M_accelerate_6lastlayer-8fold_91.41-Copy5.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=True)\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=True) # 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()*0.01\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
   },
   "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: 2.316704, Accuracy: 7.81\n",
      "Train Epoch: 0 [1280/50000 (3%)]\tLoss: 2.353208, Accuracy: 7.81\n",
      "Train Epoch: 0 [2560/50000 (6%)]\tLoss: 2.299966, Accuracy: 10.16\n",
      "Train Epoch: 0 [3840/50000 (9%)]\tLoss: 2.243143, Accuracy: 12.11\n",
      "Train Epoch: 0 [5120/50000 (11%)]\tLoss: 2.133137, Accuracy: 17.97\n",
      "Train Epoch: 0 [6400/50000 (14%)]\tLoss: 1.998923, Accuracy: 18.75\n",
      "Train Epoch: 0 [7680/50000 (17%)]\tLoss: 2.116172, Accuracy: 18.36\n",
      "Train Epoch: 0 [8960/50000 (20%)]\tLoss: 2.136685, Accuracy: 13.28\n",
      "Train Epoch: 0 [10240/50000 (23%)]\tLoss: 2.061302, Accuracy: 17.19\n",
      "Train Epoch: 0 [11520/50000 (26%)]\tLoss: 1.919407, Accuracy: 27.34\n",
      "Train Epoch: 0 [12800/50000 (28%)]\tLoss: 1.915111, Accuracy: 25.78\n",
      "Train Epoch: 0 [14080/50000 (31%)]\tLoss: 2.018020, Accuracy: 17.19\n",
      "Train Epoch: 0 [15360/50000 (34%)]\tLoss: 1.990862, Accuracy: 22.66\n",
      "Train Epoch: 0 [16640/50000 (37%)]\tLoss: 1.868446, Accuracy: 23.83\n",
      "Train Epoch: 0 [17920/50000 (40%)]\tLoss: 1.933813, Accuracy: 21.09\n",
      "Train Epoch: 0 [19200/50000 (43%)]\tLoss: 1.868474, Accuracy: 22.66\n",
      "Train Epoch: 0 [20480/50000 (45%)]\tLoss: 1.916965, Accuracy: 23.44\n",
      "Train Epoch: 0 [21760/50000 (48%)]\tLoss: 1.861632, Accuracy: 30.86\n",
      "Train Epoch: 0 [23040/50000 (51%)]\tLoss: 1.805478, Accuracy: 29.69\n",
      "Train Epoch: 0 [24320/50000 (54%)]\tLoss: 1.812276, Accuracy: 27.73\n",
      "Train Epoch: 0 [25600/50000 (57%)]\tLoss: 1.898470, Accuracy: 27.73\n",
      "Train Epoch: 0 [26880/50000 (60%)]\tLoss: 1.806492, Accuracy: 26.56\n",
      "Train Epoch: 0 [28160/50000 (62%)]\tLoss: 1.776056, Accuracy: 30.08\n",
      "Train Epoch: 0 [29440/50000 (65%)]\tLoss: 1.734005, Accuracy: 23.83\n",
      "Train Epoch: 0 [30720/50000 (68%)]\tLoss: 1.742873, Accuracy: 30.08\n",
      "Train Epoch: 0 [32000/50000 (71%)]\tLoss: 1.627363, Accuracy: 32.81\n",
      "Train Epoch: 0 [33280/50000 (74%)]\tLoss: 1.667299, Accuracy: 31.64\n",
      "Train Epoch: 0 [34560/50000 (77%)]\tLoss: 1.704336, Accuracy: 29.30\n",
      "Train Epoch: 0 [35840/50000 (80%)]\tLoss: 1.783468, Accuracy: 30.47\n",
      "Train Epoch: 0 [37120/50000 (82%)]\tLoss: 1.812645, Accuracy: 28.91\n",
      "Train Epoch: 0 [38400/50000 (85%)]\tLoss: 1.729903, Accuracy: 29.30\n",
      "Train Epoch: 0 [39680/50000 (88%)]\tLoss: 1.669595, Accuracy: 37.11\n",
      "Train Epoch: 0 [40960/50000 (91%)]\tLoss: 1.754832, Accuracy: 31.25\n",
      "Train Epoch: 0 [42240/50000 (94%)]\tLoss: 1.694833, Accuracy: 35.16\n",
      "Train Epoch: 0 [43520/50000 (97%)]\tLoss: 1.660976, Accuracy: 35.16\n",
      "Train Epoch: 0 [35000/50000 (99%)]\tLoss: 1.677888, Accuracy: 34.50\n",
      "\n",
      "Validation set: Average loss: 1.7957, Accuracy: 1459/5000 (29.00%)\n",
      "\n",
      "the time of this epoch:[36.57642912864685 s]\n",
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 1.636138, Accuracy: 38.28\n",
      "Train Epoch: 1 [1280/50000 (3%)]\tLoss: 1.673028, Accuracy: 34.38\n",
      "Train Epoch: 1 [2560/50000 (6%)]\tLoss: 1.543464, Accuracy: 35.55\n",
      "Train Epoch: 1 [3840/50000 (9%)]\tLoss: 1.639641, Accuracy: 37.89\n",
      "Train Epoch: 1 [5120/50000 (11%)]\tLoss: 1.619625, Accuracy: 36.72\n",
      "Train Epoch: 1 [6400/50000 (14%)]\tLoss: 1.672857, Accuracy: 38.67\n",
      "Train Epoch: 1 [7680/50000 (17%)]\tLoss: 1.631667, Accuracy: 33.98\n",
      "Train Epoch: 1 [8960/50000 (20%)]\tLoss: 1.540546, Accuracy: 37.11\n",
      "Train Epoch: 1 [10240/50000 (23%)]\tLoss: 1.630267, Accuracy: 36.33\n",
      "Train Epoch: 1 [11520/50000 (26%)]\tLoss: 1.642856, Accuracy: 36.33\n",
      "Train Epoch: 1 [12800/50000 (28%)]\tLoss: 1.517291, Accuracy: 41.80\n",
      "Train Epoch: 1 [14080/50000 (31%)]\tLoss: 1.513350, Accuracy: 39.06\n",
      "Train Epoch: 1 [15360/50000 (34%)]\tLoss: 1.489782, Accuracy: 41.02\n",
      "Train Epoch: 1 [16640/50000 (37%)]\tLoss: 1.626507, Accuracy: 34.38\n",
      "Train Epoch: 1 [17920/50000 (40%)]\tLoss: 1.560713, Accuracy: 43.36\n",
      "Train Epoch: 1 [19200/50000 (43%)]\tLoss: 1.497349, Accuracy: 41.80\n",
      "Train Epoch: 1 [20480/50000 (45%)]\tLoss: 1.527145, Accuracy: 39.84\n",
      "Train Epoch: 1 [21760/50000 (48%)]\tLoss: 1.533380, Accuracy: 40.23\n",
      "Train Epoch: 1 [23040/50000 (51%)]\tLoss: 1.443560, Accuracy: 45.70\n",
      "Train Epoch: 1 [24320/50000 (54%)]\tLoss: 1.387918, Accuracy: 45.31\n",
      "Train Epoch: 1 [25600/50000 (57%)]\tLoss: 1.578513, Accuracy: 39.45\n",
      "Train Epoch: 1 [26880/50000 (60%)]\tLoss: 1.557259, Accuracy: 40.23\n",
      "Train Epoch: 1 [28160/50000 (62%)]\tLoss: 1.422958, Accuracy: 44.92\n",
      "Train Epoch: 1 [29440/50000 (65%)]\tLoss: 1.648400, Accuracy: 39.06\n",
      "Train Epoch: 1 [30720/50000 (68%)]\tLoss: 1.582535, Accuracy: 36.33\n",
      "Train Epoch: 1 [32000/50000 (71%)]\tLoss: 1.412092, Accuracy: 43.75\n",
      "Train Epoch: 1 [33280/50000 (74%)]\tLoss: 1.487372, Accuracy: 41.80\n",
      "Train Epoch: 1 [34560/50000 (77%)]\tLoss: 1.371019, Accuracy: 47.66\n",
      "Train Epoch: 1 [35840/50000 (80%)]\tLoss: 1.522084, Accuracy: 41.80\n",
      "Train Epoch: 1 [37120/50000 (82%)]\tLoss: 1.427002, Accuracy: 45.70\n",
      "Train Epoch: 1 [38400/50000 (85%)]\tLoss: 1.469273, Accuracy: 46.48\n",
      "Train Epoch: 1 [39680/50000 (88%)]\tLoss: 1.315190, Accuracy: 50.00\n",
      "Train Epoch: 1 [40960/50000 (91%)]\tLoss: 1.464359, Accuracy: 44.53\n",
      "Train Epoch: 1 [42240/50000 (94%)]\tLoss: 1.386425, Accuracy: 48.83\n",
      "Train Epoch: 1 [43520/50000 (97%)]\tLoss: 1.309160, Accuracy: 51.95\n",
      "Train Epoch: 1 [35000/50000 (99%)]\tLoss: 1.430823, Accuracy: 48.00\n",
      "\n",
      "Validation set: Average loss: 1.5254, Accuracy: 2233/5000 (44.00%)\n",
      "\n",
      "the time of this epoch:[36.41851210594177 s]\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.432113, Accuracy: 45.70\n",
      "Train Epoch: 2 [1280/50000 (3%)]\tLoss: 1.384920, Accuracy: 50.39\n",
      "Train Epoch: 2 [2560/50000 (6%)]\tLoss: 1.336313, Accuracy: 53.52\n",
      "Train Epoch: 2 [3840/50000 (9%)]\tLoss: 1.358443, Accuracy: 51.56\n",
      "Train Epoch: 2 [5120/50000 (11%)]\tLoss: 1.355717, Accuracy: 45.31\n",
      "Train Epoch: 2 [6400/50000 (14%)]\tLoss: 1.270383, Accuracy: 51.56\n",
      "Train Epoch: 2 [7680/50000 (17%)]\tLoss: 1.369348, Accuracy: 51.95\n",
      "Train Epoch: 2 [8960/50000 (20%)]\tLoss: 1.317390, Accuracy: 52.73\n",
      "Train Epoch: 2 [10240/50000 (23%)]\tLoss: 1.255899, Accuracy: 54.30\n",
      "Train Epoch: 2 [11520/50000 (26%)]\tLoss: 1.343368, Accuracy: 47.66\n",
      "Train Epoch: 2 [12800/50000 (28%)]\tLoss: 1.291830, Accuracy: 57.42\n",
      "Train Epoch: 2 [14080/50000 (31%)]\tLoss: 1.149176, Accuracy: 58.59\n",
      "Train Epoch: 2 [15360/50000 (34%)]\tLoss: 1.243597, Accuracy: 53.12\n",
      "Train Epoch: 2 [16640/50000 (37%)]\tLoss: 1.186895, Accuracy: 58.98\n",
      "Train Epoch: 2 [17920/50000 (40%)]\tLoss: 1.217560, Accuracy: 55.47\n",
      "Train Epoch: 2 [19200/50000 (43%)]\tLoss: 1.408130, Accuracy: 44.14\n",
      "Train Epoch: 2 [20480/50000 (45%)]\tLoss: 1.339621, Accuracy: 51.56\n",
      "Train Epoch: 2 [21760/50000 (48%)]\tLoss: 1.200476, Accuracy: 51.56\n",
      "Train Epoch: 2 [23040/50000 (51%)]\tLoss: 1.135905, Accuracy: 57.42\n",
      "Train Epoch: 2 [24320/50000 (54%)]\tLoss: 1.026734, Accuracy: 64.06\n",
      "Train Epoch: 2 [25600/50000 (57%)]\tLoss: 1.298464, Accuracy: 55.86\n",
      "Train Epoch: 2 [26880/50000 (60%)]\tLoss: 1.217263, Accuracy: 57.03\n",
      "Train Epoch: 2 [28160/50000 (62%)]\tLoss: 1.132970, Accuracy: 57.81\n",
      "Train Epoch: 2 [29440/50000 (65%)]\tLoss: 1.103492, Accuracy: 62.50\n",
      "Train Epoch: 2 [30720/50000 (68%)]\tLoss: 1.172130, Accuracy: 56.25\n",
      "Train Epoch: 2 [32000/50000 (71%)]\tLoss: 1.124180, Accuracy: 60.94\n",
      "Train Epoch: 2 [33280/50000 (74%)]\tLoss: 1.250224, Accuracy: 53.91\n",
      "Train Epoch: 2 [34560/50000 (77%)]\tLoss: 1.136258, Accuracy: 62.11\n",
      "Train Epoch: 2 [35840/50000 (80%)]\tLoss: 1.031737, Accuracy: 66.41\n",
      "Train Epoch: 2 [37120/50000 (82%)]\tLoss: 1.095419, Accuracy: 60.16\n",
      "Train Epoch: 2 [38400/50000 (85%)]\tLoss: 1.181455, Accuracy: 58.20\n",
      "Train Epoch: 2 [39680/50000 (88%)]\tLoss: 1.045014, Accuracy: 65.62\n",
      "Train Epoch: 2 [40960/50000 (91%)]\tLoss: 1.042509, Accuracy: 66.02\n",
      "Train Epoch: 2 [42240/50000 (94%)]\tLoss: 1.152499, Accuracy: 59.77\n",
      "Train Epoch: 2 [43520/50000 (97%)]\tLoss: 1.146298, Accuracy: 56.25\n",
      "Train Epoch: 2 [35000/50000 (99%)]\tLoss: 1.045385, Accuracy: 59.00\n",
      "\n",
      "Validation set: Average loss: 1.1258, Accuracy: 2961/5000 (59.00%)\n",
      "\n",
      "the time of this epoch:[36.50461554527283 s]\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.012128, Accuracy: 62.50\n",
      "Train Epoch: 3 [1280/50000 (3%)]\tLoss: 1.137731, Accuracy: 58.20\n",
      "Train Epoch: 3 [2560/50000 (6%)]\tLoss: 0.951903, Accuracy: 64.84\n",
      "Train Epoch: 3 [3840/50000 (9%)]\tLoss: 1.073613, Accuracy: 63.28\n",
      "Train Epoch: 3 [5120/50000 (11%)]\tLoss: 0.896744, Accuracy: 69.92\n",
      "Train Epoch: 3 [6400/50000 (14%)]\tLoss: 0.957336, Accuracy: 64.84\n",
      "Train Epoch: 3 [7680/50000 (17%)]\tLoss: 1.031833, Accuracy: 64.84\n",
      "Train Epoch: 3 [8960/50000 (20%)]\tLoss: 0.934945, Accuracy: 66.80\n",
      "Train Epoch: 3 [10240/50000 (23%)]\tLoss: 1.072113, Accuracy: 62.89\n",
      "Train Epoch: 3 [11520/50000 (26%)]\tLoss: 1.147725, Accuracy: 59.38\n",
      "Train Epoch: 3 [12800/50000 (28%)]\tLoss: 0.909358, Accuracy: 71.48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [14080/50000 (31%)]\tLoss: 0.973045, Accuracy: 66.02\n",
      "Train Epoch: 3 [15360/50000 (34%)]\tLoss: 0.970531, Accuracy: 66.80\n",
      "Train Epoch: 3 [16640/50000 (37%)]\tLoss: 1.078942, Accuracy: 62.50\n",
      "Train Epoch: 3 [17920/50000 (40%)]\tLoss: 0.980111, Accuracy: 64.45\n",
      "Train Epoch: 3 [19200/50000 (43%)]\tLoss: 1.004504, Accuracy: 66.80\n",
      "Train Epoch: 3 [20480/50000 (45%)]\tLoss: 1.072742, Accuracy: 62.11\n",
      "Train Epoch: 3 [21760/50000 (48%)]\tLoss: 0.958648, Accuracy: 66.41\n",
      "Train Epoch: 3 [23040/50000 (51%)]\tLoss: 0.969537, Accuracy: 66.02\n",
      "Train Epoch: 3 [24320/50000 (54%)]\tLoss: 0.966663, Accuracy: 65.23\n",
      "Train Epoch: 3 [25600/50000 (57%)]\tLoss: 1.027152, Accuracy: 65.62\n",
      "Train Epoch: 3 [26880/50000 (60%)]\tLoss: 0.933597, Accuracy: 69.14\n",
      "Train Epoch: 3 [28160/50000 (62%)]\tLoss: 1.016932, Accuracy: 66.02\n",
      "Train Epoch: 3 [29440/50000 (65%)]\tLoss: 1.018487, Accuracy: 61.72\n",
      "Train Epoch: 3 [30720/50000 (68%)]\tLoss: 0.984311, Accuracy: 66.41\n",
      "Train Epoch: 3 [32000/50000 (71%)]\tLoss: 1.079893, Accuracy: 67.19\n",
      "Train Epoch: 3 [33280/50000 (74%)]\tLoss: 0.975791, Accuracy: 62.11\n",
      "Train Epoch: 3 [34560/50000 (77%)]\tLoss: 0.911053, Accuracy: 66.41\n",
      "Train Epoch: 3 [35840/50000 (80%)]\tLoss: 0.996169, Accuracy: 67.58\n",
      "Train Epoch: 3 [37120/50000 (82%)]\tLoss: 0.942726, Accuracy: 66.80\n",
      "Train Epoch: 3 [38400/50000 (85%)]\tLoss: 0.890662, Accuracy: 67.19\n",
      "Train Epoch: 3 [39680/50000 (88%)]\tLoss: 0.931999, Accuracy: 67.19\n",
      "Train Epoch: 3 [40960/50000 (91%)]\tLoss: 1.046035, Accuracy: 62.50\n",
      "Train Epoch: 3 [42240/50000 (94%)]\tLoss: 0.985704, Accuracy: 65.62\n",
      "Train Epoch: 3 [43520/50000 (97%)]\tLoss: 0.968066, Accuracy: 65.23\n",
      "Train Epoch: 3 [35000/50000 (99%)]\tLoss: 0.974163, Accuracy: 62.00\n",
      "\n",
      "Validation set: Average loss: 1.0668, Accuracy: 3141/5000 (62.00%)\n",
      "\n",
      "the time of this epoch:[36.43748998641968 s]\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 0.908635, Accuracy: 67.58\n",
      "Train Epoch: 4 [1280/50000 (3%)]\tLoss: 0.970902, Accuracy: 65.23\n",
      "Train Epoch: 4 [2560/50000 (6%)]\tLoss: 0.799671, Accuracy: 72.27\n",
      "Train Epoch: 4 [3840/50000 (9%)]\tLoss: 0.909662, Accuracy: 68.75\n",
      "Train Epoch: 4 [5120/50000 (11%)]\tLoss: 0.855758, Accuracy: 70.31\n",
      "Train Epoch: 4 [6400/50000 (14%)]\tLoss: 0.805115, Accuracy: 73.05\n",
      "Train Epoch: 4 [7680/50000 (17%)]\tLoss: 0.819735, Accuracy: 71.48\n",
      "Train Epoch: 4 [8960/50000 (20%)]\tLoss: 0.917356, Accuracy: 65.62\n",
      "Train Epoch: 4 [10240/50000 (23%)]\tLoss: 0.853406, Accuracy: 72.27\n",
      "Train Epoch: 4 [11520/50000 (26%)]\tLoss: 0.962338, Accuracy: 63.28\n",
      "Train Epoch: 4 [12800/50000 (28%)]\tLoss: 0.933582, Accuracy: 66.02\n",
      "Train Epoch: 4 [14080/50000 (31%)]\tLoss: 0.971344, Accuracy: 65.23\n",
      "Train Epoch: 4 [15360/50000 (34%)]\tLoss: 0.934492, Accuracy: 63.67\n",
      "Train Epoch: 4 [16640/50000 (37%)]\tLoss: 0.856536, Accuracy: 71.88\n",
      "Train Epoch: 4 [17920/50000 (40%)]\tLoss: 0.995990, Accuracy: 65.23\n",
      "Train Epoch: 4 [19200/50000 (43%)]\tLoss: 0.975678, Accuracy: 66.02\n",
      "Train Epoch: 4 [20480/50000 (45%)]\tLoss: 0.869763, Accuracy: 70.31\n",
      "Train Epoch: 4 [21760/50000 (48%)]\tLoss: 0.815325, Accuracy: 72.66\n",
      "Train Epoch: 4 [23040/50000 (51%)]\tLoss: 0.895461, Accuracy: 73.44\n",
      "Train Epoch: 4 [24320/50000 (54%)]\tLoss: 0.987028, Accuracy: 64.06\n",
      "Train Epoch: 4 [25600/50000 (57%)]\tLoss: 0.919843, Accuracy: 68.36\n",
      "Train Epoch: 4 [26880/50000 (60%)]\tLoss: 0.905033, Accuracy: 67.97\n",
      "Train Epoch: 4 [28160/50000 (62%)]\tLoss: 0.770173, Accuracy: 72.66\n",
      "Train Epoch: 4 [29440/50000 (65%)]\tLoss: 0.858997, Accuracy: 69.92\n",
      "Train Epoch: 4 [30720/50000 (68%)]\tLoss: 0.841577, Accuracy: 73.83\n",
      "Train Epoch: 4 [32000/50000 (71%)]\tLoss: 0.844353, Accuracy: 71.48\n",
      "Train Epoch: 4 [33280/50000 (74%)]\tLoss: 0.761355, Accuracy: 72.66\n",
      "Train Epoch: 4 [34560/50000 (77%)]\tLoss: 0.886752, Accuracy: 69.53\n",
      "Train Epoch: 4 [35840/50000 (80%)]\tLoss: 0.775040, Accuracy: 71.48\n",
      "Train Epoch: 4 [37120/50000 (82%)]\tLoss: 0.896410, Accuracy: 67.19\n",
      "Train Epoch: 4 [38400/50000 (85%)]\tLoss: 0.701901, Accuracy: 75.78\n",
      "Train Epoch: 4 [39680/50000 (88%)]\tLoss: 0.945440, Accuracy: 69.14\n",
      "Train Epoch: 4 [40960/50000 (91%)]\tLoss: 0.924315, Accuracy: 70.70\n",
      "Train Epoch: 4 [42240/50000 (94%)]\tLoss: 0.844580, Accuracy: 67.97\n",
      "Train Epoch: 4 [43520/50000 (97%)]\tLoss: 0.813747, Accuracy: 72.66\n",
      "Train Epoch: 4 [35000/50000 (99%)]\tLoss: 0.837670, Accuracy: 74.00\n",
      "\n",
      "Validation set: Average loss: 1.3396, Accuracy: 2961/5000 (59.00%)\n",
      "\n",
      "the time of this epoch:[36.43453240394592 s]\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.854429, Accuracy: 71.09\n",
      "Train Epoch: 5 [1280/50000 (3%)]\tLoss: 0.839831, Accuracy: 70.31\n",
      "Train Epoch: 5 [2560/50000 (6%)]\tLoss: 0.910769, Accuracy: 71.88\n",
      "Train Epoch: 5 [3840/50000 (9%)]\tLoss: 0.778616, Accuracy: 73.44\n",
      "Train Epoch: 5 [5120/50000 (11%)]\tLoss: 0.804285, Accuracy: 71.48\n",
      "Train Epoch: 5 [6400/50000 (14%)]\tLoss: 0.719457, Accuracy: 74.22\n",
      "Train Epoch: 5 [7680/50000 (17%)]\tLoss: 0.839091, Accuracy: 72.27\n",
      "Train Epoch: 5 [8960/50000 (20%)]\tLoss: 0.738168, Accuracy: 76.17\n",
      "Train Epoch: 5 [10240/50000 (23%)]\tLoss: 0.812399, Accuracy: 73.05\n",
      "Train Epoch: 5 [11520/50000 (26%)]\tLoss: 0.769267, Accuracy: 75.00\n",
      "Train Epoch: 5 [12800/50000 (28%)]\tLoss: 0.791383, Accuracy: 73.83\n",
      "Train Epoch: 5 [14080/50000 (31%)]\tLoss: 0.818897, Accuracy: 72.27\n",
      "Train Epoch: 5 [15360/50000 (34%)]\tLoss: 0.815926, Accuracy: 73.44\n",
      "Train Epoch: 5 [16640/50000 (37%)]\tLoss: 0.670639, Accuracy: 78.52\n",
