000 (60%)]\tLoss: 0.484491, Accuracy: 84.77\n",
      "Train Epoch: 39 [28160/50000 (62%)]\tLoss: 0.446175, Accuracy: 82.81\n",
      "Train Epoch: 39 [29440/50000 (65%)]\tLoss: 0.392224, Accuracy: 84.38\n",
      "Train Epoch: 39 [30720/50000 (68%)]\tLoss: 0.346542, Accuracy: 89.45\n",
      "Train Epoch: 39 [32000/50000 (71%)]\tLoss: 0.351220, Accuracy: 87.50\n",
      "Train Epoch: 39 [33280/50000 (74%)]\tLoss: 0.351401, Accuracy: 88.28\n",
      "Train Epoch: 39 [34560/50000 (77%)]\tLoss: 0.391590, Accuracy: 85.94\n",
      "Train Epoch: 39 [35840/50000 (80%)]\tLoss: 0.347243, Accuracy: 86.72\n",
      "Train Epoch: 39 [37120/50000 (82%)]\tLoss: 0.436000, Accuracy: 85.55\n",
      "Train Epoch: 39 [38400/50000 (85%)]\tLoss: 0.316225, Accuracy: 89.45\n",
      "Train Epoch: 39 [39680/50000 (88%)]\tLoss: 0.391877, Accuracy: 86.72\n",
      "Train Epoch: 39 [40960/50000 (91%)]\tLoss: 0.305688, Accuracy: 91.41\n",
      "Train Epoch: 39 [42240/50000 (94%)]\tLoss: 0.444638, Accuracy: 84.77\n",
      "Train Epoch: 39 [43520/50000 (97%)]\tLoss: 0.385794, Accuracy: 85.16\n",
      "Train Epoch: 39 [35000/50000 (99%)]\tLoss: 0.475624, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.9167, Accuracy: 3634/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[38.70156002044678 s]\n",
      "Train Epoch: 40 [0/50000 (0%)]\tLoss: 0.313528, Accuracy: 90.62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 40 [1280/50000 (3%)]\tLoss: 0.383493, Accuracy: 87.89\n",
      "Train Epoch: 40 [2560/50000 (6%)]\tLoss: 0.341826, Accuracy: 89.84\n",
      "Train Epoch: 40 [3840/50000 (9%)]\tLoss: 0.318757, Accuracy: 87.89\n",
      "Train Epoch: 40 [5120/50000 (11%)]\tLoss: 0.400480, Accuracy: 84.38\n",
      "Train Epoch: 40 [6400/50000 (14%)]\tLoss: 0.319145, Accuracy: 87.11\n",
      "Train Epoch: 40 [7680/50000 (17%)]\tLoss: 0.392847, Accuracy: 87.89\n",
      "Train Epoch: 40 [8960/50000 (20%)]\tLoss: 0.378811, Accuracy: 88.28\n",
      "Train Epoch: 40 [10240/50000 (23%)]\tLoss: 0.263613, Accuracy: 91.41\n",
      "Train Epoch: 40 [11520/50000 (26%)]\tLoss: 0.428231, Accuracy: 85.94\n",
      "Train Epoch: 40 [12800/50000 (28%)]\tLoss: 0.454602, Accuracy: 84.38\n",
      "Train Epoch: 40 [14080/50000 (31%)]\tLoss: 0.332892, Accuracy: 88.28\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 1.0442e+00]],\n",
      "\n",
      "        [[-2.5395e-02]],\n",
      "\n",
      "        [[-8.1968e-01]],\n",
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      "        [[ 5.5749e-01]],\n",
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      "        [[-1.8767e+00]],\n",
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      "        [[ 8.4855e-01]],\n",
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      "        [[ 1.1047e+00]],\n",
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      "        [[-1.8991e+00]],\n",
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      "        [[ 6.2783e-01]],\n",
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      "        [[ 8.6015e-02]],\n",
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      "        [[ 1.2027e-02]],\n",
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      "\n",
      "        [[ 2.9365e+00]],\n",
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      "        [[-1.0320e+00]],\n",
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      "        [[-7.6437e-01]],\n",
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      "        [[ 4.2139e-01]],\n",
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      "        [[-1.2203e-01]],\n",
      "\n",
      "        [[-1.4646e+00]],\n",
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      "        [[-6.6628e-01]],\n",
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      "        [[-1.4423e-01]],\n",
      "\n",
      "        [[ 6.0711e-02]],\n",
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      "        [[-9.0215e-01]],\n",
      "\n",
      "        [[ 3.8956e-01]],\n",
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      "        [[ 1.9146e+00]],\n",
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      "        [[-1.4651e-01]],\n",
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      "        [[ 4.0579e-01]],\n",
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      "        [[-1.2592e+00]],\n",
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      "        [[ 1.2421e+00]],\n",
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      "        [[ 3.4257e-01]],\n",
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      "        [[-1.7937e+00]],\n",
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      "        [[-9.9532e-01]],\n",
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      "        [[ 9.0588e-01]],\n",
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      "        [[ 8.0559e-01]],\n",
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      "        [[ 6.4246e-01]],\n",
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      "        [[ 1.7141e+00]],\n",
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      "        [[-1.4614e+00]],\n",
      "\n",
      "        [[-1.3003e-01]],\n",
      "\n",
      "        [[ 1.3361e+00]],\n",
      "\n",
      "        [[-1.0147e+00]],\n",
      "\n",
      "        [[ 1.5359e+00]],\n",
      "\n",
      "        [[ 4.5278e-01]],\n",
      "\n",
      "        [[ 8.4292e-01]],\n",
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      "        [[-1.7467e+00]],\n",
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      "        [[-8.8909e-01]],\n",
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      "        [[-1.0843e+00]],\n",
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      "        [[-7.2935e-01]],\n",
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      "        [[ 4.9830e-01]],\n",
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      "        [[ 2.2823e-01]],\n",
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      "        [[ 3.8137e-01]],\n",
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      "        [[ 8.3640e-01]],\n",
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      "        [[ 1.4000e-01]],\n",
      "\n",
      "        [[ 4.9992e-01]],\n",
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      "        [[ 2.6606e-01]],\n",
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      "        [[ 2.9173e-01]],\n",
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      "        [[ 2.4809e-01]],\n",
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      "        [[ 3.5307e-01]],\n",
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      "\n",
      "        [[ 8.1279e-01]],\n",
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      "        [[-4.6037e-01]],\n",
      "\n",
      "        [[ 2.5690e-01]],\n",
      "\n",
      "        [[-1.0724e+00]],\n",
      "\n",
      "        [[ 1.7639e+00]],\n",
      "\n",
      "        [[ 2.2008e+00]],\n",
      "\n",
      "        [[ 5.6153e-03]],\n",
      "\n",
      "        [[ 4.9611e-01]],\n",
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      "        [[ 6.7430e-01]],\n",
      "\n",
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      "        [[ 1.7962e-01]],\n",
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      "        [[-1.1451e+00]],\n",
      "\n",
      "        [[ 9.0799e-01]],\n",
      "\n",
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      "        [[ 2.5645e-01]],\n",
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      "\n",
      "        [[ 2.1885e+00]],\n",
      "\n",
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      "\n",
      "        [[-8.3577e-03]],\n",
      "\n",
      "        [[-6.6818e-01]],\n",
      "\n",
      "        [[ 9.7723e-01]],\n",
      "\n",
      "        [[-1.3102e+00]],\n",
      "\n",
      "        [[ 8.8672e-01]],\n",
      "\n",
      "        [[ 5.5974e-01]],\n",
      "\n",
      "        [[ 8.0410e-02]],\n",
      "\n",
      "        [[ 2.4822e+00]],\n",
      "\n",
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      "\n",
      "        [[ 5.5955e-01]],\n",
      "\n",
      "        [[-1.1996e+00]],\n",
      "\n",
      "        [[ 2.1272e+00]],\n",
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      "\n",
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      "\n",
      "        [[ 2.0100e-01]],\n",
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      "        [[ 1.6505e+00]],\n",
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      "\n",
      "        [[ 2.3919e-05]],\n",
      "\n",
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      "\n",
      "        [[ 1.3094e-02]],\n",
      "\n",
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      "\n",
      "        [[ 6.8470e-01]],\n",
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      "        [[ 2.7113e-02]],\n",
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      "        [[-1.3691e+00]],\n",
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      "        [[-1.8243e+00]],\n",
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      "        [[-7.1896e-01]],\n",
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      "        [[-7.0552e-01]],\n",
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      "        [[-4.1035e-01]],\n",
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      "        [[ 3.3841e-01]],\n",
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      "        [[-3.3876e-01]],\n",
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      "        [[ 1.0635e+00]],\n",
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      "        [[-1.3147e+00]],\n",
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      "        [[ 1.7022e-01]],\n",
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      "        [[ 6.4385e-01]],\n",
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      "        [[-3.0824e-01]],\n",
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      "        [[ 1.2802e+00]],\n",
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      "        [[-4.8675e-01]],\n",
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      "        [[-2.4063e-01]],\n",
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      "        [[ 4.6880e-01]],\n",
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      "        [[ 4.3439e-01]],\n",
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      "        [[-4.9321e-01]],\n",
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      "        [[ 9.8318e-01]],\n",
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      "        [[-6.0430e-01]],\n",
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      "        [[ 1.5650e+00]],\n",
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      "        [[-2.5891e-01]],\n",
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      "        [[ 2.2876e+00]],\n",
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      "        [[-1.1382e+00]],\n",
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      "        [[ 5.7267e-01]],\n",
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      "        [[-9.4232e-01]],\n",
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      "        [[ 5.0175e-01]],\n",
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      "        [[ 4.1953e-01]],\n",
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      "        [[-5.1876e-01]],\n",
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      "        [[ 3.5794e-01]],\n",
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      "        [[ 2.8165e+00]],\n",
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      "        [[-1.2103e+00]],\n",
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      "        [[ 8.2153e-01]],\n",
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      "        [[-1.0957e+00]],\n",
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      "        [[-3.2917e-01]],\n",
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      "        [[ 2.7290e-01]],\n",
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      "        [[ 1.3128e+00]],\n",
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      "        [[-3.2406e+00]],\n",
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      "        [[ 2.2490e+00]],\n",
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      "        [[ 2.8883e-01]],\n",
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      "        [[-1.4092e+00]],\n",
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      "\n",
      "        [[-1.6253e+00]],\n",
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      "torch.Size([1, 448, 512])\n",
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      "           8.6030e-04,  1.2721e-03],\n",
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      "         ...,\n",
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      "         [-1.2989e-03, -1.3257e-03,  4.6501e-03,  ..., -1.8200e-03,\n",
      "          -1.3517e-03, -1.7213e-03],\n",
      "         [ 1.1442e-03, -1.3643e-03, -1.3913e-03,  ..., -1.9920e-03,\n",
      "          -2.3337e-03, -6.3718e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 40 [15360/50000 (34%)]\tLoss: 0.373904, Accuracy: 87.11\n",
      "Train Epoch: 40 [16640/50000 (37%)]\tLoss: 0.354775, Accuracy: 87.89\n",
      "Train Epoch: 40 [17920/50000 (40%)]\tLoss: 0.442323, Accuracy: 85.16\n",
      "Train Epoch: 40 [19200/50000 (43%)]\tLoss: 0.397162, Accuracy: 87.50\n",
      "Train Epoch: 40 [20480/50000 (45%)]\tLoss: 0.427829, Accuracy: 84.38\n",
      "Train Epoch: 40 [21760/50000 (48%)]\tLoss: 0.363176, Accuracy: 87.50\n",
      "Train Epoch: 40 [23040/50000 (51%)]\tLoss: 0.432199, Accuracy: 86.33\n",
      "Train Epoch: 40 [24320/50000 (54%)]\tLoss: 0.293947, Accuracy: 89.45\n",
      "Train Epoch: 40 [25600/50000 (57%)]\tLoss: 0.510069, Accuracy: 84.77\n",
      "Train Epoch: 40 [26880/50000 (60%)]\tLoss: 0.370854, Accuracy: 85.16\n",
      "Train Epoch: 40 [28160/50000 (62%)]\tLoss: 0.413671, Accuracy: 83.59\n",
      "Train Epoch: 40 [29440/50000 (65%)]\tLoss: 0.335925, Accuracy: 87.89\n",
      "Train Epoch: 40 [30720/50000 (68%)]\tLoss: 0.419799, Accuracy: 85.16\n",
      "Train Epoch: 40 [32000/50000 (71%)]\tLoss: 0.470080, Accuracy: 84.38\n",
      "Train Epoch: 40 [33280/50000 (74%)]\tLoss: 0.317709, Accuracy: 88.28\n",
      "Train Epoch: 40 [34560/50000 (77%)]\tLoss: 0.385169, Accuracy: 87.11\n",
      "Train Epoch: 40 [35840/50000 (80%)]\tLoss: 0.380306, Accuracy: 86.33\n",
      "Train Epoch: 40 [37120/50000 (82%)]\tLoss: 0.378033, Accuracy: 88.28\n",
      "Train Epoch: 40 [38400/50000 (85%)]\tLoss: 0.350789, Accuracy: 86.72\n",
      "Train Epoch: 40 [39680/50000 (88%)]\tLoss: 0.402865, Accuracy: 83.59\n",
      "Train Epoch: 40 [40960/50000 (91%)]\tLoss: 0.294747, Accuracy: 90.23\n",
      "Train Epoch: 40 [42240/50000 (94%)]\tLoss: 0.378821, Accuracy: 86.33\n",
      "Train Epoch: 40 [43520/50000 (97%)]\tLoss: 0.420646, Accuracy: 85.16\n",
      "Train Epoch: 40 [35000/50000 (99%)]\tLoss: 0.375969, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.6469, Accuracy: 3918/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[35.6479651927948 s]\n",
      "\n",
      "Test set: Average loss: 0.6529, Accuracy: 7855/10000 (78.55%)\n",
      "\n",
      "Train Epoch: 41 [0/50000 (0%)]\tLoss: 0.546348, Accuracy: 81.64\n",
      "Train Epoch: 41 [1280/50000 (3%)]\tLoss: 0.375921, Accuracy: 86.72\n",
      "Train Epoch: 41 [2560/50000 (6%)]\tLoss: 0.397202, Accuracy: 87.11\n",
      "Train Epoch: 41 [3840/50000 (9%)]\tLoss: 0.328672, Accuracy: 87.89\n",
      "Train Epoch: 41 [5120/50000 (11%)]\tLoss: 0.410379, Accuracy: 86.72\n",
      "Train Epoch: 41 [6400/50000 (14%)]\tLoss: 0.451192, Accuracy: 83.59\n",
      "Train Epoch: 41 [7680/50000 (17%)]\tLoss: 0.331255, Accuracy: 88.28\n",
      "Train Epoch: 41 [8960/50000 (20%)]\tLoss: 0.410759, Accuracy: 85.16\n",
      "Train Epoch: 41 [10240/50000 (23%)]\tLoss: 0.463670, Accuracy: 83.59\n",
      "Train Epoch: 41 [11520/50000 (26%)]\tLoss: 0.317582, Accuracy: 91.02\n",
      "Train Epoch: 41 [12800/50000 (28%)]\tLoss: 0.368570, Accuracy: 87.89\n",
      "Train Epoch: 41 [14080/50000 (31%)]\tLoss: 0.286353, Accuracy: 91.41\n",
      "Train Epoch: 41 [15360/50000 (34%)]\tLoss: 0.380892, Accuracy: 86.72\n",
      "Train Epoch: 41 [16640/50000 (37%)]\tLoss: 0.421109, Accuracy: 85.55\n",
      "Train Epoch: 41 [17920/50000 (40%)]\tLoss: 0.378716, Accuracy: 84.77\n",
      "Train Epoch: 41 [19200/50000 (43%)]\tLoss: 0.372214, Accuracy: 87.89\n",
      "Train Epoch: 41 [20480/50000 (45%)]\tLoss: 0.508089, Accuracy: 82.81\n",
      "Train Epoch: 41 [21760/50000 (48%)]\tLoss: 0.372281, Accuracy: 86.72\n",
      "Train Epoch: 41 [23040/50000 (51%)]\tLoss: 0.338695, Accuracy: 89.06\n",
      "Train Epoch: 41 [24320/50000 (54%)]\tLoss: 0.364968, Accuracy: 88.28\n",
      "Train Epoch: 41 [25600/50000 (57%)]\tLoss: 0.352683, Accuracy: 87.89\n",
      "Train Epoch: 41 [26880/50000 (60%)]\tLoss: 0.339277, Accuracy: 87.89\n",
      "Train Epoch: 41 [28160/50000 (62%)]\tLoss: 0.436791, Accuracy: 85.94\n",
      "Train Epoch: 41 [29440/50000 (65%)]\tLoss: 0.322169, Accuracy: 88.67\n",
      "Train Epoch: 41 [30720/50000 (68%)]\tLoss: 0.432880, Accuracy: 84.77\n",
      "Train Epoch: 41 [32000/50000 (71%)]\tLoss: 0.371542, Accuracy: 85.94\n",
      "Train Epoch: 41 [33280/50000 (74%)]\tLoss: 0.435150, Accuracy: 81.64\n",
      "Train Epoch: 41 [34560/50000 (77%)]\tLoss: 0.365670, Accuracy: 86.33\n",
      "Train Epoch: 41 [35840/50000 (80%)]\tLoss: 0.398535, Accuracy: 85.16\n",
      "Train Epoch: 41 [37120/50000 (82%)]\tLoss: 0.465873, Accuracy: 84.38\n",
      "Train Epoch: 41 [38400/50000 (85%)]\tLoss: 0.327454, Accuracy: 91.02\n",
      "Train Epoch: 41 [39680/50000 (88%)]\tLoss: 0.322090, Accuracy: 86.72\n",
      "Train Epoch: 41 [40960/50000 (91%)]\tLoss: 0.343987, Accuracy: 89.45\n",
      "Train Epoch: 41 [42240/50000 (94%)]\tLoss: 0.393132, Accuracy: 85.94\n",
      "Train Epoch: 41 [43520/50000 (97%)]\tLoss: 0.370401, Accuracy: 88.67\n",
      "Train Epoch: 41 [35000/50000 (99%)]\tLoss: 0.419751, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.9478, Accuracy: 3561/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[38.65071487426758 s]\n",
      "Train Epoch: 42 [0/50000 (0%)]\tLoss: 0.411590, Accuracy: 85.94\n",
      "Train Epoch: 42 [1280/50000 (3%)]\tLoss: 0.394572, Accuracy: 88.28\n",
      "Train Epoch: 42 [2560/50000 (6%)]\tLoss: 0.299713, Accuracy: 89.06\n",
      "Train Epoch: 42 [3840/50000 (9%)]\tLoss: 0.326531, Accuracy: 89.06\n",
      "Train Epoch: 42 [5120/50000 (11%)]\tLoss: 0.434344, Accuracy: 84.77\n",
      "Train Epoch: 42 [6400/50000 (14%)]\tLoss: 0.311056, Accuracy: 89.45\n",
      "Train Epoch: 42 [7680/50000 (17%)]\tLoss: 0.377698, Accuracy: 85.94\n",
      "Train Epoch: 42 [8960/50000 (20%)]\tLoss: 0.345733, Accuracy: 88.67\n",
      "Train Epoch: 42 [10240/50000 (23%)]\tLoss: 0.316796, Accuracy: 89.06\n",
      "Train Epoch: 42 [11520/50000 (26%)]\tLoss: 0.419163, Accuracy: 85.16\n",
      "Train Epoch: 42 [12800/50000 (28%)]\tLoss: 0.315814, Accuracy: 88.67\n",
      "Train Epoch: 42 [14080/50000 (31%)]\tLoss: 0.406288, Accuracy: 87.11\n",
      "Train Epoch: 42 [15360/50000 (34%)]\tLoss: 0.344383, Accuracy: 88.28\n",
      "Train Epoch: 42 [16640/50000 (37%)]\tLoss: 0.413779, Accuracy: 86.72\n",
      "Train Epoch: 42 [17920/50000 (40%)]\tLoss: 0.536827, Accuracy: 84.38\n",
      "Train Epoch: 42 [19200/50000 (43%)]\tLoss: 0.413817, Accuracy: 87.11\n",