      "Train Epoch: 5 [17920/50000 (40%)]\tLoss: 0.828891, Accuracy: 73.44\n",
      "Train Epoch: 5 [19200/50000 (43%)]\tLoss: 0.718197, Accuracy: 74.22\n",
      "Train Epoch: 5 [20480/50000 (45%)]\tLoss: 0.729484, Accuracy: 73.05\n",
      "Train Epoch: 5 [21760/50000 (48%)]\tLoss: 0.801748, Accuracy: 72.27\n",
      "Train Epoch: 5 [23040/50000 (51%)]\tLoss: 0.642490, Accuracy: 77.34\n",
      "Train Epoch: 5 [24320/50000 (54%)]\tLoss: 0.782418, Accuracy: 71.09\n",
      "Train Epoch: 5 [25600/50000 (57%)]\tLoss: 0.857297, Accuracy: 70.31\n",
      "Train Epoch: 5 [26880/50000 (60%)]\tLoss: 0.739356, Accuracy: 70.70\n",
      "Train Epoch: 5 [28160/50000 (62%)]\tLoss: 0.891357, Accuracy: 68.36\n",
      "Train Epoch: 5 [29440/50000 (65%)]\tLoss: 0.675707, Accuracy: 78.12\n",
      "Train Epoch: 5 [30720/50000 (68%)]\tLoss: 0.841446, Accuracy: 73.05\n",
      "Train Epoch: 5 [32000/50000 (71%)]\tLoss: 0.843505, Accuracy: 73.83\n",
      "Train Epoch: 5 [33280/50000 (74%)]\tLoss: 0.752079, Accuracy: 74.22\n",
      "Train Epoch: 5 [34560/50000 (77%)]\tLoss: 0.721586, Accuracy: 75.00\n",
      "Train Epoch: 5 [35840/50000 (80%)]\tLoss: 0.678073, Accuracy: 75.39\n",
      "Train Epoch: 5 [37120/50000 (82%)]\tLoss: 0.804575, Accuracy: 71.88\n",
      "Train Epoch: 5 [38400/50000 (85%)]\tLoss: 0.651799, Accuracy: 79.30\n",
      "Train Epoch: 5 [39680/50000 (88%)]\tLoss: 0.584983, Accuracy: 80.08\n",
      "Train Epoch: 5 [40960/50000 (91%)]\tLoss: 0.755712, Accuracy: 74.22\n",
      "Train Epoch: 5 [42240/50000 (94%)]\tLoss: 0.682062, Accuracy: 76.17\n",
      "Train Epoch: 5 [43520/50000 (97%)]\tLoss: 0.733041, Accuracy: 76.56\n",
      "Train Epoch: 5 [35000/50000 (99%)]\tLoss: 0.939157, Accuracy: 68.00\n",
      "\n",
      "Validation set: Average loss: 0.9536, Accuracy: 3413/5000 (68.00%)\n",
      "\n",
      "the time of this epoch:[36.45118284225464 s]\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.749579, Accuracy: 73.05\n",
      "Train Epoch: 6 [1280/50000 (3%)]\tLoss: 0.831290, Accuracy: 72.27\n",
      "Train Epoch: 6 [2560/50000 (6%)]\tLoss: 0.867055, Accuracy: 71.09\n",
      "Train Epoch: 6 [3840/50000 (9%)]\tLoss: 0.769790, Accuracy: 71.88\n",
      "Train Epoch: 6 [5120/50000 (11%)]\tLoss: 0.787208, Accuracy: 72.27\n",
      "Train Epoch: 6 [6400/50000 (14%)]\tLoss: 0.666143, Accuracy: 76.17\n",
      "Train Epoch: 6 [7680/50000 (17%)]\tLoss: 0.677187, Accuracy: 77.73\n",
      "Train Epoch: 6 [8960/50000 (20%)]\tLoss: 0.790438, Accuracy: 75.00\n",
      "Train Epoch: 6 [10240/50000 (23%)]\tLoss: 0.751260, Accuracy: 74.61\n",
      "Train Epoch: 6 [11520/50000 (26%)]\tLoss: 0.701516, Accuracy: 74.61\n",
      "Train Epoch: 6 [12800/50000 (28%)]\tLoss: 0.737697, Accuracy: 74.22\n",
      "Train Epoch: 6 [14080/50000 (31%)]\tLoss: 0.660075, Accuracy: 75.78\n",
      "Train Epoch: 6 [15360/50000 (34%)]\tLoss: 0.692040, Accuracy: 74.61\n",
      "Train Epoch: 6 [16640/50000 (37%)]\tLoss: 0.676585, Accuracy: 74.61\n",
      "Train Epoch: 6 [17920/50000 (40%)]\tLoss: 0.739142, Accuracy: 75.00\n",
      "Train Epoch: 6 [19200/50000 (43%)]\tLoss: 0.679692, Accuracy: 75.78\n",
      "Train Epoch: 6 [20480/50000 (45%)]\tLoss: 0.762085, Accuracy: 73.44\n",
      "Train Epoch: 6 [21760/50000 (48%)]\tLoss: 0.701696, Accuracy: 79.30\n",
      "Train Epoch: 6 [23040/50000 (51%)]\tLoss: 0.645316, Accuracy: 79.30\n",
      "Train Epoch: 6 [24320/50000 (54%)]\tLoss: 0.700677, Accuracy: 75.78\n",
      "Train Epoch: 6 [25600/50000 (57%)]\tLoss: 0.616819, Accuracy: 75.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [26880/50000 (60%)]\tLoss: 0.781744, Accuracy: 74.22\n",
      "Train Epoch: 6 [28160/50000 (62%)]\tLoss: 0.702936, Accuracy: 75.39\n",
      "Train Epoch: 6 [29440/50000 (65%)]\tLoss: 0.698285, Accuracy: 76.17\n",
      "Train Epoch: 6 [30720/50000 (68%)]\tLoss: 0.838803, Accuracy: 71.09\n",
      "Train Epoch: 6 [32000/50000 (71%)]\tLoss: 0.715902, Accuracy: 74.61\n",
      "Train Epoch: 6 [33280/50000 (74%)]\tLoss: 0.730193, Accuracy: 76.56\n",
      "Train Epoch: 6 [34560/50000 (77%)]\tLoss: 0.709040, Accuracy: 73.83\n",
      "Train Epoch: 6 [35840/50000 (80%)]\tLoss: 0.762147, Accuracy: 74.61\n",
      "Train Epoch: 6 [37120/50000 (82%)]\tLoss: 0.569397, Accuracy: 80.86\n",
      "Train Epoch: 6 [38400/50000 (85%)]\tLoss: 0.690112, Accuracy: 76.56\n",
      "Train Epoch: 6 [39680/50000 (88%)]\tLoss: 0.705382, Accuracy: 74.22\n",
      "Train Epoch: 6 [40960/50000 (91%)]\tLoss: 0.700689, Accuracy: 73.83\n",
      "Train Epoch: 6 [42240/50000 (94%)]\tLoss: 0.694677, Accuracy: 76.56\n",
      "Train Epoch: 6 [43520/50000 (97%)]\tLoss: 0.667818, Accuracy: 76.95\n",
      "Train Epoch: 6 [35000/50000 (99%)]\tLoss: 0.554533, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.8881, Accuracy: 3526/5000 (70.00%)\n",
      "\n",
      "the time of this epoch:[36.39584755897522 s]\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.647474, Accuracy: 75.78\n",
      "Train Epoch: 7 [1280/50000 (3%)]\tLoss: 0.638253, Accuracy: 79.69\n",
      "Train Epoch: 7 [2560/50000 (6%)]\tLoss: 0.713667, Accuracy: 73.05\n",
      "Train Epoch: 7 [3840/50000 (9%)]\tLoss: 0.744769, Accuracy: 73.05\n",
      "Train Epoch: 7 [5120/50000 (11%)]\tLoss: 0.665683, Accuracy: 80.08\n",
      "Train Epoch: 7 [6400/50000 (14%)]\tLoss: 0.675355, Accuracy: 78.12\n",
      "Train Epoch: 7 [7680/50000 (17%)]\tLoss: 0.660262, Accuracy: 77.34\n",
      "Train Epoch: 7 [8960/50000 (20%)]\tLoss: 0.618572, Accuracy: 81.25\n",
      "Train Epoch: 7 [10240/50000 (23%)]\tLoss: 0.712038, Accuracy: 74.22\n",
      "Train Epoch: 7 [11520/50000 (26%)]\tLoss: 0.640773, Accuracy: 79.69\n",
      "Train Epoch: 7 [12800/50000 (28%)]\tLoss: 0.702234, Accuracy: 75.39\n",
      "Train Epoch: 7 [14080/50000 (31%)]\tLoss: 0.656105, Accuracy: 78.52\n",
      "Train Epoch: 7 [15360/50000 (34%)]\tLoss: 0.608829, Accuracy: 80.86\n",
      "Train Epoch: 7 [16640/50000 (37%)]\tLoss: 0.592631, Accuracy: 80.47\n",
      "Train Epoch: 7 [17920/50000 (40%)]\tLoss: 0.730256, Accuracy: 75.00\n",
      "Train Epoch: 7 [19200/50000 (43%)]\tLoss: 0.667718, Accuracy: 74.61\n",
      "Train Epoch: 7 [20480/50000 (45%)]\tLoss: 0.648006, Accuracy: 77.73\n",
      "Train Epoch: 7 [21760/50000 (48%)]\tLoss: 0.621770, Accuracy: 78.12\n",
      "Train Epoch: 7 [23040/50000 (51%)]\tLoss: 0.591202, Accuracy: 80.47\n",
      "Train Epoch: 7 [24320/50000 (54%)]\tLoss: 0.693908, Accuracy: 78.91\n",
      "Train Epoch: 7 [25600/50000 (57%)]\tLoss: 0.718974, Accuracy: 77.34\n",
      "Train Epoch: 7 [26880/50000 (60%)]\tLoss: 0.682027, Accuracy: 73.83\n",
      "Train Epoch: 7 [28160/50000 (62%)]\tLoss: 0.646310, Accuracy: 75.78\n",
      "Train Epoch: 7 [29440/50000 (65%)]\tLoss: 0.668853, Accuracy: 78.12\n",
      "Train Epoch: 7 [30720/50000 (68%)]\tLoss: 0.640837, Accuracy: 80.86\n",
      "Train Epoch: 7 [32000/50000 (71%)]\tLoss: 0.760671, Accuracy: 74.22\n",
      "Train Epoch: 7 [33280/50000 (74%)]\tLoss: 0.591830, Accuracy: 79.69\n",
      "Train Epoch: 7 [34560/50000 (77%)]\tLoss: 0.629583, Accuracy: 78.12\n",
      "Train Epoch: 7 [35840/50000 (80%)]\tLoss: 0.634472, Accuracy: 80.08\n",
      "Train Epoch: 7 [37120/50000 (82%)]\tLoss: 0.640334, Accuracy: 77.73\n",
      "Train Epoch: 7 [38400/50000 (85%)]\tLoss: 0.756655, Accuracy: 75.78\n",
      "Train Epoch: 7 [39680/50000 (88%)]\tLoss: 0.680844, Accuracy: 79.30\n",
      "Train Epoch: 7 [40960/50000 (91%)]\tLoss: 0.571522, Accuracy: 80.08\n",
      "Train Epoch: 7 [42240/50000 (94%)]\tLoss: 0.524943, Accuracy: 82.03\n",
      "Train Epoch: 7 [43520/50000 (97%)]\tLoss: 0.521845, Accuracy: 84.38\n",
      "Train Epoch: 7 [35000/50000 (99%)]\tLoss: 0.609321, Accuracy: 79.50\n",
      "\n",
      "Validation set: Average loss: 0.7722, Accuracy: 3673/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[36.43122482299805 s]\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 0.718672, Accuracy: 74.22\n",
      "Train Epoch: 8 [1280/50000 (3%)]\tLoss: 0.521352, Accuracy: 80.08\n",
      "Train Epoch: 8 [2560/50000 (6%)]\tLoss: 0.621379, Accuracy: 81.25\n",
      "Train Epoch: 8 [3840/50000 (9%)]\tLoss: 0.620907, Accuracy: 78.52\n",
      "Train Epoch: 8 [5120/50000 (11%)]\tLoss: 0.546151, Accuracy: 82.42\n",
      "Train Epoch: 8 [6400/50000 (14%)]\tLoss: 0.604796, Accuracy: 78.91\n",
      "Train Epoch: 8 [7680/50000 (17%)]\tLoss: 0.685223, Accuracy: 79.69\n",
      "Train Epoch: 8 [8960/50000 (20%)]\tLoss: 0.568854, Accuracy: 82.03\n",
      "Train Epoch: 8 [10240/50000 (23%)]\tLoss: 0.582030, Accuracy: 80.47\n",
      "Train Epoch: 8 [11520/50000 (26%)]\tLoss: 0.676708, Accuracy: 75.78\n",
      "Train Epoch: 8 [12800/50000 (28%)]\tLoss: 0.792599, Accuracy: 74.22\n",
      "Train Epoch: 8 [14080/50000 (31%)]\tLoss: 0.548219, Accuracy: 82.03\n",
      "Train Epoch: 8 [15360/50000 (34%)]\tLoss: 0.657342, Accuracy: 78.52\n",
      "Train Epoch: 8 [16640/50000 (37%)]\tLoss: 0.555766, Accuracy: 79.69\n",
      "Train Epoch: 8 [17920/50000 (40%)]\tLoss: 0.481805, Accuracy: 83.59\n",
      "Train Epoch: 8 [19200/50000 (43%)]\tLoss: 0.610952, Accuracy: 77.73\n",
      "Train Epoch: 8 [20480/50000 (45%)]\tLoss: 0.604153, Accuracy: 81.25\n",
      "Train Epoch: 8 [21760/50000 (48%)]\tLoss: 0.669066, Accuracy: 78.91\n",
      "Train Epoch: 8 [23040/50000 (51%)]\tLoss: 0.617406, Accuracy: 79.69\n",
      "Train Epoch: 8 [24320/50000 (54%)]\tLoss: 0.515249, Accuracy: 82.42\n",
      "Train Epoch: 8 [25600/50000 (57%)]\tLoss: 0.641819, Accuracy: 81.64\n",
      "Train Epoch: 8 [26880/50000 (60%)]\tLoss: 0.541584, Accuracy: 81.25\n",
      "Train Epoch: 8 [28160/50000 (62%)]\tLoss: 0.698910, Accuracy: 75.78\n",
      "Train Epoch: 8 [29440/50000 (65%)]\tLoss: 0.574574, Accuracy: 80.86\n",
      "Train Epoch: 8 [30720/50000 (68%)]\tLoss: 0.575723, Accuracy: 78.12\n",
      "Train Epoch: 8 [32000/50000 (71%)]\tLoss: 0.494144, Accuracy: 84.77\n",
      "Train Epoch: 8 [33280/50000 (74%)]\tLoss: 0.641979, Accuracy: 77.34\n",
      "Train Epoch: 8 [34560/50000 (77%)]\tLoss: 0.561423, Accuracy: 82.03\n",
      "Train Epoch: 8 [35840/50000 (80%)]\tLoss: 0.675691, Accuracy: 78.91\n",
      "Train Epoch: 8 [37120/50000 (82%)]\tLoss: 0.542370, Accuracy: 81.64\n",
      "Train Epoch: 8 [38400/50000 (85%)]\tLoss: 0.560167, Accuracy: 78.91\n",
      "Train Epoch: 8 [39680/50000 (88%)]\tLoss: 0.527487, Accuracy: 80.86\n",
      "Train Epoch: 8 [40960/50000 (91%)]\tLoss: 0.691953, Accuracy: 80.47\n",
      "Train Epoch: 8 [42240/50000 (94%)]\tLoss: 0.571614, Accuracy: 81.64\n",
      "Train Epoch: 8 [43520/50000 (97%)]\tLoss: 0.645235, Accuracy: 77.34\n",
      "Train Epoch: 8 [35000/50000 (99%)]\tLoss: 0.674479, Accuracy: 78.00\n",
      "\n",
      "Validation set: Average loss: 0.6931, Accuracy: 3820/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.477041244506836 s]\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 0.533798, Accuracy: 81.64\n",
      "Train Epoch: 9 [1280/50000 (3%)]\tLoss: 0.527471, Accuracy: 83.20\n",
      "Train Epoch: 9 [2560/50000 (6%)]\tLoss: 0.596990, Accuracy: 78.91\n",
      "Train Epoch: 9 [3840/50000 (9%)]\tLoss: 0.463175, Accuracy: 83.20\n",
      "Train Epoch: 9 [5120/50000 (11%)]\tLoss: 0.655336, Accuracy: 78.12\n",
      "Train Epoch: 9 [6400/50000 (14%)]\tLoss: 0.553164, Accuracy: 79.30\n",
      "Train Epoch: 9 [7680/50000 (17%)]\tLoss: 0.740773, Accuracy: 75.39\n",
      "Train Epoch: 9 [8960/50000 (20%)]\tLoss: 0.598609, Accuracy: 78.12\n",
      "Train Epoch: 9 [10240/50000 (23%)]\tLoss: 0.593803, Accuracy: 80.47\n",
      "Train Epoch: 9 [11520/50000 (26%)]\tLoss: 0.643701, Accuracy: 79.30\n",
      "Train Epoch: 9 [12800/50000 (28%)]\tLoss: 0.583851, Accuracy: 80.47\n",
      "Train Epoch: 9 [14080/50000 (31%)]\tLoss: 0.556536, Accuracy: 82.03\n",
      "Train Epoch: 9 [15360/50000 (34%)]\tLoss: 0.607454, Accuracy: 81.25\n",
      "Train Epoch: 9 [16640/50000 (37%)]\tLoss: 0.584227, Accuracy: 80.47\n",
      "Train Epoch: 9 [17920/50000 (40%)]\tLoss: 0.496273, Accuracy: 84.38\n",
      "Train Epoch: 9 [19200/50000 (43%)]\tLoss: 0.633082, Accuracy: 77.73\n",
      "Train Epoch: 9 [20480/50000 (45%)]\tLoss: 0.528787, Accuracy: 83.98\n",
      "Train Epoch: 9 [21760/50000 (48%)]\tLoss: 0.663040, Accuracy: 79.69\n",
      "Train Epoch: 9 [23040/50000 (51%)]\tLoss: 0.577483, Accuracy: 78.52\n",
      "Train Epoch: 9 [24320/50000 (54%)]\tLoss: 0.617415, Accuracy: 80.08\n",
      "Train Epoch: 9 [25600/50000 (57%)]\tLoss: 0.651211, Accuracy: 77.73\n",
      "Train Epoch: 9 [26880/50000 (60%)]\tLoss: 0.500203, Accuracy: 84.77\n",
      "Train Epoch: 9 [28160/50000 (62%)]\tLoss: 0.619293, Accuracy: 81.25\n",
      "Train Epoch: 9 [29440/50000 (65%)]\tLoss: 0.540733, Accuracy: 81.25\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.4733]],\n",
      "\n",
      "        [[-0.0420]],\n",
      "\n",
      "        [[ 0.4922]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.5030]],\n",
      "\n",
      "        [[ 0.2338]],\n",
      "\n",
      "        [[-0.5752]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.1312,  0.1311,  0.1350,  ...,  0.1208,  0.1074,  0.0956],\n",
      "         [ 0.2035,  0.2039,  0.2006,  ...,  0.1767,  0.1790,  0.1861],\n",
      "         [ 0.3559,  0.3365,  0.3345,  ...,  0.3559,  0.3220,  0.3227],\n",
      "         ...,\n",
      "         [ 0.3002,  0.3119,  0.3184,  ...,  0.3457,  0.3347,  0.3103],\n",
      "         [ 0.5063,  0.5182,  0.4946,  ...,  0.4723,  0.5046,  0.4808],\n",
      "         [ 0.3912,  0.4155,  0.3917,  ...,  0.3772,  0.3927,  0.3874]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [30720/50000 (68%)]\tLoss: 0.449797, Accuracy: 86.33\n",
      "Train Epoch: 9 [32000/50000 (71%)]\tLoss: 0.679902, Accuracy: 75.78\n",
      "Train Epoch: 9 [33280/50000 (74%)]\tLoss: 0.541111, Accuracy: 83.98\n",
      "Train Epoch: 9 [34560/50000 (77%)]\tLoss: 0.544761, Accuracy: 83.59\n",
      "Train Epoch: 9 [35840/50000 (80%)]\tLoss: 0.656859, Accuracy: 76.95\n",
      "Train Epoch: 9 [37120/50000 (82%)]\tLoss: 0.628509, Accuracy: 78.52\n",
      "Train Epoch: 9 [38400/50000 (85%)]\tLoss: 0.576768, Accuracy: 78.12\n",
      "Train Epoch: 9 [39680/50000 (88%)]\tLoss: 0.624049, Accuracy: 82.03\n",
      "Train Epoch: 9 [40960/50000 (91%)]\tLoss: 0.631110, Accuracy: 78.52\n",
      "Train Epoch: 9 [42240/50000 (94%)]\tLoss: 0.583086, Accuracy: 80.08\n",
      "Train Epoch: 9 [43520/50000 (97%)]\tLoss: 0.617956, Accuracy: 79.30\n",
      "Train Epoch: 9 [35000/50000 (99%)]\tLoss: 0.559871, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 0.7001, Accuracy: 3851/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.513710737228394 s]\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 0.541178, Accuracy: 81.25\n",
      "Train Epoch: 10 [1280/50000 (3%)]\tLoss: 0.603670, Accuracy: 79.30\n",
      "Train Epoch: 10 [2560/50000 (6%)]\tLoss: 0.438119, Accuracy: 82.42\n",
      "Train Epoch: 10 [3840/50000 (9%)]\tLoss: 0.676147, Accuracy: 76.17\n",
      "Train Epoch: 10 [5120/50000 (11%)]\tLoss: 0.509340, Accuracy: 82.42\n",
      "Train Epoch: 10 [6400/50000 (14%)]\tLoss: 0.524225, Accuracy: 81.25\n",
      "Train Epoch: 10 [7680/50000 (17%)]\tLoss: 0.524760, Accuracy: 83.59\n",
      "Train Epoch: 10 [8960/50000 (20%)]\tLoss: 0.592571, Accuracy: 81.64\n",
      "Train Epoch: 10 [10240/50000 (23%)]\tLoss: 0.586478, Accuracy: 79.69\n",
      "Train Epoch: 10 [11520/50000 (26%)]\tLoss: 0.489440, Accuracy: 84.77\n",
      "Train Epoch: 10 [12800/50000 (28%)]\tLoss: 0.483382, Accuracy: 83.59\n",
      "Train Epoch: 10 [14080/50000 (31%)]\tLoss: 0.642353, Accuracy: 76.95\n",
      "Train Epoch: 10 [15360/50000 (34%)]\tLoss: 0.514621, Accuracy: 80.47\n",
      "Train Epoch: 10 [16640/50000 (37%)]\tLoss: 0.461017, Accuracy: 85.55\n",
      "Train Epoch: 10 [17920/50000 (40%)]\tLoss: 0.536500, Accuracy: 84.38\n",
      "Train Epoch: 10 [19200/50000 (43%)]\tLoss: 0.592242, Accuracy: 80.86\n",
      "Train Epoch: 10 [20480/50000 (45%)]\tLoss: 0.576746, Accuracy: 80.47\n",
      "Train Epoch: 10 [21760/50000 (48%)]\tLoss: 0.547157, Accuracy: 82.81\n",
      "Train Epoch: 10 [23040/50000 (51%)]\tLoss: 0.626044, Accuracy: 79.30\n",
      "Train Epoch: 10 [24320/50000 (54%)]\tLoss: 0.539880, Accuracy: 81.64\n",
      "Train Epoch: 10 [25600/50000 (57%)]\tLoss: 0.650973, Accuracy: 79.69\n",
      "Train Epoch: 10 [26880/50000 (60%)]\tLoss: 0.640323, Accuracy: 78.12\n",
      "Train Epoch: 10 [28160/50000 (62%)]\tLoss: 0.525397, Accuracy: 82.03\n",
      "Train Epoch: 10 [29440/50000 (65%)]\tLoss: 0.566041, Accuracy: 81.64\n",
      "Train Epoch: 10 [30720/50000 (68%)]\tLoss: 0.550678, Accuracy: 79.69\n",
      "Train Epoch: 10 [32000/50000 (71%)]\tLoss: 0.628902, Accuracy: 79.30\n",
      "Train Epoch: 10 [33280/50000 (74%)]\tLoss: 0.540846, Accuracy: 81.25\n",
      "Train Epoch: 10 [34560/50000 (77%)]\tLoss: 0.463341, Accuracy: 84.38\n",
      "Train Epoch: 10 [35840/50000 (80%)]\tLoss: 0.657843, Accuracy: 78.91\n",
      "Train Epoch: 10 [37120/50000 (82%)]\tLoss: 0.512014, Accuracy: 83.59\n",
      "Train Epoch: 10 [38400/50000 (85%)]\tLoss: 0.604501, Accuracy: 77.73\n",
      "Train Epoch: 10 [39680/50000 (88%)]\tLoss: 0.580766, Accuracy: 80.08\n",
      "Train Epoch: 10 [40960/50000 (91%)]\tLoss: 0.614307, Accuracy: 77.34\n",
      "Train Epoch: 10 [42240/50000 (94%)]\tLoss: 0.573530, Accuracy: 81.25\n",
      "Train Epoch: 10 [43520/50000 (97%)]\tLoss: 0.606549, Accuracy: 80.08\n",
      "Train Epoch: 10 [35000/50000 (99%)]\tLoss: 0.496550, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.8138, Accuracy: 3612/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[36.43876934051514 s]\n",
      "\n",
      "Test set: Average loss: 0.8196, Accuracy: 7241/10000 (72.41%)\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 0.569712, Accuracy: 82.81\n",
      "Train Epoch: 11 [1280/50000 (3%)]\tLoss: 0.500503, Accuracy: 83.98\n",
      "Train Epoch: 11 [2560/50000 (6%)]\tLoss: 0.698395, Accuracy: 77.73\n",
      "Train Epoch: 11 [3840/50000 (9%)]\tLoss: 0.593665, Accuracy: 81.25\n",
      "Train Epoch: 11 [5120/50000 (11%)]\tLoss: 0.473312, Accuracy: 83.59\n",
      "Train Epoch: 11 [6400/50000 (14%)]\tLoss: 0.484766, Accuracy: 81.64\n",
      "Train Epoch: 11 [7680/50000 (17%)]\tLoss: 0.533456, Accuracy: 83.20\n",
      "Train Epoch: 11 [8960/50000 (20%)]\tLoss: 0.549107, Accuracy: 82.03\n",
      "Train Epoch: 11 [10240/50000 (23%)]\tLoss: 0.460118, Accuracy: 84.38\n",
      "Train Epoch: 11 [11520/50000 (26%)]\tLoss: 0.646073, Accuracy: 76.95\n",
      "Train Epoch: 11 [12800/50000 (28%)]\tLoss: 0.631727, Accuracy: 78.91\n",
      "Train Epoch: 11 [14080/50000 (31%)]\tLoss: 0.640583, Accuracy: 80.47\n",
      "Train Epoch: 11 [15360/50000 (34%)]\tLoss: 0.496433, Accuracy: 81.25\n",
      "Train Epoch: 11 [16640/50000 (37%)]\tLoss: 0.497379, Accuracy: 83.59\n",
      "Train Epoch: 11 [17920/50000 (40%)]\tLoss: 0.553384, Accuracy: 78.52\n",
      "Train Epoch: 11 [19200/50000 (43%)]\tLoss: 0.552590, Accuracy: 81.25\n",
      "Train Epoch: 11 [20480/50000 (45%)]\tLoss: 0.559218, Accuracy: 80.47\n",
      "Train Epoch: 11 [21760/50000 (48%)]\tLoss: 0.590687, Accuracy: 80.86\n",
      "Train Epoch: 11 [23040/50000 (51%)]\tLoss: 0.462661, Accuracy: 83.20\n",
      "Train Epoch: 11 [24320/50000 (54%)]\tLoss: 0.658156, Accuracy: 77.73\n",
      "Train Epoch: 11 [25600/50000 (57%)]\tLoss: 0.504297, Accuracy: 81.25\n",
      "Train Epoch: 11 [26880/50000 (60%)]\tLoss: 0.439110, Accuracy: 84.77\n",
      "Train Epoch: 11 [28160/50000 (62%)]\tLoss: 0.361062, Accuracy: 87.11\n",
      "Train Epoch: 11 [29440/50000 (65%)]\tLoss: 0.601747, Accuracy: 79.69\n",
      "Train Epoch: 11 [30720/50000 (68%)]\tLoss: 0.492620, Accuracy: 82.42\n",
      "Train Epoch: 11 [32000/50000 (71%)]\tLoss: 0.656400, Accuracy: 77.73\n",
      "Train Epoch: 11 [33280/50000 (74%)]\tLoss: 0.507236, Accuracy: 84.77\n",
      "Train Epoch: 11 [34560/50000 (77%)]\tLoss: 0.528274, Accuracy: 83.98\n",
      "Train Epoch: 11 [35840/50000 (80%)]\tLoss: 0.483016, Accuracy: 80.86\n",
      "Train Epoch: 11 [37120/50000 (82%)]\tLoss: 0.628558, Accuracy: 77.73\n",
      "Train Epoch: 11 [38400/50000 (85%)]\tLoss: 0.603388, Accuracy: 78.91\n",
      "Train Epoch: 11 [39680/50000 (88%)]\tLoss: 0.599237, Accuracy: 79.30\n",
      "Train Epoch: 11 [40960/50000 (91%)]\tLoss: 0.525268, Accuracy: 84.38\n",
      "Train Epoch: 11 [42240/50000 (94%)]\tLoss: 0.499605, Accuracy: 80.47\n",
      "Train Epoch: 11 [43520/50000 (97%)]\tLoss: 0.460721, Accuracy: 81.64\n",
      "Train Epoch: 11 [35000/50000 (99%)]\tLoss: 0.578306, Accuracy: 79.50\n",
      "\n",
      "Validation set: Average loss: 0.6624, Accuracy: 3921/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.59635305404663 s]\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 0.439022, Accuracy: 86.33\n",
      "Train Epoch: 12 [1280/50000 (3%)]\tLoss: 0.437028, Accuracy: 85.16\n",
      "Train Epoch: 12 [2560/50000 (6%)]\tLoss: 0.468789, Accuracy: 84.38\n",
      "Train Epoch: 12 [3840/50000 (9%)]\tLoss: 0.498633, Accuracy: 83.20\n",
      "Train Epoch: 12 [5120/50000 (11%)]\tLoss: 0.495222, Accuracy: 80.86\n",
      "Train Epoch: 12 [6400/50000 (14%)]\tLoss: 0.507810, Accuracy: 83.98\n",
      "Train Epoch: 12 [7680/50000 (17%)]\tLoss: 0.481941, Accuracy: 85.16\n",
      "Train Epoch: 12 [8960/50000 (20%)]\tLoss: 0.455875, Accuracy: 87.11\n",
      "Train Epoch: 12 [10240/50000 (23%)]\tLoss: 0.489217, Accuracy: 83.98\n",
      "Train Epoch: 12 [11520/50000 (26%)]\tLoss: 0.439362, Accuracy: 83.59\n",
      "Train Epoch: 12 [12800/50000 (28%)]\tLoss: 0.529870, Accuracy: 80.86\n",
      "Train Epoch: 12 [14080/50000 (31%)]\tLoss: 0.574006, Accuracy: 82.03\n",
      "Train Epoch: 12 [15360/50000 (34%)]\tLoss: 0.535585, Accuracy: 82.81\n",
      "Train Epoch: 12 [16640/50000 (37%)]\tLoss: 0.650319, Accuracy: 78.52\n",
      "Train Epoch: 12 [17920/50000 (40%)]\tLoss: 0.654486, Accuracy: 80.86\n",
      "Train Epoch: 12 [19200/50000 (43%)]\tLoss: 0.405691, Accuracy: 86.72\n",
      "Train Epoch: 12 [20480/50000 (45%)]\tLoss: 0.576404, Accuracy: 79.30\n",
      "Train Epoch: 12 [21760/50000 (48%)]\tLoss: 0.526881, Accuracy: 80.47\n",
      "Train Epoch: 12 [23040/50000 (51%)]\tLoss: 0.566046, Accuracy: 80.86\n",
      "Train Epoch: 12 [24320/50000 (54%)]\tLoss: 0.475626, Accuracy: 86.33\n",
      "Train Epoch: 12 [25600/50000 (57%)]\tLoss: 0.520789, Accuracy: 84.77\n",
      "Train Epoch: 12 [26880/50000 (60%)]\tLoss: 0.494854, Accuracy: 83.20\n",
      "Train Epoch: 12 [28160/50000 (62%)]\tLoss: 0.436972, Accuracy: 85.55\n",
      "Train Epoch: 12 [29440/50000 (65%)]\tLoss: 0.503007, Accuracy: 82.81\n",
      "Train Epoch: 12 [30720/50000 (68%)]\tLoss: 0.542426, Accuracy: 80.47\n",
      "Train Epoch: 12 [32000/50000 (71%)]\tLoss: 0.566142, Accuracy: 80.47\n",
      "Train Epoch: 12 [33280/50000 (74%)]\tLoss: 0.529155, Accuracy: 80.47\n",
      "Train Epoch: 12 [34560/50000 (77%)]\tLoss: 0.519204, Accuracy: 82.81\n",
      "Train Epoch: 12 [35840/50000 (80%)]\tLoss: 0.429503, Accuracy: 85.94\n",
      "Train Epoch: 12 [37120/50000 (82%)]\tLoss: 0.531968, Accuracy: 82.03\n",
      "Train Epoch: 12 [38400/50000 (85%)]\tLoss: 0.665269, Accuracy: 76.56\n",
      "Train Epoch: 12 [39680/50000 (88%)]\tLoss: 0.518874, Accuracy: 82.81\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 12 [40960/50000 (91%)]\tLoss: 0.580739, Accuracy: 81.64\n",
      "Train Epoch: 12 [42240/50000 (94%)]\tLoss: 0.464193, Accuracy: 84.38\n",
      "Train Epoch: 12 [43520/50000 (97%)]\tLoss: 0.564938, Accuracy: 81.64\n",
      "Train Epoch: 12 [35000/50000 (99%)]\tLoss: 0.528122, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.7271, Accuracy: 3820/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.441784381866455 s]\n",
      "\n",
      "Test set: Average loss: 0.7514, Accuracy: 7488/10000 (74.88%)\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 0.417827, Accuracy: 85.94\n",
      "Train Epoch: 13 [1280/50000 (3%)]\tLoss: 0.409086, Accuracy: 87.50\n",
      "Train Epoch: 13 [2560/50000 (6%)]\tLoss: 0.556519, Accuracy: 80.47\n",
      "Train Epoch: 13 [3840/50000 (9%)]\tLoss: 0.447745, Accuracy: 87.89\n",
      "Train Epoch: 13 [5120/50000 (11%)]\tLoss: 0.485698, Accuracy: 82.03\n",
      "Train Epoch: 13 [6400/50000 (14%)]\tLoss: 0.449324, Accuracy: 86.33\n",
      "Train Epoch: 13 [7680/50000 (17%)]\tLoss: 0.502645, Accuracy: 83.20\n",
      "Train Epoch: 13 [8960/50000 (20%)]\tLoss: 0.542426, Accuracy: 83.20\n",
      "Train Epoch: 13 [10240/50000 (23%)]\tLoss: 0.517737, Accuracy: 83.20\n",
      "Train Epoch: 13 [11520/50000 (26%)]\tLoss: 0.529828, Accuracy: 80.08\n",
      "Train Epoch: 13 [12800/50000 (28%)]\tLoss: 0.420525, Accuracy: 85.55\n",
      "Train Epoch: 13 [14080/50000 (31%)]\tLoss: 0.350645, Accuracy: 88.28\n",
      "Train Epoch: 13 [15360/50000 (34%)]\tLoss: 0.498583, Accuracy: 83.20\n",
      "Train Epoch: 13 [16640/50000 (37%)]\tLoss: 0.545214, Accuracy: 82.42\n",
      "Train Epoch: 13 [17920/50000 (40%)]\tLoss: 0.536864, Accuracy: 81.64\n",
      "Train Epoch: 13 [19200/50000 (43%)]\tLoss: 0.486924, Accuracy: 83.98\n",
      "Train Epoch: 13 [20480/50000 (45%)]\tLoss: 0.518716, Accuracy: 82.03\n",
      "Train Epoch: 13 [21760/50000 (48%)]\tLoss: 0.426408, Accuracy: 85.55\n",
      "Train Epoch: 13 [23040/50000 (51%)]\tLoss: 0.491417, Accuracy: 82.81\n",
      "Train Epoch: 13 [24320/50000 (54%)]\tLoss: 0.597885, Accuracy: 80.47\n",
      "Train Epoch: 13 [25600/50000 (57%)]\tLoss: 0.456899, Accuracy: 85.55\n",
      "Train Epoch: 13 [26880/50000 (60%)]\tLoss: 0.506730, Accuracy: 83.59\n",
      "Train Epoch: 13 [28160/50000 (62%)]\tLoss: 0.412012, Accuracy: 85.94\n",
      "Train Epoch: 13 [29440/50000 (65%)]\tLoss: 0.632349, Accuracy: 80.86\n",
      "Train Epoch: 13 [30720/50000 (68%)]\tLoss: 0.458718, Accuracy: 84.77\n",
      "Train Epoch: 13 [32000/50000 (71%)]\tLoss: 0.407904, Accuracy: 85.94\n",
      "Train Epoch: 13 [33280/50000 (74%)]\tLoss: 0.428304, Accuracy: 87.50\n",
      "Train Epoch: 13 [34560/50000 (77%)]\tLoss: 0.372216, Accuracy: 88.67\n",
      "Train Epoch: 13 [35840/50000 (80%)]\tLoss: 0.531423, Accuracy: 83.59\n",
      "Train Epoch: 13 [37120/50000 (82%)]\tLoss: 0.532590, Accuracy: 82.03\n",
      "Train Epoch: 13 [38400/50000 (85%)]\tLoss: 0.516488, Accuracy: 83.20\n",
      "Train Epoch: 13 [39680/50000 (88%)]\tLoss: 0.401882, Accuracy: 83.98\n",
      "Train Epoch: 13 [40960/50000 (91%)]\tLoss: 0.501682, Accuracy: 82.03\n",
      "Train Epoch: 13 [42240/50000 (94%)]\tLoss: 0.447645, Accuracy: 84.77\n",
      "Train Epoch: 13 [43520/50000 (97%)]\tLoss: 0.499978, Accuracy: 83.59\n",
      "Train Epoch: 13 [35000/50000 (99%)]\tLoss: 0.402891, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.6173, Accuracy: 3915/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.66534423828125 s]\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 0.469535, Accuracy: 82.81\n",
      "Train Epoch: 14 [1280/50000 (3%)]\tLoss: 0.435372, Accuracy: 85.16\n",
      "Train Epoch: 14 [2560/50000 (6%)]\tLoss: 0.412669, Accuracy: 86.33\n",
      "Train Epoch: 14 [3840/50000 (9%)]\tLoss: 0.434836, Accuracy: 85.16\n",
      "Train Epoch: 14 [5120/50000 (11%)]\tLoss: 0.398055, Accuracy: 89.06\n",
      "Train Epoch: 14 [6400/50000 (14%)]\tLoss: 0.397477, Accuracy: 88.28\n",
      "Train Epoch: 14 [7680/50000 (17%)]\tLoss: 0.381378, Accuracy: 86.72\n",
      "Train Epoch: 14 [8960/50000 (20%)]\tLoss: 0.425536, Accuracy: 85.55\n",
      "Train Epoch: 14 [10240/50000 (23%)]\tLoss: 0.401425, Accuracy: 85.94\n",
      "Train Epoch: 14 [11520/50000 (26%)]\tLoss: 0.387141, Accuracy: 86.33\n",
      "Train Epoch: 14 [12800/50000 (28%)]\tLoss: 0.467745, Accuracy: 85.94\n",
      "Train Epoch: 14 [14080/50000 (31%)]\tLoss: 0.480488, Accuracy: 81.64\n",
      "Train Epoch: 14 [15360/50000 (34%)]\tLoss: 0.629121, Accuracy: 79.30\n",
      "Train Epoch: 14 [16640/50000 (37%)]\tLoss: 0.534149, Accuracy: 81.25\n",
      "Train Epoch: 14 [17920/50000 (40%)]\tLoss: 0.363306, Accuracy: 88.28\n",
      "Train Epoch: 14 [19200/50000 (43%)]\tLoss: 0.500670, Accuracy: 85.55\n",
      "Train Epoch: 14 [20480/50000 (45%)]\tLoss: 0.405232, Accuracy: 84.77\n",
      "Train Epoch: 14 [21760/50000 (48%)]\tLoss: 0.494942, Accuracy: 82.03\n",
      "Train Epoch: 14 [23040/50000 (51%)]\tLoss: 0.448962, Accuracy: 85.55\n",
      "Train Epoch: 14 [24320/50000 (54%)]\tLoss: 0.598083, Accuracy: 78.12\n",
      "Train Epoch: 14 [25600/50000 (57%)]\tLoss: 0.585232, Accuracy: 80.86\n",
      "Train Epoch: 14 [26880/50000 (60%)]\tLoss: 0.479876, Accuracy: 82.81\n",
      "Train Epoch: 14 [28160/50000 (62%)]\tLoss: 0.472171, Accuracy: 85.55\n",
      "Train Epoch: 14 [29440/50000 (65%)]\tLoss: 0.604603, Accuracy: 79.30\n",
      "Train Epoch: 14 [30720/50000 (68%)]\tLoss: 0.504661, Accuracy: 81.25\n",
      "Train Epoch: 14 [32000/50000 (71%)]\tLoss: 0.497818, Accuracy: 82.03\n",
      "Train Epoch: 14 [33280/50000 (74%)]\tLoss: 0.392426, Accuracy: 85.55\n",
      "Train Epoch: 14 [34560/50000 (77%)]\tLoss: 0.451549, Accuracy: 83.59\n",
      "Train Epoch: 14 [35840/50000 (80%)]\tLoss: 0.490809, Accuracy: 82.81\n",
      "Train Epoch: 14 [37120/50000 (82%)]\tLoss: 0.494132, Accuracy: 84.38\n",
      "Train Epoch: 14 [38400/50000 (85%)]\tLoss: 0.444736, Accuracy: 83.98\n",
      "Train Epoch: 14 [39680/50000 (88%)]\tLoss: 0.449464, Accuracy: 85.16\n",
      "Train Epoch: 14 [40960/50000 (91%)]\tLoss: 0.481155, Accuracy: 83.98\n",
      "Train Epoch: 14 [42240/50000 (94%)]\tLoss: 0.571912, Accuracy: 79.69\n",
      "Train Epoch: 14 [43520/50000 (97%)]\tLoss: 0.388695, Accuracy: 86.72\n",
      "Train Epoch: 14 [35000/50000 (99%)]\tLoss: 0.561295, Accuracy: 79.50\n",
      "\n",
      "Validation set: Average loss: 0.5697, Accuracy: 4035/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[36.45839762687683 s]\n",
      "\n",
      "Test set: Average loss: 0.5987, Accuracy: 7988/10000 (79.88%)\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 0.427231, Accuracy: 85.55\n",
      "Train Epoch: 15 [1280/50000 (3%)]\tLoss: 0.425835, Accuracy: 85.16\n",
      "Train Epoch: 15 [2560/50000 (6%)]\tLoss: 0.450115, Accuracy: 84.38\n",
      "Train Epoch: 15 [3840/50000 (9%)]\tLoss: 0.435746, Accuracy: 85.94\n",
      "Train Epoch: 15 [5120/50000 (11%)]\tLoss: 0.410871, Accuracy: 85.55\n",
      "Train Epoch: 15 [6400/50000 (14%)]\tLoss: 0.455763, Accuracy: 85.94\n",
      "Train Epoch: 15 [7680/50000 (17%)]\tLoss: 0.491823, Accuracy: 83.59\n",
      "Train Epoch: 15 [8960/50000 (20%)]\tLoss: 0.439533, Accuracy: 83.59\n",
      "Train Epoch: 15 [10240/50000 (23%)]\tLoss: 0.577385, Accuracy: 81.25\n",
      "Train Epoch: 15 [11520/50000 (26%)]\tLoss: 0.468384, Accuracy: 83.20\n",
      "Train Epoch: 15 [12800/50000 (28%)]\tLoss: 0.550147, Accuracy: 83.98\n",
      "Train Epoch: 15 [14080/50000 (31%)]\tLoss: 0.510305, Accuracy: 85.16\n",
      "Train Epoch: 15 [15360/50000 (34%)]\tLoss: 0.509516, Accuracy: 83.59\n",
      "Train Epoch: 15 [16640/50000 (37%)]\tLoss: 0.421178, Accuracy: 84.77\n",
      "Train Epoch: 15 [17920/50000 (40%)]\tLoss: 0.540833, Accuracy: 79.69\n",
      "Train Epoch: 15 [19200/50000 (43%)]\tLoss: 0.494979, Accuracy: 83.59\n",
      "Train Epoch: 15 [20480/50000 (45%)]\tLoss: 0.435422, Accuracy: 85.94\n",
      "Train Epoch: 15 [21760/50000 (48%)]\tLoss: 0.366155, Accuracy: 87.50\n",
      "Train Epoch: 15 [23040/50000 (51%)]\tLoss: 0.497008, Accuracy: 83.20\n",
      "Train Epoch: 15 [24320/50000 (54%)]\tLoss: 0.518899, Accuracy: 83.98\n",
      "Train Epoch: 15 [25600/50000 (57%)]\tLoss: 0.480431, Accuracy: 81.64\n",
      "Train Epoch: 15 [26880/50000 (60%)]\tLoss: 0.445693, Accuracy: 86.72\n",
      "Train Epoch: 15 [28160/50000 (62%)]\tLoss: 0.407524, Accuracy: 86.33\n",
      "Train Epoch: 15 [29440/50000 (65%)]\tLoss: 0.559676, Accuracy: 82.03\n",
      "Train Epoch: 15 [30720/50000 (68%)]\tLoss: 0.461330, Accuracy: 86.72\n",
      "Train Epoch: 15 [32000/50000 (71%)]\tLoss: 0.434381, Accuracy: 85.94\n",
      "Train Epoch: 15 [33280/50000 (74%)]\tLoss: 0.422446, Accuracy: 84.38\n",
      "Train Epoch: 15 [34560/50000 (77%)]\tLoss: 0.450909, Accuracy: 83.98\n",
      "Train Epoch: 15 [35840/50000 (80%)]\tLoss: 0.473898, Accuracy: 81.64\n",
      "Train Epoch: 15 [37120/50000 (82%)]\tLoss: 0.478727, Accuracy: 81.64\n",
      "Train Epoch: 15 [38400/50000 (85%)]\tLoss: 0.462238, Accuracy: 83.98\n",
      "Train Epoch: 15 [39680/50000 (88%)]\tLoss: 0.501202, Accuracy: 83.20\n",
      "Train Epoch: 15 [40960/50000 (91%)]\tLoss: 0.527611, Accuracy: 82.81\n",
      "Train Epoch: 15 [42240/50000 (94%)]\tLoss: 0.535201, Accuracy: 82.03\n",
      "Train Epoch: 15 [43520/50000 (97%)]\tLoss: 0.495791, Accuracy: 83.98\n",