      "Train Epoch: 42 [20480/50000 (45%)]\tLoss: 0.385727, Accuracy: 88.67\n",
      "Train Epoch: 42 [21760/50000 (48%)]\tLoss: 0.423923, Accuracy: 86.33\n",
      "Train Epoch: 42 [23040/50000 (51%)]\tLoss: 0.336639, Accuracy: 89.06\n",
      "Train Epoch: 42 [24320/50000 (54%)]\tLoss: 0.334085, Accuracy: 89.45\n",
      "Train Epoch: 42 [25600/50000 (57%)]\tLoss: 0.396465, Accuracy: 83.98\n",
      "Train Epoch: 42 [26880/50000 (60%)]\tLoss: 0.337783, Accuracy: 88.28\n",
      "Train Epoch: 42 [28160/50000 (62%)]\tLoss: 0.395792, Accuracy: 84.38\n",
      "Train Epoch: 42 [29440/50000 (65%)]\tLoss: 0.385425, Accuracy: 86.33\n",
      "Train Epoch: 42 [30720/50000 (68%)]\tLoss: 0.511971, Accuracy: 82.03\n",
      "Train Epoch: 42 [32000/50000 (71%)]\tLoss: 0.326349, Accuracy: 89.06\n",
      "Train Epoch: 42 [33280/50000 (74%)]\tLoss: 0.468233, Accuracy: 82.81\n",
      "Train Epoch: 42 [34560/50000 (77%)]\tLoss: 0.397691, Accuracy: 86.33\n",
      "Train Epoch: 42 [35840/50000 (80%)]\tLoss: 0.394589, Accuracy: 87.89\n",
      "Train Epoch: 42 [37120/50000 (82%)]\tLoss: 0.388175, Accuracy: 86.72\n",
      "Train Epoch: 42 [38400/50000 (85%)]\tLoss: 0.390471, Accuracy: 85.94\n",
      "Train Epoch: 42 [39680/50000 (88%)]\tLoss: 0.405536, Accuracy: 85.94\n",
      "Train Epoch: 42 [40960/50000 (91%)]\tLoss: 0.398188, Accuracy: 85.55\n",
      "Train Epoch: 42 [42240/50000 (94%)]\tLoss: 0.267659, Accuracy: 89.84\n",
      "Train Epoch: 42 [43520/50000 (97%)]\tLoss: 0.337498, Accuracy: 87.50\n",
      "Train Epoch: 42 [35000/50000 (99%)]\tLoss: 0.360830, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 1.0896, Accuracy: 3452/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[35.671298027038574 s]\n",
      "\n",
      "Test set: Average loss: 1.0902, Accuracy: 6938/10000 (69.38%)\n",
      "\n",
      "Train Epoch: 43 [0/50000 (0%)]\tLoss: 0.318752, Accuracy: 87.50\n",
      "Train Epoch: 43 [1280/50000 (3%)]\tLoss: 0.325664, Accuracy: 89.84\n",
      "Train Epoch: 43 [2560/50000 (6%)]\tLoss: 0.340007, Accuracy: 87.89\n",
      "Train Epoch: 43 [3840/50000 (9%)]\tLoss: 0.427430, Accuracy: 86.72\n",
      "Train Epoch: 43 [5120/50000 (11%)]\tLoss: 0.375759, Accuracy: 85.55\n",
      "Train Epoch: 43 [6400/50000 (14%)]\tLoss: 0.460487, Accuracy: 85.16\n",
      "Train Epoch: 43 [7680/50000 (17%)]\tLoss: 0.361591, Accuracy: 88.28\n",
      "Train Epoch: 43 [8960/50000 (20%)]\tLoss: 0.334600, Accuracy: 89.84\n",
      "Train Epoch: 43 [10240/50000 (23%)]\tLoss: 0.321243, Accuracy: 89.84\n",
      "Train Epoch: 43 [11520/50000 (26%)]\tLoss: 0.338995, Accuracy: 87.11\n",
      "Train Epoch: 43 [12800/50000 (28%)]\tLoss: 0.370920, Accuracy: 88.67\n",
      "Train Epoch: 43 [14080/50000 (31%)]\tLoss: 0.497429, Accuracy: 83.20\n",
      "Train Epoch: 43 [15360/50000 (34%)]\tLoss: 0.440586, Accuracy: 84.38\n",
      "Train Epoch: 43 [16640/50000 (37%)]\tLoss: 0.492734, Accuracy: 82.42\n",
      "Train Epoch: 43 [17920/50000 (40%)]\tLoss: 0.426005, Accuracy: 84.38\n",
      "Train Epoch: 43 [19200/50000 (43%)]\tLoss: 0.337795, Accuracy: 89.06\n",
      "Train Epoch: 43 [20480/50000 (45%)]\tLoss: 0.311079, Accuracy: 89.45\n",
      "Train Epoch: 43 [21760/50000 (48%)]\tLoss: 0.446725, Accuracy: 85.55\n",
      "Train Epoch: 43 [23040/50000 (51%)]\tLoss: 0.349411, Accuracy: 89.84\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 43 [24320/50000 (54%)]\tLoss: 0.404215, Accuracy: 84.77\n",
      "Train Epoch: 43 [25600/50000 (57%)]\tLoss: 0.354135, Accuracy: 88.67\n",
      "Train Epoch: 43 [26880/50000 (60%)]\tLoss: 0.320700, Accuracy: 91.02\n",
      "Train Epoch: 43 [28160/50000 (62%)]\tLoss: 0.464369, Accuracy: 85.16\n",
      "Train Epoch: 43 [29440/50000 (65%)]\tLoss: 0.334197, Accuracy: 89.84\n",
      "Train Epoch: 43 [30720/50000 (68%)]\tLoss: 0.339557, Accuracy: 87.89\n",
      "Train Epoch: 43 [32000/50000 (71%)]\tLoss: 0.390028, Accuracy: 85.55\n",
      "Train Epoch: 43 [33280/50000 (74%)]\tLoss: 0.453454, Accuracy: 82.42\n",
      "Train Epoch: 43 [34560/50000 (77%)]\tLoss: 0.379033, Accuracy: 86.72\n",
      "Train Epoch: 43 [35840/50000 (80%)]\tLoss: 0.397941, Accuracy: 85.55\n",
      "Train Epoch: 43 [37120/50000 (82%)]\tLoss: 0.390893, Accuracy: 85.94\n",
      "Train Epoch: 43 [38400/50000 (85%)]\tLoss: 0.429131, Accuracy: 85.94\n",
      "Train Epoch: 43 [39680/50000 (88%)]\tLoss: 0.435324, Accuracy: 88.28\n",
      "Train Epoch: 43 [40960/50000 (91%)]\tLoss: 0.468446, Accuracy: 83.20\n",
      "Train Epoch: 43 [42240/50000 (94%)]\tLoss: 0.354382, Accuracy: 87.50\n",
      "Train Epoch: 43 [43520/50000 (97%)]\tLoss: 0.375975, Accuracy: 86.72\n",
      "Train Epoch: 43 [35000/50000 (99%)]\tLoss: 0.260183, Accuracy: 93.00\n",
      "\n",
      "Validation set: Average loss: 0.7558, Accuracy: 3901/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[38.8174045085907 s]\n",
      "Train Epoch: 44 [0/50000 (0%)]\tLoss: 0.483758, Accuracy: 82.03\n",
      "Train Epoch: 44 [1280/50000 (3%)]\tLoss: 0.322526, Accuracy: 88.28\n",
      "Train Epoch: 44 [2560/50000 (6%)]\tLoss: 0.434001, Accuracy: 82.03\n",
      "Train Epoch: 44 [3840/50000 (9%)]\tLoss: 0.317145, Accuracy: 87.11\n",
      "Train Epoch: 44 [5120/50000 (11%)]\tLoss: 0.352690, Accuracy: 89.84\n",
      "Train Epoch: 44 [6400/50000 (14%)]\tLoss: 0.438408, Accuracy: 86.72\n",
      "Train Epoch: 44 [7680/50000 (17%)]\tLoss: 0.452557, Accuracy: 83.20\n",
      "Train Epoch: 44 [8960/50000 (20%)]\tLoss: 0.401967, Accuracy: 84.77\n",
      "Train Epoch: 44 [10240/50000 (23%)]\tLoss: 0.451017, Accuracy: 83.20\n",
      "Train Epoch: 44 [11520/50000 (26%)]\tLoss: 0.371648, Accuracy: 87.50\n",
      "Train Epoch: 44 [12800/50000 (28%)]\tLoss: 0.365261, Accuracy: 89.06\n",
      "Train Epoch: 44 [14080/50000 (31%)]\tLoss: 0.327430, Accuracy: 89.84\n",
      "Train Epoch: 44 [15360/50000 (34%)]\tLoss: 0.445028, Accuracy: 84.77\n",
      "Train Epoch: 44 [16640/50000 (37%)]\tLoss: 0.385148, Accuracy: 87.50\n",
      "Train Epoch: 44 [17920/50000 (40%)]\tLoss: 0.342579, Accuracy: 89.45\n",
      "Train Epoch: 44 [19200/50000 (43%)]\tLoss: 0.365073, Accuracy: 87.11\n",
      "Train Epoch: 44 [20480/50000 (45%)]\tLoss: 0.398408, Accuracy: 86.33\n",
      "Train Epoch: 44 [21760/50000 (48%)]\tLoss: 0.371652, Accuracy: 88.28\n",
      "Train Epoch: 44 [23040/50000 (51%)]\tLoss: 0.386337, Accuracy: 84.38\n",
      "Train Epoch: 44 [24320/50000 (54%)]\tLoss: 0.448199, Accuracy: 83.98\n",
      "Train Epoch: 44 [25600/50000 (57%)]\tLoss: 0.395098, Accuracy: 87.11\n",
      "Train Epoch: 44 [26880/50000 (60%)]\tLoss: 0.363585, Accuracy: 88.67\n",
      "Train Epoch: 44 [28160/50000 (62%)]\tLoss: 0.377787, Accuracy: 85.16\n",
      "Train Epoch: 44 [29440/50000 (65%)]\tLoss: 0.495363, Accuracy: 83.59\n",
      "Train Epoch: 44 [30720/50000 (68%)]\tLoss: 0.303402, Accuracy: 89.45\n",
      "Train Epoch: 44 [32000/50000 (71%)]\tLoss: 0.449359, Accuracy: 82.42\n",
      "Train Epoch: 44 [33280/50000 (74%)]\tLoss: 0.321548, Accuracy: 89.84\n",
      "Train Epoch: 44 [34560/50000 (77%)]\tLoss: 0.307477, Accuracy: 89.06\n",
      "Train Epoch: 44 [35840/50000 (80%)]\tLoss: 0.297629, Accuracy: 91.41\n",
      "Train Epoch: 44 [37120/50000 (82%)]\tLoss: 0.408550, Accuracy: 86.72\n",
      "Train Epoch: 44 [38400/50000 (85%)]\tLoss: 0.332708, Accuracy: 89.06\n",
      "Train Epoch: 44 [39680/50000 (88%)]\tLoss: 0.325607, Accuracy: 87.89\n",
      "Train Epoch: 44 [40960/50000 (91%)]\tLoss: 0.373245, Accuracy: 88.28\n",
      "Train Epoch: 44 [42240/50000 (94%)]\tLoss: 0.323784, Accuracy: 88.28\n",
      "Train Epoch: 44 [43520/50000 (97%)]\tLoss: 0.385331, Accuracy: 85.16\n",
      "Train Epoch: 44 [35000/50000 (99%)]\tLoss: 0.429283, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.6337, Accuracy: 3993/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[35.62658381462097 s]\n",
      "\n",
      "Test set: Average loss: 0.6595, Accuracy: 7864/10000 (78.64%)\n",
      "\n",
      "Train Epoch: 45 [0/50000 (0%)]\tLoss: 0.249042, Accuracy: 92.58\n",
      "Train Epoch: 45 [1280/50000 (3%)]\tLoss: 0.337185, Accuracy: 87.50\n",
      "Train Epoch: 45 [2560/50000 (6%)]\tLoss: 0.508927, Accuracy: 82.42\n",
      "Train Epoch: 45 [3840/50000 (9%)]\tLoss: 0.469449, Accuracy: 83.98\n",
      "Train Epoch: 45 [5120/50000 (11%)]\tLoss: 0.373278, Accuracy: 86.72\n",
      "Train Epoch: 45 [6400/50000 (14%)]\tLoss: 0.425069, Accuracy: 82.81\n",
      "Train Epoch: 45 [7680/50000 (17%)]\tLoss: 0.313608, Accuracy: 88.67\n",
      "Train Epoch: 45 [8960/50000 (20%)]\tLoss: 0.440590, Accuracy: 84.77\n",
      "Train Epoch: 45 [10240/50000 (23%)]\tLoss: 0.445255, Accuracy: 84.38\n",
      "Train Epoch: 45 [11520/50000 (26%)]\tLoss: 0.390236, Accuracy: 87.89\n",
      "Train Epoch: 45 [12800/50000 (28%)]\tLoss: 0.464178, Accuracy: 85.55\n",
      "Train Epoch: 45 [14080/50000 (31%)]\tLoss: 0.369717, Accuracy: 87.89\n",
      "Train Epoch: 45 [15360/50000 (34%)]\tLoss: 0.452674, Accuracy: 84.38\n",
      "Train Epoch: 45 [16640/50000 (37%)]\tLoss: 0.396194, Accuracy: 86.72\n",
      "Train Epoch: 45 [17920/50000 (40%)]\tLoss: 0.347274, Accuracy: 84.38\n",
      "Train Epoch: 45 [19200/50000 (43%)]\tLoss: 0.314172, Accuracy: 87.50\n",
      "Train Epoch: 45 [20480/50000 (45%)]\tLoss: 0.428067, Accuracy: 87.11\n",
      "Train Epoch: 45 [21760/50000 (48%)]\tLoss: 0.453016, Accuracy: 85.55\n",
      "Train Epoch: 45 [23040/50000 (51%)]\tLoss: 0.408064, Accuracy: 83.98\n",
      "Train Epoch: 45 [24320/50000 (54%)]\tLoss: 0.539124, Accuracy: 82.81\n",
      "Train Epoch: 45 [25600/50000 (57%)]\tLoss: 0.376278, Accuracy: 87.11\n",
      "Train Epoch: 45 [26880/50000 (60%)]\tLoss: 0.416047, Accuracy: 86.33\n",
      "Train Epoch: 45 [28160/50000 (62%)]\tLoss: 0.448738, Accuracy: 85.55\n",
      "Train Epoch: 45 [29440/50000 (65%)]\tLoss: 0.402476, Accuracy: 85.55\n",
      "Train Epoch: 45 [30720/50000 (68%)]\tLoss: 0.363997, Accuracy: 89.06\n",
      "Train Epoch: 45 [32000/50000 (71%)]\tLoss: 0.279504, Accuracy: 89.84\n",
      "Train Epoch: 45 [33280/50000 (74%)]\tLoss: 0.369891, Accuracy: 88.28\n",
      "Train Epoch: 45 [34560/50000 (77%)]\tLoss: 0.359875, Accuracy: 88.28\n",
      "Train Epoch: 45 [35840/50000 (80%)]\tLoss: 0.356780, Accuracy: 87.11\n",
      "Train Epoch: 45 [37120/50000 (82%)]\tLoss: 0.431886, Accuracy: 87.11\n",
      "Train Epoch: 45 [38400/50000 (85%)]\tLoss: 0.411030, Accuracy: 85.55\n",
      "Train Epoch: 45 [39680/50000 (88%)]\tLoss: 0.395579, Accuracy: 87.89\n",
      "Train Epoch: 45 [40960/50000 (91%)]\tLoss: 0.387242, Accuracy: 87.11\n",
      "Train Epoch: 45 [42240/50000 (94%)]\tLoss: 0.444555, Accuracy: 86.33\n",
      "Train Epoch: 45 [43520/50000 (97%)]\tLoss: 0.370759, Accuracy: 89.84\n",
      "Train Epoch: 45 [35000/50000 (99%)]\tLoss: 0.438554, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6377, Accuracy: 3898/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[38.36208701133728 s]\n",
      "Train Epoch: 46 [0/50000 (0%)]\tLoss: 0.238417, Accuracy: 94.14\n",
      "Train Epoch: 46 [1280/50000 (3%)]\tLoss: 0.395235, Accuracy: 85.94\n",
      "Train Epoch: 46 [2560/50000 (6%)]\tLoss: 0.464825, Accuracy: 83.59\n",
      "Train Epoch: 46 [3840/50000 (9%)]\tLoss: 0.384676, Accuracy: 84.77\n",
      "Train Epoch: 46 [5120/50000 (11%)]\tLoss: 0.354218, Accuracy: 90.62\n",
      "Train Epoch: 46 [6400/50000 (14%)]\tLoss: 0.367527, Accuracy: 88.28\n",
      "Train Epoch: 46 [7680/50000 (17%)]\tLoss: 0.264371, Accuracy: 92.19\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 7.5415e-02]],\n",
      "\n",
      "        [[-2.3911e+00]],\n",
      "\n",
      "        [[-1.2559e+00]],\n",
      "\n",
      "        [[ 2.3488e+00]],\n",
      "\n",
      "        [[ 1.0203e+00]],\n",
      "\n",
      "        [[-9.0897e-01]],\n",
      "\n",
      "        [[ 1.0084e+00]],\n",
      "\n",
      "        [[ 6.2170e-01]],\n",
      "\n",
      "        [[-1.8282e-01]],\n",
      "\n",
      "        [[ 1.9690e-01]],\n",
      "\n",
      "        [[ 1.2684e+00]],\n",
      "\n",
      "        [[ 8.1687e-01]],\n",
      "\n",
      "        [[-1.1830e+00]],\n",
      "\n",
      "        [[ 3.7928e-01]],\n",
      "\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 46 [8960/50000 (20%)]\tLoss: 0.481552, Accuracy: 83.98\n",
      "Train Epoch: 46 [10240/50000 (23%)]\tLoss: 0.336904, Accuracy: 88.28\n",
      "Train Epoch: 46 [11520/50000 (26%)]\tLoss: 0.348295, Accuracy: 89.06\n",
      "Train Epoch: 46 [12800/50000 (28%)]\tLoss: 0.357603, Accuracy: 88.67\n",
      "Train Epoch: 46 [14080/50000 (31%)]\tLoss: 0.428096, Accuracy: 84.38\n",
      "Train Epoch: 46 [15360/50000 (34%)]\tLoss: 0.358454, Accuracy: 87.89\n",
      "Train Epoch: 46 [16640/50000 (37%)]\tLoss: 0.351588, Accuracy: 88.67\n",
      "Train Epoch: 46 [17920/50000 (40%)]\tLoss: 0.337333, Accuracy: 89.45\n",
      "Train Epoch: 46 [19200/50000 (43%)]\tLoss: 0.380088, Accuracy: 86.72\n",
      "Train Epoch: 46 [20480/50000 (45%)]\tLoss: 0.353480, Accuracy: 85.94\n",
      "Train Epoch: 46 [21760/50000 (48%)]\tLoss: 0.314172, Accuracy: 91.80\n",
      "Train Epoch: 46 [23040/50000 (51%)]\tLoss: 0.426722, Accuracy: 87.89\n",
      "Train Epoch: 46 [24320/50000 (54%)]\tLoss: 0.381057, Accuracy: 87.50\n",
      "Train Epoch: 46 [25600/50000 (57%)]\tLoss: 0.416866, Accuracy: 88.67\n",
      "Train Epoch: 46 [26880/50000 (60%)]\tLoss: 0.363675, Accuracy: 87.89\n",
      "Train Epoch: 46 [28160/50000 (62%)]\tLoss: 0.473645, Accuracy: 83.20\n",
      "Train Epoch: 46 [29440/50000 (65%)]\tLoss: 0.395746, Accuracy: 85.55\n",
      "Train Epoch: 46 [30720/50000 (68%)]\tLoss: 0.443354, Accuracy: 85.94\n",
      "Train Epoch: 46 [32000/50000 (71%)]\tLoss: 0.456791, Accuracy: 85.16\n",
      "Train Epoch: 46 [33280/50000 (74%)]\tLoss: 0.435419, Accuracy: 86.33\n",
      "Train Epoch: 46 [34560/50000 (77%)]\tLoss: 0.330190, Accuracy: 88.28\n",
      "Train Epoch: 46 [35840/50000 (80%)]\tLoss: 0.361207, Accuracy: 87.11\n",
      "Train Epoch: 46 [37120/50000 (82%)]\tLoss: 0.373270, Accuracy: 88.67\n",
      "Train Epoch: 46 [38400/50000 (85%)]\tLoss: 0.431543, Accuracy: 85.55\n",
      "Train Epoch: 46 [39680/50000 (88%)]\tLoss: 0.383016, Accuracy: 85.55\n",
      "Train Epoch: 46 [40960/50000 (91%)]\tLoss: 0.369084, Accuracy: 87.11\n",
      "Train Epoch: 46 [42240/50000 (94%)]\tLoss: 0.325955, Accuracy: 88.67\n",
      "Train Epoch: 46 [43520/50000 (97%)]\tLoss: 0.282874, Accuracy: 90.23\n",
      "Train Epoch: 46 [35000/50000 (99%)]\tLoss: 0.286236, Accuracy: 91.50\n",
      "\n",
      "Validation set: Average loss: 0.7125, Accuracy: 3866/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[35.81555223464966 s]\n",
      "\n",
      "Test set: Average loss: 0.7075, Accuracy: 7732/10000 (77.32%)\n",
      "\n",
      "Train Epoch: 47 [0/50000 (0%)]\tLoss: 0.450251, Accuracy: 87.11\n",
      "Train Epoch: 47 [1280/50000 (3%)]\tLoss: 0.364645, Accuracy: 88.28\n",
      "Train Epoch: 47 [2560/50000 (6%)]\tLoss: 0.366297, Accuracy: 88.67\n",
      "Train Epoch: 47 [3840/50000 (9%)]\tLoss: 0.337500, Accuracy: 87.89\n",
      "Train Epoch: 47 [5120/50000 (11%)]\tLoss: 0.253673, Accuracy: 91.41\n",
      "Train Epoch: 47 [6400/50000 (14%)]\tLoss: 0.288472, Accuracy: 91.41\n",
      "Train Epoch: 47 [7680/50000 (17%)]\tLoss: 0.475976, Accuracy: 83.98\n",
      "Train Epoch: 47 [8960/50000 (20%)]\tLoss: 0.296959, Accuracy: 91.80\n",
      "Train Epoch: 47 [10240/50000 (23%)]\tLoss: 0.398778, Accuracy: 85.55\n",
      "Train Epoch: 47 [11520/50000 (26%)]\tLoss: 0.466549, Accuracy: 85.16\n",
      "Train Epoch: 47 [12800/50000 (28%)]\tLoss: 0.382804, Accuracy: 87.89\n",
      "Train Epoch: 47 [14080/50000 (31%)]\tLoss: 0.450428, Accuracy: 86.33\n",
      "Train Epoch: 47 [15360/50000 (34%)]\tLoss: 0.312137, Accuracy: 90.23\n",
      "Train Epoch: 47 [16640/50000 (37%)]\tLoss: 0.337387, Accuracy: 88.28\n",
      "Train Epoch: 47 [17920/50000 (40%)]\tLoss: 0.422937, Accuracy: 82.03\n",
      "Train Epoch: 47 [19200/50000 (43%)]\tLoss: 0.369716, Accuracy: 85.55\n",
      "Train Epoch: 47 [20480/50000 (45%)]\tLoss: 0.458869, Accuracy: 85.94\n",
      "Train Epoch: 47 [21760/50000 (48%)]\tLoss: 0.466575, Accuracy: 86.33\n",
      "Train Epoch: 47 [23040/50000 (51%)]\tLoss: 0.378382, Accuracy: 86.72\n",
      "Train Epoch: 47 [24320/50000 (54%)]\tLoss: 0.368774, Accuracy: 87.50\n",
      "Train Epoch: 47 [25600/50000 (57%)]\tLoss: 0.408173, Accuracy: 85.16\n",
      "Train Epoch: 47 [26880/50000 (60%)]\tLoss: 0.380348, Accuracy: 85.94\n",
      "Train Epoch: 47 [28160/50000 (62%)]\tLoss: 0.337406, Accuracy: 88.67\n",
      "Train Epoch: 47 [29440/50000 (65%)]\tLoss: 0.315990, Accuracy: 88.67\n",
      "Train Epoch: 47 [30720/50000 (68%)]\tLoss: 0.414026, Accuracy: 85.94\n",
      "Train Epoch: 47 [32000/50000 (71%)]\tLoss: 0.366508, Accuracy: 86.72\n",
      "Train Epoch: 47 [33280/50000 (74%)]\tLoss: 0.398735, Accuracy: 86.72\n",
      "Train Epoch: 47 [34560/50000 (77%)]\tLoss: 0.393938, Accuracy: 85.55\n",
      "Train Epoch: 47 [35840/50000 (80%)]\tLoss: 0.516565, Accuracy: 85.16\n",
      "Train Epoch: 47 [37120/50000 (82%)]\tLoss: 0.361854, Accuracy: 87.11\n",
      "Train Epoch: 47 [38400/50000 (85%)]\tLoss: 0.361481, Accuracy: 89.45\n",
      "Train Epoch: 47 [39680/50000 (88%)]\tLoss: 0.375789, Accuracy: 85.94\n",
      "Train Epoch: 47 [40960/50000 (91%)]\tLoss: 0.328857, Accuracy: 88.67\n",
      "Train Epoch: 47 [42240/50000 (94%)]\tLoss: 0.392985, Accuracy: 87.11\n",
      "Train Epoch: 47 [43520/50000 (97%)]\tLoss: 0.440379, Accuracy: 85.16\n",
      "Train Epoch: 47 [35000/50000 (99%)]\tLoss: 0.453183, Accuracy: 83.50\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[-0.2747]],\n",
      "\n",
      "        [[-0.7621]],\n",
      "\n",
      "        [[-1.6134]],\n",
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      "        [[-0.6545]],\n",
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      "        [[-0.7305]],\n",
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      "        [[ 1.7057]],\n",
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      "        [[-0.4170]],\n",
      "\n",
      "        [[ 1.0421]],\n",
      "\n",
      "        [[-0.3348]],\n",
      "\n",
      "        [[-1.5337]],\n",
      "\n",
      "        [[ 1.2796]],\n",
      "\n",
      "        [[-0.4444]],\n",
      "\n",
      "        [[ 0.4543]],\n",
      "\n",
      "        [[-1.4200]],\n",
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      "        [[-0.0057]],\n",
      "\n",
      "        [[ 0.6732]],\n",
      "\n",
      "        [[-0.3276]],\n",
      "\n",
      "        [[-0.2589]],\n",
      "\n",
      "        [[-0.6270]],\n",
      "\n",
      "        [[-0.0942]],\n",
      "\n",
      "        [[-0.0985]],\n",
      "\n",
      "        [[-1.7058]],\n",
      "\n",
      "        [[-0.3039]],\n",
      "\n",
      "        [[ 0.3482]],\n",
      "\n",
      "        [[-1.0911]],\n",
      "\n",
      "        [[-1.6630]],\n",
      "\n",
      "        [[-0.3279]],\n",
      "\n",
      "        [[-0.3788]],\n",
      "\n",
      "        [[-2.1312]],\n",
      "\n",
      "        [[ 1.4018]],\n",
      "\n",
      "        [[ 1.1639]],\n",
      "\n",
      "        [[-1.0310]],\n",
      "\n",
      "        [[ 1.2747]],\n",
      "\n",
      "        [[ 1.2130]],\n",
      "\n",
      "        [[-0.7304]],\n",
      "\n",
      "        [[-1.2708]],\n",
      "\n",
      "        [[ 0.6139]],\n",
      "\n",
      "        [[ 0.7437]],\n",
      "\n",
      "        [[ 1.4360]],\n",
      "\n",
      "        [[-0.1636]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 1.0531e-04,  1.0385e-03,  9.4448e-04,  ..., -1.4845e-03,\n",
      "          -1.6721e-03,  9.0208e-04],\n",