      "Train Epoch: 15 [35000/50000 (99%)]\tLoss: 0.538665, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.6435, Accuracy: 3944/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.621389865875244 s]\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 0.489690, Accuracy: 82.81\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 16 [1280/50000 (3%)]\tLoss: 0.483176, Accuracy: 82.81\n",
      "Train Epoch: 16 [2560/50000 (6%)]\tLoss: 0.378925, Accuracy: 86.33\n",
      "Train Epoch: 16 [3840/50000 (9%)]\tLoss: 0.577422, Accuracy: 78.91\n",
      "Train Epoch: 16 [5120/50000 (11%)]\tLoss: 0.377858, Accuracy: 89.06\n",
      "Train Epoch: 16 [6400/50000 (14%)]\tLoss: 0.356640, Accuracy: 87.11\n",
      "Train Epoch: 16 [7680/50000 (17%)]\tLoss: 0.506118, Accuracy: 85.16\n",
      "Train Epoch: 16 [8960/50000 (20%)]\tLoss: 0.438998, Accuracy: 85.55\n",
      "Train Epoch: 16 [10240/50000 (23%)]\tLoss: 0.365598, Accuracy: 85.55\n",
      "Train Epoch: 16 [11520/50000 (26%)]\tLoss: 0.463216, Accuracy: 85.55\n",
      "Train Epoch: 16 [12800/50000 (28%)]\tLoss: 0.456161, Accuracy: 83.59\n",
      "Train Epoch: 16 [14080/50000 (31%)]\tLoss: 0.428328, Accuracy: 83.59\n",
      "Train Epoch: 16 [15360/50000 (34%)]\tLoss: 0.410391, Accuracy: 88.67\n",
      "Train Epoch: 16 [16640/50000 (37%)]\tLoss: 0.421642, Accuracy: 85.16\n",
      "Train Epoch: 16 [17920/50000 (40%)]\tLoss: 0.485967, Accuracy: 82.03\n",
      "Train Epoch: 16 [19200/50000 (43%)]\tLoss: 0.460820, Accuracy: 82.42\n",
      "Train Epoch: 16 [20480/50000 (45%)]\tLoss: 0.640296, Accuracy: 81.25\n",
      "Train Epoch: 16 [21760/50000 (48%)]\tLoss: 0.570178, Accuracy: 80.86\n",
      "Train Epoch: 16 [23040/50000 (51%)]\tLoss: 0.464156, Accuracy: 85.16\n",
      "Train Epoch: 16 [24320/50000 (54%)]\tLoss: 0.462690, Accuracy: 83.20\n",
      "Train Epoch: 16 [25600/50000 (57%)]\tLoss: 0.445252, Accuracy: 83.20\n",
      "Train Epoch: 16 [26880/50000 (60%)]\tLoss: 0.309759, Accuracy: 87.50\n",
      "Train Epoch: 16 [28160/50000 (62%)]\tLoss: 0.403561, Accuracy: 85.16\n",
      "Train Epoch: 16 [29440/50000 (65%)]\tLoss: 0.488271, Accuracy: 83.98\n",
      "Train Epoch: 16 [30720/50000 (68%)]\tLoss: 0.450632, Accuracy: 86.33\n",
      "Train Epoch: 16 [32000/50000 (71%)]\tLoss: 0.380492, Accuracy: 87.11\n",
      "Train Epoch: 16 [33280/50000 (74%)]\tLoss: 0.588150, Accuracy: 80.86\n",
      "Train Epoch: 16 [34560/50000 (77%)]\tLoss: 0.463273, Accuracy: 82.03\n",
      "Train Epoch: 16 [35840/50000 (80%)]\tLoss: 0.426350, Accuracy: 85.55\n",
      "Train Epoch: 16 [37120/50000 (82%)]\tLoss: 0.440632, Accuracy: 84.38\n",
      "Train Epoch: 16 [38400/50000 (85%)]\tLoss: 0.426941, Accuracy: 85.94\n",
      "Train Epoch: 16 [39680/50000 (88%)]\tLoss: 0.419592, Accuracy: 85.55\n",
      "Train Epoch: 16 [40960/50000 (91%)]\tLoss: 0.416508, Accuracy: 85.55\n",
      "Train Epoch: 16 [42240/50000 (94%)]\tLoss: 0.472109, Accuracy: 85.94\n",
      "Train Epoch: 16 [43520/50000 (97%)]\tLoss: 0.444190, Accuracy: 85.94\n",
      "Train Epoch: 16 [35000/50000 (99%)]\tLoss: 0.444559, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.7917, Accuracy: 3716/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[36.500861167907715 s]\n",
      "\n",
      "Test set: Average loss: 0.8161, Accuracy: 7393/10000 (73.93%)\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 0.459907, Accuracy: 85.55\n",
      "Train Epoch: 17 [1280/50000 (3%)]\tLoss: 0.470045, Accuracy: 83.98\n",
      "Train Epoch: 17 [2560/50000 (6%)]\tLoss: 0.548871, Accuracy: 80.47\n",
      "Train Epoch: 17 [3840/50000 (9%)]\tLoss: 0.394006, Accuracy: 86.72\n",
      "Train Epoch: 17 [5120/50000 (11%)]\tLoss: 0.504149, Accuracy: 84.77\n",
      "Train Epoch: 17 [6400/50000 (14%)]\tLoss: 0.471278, Accuracy: 83.98\n",
      "Train Epoch: 17 [7680/50000 (17%)]\tLoss: 0.470703, Accuracy: 83.98\n",
      "Train Epoch: 17 [8960/50000 (20%)]\tLoss: 0.393738, Accuracy: 86.72\n",
      "Train Epoch: 17 [10240/50000 (23%)]\tLoss: 0.490509, Accuracy: 82.81\n",
      "Train Epoch: 17 [11520/50000 (26%)]\tLoss: 0.493416, Accuracy: 83.20\n",
      "Train Epoch: 17 [12800/50000 (28%)]\tLoss: 0.524430, Accuracy: 82.03\n",
      "Train Epoch: 17 [14080/50000 (31%)]\tLoss: 0.443832, Accuracy: 84.77\n",
      "Train Epoch: 17 [15360/50000 (34%)]\tLoss: 0.440365, Accuracy: 85.55\n",
      "Train Epoch: 17 [16640/50000 (37%)]\tLoss: 0.441751, Accuracy: 84.77\n",
      "Train Epoch: 17 [17920/50000 (40%)]\tLoss: 0.526503, Accuracy: 83.20\n",
      "Train Epoch: 17 [19200/50000 (43%)]\tLoss: 0.548877, Accuracy: 82.42\n",
      "Train Epoch: 17 [20480/50000 (45%)]\tLoss: 0.519544, Accuracy: 82.42\n",
      "Train Epoch: 17 [21760/50000 (48%)]\tLoss: 0.519424, Accuracy: 82.81\n",
      "Train Epoch: 17 [23040/50000 (51%)]\tLoss: 0.492702, Accuracy: 85.16\n",
      "Train Epoch: 17 [24320/50000 (54%)]\tLoss: 0.427262, Accuracy: 85.94\n",
      "Train Epoch: 17 [25600/50000 (57%)]\tLoss: 0.539360, Accuracy: 81.64\n",
      "Train Epoch: 17 [26880/50000 (60%)]\tLoss: 0.408029, Accuracy: 87.89\n",
      "Train Epoch: 17 [28160/50000 (62%)]\tLoss: 0.440329, Accuracy: 84.38\n",
      "Train Epoch: 17 [29440/50000 (65%)]\tLoss: 0.403117, Accuracy: 86.72\n",
      "Train Epoch: 17 [30720/50000 (68%)]\tLoss: 0.338660, Accuracy: 90.23\n",
      "Train Epoch: 17 [32000/50000 (71%)]\tLoss: 0.513608, Accuracy: 86.72\n",
      "Train Epoch: 17 [33280/50000 (74%)]\tLoss: 0.510003, Accuracy: 82.42\n",
      "Train Epoch: 17 [34560/50000 (77%)]\tLoss: 0.500428, Accuracy: 81.25\n",
      "Train Epoch: 17 [35840/50000 (80%)]\tLoss: 0.349653, Accuracy: 89.06\n",
      "Train Epoch: 17 [37120/50000 (82%)]\tLoss: 0.424995, Accuracy: 87.11\n",
      "Train Epoch: 17 [38400/50000 (85%)]\tLoss: 0.520067, Accuracy: 82.03\n",
      "Train Epoch: 17 [39680/50000 (88%)]\tLoss: 0.488782, Accuracy: 84.77\n",
      "Train Epoch: 17 [40960/50000 (91%)]\tLoss: 0.423348, Accuracy: 86.72\n",
      "Train Epoch: 17 [42240/50000 (94%)]\tLoss: 0.423510, Accuracy: 84.38\n",
      "Train Epoch: 17 [43520/50000 (97%)]\tLoss: 0.409551, Accuracy: 88.28\n",
      "Train Epoch: 17 [35000/50000 (99%)]\tLoss: 0.533411, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 1.0268, Accuracy: 3459/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[39.615416526794434 s]\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.481362, Accuracy: 85.55\n",
      "Train Epoch: 18 [1280/50000 (3%)]\tLoss: 0.497029, Accuracy: 81.25\n",
      "Train Epoch: 18 [2560/50000 (6%)]\tLoss: 0.501824, Accuracy: 84.38\n",
      "Train Epoch: 18 [3840/50000 (9%)]\tLoss: 0.409045, Accuracy: 87.50\n",
      "Train Epoch: 18 [5120/50000 (11%)]\tLoss: 0.322125, Accuracy: 88.67\n",
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      "Train Epoch: 18 [7680/50000 (17%)]\tLoss: 0.557502, Accuracy: 80.08\n",
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      "Train Epoch: 18 [10240/50000 (23%)]\tLoss: 0.391029, Accuracy: 87.50\n",
      "Train Epoch: 18 [11520/50000 (26%)]\tLoss: 0.406696, Accuracy: 85.55\n",
      "Train Epoch: 18 [12800/50000 (28%)]\tLoss: 0.595201, Accuracy: 82.03\n",
      "Train Epoch: 18 [14080/50000 (31%)]\tLoss: 0.436570, Accuracy: 85.16\n",
      "Train Epoch: 18 [15360/50000 (34%)]\tLoss: 0.374959, Accuracy: 85.94\n",
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      "Train Epoch: 18 [17920/50000 (40%)]\tLoss: 0.580832, Accuracy: 84.38\n",
      "Train Epoch: 18 [19200/50000 (43%)]\tLoss: 0.487251, Accuracy: 83.20\n",
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      "Train Epoch: 18 [21760/50000 (48%)]\tLoss: 0.341738, Accuracy: 90.23\n",
      "Train Epoch: 18 [23040/50000 (51%)]\tLoss: 0.481659, Accuracy: 84.38\n",
      "Train Epoch: 18 [24320/50000 (54%)]\tLoss: 0.382434, Accuracy: 87.89\n",
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      "Train Epoch: 18 [26880/50000 (60%)]\tLoss: 0.530944, Accuracy: 82.03\n",
      "Train Epoch: 18 [28160/50000 (62%)]\tLoss: 0.442052, Accuracy: 84.38\n",
      "Train Epoch: 18 [29440/50000 (65%)]\tLoss: 0.500814, Accuracy: 81.25\n",
      "Train Epoch: 18 [30720/50000 (68%)]\tLoss: 0.555620, Accuracy: 81.25\n",
      "Train Epoch: 18 [32000/50000 (71%)]\tLoss: 0.522410, Accuracy: 84.77\n",
      "Train Epoch: 18 [33280/50000 (74%)]\tLoss: 0.419003, Accuracy: 85.55\n",
      "Train Epoch: 18 [34560/50000 (77%)]\tLoss: 0.443125, Accuracy: 83.20\n",
      "Train Epoch: 18 [35840/50000 (80%)]\tLoss: 0.420306, Accuracy: 84.77\n",
      "Train Epoch: 18 [37120/50000 (82%)]\tLoss: 0.505298, Accuracy: 85.94\n",
      "Train Epoch: 18 [38400/50000 (85%)]\tLoss: 0.503017, Accuracy: 83.98\n",
      "Train Epoch: 18 [39680/50000 (88%)]\tLoss: 0.390298, Accuracy: 86.33\n",
      "Train Epoch: 18 [40960/50000 (91%)]\tLoss: 0.496503, Accuracy: 85.55\n",
      "Train Epoch: 18 [42240/50000 (94%)]\tLoss: 0.465370, Accuracy: 85.55\n",
      "Train Epoch: 18 [43520/50000 (97%)]\tLoss: 0.449413, Accuracy: 85.16\n",
      "Train Epoch: 18 [35000/50000 (99%)]\tLoss: 0.396234, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.8623, Accuracy: 3776/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[36.40884518623352 s]\n",
      "\n",
      "Test set: Average loss: 0.8791, Accuracy: 7471/10000 (74.71%)\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 0.367584, Accuracy: 84.77\n",
      "Train Epoch: 19 [1280/50000 (3%)]\tLoss: 0.456672, Accuracy: 85.55\n",
      "Train Epoch: 19 [2560/50000 (6%)]\tLoss: 0.487401, Accuracy: 83.98\n",
      "Train Epoch: 19 [3840/50000 (9%)]\tLoss: 0.511579, Accuracy: 81.25\n",
      "Train Epoch: 19 [5120/50000 (11%)]\tLoss: 0.402225, Accuracy: 86.33\n",
      "Train Epoch: 19 [6400/50000 (14%)]\tLoss: 0.378828, Accuracy: 87.11\n",
      "Train Epoch: 19 [7680/50000 (17%)]\tLoss: 0.450056, Accuracy: 83.59\n",
      "Train Epoch: 19 [8960/50000 (20%)]\tLoss: 0.498214, Accuracy: 82.81\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 19 [10240/50000 (23%)]\tLoss: 0.371770, Accuracy: 87.50\n",
      "Train Epoch: 19 [11520/50000 (26%)]\tLoss: 0.531536, Accuracy: 83.20\n",
      "Train Epoch: 19 [12800/50000 (28%)]\tLoss: 0.392761, Accuracy: 87.11\n",
      "Train Epoch: 19 [14080/50000 (31%)]\tLoss: 0.388224, Accuracy: 86.72\n",
      "Train Epoch: 19 [15360/50000 (34%)]\tLoss: 0.450161, Accuracy: 84.38\n",
      "Train Epoch: 19 [16640/50000 (37%)]\tLoss: 0.420607, Accuracy: 85.16\n",
      "Train Epoch: 19 [17920/50000 (40%)]\tLoss: 0.488286, Accuracy: 82.03\n",
      "Train Epoch: 19 [19200/50000 (43%)]\tLoss: 0.523002, Accuracy: 82.42\n",
      "Train Epoch: 19 [20480/50000 (45%)]\tLoss: 0.316246, Accuracy: 89.45\n",
      "Train Epoch: 19 [21760/50000 (48%)]\tLoss: 0.440568, Accuracy: 85.55\n",
      "Train Epoch: 19 [23040/50000 (51%)]\tLoss: 0.383295, Accuracy: 88.28\n",
      "Train Epoch: 19 [24320/50000 (54%)]\tLoss: 0.537066, Accuracy: 83.59\n",
      "Train Epoch: 19 [25600/50000 (57%)]\tLoss: 0.430065, Accuracy: 85.94\n",
      "Train Epoch: 19 [26880/50000 (60%)]\tLoss: 0.370623, Accuracy: 87.11\n",
      "Train Epoch: 19 [28160/50000 (62%)]\tLoss: 0.464852, Accuracy: 83.98\n",
      "Train Epoch: 19 [29440/50000 (65%)]\tLoss: 0.574241, Accuracy: 80.08\n",
      "Train Epoch: 19 [30720/50000 (68%)]\tLoss: 0.478876, Accuracy: 83.98\n",
      "Train Epoch: 19 [32000/50000 (71%)]\tLoss: 0.495559, Accuracy: 83.59\n",
      "Train Epoch: 19 [33280/50000 (74%)]\tLoss: 0.396194, Accuracy: 85.94\n",
      "Train Epoch: 19 [34560/50000 (77%)]\tLoss: 0.443220, Accuracy: 87.11\n",
      "Train Epoch: 19 [35840/50000 (80%)]\tLoss: 0.392344, Accuracy: 87.11\n",
      "Train Epoch: 19 [37120/50000 (82%)]\tLoss: 0.380428, Accuracy: 87.50\n",
      "Train Epoch: 19 [38400/50000 (85%)]\tLoss: 0.418905, Accuracy: 85.55\n",
      "Train Epoch: 19 [39680/50000 (88%)]\tLoss: 0.499728, Accuracy: 85.16\n",
      "Train Epoch: 19 [40960/50000 (91%)]\tLoss: 0.447446, Accuracy: 83.98\n",
      "Train Epoch: 19 [42240/50000 (94%)]\tLoss: 0.360571, Accuracy: 88.28\n",
      "Train Epoch: 19 [43520/50000 (97%)]\tLoss: 0.561262, Accuracy: 82.42\n",
      "Train Epoch: 19 [35000/50000 (99%)]\tLoss: 0.434581, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.6770, Accuracy: 3939/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.51133489608765 s]\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 0.494803, Accuracy: 82.03\n",
      "Train Epoch: 20 [1280/50000 (3%)]\tLoss: 0.429813, Accuracy: 83.98\n",
      "Train Epoch: 20 [2560/50000 (6%)]\tLoss: 0.500861, Accuracy: 82.81\n",
      "Train Epoch: 20 [3840/50000 (9%)]\tLoss: 0.462597, Accuracy: 82.42\n",
      "Train Epoch: 20 [5120/50000 (11%)]\tLoss: 0.441081, Accuracy: 86.33\n",
      "Train Epoch: 20 [6400/50000 (14%)]\tLoss: 0.524740, Accuracy: 82.42\n",
      "Train Epoch: 20 [7680/50000 (17%)]\tLoss: 0.444778, Accuracy: 84.38\n",
      "Train Epoch: 20 [8960/50000 (20%)]\tLoss: 0.469338, Accuracy: 82.03\n",
      "Train Epoch: 20 [10240/50000 (23%)]\tLoss: 0.430862, Accuracy: 85.55\n",
      "Train Epoch: 20 [11520/50000 (26%)]\tLoss: 0.399971, Accuracy: 87.50\n",
      "Train Epoch: 20 [12800/50000 (28%)]\tLoss: 0.469058, Accuracy: 83.59\n",
      "Train Epoch: 20 [14080/50000 (31%)]\tLoss: 0.505022, Accuracy: 83.20\n",
      "Train Epoch: 20 [15360/50000 (34%)]\tLoss: 0.450117, Accuracy: 85.55\n",
      "Train Epoch: 20 [16640/50000 (37%)]\tLoss: 0.400168, Accuracy: 86.33\n",
      "Train Epoch: 20 [17920/50000 (40%)]\tLoss: 0.430679, Accuracy: 87.50\n",
      "Train Epoch: 20 [19200/50000 (43%)]\tLoss: 0.316702, Accuracy: 91.02\n",
      "Train Epoch: 20 [20480/50000 (45%)]\tLoss: 0.457928, Accuracy: 85.16\n",
      "Train Epoch: 20 [21760/50000 (48%)]\tLoss: 0.352393, Accuracy: 87.50\n",
      "Train Epoch: 20 [23040/50000 (51%)]\tLoss: 0.251093, Accuracy: 90.23\n",
      "Train Epoch: 20 [24320/50000 (54%)]\tLoss: 0.432578, Accuracy: 82.81\n",
      "Train Epoch: 20 [25600/50000 (57%)]\tLoss: 0.371547, Accuracy: 88.67\n",
      "Train Epoch: 20 [26880/50000 (60%)]\tLoss: 0.474357, Accuracy: 83.20\n",
      "Train Epoch: 20 [28160/50000 (62%)]\tLoss: 0.440722, Accuracy: 86.72\n",
      "Train Epoch: 20 [29440/50000 (65%)]\tLoss: 0.445363, Accuracy: 85.94\n",
      "Train Epoch: 20 [30720/50000 (68%)]\tLoss: 0.406744, Accuracy: 85.94\n",
      "Train Epoch: 20 [32000/50000 (71%)]\tLoss: 0.506430, Accuracy: 82.81\n",
      "Train Epoch: 20 [33280/50000 (74%)]\tLoss: 0.448395, Accuracy: 86.33\n",
      "Train Epoch: 20 [34560/50000 (77%)]\tLoss: 0.485823, Accuracy: 81.64\n",
      "Train Epoch: 20 [35840/50000 (80%)]\tLoss: 0.491373, Accuracy: 85.55\n",
      "Train Epoch: 20 [37120/50000 (82%)]\tLoss: 0.378522, Accuracy: 84.77\n",
      "Train Epoch: 20 [38400/50000 (85%)]\tLoss: 0.461400, Accuracy: 85.55\n",
      "Train Epoch: 20 [39680/50000 (88%)]\tLoss: 0.387417, Accuracy: 85.16\n",
      "Train Epoch: 20 [40960/50000 (91%)]\tLoss: 0.438611, Accuracy: 84.77\n",
      "Train Epoch: 20 [42240/50000 (94%)]\tLoss: 0.426379, Accuracy: 83.59\n",
      "Train Epoch: 20 [43520/50000 (97%)]\tLoss: 0.489980, Accuracy: 82.03\n",
      "Train Epoch: 20 [35000/50000 (99%)]\tLoss: 0.540624, Accuracy: 83.50\n",
      "\n",
      "Validation set: Average loss: 0.8359, Accuracy: 3700/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[36.40083026885986 s]\n",
      "\n",
      "Test set: Average loss: 0.8829, Accuracy: 7301/10000 (73.01%)\n",
      "\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 0.321385, Accuracy: 89.84\n",
      "Train Epoch: 21 [1280/50000 (3%)]\tLoss: 0.460736, Accuracy: 85.16\n",
      "Train Epoch: 21 [2560/50000 (6%)]\tLoss: 0.426517, Accuracy: 84.38\n",
      "Train Epoch: 21 [3840/50000 (9%)]\tLoss: 0.474563, Accuracy: 84.38\n",
      "Train Epoch: 21 [5120/50000 (11%)]\tLoss: 0.419890, Accuracy: 85.55\n",
      "Train Epoch: 21 [6400/50000 (14%)]\tLoss: 0.454070, Accuracy: 84.77\n",
      "Train Epoch: 21 [7680/50000 (17%)]\tLoss: 0.398023, Accuracy: 84.77\n",
      "Train Epoch: 21 [8960/50000 (20%)]\tLoss: 0.462231, Accuracy: 82.81\n",
      "Train Epoch: 21 [10240/50000 (23%)]\tLoss: 0.362427, Accuracy: 87.11\n",
      "Train Epoch: 21 [11520/50000 (26%)]\tLoss: 0.407810, Accuracy: 85.16\n",
      "Train Epoch: 21 [12800/50000 (28%)]\tLoss: 0.551884, Accuracy: 83.98\n",
      "Train Epoch: 21 [14080/50000 (31%)]\tLoss: 0.411918, Accuracy: 83.20\n",
      "Train Epoch: 21 [15360/50000 (34%)]\tLoss: 0.420906, Accuracy: 84.77\n",
      "Train Epoch: 21 [16640/50000 (37%)]\tLoss: 0.407114, Accuracy: 86.72\n",
      "Train Epoch: 21 [17920/50000 (40%)]\tLoss: 0.421286, Accuracy: 86.33\n",
      "Train Epoch: 21 [19200/50000 (43%)]\tLoss: 0.511735, Accuracy: 83.20\n",
      "Train Epoch: 21 [20480/50000 (45%)]\tLoss: 0.373387, Accuracy: 86.72\n",
      "Train Epoch: 21 [21760/50000 (48%)]\tLoss: 0.393298, Accuracy: 85.55\n",
      "Train Epoch: 21 [23040/50000 (51%)]\tLoss: 0.375106, Accuracy: 87.50\n",
      "Train Epoch: 21 [24320/50000 (54%)]\tLoss: 0.432608, Accuracy: 85.55\n",
      "Train Epoch: 21 [25600/50000 (57%)]\tLoss: 0.482030, Accuracy: 83.59\n",
      "Train Epoch: 21 [26880/50000 (60%)]\tLoss: 0.459954, Accuracy: 81.25\n",
      "Train Epoch: 21 [28160/50000 (62%)]\tLoss: 0.486269, Accuracy: 84.77\n",
      "Train Epoch: 21 [29440/50000 (65%)]\tLoss: 0.357251, Accuracy: 88.28\n",
      "Train Epoch: 21 [30720/50000 (68%)]\tLoss: 0.356560, Accuracy: 89.45\n",
      "Train Epoch: 21 [32000/50000 (71%)]\tLoss: 0.452020, Accuracy: 83.59\n",
      "Train Epoch: 21 [33280/50000 (74%)]\tLoss: 0.404259, Accuracy: 87.11\n",
      "Train Epoch: 21 [34560/50000 (77%)]\tLoss: 0.457122, Accuracy: 84.38\n",
      "Train Epoch: 21 [35840/50000 (80%)]\tLoss: 0.413679, Accuracy: 84.38\n",
      "Train Epoch: 21 [37120/50000 (82%)]\tLoss: 0.379288, Accuracy: 89.06\n",
      "Train Epoch: 21 [38400/50000 (85%)]\tLoss: 0.378450, Accuracy: 86.72\n",
      "Train Epoch: 21 [39680/50000 (88%)]\tLoss: 0.403861, Accuracy: 86.72\n",
      "Train Epoch: 21 [40960/50000 (91%)]\tLoss: 0.349486, Accuracy: 87.89\n",
      "Train Epoch: 21 [42240/50000 (94%)]\tLoss: 0.388549, Accuracy: 89.06\n",
      "Train Epoch: 21 [43520/50000 (97%)]\tLoss: 0.534644, Accuracy: 83.59\n",
      "Train Epoch: 21 [35000/50000 (99%)]\tLoss: 0.507544, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.6936, Accuracy: 3933/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.650506019592285 s]\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 0.409981, Accuracy: 87.11\n",
      "Train Epoch: 22 [1280/50000 (3%)]\tLoss: 0.387986, Accuracy: 85.94\n",
      "Train Epoch: 22 [2560/50000 (6%)]\tLoss: 0.462373, Accuracy: 85.16\n",
      "Train Epoch: 22 [3840/50000 (9%)]\tLoss: 0.440558, Accuracy: 87.11\n",
      "Train Epoch: 22 [5120/50000 (11%)]\tLoss: 0.512918, Accuracy: 83.59\n",
      "Train Epoch: 22 [6400/50000 (14%)]\tLoss: 0.440040, Accuracy: 86.33\n",
      "Train Epoch: 22 [7680/50000 (17%)]\tLoss: 0.329560, Accuracy: 90.23\n",
      "Train Epoch: 22 [8960/50000 (20%)]\tLoss: 0.449305, Accuracy: 86.33\n",
      "Train Epoch: 22 [10240/50000 (23%)]\tLoss: 0.325283, Accuracy: 90.23\n",
      "Train Epoch: 22 [11520/50000 (26%)]\tLoss: 0.371493, Accuracy: 86.72\n",
      "Train Epoch: 22 [12800/50000 (28%)]\tLoss: 0.520968, Accuracy: 80.47\n",
      "Train Epoch: 22 [14080/50000 (31%)]\tLoss: 0.394325, Accuracy: 87.11\n",
      "Train Epoch: 22 [15360/50000 (34%)]\tLoss: 0.487148, Accuracy: 82.81\n",
      "Train Epoch: 22 [16640/50000 (37%)]\tLoss: 0.443742, Accuracy: 85.94\n",
      "Train Epoch: 22 [17920/50000 (40%)]\tLoss: 0.411635, Accuracy: 86.72\n",
      "Train Epoch: 22 [19200/50000 (43%)]\tLoss: 0.478470, Accuracy: 83.20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 22 [20480/50000 (45%)]\tLoss: 0.425796, Accuracy: 84.38\n",
      "Train Epoch: 22 [21760/50000 (48%)]\tLoss: 0.419174, Accuracy: 85.94\n",
      "Train Epoch: 22 [23040/50000 (51%)]\tLoss: 0.408738, Accuracy: 85.94\n",
      "Train Epoch: 22 [24320/50000 (54%)]\tLoss: 0.467688, Accuracy: 83.98\n",
      "Train Epoch: 22 [25600/50000 (57%)]\tLoss: 0.384958, Accuracy: 87.50\n",
      "Train Epoch: 22 [26880/50000 (60%)]\tLoss: 0.442602, Accuracy: 83.59\n",
      "Train Epoch: 22 [28160/50000 (62%)]\tLoss: 0.388100, Accuracy: 86.33\n",
      "Train Epoch: 22 [29440/50000 (65%)]\tLoss: 0.432566, Accuracy: 85.94\n",
      "Train Epoch: 22 [30720/50000 (68%)]\tLoss: 0.396259, Accuracy: 87.50\n",
      "Train Epoch: 22 [32000/50000 (71%)]\tLoss: 0.358817, Accuracy: 89.06\n",
      "Train Epoch: 22 [33280/50000 (74%)]\tLoss: 0.404589, Accuracy: 85.94\n",
      "Train Epoch: 22 [34560/50000 (77%)]\tLoss: 0.398503, Accuracy: 84.77\n",
      "Train Epoch: 22 [35840/50000 (80%)]\tLoss: 0.366166, Accuracy: 85.94\n",
      "Train Epoch: 22 [37120/50000 (82%)]\tLoss: 0.300754, Accuracy: 91.80\n",
      "Train Epoch: 22 [38400/50000 (85%)]\tLoss: 0.424155, Accuracy: 85.94\n",
      "Train Epoch: 22 [39680/50000 (88%)]\tLoss: 0.485444, Accuracy: 82.42\n",
      "Train Epoch: 22 [40960/50000 (91%)]\tLoss: 0.409236, Accuracy: 88.67\n",
      "Train Epoch: 22 [42240/50000 (94%)]\tLoss: 0.379000, Accuracy: 87.11\n",
      "Train Epoch: 22 [43520/50000 (97%)]\tLoss: 0.519388, Accuracy: 83.20\n",
      "Train Epoch: 22 [35000/50000 (99%)]\tLoss: 0.486077, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.7174, Accuracy: 3903/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.390061378479004 s]\n",
      "\n",
      "Test set: Average loss: 0.7447, Accuracy: 7719/10000 (77.19%)\n",
      "\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.352066, Accuracy: 87.11\n",
      "Train Epoch: 23 [1280/50000 (3%)]\tLoss: 0.380498, Accuracy: 87.89\n",
      "Train Epoch: 23 [2560/50000 (6%)]\tLoss: 0.367826, Accuracy: 88.28\n",
      "Train Epoch: 23 [3840/50000 (9%)]\tLoss: 0.369350, Accuracy: 88.67\n",
      "Train Epoch: 23 [5120/50000 (11%)]\tLoss: 0.403597, Accuracy: 86.72\n",
      "Train Epoch: 23 [6400/50000 (14%)]\tLoss: 0.510243, Accuracy: 84.38\n",
      "Train Epoch: 23 [7680/50000 (17%)]\tLoss: 0.368346, Accuracy: 88.28\n",
      "Train Epoch: 23 [8960/50000 (20%)]\tLoss: 0.390010, Accuracy: 87.50\n",
      "Train Epoch: 23 [10240/50000 (23%)]\tLoss: 0.300580, Accuracy: 89.84\n",
      "Train Epoch: 23 [11520/50000 (26%)]\tLoss: 0.354677, Accuracy: 89.45\n",
      "Train Epoch: 23 [12800/50000 (28%)]\tLoss: 0.373544, Accuracy: 86.33\n",
      "Train Epoch: 23 [14080/50000 (31%)]\tLoss: 0.418734, Accuracy: 86.33\n",
      "Train Epoch: 23 [15360/50000 (34%)]\tLoss: 0.432129, Accuracy: 87.50\n",
      "Train Epoch: 23 [16640/50000 (37%)]\tLoss: 0.424861, Accuracy: 84.38\n",
      "Train Epoch: 23 [17920/50000 (40%)]\tLoss: 0.390008, Accuracy: 87.89\n",
      "Train Epoch: 23 [19200/50000 (43%)]\tLoss: 0.518605, Accuracy: 83.98\n",
      "Train Epoch: 23 [20480/50000 (45%)]\tLoss: 0.320714, Accuracy: 91.41\n",
      "Train Epoch: 23 [21760/50000 (48%)]\tLoss: 0.533991, Accuracy: 80.86\n",
      "Train Epoch: 23 [23040/50000 (51%)]\tLoss: 0.493903, Accuracy: 82.42\n",
      "Train Epoch: 23 [24320/50000 (54%)]\tLoss: 0.476833, Accuracy: 82.03\n",
      "Train Epoch: 23 [25600/50000 (57%)]\tLoss: 0.433989, Accuracy: 85.94\n",
      "Train Epoch: 23 [26880/50000 (60%)]\tLoss: 0.458977, Accuracy: 83.98\n",
      "Train Epoch: 23 [28160/50000 (62%)]\tLoss: 0.355839, Accuracy: 88.28\n",
      "Train Epoch: 23 [29440/50000 (65%)]\tLoss: 0.342520, Accuracy: 87.89\n",
      "Train Epoch: 23 [30720/50000 (68%)]\tLoss: 0.448666, Accuracy: 84.38\n",
      "Train Epoch: 23 [32000/50000 (71%)]\tLoss: 0.562603, Accuracy: 84.77\n",
      "Train Epoch: 23 [33280/50000 (74%)]\tLoss: 0.403054, Accuracy: 86.72\n",
      "Train Epoch: 23 [34560/50000 (77%)]\tLoss: 0.517980, Accuracy: 82.42\n",
      "Train Epoch: 23 [35840/50000 (80%)]\tLoss: 0.439886, Accuracy: 85.94\n",
      "Train Epoch: 23 [37120/50000 (82%)]\tLoss: 0.393845, Accuracy: 87.89\n",
      "Train Epoch: 23 [38400/50000 (85%)]\tLoss: 0.370936, Accuracy: 86.72\n",
      "Train Epoch: 23 [39680/50000 (88%)]\tLoss: 0.357536, Accuracy: 88.67\n",
      "Train Epoch: 23 [40960/50000 (91%)]\tLoss: 0.587634, Accuracy: 80.08\n",
      "Train Epoch: 23 [42240/50000 (94%)]\tLoss: 0.362709, Accuracy: 88.67\n",
      "Train Epoch: 23 [43520/50000 (97%)]\tLoss: 0.410861, Accuracy: 85.94\n",
      "Train Epoch: 23 [35000/50000 (99%)]\tLoss: 0.445798, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.8416, Accuracy: 3671/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[39.572076082229614 s]\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.460300, Accuracy: 85.55\n",
      "Train Epoch: 24 [1280/50000 (3%)]\tLoss: 0.312920, Accuracy: 88.67\n",
      "Train Epoch: 24 [2560/50000 (6%)]\tLoss: 0.450268, Accuracy: 84.77\n",
      "Train Epoch: 24 [3840/50000 (9%)]\tLoss: 0.352477, Accuracy: 88.67\n",
      "Train Epoch: 24 [5120/50000 (11%)]\tLoss: 0.329917, Accuracy: 88.28\n",
      "Train Epoch: 24 [6400/50000 (14%)]\tLoss: 0.407239, Accuracy: 84.77\n",
      "Train Epoch: 24 [7680/50000 (17%)]\tLoss: 0.426407, Accuracy: 87.89\n",
      "Train Epoch: 24 [8960/50000 (20%)]\tLoss: 0.502440, Accuracy: 82.81\n",
      "Train Epoch: 24 [10240/50000 (23%)]\tLoss: 0.429881, Accuracy: 85.55\n",
      "Train Epoch: 24 [11520/50000 (26%)]\tLoss: 0.456611, Accuracy: 83.20\n",
      "Train Epoch: 24 [12800/50000 (28%)]\tLoss: 0.369051, Accuracy: 88.28\n",
      "Train Epoch: 24 [14080/50000 (31%)]\tLoss: 0.372476, Accuracy: 88.28\n",
      "Train Epoch: 24 [15360/50000 (34%)]\tLoss: 0.429993, Accuracy: 83.98\n",
      "Train Epoch: 24 [16640/50000 (37%)]\tLoss: 0.383809, Accuracy: 88.28\n",
      "Train Epoch: 24 [17920/50000 (40%)]\tLoss: 0.364580, Accuracy: 88.67\n",
      "Train Epoch: 24 [19200/50000 (43%)]\tLoss: 0.390776, Accuracy: 84.38\n",
      "Train Epoch: 24 [20480/50000 (45%)]\tLoss: 0.358206, Accuracy: 89.45\n",
      "Train Epoch: 24 [21760/50000 (48%)]\tLoss: 0.430035, Accuracy: 87.50\n",
      "Train Epoch: 24 [23040/50000 (51%)]\tLoss: 0.400074, Accuracy: 86.33\n",
      "Train Epoch: 24 [24320/50000 (54%)]\tLoss: 0.388275, Accuracy: 86.33\n",
      "Train Epoch: 24 [25600/50000 (57%)]\tLoss: 0.325868, Accuracy: 89.45\n",
      "Train Epoch: 24 [26880/50000 (60%)]\tLoss: 0.458730, Accuracy: 84.38\n",
      "Train Epoch: 24 [28160/50000 (62%)]\tLoss: 0.406453, Accuracy: 87.11\n",
      "Train Epoch: 24 [29440/50000 (65%)]\tLoss: 0.380156, Accuracy: 86.72\n",
      "Train Epoch: 24 [30720/50000 (68%)]\tLoss: 0.359021, Accuracy: 87.89\n",
      "Train Epoch: 24 [32000/50000 (71%)]\tLoss: 0.388967, Accuracy: 86.33\n",
      "Train Epoch: 24 [33280/50000 (74%)]\tLoss: 0.535409, Accuracy: 83.20\n",
      "Train Epoch: 24 [34560/50000 (77%)]\tLoss: 0.389043, Accuracy: 86.33\n",
      "Train Epoch: 24 [35840/50000 (80%)]\tLoss: 0.447915, Accuracy: 83.98\n",
      "Train Epoch: 24 [37120/50000 (82%)]\tLoss: 0.434884, Accuracy: 85.55\n",
      "Train Epoch: 24 [38400/50000 (85%)]\tLoss: 0.480838, Accuracy: 82.03\n",
      "Train Epoch: 24 [39680/50000 (88%)]\tLoss: 0.398033, Accuracy: 85.94\n",
      "Train Epoch: 24 [40960/50000 (91%)]\tLoss: 0.415793, Accuracy: 87.89\n",
      "Train Epoch: 24 [42240/50000 (94%)]\tLoss: 0.421580, Accuracy: 86.72\n",
      "Train Epoch: 24 [43520/50000 (97%)]\tLoss: 0.368682, Accuracy: 85.55\n",
      "Train Epoch: 24 [35000/50000 (99%)]\tLoss: 0.497182, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.6537, Accuracy: 3973/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.40605902671814 s]\n",
      "\n",
      "Test set: Average loss: 0.6844, Accuracy: 7834/10000 (78.34%)\n",
      "\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.328003, Accuracy: 88.67\n",
      "Train Epoch: 25 [1280/50000 (3%)]\tLoss: 0.424371, Accuracy: 85.55\n",
      "Train Epoch: 25 [2560/50000 (6%)]\tLoss: 0.404373, Accuracy: 84.38\n",
      "Train Epoch: 25 [3840/50000 (9%)]\tLoss: 0.369625, Accuracy: 90.23\n",
      "Train Epoch: 25 [5120/50000 (11%)]\tLoss: 0.377831, Accuracy: 85.94\n",
      "Train Epoch: 25 [6400/50000 (14%)]\tLoss: 0.467057, Accuracy: 84.38\n",
      "Train Epoch: 25 [7680/50000 (17%)]\tLoss: 0.370575, Accuracy: 87.89\n",
      "Train Epoch: 25 [8960/50000 (20%)]\tLoss: 0.396505, Accuracy: 84.38\n",
      "Train Epoch: 25 [10240/50000 (23%)]\tLoss: 0.383834, Accuracy: 84.38\n",
      "Train Epoch: 25 [11520/50000 (26%)]\tLoss: 0.399186, Accuracy: 87.11\n",
      "Train Epoch: 25 [12800/50000 (28%)]\tLoss: 0.383397, Accuracy: 85.94\n",
      "Train Epoch: 25 [14080/50000 (31%)]\tLoss: 0.365241, Accuracy: 85.94\n",
      "Train Epoch: 25 [15360/50000 (34%)]\tLoss: 0.493501, Accuracy: 83.20\n",
      "Train Epoch: 25 [16640/50000 (37%)]\tLoss: 0.383614, Accuracy: 87.50\n",
      "Train Epoch: 25 [17920/50000 (40%)]\tLoss: 0.463344, Accuracy: 83.59\n",
      "Train Epoch: 25 [19200/50000 (43%)]\tLoss: 0.396982, Accuracy: 85.55\n",
      "Train Epoch: 25 [20480/50000 (45%)]\tLoss: 0.400255, Accuracy: 86.33\n",
      "Train Epoch: 25 [21760/50000 (48%)]\tLoss: 0.447765, Accuracy: 83.20\n",
      "Train Epoch: 25 [23040/50000 (51%)]\tLoss: 0.548829, Accuracy: 78.91\n",
      "Train Epoch: 25 [24320/50000 (54%)]\tLoss: 0.442449, Accuracy: 83.98\n",
      "Train Epoch: 25 [25600/50000 (57%)]\tLoss: 0.460342, Accuracy: 84.38\n",
      "Train Epoch: 25 [26880/50000 (60%)]\tLoss: 0.445861, Accuracy: 87.11\n",
      "Train Epoch: 25 [28160/50000 (62%)]\tLoss: 0.323845, Accuracy: 88.67\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 25 [29440/50000 (65%)]\tLoss: 0.323404, Accuracy: 88.67\n",
      "Train Epoch: 25 [30720/50000 (68%)]\tLoss: 0.448584, Accuracy: 86.72\n",
      "Train Epoch: 25 [32000/50000 (71%)]\tLoss: 0.430042, Accuracy: 85.55\n",
      "Train Epoch: 25 [33280/50000 (74%)]\tLoss: 0.507318, Accuracy: 84.77\n",
      "Train Epoch: 25 [34560/50000 (77%)]\tLoss: 0.449295, Accuracy: 85.55\n",
      "Train Epoch: 25 [35840/50000 (80%)]\tLoss: 0.394930, Accuracy: 88.67\n",
      "Train Epoch: 25 [37120/50000 (82%)]\tLoss: 0.375777, Accuracy: 85.94\n",
      "Train Epoch: 25 [38400/50000 (85%)]\tLoss: 0.489016, Accuracy: 83.59\n",
      "Train Epoch: 25 [39680/50000 (88%)]\tLoss: 0.405104, Accuracy: 85.16\n",
      "Train Epoch: 25 [40960/50000 (91%)]\tLoss: 0.333655, Accuracy: 89.45\n",
      "Train Epoch: 25 [42240/50000 (94%)]\tLoss: 0.477311, Accuracy: 85.55\n",
      "Train Epoch: 25 [43520/50000 (97%)]\tLoss: 0.416453, Accuracy: 85.55\n",
      "Train Epoch: 25 [35000/50000 (99%)]\tLoss: 0.501366, Accuracy: 83.50\n",
      "\n",
      "Validation set: Average loss: 0.6879, Accuracy: 3942/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.627936124801636 s]\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 0.409257, Accuracy: 84.77\n",
      "Train Epoch: 26 [1280/50000 (3%)]\tLoss: 0.422261, Accuracy: 85.55\n",
      "Train Epoch: 26 [2560/50000 (6%)]\tLoss: 0.369892, Accuracy: 87.11\n",
      "Train Epoch: 26 [3840/50000 (9%)]\tLoss: 0.423520, Accuracy: 85.94\n",
      "Train Epoch: 26 [5120/50000 (11%)]\tLoss: 0.466646, Accuracy: 82.81\n",
      "Train Epoch: 26 [6400/50000 (14%)]\tLoss: 0.402134, Accuracy: 85.16\n",
      "Train Epoch: 26 [7680/50000 (17%)]\tLoss: 0.381280, Accuracy: 85.16\n",
      "Train Epoch: 26 [8960/50000 (20%)]\tLoss: 0.411942, Accuracy: 87.50\n",
      "Train Epoch: 26 [10240/50000 (23%)]\tLoss: 0.495887, Accuracy: 84.38\n",
      "Train Epoch: 26 [11520/50000 (26%)]\tLoss: 0.314067, Accuracy: 88.28\n",
      "Train Epoch: 26 [12800/50000 (28%)]\tLoss: 0.454207, Accuracy: 83.20\n",
      "Train Epoch: 26 [14080/50000 (31%)]\tLoss: 0.477102, Accuracy: 84.38\n",
      "Train Epoch: 26 [15360/50000 (34%)]\tLoss: 0.437911, Accuracy: 86.33\n",
      "Train Epoch: 26 [16640/50000 (37%)]\tLoss: 0.391265, Accuracy: 87.89\n",
      "Train Epoch: 26 [17920/50000 (40%)]\tLoss: 0.416923, Accuracy: 87.50\n",
      "Train Epoch: 26 [19200/50000 (43%)]\tLoss: 0.360691, Accuracy: 88.67\n",
      "Train Epoch: 26 [20480/50000 (45%)]\tLoss: 0.382636, Accuracy: 85.94\n",
      "Train Epoch: 26 [21760/50000 (48%)]\tLoss: 0.470330, Accuracy: 83.20\n",
      "Train Epoch: 26 [23040/50000 (51%)]\tLoss: 0.386580, Accuracy: 87.89\n",
      "Train Epoch: 26 [24320/50000 (54%)]\tLoss: 0.417588, Accuracy: 88.28\n",
      "Train Epoch: 26 [25600/50000 (57%)]\tLoss: 0.444632, Accuracy: 85.16\n",
      "Train Epoch: 26 [26880/50000 (60%)]\tLoss: 0.352156, Accuracy: 88.67\n",
      "Train Epoch: 26 [28160/50000 (62%)]\tLoss: 0.418803, Accuracy: 85.94\n",
      "Train Epoch: 26 [29440/50000 (65%)]\tLoss: 0.381028, Accuracy: 88.67\n",
      "Train Epoch: 26 [30720/50000 (68%)]\tLoss: 0.413922, Accuracy: 87.50\n",
      "Train Epoch: 26 [32000/50000 (71%)]\tLoss: 0.384201, Accuracy: 87.50\n",
      "Train Epoch: 26 [33280/50000 (74%)]\tLoss: 0.288868, Accuracy: 89.06\n",