      "         [-4.9231e-04,  7.4398e-04, -4.8614e-04,  ..., -2.7472e-04,\n",
      "           1.6518e-03,  9.5379e-04],\n",
      "         [-7.6877e-04, -6.9544e-04, -8.3407e-04,  ..., -6.3701e-04,\n",
      "           1.4623e-04, -5.7625e-04],\n",
      "         ...,\n",
      "         [-3.9172e-04, -5.4440e-04, -7.0800e-04,  ...,  2.8153e-04,\n",
      "           3.4683e-04,  4.6435e-04],\n",
      "         [ 1.4506e-03,  7.0932e-04,  8.8385e-04,  ..., -1.2495e-03,\n",
      "           7.3503e-04,  1.6123e-03],\n",
      "         [-1.0240e-03,  2.8667e-04, -7.3305e-04,  ..., -1.2104e-03,\n",
      "          -1.7503e-04,  7.4456e-04]]], device='cuda:0')\n",
      "\n",
      "Validation set: Average loss: 0.5921, Accuracy: 4014/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[38.83792567253113 s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 48 [0/50000 (0%)]\tLoss: 0.401424, Accuracy: 85.16\n",
      "Train Epoch: 48 [1280/50000 (3%)]\tLoss: 0.415324, Accuracy: 85.55\n",
      "Train Epoch: 48 [2560/50000 (6%)]\tLoss: 0.387319, Accuracy: 89.84\n",
      "Train Epoch: 48 [3840/50000 (9%)]\tLoss: 0.317635, Accuracy: 91.80\n",
      "Train Epoch: 48 [5120/50000 (11%)]\tLoss: 0.445667, Accuracy: 84.38\n",
      "Train Epoch: 48 [6400/50000 (14%)]\tLoss: 0.376507, Accuracy: 87.50\n",
      "Train Epoch: 48 [7680/50000 (17%)]\tLoss: 0.273356, Accuracy: 91.41\n",
      "Train Epoch: 48 [8960/50000 (20%)]\tLoss: 0.296273, Accuracy: 91.41\n",
      "Train Epoch: 48 [10240/50000 (23%)]\tLoss: 0.308455, Accuracy: 91.41\n",
      "Train Epoch: 48 [11520/50000 (26%)]\tLoss: 0.291696, Accuracy: 91.41\n",
      "Train Epoch: 48 [12800/50000 (28%)]\tLoss: 0.501413, Accuracy: 83.98\n",
      "Train Epoch: 48 [14080/50000 (31%)]\tLoss: 0.441590, Accuracy: 84.38\n",
      "Train Epoch: 48 [15360/50000 (34%)]\tLoss: 0.433535, Accuracy: 85.55\n",
      "Train Epoch: 48 [16640/50000 (37%)]\tLoss: 0.443602, Accuracy: 86.33\n",
      "Train Epoch: 48 [17920/50000 (40%)]\tLoss: 0.353536, Accuracy: 88.28\n",
      "Train Epoch: 48 [19200/50000 (43%)]\tLoss: 0.350933, Accuracy: 87.89\n",
      "Train Epoch: 48 [20480/50000 (45%)]\tLoss: 0.324749, Accuracy: 89.06\n",
      "Train Epoch: 48 [21760/50000 (48%)]\tLoss: 0.409275, Accuracy: 85.16\n",
      "Train Epoch: 48 [23040/50000 (51%)]\tLoss: 0.376840, Accuracy: 88.28\n",
      "Train Epoch: 48 [24320/50000 (54%)]\tLoss: 0.360947, Accuracy: 87.89\n",
      "Train Epoch: 48 [25600/50000 (57%)]\tLoss: 0.340215, Accuracy: 88.28\n",
      "Train Epoch: 48 [26880/50000 (60%)]\tLoss: 0.374547, Accuracy: 87.89\n",
      "Train Epoch: 48 [28160/50000 (62%)]\tLoss: 0.464442, Accuracy: 83.20\n",
      "Train Epoch: 48 [29440/50000 (65%)]\tLoss: 0.358733, Accuracy: 88.28\n",
      "Train Epoch: 48 [30720/50000 (68%)]\tLoss: 0.471055, Accuracy: 85.16\n",
      "Train Epoch: 48 [32000/50000 (71%)]\tLoss: 0.432687, Accuracy: 84.38\n",
      "Train Epoch: 48 [33280/50000 (74%)]\tLoss: 0.354627, Accuracy: 87.89\n",
      "Train Epoch: 48 [34560/50000 (77%)]\tLoss: 0.454262, Accuracy: 87.11\n",
      "Train Epoch: 48 [35840/50000 (80%)]\tLoss: 0.352213, Accuracy: 84.77\n",
      "Train Epoch: 48 [37120/50000 (82%)]\tLoss: 0.332402, Accuracy: 87.89\n",
      "Train Epoch: 48 [38400/50000 (85%)]\tLoss: 0.285347, Accuracy: 91.80\n",
      "Train Epoch: 48 [39680/50000 (88%)]\tLoss: 0.470570, Accuracy: 86.33\n",
      "Train Epoch: 48 [40960/50000 (91%)]\tLoss: 0.429814, Accuracy: 83.98\n",
      "Train Epoch: 48 [42240/50000 (94%)]\tLoss: 0.343502, Accuracy: 87.50\n",
      "Train Epoch: 48 [43520/50000 (97%)]\tLoss: 0.330315, Accuracy: 87.89\n",
      "Train Epoch: 48 [35000/50000 (99%)]\tLoss: 0.314464, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.6224, Accuracy: 3997/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[35.5733060836792 s]\n",
      "\n",
      "Test set: Average loss: 0.6206, Accuracy: 8003/10000 (80.03%)\n",
      "\n",
      "Train Epoch: 49 [0/50000 (0%)]\tLoss: 0.376903, Accuracy: 89.06\n",
      "Train Epoch: 49 [1280/50000 (3%)]\tLoss: 0.342557, Accuracy: 89.06\n",
      "Train Epoch: 49 [2560/50000 (6%)]\tLoss: 0.406797, Accuracy: 85.55\n",
      "Train Epoch: 49 [3840/50000 (9%)]\tLoss: 0.335345, Accuracy: 86.33\n",
      "Train Epoch: 49 [5120/50000 (11%)]\tLoss: 0.360870, Accuracy: 88.67\n",
      "Train Epoch: 49 [6400/50000 (14%)]\tLoss: 0.306228, Accuracy: 89.45\n",
      "Train Epoch: 49 [7680/50000 (17%)]\tLoss: 0.415280, Accuracy: 86.33\n",
      "Train Epoch: 49 [8960/50000 (20%)]\tLoss: 0.360192, Accuracy: 87.89\n",
      "Train Epoch: 49 [10240/50000 (23%)]\tLoss: 0.310933, Accuracy: 89.06\n",
      "Train Epoch: 49 [11520/50000 (26%)]\tLoss: 0.365598, Accuracy: 89.45\n",
      "Train Epoch: 49 [12800/50000 (28%)]\tLoss: 0.400224, Accuracy: 87.11\n",
      "Train Epoch: 49 [14080/50000 (31%)]\tLoss: 0.385552, Accuracy: 88.28\n",
      "Train Epoch: 49 [15360/50000 (34%)]\tLoss: 0.271012, Accuracy: 91.41\n",
      "Train Epoch: 49 [16640/50000 (37%)]\tLoss: 0.339913, Accuracy: 89.45\n",
      "Train Epoch: 49 [17920/50000 (40%)]\tLoss: 0.365532, Accuracy: 89.45\n",
      "Train Epoch: 49 [19200/50000 (43%)]\tLoss: 0.392831, Accuracy: 88.28\n",
      "Train Epoch: 49 [20480/50000 (45%)]\tLoss: 0.380904, Accuracy: 89.06\n",
      "Train Epoch: 49 [21760/50000 (48%)]\tLoss: 0.420658, Accuracy: 85.94\n",
      "Train Epoch: 49 [23040/50000 (51%)]\tLoss: 0.356719, Accuracy: 84.38\n",
      "Train Epoch: 49 [24320/50000 (54%)]\tLoss: 0.409797, Accuracy: 86.33\n",
      "Train Epoch: 49 [25600/50000 (57%)]\tLoss: 0.416500, Accuracy: 85.55\n",
      "Train Epoch: 49 [26880/50000 (60%)]\tLoss: 0.462193, Accuracy: 83.20\n",
      "Train Epoch: 49 [28160/50000 (62%)]\tLoss: 0.279392, Accuracy: 90.62\n",
      "Train Epoch: 49 [29440/50000 (65%)]\tLoss: 0.399122, Accuracy: 85.94\n",
      "Train Epoch: 49 [30720/50000 (68%)]\tLoss: 0.376336, Accuracy: 87.11\n",
      "Train Epoch: 49 [32000/50000 (71%)]\tLoss: 0.437789, Accuracy: 82.81\n",
      "Train Epoch: 49 [33280/50000 (74%)]\tLoss: 0.427880, Accuracy: 86.33\n",
      "Train Epoch: 49 [34560/50000 (77%)]\tLoss: 0.417943, Accuracy: 85.16\n",
      "Train Epoch: 49 [35840/50000 (80%)]\tLoss: 0.360475, Accuracy: 87.89\n",
      "Train Epoch: 49 [37120/50000 (82%)]\tLoss: 0.284017, Accuracy: 89.84\n",
      "Train Epoch: 49 [38400/50000 (85%)]\tLoss: 0.356927, Accuracy: 86.72\n",
      "Train Epoch: 49 [39680/50000 (88%)]\tLoss: 0.405786, Accuracy: 85.94\n",
      "Train Epoch: 49 [40960/50000 (91%)]\tLoss: 0.459116, Accuracy: 85.55\n",
      "Train Epoch: 49 [42240/50000 (94%)]\tLoss: 0.451889, Accuracy: 84.77\n",
      "Train Epoch: 49 [43520/50000 (97%)]\tLoss: 0.303482, Accuracy: 89.45\n",
      "Train Epoch: 49 [35000/50000 (99%)]\tLoss: 0.497317, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.5583, Accuracy: 4081/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[38.525331258773804 s]\n",
      "Train Epoch: 50 [0/50000 (0%)]\tLoss: 0.391495, Accuracy: 86.72\n",
      "Train Epoch: 50 [1280/50000 (3%)]\tLoss: 0.295077, Accuracy: 91.02\n",
      "Train Epoch: 50 [2560/50000 (6%)]\tLoss: 0.256466, Accuracy: 90.62\n",
      "Train Epoch: 50 [3840/50000 (9%)]\tLoss: 0.352673, Accuracy: 87.50\n",
      "Train Epoch: 50 [5120/50000 (11%)]\tLoss: 0.525876, Accuracy: 81.25\n",
      "Train Epoch: 50 [6400/50000 (14%)]\tLoss: 0.338679, Accuracy: 91.41\n",
      "Train Epoch: 50 [7680/50000 (17%)]\tLoss: 0.366629, Accuracy: 85.55\n",
      "Train Epoch: 50 [8960/50000 (20%)]\tLoss: 0.323472, Accuracy: 87.11\n",
      "Train Epoch: 50 [10240/50000 (23%)]\tLoss: 0.422704, Accuracy: 85.94\n",
      "Train Epoch: 50 [11520/50000 (26%)]\tLoss: 0.339626, Accuracy: 87.11\n",
      "Train Epoch: 50 [12800/50000 (28%)]\tLoss: 0.446405, Accuracy: 85.16\n",
      "Train Epoch: 50 [14080/50000 (31%)]\tLoss: 0.405429, Accuracy: 85.16\n",
      "Train Epoch: 50 [15360/50000 (34%)]\tLoss: 0.383819, Accuracy: 86.72\n",
      "Train Epoch: 50 [16640/50000 (37%)]\tLoss: 0.460037, Accuracy: 83.59\n",
      "Train Epoch: 50 [17920/50000 (40%)]\tLoss: 0.358132, Accuracy: 87.50\n",
      "Train Epoch: 50 [19200/50000 (43%)]\tLoss: 0.377948, Accuracy: 87.89\n",
      "Train Epoch: 50 [20480/50000 (45%)]\tLoss: 0.352345, Accuracy: 87.50\n",
      "Train Epoch: 50 [21760/50000 (48%)]\tLoss: 0.429277, Accuracy: 86.33\n",
      "Train Epoch: 50 [23040/50000 (51%)]\tLoss: 0.351405, Accuracy: 88.28\n",
      "Train Epoch: 50 [24320/50000 (54%)]\tLoss: 0.508348, Accuracy: 83.59\n",
      "Train Epoch: 50 [25600/50000 (57%)]\tLoss: 0.400711, Accuracy: 86.33\n",
      "Train Epoch: 50 [26880/50000 (60%)]\tLoss: 0.438728, Accuracy: 88.28\n",
      "Train Epoch: 50 [28160/50000 (62%)]\tLoss: 0.374521, Accuracy: 87.11\n",
      "Train Epoch: 50 [29440/50000 (65%)]\tLoss: 0.340530, Accuracy: 87.50\n",
      "Train Epoch: 50 [30720/50000 (68%)]\tLoss: 0.547033, Accuracy: 80.86\n",
      "Train Epoch: 50 [32000/50000 (71%)]\tLoss: 0.370041, Accuracy: 85.16\n",
      "Train Epoch: 50 [33280/50000 (74%)]\tLoss: 0.340529, Accuracy: 90.23\n",
      "Train Epoch: 50 [34560/50000 (77%)]\tLoss: 0.311128, Accuracy: 89.45\n",
      "Train Epoch: 50 [35840/50000 (80%)]\tLoss: 0.340646, Accuracy: 87.50\n",
      "Train Epoch: 50 [37120/50000 (82%)]\tLoss: 0.428371, Accuracy: 87.11\n",
      "Train Epoch: 50 [38400/50000 (85%)]\tLoss: 0.335629, Accuracy: 89.06\n",
      "Train Epoch: 50 [39680/50000 (88%)]\tLoss: 0.327284, Accuracy: 88.28\n",
      "Train Epoch: 50 [40960/50000 (91%)]\tLoss: 0.343398, Accuracy: 89.45\n",
      "Train Epoch: 50 [42240/50000 (94%)]\tLoss: 0.463410, Accuracy: 85.16\n",
      "Train Epoch: 50 [43520/50000 (97%)]\tLoss: 0.305351, Accuracy: 91.80\n",
      "Train Epoch: 50 [35000/50000 (99%)]\tLoss: 0.538665, Accuracy: 81.00\n",
      "\n",
      "Validation set: Average loss: 0.5248, Accuracy: 4154/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[35.58052945137024 s]\n",
      "\n",
      "Test set: Average loss: 0.5404, Accuracy: 8295/10000 (82.95%)\n",
      "\n",
      "Train Epoch: 51 [0/50000 (0%)]\tLoss: 0.474586, Accuracy: 84.38\n",
      "Train Epoch: 51 [1280/50000 (3%)]\tLoss: 0.362049, Accuracy: 87.89\n",
      "Train Epoch: 51 [2560/50000 (6%)]\tLoss: 0.279022, Accuracy: 91.80\n",
      "Train Epoch: 51 [3840/50000 (9%)]\tLoss: 0.320591, Accuracy: 90.23\n",
      "Train Epoch: 51 [5120/50000 (11%)]\tLoss: 0.210862, Accuracy: 92.97\n",
      "Train Epoch: 51 [6400/50000 (14%)]\tLoss: 0.213703, Accuracy: 93.75\n",
      "Train Epoch: 51 [7680/50000 (17%)]\tLoss: 0.302230, Accuracy: 89.45\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 51 [8960/50000 (20%)]\tLoss: 0.224357, Accuracy: 91.80\n",
      "Train Epoch: 51 [10240/50000 (23%)]\tLoss: 0.242469, Accuracy: 92.97\n",
      "Train Epoch: 51 [11520/50000 (26%)]\tLoss: 0.236150, Accuracy: 90.23\n",
      "Train Epoch: 51 [12800/50000 (28%)]\tLoss: 0.248336, Accuracy: 92.19\n",
      "Train Epoch: 51 [14080/50000 (31%)]\tLoss: 0.229930, Accuracy: 93.36\n",
      "Train Epoch: 51 [15360/50000 (34%)]\tLoss: 0.270544, Accuracy: 89.06\n",
      "Train Epoch: 51 [16640/50000 (37%)]\tLoss: 0.233126, Accuracy: 93.36\n",
      "Train Epoch: 51 [17920/50000 (40%)]\tLoss: 0.296939, Accuracy: 90.23\n",
      "Train Epoch: 51 [19200/50000 (43%)]\tLoss: 0.240736, Accuracy: 92.19\n",
      "Train Epoch: 51 [20480/50000 (45%)]\tLoss: 0.243844, Accuracy: 92.19\n",
      "Train Epoch: 51 [21760/50000 (48%)]\tLoss: 0.244759, Accuracy: 91.80\n",
      "Train Epoch: 51 [23040/50000 (51%)]\tLoss: 0.184592, Accuracy: 93.36\n",
      "Train Epoch: 51 [24320/50000 (54%)]\tLoss: 0.177756, Accuracy: 94.14\n",
      "Train Epoch: 51 [25600/50000 (57%)]\tLoss: 0.299721, Accuracy: 89.06\n",
      "Train Epoch: 51 [26880/50000 (60%)]\tLoss: 0.201714, Accuracy: 92.58\n",
      "Train Epoch: 51 [28160/50000 (62%)]\tLoss: 0.229900, Accuracy: 90.62\n",
      "Train Epoch: 51 [29440/50000 (65%)]\tLoss: 0.266296, Accuracy: 90.62\n",
      "Train Epoch: 51 [30720/50000 (68%)]\tLoss: 0.291900, Accuracy: 89.06\n",
      "Train Epoch: 51 [32000/50000 (71%)]\tLoss: 0.211319, Accuracy: 92.19\n",
      "Train Epoch: 51 [33280/50000 (74%)]\tLoss: 0.219827, Accuracy: 91.80\n",
      "Train Epoch: 51 [34560/50000 (77%)]\tLoss: 0.211281, Accuracy: 91.80\n",
      "Train Epoch: 51 [35840/50000 (80%)]\tLoss: 0.181663, Accuracy: 93.75\n",
      "Train Epoch: 51 [37120/50000 (82%)]\tLoss: 0.221662, Accuracy: 92.19\n",
      "Train Epoch: 51 [38400/50000 (85%)]\tLoss: 0.168508, Accuracy: 95.31\n",
      "Train Epoch: 51 [39680/50000 (88%)]\tLoss: 0.280094, Accuracy: 90.23\n",
      "Train Epoch: 51 [40960/50000 (91%)]\tLoss: 0.254123, Accuracy: 91.41\n",
      "Train Epoch: 51 [42240/50000 (94%)]\tLoss: 0.186983, Accuracy: 92.19\n",
      "Train Epoch: 51 [43520/50000 (97%)]\tLoss: 0.184667, Accuracy: 94.14\n",
      "Train Epoch: 51 [35000/50000 (99%)]\tLoss: 0.223192, Accuracy: 92.00\n",
      "\n",
      "Validation set: Average loss: 0.2799, Accuracy: 4520/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[38.356393575668335 s]\n",
      "Train Epoch: 52 [0/50000 (0%)]\tLoss: 0.198503, Accuracy: 93.36\n",
      "Train Epoch: 52 [1280/50000 (3%)]\tLoss: 0.148837, Accuracy: 95.31\n",
      "Train Epoch: 52 [2560/50000 (6%)]\tLoss: 0.172840, Accuracy: 94.14\n",
      "Train Epoch: 52 [3840/50000 (9%)]\tLoss: 0.144957, Accuracy: 94.92\n",
      "Train Epoch: 52 [5120/50000 (11%)]\tLoss: 0.218230, Accuracy: 93.36\n",
      "Train Epoch: 52 [6400/50000 (14%)]\tLoss: 0.285205, Accuracy: 88.67\n",
      "Train Epoch: 52 [7680/50000 (17%)]\tLoss: 0.191642, Accuracy: 94.14\n",
      "Train Epoch: 52 [8960/50000 (20%)]\tLoss: 0.226885, Accuracy: 92.97\n",
      "Train Epoch: 52 [10240/50000 (23%)]\tLoss: 0.220194, Accuracy: 93.36\n",
      "Train Epoch: 52 [11520/50000 (26%)]\tLoss: 0.208140, Accuracy: 93.75\n",
      "Train Epoch: 52 [12800/50000 (28%)]\tLoss: 0.195898, Accuracy: 92.97\n",
      "Train Epoch: 52 [14080/50000 (31%)]\tLoss: 0.185812, Accuracy: 91.41\n",
      "Train Epoch: 52 [15360/50000 (34%)]\tLoss: 0.191391, Accuracy: 93.75\n",
      "Train Epoch: 52 [16640/50000 (37%)]\tLoss: 0.253408, Accuracy: 90.62\n",
      "Train Epoch: 52 [17920/50000 (40%)]\tLoss: 0.198724, Accuracy: 93.36\n",
      "Train Epoch: 52 [19200/50000 (43%)]\tLoss: 0.160346, Accuracy: 94.14\n",
      "Train Epoch: 52 [20480/50000 (45%)]\tLoss: 0.234304, Accuracy: 91.80\n",
      "Train Epoch: 52 [21760/50000 (48%)]\tLoss: 0.233143, Accuracy: 92.58\n",
      "Train Epoch: 52 [23040/50000 (51%)]\tLoss: 0.211097, Accuracy: 91.41\n",
      "Train Epoch: 52 [24320/50000 (54%)]\tLoss: 0.194430, Accuracy: 94.14\n",
      "Train Epoch: 52 [25600/50000 (57%)]\tLoss: 0.190898, Accuracy: 93.75\n",
      "Train Epoch: 52 [26880/50000 (60%)]\tLoss: 0.196376, Accuracy: 93.36\n",
      "Train Epoch: 52 [28160/50000 (62%)]\tLoss: 0.244113, Accuracy: 91.80\n",
      "Train Epoch: 52 [29440/50000 (65%)]\tLoss: 0.191647, Accuracy: 95.31\n",
      "Train Epoch: 52 [30720/50000 (68%)]\tLoss: 0.124851, Accuracy: 94.92\n",
      "Train Epoch: 52 [32000/50000 (71%)]\tLoss: 0.217647, Accuracy: 92.58\n",
      "Train Epoch: 52 [33280/50000 (74%)]\tLoss: 0.198999, Accuracy: 93.75\n",
      "Train Epoch: 52 [34560/50000 (77%)]\tLoss: 0.144067, Accuracy: 94.92\n",
      "Train Epoch: 52 [35840/50000 (80%)]\tLoss: 0.209213, Accuracy: 91.80\n",
      "Train Epoch: 52 [37120/50000 (82%)]\tLoss: 0.154291, Accuracy: 94.53\n",
      "Train Epoch: 52 [38400/50000 (85%)]\tLoss: 0.187913, Accuracy: 93.75\n",
      "Train Epoch: 52 [39680/50000 (88%)]\tLoss: 0.219334, Accuracy: 93.36\n",
      "Train Epoch: 52 [40960/50000 (91%)]\tLoss: 0.193599, Accuracy: 93.75\n",
      "Train Epoch: 52 [42240/50000 (94%)]\tLoss: 0.162716, Accuracy: 93.36\n",
      "Train Epoch: 52 [43520/50000 (97%)]\tLoss: 0.275979, Accuracy: 92.97\n",
      "Train Epoch: 52 [35000/50000 (99%)]\tLoss: 0.155094, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2774, Accuracy: 4539/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[35.63227415084839 s]\n",
      "\n",
      "Test set: Average loss: 0.2949, Accuracy: 9030/10000 (90.30%)\n",
      "\n",
      "Train Epoch: 53 [0/50000 (0%)]\tLoss: 0.162002, Accuracy: 95.31\n",
      "Train Epoch: 53 [1280/50000 (3%)]\tLoss: 0.239951, Accuracy: 90.62\n",
      "Train Epoch: 53 [2560/50000 (6%)]\tLoss: 0.224250, Accuracy: 91.80\n",
      "Train Epoch: 53 [3840/50000 (9%)]\tLoss: 0.119965, Accuracy: 96.88\n",
      "Train Epoch: 53 [5120/50000 (11%)]\tLoss: 0.168679, Accuracy: 94.92\n",
      "Train Epoch: 53 [6400/50000 (14%)]\tLoss: 0.198539, Accuracy: 92.58\n",
      "Train Epoch: 53 [7680/50000 (17%)]\tLoss: 0.196900, Accuracy: 92.19\n",
      "Train Epoch: 53 [8960/50000 (20%)]\tLoss: 0.170814, Accuracy: 94.14\n",
      "Train Epoch: 53 [10240/50000 (23%)]\tLoss: 0.176654, Accuracy: 94.53\n",
      "Train Epoch: 53 [11520/50000 (26%)]\tLoss: 0.164930, Accuracy: 94.53\n",
      "Train Epoch: 53 [12800/50000 (28%)]\tLoss: 0.193205, Accuracy: 92.97\n",
      "Train Epoch: 53 [14080/50000 (31%)]\tLoss: 0.165709, Accuracy: 93.75\n",
      "Train Epoch: 53 [15360/50000 (34%)]\tLoss: 0.180826, Accuracy: 94.53\n",
      "Train Epoch: 53 [16640/50000 (37%)]\tLoss: 0.193032, Accuracy: 94.14\n",
      "Train Epoch: 53 [17920/50000 (40%)]\tLoss: 0.145895, Accuracy: 94.92\n",
      "Train Epoch: 53 [19200/50000 (43%)]\tLoss: 0.111833, Accuracy: 97.66\n",
      "Train Epoch: 53 [20480/50000 (45%)]\tLoss: 0.216023, Accuracy: 92.97\n",
      "Train Epoch: 53 [21760/50000 (48%)]\tLoss: 0.186617, Accuracy: 94.53\n",
      "Train Epoch: 53 [23040/50000 (51%)]\tLoss: 0.185960, Accuracy: 92.58\n",
      "Train Epoch: 53 [24320/50000 (54%)]\tLoss: 0.107091, Accuracy: 96.88\n",
      "Train Epoch: 53 [25600/50000 (57%)]\tLoss: 0.220605, Accuracy: 91.02\n",
      "Train Epoch: 53 [26880/50000 (60%)]\tLoss: 0.191511, Accuracy: 93.75\n",
      "Train Epoch: 53 [28160/50000 (62%)]\tLoss: 0.149596, Accuracy: 94.92\n",
      "Train Epoch: 53 [29440/50000 (65%)]\tLoss: 0.153775, Accuracy: 94.53\n",
      "Train Epoch: 53 [30720/50000 (68%)]\tLoss: 0.181991, Accuracy: 94.14\n",
      "Train Epoch: 53 [32000/50000 (71%)]\tLoss: 0.171983, Accuracy: 93.75\n",
      "Train Epoch: 53 [33280/50000 (74%)]\tLoss: 0.179485, Accuracy: 94.14\n",
      "Train Epoch: 53 [34560/50000 (77%)]\tLoss: 0.183800, Accuracy: 94.14\n",
      "Train Epoch: 53 [35840/50000 (80%)]\tLoss: 0.176643, Accuracy: 93.75\n",
      "Train Epoch: 53 [37120/50000 (82%)]\tLoss: 0.153800, Accuracy: 94.92\n",
      "Train Epoch: 53 [38400/50000 (85%)]\tLoss: 0.185126, Accuracy: 94.53\n",
      "Train Epoch: 53 [39680/50000 (88%)]\tLoss: 0.163916, Accuracy: 96.88\n",
      "Train Epoch: 53 [40960/50000 (91%)]\tLoss: 0.123090, Accuracy: 96.09\n",
      "Train Epoch: 53 [42240/50000 (94%)]\tLoss: 0.184066, Accuracy: 94.14\n",
      "Train Epoch: 53 [43520/50000 (97%)]\tLoss: 0.201946, Accuracy: 91.02\n",
      "Train Epoch: 53 [35000/50000 (99%)]\tLoss: 0.167036, Accuracy: 93.50\n",
      "\n",
      "Validation set: Average loss: 0.2710, Accuracy: 4549/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[38.72765398025513 s]\n",
      "Train Epoch: 54 [0/50000 (0%)]\tLoss: 0.099596, Accuracy: 96.48\n",
      "Train Epoch: 54 [1280/50000 (3%)]\tLoss: 0.198192, Accuracy: 91.80\n",
      "Train Epoch: 54 [2560/50000 (6%)]\tLoss: 0.149440, Accuracy: 95.31\n",
      "Train Epoch: 54 [3840/50000 (9%)]\tLoss: 0.145605, Accuracy: 95.31\n",
      "Train Epoch: 54 [5120/50000 (11%)]\tLoss: 0.094601, Accuracy: 97.27\n",
      "Train Epoch: 54 [6400/50000 (14%)]\tLoss: 0.174720, Accuracy: 93.75\n",
      "Train Epoch: 54 [7680/50000 (17%)]\tLoss: 0.143344, Accuracy: 96.88\n",
      "Train Epoch: 54 [8960/50000 (20%)]\tLoss: 0.146888, Accuracy: 95.31\n",