      "Train Epoch: 26 [34560/50000 (77%)]\tLoss: 0.427197, Accuracy: 85.16\n",
      "Train Epoch: 26 [35840/50000 (80%)]\tLoss: 0.411731, Accuracy: 85.16\n",
      "Train Epoch: 26 [37120/50000 (82%)]\tLoss: 0.454897, Accuracy: 84.38\n",
      "Train Epoch: 26 [38400/50000 (85%)]\tLoss: 0.471069, Accuracy: 85.16\n",
      "Train Epoch: 26 [39680/50000 (88%)]\tLoss: 0.342705, Accuracy: 88.28\n",
      "Train Epoch: 26 [40960/50000 (91%)]\tLoss: 0.332850, Accuracy: 89.84\n",
      "Train Epoch: 26 [42240/50000 (94%)]\tLoss: 0.496412, Accuracy: 82.81\n",
      "Train Epoch: 26 [43520/50000 (97%)]\tLoss: 0.545155, Accuracy: 81.25\n",
      "Train Epoch: 26 [35000/50000 (99%)]\tLoss: 0.434583, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6756, Accuracy: 3837/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.39984059333801 s]\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.1696]],\n",
      "\n",
      "        [[-0.7408]],\n",
      "\n",
      "        [[-0.7102]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.6201]],\n",
      "\n",
      "        [[-0.7252]],\n",
      "\n",
      "        [[ 0.3249]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[ 2.0081,  2.0555,  2.0083,  ...,  1.9631,  2.0287,  2.0403],\n",
      "         [ 1.8079,  1.7960,  1.8420,  ...,  1.8329,  1.7389,  1.7465],\n",
      "         [ 2.2572,  2.2941,  2.2681,  ...,  2.2898,  2.2238,  2.3015],\n",
      "         ...,\n",
      "         [ 1.9074,  1.8850,  1.9220,  ...,  1.8891,  1.8972,  1.9743],\n",
      "         [ 2.5100,  2.4935,  2.3939,  ...,  2.5667,  2.4846,  2.4853],\n",
      "         [ 1.0634,  1.0895,  1.0772,  ...,  1.1025,  1.1243,  1.0854]]], device='cuda:0')\n",
      "\n",
      "Test set: Average loss: 0.6939, Accuracy: 7729/10000 (77.29%)\n",
      "\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.308357, Accuracy: 89.06\n",
      "Train Epoch: 27 [1280/50000 (3%)]\tLoss: 0.371655, Accuracy: 87.50\n",
      "Train Epoch: 27 [2560/50000 (6%)]\tLoss: 0.380956, Accuracy: 87.50\n",
      "Train Epoch: 27 [3840/50000 (9%)]\tLoss: 0.382342, Accuracy: 87.89\n",
      "Train Epoch: 27 [5120/50000 (11%)]\tLoss: 0.393340, Accuracy: 85.55\n",
      "Train Epoch: 27 [6400/50000 (14%)]\tLoss: 0.408921, Accuracy: 85.55\n",
      "Train Epoch: 27 [7680/50000 (17%)]\tLoss: 0.392048, Accuracy: 86.33\n",
      "Train Epoch: 27 [8960/50000 (20%)]\tLoss: 0.361210, Accuracy: 87.11\n",
      "Train Epoch: 27 [10240/50000 (23%)]\tLoss: 0.407203, Accuracy: 86.72\n",
      "Train Epoch: 27 [11520/50000 (26%)]\tLoss: 0.387375, Accuracy: 88.67\n",
      "Train Epoch: 27 [12800/50000 (28%)]\tLoss: 0.414535, Accuracy: 86.72\n",
      "Train Epoch: 27 [14080/50000 (31%)]\tLoss: 0.332437, Accuracy: 88.67\n",
      "Train Epoch: 27 [15360/50000 (34%)]\tLoss: 0.395302, Accuracy: 86.33\n",
      "Train Epoch: 27 [16640/50000 (37%)]\tLoss: 0.413241, Accuracy: 85.94\n",
      "Train Epoch: 27 [17920/50000 (40%)]\tLoss: 0.454926, Accuracy: 85.16\n",
      "Train Epoch: 27 [19200/50000 (43%)]\tLoss: 0.388105, Accuracy: 85.94\n",
      "Train Epoch: 27 [20480/50000 (45%)]\tLoss: 0.355184, Accuracy: 87.89\n",
      "Train Epoch: 27 [21760/50000 (48%)]\tLoss: 0.444673, Accuracy: 85.16\n",
      "Train Epoch: 27 [23040/50000 (51%)]\tLoss: 0.540933, Accuracy: 82.03\n",
      "Train Epoch: 27 [24320/50000 (54%)]\tLoss: 0.459177, Accuracy: 87.11\n",
      "Train Epoch: 27 [25600/50000 (57%)]\tLoss: 0.428102, Accuracy: 85.16\n",
      "Train Epoch: 27 [26880/50000 (60%)]\tLoss: 0.436970, Accuracy: 86.72\n",
      "Train Epoch: 27 [28160/50000 (62%)]\tLoss: 0.345327, Accuracy: 89.84\n",
      "Train Epoch: 27 [29440/50000 (65%)]\tLoss: 0.356502, Accuracy: 88.28\n",
      "Train Epoch: 27 [30720/50000 (68%)]\tLoss: 0.361432, Accuracy: 88.28\n",
      "Train Epoch: 27 [32000/50000 (71%)]\tLoss: 0.483146, Accuracy: 81.64\n",
      "Train Epoch: 27 [33280/50000 (74%)]\tLoss: 0.435021, Accuracy: 85.55\n",
      "Train Epoch: 27 [34560/50000 (77%)]\tLoss: 0.334862, Accuracy: 87.89\n",
      "Train Epoch: 27 [35840/50000 (80%)]\tLoss: 0.443012, Accuracy: 83.98\n",
      "Train Epoch: 27 [37120/50000 (82%)]\tLoss: 0.381129, Accuracy: 87.50\n",
      "Train Epoch: 27 [38400/50000 (85%)]\tLoss: 0.348893, Accuracy: 88.28\n",
      "Train Epoch: 27 [39680/50000 (88%)]\tLoss: 0.443959, Accuracy: 84.38\n",
      "Train Epoch: 27 [40960/50000 (91%)]\tLoss: 0.406893, Accuracy: 86.33\n",
      "Train Epoch: 27 [42240/50000 (94%)]\tLoss: 0.427964, Accuracy: 87.89\n",
      "Train Epoch: 27 [43520/50000 (97%)]\tLoss: 0.461285, Accuracy: 84.38\n",
      "Train Epoch: 27 [35000/50000 (99%)]\tLoss: 0.387164, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7299, Accuracy: 3872/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[39.64931583404541 s]\n",
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 0.439421, Accuracy: 83.98\n",
      "Train Epoch: 28 [1280/50000 (3%)]\tLoss: 0.440812, Accuracy: 83.98\n",
      "Train Epoch: 28 [2560/50000 (6%)]\tLoss: 0.395673, Accuracy: 86.33\n",
      "Train Epoch: 28 [3840/50000 (9%)]\tLoss: 0.427941, Accuracy: 87.11\n",
      "Train Epoch: 28 [5120/50000 (11%)]\tLoss: 0.360706, Accuracy: 87.89\n",
      "Train Epoch: 28 [6400/50000 (14%)]\tLoss: 0.368316, Accuracy: 88.28\n",
      "Train Epoch: 28 [7680/50000 (17%)]\tLoss: 0.417889, Accuracy: 87.11\n",
      "Train Epoch: 28 [8960/50000 (20%)]\tLoss: 0.416467, Accuracy: 85.55\n",
      "Train Epoch: 28 [10240/50000 (23%)]\tLoss: 0.420898, Accuracy: 84.38\n",
      "Train Epoch: 28 [11520/50000 (26%)]\tLoss: 0.373692, Accuracy: 88.67\n",
      "Train Epoch: 28 [12800/50000 (28%)]\tLoss: 0.359386, Accuracy: 89.06\n",
      "Train Epoch: 28 [14080/50000 (31%)]\tLoss: 0.503627, Accuracy: 82.81\n",
      "Train Epoch: 28 [15360/50000 (34%)]\tLoss: 0.400963, Accuracy: 86.33\n",
      "Train Epoch: 28 [16640/50000 (37%)]\tLoss: 0.471488, Accuracy: 84.77\n",
      "Train Epoch: 28 [17920/50000 (40%)]\tLoss: 0.481591, Accuracy: 83.98\n",
      "Train Epoch: 28 [19200/50000 (43%)]\tLoss: 0.470593, Accuracy: 83.59\n",
      "Train Epoch: 28 [20480/50000 (45%)]\tLoss: 0.444618, Accuracy: 85.94\n",
      "Train Epoch: 28 [21760/50000 (48%)]\tLoss: 0.366174, Accuracy: 87.11\n",
      "Train Epoch: 28 [23040/50000 (51%)]\tLoss: 0.421822, Accuracy: 83.98\n",
      "Train Epoch: 28 [24320/50000 (54%)]\tLoss: 0.423672, Accuracy: 86.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 28 [25600/50000 (57%)]\tLoss: 0.406620, Accuracy: 87.11\n",
      "Train Epoch: 28 [26880/50000 (60%)]\tLoss: 0.445504, Accuracy: 83.59\n",
      "Train Epoch: 28 [28160/50000 (62%)]\tLoss: 0.391240, Accuracy: 88.28\n",
      "Train Epoch: 28 [29440/50000 (65%)]\tLoss: 0.366822, Accuracy: 89.45\n",
      "Train Epoch: 28 [30720/50000 (68%)]\tLoss: 0.409954, Accuracy: 86.33\n",
      "Train Epoch: 28 [32000/50000 (71%)]\tLoss: 0.497617, Accuracy: 83.98\n",
      "Train Epoch: 28 [33280/50000 (74%)]\tLoss: 0.435051, Accuracy: 83.59\n",
      "Train Epoch: 28 [34560/50000 (77%)]\tLoss: 0.468059, Accuracy: 85.16\n",
      "Train Epoch: 28 [35840/50000 (80%)]\tLoss: 0.466041, Accuracy: 84.38\n",
      "Train Epoch: 28 [37120/50000 (82%)]\tLoss: 0.462644, Accuracy: 84.38\n",
      "Train Epoch: 28 [38400/50000 (85%)]\tLoss: 0.541074, Accuracy: 81.64\n",
      "Train Epoch: 28 [39680/50000 (88%)]\tLoss: 0.418244, Accuracy: 88.28\n",
      "Train Epoch: 28 [40960/50000 (91%)]\tLoss: 0.386526, Accuracy: 89.45\n",
      "Train Epoch: 28 [42240/50000 (94%)]\tLoss: 0.334008, Accuracy: 89.45\n",
      "Train Epoch: 28 [43520/50000 (97%)]\tLoss: 0.479500, Accuracy: 82.42\n",
      "Train Epoch: 28 [35000/50000 (99%)]\tLoss: 0.429869, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6447, Accuracy: 3969/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.47909164428711 s]\n",
      "\n",
      "Test set: Average loss: 0.7029, Accuracy: 7757/10000 (77.57%)\n",
      "\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 0.417525, Accuracy: 87.50\n",
      "Train Epoch: 29 [1280/50000 (3%)]\tLoss: 0.520817, Accuracy: 84.38\n",
      "Train Epoch: 29 [2560/50000 (6%)]\tLoss: 0.310842, Accuracy: 89.45\n",
      "Train Epoch: 29 [3840/50000 (9%)]\tLoss: 0.303509, Accuracy: 90.23\n",
      "Train Epoch: 29 [5120/50000 (11%)]\tLoss: 0.331956, Accuracy: 88.28\n",
      "Train Epoch: 29 [6400/50000 (14%)]\tLoss: 0.346718, Accuracy: 87.89\n",
      "Train Epoch: 29 [7680/50000 (17%)]\tLoss: 0.455812, Accuracy: 83.98\n",
      "Train Epoch: 29 [8960/50000 (20%)]\tLoss: 0.356401, Accuracy: 89.84\n",
      "Train Epoch: 29 [10240/50000 (23%)]\tLoss: 0.396123, Accuracy: 87.11\n",
      "Train Epoch: 29 [11520/50000 (26%)]\tLoss: 0.354957, Accuracy: 87.50\n",
      "Train Epoch: 29 [12800/50000 (28%)]\tLoss: 0.360869, Accuracy: 86.33\n",
      "Train Epoch: 29 [14080/50000 (31%)]\tLoss: 0.516429, Accuracy: 84.38\n",
      "Train Epoch: 29 [15360/50000 (34%)]\tLoss: 0.389664, Accuracy: 85.94\n",
      "Train Epoch: 29 [16640/50000 (37%)]\tLoss: 0.373227, Accuracy: 85.94\n",
      "Train Epoch: 29 [17920/50000 (40%)]\tLoss: 0.492237, Accuracy: 84.77\n",
      "Train Epoch: 29 [19200/50000 (43%)]\tLoss: 0.560212, Accuracy: 80.86\n",
      "Train Epoch: 29 [20480/50000 (45%)]\tLoss: 0.381575, Accuracy: 86.72\n",
      "Train Epoch: 29 [21760/50000 (48%)]\tLoss: 0.430658, Accuracy: 80.86\n",
      "Train Epoch: 29 [23040/50000 (51%)]\tLoss: 0.380315, Accuracy: 86.72\n",
      "Train Epoch: 29 [24320/50000 (54%)]\tLoss: 0.508652, Accuracy: 83.98\n",
      "Train Epoch: 29 [25600/50000 (57%)]\tLoss: 0.381531, Accuracy: 88.67\n",
      "Train Epoch: 29 [26880/50000 (60%)]\tLoss: 0.404730, Accuracy: 88.67\n",
      "Train Epoch: 29 [28160/50000 (62%)]\tLoss: 0.352378, Accuracy: 87.50\n",
      "Train Epoch: 29 [29440/50000 (65%)]\tLoss: 0.423018, Accuracy: 87.89\n",
      "Train Epoch: 29 [30720/50000 (68%)]\tLoss: 0.404056, Accuracy: 87.50\n",
      "Train Epoch: 29 [32000/50000 (71%)]\tLoss: 0.365012, Accuracy: 87.50\n",
      "Train Epoch: 29 [33280/50000 (74%)]\tLoss: 0.384433, Accuracy: 85.94\n",
      "Train Epoch: 29 [34560/50000 (77%)]\tLoss: 0.416712, Accuracy: 86.72\n",
      "Train Epoch: 29 [35840/50000 (80%)]\tLoss: 0.450302, Accuracy: 83.20\n",
      "Train Epoch: 29 [37120/50000 (82%)]\tLoss: 0.420321, Accuracy: 84.77\n",
      "Train Epoch: 29 [38400/50000 (85%)]\tLoss: 0.423529, Accuracy: 84.77\n",
      "Train Epoch: 29 [39680/50000 (88%)]\tLoss: 0.446442, Accuracy: 85.94\n",
      "Train Epoch: 29 [40960/50000 (91%)]\tLoss: 0.401579, Accuracy: 87.89\n",
      "Train Epoch: 29 [42240/50000 (94%)]\tLoss: 0.448716, Accuracy: 83.98\n",
      "Train Epoch: 29 [43520/50000 (97%)]\tLoss: 0.436040, Accuracy: 83.98\n",
      "Train Epoch: 29 [35000/50000 (99%)]\tLoss: 0.421533, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.8576, Accuracy: 3634/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[39.59487295150757 s]\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 0.339374, Accuracy: 88.28\n",
      "Train Epoch: 30 [1280/50000 (3%)]\tLoss: 0.396197, Accuracy: 85.16\n",
      "Train Epoch: 30 [2560/50000 (6%)]\tLoss: 0.419986, Accuracy: 86.72\n",
      "Train Epoch: 30 [3840/50000 (9%)]\tLoss: 0.298300, Accuracy: 90.62\n",
      "Train Epoch: 30 [5120/50000 (11%)]\tLoss: 0.341207, Accuracy: 89.45\n",
      "Train Epoch: 30 [6400/50000 (14%)]\tLoss: 0.407570, Accuracy: 87.11\n",
      "Train Epoch: 30 [7680/50000 (17%)]\tLoss: 0.418353, Accuracy: 84.38\n",
      "Train Epoch: 30 [8960/50000 (20%)]\tLoss: 0.399317, Accuracy: 86.33\n",
      "Train Epoch: 30 [10240/50000 (23%)]\tLoss: 0.352466, Accuracy: 86.72\n",
      "Train Epoch: 30 [11520/50000 (26%)]\tLoss: 0.362822, Accuracy: 88.67\n",
      "Train Epoch: 30 [12800/50000 (28%)]\tLoss: 0.379459, Accuracy: 89.45\n",
      "Train Epoch: 30 [14080/50000 (31%)]\tLoss: 0.405780, Accuracy: 85.94\n",
      "Train Epoch: 30 [15360/50000 (34%)]\tLoss: 0.537927, Accuracy: 82.03\n",
      "Train Epoch: 30 [16640/50000 (37%)]\tLoss: 0.369763, Accuracy: 89.06\n",
      "Train Epoch: 30 [17920/50000 (40%)]\tLoss: 0.393469, Accuracy: 88.67\n",
      "Train Epoch: 30 [19200/50000 (43%)]\tLoss: 0.379009, Accuracy: 85.94\n",
      "Train Epoch: 30 [20480/50000 (45%)]\tLoss: 0.420910, Accuracy: 84.38\n",
      "Train Epoch: 30 [21760/50000 (48%)]\tLoss: 0.414752, Accuracy: 85.94\n",
      "Train Epoch: 30 [23040/50000 (51%)]\tLoss: 0.479269, Accuracy: 83.98\n",
      "Train Epoch: 30 [24320/50000 (54%)]\tLoss: 0.368316, Accuracy: 87.89\n",
      "Train Epoch: 30 [25600/50000 (57%)]\tLoss: 0.367124, Accuracy: 87.11\n",
      "Train Epoch: 30 [26880/50000 (60%)]\tLoss: 0.425694, Accuracy: 85.55\n",
      "Train Epoch: 30 [28160/50000 (62%)]\tLoss: 0.322640, Accuracy: 89.84\n",
      "Train Epoch: 30 [29440/50000 (65%)]\tLoss: 0.427201, Accuracy: 87.50\n",
      "Train Epoch: 30 [30720/50000 (68%)]\tLoss: 0.479419, Accuracy: 83.59\n",
      "Train Epoch: 30 [32000/50000 (71%)]\tLoss: 0.321450, Accuracy: 87.89\n",
      "Train Epoch: 30 [33280/50000 (74%)]\tLoss: 0.311802, Accuracy: 89.84\n",
      "Train Epoch: 30 [34560/50000 (77%)]\tLoss: 0.334829, Accuracy: 88.28\n",
      "Train Epoch: 30 [35840/50000 (80%)]\tLoss: 0.443426, Accuracy: 87.50\n",
      "Train Epoch: 30 [37120/50000 (82%)]\tLoss: 0.434811, Accuracy: 87.11\n",
      "Train Epoch: 30 [38400/50000 (85%)]\tLoss: 0.324219, Accuracy: 89.45\n",
      "Train Epoch: 30 [39680/50000 (88%)]\tLoss: 0.472219, Accuracy: 83.98\n",
      "Train Epoch: 30 [40960/50000 (91%)]\tLoss: 0.421623, Accuracy: 85.16\n",
      "Train Epoch: 30 [42240/50000 (94%)]\tLoss: 0.507886, Accuracy: 82.81\n",
      "Train Epoch: 30 [43520/50000 (97%)]\tLoss: 0.370841, Accuracy: 86.33\n",
      "Train Epoch: 30 [35000/50000 (99%)]\tLoss: 0.382429, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.7189, Accuracy: 3859/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.36068081855774 s]\n",
      "\n",
      "Test set: Average loss: 0.7495, Accuracy: 7679/10000 (76.79%)\n",
      "\n",
      "Train Epoch: 31 [0/50000 (0%)]\tLoss: 0.296130, Accuracy: 89.45\n",
      "Train Epoch: 31 [1280/50000 (3%)]\tLoss: 0.346948, Accuracy: 88.67\n",
      "Train Epoch: 31 [2560/50000 (6%)]\tLoss: 0.288238, Accuracy: 89.84\n",
      "Train Epoch: 31 [3840/50000 (9%)]\tLoss: 0.364689, Accuracy: 87.89\n",
      "Train Epoch: 31 [5120/50000 (11%)]\tLoss: 0.410767, Accuracy: 85.55\n",
      "Train Epoch: 31 [6400/50000 (14%)]\tLoss: 0.354604, Accuracy: 87.50\n",
      "Train Epoch: 31 [7680/50000 (17%)]\tLoss: 0.362633, Accuracy: 87.50\n",
      "Train Epoch: 31 [8960/50000 (20%)]\tLoss: 0.296317, Accuracy: 91.80\n",
      "Train Epoch: 31 [10240/50000 (23%)]\tLoss: 0.322702, Accuracy: 87.89\n",
      "Train Epoch: 31 [11520/50000 (26%)]\tLoss: 0.435093, Accuracy: 84.77\n",
      "Train Epoch: 31 [12800/50000 (28%)]\tLoss: 0.427725, Accuracy: 87.89\n",
      "Train Epoch: 31 [14080/50000 (31%)]\tLoss: 0.372761, Accuracy: 88.28\n",
      "Train Epoch: 31 [15360/50000 (34%)]\tLoss: 0.367221, Accuracy: 87.11\n",
      "Train Epoch: 31 [16640/50000 (37%)]\tLoss: 0.375463, Accuracy: 89.06\n",
      "Train Epoch: 31 [17920/50000 (40%)]\tLoss: 0.414518, Accuracy: 86.72\n",
      "Train Epoch: 31 [19200/50000 (43%)]\tLoss: 0.427061, Accuracy: 83.98\n",
      "Train Epoch: 31 [20480/50000 (45%)]\tLoss: 0.480016, Accuracy: 83.59\n",
      "Train Epoch: 31 [21760/50000 (48%)]\tLoss: 0.390509, Accuracy: 87.11\n",
      "Train Epoch: 31 [23040/50000 (51%)]\tLoss: 0.543183, Accuracy: 83.98\n",
      "Train Epoch: 31 [24320/50000 (54%)]\tLoss: 0.517668, Accuracy: 81.64\n",
      "Train Epoch: 31 [25600/50000 (57%)]\tLoss: 0.346809, Accuracy: 87.89\n",
      "Train Epoch: 31 [26880/50000 (60%)]\tLoss: 0.401507, Accuracy: 86.33\n",
      "Train Epoch: 31 [28160/50000 (62%)]\tLoss: 0.433214, Accuracy: 85.94\n",
      "Train Epoch: 31 [29440/50000 (65%)]\tLoss: 0.408185, Accuracy: 86.33\n",
      "Train Epoch: 31 [30720/50000 (68%)]\tLoss: 0.463523, Accuracy: 85.55\n",
      "Train Epoch: 31 [32000/50000 (71%)]\tLoss: 0.382888, Accuracy: 87.50\n",
      "Train Epoch: 31 [33280/50000 (74%)]\tLoss: 0.387380, Accuracy: 87.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 31 [34560/50000 (77%)]\tLoss: 0.420852, Accuracy: 86.72\n",
      "Train Epoch: 31 [35840/50000 (80%)]\tLoss: 0.313451, Accuracy: 91.02\n",
      "Train Epoch: 31 [37120/50000 (82%)]\tLoss: 0.363924, Accuracy: 87.50\n",
      "Train Epoch: 31 [38400/50000 (85%)]\tLoss: 0.423118, Accuracy: 86.72\n",
      "Train Epoch: 31 [39680/50000 (88%)]\tLoss: 0.410131, Accuracy: 86.33\n",
      "Train Epoch: 31 [40960/50000 (91%)]\tLoss: 0.341584, Accuracy: 87.89\n",
      "Train Epoch: 31 [42240/50000 (94%)]\tLoss: 0.395404, Accuracy: 86.72\n",
      "Train Epoch: 31 [43520/50000 (97%)]\tLoss: 0.377728, Accuracy: 87.89\n",
      "Train Epoch: 31 [35000/50000 (99%)]\tLoss: 0.383232, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.5522, Accuracy: 4110/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[39.55836629867554 s]\n",
      "Train Epoch: 32 [0/50000 (0%)]\tLoss: 0.368619, Accuracy: 88.67\n",
      "Train Epoch: 32 [1280/50000 (3%)]\tLoss: 0.389229, Accuracy: 87.11\n",
      "Train Epoch: 32 [2560/50000 (6%)]\tLoss: 0.362747, Accuracy: 87.11\n",
      "Train Epoch: 32 [3840/50000 (9%)]\tLoss: 0.364577, Accuracy: 88.28\n",
      "Train Epoch: 32 [5120/50000 (11%)]\tLoss: 0.295621, Accuracy: 91.02\n",
      "Train Epoch: 32 [6400/50000 (14%)]\tLoss: 0.359262, Accuracy: 89.84\n",
      "Train Epoch: 32 [7680/50000 (17%)]\tLoss: 0.366253, Accuracy: 87.50\n",
      "Train Epoch: 32 [8960/50000 (20%)]\tLoss: 0.376085, Accuracy: 86.72\n",
      "Train Epoch: 32 [10240/50000 (23%)]\tLoss: 0.412859, Accuracy: 86.72\n",
      "Train Epoch: 32 [11520/50000 (26%)]\tLoss: 0.502871, Accuracy: 84.38\n",
      "Train Epoch: 32 [12800/50000 (28%)]\tLoss: 0.275331, Accuracy: 91.02\n",
      "Train Epoch: 32 [14080/50000 (31%)]\tLoss: 0.430475, Accuracy: 85.16\n",
      "Train Epoch: 32 [15360/50000 (34%)]\tLoss: 0.347007, Accuracy: 89.06\n",
      "Train Epoch: 32 [16640/50000 (37%)]\tLoss: 0.468231, Accuracy: 83.98\n",
      "Train Epoch: 32 [17920/50000 (40%)]\tLoss: 0.467000, Accuracy: 83.98\n",
      "Train Epoch: 32 [19200/50000 (43%)]\tLoss: 0.360330, Accuracy: 87.50\n",
      "Train Epoch: 32 [20480/50000 (45%)]\tLoss: 0.351381, Accuracy: 87.50\n",
      "Train Epoch: 32 [21760/50000 (48%)]\tLoss: 0.400420, Accuracy: 85.55\n",
      "Train Epoch: 32 [23040/50000 (51%)]\tLoss: 0.356728, Accuracy: 88.28\n",
      "Train Epoch: 32 [24320/50000 (54%)]\tLoss: 0.328028, Accuracy: 91.41\n",
      "Train Epoch: 32 [25600/50000 (57%)]\tLoss: 0.447710, Accuracy: 87.50\n",
      "Train Epoch: 32 [26880/50000 (60%)]\tLoss: 0.436623, Accuracy: 86.72\n",
      "Train Epoch: 32 [28160/50000 (62%)]\tLoss: 0.366968, Accuracy: 87.89\n",
      "Train Epoch: 32 [29440/50000 (65%)]\tLoss: 0.476400, Accuracy: 84.38\n",
      "Train Epoch: 32 [30720/50000 (68%)]\tLoss: 0.427533, Accuracy: 86.72\n",
      "Train Epoch: 32 [32000/50000 (71%)]\tLoss: 0.446420, Accuracy: 86.33\n",
      "Train Epoch: 32 [33280/50000 (74%)]\tLoss: 0.341316, Accuracy: 87.50\n",
      "Train Epoch: 32 [34560/50000 (77%)]\tLoss: 0.314517, Accuracy: 91.80\n",
      "Train Epoch: 32 [35840/50000 (80%)]\tLoss: 0.367027, Accuracy: 88.28\n",
      "Train Epoch: 32 [37120/50000 (82%)]\tLoss: 0.432297, Accuracy: 86.72\n",
      "Train Epoch: 32 [38400/50000 (85%)]\tLoss: 0.344596, Accuracy: 87.50\n",
      "Train Epoch: 32 [39680/50000 (88%)]\tLoss: 0.571075, Accuracy: 79.30\n",
      "Train Epoch: 32 [40960/50000 (91%)]\tLoss: 0.299752, Accuracy: 89.84\n",
      "Train Epoch: 32 [42240/50000 (94%)]\tLoss: 0.399260, Accuracy: 88.67\n",
      "Train Epoch: 32 [43520/50000 (97%)]\tLoss: 0.361660, Accuracy: 88.67\n",
      "Train Epoch: 32 [35000/50000 (99%)]\tLoss: 0.283430, Accuracy: 93.50\n",
      "\n",
      "Validation set: Average loss: 0.5759, Accuracy: 4061/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[36.33422565460205 s]\n",
      "\n",
      "Test set: Average loss: 0.6046, Accuracy: 8071/10000 (80.71%)\n",
      "\n",
      "Train Epoch: 33 [0/50000 (0%)]\tLoss: 0.494290, Accuracy: 81.25\n",
      "Train Epoch: 33 [1280/50000 (3%)]\tLoss: 0.372404, Accuracy: 87.11\n",
      "Train Epoch: 33 [2560/50000 (6%)]\tLoss: 0.313101, Accuracy: 87.89\n",
      "Train Epoch: 33 [3840/50000 (9%)]\tLoss: 0.364082, Accuracy: 88.67\n",
      "Train Epoch: 33 [5120/50000 (11%)]\tLoss: 0.383213, Accuracy: 85.55\n",
      "Train Epoch: 33 [6400/50000 (14%)]\tLoss: 0.403005, Accuracy: 86.33\n",
      "Train Epoch: 33 [7680/50000 (17%)]\tLoss: 0.361809, Accuracy: 87.11\n",
      "Train Epoch: 33 [8960/50000 (20%)]\tLoss: 0.356713, Accuracy: 87.89\n",
      "Train Epoch: 33 [10240/50000 (23%)]\tLoss: 0.408984, Accuracy: 84.38\n",
      "Train Epoch: 33 [11520/50000 (26%)]\tLoss: 0.429478, Accuracy: 85.55\n",
      "Train Epoch: 33 [12800/50000 (28%)]\tLoss: 0.380396, Accuracy: 85.55\n",
      "Train Epoch: 33 [14080/50000 (31%)]\tLoss: 0.363209, Accuracy: 86.72\n",
      "Train Epoch: 33 [15360/50000 (34%)]\tLoss: 0.314176, Accuracy: 89.45\n",
      "Train Epoch: 33 [16640/50000 (37%)]\tLoss: 0.382159, Accuracy: 87.50\n",
      "Train Epoch: 33 [17920/50000 (40%)]\tLoss: 0.421951, Accuracy: 87.89\n",
      "Train Epoch: 33 [19200/50000 (43%)]\tLoss: 0.455531, Accuracy: 85.55\n",
      "Train Epoch: 33 [20480/50000 (45%)]\tLoss: 0.422464, Accuracy: 85.16\n",
      "Train Epoch: 33 [21760/50000 (48%)]\tLoss: 0.456856, Accuracy: 85.16\n",
      "Train Epoch: 33 [23040/50000 (51%)]\tLoss: 0.502020, Accuracy: 83.59\n",
      "Train Epoch: 33 [24320/50000 (54%)]\tLoss: 0.376911, Accuracy: 87.11\n",
      "Train Epoch: 33 [25600/50000 (57%)]\tLoss: 0.468832, Accuracy: 84.77\n",
      "Train Epoch: 33 [26880/50000 (60%)]\tLoss: 0.382655, Accuracy: 87.50\n",
      "Train Epoch: 33 [28160/50000 (62%)]\tLoss: 0.424944, Accuracy: 83.98\n",
      "Train Epoch: 33 [29440/50000 (65%)]\tLoss: 0.367753, Accuracy: 87.89\n",
      "Train Epoch: 33 [30720/50000 (68%)]\tLoss: 0.401695, Accuracy: 85.55\n",
      "Train Epoch: 33 [32000/50000 (71%)]\tLoss: 0.450179, Accuracy: 83.59\n",
      "Train Epoch: 33 [33280/50000 (74%)]\tLoss: 0.356550, Accuracy: 89.06\n",
      "Train Epoch: 33 [34560/50000 (77%)]\tLoss: 0.298390, Accuracy: 89.45\n",
      "Train Epoch: 33 [35840/50000 (80%)]\tLoss: 0.478986, Accuracy: 83.98\n",
      "Train Epoch: 33 [37120/50000 (82%)]\tLoss: 0.552160, Accuracy: 83.59\n",
      "Train Epoch: 33 [38400/50000 (85%)]\tLoss: 0.481016, Accuracy: 86.72\n",
      "Train Epoch: 33 [39680/50000 (88%)]\tLoss: 0.323853, Accuracy: 90.23\n",
      "Train Epoch: 33 [40960/50000 (91%)]\tLoss: 0.359063, Accuracy: 87.50\n",
      "Train Epoch: 33 [42240/50000 (94%)]\tLoss: 0.482148, Accuracy: 82.03\n",
      "Train Epoch: 33 [43520/50000 (97%)]\tLoss: 0.453626, Accuracy: 84.38\n",
      "Train Epoch: 33 [35000/50000 (99%)]\tLoss: 0.439348, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.7662, Accuracy: 3823/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.45626449584961 s]\n",
      "Train Epoch: 34 [0/50000 (0%)]\tLoss: 0.353162, Accuracy: 89.45\n",
      "Train Epoch: 34 [1280/50000 (3%)]\tLoss: 0.357309, Accuracy: 88.67\n",
      "Train Epoch: 34 [2560/50000 (6%)]\tLoss: 0.390393, Accuracy: 86.72\n",
      "Train Epoch: 34 [3840/50000 (9%)]\tLoss: 0.361230, Accuracy: 86.72\n",
      "Train Epoch: 34 [5120/50000 (11%)]\tLoss: 0.331873, Accuracy: 89.06\n",
      "Train Epoch: 34 [6400/50000 (14%)]\tLoss: 0.359509, Accuracy: 86.72\n",
      "Train Epoch: 34 [7680/50000 (17%)]\tLoss: 0.338693, Accuracy: 89.45\n",
      "Train Epoch: 34 [8960/50000 (20%)]\tLoss: 0.433206, Accuracy: 85.55\n",
      "Train Epoch: 34 [10240/50000 (23%)]\tLoss: 0.422255, Accuracy: 84.77\n",
      "Train Epoch: 34 [11520/50000 (26%)]\tLoss: 0.325181, Accuracy: 89.06\n",
      "Train Epoch: 34 [12800/50000 (28%)]\tLoss: 0.353785, Accuracy: 89.45\n",
      "Train Epoch: 34 [14080/50000 (31%)]\tLoss: 0.503906, Accuracy: 83.98\n",
      "Train Epoch: 34 [15360/50000 (34%)]\tLoss: 0.382334, Accuracy: 86.33\n",
      "Train Epoch: 34 [16640/50000 (37%)]\tLoss: 0.313914, Accuracy: 91.02\n",
      "Train Epoch: 34 [17920/50000 (40%)]\tLoss: 0.353037, Accuracy: 86.72\n",
      "Train Epoch: 34 [19200/50000 (43%)]\tLoss: 0.391521, Accuracy: 87.50\n",
      "Train Epoch: 34 [20480/50000 (45%)]\tLoss: 0.364479, Accuracy: 87.89\n",
      "Train Epoch: 34 [21760/50000 (48%)]\tLoss: 0.463354, Accuracy: 85.16\n",
      "Train Epoch: 34 [23040/50000 (51%)]\tLoss: 0.391368, Accuracy: 86.72\n",
      "Train Epoch: 34 [24320/50000 (54%)]\tLoss: 0.413129, Accuracy: 87.11\n",
      "Train Epoch: 34 [25600/50000 (57%)]\tLoss: 0.418133, Accuracy: 82.81\n",
      "Train Epoch: 34 [26880/50000 (60%)]\tLoss: 0.430652, Accuracy: 83.98\n",
      "Train Epoch: 34 [28160/50000 (62%)]\tLoss: 0.299478, Accuracy: 89.84\n",
      "Train Epoch: 34 [29440/50000 (65%)]\tLoss: 0.316754, Accuracy: 89.84\n",
      "Train Epoch: 34 [30720/50000 (68%)]\tLoss: 0.468738, Accuracy: 83.20\n",
      "Train Epoch: 34 [32000/50000 (71%)]\tLoss: 0.402670, Accuracy: 84.77\n",
      "Train Epoch: 34 [33280/50000 (74%)]\tLoss: 0.386344, Accuracy: 88.67\n",
      "Train Epoch: 34 [34560/50000 (77%)]\tLoss: 0.442317, Accuracy: 83.20\n",
      "Train Epoch: 34 [35840/50000 (80%)]\tLoss: 0.407330, Accuracy: 87.50\n",
      "Train Epoch: 34 [37120/50000 (82%)]\tLoss: 0.491955, Accuracy: 84.77\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.8229]],\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 34 [38400/50000 (85%)]\tLoss: 0.381778, Accuracy: 87.50\n",
      "Train Epoch: 34 [39680/50000 (88%)]\tLoss: 0.428820, Accuracy: 84.38\n",
      "Train Epoch: 34 [40960/50000 (91%)]\tLoss: 0.358834, Accuracy: 89.06\n",
      "Train Epoch: 34 [42240/50000 (94%)]\tLoss: 0.530289, Accuracy: 83.98\n",
      "Train Epoch: 34 [43520/50000 (97%)]\tLoss: 0.399432, Accuracy: 85.94\n",
      "Train Epoch: 34 [35000/50000 (99%)]\tLoss: 0.383538, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6161, Accuracy: 3962/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.29870104789734 s]\n",
      "\n",
      "Test set: Average loss: 0.6155, Accuracy: 7971/10000 (79.71%)\n",
      "\n",
      "Train Epoch: 35 [0/50000 (0%)]\tLoss: 0.386020, Accuracy: 88.67\n",
      "Train Epoch: 35 [1280/50000 (3%)]\tLoss: 0.321128, Accuracy: 89.84\n",
      "Train Epoch: 35 [2560/50000 (6%)]\tLoss: 0.325898, Accuracy: 89.45\n",
      "Train Epoch: 35 [3840/50000 (9%)]\tLoss: 0.360748, Accuracy: 85.94\n",
      "Train Epoch: 35 [5120/50000 (11%)]\tLoss: 0.368207, Accuracy: 87.50\n",
      "Train Epoch: 35 [6400/50000 (14%)]\tLoss: 0.339880, Accuracy: 89.06\n",
      "Train Epoch: 35 [7680/50000 (17%)]\tLoss: 0.369783, Accuracy: 88.28\n",
      "Train Epoch: 35 [8960/50000 (20%)]\tLoss: 0.526831, Accuracy: 82.03\n",
      "Train Epoch: 35 [10240/50000 (23%)]\tLoss: 0.347681, Accuracy: 87.50\n",
      "Train Epoch: 35 [11520/50000 (26%)]\tLoss: 0.451053, Accuracy: 85.16\n",
      "Train Epoch: 35 [12800/50000 (28%)]\tLoss: 0.376296, Accuracy: 87.50\n",
      "Train Epoch: 35 [14080/50000 (31%)]\tLoss: 0.456640, Accuracy: 83.59\n",
      "Train Epoch: 35 [15360/50000 (34%)]\tLoss: 0.401376, Accuracy: 85.55\n",
      "Train Epoch: 35 [16640/50000 (37%)]\tLoss: 0.352919, Accuracy: 88.67\n",
      "Train Epoch: 35 [17920/50000 (40%)]\tLoss: 0.365559, Accuracy: 88.67\n",
      "Train Epoch: 35 [19200/50000 (43%)]\tLoss: 0.364179, Accuracy: 87.50\n",
      "Train Epoch: 35 [20480/50000 (45%)]\tLoss: 0.403200, Accuracy: 86.33\n",
      "Train Epoch: 35 [21760/50000 (48%)]\tLoss: 0.381749, Accuracy: 86.33\n",
      "Train Epoch: 35 [23040/50000 (51%)]\tLoss: 0.353943, Accuracy: 87.89\n",
      "Train Epoch: 35 [24320/50000 (54%)]\tLoss: 0.433715, Accuracy: 84.77\n",
      "Train Epoch: 35 [25600/50000 (57%)]\tLoss: 0.267566, Accuracy: 92.19\n",
      "Train Epoch: 35 [26880/50000 (60%)]\tLoss: 0.367664, Accuracy: 87.89\n",
      "Train Epoch: 35 [28160/50000 (62%)]\tLoss: 0.332037, Accuracy: 89.06\n",
      "Train Epoch: 35 [29440/50000 (65%)]\tLoss: 0.386515, Accuracy: 87.50\n",
      "Train Epoch: 35 [30720/50000 (68%)]\tLoss: 0.369715, Accuracy: 88.67\n",
      "Train Epoch: 35 [32000/50000 (71%)]\tLoss: 0.457991, Accuracy: 85.16\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
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      "tensor([[[ 4.5949e-05,  3.0979e-06,  3.9875e-05,  ...,  3.1752e-05,\n",
      "           3.5462e-05,  1.5355e-05],\n",
      "         [ 1.8960e-03,  1.9158e-03,  1.8989e-03,  ...,  1.8446e-03,\n",
      "           1.9127e-03,  1.9287e-03],\n",
      "         [-4.2698e-04, -4.3958e-04, -4.4581e-04,  ..., -4.0067e-04,\n",
      "          -4.3296e-04, -4.4617e-04],\n",
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      "         [ 8.4052e-04,  8.0928e-04,  8.1799e-04,  ...,  8.2342e-04,\n",
      "           8.1276e-04,  8.2779e-04],\n",
      "         [ 1.4298e-03,  1.4348e-03,  1.4304e-03,  ...,  1.4018e-03,\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 35 [33280/50000 (74%)]\tLoss: 0.444279, Accuracy: 86.33\n",
      "Train Epoch: 35 [34560/50000 (77%)]\tLoss: 0.474252, Accuracy: 83.98\n",
      "Train Epoch: 35 [35840/50000 (80%)]\tLoss: 0.421689, Accuracy: 87.89\n",
      "Train Epoch: 35 [37120/50000 (82%)]\tLoss: 0.437137, Accuracy: 84.38\n",
      "Train Epoch: 35 [38400/50000 (85%)]\tLoss: 0.338578, Accuracy: 89.06\n",
      "Train Epoch: 35 [39680/50000 (88%)]\tLoss: 0.491081, Accuracy: 82.81\n",
      "Train Epoch: 35 [40960/50000 (91%)]\tLoss: 0.439332, Accuracy: 87.11\n",
      "Train Epoch: 35 [42240/50000 (94%)]\tLoss: 0.388331, Accuracy: 85.94\n",
      "Train Epoch: 35 [43520/50000 (97%)]\tLoss: 0.395103, Accuracy: 85.94\n",
      "Train Epoch: 35 [35000/50000 (99%)]\tLoss: 0.404540, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7671, Accuracy: 3837/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.43820571899414 s]\n",
      "Train Epoch: 36 [0/50000 (0%)]\tLoss: 0.444071, Accuracy: 85.16\n",
      "Train Epoch: 36 [1280/50000 (3%)]\tLoss: 0.375584, Accuracy: 86.72\n",
      "Train Epoch: 36 [2560/50000 (6%)]\tLoss: 0.377513, Accuracy: 86.72\n",
      "Train Epoch: 36 [3840/50000 (9%)]\tLoss: 0.423385, Accuracy: 84.38\n",
      "Train Epoch: 36 [5120/50000 (11%)]\tLoss: 0.414035, Accuracy: 84.77\n",
      "Train Epoch: 36 [6400/50000 (14%)]\tLoss: 0.375099, Accuracy: 88.28\n",
      "Train Epoch: 36 [7680/50000 (17%)]\tLoss: 0.469814, Accuracy: 85.94\n",
      "Train Epoch: 36 [8960/50000 (20%)]\tLoss: 0.420899, Accuracy: 86.72\n",
      "Train Epoch: 36 [10240/50000 (23%)]\tLoss: 0.371929, Accuracy: 87.50\n",
      "Train Epoch: 36 [11520/50000 (26%)]\tLoss: 0.425815, Accuracy: 84.38\n",
      "Train Epoch: 36 [12800/50000 (28%)]\tLoss: 0.416140, Accuracy: 86.33\n",
      "Train Epoch: 36 [14080/50000 (31%)]\tLoss: 0.496979, Accuracy: 82.42\n",
      "Train Epoch: 36 [15360/50000 (34%)]\tLoss: 0.507089, Accuracy: 82.81\n",
      "Train Epoch: 36 [16640/50000 (37%)]\tLoss: 0.392694, Accuracy: 87.11\n",
      "Train Epoch: 36 [17920/50000 (40%)]\tLoss: 0.380693, Accuracy: 87.50\n",
      "Train Epoch: 36 [19200/50000 (43%)]\tLoss: 0.413190, Accuracy: 87.11\n",
      "Train Epoch: 36 [20480/50000 (45%)]\tLoss: 0.382495, Accuracy: 87.11\n",
      "Train Epoch: 36 [21760/50000 (48%)]\tLoss: 0.339867, Accuracy: 90.23\n",
      "Train Epoch: 36 [23040/50000 (51%)]\tLoss: 0.432394, Accuracy: 84.77\n",
      "Train Epoch: 36 [24320/50000 (54%)]\tLoss: 0.435082, Accuracy: 84.77\n",
      "Train Epoch: 36 [25600/50000 (57%)]\tLoss: 0.361033, Accuracy: 87.11\n",
      "Train Epoch: 36 [26880/50000 (60%)]\tLoss: 0.398513, Accuracy: 86.72\n",
      "Train Epoch: 36 [28160/50000 (62%)]\tLoss: 0.323851, Accuracy: 89.06\n",
      "Train Epoch: 36 [29440/50000 (65%)]\tLoss: 0.426065, Accuracy: 85.94\n",
      "Train Epoch: 36 [30720/50000 (68%)]\tLoss: 0.401129, Accuracy: 84.77\n",
      "Train Epoch: 36 [32000/50000 (71%)]\tLoss: 0.419579, Accuracy: 85.55\n",
      "Train Epoch: 36 [33280/50000 (74%)]\tLoss: 0.495708, Accuracy: 84.38\n",
      "Train Epoch: 36 [34560/50000 (77%)]\tLoss: 0.377208, Accuracy: 85.55\n",
      "Train Epoch: 36 [35840/50000 (80%)]\tLoss: 0.403544, Accuracy: 87.50\n",
      "Train Epoch: 36 [37120/50000 (82%)]\tLoss: 0.424289, Accuracy: 84.38\n",
      "Train Epoch: 36 [38400/50000 (85%)]\tLoss: 0.403637, Accuracy: 87.11\n",
      "Train Epoch: 36 [39680/50000 (88%)]\tLoss: 0.389285, Accuracy: 85.16\n",
      "Train Epoch: 36 [40960/50000 (91%)]\tLoss: 0.448471, Accuracy: 82.42\n",
      "Train Epoch: 36 [42240/50000 (94%)]\tLoss: 0.461209, Accuracy: 85.55\n",