      "Train Epoch: 54 [10240/50000 (23%)]\tLoss: 0.123809, Accuracy: 95.31\n",
      "Train Epoch: 54 [11520/50000 (26%)]\tLoss: 0.105500, Accuracy: 97.66\n",
      "Train Epoch: 54 [12800/50000 (28%)]\tLoss: 0.150963, Accuracy: 94.53\n",
      "Train Epoch: 54 [14080/50000 (31%)]\tLoss: 0.145227, Accuracy: 94.92\n",
      "Train Epoch: 54 [15360/50000 (34%)]\tLoss: 0.193728, Accuracy: 93.36\n",
      "Train Epoch: 54 [16640/50000 (37%)]\tLoss: 0.170595, Accuracy: 93.75\n",
      "Train Epoch: 54 [17920/50000 (40%)]\tLoss: 0.125659, Accuracy: 95.70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 54 [19200/50000 (43%)]\tLoss: 0.131976, Accuracy: 95.70\n",
      "Train Epoch: 54 [20480/50000 (45%)]\tLoss: 0.221398, Accuracy: 92.19\n",
      "Train Epoch: 54 [21760/50000 (48%)]\tLoss: 0.162728, Accuracy: 95.70\n",
      "Train Epoch: 54 [23040/50000 (51%)]\tLoss: 0.200011, Accuracy: 93.36\n",
      "Train Epoch: 54 [24320/50000 (54%)]\tLoss: 0.159967, Accuracy: 93.36\n",
      "Train Epoch: 54 [25600/50000 (57%)]\tLoss: 0.145253, Accuracy: 94.53\n",
      "Train Epoch: 54 [26880/50000 (60%)]\tLoss: 0.134353, Accuracy: 95.31\n",
      "Train Epoch: 54 [28160/50000 (62%)]\tLoss: 0.159640, Accuracy: 95.31\n",
      "Train Epoch: 54 [29440/50000 (65%)]\tLoss: 0.138484, Accuracy: 96.09\n",
      "Train Epoch: 54 [30720/50000 (68%)]\tLoss: 0.204034, Accuracy: 92.19\n",
      "Train Epoch: 54 [32000/50000 (71%)]\tLoss: 0.183372, Accuracy: 93.75\n",
      "Train Epoch: 54 [33280/50000 (74%)]\tLoss: 0.102325, Accuracy: 96.48\n",
      "Train Epoch: 54 [34560/50000 (77%)]\tLoss: 0.239256, Accuracy: 92.58\n",
      "Train Epoch: 54 [35840/50000 (80%)]\tLoss: 0.124541, Accuracy: 96.09\n",
      "Train Epoch: 54 [37120/50000 (82%)]\tLoss: 0.157573, Accuracy: 94.92\n",
      "Train Epoch: 54 [38400/50000 (85%)]\tLoss: 0.081245, Accuracy: 97.66\n",
      "Train Epoch: 54 [39680/50000 (88%)]\tLoss: 0.179949, Accuracy: 94.53\n",
      "Train Epoch: 54 [40960/50000 (91%)]\tLoss: 0.122813, Accuracy: 96.48\n",
      "Train Epoch: 54 [42240/50000 (94%)]\tLoss: 0.129264, Accuracy: 94.14\n",
      "Train Epoch: 54 [43520/50000 (97%)]\tLoss: 0.155870, Accuracy: 94.92\n",
      "Train Epoch: 54 [35000/50000 (99%)]\tLoss: 0.102121, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.2743, Accuracy: 4555/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.55281209945679 s]\n",
      "\n",
      "Test set: Average loss: 0.2866, Accuracy: 9081/10000 (90.81%)\n",
      "\n",
      "Train Epoch: 55 [0/50000 (0%)]\tLoss: 0.119821, Accuracy: 96.48\n",
      "Train Epoch: 55 [1280/50000 (3%)]\tLoss: 0.200544, Accuracy: 92.97\n",
      "Train Epoch: 55 [2560/50000 (6%)]\tLoss: 0.212442, Accuracy: 93.75\n",
      "Train Epoch: 55 [3840/50000 (9%)]\tLoss: 0.163990, Accuracy: 94.92\n",
      "Train Epoch: 55 [5120/50000 (11%)]\tLoss: 0.129201, Accuracy: 96.88\n",
      "Train Epoch: 55 [6400/50000 (14%)]\tLoss: 0.105889, Accuracy: 97.27\n",
      "Train Epoch: 55 [7680/50000 (17%)]\tLoss: 0.216311, Accuracy: 91.80\n",
      "Train Epoch: 55 [8960/50000 (20%)]\tLoss: 0.120760, Accuracy: 95.31\n",
      "Train Epoch: 55 [10240/50000 (23%)]\tLoss: 0.111462, Accuracy: 96.48\n",
      "Train Epoch: 55 [11520/50000 (26%)]\tLoss: 0.141492, Accuracy: 93.75\n",
      "Train Epoch: 55 [12800/50000 (28%)]\tLoss: 0.119394, Accuracy: 96.09\n",
      "Train Epoch: 55 [14080/50000 (31%)]\tLoss: 0.114648, Accuracy: 96.88\n",
      "Train Epoch: 55 [15360/50000 (34%)]\tLoss: 0.133275, Accuracy: 95.70\n",
      "Train Epoch: 55 [16640/50000 (37%)]\tLoss: 0.217612, Accuracy: 92.58\n",
      "Train Epoch: 55 [17920/50000 (40%)]\tLoss: 0.121041, Accuracy: 95.31\n",
      "Train Epoch: 55 [19200/50000 (43%)]\tLoss: 0.113446, Accuracy: 96.09\n",
      "Train Epoch: 55 [20480/50000 (45%)]\tLoss: 0.121227, Accuracy: 95.31\n",
      "Train Epoch: 55 [21760/50000 (48%)]\tLoss: 0.124045, Accuracy: 97.27\n",
      "Train Epoch: 55 [23040/50000 (51%)]\tLoss: 0.117911, Accuracy: 96.88\n",
      "Train Epoch: 55 [24320/50000 (54%)]\tLoss: 0.190618, Accuracy: 94.53\n",
      "Train Epoch: 55 [25600/50000 (57%)]\tLoss: 0.156082, Accuracy: 94.53\n",
      "Train Epoch: 55 [26880/50000 (60%)]\tLoss: 0.163131, Accuracy: 95.31\n",
      "Train Epoch: 55 [28160/50000 (62%)]\tLoss: 0.147042, Accuracy: 95.70\n",
      "Train Epoch: 55 [29440/50000 (65%)]\tLoss: 0.211368, Accuracy: 92.97\n",
      "Train Epoch: 55 [30720/50000 (68%)]\tLoss: 0.148753, Accuracy: 94.92\n",
      "Train Epoch: 55 [32000/50000 (71%)]\tLoss: 0.222888, Accuracy: 93.36\n",
      "Train Epoch: 55 [33280/50000 (74%)]\tLoss: 0.116729, Accuracy: 96.88\n",
      "Train Epoch: 55 [34560/50000 (77%)]\tLoss: 0.178985, Accuracy: 94.53\n",
      "Train Epoch: 55 [35840/50000 (80%)]\tLoss: 0.171074, Accuracy: 92.58\n",
      "Train Epoch: 55 [37120/50000 (82%)]\tLoss: 0.142156, Accuracy: 92.97\n",
      "Train Epoch: 55 [38400/50000 (85%)]\tLoss: 0.093955, Accuracy: 96.88\n",
      "Train Epoch: 55 [39680/50000 (88%)]\tLoss: 0.166213, Accuracy: 95.31\n",
      "Train Epoch: 55 [40960/50000 (91%)]\tLoss: 0.110323, Accuracy: 96.48\n",
      "Train Epoch: 55 [42240/50000 (94%)]\tLoss: 0.139055, Accuracy: 94.53\n",
      "Train Epoch: 55 [43520/50000 (97%)]\tLoss: 0.185428, Accuracy: 94.53\n",
      "Train Epoch: 55 [35000/50000 (99%)]\tLoss: 0.106184, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.2702, Accuracy: 4561/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.67844581604004 s]\n",
      "Train Epoch: 56 [0/50000 (0%)]\tLoss: 0.105964, Accuracy: 96.48\n",
      "Train Epoch: 56 [1280/50000 (3%)]\tLoss: 0.131376, Accuracy: 94.92\n",
      "Train Epoch: 56 [2560/50000 (6%)]\tLoss: 0.123547, Accuracy: 95.31\n",
      "Train Epoch: 56 [3840/50000 (9%)]\tLoss: 0.157001, Accuracy: 95.31\n",
      "Train Epoch: 56 [5120/50000 (11%)]\tLoss: 0.127935, Accuracy: 96.48\n",
      "Train Epoch: 56 [6400/50000 (14%)]\tLoss: 0.179094, Accuracy: 93.36\n",
      "Train Epoch: 56 [7680/50000 (17%)]\tLoss: 0.195011, Accuracy: 94.14\n",
      "Train Epoch: 56 [8960/50000 (20%)]\tLoss: 0.078235, Accuracy: 96.48\n",
      "Train Epoch: 56 [10240/50000 (23%)]\tLoss: 0.169012, Accuracy: 92.58\n",
      "Train Epoch: 56 [11520/50000 (26%)]\tLoss: 0.112866, Accuracy: 96.09\n",
      "Train Epoch: 56 [12800/50000 (28%)]\tLoss: 0.180061, Accuracy: 94.53\n",
      "Train Epoch: 56 [14080/50000 (31%)]\tLoss: 0.159341, Accuracy: 94.53\n",
      "Train Epoch: 56 [15360/50000 (34%)]\tLoss: 0.109455, Accuracy: 96.48\n",
      "Train Epoch: 56 [16640/50000 (37%)]\tLoss: 0.151004, Accuracy: 95.70\n",
      "Train Epoch: 56 [17920/50000 (40%)]\tLoss: 0.115322, Accuracy: 96.09\n",
      "Train Epoch: 56 [19200/50000 (43%)]\tLoss: 0.147978, Accuracy: 94.53\n",
      "Train Epoch: 56 [20480/50000 (45%)]\tLoss: 0.128375, Accuracy: 96.88\n",
      "Train Epoch: 56 [21760/50000 (48%)]\tLoss: 0.080700, Accuracy: 98.44\n",
      "Train Epoch: 56 [23040/50000 (51%)]\tLoss: 0.168370, Accuracy: 94.53\n",
      "Train Epoch: 56 [24320/50000 (54%)]\tLoss: 0.109820, Accuracy: 96.48\n",
      "Train Epoch: 56 [25600/50000 (57%)]\tLoss: 0.203984, Accuracy: 94.14\n",
      "Train Epoch: 56 [26880/50000 (60%)]\tLoss: 0.134041, Accuracy: 96.09\n",
      "Train Epoch: 56 [28160/50000 (62%)]\tLoss: 0.146583, Accuracy: 95.31\n",
      "Train Epoch: 56 [29440/50000 (65%)]\tLoss: 0.153044, Accuracy: 94.92\n",
      "Train Epoch: 56 [30720/50000 (68%)]\tLoss: 0.118776, Accuracy: 94.92\n",
      "Train Epoch: 56 [32000/50000 (71%)]\tLoss: 0.104571, Accuracy: 96.09\n",
      "Train Epoch: 56 [33280/50000 (74%)]\tLoss: 0.146744, Accuracy: 93.75\n",
      "Train Epoch: 56 [34560/50000 (77%)]\tLoss: 0.126939, Accuracy: 95.70\n",
      "Train Epoch: 56 [35840/50000 (80%)]\tLoss: 0.158583, Accuracy: 94.92\n",
      "Train Epoch: 56 [37120/50000 (82%)]\tLoss: 0.141416, Accuracy: 95.70\n",
      "Train Epoch: 56 [38400/50000 (85%)]\tLoss: 0.148522, Accuracy: 95.31\n",
      "Train Epoch: 56 [39680/50000 (88%)]\tLoss: 0.171096, Accuracy: 94.14\n",
      "Train Epoch: 56 [40960/50000 (91%)]\tLoss: 0.151152, Accuracy: 94.92\n",
      "Train Epoch: 56 [42240/50000 (94%)]\tLoss: 0.150306, Accuracy: 94.14\n",
      "Train Epoch: 56 [43520/50000 (97%)]\tLoss: 0.230768, Accuracy: 92.97\n",
      "Train Epoch: 56 [35000/50000 (99%)]\tLoss: 0.159058, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2717, Accuracy: 4573/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.534573554992676 s]\n",
      "\n",
      "Test set: Average loss: 0.2828, Accuracy: 9108/10000 (91.08%)\n",
      "\n",
      "Train Epoch: 57 [0/50000 (0%)]\tLoss: 0.116527, Accuracy: 96.48\n",
      "Train Epoch: 57 [1280/50000 (3%)]\tLoss: 0.155429, Accuracy: 95.31\n",
      "Train Epoch: 57 [2560/50000 (6%)]\tLoss: 0.080868, Accuracy: 98.44\n",
      "Train Epoch: 57 [3840/50000 (9%)]\tLoss: 0.110080, Accuracy: 96.88\n",
      "Train Epoch: 57 [5120/50000 (11%)]\tLoss: 0.153398, Accuracy: 94.92\n",
      "Train Epoch: 57 [6400/50000 (14%)]\tLoss: 0.176357, Accuracy: 93.36\n",
      "Train Epoch: 57 [7680/50000 (17%)]\tLoss: 0.095575, Accuracy: 95.70\n",
      "Train Epoch: 57 [8960/50000 (20%)]\tLoss: 0.140631, Accuracy: 96.09\n",
      "Train Epoch: 57 [10240/50000 (23%)]\tLoss: 0.128365, Accuracy: 95.31\n",
      "Train Epoch: 57 [11520/50000 (26%)]\tLoss: 0.132364, Accuracy: 94.92\n",
      "Train Epoch: 57 [12800/50000 (28%)]\tLoss: 0.190812, Accuracy: 93.36\n",
      "Train Epoch: 57 [14080/50000 (31%)]\tLoss: 0.092227, Accuracy: 97.66\n",
      "Train Epoch: 57 [15360/50000 (34%)]\tLoss: 0.115742, Accuracy: 96.09\n",
      "Train Epoch: 57 [16640/50000 (37%)]\tLoss: 0.117247, Accuracy: 96.48\n",
      "Train Epoch: 57 [17920/50000 (40%)]\tLoss: 0.119669, Accuracy: 95.31\n",
      "Train Epoch: 57 [19200/50000 (43%)]\tLoss: 0.134240, Accuracy: 94.92\n",
      "Train Epoch: 57 [20480/50000 (45%)]\tLoss: 0.117764, Accuracy: 95.70\n",
      "Train Epoch: 57 [21760/50000 (48%)]\tLoss: 0.110791, Accuracy: 96.88\n",
      "Train Epoch: 57 [23040/50000 (51%)]\tLoss: 0.113726, Accuracy: 96.88\n",
      "Train Epoch: 57 [24320/50000 (54%)]\tLoss: 0.131428, Accuracy: 95.70\n",
      "Train Epoch: 57 [25600/50000 (57%)]\tLoss: 0.120554, Accuracy: 96.48\n",
      "Train Epoch: 57 [26880/50000 (60%)]\tLoss: 0.155410, Accuracy: 94.14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 57 [28160/50000 (62%)]\tLoss: 0.131040, Accuracy: 95.70\n",
      "Train Epoch: 57 [29440/50000 (65%)]\tLoss: 0.094729, Accuracy: 97.27\n",
      "Train Epoch: 57 [30720/50000 (68%)]\tLoss: 0.160099, Accuracy: 94.53\n",
      "Train Epoch: 57 [32000/50000 (71%)]\tLoss: 0.154684, Accuracy: 94.92\n",
      "Train Epoch: 57 [33280/50000 (74%)]\tLoss: 0.101756, Accuracy: 97.27\n",
      "Train Epoch: 57 [34560/50000 (77%)]\tLoss: 0.143079, Accuracy: 94.53\n",
      "Train Epoch: 57 [35840/50000 (80%)]\tLoss: 0.166293, Accuracy: 93.75\n",
      "Train Epoch: 57 [37120/50000 (82%)]\tLoss: 0.210754, Accuracy: 92.58\n",
      "Train Epoch: 57 [38400/50000 (85%)]\tLoss: 0.123883, Accuracy: 95.70\n",
      "Train Epoch: 57 [39680/50000 (88%)]\tLoss: 0.114431, Accuracy: 96.48\n",
      "Train Epoch: 57 [40960/50000 (91%)]\tLoss: 0.133648, Accuracy: 96.48\n",
      "Train Epoch: 57 [42240/50000 (94%)]\tLoss: 0.133741, Accuracy: 93.36\n",
      "Train Epoch: 57 [43520/50000 (97%)]\tLoss: 0.194914, Accuracy: 92.97\n",
      "Train Epoch: 57 [35000/50000 (99%)]\tLoss: 0.172517, Accuracy: 92.50\n",
      "\n",
      "Validation set: Average loss: 0.2801, Accuracy: 4549/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[38.55243992805481 s]\n",
      "Train Epoch: 58 [0/50000 (0%)]\tLoss: 0.101419, Accuracy: 96.09\n",
      "Train Epoch: 58 [1280/50000 (3%)]\tLoss: 0.074381, Accuracy: 97.66\n",
      "Train Epoch: 58 [2560/50000 (6%)]\tLoss: 0.133196, Accuracy: 97.66\n",
      "Train Epoch: 58 [3840/50000 (9%)]\tLoss: 0.103590, Accuracy: 95.70\n",
      "Train Epoch: 58 [5120/50000 (11%)]\tLoss: 0.130855, Accuracy: 95.70\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.0490]],\n",
      "\n",
      "        [[ 0.8348]],\n",
      "\n",
      "        [[ 0.3804]],\n",
      "\n",
      "        [[-0.6389]],\n",
      "\n",
      "        [[-1.0833]],\n",
      "\n",
      "        [[ 1.6287]],\n",
      "\n",
      "        [[ 0.2320]],\n",
      "\n",
      "        [[-0.0762]],\n",
      "\n",
      "        [[ 0.5134]],\n",
      "\n",
      "        [[ 0.7206]],\n",
      "\n",
      "        [[ 0.4495]],\n",
      "\n",
      "        [[ 0.5243]],\n",
      "\n",
      "        [[-0.0806]],\n",
      "\n",
      "        [[ 0.0211]],\n",
      "\n",
      "        [[ 0.2867]],\n",
      "\n",
      "        [[-1.8904]],\n",
      "\n",
      "        [[-0.9845]],\n",
      "\n",
      "        [[ 1.1643]],\n",
      "\n",
      "        [[ 0.0576]],\n",
      "\n",
      "        [[ 0.6862]],\n",
      "\n",
      "        [[ 0.6098]],\n",
      "\n",
      "        [[-1.5730]],\n",
      "\n",
      "        [[ 0.8768]],\n",
      "\n",
      "        [[ 0.0153]],\n",
      "\n",
      "        [[-1.9590]],\n",
      "\n",
      "        [[ 0.6754]],\n",
      "\n",
      "        [[-0.2232]],\n",
      "\n",
      "        [[ 0.6325]],\n",
      "\n",
      "        [[-0.4504]],\n",
      "\n",
      "        [[-1.5832]],\n",
      "\n",
      "        [[ 0.8099]],\n",
      "\n",
      "        [[-0.6347]],\n",
      "\n",
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      "        [[ 0.4220]],\n",
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      "        [[ 0.5099]],\n",
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      "        [[-0.2840]],\n",
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      "        [[-0.6984]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
      "tensor([[[ 3.8813e-04, -9.3177e-05,  1.0515e-03,  ..., -5.1214e-04,\n",
      "          -3.4042e-04,  2.9617e-04],\n",
      "         [-1.3778e-04,  1.9745e-04,  9.2594e-05,  ...,  3.1678e-04,\n",
      "          -4.4735e-04, -1.5840e-04],\n",
      "         [-3.7652e-04,  6.4411e-05, -3.7446e-04,  ...,  4.3202e-04,\n",
      "           3.4343e-04,  3.3182e-04],\n",
      "         ...,\n",
      "         [ 1.9192e-04,  3.0463e-05, -2.5699e-04,  ..., -7.0841e-05,\n",
      "           2.4803e-04, -1.2577e-04],\n",
      "         [ 4.6425e-04,  3.2149e-04, -4.2036e-04,  ...,  1.0806e-05,\n",
      "          -2.5716e-05,  3.4674e-05],\n",
      "         [ 2.5385e-05,  1.7079e-04,  5.1078e-04,  ...,  4.5133e-04,\n",
      "          -6.7136e-05, -2.1370e-05]]], device='cuda:0')\n",
      "Train Epoch: 58 [6400/50000 (14%)]\tLoss: 0.084058, Accuracy: 97.27\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 58 [7680/50000 (17%)]\tLoss: 0.083892, Accuracy: 97.66\n",
      "Train Epoch: 58 [8960/50000 (20%)]\tLoss: 0.125161, Accuracy: 94.92\n",
      "Train Epoch: 58 [10240/50000 (23%)]\tLoss: 0.145515, Accuracy: 95.31\n",
      "Train Epoch: 58 [11520/50000 (26%)]\tLoss: 0.066238, Accuracy: 98.44\n",
      "Train Epoch: 58 [12800/50000 (28%)]\tLoss: 0.131774, Accuracy: 96.09\n",
      "Train Epoch: 58 [14080/50000 (31%)]\tLoss: 0.164351, Accuracy: 94.14\n",
      "Train Epoch: 58 [15360/50000 (34%)]\tLoss: 0.155366, Accuracy: 93.36\n",
      "Train Epoch: 58 [16640/50000 (37%)]\tLoss: 0.082641, Accuracy: 97.66\n",
      "Train Epoch: 58 [17920/50000 (40%)]\tLoss: 0.126772, Accuracy: 96.09\n",
      "Train Epoch: 58 [19200/50000 (43%)]\tLoss: 0.098789, Accuracy: 96.48\n",
      "Train Epoch: 58 [20480/50000 (45%)]\tLoss: 0.123690, Accuracy: 94.92\n",
      "Train Epoch: 58 [21760/50000 (48%)]\tLoss: 0.126388, Accuracy: 95.31\n",
      "Train Epoch: 58 [23040/50000 (51%)]\tLoss: 0.090970, Accuracy: 98.44\n",
      "Train Epoch: 58 [24320/50000 (54%)]\tLoss: 0.079768, Accuracy: 97.27\n",
      "Train Epoch: 58 [25600/50000 (57%)]\tLoss: 0.191976, Accuracy: 94.14\n",
      "Train Epoch: 58 [26880/50000 (60%)]\tLoss: 0.106807, Accuracy: 96.48\n",
      "Train Epoch: 58 [28160/50000 (62%)]\tLoss: 0.212793, Accuracy: 92.58\n",
      "Train Epoch: 58 [29440/50000 (65%)]\tLoss: 0.100964, Accuracy: 97.27\n",
      "Train Epoch: 58 [30720/50000 (68%)]\tLoss: 0.087855, Accuracy: 96.88\n",
      "Train Epoch: 58 [32000/50000 (71%)]\tLoss: 0.111470, Accuracy: 95.31\n",
      "Train Epoch: 58 [33280/50000 (74%)]\tLoss: 0.087922, Accuracy: 97.27\n",
      "Train Epoch: 58 [34560/50000 (77%)]\tLoss: 0.104152, Accuracy: 95.31\n",
      "Train Epoch: 58 [35840/50000 (80%)]\tLoss: 0.117631, Accuracy: 96.48\n",
      "Train Epoch: 58 [37120/50000 (82%)]\tLoss: 0.170500, Accuracy: 94.92\n",
      "Train Epoch: 58 [38400/50000 (85%)]\tLoss: 0.142173, Accuracy: 94.92\n",
      "Train Epoch: 58 [39680/50000 (88%)]\tLoss: 0.111191, Accuracy: 96.48\n",
      "Train Epoch: 58 [40960/50000 (91%)]\tLoss: 0.156436, Accuracy: 95.31\n",
      "Train Epoch: 58 [42240/50000 (94%)]\tLoss: 0.122662, Accuracy: 95.70\n",
      "Train Epoch: 58 [43520/50000 (97%)]\tLoss: 0.065630, Accuracy: 99.22\n",
      "Train Epoch: 58 [35000/50000 (99%)]\tLoss: 0.088177, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2861, Accuracy: 4559/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.46881580352783 s]\n",
      "\n",
      "Test set: Average loss: 0.2985, Accuracy: 9076/10000 (90.76%)\n",
      "\n",
      "Train Epoch: 59 [0/50000 (0%)]\tLoss: 0.106296, Accuracy: 96.09\n",
      "Train Epoch: 59 [1280/50000 (3%)]\tLoss: 0.100753, Accuracy: 96.88\n",
      "Train Epoch: 59 [2560/50000 (6%)]\tLoss: 0.101380, Accuracy: 96.88\n",
      "Train Epoch: 59 [3840/50000 (9%)]\tLoss: 0.110937, Accuracy: 95.31\n",
      "Train Epoch: 59 [5120/50000 (11%)]\tLoss: 0.168082, Accuracy: 94.14\n",
      "Train Epoch: 59 [6400/50000 (14%)]\tLoss: 0.116077, Accuracy: 96.09\n",
      "Train Epoch: 59 [7680/50000 (17%)]\tLoss: 0.165100, Accuracy: 94.53\n",
      "Train Epoch: 59 [8960/50000 (20%)]\tLoss: 0.078877, Accuracy: 97.27\n",
      "Train Epoch: 59 [10240/50000 (23%)]\tLoss: 0.123228, Accuracy: 94.14\n",
      "Train Epoch: 59 [11520/50000 (26%)]\tLoss: 0.107243, Accuracy: 96.09\n",
      "Train Epoch: 59 [12800/50000 (28%)]\tLoss: 0.101245, Accuracy: 97.27\n",
      "Train Epoch: 59 [14080/50000 (31%)]\tLoss: 0.107457, Accuracy: 97.66\n",
      "Train Epoch: 59 [15360/50000 (34%)]\tLoss: 0.120744, Accuracy: 95.70\n",
      "Train Epoch: 59 [16640/50000 (37%)]\tLoss: 0.100736, Accuracy: 96.88\n",
      "Train Epoch: 59 [17920/50000 (40%)]\tLoss: 0.096945, Accuracy: 96.88\n",
      "Train Epoch: 59 [19200/50000 (43%)]\tLoss: 0.145928, Accuracy: 95.31\n",
      "Train Epoch: 59 [20480/50000 (45%)]\tLoss: 0.057405, Accuracy: 98.44\n",
      "Train Epoch: 59 [21760/50000 (48%)]\tLoss: 0.077065, Accuracy: 98.05\n",
      "Train Epoch: 59 [23040/50000 (51%)]\tLoss: 0.106265, Accuracy: 95.70\n",
      "Train Epoch: 59 [24320/50000 (54%)]\tLoss: 0.058900, Accuracy: 98.83\n",
      "Train Epoch: 59 [25600/50000 (57%)]\tLoss: 0.151360, Accuracy: 93.36\n",
      "Train Epoch: 59 [26880/50000 (60%)]\tLoss: 0.170992, Accuracy: 93.75\n",
      "Train Epoch: 59 [28160/50000 (62%)]\tLoss: 0.126922, Accuracy: 95.70\n",
      "Train Epoch: 59 [29440/50000 (65%)]\tLoss: 0.110702, Accuracy: 96.48\n",
      "Train Epoch: 59 [30720/50000 (68%)]\tLoss: 0.111056, Accuracy: 95.31\n",
      "Train Epoch: 59 [32000/50000 (71%)]\tLoss: 0.082627, Accuracy: 97.27\n",
      "Train Epoch: 59 [33280/50000 (74%)]\tLoss: 0.126534, Accuracy: 96.48\n",
      "Train Epoch: 59 [34560/50000 (77%)]\tLoss: 0.096665, Accuracy: 96.88\n",
      "Train Epoch: 59 [35840/50000 (80%)]\tLoss: 0.117545, Accuracy: 96.48\n",
      "Train Epoch: 59 [37120/50000 (82%)]\tLoss: 0.127753, Accuracy: 95.70\n",
      "Train Epoch: 59 [38400/50000 (85%)]\tLoss: 0.062211, Accuracy: 96.88\n",
      "Train Epoch: 59 [39680/50000 (88%)]\tLoss: 0.090521, Accuracy: 96.09\n",
      "Train Epoch: 59 [40960/50000 (91%)]\tLoss: 0.090820, Accuracy: 97.27\n",
      "Train Epoch: 59 [42240/50000 (94%)]\tLoss: 0.143928, Accuracy: 94.53\n",
      "Train Epoch: 59 [43520/50000 (97%)]\tLoss: 0.103797, Accuracy: 95.31\n",
      "Train Epoch: 59 [35000/50000 (99%)]\tLoss: 0.086360, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2790, Accuracy: 4562/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.47732973098755 s]\n",
      "Train Epoch: 60 [0/50000 (0%)]\tLoss: 0.120362, Accuracy: 95.31\n",
      "Train Epoch: 60 [1280/50000 (3%)]\tLoss: 0.061332, Accuracy: 98.44\n",
      "Train Epoch: 60 [2560/50000 (6%)]\tLoss: 0.100832, Accuracy: 96.88\n",
      "Train Epoch: 60 [3840/50000 (9%)]\tLoss: 0.117569, Accuracy: 94.53\n",
      "Train Epoch: 60 [5120/50000 (11%)]\tLoss: 0.111823, Accuracy: 96.09\n",
      "Train Epoch: 60 [6400/50000 (14%)]\tLoss: 0.131663, Accuracy: 95.70\n",
      "Train Epoch: 60 [7680/50000 (17%)]\tLoss: 0.104927, Accuracy: 96.09\n",
      "Train Epoch: 60 [8960/50000 (20%)]\tLoss: 0.110530, Accuracy: 95.70\n",
      "Train Epoch: 60 [10240/50000 (23%)]\tLoss: 0.111751, Accuracy: 95.70\n",
      "Train Epoch: 60 [11520/50000 (26%)]\tLoss: 0.071729, Accuracy: 97.66\n",
      "Train Epoch: 60 [12800/50000 (28%)]\tLoss: 0.161499, Accuracy: 96.48\n",
      "Train Epoch: 60 [14080/50000 (31%)]\tLoss: 0.116778, Accuracy: 94.92\n",
      "Train Epoch: 60 [15360/50000 (34%)]\tLoss: 0.117483, Accuracy: 95.31\n",
      "Train Epoch: 60 [16640/50000 (37%)]\tLoss: 0.110690, Accuracy: 96.88\n",
      "Train Epoch: 60 [17920/50000 (40%)]\tLoss: 0.100303, Accuracy: 97.27\n",
      "Train Epoch: 60 [19200/50000 (43%)]\tLoss: 0.121449, Accuracy: 96.88\n",
      "Train Epoch: 60 [20480/50000 (45%)]\tLoss: 0.080153, Accuracy: 97.27\n",
      "Train Epoch: 60 [21760/50000 (48%)]\tLoss: 0.102495, Accuracy: 96.88\n",
      "Train Epoch: 60 [23040/50000 (51%)]\tLoss: 0.144618, Accuracy: 96.48\n",
      "Train Epoch: 60 [24320/50000 (54%)]\tLoss: 0.097254, Accuracy: 96.09\n",
      "Train Epoch: 60 [25600/50000 (57%)]\tLoss: 0.116440, Accuracy: 94.92\n",
      "Train Epoch: 60 [26880/50000 (60%)]\tLoss: 0.140560, Accuracy: 93.75\n",
      "Train Epoch: 60 [28160/50000 (62%)]\tLoss: 0.124611, Accuracy: 95.70\n",
      "Train Epoch: 60 [29440/50000 (65%)]\tLoss: 0.148749, Accuracy: 94.53\n",
      "Train Epoch: 60 [30720/50000 (68%)]\tLoss: 0.120605, Accuracy: 96.88\n",
      "Train Epoch: 60 [32000/50000 (71%)]\tLoss: 0.132669, Accuracy: 95.70\n",
      "Train Epoch: 60 [33280/50000 (74%)]\tLoss: 0.080550, Accuracy: 96.88\n",
      "Train Epoch: 60 [34560/50000 (77%)]\tLoss: 0.100678, Accuracy: 96.09\n",
      "Train Epoch: 60 [35840/50000 (80%)]\tLoss: 0.074389, Accuracy: 98.05\n",
      "Train Epoch: 60 [37120/50000 (82%)]\tLoss: 0.114730, Accuracy: 96.88\n",
      "Train Epoch: 60 [38400/50000 (85%)]\tLoss: 0.124498, Accuracy: 95.31\n",
      "Train Epoch: 60 [39680/50000 (88%)]\tLoss: 0.092546, Accuracy: 95.70\n",
      "Train Epoch: 60 [40960/50000 (91%)]\tLoss: 0.159998, Accuracy: 93.36\n",
      "Train Epoch: 60 [42240/50000 (94%)]\tLoss: 0.123714, Accuracy: 96.09\n",
      "Train Epoch: 60 [43520/50000 (97%)]\tLoss: 0.183988, Accuracy: 94.53\n",
      "Train Epoch: 60 [35000/50000 (99%)]\tLoss: 0.097409, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2811, Accuracy: 4561/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.4312629699707 s]\n",
      "\n",
      "Test set: Average loss: 0.2957, Accuracy: 9088/10000 (90.88%)\n",
      "\n",
      "Train Epoch: 61 [0/50000 (0%)]\tLoss: 0.111611, Accuracy: 95.31\n",
      "Train Epoch: 61 [1280/50000 (3%)]\tLoss: 0.126961, Accuracy: 94.92\n",
      "Train Epoch: 61 [2560/50000 (6%)]\tLoss: 0.101334, Accuracy: 96.09\n",
      "Train Epoch: 61 [3840/50000 (9%)]\tLoss: 0.137462, Accuracy: 95.70\n",
      "Train Epoch: 61 [5120/50000 (11%)]\tLoss: 0.128284, Accuracy: 94.92\n",
      "Train Epoch: 61 [6400/50000 (14%)]\tLoss: 0.113410, Accuracy: 97.27\n",
      "Train Epoch: 61 [7680/50000 (17%)]\tLoss: 0.068843, Accuracy: 98.44\n",
      "Train Epoch: 61 [8960/50000 (20%)]\tLoss: 0.072117, Accuracy: 97.66\n",
      "Train Epoch: 61 [10240/50000 (23%)]\tLoss: 0.062893, Accuracy: 99.22\n",
      "Train Epoch: 61 [11520/50000 (26%)]\tLoss: 0.110677, Accuracy: 95.70\n",
      "Train Epoch: 61 [12800/50000 (28%)]\tLoss: 0.120650, Accuracy: 95.31\n",
      "Train Epoch: 61 [14080/50000 (31%)]\tLoss: 0.185150, Accuracy: 93.36\n",
      "Train Epoch: 61 [15360/50000 (34%)]\tLoss: 0.117665, Accuracy: 95.70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 61 [16640/50000 (37%)]\tLoss: 0.105590, Accuracy: 97.27\n",
      "Train Epoch: 61 [17920/50000 (40%)]\tLoss: 0.115591, Accuracy: 95.70\n",
      "Train Epoch: 61 [19200/50000 (43%)]\tLoss: 0.106499, Accuracy: 96.48\n",
      "Train Epoch: 61 [20480/50000 (45%)]\tLoss: 0.116617, Accuracy: 96.48\n",
      "Train Epoch: 61 [21760/50000 (48%)]\tLoss: 0.140500, Accuracy: 94.53\n",
      "Train Epoch: 61 [23040/50000 (51%)]\tLoss: 0.104822, Accuracy: 96.09\n",
      "Train Epoch: 61 [24320/50000 (54%)]\tLoss: 0.123377, Accuracy: 96.88\n",
      "Train Epoch: 61 [25600/50000 (57%)]\tLoss: 0.115155, Accuracy: 96.09\n",
      "Train Epoch: 61 [26880/50000 (60%)]\tLoss: 0.090788, Accuracy: 97.27\n",
      "Train Epoch: 61 [28160/50000 (62%)]\tLoss: 0.075784, Accuracy: 98.05\n",
      "Train Epoch: 61 [29440/50000 (65%)]\tLoss: 0.095356, Accuracy: 97.27\n",
      "Train Epoch: 61 [30720/50000 (68%)]\tLoss: 0.087719, Accuracy: 96.48\n",
      "Train Epoch: 61 [32000/50000 (71%)]\tLoss: 0.090192, Accuracy: 96.48\n",
      "Train Epoch: 61 [33280/50000 (74%)]\tLoss: 0.079034, Accuracy: 96.48\n",
      "Train Epoch: 61 [34560/50000 (77%)]\tLoss: 0.148946, Accuracy: 94.14\n",
      "Train Epoch: 61 [35840/50000 (80%)]\tLoss: 0.114476, Accuracy: 96.09\n",
      "Train Epoch: 61 [37120/50000 (82%)]\tLoss: 0.177484, Accuracy: 94.53\n",
      "Train Epoch: 61 [38400/50000 (85%)]\tLoss: 0.125992, Accuracy: 95.31\n",
      "Train Epoch: 61 [39680/50000 (88%)]\tLoss: 0.104383, Accuracy: 96.09\n",
      "Train Epoch: 61 [40960/50000 (91%)]\tLoss: 0.189650, Accuracy: 93.75\n",
      "Train Epoch: 61 [42240/50000 (94%)]\tLoss: 0.062789, Accuracy: 98.83\n",
      "Train Epoch: 61 [43520/50000 (97%)]\tLoss: 0.066826, Accuracy: 98.05\n",
      "Train Epoch: 61 [35000/50000 (99%)]\tLoss: 0.089198, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2953, Accuracy: 4552/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.552974700927734 s]\n",
      "Train Epoch: 62 [0/50000 (0%)]\tLoss: 0.134485, Accuracy: 94.53\n",
      "Train Epoch: 62 [1280/50000 (3%)]\tLoss: 0.122961, Accuracy: 96.09\n",
      "Train Epoch: 62 [2560/50000 (6%)]\tLoss: 0.087665, Accuracy: 96.48\n",
      "Train Epoch: 62 [3840/50000 (9%)]\tLoss: 0.111374, Accuracy: 96.48\n",
      "Train Epoch: 62 [5120/50000 (11%)]\tLoss: 0.127667, Accuracy: 94.92\n",
      "Train Epoch: 62 [6400/50000 (14%)]\tLoss: 0.119300, Accuracy: 95.70\n",
      "Train Epoch: 62 [7680/50000 (17%)]\tLoss: 0.112818, Accuracy: 96.48\n",
      "Train Epoch: 62 [8960/50000 (20%)]\tLoss: 0.100796, Accuracy: 96.09\n",
      "Train Epoch: 62 [10240/50000 (23%)]\tLoss: 0.131185, Accuracy: 96.48\n",
      "Train Epoch: 62 [11520/50000 (26%)]\tLoss: 0.116909, Accuracy: 96.88\n",
      "Train Epoch: 62 [12800/50000 (28%)]\tLoss: 0.086544, Accuracy: 97.27\n",
      "Train Epoch: 62 [14080/50000 (31%)]\tLoss: 0.084560, Accuracy: 97.66\n",
      "Train Epoch: 62 [15360/50000 (34%)]\tLoss: 0.141904, Accuracy: 94.14\n",
      "Train Epoch: 62 [16640/50000 (37%)]\tLoss: 0.090359, Accuracy: 96.88\n",
      "Train Epoch: 62 [17920/50000 (40%)]\tLoss: 0.077551, Accuracy: 98.05\n",
      "Train Epoch: 62 [19200/50000 (43%)]\tLoss: 0.128470, Accuracy: 95.70\n",
      "Train Epoch: 62 [20480/50000 (45%)]\tLoss: 0.108923, Accuracy: 96.09\n",
      "Train Epoch: 62 [21760/50000 (48%)]\tLoss: 0.091208, Accuracy: 97.27\n",
      "Train Epoch: 62 [23040/50000 (51%)]\tLoss: 0.082699, Accuracy: 96.88\n",
      "Train Epoch: 62 [24320/50000 (54%)]\tLoss: 0.146650, Accuracy: 95.70\n",
      "Train Epoch: 62 [25600/50000 (57%)]\tLoss: 0.061285, Accuracy: 97.66\n",
      "Train Epoch: 62 [26880/50000 (60%)]\tLoss: 0.077963, Accuracy: 96.88\n",
      "Train Epoch: 62 [28160/50000 (62%)]\tLoss: 0.135784, Accuracy: 94.14\n",
      "Train Epoch: 62 [29440/50000 (65%)]\tLoss: 0.121210, Accuracy: 95.70\n",
      "Train Epoch: 62 [30720/50000 (68%)]\tLoss: 0.121045, Accuracy: 96.48\n",
      "Train Epoch: 62 [32000/50000 (71%)]\tLoss: 0.096956, Accuracy: 97.66\n",
      "Train Epoch: 62 [33280/50000 (74%)]\tLoss: 0.160028, Accuracy: 94.14\n",
      "Train Epoch: 62 [34560/50000 (77%)]\tLoss: 0.081874, Accuracy: 97.66\n",
      "Train Epoch: 62 [35840/50000 (80%)]\tLoss: 0.122136, Accuracy: 95.31\n",
      "Train Epoch: 62 [37120/50000 (82%)]\tLoss: 0.126664, Accuracy: 95.70\n",
      "Train Epoch: 62 [38400/50000 (85%)]\tLoss: 0.130275, Accuracy: 95.31\n",
      "Train Epoch: 62 [39680/50000 (88%)]\tLoss: 0.107410, Accuracy: 96.88\n",
      "Train Epoch: 62 [40960/50000 (91%)]\tLoss: 0.074307, Accuracy: 98.05\n",
      "Train Epoch: 62 [42240/50000 (94%)]\tLoss: 0.100907, Accuracy: 97.27\n",
      "Train Epoch: 62 [43520/50000 (97%)]\tLoss: 0.070056, Accuracy: 97.66\n",
      "Train Epoch: 62 [35000/50000 (99%)]\tLoss: 0.070375, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2903, Accuracy: 4553/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.511550188064575 s]\n",
      "\n",
      "Test set: Average loss: 0.2958, Accuracy: 9100/10000 (91.00%)\n",
      "\n",
      "Train Epoch: 63 [0/50000 (0%)]\tLoss: 0.093804, Accuracy: 96.09\n",
      "Train Epoch: 63 [1280/50000 (3%)]\tLoss: 0.114910, Accuracy: 95.70\n",
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      "Train Epoch: 63 [6400/50000 (14%)]\tLoss: 0.055610, Accuracy: 99.22\n",
      "Train Epoch: 63 [7680/50000 (17%)]\tLoss: 0.094593, Accuracy: 96.48\n",
      "Train Epoch: 63 [8960/50000 (20%)]\tLoss: 0.107014, Accuracy: 96.48\n",
      "Train Epoch: 63 [10240/50000 (23%)]\tLoss: 0.072613, Accuracy: 96.09\n",
      "Train Epoch: 63 [11520/50000 (26%)]\tLoss: 0.120219, Accuracy: 96.88\n",
      "Train Epoch: 63 [12800/50000 (28%)]\tLoss: 0.075148, Accuracy: 97.27\n",
      "Train Epoch: 63 [14080/50000 (31%)]\tLoss: 0.052570, Accuracy: 98.05\n",
      "Train Epoch: 63 [15360/50000 (34%)]\tLoss: 0.066955, Accuracy: 97.27\n",
      "Train Epoch: 63 [16640/50000 (37%)]\tLoss: 0.079246, Accuracy: 98.05\n",
      "Train Epoch: 63 [17920/50000 (40%)]\tLoss: 0.131092, Accuracy: 94.14\n",
      "Train Epoch: 63 [19200/50000 (43%)]\tLoss: 0.088489, Accuracy: 97.27\n",
      "Train Epoch: 63 [20480/50000 (45%)]\tLoss: 0.120880, Accuracy: 95.31\n",
      "Train Epoch: 63 [21760/50000 (48%)]\tLoss: 0.130674, Accuracy: 96.09\n",
      "Train Epoch: 63 [23040/50000 (51%)]\tLoss: 0.062238, Accuracy: 98.05\n",
      "Train Epoch: 63 [24320/50000 (54%)]\tLoss: 0.104880, Accuracy: 96.88\n",
      "Train Epoch: 63 [25600/50000 (57%)]\tLoss: 0.072769, Accuracy: 97.66\n",
      "Train Epoch: 63 [26880/50000 (60%)]\tLoss: 0.091600, Accuracy: 97.66\n",
      "Train Epoch: 63 [28160/50000 (62%)]\tLoss: 0.131056, Accuracy: 95.70\n",
      "Train Epoch: 63 [29440/50000 (65%)]\tLoss: 0.116807, Accuracy: 95.31\n",
      "Train Epoch: 63 [30720/50000 (68%)]\tLoss: 0.078105, Accuracy: 96.88\n",
      "Train Epoch: 63 [32000/50000 (71%)]\tLoss: 0.103388, Accuracy: 97.66\n",
      "Train Epoch: 63 [33280/50000 (74%)]\tLoss: 0.114567, Accuracy: 95.31\n",
      "Train Epoch: 63 [34560/50000 (77%)]\tLoss: 0.147744, Accuracy: 94.53\n",
      "Train Epoch: 63 [35840/50000 (80%)]\tLoss: 0.122338, Accuracy: 95.31\n",
      "Train Epoch: 63 [37120/50000 (82%)]\tLoss: 0.140008, Accuracy: 95.70\n",
      "Train Epoch: 63 [38400/50000 (85%)]\tLoss: 0.094511, Accuracy: 96.48\n",
      "Train Epoch: 63 [39680/50000 (88%)]\tLoss: 0.111506, Accuracy: 96.09\n",
      "Train Epoch: 63 [40960/50000 (91%)]\tLoss: 0.107884, Accuracy: 96.09\n",
      "Train Epoch: 63 [42240/50000 (94%)]\tLoss: 0.062076, Accuracy: 97.66\n",
      "Train Epoch: 63 [43520/50000 (97%)]\tLoss: 0.109363, Accuracy: 96.88\n",
      "Train Epoch: 63 [35000/50000 (99%)]\tLoss: 0.104191, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.3008, Accuracy: 4552/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.51643133163452 s]\n",
      "Train Epoch: 64 [0/50000 (0%)]\tLoss: 0.079836, Accuracy: 96.48\n",
      "Train Epoch: 64 [1280/50000 (3%)]\tLoss: 0.088077, Accuracy: 97.27\n",
      "Train Epoch: 64 [2560/50000 (6%)]\tLoss: 0.072372, Accuracy: 96.88\n",
      "Train Epoch: 64 [3840/50000 (9%)]\tLoss: 0.078905, Accuracy: 97.66\n",
      "Train Epoch: 64 [5120/50000 (11%)]\tLoss: 0.082269, Accuracy: 97.27\n",
      "Train Epoch: 64 [6400/50000 (14%)]\tLoss: 0.099119, Accuracy: 97.27\n",
      "Train Epoch: 64 [7680/50000 (17%)]\tLoss: 0.049045, Accuracy: 98.83\n",
      "Train Epoch: 64 [8960/50000 (20%)]\tLoss: 0.089366, Accuracy: 97.27\n",
      "Train Epoch: 64 [10240/50000 (23%)]\tLoss: 0.097453, Accuracy: 96.48\n",
      "Train Epoch: 64 [11520/50000 (26%)]\tLoss: 0.059500, Accuracy: 98.44\n",
      "Train Epoch: 64 [12800/50000 (28%)]\tLoss: 0.067893, Accuracy: 98.05\n",
      "Train Epoch: 64 [14080/50000 (31%)]\tLoss: 0.076434, Accuracy: 97.66\n",
      "Train Epoch: 64 [15360/50000 (34%)]\tLoss: 0.099013, Accuracy: 96.48\n",
      "Train Epoch: 64 [16640/50000 (37%)]\tLoss: 0.090400, Accuracy: 96.48\n",
      "Train Epoch: 64 [17920/50000 (40%)]\tLoss: 0.108115, Accuracy: 96.09\n",
      "Train Epoch: 64 [19200/50000 (43%)]\tLoss: 0.122426, Accuracy: 94.92\n",
      "Train Epoch: 64 [20480/50000 (45%)]\tLoss: 0.064428, Accuracy: 98.44\n",
      "Train Epoch: 64 [21760/50000 (48%)]\tLoss: 0.118689, Accuracy: 96.88\n",
      "Train Epoch: 64 [23040/50000 (51%)]\tLoss: 0.081508, Accuracy: 97.27\n",
      "Train Epoch: 64 [24320/50000 (54%)]\tLoss: 0.119669, Accuracy: 95.70\n",
      "Train Epoch: 64 [25600/50000 (57%)]\tLoss: 0.078111, Accuracy: 96.88\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 64 [26880/50000 (60%)]\tLoss: 0.077980, Accuracy: 97.27\n",
      "Train Epoch: 64 [28160/50000 (62%)]\tLoss: 0.084627, Accuracy: 96.88\n",
      "Train Epoch: 64 [29440/50000 (65%)]\tLoss: 0.092792, Accuracy: 98.44\n",
      "Train Epoch: 64 [30720/50000 (68%)]\tLoss: 0.061855, Accuracy: 98.05\n",
      "Train Epoch: 64 [32000/50000 (71%)]\tLoss: 0.084916, Accuracy: 95.70\n",
      "Train Epoch: 64 [33280/50000 (74%)]\tLoss: 0.093857, Accuracy: 97.27\n",
      "Train Epoch: 64 [34560/50000 (77%)]\tLoss: 0.150682, Accuracy: 94.92\n",
      "Train Epoch: 64 [35840/50000 (80%)]\tLoss: 0.117848, Accuracy: 95.70\n",
      "Train Epoch: 64 [37120/50000 (82%)]\tLoss: 0.159816, Accuracy: 94.14\n",
      "Train Epoch: 64 [38400/50000 (85%)]\tLoss: 0.061521, Accuracy: 97.27\n",
      "Train Epoch: 64 [39680/50000 (88%)]\tLoss: 0.054463, Accuracy: 97.66\n",
      "Train Epoch: 64 [40960/50000 (91%)]\tLoss: 0.130532, Accuracy: 94.53\n",
      "Train Epoch: 64 [42240/50000 (94%)]\tLoss: 0.129586, Accuracy: 96.09\n",
      "Train Epoch: 64 [43520/50000 (97%)]\tLoss: 0.128632, Accuracy: 95.31\n",
      "Train Epoch: 64 [35000/50000 (99%)]\tLoss: 0.087072, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2994, Accuracy: 4557/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.45029425621033 s]\n",
      "\n",
      "Test set: Average loss: 0.3094, Accuracy: 9091/10000 (90.91%)\n",
      "\n",
      "Train Epoch: 65 [0/50000 (0%)]\tLoss: 0.112761, Accuracy: 94.92\n",
      "Train Epoch: 65 [1280/50000 (3%)]\tLoss: 0.081360, Accuracy: 97.27\n",
      "Train Epoch: 65 [2560/50000 (6%)]\tLoss: 0.034085, Accuracy: 99.22\n",
      "Train Epoch: 65 [3840/50000 (9%)]\tLoss: 0.069615, Accuracy: 98.05\n",
      "Train Epoch: 65 [5120/50000 (11%)]\tLoss: 0.106097, Accuracy: 96.09\n",
      "Train Epoch: 65 [6400/50000 (14%)]\tLoss: 0.094771, Accuracy: 98.05\n",
      "Train Epoch: 65 [7680/50000 (17%)]\tLoss: 0.100894, Accuracy: 97.27\n",
      "Train Epoch: 65 [8960/50000 (20%)]\tLoss: 0.080488, Accuracy: 96.48\n",
      "Train Epoch: 65 [10240/50000 (23%)]\tLoss: 0.112581, Accuracy: 94.53\n",
      "Train Epoch: 65 [11520/50000 (26%)]\tLoss: 0.087185, Accuracy: 97.66\n",
      "Train Epoch: 65 [12800/50000 (28%)]\tLoss: 0.124578, Accuracy: 95.31\n",
      "Train Epoch: 65 [14080/50000 (31%)]\tLoss: 0.042233, Accuracy: 98.83\n",
      "Train Epoch: 65 [15360/50000 (34%)]\tLoss: 0.113161, Accuracy: 95.70\n",
      "Train Epoch: 65 [16640/50000 (37%)]\tLoss: 0.093953, Accuracy: 97.27\n",
      "Train Epoch: 65 [17920/50000 (40%)]\tLoss: 0.063920, Accuracy: 98.05\n",
      "Train Epoch: 65 [19200/50000 (43%)]\tLoss: 0.084164, Accuracy: 97.66\n",
      "Train Epoch: 65 [20480/50000 (45%)]\tLoss: 0.071078, Accuracy: 98.44\n",
      "Train Epoch: 65 [21760/50000 (48%)]\tLoss: 0.089002, Accuracy: 97.66\n",
      "Train Epoch: 65 [23040/50000 (51%)]\tLoss: 0.074317, Accuracy: 98.44\n",
      "Train Epoch: 65 [24320/50000 (54%)]\tLoss: 0.088069, Accuracy: 97.27\n",
      "Train Epoch: 65 [25600/50000 (57%)]\tLoss: 0.040768, Accuracy: 98.05\n",
      "Train Epoch: 65 [26880/50000 (60%)]\tLoss: 0.065986, Accuracy: 98.05\n",
      "Train Epoch: 65 [28160/50000 (62%)]\tLoss: 0.068509, Accuracy: 98.05\n",
      "Train Epoch: 65 [29440/50000 (65%)]\tLoss: 0.064875, Accuracy: 97.27\n",
      "Train Epoch: 65 [30720/50000 (68%)]\tLoss: 0.068879, Accuracy: 96.88\n",
      "Train Epoch: 65 [32000/50000 (71%)]\tLoss: 0.123632, Accuracy: 95.70\n",
      "Train Epoch: 65 [33280/50000 (74%)]\tLoss: 0.102295, Accuracy: 96.09\n",
      "Train Epoch: 65 [34560/50000 (77%)]\tLoss: 0.034271, Accuracy: 99.22\n",
      "Train Epoch: 65 [35840/50000 (80%)]\tLoss: 0.137345, Accuracy: 95.31\n",
      "Train Epoch: 65 [37120/50000 (82%)]\tLoss: 0.167365, Accuracy: 95.31\n",
      "Train Epoch: 65 [38400/50000 (85%)]\tLoss: 0.077173, Accuracy: 98.05\n",
      "Train Epoch: 65 [39680/50000 (88%)]\tLoss: 0.095579, Accuracy: 96.09\n",
      "Train Epoch: 65 [40960/50000 (91%)]\tLoss: 0.067577, Accuracy: 98.05\n",
      "Train Epoch: 65 [42240/50000 (94%)]\tLoss: 0.112880, Accuracy: 96.09\n",
      "Train Epoch: 65 [43520/50000 (97%)]\tLoss: 0.090553, Accuracy: 97.66\n",
      "Train Epoch: 65 [35000/50000 (99%)]\tLoss: 0.087633, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.3212, Accuracy: 4527/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[38.56570553779602 s]\n",
      "Train Epoch: 66 [0/50000 (0%)]\tLoss: 0.069089, Accuracy: 96.48\n",
      "Train Epoch: 66 [1280/50000 (3%)]\tLoss: 0.085330, Accuracy: 96.88\n",
      "Train Epoch: 66 [2560/50000 (6%)]\tLoss: 0.101025, Accuracy: 96.48\n",
      "Train Epoch: 66 [3840/50000 (9%)]\tLoss: 0.075925, Accuracy: 97.66\n",
      "Train Epoch: 66 [5120/50000 (11%)]\tLoss: 0.089377, Accuracy: 96.88\n",
      "Train Epoch: 66 [6400/50000 (14%)]\tLoss: 0.084821, Accuracy: 98.44\n",
      "Train Epoch: 66 [7680/50000 (17%)]\tLoss: 0.062961, Accuracy: 98.05\n",
      "Train Epoch: 66 [8960/50000 (20%)]\tLoss: 0.097329, Accuracy: 97.66\n",
      "Train Epoch: 66 [10240/50000 (23%)]\tLoss: 0.076899, Accuracy: 97.27\n",
      "Train Epoch: 66 [11520/50000 (26%)]\tLoss: 0.069266, Accuracy: 97.66\n",
      "Train Epoch: 66 [12800/50000 (28%)]\tLoss: 0.091626, Accuracy: 97.27\n",
      "Train Epoch: 66 [14080/50000 (31%)]\tLoss: 0.147232, Accuracy: 94.53\n",
      "Train Epoch: 66 [15360/50000 (34%)]\tLoss: 0.064272, Accuracy: 97.27\n",
      "Train Epoch: 66 [16640/50000 (37%)]\tLoss: 0.085037, Accuracy: 96.09\n",
      "Train Epoch: 66 [17920/50000 (40%)]\tLoss: 0.066447, Accuracy: 98.05\n",
      "Train Epoch: 66 [19200/50000 (43%)]\tLoss: 0.088567, Accuracy: 96.48\n",
      "Train Epoch: 66 [20480/50000 (45%)]\tLoss: 0.112894, Accuracy: 95.70\n",
      "Train Epoch: 66 [21760/50000 (48%)]\tLoss: 0.099886, Accuracy: 97.27\n",
      "Train Epoch: 66 [23040/50000 (51%)]\tLoss: 0.082613, Accuracy: 97.27\n",
      "Train Epoch: 66 [24320/50000 (54%)]\tLoss: 0.105089, Accuracy: 96.88\n",
      "Train Epoch: 66 [25600/50000 (57%)]\tLoss: 0.088558, Accuracy: 96.09\n",
      "Train Epoch: 66 [26880/50000 (60%)]\tLoss: 0.113434, Accuracy: 94.53\n",
      "Train Epoch: 66 [28160/50000 (62%)]\tLoss: 0.083376, Accuracy: 96.09\n",
      "Train Epoch: 66 [29440/50000 (65%)]\tLoss: 0.109489, Accuracy: 96.09\n",
      "Train Epoch: 66 [30720/50000 (68%)]\tLoss: 0.082157, Accuracy: 96.88\n",
      "Train Epoch: 66 [32000/50000 (71%)]\tLoss: 0.092241, Accuracy: 97.66\n",
      "Train Epoch: 66 [33280/50000 (74%)]\tLoss: 0.112939, Accuracy: 96.09\n",
      "Train Epoch: 66 [34560/50000 (77%)]\tLoss: 0.110521, Accuracy: 97.27\n",
      "Train Epoch: 66 [35840/50000 (80%)]\tLoss: 0.083041, Accuracy: 96.88\n",
      "Train Epoch: 66 [37120/50000 (82%)]\tLoss: 0.105914, Accuracy: 96.48\n",
      "Train Epoch: 66 [38400/50000 (85%)]\tLoss: 0.104600, Accuracy: 96.88\n",
      "Train Epoch: 66 [39680/50000 (88%)]\tLoss: 0.102039, Accuracy: 97.27\n",
      "Train Epoch: 66 [40960/50000 (91%)]\tLoss: 0.114830, Accuracy: 95.70\n",
      "Train Epoch: 66 [42240/50000 (94%)]\tLoss: 0.105731, Accuracy: 95.70\n",
      "Train Epoch: 66 [43520/50000 (97%)]\tLoss: 0.109188, Accuracy: 96.48\n",
      "Train Epoch: 66 [35000/50000 (99%)]\tLoss: 0.081896, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.3381, Accuracy: 4526/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[35.43313694000244 s]\n",
      "\n",
      "Test set: Average loss: 0.3437, Accuracy: 9022/10000 (90.22%)\n",
      "\n",
      "Train Epoch: 67 [0/50000 (0%)]\tLoss: 0.092954, Accuracy: 97.66\n",
      "Train Epoch: 67 [1280/50000 (3%)]\tLoss: 0.065473, Accuracy: 97.66\n",
      "Train Epoch: 67 [2560/50000 (6%)]\tLoss: 0.052417, Accuracy: 98.44\n",
      "Train Epoch: 67 [3840/50000 (9%)]\tLoss: 0.060636, Accuracy: 98.44\n",
      "Train Epoch: 67 [5120/50000 (11%)]\tLoss: 0.106450, Accuracy: 96.09\n",
      "Train Epoch: 67 [6400/50000 (14%)]\tLoss: 0.066200, Accuracy: 97.27\n",
      "Train Epoch: 67 [7680/50000 (17%)]\tLoss: 0.068293, Accuracy: 96.88\n",
      "Train Epoch: 67 [8960/50000 (20%)]\tLoss: 0.086981, Accuracy: 96.09\n",
      "Train Epoch: 67 [10240/50000 (23%)]\tLoss: 0.058497, Accuracy: 98.83\n",
      "Train Epoch: 67 [11520/50000 (26%)]\tLoss: 0.076317, Accuracy: 96.09\n",
      "Train Epoch: 67 [12800/50000 (28%)]\tLoss: 0.120127, Accuracy: 95.31\n",
      "Train Epoch: 67 [14080/50000 (31%)]\tLoss: 0.101522, Accuracy: 97.66\n",
      "Train Epoch: 67 [15360/50000 (34%)]\tLoss: 0.079132, Accuracy: 98.44\n",
      "Train Epoch: 67 [16640/50000 (37%)]\tLoss: 0.097334, Accuracy: 97.27\n",
      "Train Epoch: 67 [17920/50000 (40%)]\tLoss: 0.065420, Accuracy: 97.27\n",
      "Train Epoch: 67 [19200/50000 (43%)]\tLoss: 0.103972, Accuracy: 96.88\n",
      "Train Epoch: 67 [20480/50000 (45%)]\tLoss: 0.080701, Accuracy: 97.66\n",
      "Train Epoch: 67 [21760/50000 (48%)]\tLoss: 0.091039, Accuracy: 97.66\n",
      "Train Epoch: 67 [23040/50000 (51%)]\tLoss: 0.071902, Accuracy: 98.05\n",
      "Train Epoch: 67 [24320/50000 (54%)]\tLoss: 0.101807, Accuracy: 96.48\n",
      "Train Epoch: 67 [25600/50000 (57%)]\tLoss: 0.085908, Accuracy: 96.48\n",
      "Train Epoch: 67 [26880/50000 (60%)]\tLoss: 0.069134, Accuracy: 97.66\n",
      "Train Epoch: 67 [28160/50000 (62%)]\tLoss: 0.064249, Accuracy: 97.66\n",
      "Train Epoch: 67 [29440/50000 (65%)]\tLoss: 0.109003, Accuracy: 96.48\n",
      "Train Epoch: 67 [30720/50000 (68%)]\tLoss: 0.143189, Accuracy: 95.70\n",
      "Train Epoch: 67 [32000/50000 (71%)]\tLoss: 0.113584, Accuracy: 96.48\n",
      "Train Epoch: 67 [33280/50000 (74%)]\tLoss: 0.048194, Accuracy: 99.22\n",
      "Train Epoch: 67 [34560/50000 (77%)]\tLoss: 0.052117, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 67 [35840/50000 (80%)]\tLoss: 0.152347, Accuracy: 94.92\n",
      "Train Epoch: 67 [37120/50000 (82%)]\tLoss: 0.077959, Accuracy: 96.88\n",
      "Train Epoch: 67 [38400/50000 (85%)]\tLoss: 0.054895, Accuracy: 97.66\n",
      "Train Epoch: 67 [39680/50000 (88%)]\tLoss: 0.086125, Accuracy: 96.48\n",
      "Train Epoch: 67 [40960/50000 (91%)]\tLoss: 0.059907, Accuracy: 98.05\n",
      "Train Epoch: 67 [42240/50000 (94%)]\tLoss: 0.127473, Accuracy: 95.31\n",
      "Train Epoch: 67 [43520/50000 (97%)]\tLoss: 0.098831, Accuracy: 97.27\n",
      "Train Epoch: 67 [35000/50000 (99%)]\tLoss: 0.095793, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.3062, Accuracy: 4534/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[38.536805152893066 s]\n",
      "Train Epoch: 68 [0/50000 (0%)]\tLoss: 0.045004, Accuracy: 98.83\n",
      "Train Epoch: 68 [1280/50000 (3%)]\tLoss: 0.071644, Accuracy: 97.66\n",
      "Train Epoch: 68 [2560/50000 (6%)]\tLoss: 0.108619, Accuracy: 97.27\n",
      "Train Epoch: 68 [3840/50000 (9%)]\tLoss: 0.064414, Accuracy: 98.05\n",
      "Train Epoch: 68 [5120/50000 (11%)]\tLoss: 0.052400, Accuracy: 97.66\n",
      "Train Epoch: 68 [6400/50000 (14%)]\tLoss: 0.093120, Accuracy: 96.48\n",
      "Train Epoch: 68 [7680/50000 (17%)]\tLoss: 0.032183, Accuracy: 99.22\n",
      "Train Epoch: 68 [8960/50000 (20%)]\tLoss: 0.092248, Accuracy: 96.88\n",
      "Train Epoch: 68 [10240/50000 (23%)]\tLoss: 0.118338, Accuracy: 96.88\n",
      "Train Epoch: 68 [11520/50000 (26%)]\tLoss: 0.096717, Accuracy: 96.09\n",
      "Train Epoch: 68 [12800/50000 (28%)]\tLoss: 0.068716, Accuracy: 96.88\n",
      "Train Epoch: 68 [14080/50000 (31%)]\tLoss: 0.087190, Accuracy: 97.66\n",
      "Train Epoch: 68 [15360/50000 (34%)]\tLoss: 0.087060, Accuracy: 97.27\n",
      "Train Epoch: 68 [16640/50000 (37%)]\tLoss: 0.081652, Accuracy: 97.66\n",
      "Train Epoch: 68 [17920/50000 (40%)]\tLoss: 0.057260, Accuracy: 97.66\n",
      "Train Epoch: 68 [19200/50000 (43%)]\tLoss: 0.090301, Accuracy: 95.70\n",
      "Train Epoch: 68 [20480/50000 (45%)]\tLoss: 0.074211, Accuracy: 97.66\n",
      "Train Epoch: 68 [21760/50000 (48%)]\tLoss: 0.083878, Accuracy: 96.88\n",
      "Train Epoch: 68 [23040/50000 (51%)]\tLoss: 0.076197, Accuracy: 97.27\n",
      "Train Epoch: 68 [24320/50000 (54%)]\tLoss: 0.119602, Accuracy: 96.09\n",
      "Train Epoch: 68 [25600/50000 (57%)]\tLoss: 0.052817, Accuracy: 98.05\n",
      "Train Epoch: 68 [26880/50000 (60%)]\tLoss: 0.094869, Accuracy: 95.31\n",
      "Train Epoch: 68 [28160/50000 (62%)]\tLoss: 0.060627, Accuracy: 98.44\n",
      "Train Epoch: 68 [29440/50000 (65%)]\tLoss: 0.120746, Accuracy: 96.48\n",
      "Train Epoch: 68 [30720/50000 (68%)]\tLoss: 0.073122, Accuracy: 98.44\n",
      "Train Epoch: 68 [32000/50000 (71%)]\tLoss: 0.112446, Accuracy: 95.70\n",
      "Train Epoch: 68 [33280/50000 (74%)]\tLoss: 0.086635, Accuracy: 96.88\n",
      "Train Epoch: 68 [34560/50000 (77%)]\tLoss: 0.085989, Accuracy: 96.88\n",
      "Train Epoch: 68 [35840/50000 (80%)]\tLoss: 0.136051, Accuracy: 93.75\n",
      "Train Epoch: 68 [37120/50000 (82%)]\tLoss: 0.116248, Accuracy: 96.48\n",
      "Train Epoch: 68 [38400/50000 (85%)]\tLoss: 0.099005, Accuracy: 96.88\n",
      "Train Epoch: 68 [39680/50000 (88%)]\tLoss: 0.153100, Accuracy: 94.53\n",
      "Train Epoch: 68 [40960/50000 (91%)]\tLoss: 0.089213, Accuracy: 96.09\n",
      "Train Epoch: 68 [42240/50000 (94%)]\tLoss: 0.076500, Accuracy: 97.27\n",
      "Train Epoch: 68 [43520/50000 (97%)]\tLoss: 0.071678, Accuracy: 98.05\n",
      "Train Epoch: 68 [35000/50000 (99%)]\tLoss: 0.076091, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.3402, Accuracy: 4515/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[35.437325954437256 s]\n",
      "\n",
      "Test set: Average loss: 0.3372, Accuracy: 9048/10000 (90.48%)\n",
      "\n",
      "Train Epoch: 69 [0/50000 (0%)]\tLoss: 0.051396, Accuracy: 98.05\n",
      "Train Epoch: 69 [1280/50000 (3%)]\tLoss: 0.094279, Accuracy: 97.27\n",
      "Train Epoch: 69 [2560/50000 (6%)]\tLoss: 0.033413, Accuracy: 98.44\n",
      "Train Epoch: 69 [3840/50000 (9%)]\tLoss: 0.106899, Accuracy: 96.48\n",
      "Train Epoch: 69 [5120/50000 (11%)]\tLoss: 0.068396, Accuracy: 98.05\n",
      "Train Epoch: 69 [6400/50000 (14%)]\tLoss: 0.053037, Accuracy: 98.05\n",
      "Train Epoch: 69 [7680/50000 (17%)]\tLoss: 0.052808, Accuracy: 98.44\n",
      "Train Epoch: 69 [8960/50000 (20%)]\tLoss: 0.066661, Accuracy: 98.44\n",
      "Train Epoch: 69 [10240/50000 (23%)]\tLoss: 0.055478, Accuracy: 98.83\n",
      "Train Epoch: 69 [11520/50000 (26%)]\tLoss: 0.083996, Accuracy: 96.88\n",
      "Train Epoch: 69 [12800/50000 (28%)]\tLoss: 0.039407, Accuracy: 98.83\n",
      "Train Epoch: 69 [14080/50000 (31%)]\tLoss: 0.085965, Accuracy: 96.88\n",
      "Train Epoch: 69 [15360/50000 (34%)]\tLoss: 0.080537, Accuracy: 96.09\n",
      "Train Epoch: 69 [16640/50000 (37%)]\tLoss: 0.062179, Accuracy: 97.27\n",
      "Train Epoch: 69 [17920/50000 (40%)]\tLoss: 0.076511, Accuracy: 96.88\n",
      "Train Epoch: 69 [19200/50000 (43%)]\tLoss: 0.103493, Accuracy: 96.88\n",
      "Train Epoch: 69 [20480/50000 (45%)]\tLoss: 0.083928, Accuracy: 96.09\n",
      "Train Epoch: 69 [21760/50000 (48%)]\tLoss: 0.077096, Accuracy: 97.66\n",
      "Train Epoch: 69 [23040/50000 (51%)]\tLoss: 0.051173, Accuracy: 99.22\n",
      "Train Epoch: 69 [24320/50000 (54%)]\tLoss: 0.094255, Accuracy: 96.09\n",
      "Train Epoch: 69 [25600/50000 (57%)]\tLoss: 0.083322, Accuracy: 96.88\n",
      "Train Epoch: 69 [26880/50000 (60%)]\tLoss: 0.053506, Accuracy: 98.44\n",
      "Train Epoch: 69 [28160/50000 (62%)]\tLoss: 0.045259, Accuracy: 97.66\n",
      "Train Epoch: 69 [29440/50000 (65%)]\tLoss: 0.118385, Accuracy: 95.31\n",
      "Train Epoch: 69 [30720/50000 (68%)]\tLoss: 0.113363, Accuracy: 94.92\n",
      "Train Epoch: 69 [32000/50000 (71%)]\tLoss: 0.062995, Accuracy: 97.27\n",
      "Train Epoch: 69 [33280/50000 (74%)]\tLoss: 0.107401, Accuracy: 95.70\n",
      "Train Epoch: 69 [34560/50000 (77%)]\tLoss: 0.093034, Accuracy: 96.88\n",
      "Train Epoch: 69 [35840/50000 (80%)]\tLoss: 0.097336, Accuracy: 95.70\n",
      "Train Epoch: 69 [37120/50000 (82%)]\tLoss: 0.061455, Accuracy: 97.27\n",
      "Train Epoch: 69 [38400/50000 (85%)]\tLoss: 0.051941, Accuracy: 98.44\n",
      "Train Epoch: 69 [39680/50000 (88%)]\tLoss: 0.078938, Accuracy: 97.66\n",
      "Train Epoch: 69 [40960/50000 (91%)]\tLoss: 0.066117, Accuracy: 98.05\n",
      "Train Epoch: 69 [42240/50000 (94%)]\tLoss: 0.109074, Accuracy: 96.88\n",
      "Train Epoch: 69 [43520/50000 (97%)]\tLoss: 0.082460, Accuracy: 97.66\n",
      "Train Epoch: 69 [35000/50000 (99%)]\tLoss: 0.104396, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.3365, Accuracy: 4561/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.51046538352966 s]\n",
      "Train Epoch: 70 [0/50000 (0%)]\tLoss: 0.085301, Accuracy: 96.09\n",
      "Train Epoch: 70 [1280/50000 (3%)]\tLoss: 0.083488, Accuracy: 96.48\n",
      "Train Epoch: 70 [2560/50000 (6%)]\tLoss: 0.067573, Accuracy: 98.83\n",
      "Train Epoch: 70 [3840/50000 (9%)]\tLoss: 0.060972, Accuracy: 97.27\n",
      "Train Epoch: 70 [5120/50000 (11%)]\tLoss: 0.071167, Accuracy: 98.44\n",
      "Train Epoch: 70 [6400/50000 (14%)]\tLoss: 0.041544, Accuracy: 99.22\n",
      "Train Epoch: 70 [7680/50000 (17%)]\tLoss: 0.091520, Accuracy: 97.66\n",
      "Train Epoch: 70 [8960/50000 (20%)]\tLoss: 0.101357, Accuracy: 95.70\n",
      "Train Epoch: 70 [10240/50000 (23%)]\tLoss: 0.070911, Accuracy: 98.05\n",
      "Train Epoch: 70 [11520/50000 (26%)]\tLoss: 0.050784, Accuracy: 98.44\n",
      "Train Epoch: 70 [12800/50000 (28%)]\tLoss: 0.038744, Accuracy: 99.22\n",
      "Train Epoch: 70 [14080/50000 (31%)]\tLoss: 0.087430, Accuracy: 97.27\n",
      "Train Epoch: 70 [15360/50000 (34%)]\tLoss: 0.088331, Accuracy: 95.31\n",
      "Train Epoch: 70 [16640/50000 (37%)]\tLoss: 0.044005, Accuracy: 97.66\n",
      "Train Epoch: 70 [17920/50000 (40%)]\tLoss: 0.126501, Accuracy: 95.31\n",
      "Train Epoch: 70 [19200/50000 (43%)]\tLoss: 0.075693, Accuracy: 97.27\n",
      "Train Epoch: 70 [20480/50000 (45%)]\tLoss: 0.066305, Accuracy: 97.66\n",
      "Train Epoch: 70 [21760/50000 (48%)]\tLoss: 0.079079, Accuracy: 96.09\n",
      "Train Epoch: 70 [23040/50000 (51%)]\tLoss: 0.137552, Accuracy: 96.88\n",
      "Train Epoch: 70 [24320/50000 (54%)]\tLoss: 0.062305, Accuracy: 97.66\n",
      "Train Epoch: 70 [25600/50000 (57%)]\tLoss: 0.063961, Accuracy: 97.27\n",
      "Train Epoch: 70 [26880/50000 (60%)]\tLoss: 0.085798, Accuracy: 98.44\n",
      "Train Epoch: 70 [28160/50000 (62%)]\tLoss: 0.125740, Accuracy: 94.14\n",
      "Train Epoch: 70 [29440/50000 (65%)]\tLoss: 0.081257, Accuracy: 96.88\n",
      "Train Epoch: 70 [30720/50000 (68%)]\tLoss: 0.111905, Accuracy: 96.48\n",
      "Train Epoch: 70 [32000/50000 (71%)]\tLoss: 0.058634, Accuracy: 98.05\n",
      "Train Epoch: 70 [33280/50000 (74%)]\tLoss: 0.138382, Accuracy: 96.48\n",
      "Train Epoch: 70 [34560/50000 (77%)]\tLoss: 0.040913, Accuracy: 98.44\n",
      "Train Epoch: 70 [35840/50000 (80%)]\tLoss: 0.067890, Accuracy: 97.27\n",
      "Train Epoch: 70 [37120/50000 (82%)]\tLoss: 0.083613, Accuracy: 96.88\n",
      "Train Epoch: 70 [38400/50000 (85%)]\tLoss: 0.104048, Accuracy: 98.05\n",
      "Train Epoch: 70 [39680/50000 (88%)]\tLoss: 0.080781, Accuracy: 96.48\n",
      "Train Epoch: 70 [40960/50000 (91%)]\tLoss: 0.064828, Accuracy: 97.27\n",
      "Train Epoch: 70 [42240/50000 (94%)]\tLoss: 0.110322, Accuracy: 96.09\n",
      "Train Epoch: 70 [43520/50000 (97%)]\tLoss: 0.095847, Accuracy: 95.70\n",
      "Train Epoch: 70 [35000/50000 (99%)]\tLoss: 0.100980, Accuracy: 96.50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 0.3493, Accuracy: 4530/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[35.45314645767212 s]\n",
      "\n",
      "Test set: Average loss: 0.3503, Accuracy: 9029/10000 (90.29%)\n",
      "\n",
      "Train Epoch: 71 [0/50000 (0%)]\tLoss: 0.063958, Accuracy: 97.66\n",
      "Train Epoch: 71 [1280/50000 (3%)]\tLoss: 0.102680, Accuracy: 96.48\n",
      "Train Epoch: 71 [2560/50000 (6%)]\tLoss: 0.062120, Accuracy: 97.66\n",
      "Train Epoch: 71 [3840/50000 (9%)]\tLoss: 0.081578, Accuracy: 96.48\n",
      "Train Epoch: 71 [5120/50000 (11%)]\tLoss: 0.087423, Accuracy: 97.66\n",
      "Train Epoch: 71 [6400/50000 (14%)]\tLoss: 0.082355, Accuracy: 97.27\n",
      "Train Epoch: 71 [7680/50000 (17%)]\tLoss: 0.075287, Accuracy: 96.48\n",
      "Train Epoch: 71 [8960/50000 (20%)]\tLoss: 0.055454, Accuracy: 97.66\n",
      "Train Epoch: 71 [10240/50000 (23%)]\tLoss: 0.068696, Accuracy: 97.66\n",
      "Train Epoch: 71 [11520/50000 (26%)]\tLoss: 0.069526, Accuracy: 98.44\n",
      "Train Epoch: 71 [12800/50000 (28%)]\tLoss: 0.063967, Accuracy: 98.05\n",
      "Train Epoch: 71 [14080/50000 (31%)]\tLoss: 0.058727, Accuracy: 98.83\n",
      "Train Epoch: 71 [15360/50000 (34%)]\tLoss: 0.070844, Accuracy: 97.66\n",
      "Train Epoch: 71 [16640/50000 (37%)]\tLoss: 0.038818, Accuracy: 98.44\n",
      "Train Epoch: 71 [17920/50000 (40%)]\tLoss: 0.069420, Accuracy: 97.27\n",
      "Train Epoch: 71 [19200/50000 (43%)]\tLoss: 0.059915, Accuracy: 98.44\n",
      "Train Epoch: 71 [20480/50000 (45%)]\tLoss: 0.063357, Accuracy: 97.27\n",
      "Train Epoch: 71 [21760/50000 (48%)]\tLoss: 0.029329, Accuracy: 99.61\n",
      "Train Epoch: 71 [23040/50000 (51%)]\tLoss: 0.046636, Accuracy: 97.66\n",
      "Train Epoch: 71 [24320/50000 (54%)]\tLoss: 0.042375, Accuracy: 98.44\n",
      "Train Epoch: 71 [25600/50000 (57%)]\tLoss: 0.054745, Accuracy: 97.66\n",
      "Train Epoch: 71 [26880/50000 (60%)]\tLoss: 0.030967, Accuracy: 99.61\n",
      "Train Epoch: 71 [28160/50000 (62%)]\tLoss: 0.065128, Accuracy: 96.88\n",
      "Train Epoch: 71 [29440/50000 (65%)]\tLoss: 0.048652, Accuracy: 99.22\n",
      "Train Epoch: 71 [30720/50000 (68%)]\tLoss: 0.049919, Accuracy: 98.05\n",
      "Train Epoch: 71 [32000/50000 (71%)]\tLoss: 0.068625, Accuracy: 98.05\n",
      "Train Epoch: 71 [33280/50000 (74%)]\tLoss: 0.062775, Accuracy: 97.66\n",
      "Train Epoch: 71 [34560/50000 (77%)]\tLoss: 0.044611, Accuracy: 98.44\n",
      "Train Epoch: 71 [35840/50000 (80%)]\tLoss: 0.041736, Accuracy: 98.83\n",
      "Train Epoch: 71 [37120/50000 (82%)]\tLoss: 0.063170, Accuracy: 97.27\n",
      "Train Epoch: 71 [38400/50000 (85%)]\tLoss: 0.057617, Accuracy: 97.66\n",
      "Train Epoch: 71 [39680/50000 (88%)]\tLoss: 0.045110, Accuracy: 99.22\n",
      "Train Epoch: 71 [40960/50000 (91%)]\tLoss: 0.055719, Accuracy: 98.83\n",
      "Train Epoch: 71 [42240/50000 (94%)]\tLoss: 0.059446, Accuracy: 97.27\n",
      "Train Epoch: 71 [43520/50000 (97%)]\tLoss: 0.068770, Accuracy: 97.27\n",
      "Train Epoch: 71 [35000/50000 (99%)]\tLoss: 0.033887, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.2960, Accuracy: 4580/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.529736280441284 s]\n",
      "Train Epoch: 72 [0/50000 (0%)]\tLoss: 0.056005, Accuracy: 97.27\n",
      "Train Epoch: 72 [1280/50000 (3%)]\tLoss: 0.042212, Accuracy: 98.05\n",
      "Train Epoch: 72 [2560/50000 (6%)]\tLoss: 0.036981, Accuracy: 99.22\n",
      "Train Epoch: 72 [3840/50000 (9%)]\tLoss: 0.042156, Accuracy: 98.83\n",
      "Train Epoch: 72 [5120/50000 (11%)]\tLoss: 0.062548, Accuracy: 97.27\n",
      "Train Epoch: 72 [6400/50000 (14%)]\tLoss: 0.055274, Accuracy: 97.66\n",
      "Train Epoch: 72 [7680/50000 (17%)]\tLoss: 0.065787, Accuracy: 98.44\n",
      "Train Epoch: 72 [8960/50000 (20%)]\tLoss: 0.069077, Accuracy: 96.88\n",
      "Train Epoch: 72 [10240/50000 (23%)]\tLoss: 0.031990, Accuracy: 99.22\n",
      "Train Epoch: 72 [11520/50000 (26%)]\tLoss: 0.081086, Accuracy: 98.05\n",
      "Train Epoch: 72 [12800/50000 (28%)]\tLoss: 0.072107, Accuracy: 96.88\n",
      "Train Epoch: 72 [14080/50000 (31%)]\tLoss: 0.039370, Accuracy: 99.22\n",
      "Train Epoch: 72 [15360/50000 (34%)]\tLoss: 0.026899, Accuracy: 99.22\n",
      "Train Epoch: 72 [16640/50000 (37%)]\tLoss: 0.063290, Accuracy: 98.44\n",
      "Train Epoch: 72 [17920/50000 (40%)]\tLoss: 0.067036, Accuracy: 98.05\n",
      "Train Epoch: 72 [19200/50000 (43%)]\tLoss: 0.063673, Accuracy: 98.05\n",
      "Train Epoch: 72 [20480/50000 (45%)]\tLoss: 0.090435, Accuracy: 95.70\n",
      "Train Epoch: 72 [21760/50000 (48%)]\tLoss: 0.061025, Accuracy: 97.27\n",
      "Train Epoch: 72 [23040/50000 (51%)]\tLoss: 0.046823, Accuracy: 98.05\n",
      "Train Epoch: 72 [24320/50000 (54%)]\tLoss: 0.022253, Accuracy: 99.61\n",
      "Train Epoch: 72 [25600/50000 (57%)]\tLoss: 0.040233, Accuracy: 99.22\n",
      "Train Epoch: 72 [26880/50000 (60%)]\tLoss: 0.054006, Accuracy: 98.83\n",
      "Train Epoch: 72 [28160/50000 (62%)]\tLoss: 0.041934, Accuracy: 98.83\n",
      "Train Epoch: 72 [29440/50000 (65%)]\tLoss: 0.025014, Accuracy: 98.83\n",
      "Train Epoch: 72 [30720/50000 (68%)]\tLoss: 0.073900, Accuracy: 97.66\n",
      "Train Epoch: 72 [32000/50000 (71%)]\tLoss: 0.041567, Accuracy: 98.05\n",
      "Train Epoch: 72 [33280/50000 (74%)]\tLoss: 0.044083, Accuracy: 98.83\n",
      "Train Epoch: 72 [34560/50000 (77%)]\tLoss: 0.082048, Accuracy: 96.09\n",
      "Train Epoch: 72 [35840/50000 (80%)]\tLoss: 0.068816, Accuracy: 98.05\n",
      "Train Epoch: 72 [37120/50000 (82%)]\tLoss: 0.030565, Accuracy: 99.61\n",
      "Train Epoch: 72 [38400/50000 (85%)]\tLoss: 0.087837, Accuracy: 97.66\n",
      "Train Epoch: 72 [39680/50000 (88%)]\tLoss: 0.037354, Accuracy: 98.05\n",
      "Train Epoch: 72 [40960/50000 (91%)]\tLoss: 0.042917, Accuracy: 99.22\n",
      "Train Epoch: 72 [42240/50000 (94%)]\tLoss: 0.052790, Accuracy: 97.66\n",
      "Train Epoch: 72 [43520/50000 (97%)]\tLoss: 0.054203, Accuracy: 98.44\n",
      "Train Epoch: 72 [35000/50000 (99%)]\tLoss: 0.036711, Accuracy: 99.00\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 1.5885]],\n",
      "\n",
      "        [[ 0.0115]],\n",
      "\n",
      "        [[-2.4132]],\n",
      "\n",
      "        [[ 0.9750]],\n",
      "\n",
      "        [[-0.8674]],\n",
      "\n",
      "        [[-1.0833]],\n",
      "\n",
      "        [[ 1.0773]],\n",
      "\n",
      "        [[ 1.3794]],\n",
      "\n",
      "        [[ 0.1025]],\n",
      "\n",
      "        [[-0.2040]],\n",
      "\n",
      "        [[-0.6442]],\n",
      "\n",
      "        [[ 0.4067]],\n",
      "\n",
      "        [[ 1.0863]],\n",
      "\n",
      "        [[-1.7045]],\n",
      "\n",
      "        [[-0.4829]],\n",
      "\n",
      "        [[-0.0569]],\n",
      "\n",
      "        [[ 0.7632]],\n",
      "\n",
      "        [[-0.6919]],\n",
      "\n",
      "        [[-0.9831]],\n",
      "\n",
      "        [[ 1.0705]],\n",
      "\n",
      "        [[-0.7974]],\n",
      "\n",
      "        [[ 2.2274]],\n",
      "\n",
      "        [[-0.6585]],\n",
      "\n",
      "        [[ 2.1615]],\n",
      "\n",
      "        [[-0.2475]],\n",
      "\n",
      "        [[ 1.1693]],\n",
      "\n",
      "        [[ 1.2582]],\n",
      "\n",
      "        [[-1.0603]],\n",
      "\n",
      "        [[ 0.1915]],\n",
      "\n",
      "        [[-1.0968]],\n",
      "\n",
      "        [[ 1.8371]],\n",
      "\n",
      "        [[ 0.1127]],\n",
      "\n",
      "        [[-0.5816]],\n",
      "\n",
      "        [[-1.2189]],\n",
      "\n",
      "        [[-1.1478]],\n",
      "\n",
      "        [[ 2.1078]],\n",
      "\n",
      "        [[-0.8133]],\n",
      "\n",
      "        [[-0.2760]],\n",
      "\n",
      "        [[-0.7652]],\n",
      "\n",
      "        [[ 1.1091]],\n",
      "\n",
      "        [[-1.6415]],\n",
      "\n",
      "        [[ 0.7859]],\n",
      "\n",
      "        [[-1.1269]],\n",
      "\n",
      "        [[ 1.1844]],\n",
      "\n",
      "        [[ 0.2848]],\n",
      "\n",
      "        [[ 2.5051]],\n",
      "\n",
      "        [[ 0.9471]],\n",
      "\n",
      "        [[-0.8691]],\n",
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      "        [[ 1.0561]],\n",
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      "        [[-0.1865]],\n",
      "\n",
      "        [[-0.6693]],\n",
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      "        [[-0.2775]],\n",
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      "        [[ 1.4800]],\n",
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      "        [[ 0.4465]],\n",
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      "        [[-0.8441]],\n",
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      "        [[ 0.6719]],\n",
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      "        [[-0.6727]],\n",
      "\n",
      "        [[-0.2336]],\n",
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      "        [[-0.4563]],\n",
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      "        [[ 0.1761]],\n",
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      "        [[ 0.5136]],\n",
      "\n",
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      "        [[-0.8374]],\n",
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      "        [[-0.0098]],\n",
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      "        [[ 0.1051]],\n",
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      "        [[-0.1701]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[-2.4359e-04, -1.2401e-04,  2.5187e-04,  ...,  1.4102e-04,\n",
      "          -9.4448e-05,  4.2242e-04],\n",
      "         [ 5.6939e-04, -7.3343e-04, -2.1351e-04,  ...,  3.2799e-04,\n",
      "          -1.9491e-05, -1.7693e-04],\n",
      "         [ 6.5587e-05,  1.7056e-04,  1.7395e-04,  ..., -5.0466e-05,\n",
      "          -1.9229e-04,  1.5681e-04],\n",
      "         ...,\n",
      "         [ 1.8539e-04, -4.6777e-04, -1.4311e-05,  ...,  2.1569e-04,\n",
      "          -7.6509e-04,  2.4802e-04],\n",
      "         [-5.4289e-04, -3.9912e-04,  1.0818e-04,  ..., -1.6827e-04,\n",
      "           1.4098e-04,  1.6255e-05],\n",
      "         [ 6.8715e-05, -4.7284e-05, -6.0697e-04,  ...,  1.5438e-04,\n",
      "           2.8198e-04, -5.3328e-05]]], device='cuda:0')\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 0.2939, Accuracy: 4579/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.44555330276489 s]\n",
      "\n",
      "Test set: Average loss: 0.3072, Accuracy: 9139/10000 (91.39%)\n",
      "\n",
      "Train Epoch: 73 [0/50000 (0%)]\tLoss: 0.032727, Accuracy: 99.22\n",
      "Train Epoch: 73 [1280/50000 (3%)]\tLoss: 0.044157, Accuracy: 98.44\n",
      "Train Epoch: 73 [2560/50000 (6%)]\tLoss: 0.045993, Accuracy: 98.83\n",
      "Train Epoch: 73 [3840/50000 (9%)]\tLoss: 0.028941, Accuracy: 98.44\n",
      "Train Epoch: 73 [5120/50000 (11%)]\tLoss: 0.090502, Accuracy: 95.70\n",
      "Train Epoch: 73 [6400/50000 (14%)]\tLoss: 0.054153, Accuracy: 97.66\n",
      "Train Epoch: 73 [7680/50000 (17%)]\tLoss: 0.035367, Accuracy: 99.22\n",
      "Train Epoch: 73 [8960/50000 (20%)]\tLoss: 0.061317, Accuracy: 98.05\n",
      "Train Epoch: 73 [10240/50000 (23%)]\tLoss: 0.052941, Accuracy: 98.44\n",
      "Train Epoch: 73 [11520/50000 (26%)]\tLoss: 0.043059, Accuracy: 98.83\n",
      "Train Epoch: 73 [12800/50000 (28%)]\tLoss: 0.042233, Accuracy: 98.83\n",
      "Train Epoch: 73 [14080/50000 (31%)]\tLoss: 0.060299, Accuracy: 98.05\n",
      "Train Epoch: 73 [15360/50000 (34%)]\tLoss: 0.043283, Accuracy: 98.44\n",
      "Train Epoch: 73 [16640/50000 (37%)]\tLoss: 0.040874, Accuracy: 98.44\n",
      "Train Epoch: 73 [17920/50000 (40%)]\tLoss: 0.048970, Accuracy: 98.44\n",
      "Train Epoch: 73 [19200/50000 (43%)]\tLoss: 0.036510, Accuracy: 99.61\n",
      "Train Epoch: 73 [20480/50000 (45%)]\tLoss: 0.060810, Accuracy: 98.05\n",
      "Train Epoch: 73 [21760/50000 (48%)]\tLoss: 0.054621, Accuracy: 98.05\n",
      "Train Epoch: 73 [23040/50000 (51%)]\tLoss: 0.055879, Accuracy: 98.44\n",
      "Train Epoch: 73 [24320/50000 (54%)]\tLoss: 0.052268, Accuracy: 99.22\n",
      "Train Epoch: 73 [25600/50000 (57%)]\tLoss: 0.072315, Accuracy: 98.05\n",
      "Train Epoch: 73 [26880/50000 (60%)]\tLoss: 0.051890, Accuracy: 98.83\n",
      "Train Epoch: 73 [28160/50000 (62%)]\tLoss: 0.021023, Accuracy: 99.61\n",
      "Train Epoch: 73 [29440/50000 (65%)]\tLoss: 0.033943, Accuracy: 99.22\n",
      "Train Epoch: 73 [30720/50000 (68%)]\tLoss: 0.041079, Accuracy: 98.83\n",
      "Train Epoch: 73 [32000/50000 (71%)]\tLoss: 0.042212, Accuracy: 98.83\n",
      "Train Epoch: 73 [33280/50000 (74%)]\tLoss: 0.062900, Accuracy: 98.05\n",
      "Train Epoch: 73 [34560/50000 (77%)]\tLoss: 0.030899, Accuracy: 99.22\n",
      "Train Epoch: 73 [35840/50000 (80%)]\tLoss: 0.039217, Accuracy: 98.44\n",
      "Train Epoch: 73 [37120/50000 (82%)]\tLoss: 0.051155, Accuracy: 98.83\n",
      "Train Epoch: 73 [38400/50000 (85%)]\tLoss: 0.044292, Accuracy: 98.44\n",
      "Train Epoch: 73 [39680/50000 (88%)]\tLoss: 0.077741, Accuracy: 98.44\n",
      "Train Epoch: 73 [40960/50000 (91%)]\tLoss: 0.059268, Accuracy: 97.66\n",
      "Train Epoch: 73 [42240/50000 (94%)]\tLoss: 0.047024, Accuracy: 98.05\n",
      "Train Epoch: 73 [43520/50000 (97%)]\tLoss: 0.040794, Accuracy: 98.44\n",
      "Train Epoch: 73 [35000/50000 (99%)]\tLoss: 0.023728, Accuracy: 99.50\n",
      "\n",
      "Validation set: Average loss: 0.2953, Accuracy: 4587/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.424445152282715 s]\n",
      "Train Epoch: 74 [0/50000 (0%)]\tLoss: 0.030295, Accuracy: 98.44\n",
      "Train Epoch: 74 [1280/50000 (3%)]\tLoss: 0.052017, Accuracy: 98.83\n",
      "Train Epoch: 74 [2560/50000 (6%)]\tLoss: 0.088336, Accuracy: 97.27\n",
      "Train Epoch: 74 [3840/50000 (9%)]\tLoss: 0.047768, Accuracy: 98.05\n",
      "Train Epoch: 74 [5120/50000 (11%)]\tLoss: 0.035781, Accuracy: 98.44\n",
      "Train Epoch: 74 [6400/50000 (14%)]\tLoss: 0.030094, Accuracy: 99.22\n",
      "Train Epoch: 74 [7680/50000 (17%)]\tLoss: 0.044808, Accuracy: 98.44\n",
      "Train Epoch: 74 [8960/50000 (20%)]\tLoss: 0.037351, Accuracy: 99.61\n",
      "Train Epoch: 74 [10240/50000 (23%)]\tLoss: 0.071067, Accuracy: 96.48\n",
      "Train Epoch: 74 [11520/50000 (26%)]\tLoss: 0.042333, Accuracy: 98.83\n",
      "Train Epoch: 74 [12800/50000 (28%)]\tLoss: 0.068875, Accuracy: 97.27\n",
      "Train Epoch: 74 [14080/50000 (31%)]\tLoss: 0.019562, Accuracy: 100.00\n",
      "Train Epoch: 74 [15360/50000 (34%)]\tLoss: 0.085378, Accuracy: 97.66\n",
      "Train Epoch: 74 [16640/50000 (37%)]\tLoss: 0.058323, Accuracy: 98.44\n",
      "Train Epoch: 74 [17920/50000 (40%)]\tLoss: 0.067234, Accuracy: 98.05\n",
      "Train Epoch: 74 [19200/50000 (43%)]\tLoss: 0.089101, Accuracy: 96.88\n",
      "Train Epoch: 74 [20480/50000 (45%)]\tLoss: 0.093369, Accuracy: 96.48\n",
      "Train Epoch: 74 [21760/50000 (48%)]\tLoss: 0.068039, Accuracy: 98.05\n",
      "Train Epoch: 74 [23040/50000 (51%)]\tLoss: 0.044321, Accuracy: 98.83\n",
      "Train Epoch: 74 [24320/50000 (54%)]\tLoss: 0.083824, Accuracy: 96.09\n",
      "Train Epoch: 74 [25600/50000 (57%)]\tLoss: 0.054465, Accuracy: 97.66\n",
      "Train Epoch: 74 [26880/50000 (60%)]\tLoss: 0.053568, Accuracy: 98.44\n",
      "Train Epoch: 74 [28160/50000 (62%)]\tLoss: 0.073380, Accuracy: 98.05\n",
      "Train Epoch: 74 [29440/50000 (65%)]\tLoss: 0.064065, Accuracy: 96.88\n",
      "Train Epoch: 74 [30720/50000 (68%)]\tLoss: 0.028745, Accuracy: 99.22\n",
      "Train Epoch: 74 [32000/50000 (71%)]\tLoss: 0.056785, Accuracy: 97.27\n",
      "Train Epoch: 74 [33280/50000 (74%)]\tLoss: 0.038559, Accuracy: 98.44\n",
      "Train Epoch: 74 [34560/50000 (77%)]\tLoss: 0.041515, Accuracy: 98.83\n",
      "Train Epoch: 74 [35840/50000 (80%)]\tLoss: 0.050463, Accuracy: 98.44\n",
      "Train Epoch: 74 [37120/50000 (82%)]\tLoss: 0.044635, Accuracy: 98.44\n",
      "Train Epoch: 74 [38400/50000 (85%)]\tLoss: 0.048106, Accuracy: 98.44\n",
      "Train Epoch: 74 [39680/50000 (88%)]\tLoss: 0.087250, Accuracy: 97.66\n",
      "Train Epoch: 74 [40960/50000 (91%)]\tLoss: 0.095062, Accuracy: 97.27\n",
      "Train Epoch: 74 [42240/50000 (94%)]\tLoss: 0.046450, Accuracy: 98.83\n",
      "Train Epoch: 74 [43520/50000 (97%)]\tLoss: 0.025746, Accuracy: 99.61\n",
      "Train Epoch: 74 [35000/50000 (99%)]\tLoss: 0.034077, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.2968, Accuracy: 4586/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.35649275779724 s]\n",
      "\n",
      "Test set: Average loss: 0.3112, Accuracy: 9124/10000 (91.24%)\n",
      "\n",
      "Train Epoch: 75 [0/50000 (0%)]\tLoss: 0.022763, Accuracy: 99.22\n",
      "Train Epoch: 75 [1280/50000 (3%)]\tLoss: 0.063773, Accuracy: 98.05\n",
      "Train Epoch: 75 [2560/50000 (6%)]\tLoss: 0.030695, Accuracy: 98.83\n",
      "Train Epoch: 75 [3840/50000 (9%)]\tLoss: 0.054275, Accuracy: 98.05\n",
      "Train Epoch: 75 [5120/50000 (11%)]\tLoss: 0.046431, Accuracy: 98.44\n",
      "Train Epoch: 75 [6400/50000 (14%)]\tLoss: 0.053871, Accuracy: 98.44\n",
      "Train Epoch: 75 [7680/50000 (17%)]\tLoss: 0.039081, Accuracy: 98.83\n",
      "Train Epoch: 75 [8960/50000 (20%)]\tLoss: 0.071489, Accuracy: 96.88\n",
      "Train Epoch: 75 [10240/50000 (23%)]\tLoss: 0.027082, Accuracy: 99.22\n",
      "Train Epoch: 75 [11520/50000 (26%)]\tLoss: 0.044638, Accuracy: 99.22\n",
      "Train Epoch: 75 [12800/50000 (28%)]\tLoss: 0.058585, Accuracy: 97.66\n",
      "Train Epoch: 75 [14080/50000 (31%)]\tLoss: 0.043985, Accuracy: 99.61\n",
      "Train Epoch: 75 [15360/50000 (34%)]\tLoss: 0.048402, Accuracy: 99.22\n",
      "Train Epoch: 75 [16640/50000 (37%)]\tLoss: 0.042658, Accuracy: 98.44\n",
      "Train Epoch: 75 [17920/50000 (40%)]\tLoss: 0.031230, Accuracy: 99.22\n",
      "Train Epoch: 75 [19200/50000 (43%)]\tLoss: 0.036821, Accuracy: 98.83\n",
      "Train Epoch: 75 [20480/50000 (45%)]\tLoss: 0.063036, Accuracy: 98.44\n",
      "Train Epoch: 75 [21760/50000 (48%)]\tLoss: 0.095653, Accuracy: 97.27\n",
      "Train Epoch: 75 [23040/50000 (51%)]\tLoss: 0.047057, Accuracy: 99.22\n",
      "Train Epoch: 75 [24320/50000 (54%)]\tLoss: 0.034227, Accuracy: 98.83\n",
      "Train Epoch: 75 [25600/50000 (57%)]\tLoss: 0.091127, Accuracy: 96.48\n",
      "Train Epoch: 75 [26880/50000 (60%)]\tLoss: 0.045904, Accuracy: 99.22\n",
      "Train Epoch: 75 [28160/50000 (62%)]\tLoss: 0.046712, Accuracy: 98.83\n",
      "Train Epoch: 75 [29440/50000 (65%)]\tLoss: 0.047143, Accuracy: 98.44\n",
      "Train Epoch: 75 [30720/50000 (68%)]\tLoss: 0.047277, Accuracy: 98.44\n",
      "Train Epoch: 75 [32000/50000 (71%)]\tLoss: 0.037091, Accuracy: 98.05\n",
      "Train Epoch: 75 [33280/50000 (74%)]\tLoss: 0.036243, Accuracy: 99.22\n",
      "Train Epoch: 75 [34560/50000 (77%)]\tLoss: 0.041141, Accuracy: 98.83\n",
      "Train Epoch: 75 [35840/50000 (80%)]\tLoss: 0.024092, Accuracy: 99.61\n",
      "Train Epoch: 75 [37120/50000 (82%)]\tLoss: 0.034439, Accuracy: 98.44\n",
      "Train Epoch: 75 [38400/50000 (85%)]\tLoss: 0.079140, Accuracy: 97.27\n",
      "Train Epoch: 75 [39680/50000 (88%)]\tLoss: 0.022201, Accuracy: 99.22\n",
      "Train Epoch: 75 [40960/50000 (91%)]\tLoss: 0.052957, Accuracy: 98.83\n",
      "Train Epoch: 75 [42240/50000 (94%)]\tLoss: 0.049251, Accuracy: 98.83\n",
      "Train Epoch: 75 [43520/50000 (97%)]\tLoss: 0.067851, Accuracy: 98.83\n",
      "Train Epoch: 75 [35000/50000 (99%)]\tLoss: 0.063095, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2972, Accuracy: 4579/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.43083190917969 s]\n",
      "Train Epoch: 76 [0/50000 (0%)]\tLoss: 0.040246, Accuracy: 98.83\n",
      "Train Epoch: 76 [1280/50000 (3%)]\tLoss: 0.031708, Accuracy: 98.83\n",
      "Train Epoch: 76 [2560/50000 (6%)]\tLoss: 0.045754, Accuracy: 98.05\n",
      "Train Epoch: 76 [3840/50000 (9%)]\tLoss: 0.028720, Accuracy: 99.22\n",
      "Train Epoch: 76 [5120/50000 (11%)]\tLoss: 0.055369, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 76 [6400/50000 (14%)]\tLoss: 0.078031, Accuracy: 97.27\n",
      "Train Epoch: 76 [7680/50000 (17%)]\tLoss: 0.035191, Accuracy: 99.22\n",
      "Train Epoch: 76 [8960/50000 (20%)]\tLoss: 0.040510, Accuracy: 99.22\n",
      "Train Epoch: 76 [10240/50000 (23%)]\tLoss: 0.027895, Accuracy: 99.61\n",
      "Train Epoch: 76 [11520/50000 (26%)]\tLoss: 0.068688, Accuracy: 97.66\n",
      "Train Epoch: 76 [12800/50000 (28%)]\tLoss: 0.042177, Accuracy: 98.44\n",
      "Train Epoch: 76 [14080/50000 (31%)]\tLoss: 0.053524, Accuracy: 97.66\n",
      "Train Epoch: 76 [15360/50000 (34%)]\tLoss: 0.079444, Accuracy: 96.48\n",
      "Train Epoch: 76 [16640/50000 (37%)]\tLoss: 0.047946, Accuracy: 98.44\n",
      "Train Epoch: 76 [17920/50000 (40%)]\tLoss: 0.086911, Accuracy: 96.88\n",
      "Train Epoch: 76 [19200/50000 (43%)]\tLoss: 0.052065, Accuracy: 98.44\n",
      "Train Epoch: 76 [20480/50000 (45%)]\tLoss: 0.042702, Accuracy: 98.44\n",
      "Train Epoch: 76 [21760/50000 (48%)]\tLoss: 0.022516, Accuracy: 99.22\n",
      "Train Epoch: 76 [23040/50000 (51%)]\tLoss: 0.022023, Accuracy: 99.22\n",
      "Train Epoch: 76 [24320/50000 (54%)]\tLoss: 0.040394, Accuracy: 98.83\n",
      "Train Epoch: 76 [25600/50000 (57%)]\tLoss: 0.043730, Accuracy: 98.83\n",
      "Train Epoch: 76 [26880/50000 (60%)]\tLoss: 0.032616, Accuracy: 98.83\n",
      "Train Epoch: 76 [28160/50000 (62%)]\tLoss: 0.034863, Accuracy: 98.83\n",
      "Train Epoch: 76 [29440/50000 (65%)]\tLoss: 0.035077, Accuracy: 99.22\n",
      "Train Epoch: 76 [30720/50000 (68%)]\tLoss: 0.028357, Accuracy: 99.61\n",
      "Train Epoch: 76 [32000/50000 (71%)]\tLoss: 0.045784, Accuracy: 98.83\n",
      "Train Epoch: 76 [33280/50000 (74%)]\tLoss: 0.068980, Accuracy: 97.66\n",
      "Train Epoch: 76 [34560/50000 (77%)]\tLoss: 0.100582, Accuracy: 96.88\n",
      "Train Epoch: 76 [35840/50000 (80%)]\tLoss: 0.058621, Accuracy: 97.66\n",
      "Train Epoch: 76 [37120/50000 (82%)]\tLoss: 0.060029, Accuracy: 98.05\n",
      "Train Epoch: 76 [38400/50000 (85%)]\tLoss: 0.050625, Accuracy: 98.44\n",
      "Train Epoch: 76 [39680/50000 (88%)]\tLoss: 0.032730, Accuracy: 99.61\n",
      "Train Epoch: 76 [40960/50000 (91%)]\tLoss: 0.062210, Accuracy: 98.05\n",
      "Train Epoch: 76 [42240/50000 (94%)]\tLoss: 0.066010, Accuracy: 98.05\n",
      "Train Epoch: 76 [43520/50000 (97%)]\tLoss: 0.027559, Accuracy: 99.61\n",
      "Train Epoch: 76 [35000/50000 (99%)]\tLoss: 0.087926, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2986, Accuracy: 4590/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.30670094490051 s]\n",
      "\n",
      "Test set: Average loss: 0.3123, Accuracy: 9136/10000 (91.36%)\n",
      "\n",
      "Train Epoch: 77 [0/50000 (0%)]\tLoss: 0.054934, Accuracy: 98.44\n",
      "Train Epoch: 77 [1280/50000 (3%)]\tLoss: 0.029245, Accuracy: 99.22\n",
      "Train Epoch: 77 [2560/50000 (6%)]\tLoss: 0.061231, Accuracy: 96.48\n",
      "Train Epoch: 77 [3840/50000 (9%)]\tLoss: 0.076968, Accuracy: 97.27\n",
      "Train Epoch: 77 [5120/50000 (11%)]\tLoss: 0.044290, Accuracy: 99.22\n",
      "Train Epoch: 77 [6400/50000 (14%)]\tLoss: 0.059308, Accuracy: 98.05\n",
      "Train Epoch: 77 [7680/50000 (17%)]\tLoss: 0.029428, Accuracy: 99.22\n",
      "Train Epoch: 77 [8960/50000 (20%)]\tLoss: 0.028417, Accuracy: 99.22\n",
      "Train Epoch: 77 [10240/50000 (23%)]\tLoss: 0.045375, Accuracy: 99.22\n",
      "Train Epoch: 77 [11520/50000 (26%)]\tLoss: 0.076939, Accuracy: 97.66\n",
      "Train Epoch: 77 [12800/50000 (28%)]\tLoss: 0.029898, Accuracy: 98.44\n",
      "Train Epoch: 77 [14080/50000 (31%)]\tLoss: 0.052332, Accuracy: 98.44\n",
      "Train Epoch: 77 [15360/50000 (34%)]\tLoss: 0.062392, Accuracy: 98.05\n",
      "Train Epoch: 77 [16640/50000 (37%)]\tLoss: 0.046796, Accuracy: 97.66\n",
      "Train Epoch: 77 [17920/50000 (40%)]\tLoss: 0.038591, Accuracy: 98.05\n",
      "Train Epoch: 77 [19200/50000 (43%)]\tLoss: 0.042804, Accuracy: 98.83\n",
      "Train Epoch: 77 [20480/50000 (45%)]\tLoss: 0.064975, Accuracy: 98.83\n",
      "Train Epoch: 77 [21760/50000 (48%)]\tLoss: 0.018138, Accuracy: 100.00\n",
      "Train Epoch: 77 [23040/50000 (51%)]\tLoss: 0.042616, Accuracy: 98.83\n",
      "Train Epoch: 77 [24320/50000 (54%)]\tLoss: 0.040213, Accuracy: 98.83\n",
      "Train Epoch: 77 [25600/50000 (57%)]\tLoss: 0.037630, Accuracy: 99.22\n",
      "Train Epoch: 77 [26880/50000 (60%)]\tLoss: 0.047047, Accuracy: 98.44\n",
      "Train Epoch: 77 [28160/50000 (62%)]\tLoss: 0.063239, Accuracy: 98.05\n",
      "Train Epoch: 77 [29440/50000 (65%)]\tLoss: 0.049973, Accuracy: 98.05\n",
      "Train Epoch: 77 [30720/50000 (68%)]\tLoss: 0.016073, Accuracy: 100.00\n",
      "Train Epoch: 77 [32000/50000 (71%)]\tLoss: 0.038765, Accuracy: 98.83\n",
      "Train Epoch: 77 [33280/50000 (74%)]\tLoss: 0.062472, Accuracy: 97.27\n",
      "Train Epoch: 77 [34560/50000 (77%)]\tLoss: 0.058216, Accuracy: 98.05\n",
      "Train Epoch: 77 [35840/50000 (80%)]\tLoss: 0.034806, Accuracy: 99.22\n",
      "Train Epoch: 77 [37120/50000 (82%)]\tLoss: 0.020668, Accuracy: 99.61\n",
      "Train Epoch: 77 [38400/50000 (85%)]\tLoss: 0.050316, Accuracy: 98.44\n",
      "Train Epoch: 77 [39680/50000 (88%)]\tLoss: 0.040335, Accuracy: 98.83\n",
      "Train Epoch: 77 [40960/50000 (91%)]\tLoss: 0.043920, Accuracy: 98.83\n",
      "Train Epoch: 77 [42240/50000 (94%)]\tLoss: 0.037125, Accuracy: 98.05\n",
      "Train Epoch: 77 [43520/50000 (97%)]\tLoss: 0.031607, Accuracy: 99.61\n",
      "Train Epoch: 77 [35000/50000 (99%)]\tLoss: 0.052506, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2988, Accuracy: 4588/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.40630125999451 s]\n",
      "Train Epoch: 78 [0/50000 (0%)]\tLoss: 0.039937, Accuracy: 99.22\n",
      "Train Epoch: 78 [1280/50000 (3%)]\tLoss: 0.068191, Accuracy: 97.66\n",
      "Train Epoch: 78 [2560/50000 (6%)]\tLoss: 0.034056, Accuracy: 98.44\n",
      "Train Epoch: 78 [3840/50000 (9%)]\tLoss: 0.030665, Accuracy: 98.83\n",
      "Train Epoch: 78 [5120/50000 (11%)]\tLoss: 0.026638, Accuracy: 99.22\n",
      "Train Epoch: 78 [6400/50000 (14%)]\tLoss: 0.029639, Accuracy: 99.22\n",
      "Train Epoch: 78 [7680/50000 (17%)]\tLoss: 0.079650, Accuracy: 96.48\n",
      "Train Epoch: 78 [8960/50000 (20%)]\tLoss: 0.032823, Accuracy: 99.61\n",
      "Train Epoch: 78 [10240/50000 (23%)]\tLoss: 0.053723, Accuracy: 98.83\n",
      "Train Epoch: 78 [11520/50000 (26%)]\tLoss: 0.065322, Accuracy: 97.66\n",
      "Train Epoch: 78 [12800/50000 (28%)]\tLoss: 0.045008, Accuracy: 98.44\n",
      "Train Epoch: 78 [14080/50000 (31%)]\tLoss: 0.042398, Accuracy: 98.83\n",
      "Train Epoch: 78 [15360/50000 (34%)]\tLoss: 0.052702, Accuracy: 98.83\n",
      "Train Epoch: 78 [16640/50000 (37%)]\tLoss: 0.029691, Accuracy: 98.83\n",
      "Train Epoch: 78 [17920/50000 (40%)]\tLoss: 0.035609, Accuracy: 99.22\n",
      "Train Epoch: 78 [19200/50000 (43%)]\tLoss: 0.026434, Accuracy: 99.61\n",
      "Train Epoch: 78 [20480/50000 (45%)]\tLoss: 0.042900, Accuracy: 98.83\n",
      "Train Epoch: 78 [21760/50000 (48%)]\tLoss: 0.055141, Accuracy: 97.27\n",
      "Train Epoch: 78 [23040/50000 (51%)]\tLoss: 0.015981, Accuracy: 100.00\n",
      "Train Epoch: 78 [24320/50000 (54%)]\tLoss: 0.029112, Accuracy: 98.83\n",
      "Train Epoch: 78 [25600/50000 (57%)]\tLoss: 0.051878, Accuracy: 98.44\n",
      "Train Epoch: 78 [26880/50000 (60%)]\tLoss: 0.048360, Accuracy: 98.44\n",
      "Train Epoch: 78 [28160/50000 (62%)]\tLoss: 0.073824, Accuracy: 97.27\n",
      "Train Epoch: 78 [29440/50000 (65%)]\tLoss: 0.030597, Accuracy: 99.61\n",
      "Train Epoch: 78 [30720/50000 (68%)]\tLoss: 0.022155, Accuracy: 99.61\n",
      "Train Epoch: 78 [32000/50000 (71%)]\tLoss: 0.032536, Accuracy: 98.83\n",
      "Train Epoch: 78 [33280/50000 (74%)]\tLoss: 0.055390, Accuracy: 98.05\n",
      "Train Epoch: 78 [34560/50000 (77%)]\tLoss: 0.041879, Accuracy: 98.83\n",
      "Train Epoch: 78 [35840/50000 (80%)]\tLoss: 0.037620, Accuracy: 98.05\n",
      "Train Epoch: 78 [37120/50000 (82%)]\tLoss: 0.065851, Accuracy: 98.05\n",
      "Train Epoch: 78 [38400/50000 (85%)]\tLoss: 0.070747, Accuracy: 96.09\n",
      "Train Epoch: 78 [39680/50000 (88%)]\tLoss: 0.047500, Accuracy: 98.44\n",
      "Train Epoch: 78 [40960/50000 (91%)]\tLoss: 0.037017, Accuracy: 98.83\n",
      "Train Epoch: 78 [42240/50000 (94%)]\tLoss: 0.052622, Accuracy: 98.83\n",
      "Train Epoch: 78 [43520/50000 (97%)]\tLoss: 0.043782, Accuracy: 99.22\n",
      "Train Epoch: 78 [35000/50000 (99%)]\tLoss: 0.057183, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2983, Accuracy: 4593/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[35.37246608734131 s]\n",
      "\n",
      "Test set: Average loss: 0.3127, Accuracy: 9136/10000 (91.36%)\n",
      "\n",
      "Train Epoch: 79 [0/50000 (0%)]\tLoss: 0.039833, Accuracy: 99.22\n",
      "Train Epoch: 79 [1280/50000 (3%)]\tLoss: 0.047098, Accuracy: 98.83\n",
      "Train Epoch: 79 [2560/50000 (6%)]\tLoss: 0.024354, Accuracy: 99.22\n",
      "Train Epoch: 79 [3840/50000 (9%)]\tLoss: 0.061714, Accuracy: 97.66\n",
      "Train Epoch: 79 [5120/50000 (11%)]\tLoss: 0.035064, Accuracy: 98.83\n",
      "Train Epoch: 79 [6400/50000 (14%)]\tLoss: 0.044338, Accuracy: 98.44\n",
      "Train Epoch: 79 [7680/50000 (17%)]\tLoss: 0.040276, Accuracy: 99.61\n",
      "Train Epoch: 79 [8960/50000 (20%)]\tLoss: 0.039711, Accuracy: 99.22\n",
      "Train Epoch: 79 [10240/50000 (23%)]\tLoss: 0.048557, Accuracy: 98.83\n",
      "Train Epoch: 79 [11520/50000 (26%)]\tLoss: 0.059935, Accuracy: 97.66\n",
      "Train Epoch: 79 [12800/50000 (28%)]\tLoss: 0.034806, Accuracy: 98.83\n",
      "Train Epoch: 79 [14080/50000 (31%)]\tLoss: 0.043166, Accuracy: 98.83\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 79 [15360/50000 (34%)]\tLoss: 0.031476, Accuracy: 99.22\n",
      "Train Epoch: 79 [16640/50000 (37%)]\tLoss: 0.045120, Accuracy: 98.83\n",
      "Train Epoch: 79 [17920/50000 (40%)]\tLoss: 0.052362, Accuracy: 97.66\n",
      "Train Epoch: 79 [19200/50000 (43%)]\tLoss: 0.019260, Accuracy: 99.61\n",
      "Train Epoch: 79 [20480/50000 (45%)]\tLoss: 0.026355, Accuracy: 99.61\n",
      "Train Epoch: 79 [21760/50000 (48%)]\tLoss: 0.050151, Accuracy: 98.83\n",
      "Train Epoch: 79 [23040/50000 (51%)]\tLoss: 0.035882, Accuracy: 98.83\n",
      "Train Epoch: 79 [24320/50000 (54%)]\tLoss: 0.025629, Accuracy: 99.22\n",
      "Train Epoch: 79 [25600/50000 (57%)]\tLoss: 0.038325, Accuracy: 98.05\n",
      "Train Epoch: 79 [26880/50000 (60%)]\tLoss: 0.035156, Accuracy: 98.83\n",
      "Train Epoch: 79 [28160/50000 (62%)]\tLoss: 0.118523, Accuracy: 95.70\n",
      "Train Epoch: 79 [29440/50000 (65%)]\tLoss: 0.037910, Accuracy: 98.83\n",
      "Train Epoch: 79 [30720/50000 (68%)]\tLoss: 0.048825, Accuracy: 99.22\n",
      "Train Epoch: 79 [32000/50000 (71%)]\tLoss: 0.048501, Accuracy: 98.05\n",
      "Train Epoch: 79 [33280/50000 (74%)]\tLoss: 0.054447, Accuracy: 97.66\n",
      "Train Epoch: 79 [34560/50000 (77%)]\tLoss: 0.050655, Accuracy: 97.66\n",
      "Train Epoch: 79 [35840/50000 (80%)]\tLoss: 0.089981, Accuracy: 97.27\n",
      "Train Epoch: 79 [37120/50000 (82%)]\tLoss: 0.041251, Accuracy: 98.83\n",
      "Train Epoch: 79 [38400/50000 (85%)]\tLoss: 0.030944, Accuracy: 99.61\n",
      "Train Epoch: 79 [39680/50000 (88%)]\tLoss: 0.086691, Accuracy: 98.05\n",
      "Train Epoch: 79 [40960/50000 (91%)]\tLoss: 0.046554, Accuracy: 98.44\n",
      "Train Epoch: 79 [42240/50000 (94%)]\tLoss: 0.026098, Accuracy: 99.22\n",
      "Train Epoch: 79 [43520/50000 (97%)]\tLoss: 0.099178, Accuracy: 96.48\n",
      "Train Epoch: 79 [35000/50000 (99%)]\tLoss: 0.059812, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.3003, Accuracy: 4584/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[38.44213819503784 s]\n",
      "\n",
      "Test set: Average loss: 0.3165, Accuracy: 9124/10000 (91.24%)\n",
      "\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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.6902, Accuracy: 8877/10000 (88.77%)\n",
      "\n"
     ]
    }
   ],
   "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": 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X31/fe9LvZ2vJWUnW0hr5uH35k3EJth8Y1/tcVGLf/6o7fBVAgd5VXN0TfcmyvzdZ82xC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\n/x9BnCF17x72RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "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": 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B79YtXUHXO6N9gp5FvFmdL+sbhwt9tG0D0thdvmmRRgx9yy2lkO+wA3DJJfb2\nqrMxrbTFRYuKj7B33umuz5JU0E1Rvfhi+ad4+GE5KERNzEAkY5jXXCNfuwRdoUIutk4ys1a4jZAs\nl0mTSgeuxPW4TzopXnsbvuJc69cXPdiQkItNhE3BVF7qUUeV3ywrEdoQT1v9L+PeLLbbTi59v43t\nty9vb9LRPXQG6XeKugZKKIFMkl1gEyvTe1y2zD6yL07aIuD20E0WLJBLNWGCa4Z4X1EvPe9dxxyy\n79pfx3aeRYv8+1SDqCwXJZRxPHT9szY/v002kd+Fvn3Ln9z0tnFE99NPw24ASTNY5s8HVqwA+vVz\nt9l5Z/kE2L27++aSs5GdacOCHkLSGLqrU9SXuWFrbyMkFTLU2/TF0G24PHTXcV0iGSXoKm3RJhRJ\nPPQkcfe06N8fWLnSf05fyAVIL+TS1FQsqpVWDL1377B2SQW9R4+wMgNbbpns+D6ymBglIzjkEkKl\nHnro9qRFwFz2hJaP1YWikhi66/yu6/LVbFHHd3noIYIeWg3St09a+MQxStArDbmYn58tZdBmZxYx\n9DhzmDKxYUEPIUrQ16+Xk9G+8ELpfuaISEUcD33lSpmCFjXJrCmKs2aVp/t99JGcPEMvxgWUCkWa\nIRfTQ3fVtEnioccNuZxwQthNuLnZniZZKT5BX7FCfjb/+Efp9kpCLjpmR2CooGeZ5VJPKJuzGMGa\nMizocXAV53r9ddkBeOKJpe1dQhYl6PoP6Y475AjKiRP9tpnCa9oCALfdJuPZf/xj6Xbd242TthgV\ncnF5nub7Ls+5Ug9dF/ApU8IE/Yc/lFUT0yZEHM85p/T1v/9duaAfcAAwbVrpNttUcgr9u9pIgv7o\no8lz3A8/HDj77GJnfo6pw9tlDYiKoasaNWZpg7gjRW3tP/9cLs1Z0KNi6CHlQBU2Dz0khh7qoUcV\nKcsqhp4k5JIVScTxs8/ix9DNIlQXXFAee/Z56Pr/opEE3TWDUgidO8tR2XUAe+ghRIVcXIIeOiWb\n2d4m6D16+G0M8T5dXrdP0CsJuajjRl2vT9DjeuhxJ7ioFknEsaUlfgzdPI/t/6d/nj5BzzIPnckE\nFvQonn66WBZV/4IvWwY89ZRcV4L+zDOlYYg0Qi6qgt/nn8vJJhRZeOirVhXDDb4Y+rp10tZ773Wf\nQz9ulId+//32/dvaksXQn39eDn9/7jn7+WpBJR66EvS//S2d8+iiat4s9f93I3noHQT+dKPYf//i\nui5I++wjf2xCAB9/LLctXy4fzc4/X752dYomCblMmCD/WlrsgyNCBN3ldStv96CDynPGXR769dcD\nixfbr0MR4qE/+WRpnRGdJB7olpyeAAAeOklEQVS6EKV1TMz3akWSsQUtLVIAk4RcfOiianb06Z9R\n1lkuffvK31Fe+M53gL//vdZWVAR76ElRPy4hSgVLzyBJGkO3eegKV1VFU6ziCLoS3pdfLm7zebPr\n10eLuWoH+MsIr1rl3t/noYfE0E3Ma6rmIBPzGrbdNnqf1lZZDiKOnbaBRSZmyEUIYNw4+bqaMfRV\nq4DpOarpN21a8pr2OYEFvVJMQde/sK5Qg8vTsgm98tAVLkEPCSe40gRtsVlfBsr69WH52lEeetQx\nknjoPszrrmYamimOW20Vtl97ezxBD5lU2xb2sI0J4Bh63cGCHgebaJqCrouPS7BcMXRbJ6Ep6EuX\nymWSGLrLHiWO+j4+sU0q6LbZj3yisXZtZXnoJub5aynoX7BO8GUnjp0hWUq2G6StzyQLDz2LSTOY\n/4MF3YcpAK70RV3crr0WePttuR7aKUokZ/ixCZ8SdOXZfO97wIgR5XaYczfacHnd69bJjklV7dHX\nFpCCHlLt0Ay5+Kazs3HxxfE99Pffdx+vlh66+R0YODB83zgeurpGNYnzJpuUt7F5yUq8sw65NPiM\nQbWGBd1HSNpbe3v5dtWx6OoU1dsrkZs40V4/XHmi+o9w/vxo220/HJ+gT5oU1hYI99CV7Uk99E6d\nso2h19JDN8cV+DAF/YIL3G3VNV53ncyKsd38Q0MuDT7/ZiPCAS0fIbVQTA9d3y9E9PRz2IRPhS3i\nZmjEFXRXCKcSD13t64rdR3no6v2k1RZd9iiqKeiuSSZCMAXdlxmivkPdu8tyFCG2ACzoDQL/x3yE\neuguQY8aOAOU7mvz0FXYIk6euet9l0i3tpZfQxoxdJO4Hrpq04geelQYRa9eaNqppmyzEdI57gu5\nZJ22yGQKe+g+QmLoZqcoUO6h+7xrW+lavb0SLvOHqiZg1rn9djnJr547b7PruutKtz/9dPnw8PZ2\n4NJLgTfeKD/O5MlhMXuTefNKX7/+enlBKhs2j/LKK+OfP0+CHuWh9+xZnKneFH+foIfcaG03yGp1\nijKZwoLuo1IPPUTQ9RRGm4fuSv2zFQpSBbmE8HvoNszZjD7+GLjxRnf7V191vxfKww+HtUtLWPIu\n6KNHy2qYEyeW/v9MQfeVgQgRdNt34/zzgX/9CzjyyOK20aOBMWOkrYcfHn1cH3ffLQeRMZkS+Ush\noq2I6EkiWkhErxLR6YXtFxLRB0Q0r/DnCNjVMaExdJfwhwi6rVSA3j5kdGAocYa+13KYvElaqW5m\n3D9O9sjxx1d27ihB/9Of5FPXmWeW75t2yMXG4MHyaUmf77Z7d+Dxx+WN9+STkx1XMW4ccMstlR2D\niSTEQ98A4JdCiJeIqBeAOUSk6otOFEL8PjvzakxaMfRKPPQksWIgvoduUoup2Fyk5aGb1xTHQ+/U\nSf4lFUzzGnx1yIHS70yckEuebsRM1Yn8pQghlgohXiqstwBYCCDGqIga8957xVnkXaiZ6BcutKcU\nKmzDgkNCLlHD6M39bFkucRBCXotJHJEOyWKpFml56B9+WPraJ4w2Gyq5sfhmDQL8Q/bTjqEzDUus\nbygRDQYwEsCswqbTiGgBEU0mok0d+4wnomYial6hqhJWk222kbFAH4MGyUfNHXeUHYEKU4gvuaR8\nX1+naEi4xBZyscXQ43DXXe6bTyhphnoqJS0P/Sc/KX0dJ3WwUyfgmGOSnzvKQ1dCrrJbvvvd4jbT\nTt+TBQt6hyb4l0JEPQFMA3CGEGINgJsAbAtgBIClAP5g208IcasQYpQQYtQAs154tdCLTkXx7LPF\n9RAB9MXQlaj65lGMiqEn+YG6Bh7FEfQkN5I00T1aff2uu4rrBxxQvt+QIcX1oUOBRx5xnyOOoDc1\nyTj3kiXJ4slK0G+6ST4Nmt8J9X6vXrJqp94xarbt3FlOTXjttcVtato8Drl0aIIEnYi6QIr5XUKI\n+wFACLFMCNEmhGgH8CcAe2RnZhXRxTRU0F0eugr1ROV0K0wPPWm5V1f+cJybQ609dF3Ede92662L\n6+ZcmZ06AbvsUnw9fHj5pCM6cT30Ll1kDZYkEx2ra+jZU3rhLkEHpM16iMfWtl+/0gJfakQoe+gd\nmpAsFwIwCcBCIcTV2na9GMW3AbySvnk1plJB/+wzufTFo22CroQ8aYeoK0QRJy6etaBHdUjq53eJ\nu0lTU7ln7xPtOMKsH9csmKaIKmOgt4nqFNXbuuzU91H2sYfeoQnx0EcDOA7AGCNF8UoiepmIFgD4\nGoBfZGloxcyYYd/+s5+Vvn7kEeCXv5RxdT384sIm6D//OfDAA0UP/Z133PvbOkWfe07G8rfcMvr8\nNq64IvpcUWQt6LZJOnRcIu7rIO3cWU6aoLf1pSbG9dAVrptRSAVFn9dtEiXo+g1Exd7jdPQyDUdk\n2qIQ4p8AbK7HQ+mbkyE//Slw6KHl22+4oXzb1YUHEV8RJIWtUxQATjoJ2Guv6P1tHjoAnHde9L5x\niRMXz1LQ995bxolXrnS3+epXZQ404BZ30yNuapL/O1VoLMpDHzxYDqh5/305oOZb33K31W249FJZ\nxVDvQL/gAuCEE4C5c2UoZPfd7cdxeeg2794l/ub7APCLX8jv4qmnuq/hxRdl1hfTsHScsb1Jwhch\n2RW2TlG1PSpdErB3imaFTaSHDy+tG+JrmxYnnxz92fboARx9tFzX20aFXHr3Bs4+u/jaJuh7FLp7\niGSJ3ilTomeF18+rn0Pxk5/IG8S3vw2MGhV9nLRDLl27Auee67+BjRolp1ljGpbGFvRK87lD8p9t\nIRe1XcXQfbg89CywiXSPHvbrzFLQ1SAdHz16FNuExtCVSCoBdAm6Op7+/YgKv5ifkXkTj7oeda6s\nQi4Mg0ar5fLyy8Dq1cB++8nXukCagr58uSxK5SNkuiyfoId46CqsoPbJEtv0dS5BzzJtMVTQbROE\nRMXQgaIAEoXPzhMljr6a9rb3XbhEuhIPnWcBYgo0lqDvuqtcqh+qLrRmyOWQQ4DmZv/xKvXQQwR9\nwoTiei1SzlyCHpoRM3BgcVq8UDp1iv5su3cvZpMIARx7rKz8qAufKarqmMrb3rDBPpFEEgE097Gl\nTOocdZS8gR5wgMwpNz10c5Lo0Bi6nrapOnw5VZEp0NghF1tpWoUv80QR8sNXnaI771y6PTTkopMn\nQbeFXIQAttuu+Pq442TJXhtCyL/zzy9/L9RD1ydduPNOYPbs4n6dOpWnDyoPXWV6bNggt5lFoWwe\nehSmvV26lE7ZZ36Gf/mLLHZ1wQXyqVGhrqlXL3n+Qw6xH19vqwu6XhVziy3C7Wc6BB1H0E1Cfsyh\nHnp7e3l4ZsOG+hb0kJBL587Rn5EtPTFuDF2/GeteuFneQL2n0grVTck8ly2GHoUvdGM7h4npoUdt\nB6JDLknTWpmGpb4Ffd06d9x53bpygRRCFmgK/SGHCLqa7cf80SUpblULQd944+SdoiGhEzM0ofaL\nEsDu3f0TF2+0UbmHrmxRHrq6KZnhjDQ8dHNb3Bi6Ql2b7/iuY9s+W6ZDU9+C3rWrzC83Wb1avnfZ\nZaXbb7xRDv6YOjU9D33kSCnEIR2oUdRilN+uu9ptD/HQ29qAzTf3t0nqoe+wg38WHZugq+vIwkO3\nxbj1baGCbrZTpQps4ZO995ZL1SdgDpKK8uCZDkf9CroSnFtvLX9v2TK5vO220u1qlp0lS9ITdMDu\noSeh2h76U08Bp5ySPOTS3i4rVD73XOn2uXOL6zYv0leKdvx4mX307W/7Jy72eehK0NU1mOdyeehL\nlshZe2zocXDzOLZzmLhCKxMmyIktRo4s3+eee+RgoF69gNdeK59VCpAdxYsX+8/NdBjqN8tFZZD4\n8oxNUVLle9euTVfQbTH0JFRb0L/6VbmMk/VhK15mjojdccfietyQS+fOxbRTm/Cq9Y02Ks8iMkMu\nUR66iW/ovnISdJJ46Kan36WLu7xzz57FQUrDh9vbfPGLYedlOgT166GrDkdbWpr6cZlxYJVep2cn\n+Ah9JFfZFJVSq/SzpHnMLnv1p5W4IReb12urD9+1a3TIJc0Yuk3Qbcd04ev8ZJiUqN9vlxJ024S5\n6gdsdkyqYltXXy1rUkcROlqypSUdQX/ppcqPkYQ4gq6Loyvmr7eJ66GHCnqXLuVPYKaHrv7/acTQ\nbSEXnaQeOsOkSP0Kunrctgm6bWBREkIF/ZNP6qtjavhwGT9XxPEaH3ywWNFQF8QbbpAFon7zm9L2\nLg/96KOLoRXzPYUthr7TTrJuyrRp8gath9xC0xajPPTf/rZ0FPEpp8iOdB/soTM5oH5j6D4PPa1s\nkVBB//TTdDz0avHaa6Wv49z4hg8Hbr4Z+N73SvdzVflzeegnnyz/XOEQfV0X3qYmeX7FFVfIGwlQ\n/B+obBBXp2iUh/6735W+/uMf7e10Qj1v9tCZDKlfd8HnoacViw6tZ5JW2mKtiPt5xZlMweWhu4jy\n0E1sdV7U01JSDz1L2ENnMqR+v10+Dz0tQY9TcTCuoPsmXqg2cT8vW2zbhS0LKVTQ45wHKK/l4uoU\nrWUxK/bQmQypf0G3zdBSbQ8dKBeJrl2lIKnUQJNqzywzaJD7vbifV4jn7CNU0FWM3Vdf/MtfLq6r\ndMlNNpHLo46yn2/MGLm05X5nRS2eBpgOR/0Kuko9tHm6tfDQdUFfsgRYtUrG1h95xD4Tju3JQueZ\nZ8LPHYJrwAzg/7xs0/DF9ZzNDBGboB93XPl73/qWLHOsBNjGPvvIlMLXXgP+8Ae5beON5ec/caL9\nfMcdJ4+7775h9qeBEnT20JkMqd/A79q1cml7pK92pyhQKujmABV9nktFVFbMppuGnzsE3xOBT9Bt\ndsSdkLhPn9LXNkFXnafmk86AAdHH32wz+aejf+bm+Tp3DjtuFrCgMxkS6aET0VZE9CQRLSSiV4no\n9ML2vkQ0k4gWFZYpK1AEykO3iUMtQi5RU6OZRBXvqmac1/d5+aoMphlyUUKXRadhHjoiOeTCVIEQ\nD30DgF8KIV4iol4A5hDRTAA/AvC4EOJyIjoHwDkAzvYcJz1+8Qvgmmvkenu7nOB3yJDi+7UOuZgk\n8dCrKei+m4vNDhUuMj3vUHzXloUHy14x00GIdF2EEEuFEC8V1lsALATwBQCHA1CjLaYCOCIrI8tQ\nYg5IQZ82Dfj974vbfII+fnz4eeIIuk+gL7qofNv++9vbKrGzid6ddwJ33AFcdVWYTbNny8JZDz/s\nb/fJJ6WvH3iguG7zbvfdV8anb7opzA4AmDPHf0z1P8tigFZSD/2RR2ThLIapE2J904loMICRAGYB\n2FwIsRSQog9gM/eeGWITb5/HaRaS8hFH0H1piD16lA+86dVLjng08Qn6scfKDr2zzgqzaffd5fWO\nHetvZ9a2GTq03B4dIuCMM+LF+XfbrbhuE1j1P8tTOuc3vuEunJUUflpgMiRY0ImoJ4BpAM4QQqyJ\nsd94ImomouYVqtphJZixSFsc1yfocQQjTgw9atZ4c3adXr3sP251fbXMlbYNp08Tm6Crm2fU51iv\ncAydqQJBgk5EXSDF/C4hxP2FzcuIaGDh/YEAltv2FULcKoQYJYQYNaDSzIKVK4Hrry/ddv/95e18\nnnUcgYoTi48SIrMyYM+edmHLg6DrN71qCbq6CWch6HkQU05bZKpAZKcoERGASQAWCiGu1t56EMAP\nAVxeWE7PxEKdww8vn0zBhs+zzirjIcrzD/XQ1ROHLqTbbRc+CGa77YA33wxrqzjrLODRR4E1a2TR\nLNND33FH4MAD4x3Th35t55wD3Hdf8SacRchFF/TTT0//+HFgQWcyJETdRgM4DsAYIppX+DsYUsgP\nJKJFAA4svM4WfSYcHz4P3faDmjw53IZhw4B33infHjfk0rOnX9D1UgIzZ8pZ5EO44IKwdjpXXQXM\nny+va8KE0mvp1EnO9KR3RFeKnhN/2WXAokXZhlyUoB90ULrXkcQGhsmQSA9dCPFPAC634uvpmhNB\n6MQUcTozgXh1WNrb7V5kXEHv1i08dz1O2CONQVVZh1xUeVudaoRc2DtmGpwcjLjIAFXnJZS4cXWb\n6ESFCswY+r//7RcY3aY4YaI0BD3rTlHbqNU8ZrkwTJ3RmILuyzXfcsvybZ07lxevcono+vV2QY/y\nLM0Y+BZblJ9Dn04vqYeexqO9ngueRVlgm4dejZBLHjz0PNjANCyNKegudtnFnoe+8cbAvHkyVqzY\nYovi+p//DNx6q1zfsCGZoN9yS3H9f/8XOOSQ0h/3lVfKgUCKOIJ+2WVywgnA76F/+GFYh6l+Ppv4\nVopt8FCjCzrH0Jkq0NiCvvnmpa9dg4p69QL69ZPZHKrNVlsV39911+Ks6y5BjwoV6NUVzVntATmI\nRZV/BeIJ+m67FYtb+YRj4EBg2239xzKp1lB8FXLJYqRongSdPXQmQxpb0EO9PX1WHfXD23rr4rZO\nnYrHamuz/yjjeJZqf/04rll1gGhB79y58oJZtUZ56PU881MILOhMhjS2oId2sOnzXipB1D30Tp2K\nx3Jl0CQRdF2ozR+6T+xNmpqSzWSfJ5SHnoWg58E7rtf/C1NXNLagh+aX6x66EnS9vrbuoSvh+dGP\nSo8RJxNFCcvZluKUL7wAnHde6bYoD71TJ+Dii4ETTgCOPz7cDh9XXBFd1CtN1I2yUUMuDFMFGvv5\ndpttpMcXVXvc5qHrGSdEpSEXQA7gmTKl2CZOmQAl/n36ADvtJDtjleh85SvyTydK0NeuBfr3jzdA\nKopf/zq9Y4XAgs4wFVM/HnqSR9bQdD+9w1KdR9+mh1zUzcEUhzixa31fte67vqjrCB1wlWeyDLko\n8iDoebCBaVjqR9CTTFoRKuh6uEQJsz74xewUtVFLQTcHLdUj1chyqSV5sIFpeOpH0OMO5weSjXK0\nhVx0QTfbKYYNCz+HfgPxCfopp5S2cbHLLqWvhwzJZ7bIJpvYB3YBMv4PyNBR2qjP5zvfSf/YoRxz\njFxus03tbGAanvoV9Ndei94nSWVFJdSukItCCfCQITKGrU8KEUWoh3799UBrq/9Ya9fKOLzOokX5\nDMOsXAm8+679vd/+Vl6r3p+RFl/8ovycjj02/WOHcvrp8vrMsREMkyI5dOMcmIKue9Auknjothi6\n3imqUMJvE/soQgXd9mRgYjt3LWup+/A9Ndg+4zSpdY2YrK+PYVDPHnpISKGpKX7s0uWhmyJZSeaE\nTdAZhmEqpPEFPS5qQNEmmxS36aEbNdO98vj0EaWh6MdT58uiZgrDMB2K+g25hGRDuAR91ixg9Wr7\n5Ml33y1n7xkypLhNTYb8178CO+8s17fZRrZNMpOP7pVPnQr87W+ldVxCeOMNdzyaqR6zZ3MGC5Mb\nSFTxyzhq1CjR3NycbOc33ijNJFmzBujd27/PunUy/VClGo4fX6x6+OmnRW/b9RmEpBTGaa/eb2/n\nUAvDMMEQ0RwhxKiodh035FLLjkMWc4ZhMqCxBd2XtqhElTMPGIZpECIFnYgmE9FyInpF23YhEX1g\nTBqdLaagR3nY/fr53+/WTeY833STu80BBwCnnhpmH8MwTI0J6RSdAuAGAHcY2ycKIX6fukUuTEGP\nGjS0cqVcuuLZTU0yDu9j5sww2xiGYXJApIcuhPgHgI+rYIufJEP/GYZhOhCVxNBPI6IFhZDMpqlZ\n5OKzzzI/BcMwTD2TVNBvArAtgBEAlgL4g6shEY0nomYial6xYkXC00FOhGyicsJ97L9/8nMyDMPU\nEYkEXQixTAjRJoRoB/AnAHt42t4qhBglhBg1YMCApHbKkZldugCrVhULcz3zDHDIIXL9+OPlgCGT\n6dO5Y5NhmA5BIkEnooHay28DeMXVNjVaW4H99gP69gWGD5fb+vQpeunDhtlL2PbsWV5elmEYpgGJ\nzHIhorsB7A+gPxEtAfBfAPYnohEABIDFAH6SoY2SdeuKIzt19Nnusyi9yjAMUydECroQ4geWzZMy\nsMXPunX2QUBqgJEQ7hGYajuP0GQYpoGpn+Jcra12QT/rLODDD+UEAgBw883ls94cdxzQ3AxMmJCt\njVOn8shThmFqRv0U5xo6FNhnH+AOc3xTHRG32BfDMAwasTiXK+TCMAzDAGBBZxiGaRjqR9BbW2s/\nL2Sl1HLWeYZhGp766RRtBA/9f/6H4+cMw2QGC3o1iaoQyTAMUwH1oTBtbXLgUL0LOsMwTIbUh6C3\ntsplvcfQGYZhMqQ+BH3dOrlkD51hGMYJCzrDMEyDUF+CziEXhmEYJ/Uh6CqGzh46wzCMk/oQ9P/8\nRy67dautHQzDMDmmPgRdzSfK9c4ZhmGc1Iegt7TIJQs6wzCMk/oQdOWh9+xZWzsYhmFyTH0IOnvo\nDMMwkdSXoLOHzjAM4yRS0IloMhEtJ6JXtG19iWgmES0qLDfN1EruFGUYhokkxEOfAmCsse0cAI8L\nIbYH8HjhdXa0tMhKhZy2yDAM4yRS0IUQ/wDwsbH5cABTC+tTARyRsl2ltLRI71zNyckwDMOUkTSG\nvrkQYikAFJabpWeShV12AY48MtNTMAzD1DuZd4oS0Xgiaiai5hUrViQ7yEknAbfdlq5hDMMwDUZS\nQV9GRAMBoLBc7moohLhVCDFKCDFqwIABCU/HMAzDRJFU0B8E8MPC+g8BTE/HHIZhGCYpIWmLdwN4\nHsAORLSEiH4M4HIABxLRIgAHFl4zDMMwNSRykmghxA8cb309ZVsYhmGYCqiPkaIMwzBMJCzoDMMw\nDQILOsMwTIPAgs4wDNMgkBCieicjWgHg3YS79wewMkVzsoRtzY56spdtzYaOaOs2QojIgTxVFfRK\nIKJmIcSoWtsRAtuaHfVkL9uaDWyrGw65MAzDNAgs6AzDMA1CPQn6rbU2IAZsa3bUk71sazawrQ7q\nJobOMAzD+KknD51hGIbxUBeCTkRjiegNInqTiLKd7i7MnuB5VklyXcH2BUS0W5Vt3YqIniSihUT0\nKhGdnld7iagbEc0movkFWy8qbB9CRLMKtt5LRBsVtnctvH6z8P7gatmq2dxERHOJaEaebSWixUT0\nMhHNI6LmwrbcfQcK5+9DRPcR0euF7+1eebSViHYofJ7qbw0RnVFTW4UQuf4D0ATgLQBDAWwEYD6A\nHWts034AdgPwirbtSgDnFNbPAXBFYf1gAA8DIAB7AphVZVsHAtitsN4LwL8A7JhHewvn7FlY7wJg\nVsGGvwAYV9h+M4CfFtZPAXBzYX0cgHtr8F04E8B/A5hReJ1LWwEsBtDf2Ja770Dh/FMBnFRY3whA\nn7zaqtncBOAjANvU0taqX3iCD2ovAI9or88FcG4O7BpsCPobAAYW1gcCeKOwfguAH9ja1cju6ZAl\nj3NtL4AeAF4C8BXIgRmdze8DgEcA7FVY71xoR1W0cRDkJOljAMwo/FDzaqtN0HP3HQDQG8A75meT\nR1sN+74B4Nla21oPIZcvAHhfe72ksC1vuOZZzY39hcf8kZCeby7tLYQw5kHOgjUT8unsEyHEBos9\n/2dr4f1PAfSrlq0ArgHwawDthdf9kF9bBYBHiWgOEY0vbMvjd2AogBUAbi+Esm4joo1zaqvOOAB3\nF9ZrZms9CDpZttVTak4u7CeingCmAThDCLHG19SyrWr2CiHahBAjIL3fPQAM99hTM1uJ6FAAy4UQ\nc/TNHntq/T0YLYTYDcBBAE4lov08bWtpa2fIcOZNQoiRAP4NGbZwUevPFYV+ksMA/E9UU8u2VG2t\nB0FfAmAr7fUgAB/WyBYfrnlWa24/EXWBFPO7hBD3Fzbn1l4AEEJ8AuApyFhjHyJSk7Ho9vyfrYX3\nNwHwcZVMHA3gMCJaDOAeyLDLNTm1FUKIDwvL5QAegLxZ5vE7sATAEiHErMLr+yAFPo+2Kg4C8JIQ\nYlnhdc1srQdBfxHA9oXsgY0gH20erLFNNlzzrD4I4PhCD/eeAD5Vj2PVgIgIwCQAC4UQV+fZXiIa\nQER9CuvdARwAYCGAJwEc6bBVXcORAJ4QheBk1gghzhVCDBJCDIb8Tj4hhDgmj7YS0cZE1EutQ8Z7\nX0EOvwNCiI8AvE9EOxQ2fR3Aa3m0VeMHKIZblE21sbXanQcJOxwOhszOeAvAeTmw524ASwGsh7zr\n/hgyHvo4gEWFZd9CWwLwx4LtLwMYVWVb94F8rFsAYF7h7+A82gtgVwBzC7a+AuCCwvahAGYDeBPy\nsbZrYXu3wus3C+8PrdH3YX8Us1xyZ2vBpvmFv1fVbyiP34HC+UcAaC58D/4XwKY5trUHgFUANtG2\n1cxWHinKMAzTINRDyIVhGIYJgAWdYRimQWBBZxiGaRBY0BmGYRoEFnSGYZgGgQWdYRimQWBBZxiG\naRBY0BmGYRqE/w/U/zxNDJVCvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hDBNo+60g7Jjl3rBJxD1uoPQ49t5b3+h2SMt47qbUbxRpPfcwcTcC0tlZ3JDt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\ngFDcCSGkAaG4E0JIA0JxJ4SQBuT/AHC9SchdaANwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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88UeowtCh9vi112J3VkyG4mKbi3XaNJg9O3H78fq+cznJE39Eaakl2TPOsKtY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\nDuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Med4Y4zPG+AoKCiw4NJFH/fznWideskQX0LbK2LE6oJgjpQl89JH22xk/Xn+/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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
   },
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