      "Train Epoch: 36 [43520/50000 (97%)]\tLoss: 0.452444, Accuracy: 84.77\n",
      "Train Epoch: 36 [35000/50000 (99%)]\tLoss: 0.458198, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.7164, Accuracy: 3877/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.276729583740234 s]\n",
      "\n",
      "Test set: Average loss: 0.7524, Accuracy: 7646/10000 (76.46%)\n",
      "\n",
      "Train Epoch: 37 [0/50000 (0%)]\tLoss: 0.393044, Accuracy: 84.38\n",
      "Train Epoch: 37 [1280/50000 (3%)]\tLoss: 0.420254, Accuracy: 84.77\n",
      "Train Epoch: 37 [2560/50000 (6%)]\tLoss: 0.317519, Accuracy: 90.23\n",
      "Train Epoch: 37 [3840/50000 (9%)]\tLoss: 0.404893, Accuracy: 87.50\n",
      "Train Epoch: 37 [5120/50000 (11%)]\tLoss: 0.306665, Accuracy: 89.06\n",
      "Train Epoch: 37 [6400/50000 (14%)]\tLoss: 0.364520, Accuracy: 87.11\n",
      "Train Epoch: 37 [7680/50000 (17%)]\tLoss: 0.388283, Accuracy: 86.33\n",
      "Train Epoch: 37 [8960/50000 (20%)]\tLoss: 0.478116, Accuracy: 81.25\n",
      "Train Epoch: 37 [10240/50000 (23%)]\tLoss: 0.285316, Accuracy: 89.06\n",
      "Train Epoch: 37 [11520/50000 (26%)]\tLoss: 0.424723, Accuracy: 84.77\n",
      "Train Epoch: 37 [12800/50000 (28%)]\tLoss: 0.513020, Accuracy: 80.47\n",
      "Train Epoch: 37 [14080/50000 (31%)]\tLoss: 0.546150, Accuracy: 78.91\n",
      "Train Epoch: 37 [15360/50000 (34%)]\tLoss: 0.402826, Accuracy: 86.72\n",
      "Train Epoch: 37 [16640/50000 (37%)]\tLoss: 0.373036, Accuracy: 88.67\n",
      "Train Epoch: 37 [17920/50000 (40%)]\tLoss: 0.393717, Accuracy: 87.50\n",
      "Train Epoch: 37 [19200/50000 (43%)]\tLoss: 0.293772, Accuracy: 91.02\n",
      "Train Epoch: 37 [20480/50000 (45%)]\tLoss: 0.406549, Accuracy: 85.16\n",
      "Train Epoch: 37 [21760/50000 (48%)]\tLoss: 0.459736, Accuracy: 84.38\n",
      "Train Epoch: 37 [23040/50000 (51%)]\tLoss: 0.467596, Accuracy: 84.38\n",
      "Train Epoch: 37 [24320/50000 (54%)]\tLoss: 0.361844, Accuracy: 87.11\n",
      "Train Epoch: 37 [25600/50000 (57%)]\tLoss: 0.310088, Accuracy: 89.84\n",
      "Train Epoch: 37 [26880/50000 (60%)]\tLoss: 0.469384, Accuracy: 83.20\n",
      "Train Epoch: 37 [28160/50000 (62%)]\tLoss: 0.314440, Accuracy: 90.23\n",
      "Train Epoch: 37 [29440/50000 (65%)]\tLoss: 0.390324, Accuracy: 86.33\n",
      "Train Epoch: 37 [30720/50000 (68%)]\tLoss: 0.422337, Accuracy: 85.94\n",
      "Train Epoch: 37 [32000/50000 (71%)]\tLoss: 0.398520, Accuracy: 86.33\n",
      "Train Epoch: 37 [33280/50000 (74%)]\tLoss: 0.422355, Accuracy: 83.98\n",
      "Train Epoch: 37 [34560/50000 (77%)]\tLoss: 0.361772, Accuracy: 90.23\n",
      "Train Epoch: 37 [35840/50000 (80%)]\tLoss: 0.460463, Accuracy: 86.72\n",
      "Train Epoch: 37 [37120/50000 (82%)]\tLoss: 0.433627, Accuracy: 85.55\n",
      "Train Epoch: 37 [38400/50000 (85%)]\tLoss: 0.423046, Accuracy: 84.38\n",
      "Train Epoch: 37 [39680/50000 (88%)]\tLoss: 0.346653, Accuracy: 87.50\n",
      "Train Epoch: 37 [40960/50000 (91%)]\tLoss: 0.383708, Accuracy: 89.06\n",
      "Train Epoch: 37 [42240/50000 (94%)]\tLoss: 0.463080, Accuracy: 85.16\n",
      "Train Epoch: 37 [43520/50000 (97%)]\tLoss: 0.382049, Accuracy: 87.50\n",
      "Train Epoch: 37 [35000/50000 (99%)]\tLoss: 0.430342, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6676, Accuracy: 3821/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.406681060791016 s]\n",
      "Train Epoch: 38 [0/50000 (0%)]\tLoss: 0.383171, Accuracy: 85.16\n",
      "Train Epoch: 38 [1280/50000 (3%)]\tLoss: 0.346714, Accuracy: 87.11\n",
      "Train Epoch: 38 [2560/50000 (6%)]\tLoss: 0.346339, Accuracy: 90.62\n",
      "Train Epoch: 38 [3840/50000 (9%)]\tLoss: 0.353714, Accuracy: 88.28\n",
      "Train Epoch: 38 [5120/50000 (11%)]\tLoss: 0.272977, Accuracy: 92.19\n",
      "Train Epoch: 38 [6400/50000 (14%)]\tLoss: 0.506340, Accuracy: 82.81\n",
      "Train Epoch: 38 [7680/50000 (17%)]\tLoss: 0.408510, Accuracy: 86.72\n",
      "Train Epoch: 38 [8960/50000 (20%)]\tLoss: 0.446601, Accuracy: 85.94\n",
      "Train Epoch: 38 [10240/50000 (23%)]\tLoss: 0.315938, Accuracy: 90.62\n",
      "Train Epoch: 38 [11520/50000 (26%)]\tLoss: 0.362714, Accuracy: 85.94\n",
      "Train Epoch: 38 [12800/50000 (28%)]\tLoss: 0.373034, Accuracy: 87.89\n",
      "Train Epoch: 38 [14080/50000 (31%)]\tLoss: 0.436570, Accuracy: 86.33\n",
      "Train Epoch: 38 [15360/50000 (34%)]\tLoss: 0.363255, Accuracy: 88.28\n",
      "Train Epoch: 38 [16640/50000 (37%)]\tLoss: 0.455094, Accuracy: 85.55\n",
      "Train Epoch: 38 [17920/50000 (40%)]\tLoss: 0.516383, Accuracy: 83.59\n",
      "Train Epoch: 38 [19200/50000 (43%)]\tLoss: 0.418854, Accuracy: 83.98\n",
      "Train Epoch: 38 [20480/50000 (45%)]\tLoss: 0.446048, Accuracy: 83.20\n",
      "Train Epoch: 38 [21760/50000 (48%)]\tLoss: 0.506241, Accuracy: 84.38\n",
      "Train Epoch: 38 [23040/50000 (51%)]\tLoss: 0.374721, Accuracy: 85.94\n",
      "Train Epoch: 38 [24320/50000 (54%)]\tLoss: 0.439467, Accuracy: 83.98\n",
      "Train Epoch: 38 [25600/50000 (57%)]\tLoss: 0.384880, Accuracy: 88.28\n",
      "Train Epoch: 38 [26880/50000 (60%)]\tLoss: 0.304274, Accuracy: 91.02\n",
      "Train Epoch: 38 [28160/50000 (62%)]\tLoss: 0.439877, Accuracy: 85.16\n",
      "Train Epoch: 38 [29440/50000 (65%)]\tLoss: 0.382494, Accuracy: 86.72\n",
      "Train Epoch: 38 [30720/50000 (68%)]\tLoss: 0.419602, Accuracy: 86.33\n",
      "Train Epoch: 38 [32000/50000 (71%)]\tLoss: 0.377404, Accuracy: 85.94\n",
      "Train Epoch: 38 [33280/50000 (74%)]\tLoss: 0.310060, Accuracy: 89.84\n",
      "Train Epoch: 38 [34560/50000 (77%)]\tLoss: 0.435542, Accuracy: 85.55\n",
      "Train Epoch: 38 [35840/50000 (80%)]\tLoss: 0.424215, Accuracy: 85.55\n",
      "Train Epoch: 38 [37120/50000 (82%)]\tLoss: 0.368300, Accuracy: 86.33\n",
      "Train Epoch: 38 [38400/50000 (85%)]\tLoss: 0.315418, Accuracy: 89.84\n",
      "Train Epoch: 38 [39680/50000 (88%)]\tLoss: 0.373914, Accuracy: 88.67\n",
      "Train Epoch: 38 [40960/50000 (91%)]\tLoss: 0.300823, Accuracy: 91.02\n",
      "Train Epoch: 38 [42240/50000 (94%)]\tLoss: 0.381599, Accuracy: 86.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 38 [43520/50000 (97%)]\tLoss: 0.346661, Accuracy: 89.84\n",
      "Train Epoch: 38 [35000/50000 (99%)]\tLoss: 0.424610, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6340, Accuracy: 4038/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[36.32576942443848 s]\n",
      "\n",
      "Test set: Average loss: 0.6389, Accuracy: 8017/10000 (80.17%)\n",
      "\n",
      "Train Epoch: 39 [0/50000 (0%)]\tLoss: 0.380700, Accuracy: 87.11\n",
      "Train Epoch: 39 [1280/50000 (3%)]\tLoss: 0.335637, Accuracy: 87.11\n",
      "Train Epoch: 39 [2560/50000 (6%)]\tLoss: 0.438443, Accuracy: 85.55\n",
      "Train Epoch: 39 [3840/50000 (9%)]\tLoss: 0.390839, Accuracy: 85.94\n",
      "Train Epoch: 39 [5120/50000 (11%)]\tLoss: 0.318189, Accuracy: 88.67\n",
      "Train Epoch: 39 [6400/50000 (14%)]\tLoss: 0.516025, Accuracy: 83.59\n",
      "Train Epoch: 39 [7680/50000 (17%)]\tLoss: 0.337853, Accuracy: 88.67\n",
      "Train Epoch: 39 [8960/50000 (20%)]\tLoss: 0.423792, Accuracy: 86.33\n",
      "Train Epoch: 39 [10240/50000 (23%)]\tLoss: 0.363144, Accuracy: 87.50\n",
      "Train Epoch: 39 [11520/50000 (26%)]\tLoss: 0.455338, Accuracy: 83.59\n",
      "Train Epoch: 39 [12800/50000 (28%)]\tLoss: 0.375507, Accuracy: 87.89\n",
      "Train Epoch: 39 [14080/50000 (31%)]\tLoss: 0.374195, Accuracy: 87.89\n",
      "Train Epoch: 39 [15360/50000 (34%)]\tLoss: 0.448607, Accuracy: 84.38\n",
      "Train Epoch: 39 [16640/50000 (37%)]\tLoss: 0.386380, Accuracy: 88.28\n",
      "Train Epoch: 39 [17920/50000 (40%)]\tLoss: 0.379661, Accuracy: 87.89\n",
      "Train Epoch: 39 [19200/50000 (43%)]\tLoss: 0.316972, Accuracy: 90.62\n",
      "Train Epoch: 39 [20480/50000 (45%)]\tLoss: 0.368117, Accuracy: 88.67\n",
      "Train Epoch: 39 [21760/50000 (48%)]\tLoss: 0.422500, Accuracy: 88.67\n",
      "Train Epoch: 39 [23040/50000 (51%)]\tLoss: 0.302979, Accuracy: 89.45\n",
      "Train Epoch: 39 [24320/50000 (54%)]\tLoss: 0.332838, Accuracy: 87.50\n",
      "Train Epoch: 39 [25600/50000 (57%)]\tLoss: 0.401368, Accuracy: 87.50\n",
      "Train Epoch: 39 [26880/50000 (60%)]\tLoss: 0.368779, Accuracy: 88.67\n",
      "Train Epoch: 39 [28160/50000 (62%)]\tLoss: 0.348855, Accuracy: 86.72\n",
      "Train Epoch: 39 [29440/50000 (65%)]\tLoss: 0.340836, Accuracy: 87.11\n",
      "Train Epoch: 39 [30720/50000 (68%)]\tLoss: 0.306096, Accuracy: 90.62\n",
      "Train Epoch: 39 [32000/50000 (71%)]\tLoss: 0.434892, Accuracy: 85.94\n",
      "Train Epoch: 39 [33280/50000 (74%)]\tLoss: 0.437019, Accuracy: 86.33\n",
      "Train Epoch: 39 [34560/50000 (77%)]\tLoss: 0.421595, Accuracy: 87.11\n",
      "Train Epoch: 39 [35840/50000 (80%)]\tLoss: 0.422853, Accuracy: 84.77\n",
      "Train Epoch: 39 [37120/50000 (82%)]\tLoss: 0.441893, Accuracy: 85.55\n",
      "Train Epoch: 39 [38400/50000 (85%)]\tLoss: 0.414963, Accuracy: 84.77\n",
      "Train Epoch: 39 [39680/50000 (88%)]\tLoss: 0.415270, Accuracy: 84.38\n",
      "Train Epoch: 39 [40960/50000 (91%)]\tLoss: 0.358870, Accuracy: 87.11\n",
      "Train Epoch: 39 [42240/50000 (94%)]\tLoss: 0.383229, Accuracy: 86.72\n",
      "Train Epoch: 39 [43520/50000 (97%)]\tLoss: 0.428478, Accuracy: 86.33\n",
      "Train Epoch: 39 [35000/50000 (99%)]\tLoss: 0.499525, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.8116, Accuracy: 3746/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[39.41126775741577 s]\n",
      "Train Epoch: 40 [0/50000 (0%)]\tLoss: 0.398017, Accuracy: 85.16\n",
      "Train Epoch: 40 [1280/50000 (3%)]\tLoss: 0.399271, Accuracy: 87.11\n",
      "Train Epoch: 40 [2560/50000 (6%)]\tLoss: 0.369287, Accuracy: 85.94\n",
      "Train Epoch: 40 [3840/50000 (9%)]\tLoss: 0.435088, Accuracy: 85.94\n",
      "Train Epoch: 40 [5120/50000 (11%)]\tLoss: 0.309668, Accuracy: 91.80\n",
      "Train Epoch: 40 [6400/50000 (14%)]\tLoss: 0.278411, Accuracy: 90.62\n",
      "Train Epoch: 40 [7680/50000 (17%)]\tLoss: 0.367456, Accuracy: 86.72\n",
      "Train Epoch: 40 [8960/50000 (20%)]\tLoss: 0.389369, Accuracy: 86.33\n",
      "Train Epoch: 40 [10240/50000 (23%)]\tLoss: 0.340615, Accuracy: 87.89\n",
      "Train Epoch: 40 [11520/50000 (26%)]\tLoss: 0.388397, Accuracy: 87.11\n",
      "Train Epoch: 40 [12800/50000 (28%)]\tLoss: 0.302801, Accuracy: 88.67\n",
      "Train Epoch: 40 [14080/50000 (31%)]\tLoss: 0.337999, Accuracy: 87.89\n",
      "Train Epoch: 40 [15360/50000 (34%)]\tLoss: 0.358759, Accuracy: 89.06\n",
      "Train Epoch: 40 [16640/50000 (37%)]\tLoss: 0.492750, Accuracy: 85.16\n",
      "Train Epoch: 40 [17920/50000 (40%)]\tLoss: 0.355639, Accuracy: 90.23\n",
      "Train Epoch: 40 [19200/50000 (43%)]\tLoss: 0.294918, Accuracy: 90.23\n",
      "Train Epoch: 40 [20480/50000 (45%)]\tLoss: 0.382166, Accuracy: 85.16\n",
      "Train Epoch: 40 [21760/50000 (48%)]\tLoss: 0.348867, Accuracy: 88.67\n",
      "Train Epoch: 40 [23040/50000 (51%)]\tLoss: 0.463425, Accuracy: 85.94\n",
      "Train Epoch: 40 [24320/50000 (54%)]\tLoss: 0.425744, Accuracy: 87.50\n",
      "Train Epoch: 40 [25600/50000 (57%)]\tLoss: 0.451467, Accuracy: 85.16\n",
      "Train Epoch: 40 [26880/50000 (60%)]\tLoss: 0.318906, Accuracy: 88.28\n",
      "torch.Size([512, 1, 1])\n",
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      "torch.Size([1, 896, 512])\n",
      "tensor([[[-8.5320e-05, -1.3335e-04, -8.3502e-05,  ..., -7.8700e-05,\n",
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      "          -3.7220e-04, -3.6398e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 40 [28160/50000 (62%)]\tLoss: 0.428981, Accuracy: 85.94\n",
      "Train Epoch: 40 [29440/50000 (65%)]\tLoss: 0.379405, Accuracy: 88.28\n",
      "Train Epoch: 40 [30720/50000 (68%)]\tLoss: 0.273812, Accuracy: 87.89\n",
      "Train Epoch: 40 [32000/50000 (71%)]\tLoss: 0.332366, Accuracy: 88.28\n",
      "Train Epoch: 40 [33280/50000 (74%)]\tLoss: 0.403153, Accuracy: 85.94\n",
      "Train Epoch: 40 [34560/50000 (77%)]\tLoss: 0.341865, Accuracy: 89.84\n",
      "Train Epoch: 40 [35840/50000 (80%)]\tLoss: 0.346085, Accuracy: 87.89\n",
      "Train Epoch: 40 [37120/50000 (82%)]\tLoss: 0.347020, Accuracy: 86.72\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 40 [38400/50000 (85%)]\tLoss: 0.416124, Accuracy: 85.55\n",
      "Train Epoch: 40 [39680/50000 (88%)]\tLoss: 0.357095, Accuracy: 89.06\n",
      "Train Epoch: 40 [40960/50000 (91%)]\tLoss: 0.385775, Accuracy: 88.67\n",
      "Train Epoch: 40 [42240/50000 (94%)]\tLoss: 0.467713, Accuracy: 86.33\n",
      "Train Epoch: 40 [43520/50000 (97%)]\tLoss: 0.350438, Accuracy: 88.28\n",
      "Train Epoch: 40 [35000/50000 (99%)]\tLoss: 0.411136, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.7424, Accuracy: 3846/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.31203246116638 s]\n",
      "\n",
      "Test set: Average loss: 0.7649, Accuracy: 7612/10000 (76.12%)\n",
      "\n",
      "Train Epoch: 41 [0/50000 (0%)]\tLoss: 0.354475, Accuracy: 86.72\n",
      "Train Epoch: 41 [1280/50000 (3%)]\tLoss: 0.341551, Accuracy: 86.72\n",
      "Train Epoch: 41 [2560/50000 (6%)]\tLoss: 0.427171, Accuracy: 84.77\n",
      "Train Epoch: 41 [3840/50000 (9%)]\tLoss: 0.370378, Accuracy: 87.50\n",
      "Train Epoch: 41 [5120/50000 (11%)]\tLoss: 0.339996, Accuracy: 87.89\n",
      "Train Epoch: 41 [6400/50000 (14%)]\tLoss: 0.316873, Accuracy: 89.06\n",
      "Train Epoch: 41 [7680/50000 (17%)]\tLoss: 0.366736, Accuracy: 87.11\n",
      "Train Epoch: 41 [8960/50000 (20%)]\tLoss: 0.369290, Accuracy: 86.72\n",
      "Train Epoch: 41 [10240/50000 (23%)]\tLoss: 0.488383, Accuracy: 83.98\n",
      "Train Epoch: 41 [11520/50000 (26%)]\tLoss: 0.393860, Accuracy: 86.72\n",
      "Train Epoch: 41 [12800/50000 (28%)]\tLoss: 0.360421, Accuracy: 87.11\n",
      "Train Epoch: 41 [14080/50000 (31%)]\tLoss: 0.325873, Accuracy: 87.89\n",
      "Train Epoch: 41 [15360/50000 (34%)]\tLoss: 0.373887, Accuracy: 85.55\n",
      "Train Epoch: 41 [16640/50000 (37%)]\tLoss: 0.392540, Accuracy: 88.28\n",
      "Train Epoch: 41 [17920/50000 (40%)]\tLoss: 0.304697, Accuracy: 91.80\n",
      "Train Epoch: 41 [19200/50000 (43%)]\tLoss: 0.397804, Accuracy: 86.72\n",
      "Train Epoch: 41 [20480/50000 (45%)]\tLoss: 0.474264, Accuracy: 84.77\n",
      "Train Epoch: 41 [21760/50000 (48%)]\tLoss: 0.384301, Accuracy: 86.72\n",
      "Train Epoch: 41 [23040/50000 (51%)]\tLoss: 0.315751, Accuracy: 89.84\n",
      "Train Epoch: 41 [24320/50000 (54%)]\tLoss: 0.388676, Accuracy: 88.67\n",
      "Train Epoch: 41 [25600/50000 (57%)]\tLoss: 0.397659, Accuracy: 86.72\n",
      "Train Epoch: 41 [26880/50000 (60%)]\tLoss: 0.351577, Accuracy: 87.89\n",
      "Train Epoch: 41 [28160/50000 (62%)]\tLoss: 0.401998, Accuracy: 83.98\n",
      "Train Epoch: 41 [29440/50000 (65%)]\tLoss: 0.440707, Accuracy: 85.16\n",
      "Train Epoch: 41 [30720/50000 (68%)]\tLoss: 0.416203, Accuracy: 85.55\n",
      "Train Epoch: 41 [32000/50000 (71%)]\tLoss: 0.395298, Accuracy: 86.33\n",
      "Train Epoch: 41 [33280/50000 (74%)]\tLoss: 0.403714, Accuracy: 87.11\n",
      "Train Epoch: 41 [34560/50000 (77%)]\tLoss: 0.484437, Accuracy: 83.59\n",
      "Train Epoch: 41 [35840/50000 (80%)]\tLoss: 0.337739, Accuracy: 89.06\n",
      "Train Epoch: 41 [37120/50000 (82%)]\tLoss: 0.379778, Accuracy: 86.72\n",
      "Train Epoch: 41 [38400/50000 (85%)]\tLoss: 0.361459, Accuracy: 87.50\n",
      "Train Epoch: 41 [39680/50000 (88%)]\tLoss: 0.333963, Accuracy: 89.06\n",
      "Train Epoch: 41 [40960/50000 (91%)]\tLoss: 0.352739, Accuracy: 88.28\n",
      "Train Epoch: 41 [42240/50000 (94%)]\tLoss: 0.444328, Accuracy: 86.72\n",
      "Train Epoch: 41 [43520/50000 (97%)]\tLoss: 0.329832, Accuracy: 89.45\n",
      "Train Epoch: 41 [35000/50000 (99%)]\tLoss: 0.462570, Accuracy: 83.50\n",
      "\n",
      "Validation set: Average loss: 0.7324, Accuracy: 3814/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.415035009384155 s]\n",
      "Train Epoch: 42 [0/50000 (0%)]\tLoss: 0.368213, Accuracy: 85.55\n",
      "Train Epoch: 42 [1280/50000 (3%)]\tLoss: 0.321633, Accuracy: 89.84\n",
      "Train Epoch: 42 [2560/50000 (6%)]\tLoss: 0.326432, Accuracy: 88.67\n",
      "Train Epoch: 42 [3840/50000 (9%)]\tLoss: 0.347017, Accuracy: 88.67\n",
      "Train Epoch: 42 [5120/50000 (11%)]\tLoss: 0.355919, Accuracy: 88.28\n",
      "Train Epoch: 42 [6400/50000 (14%)]\tLoss: 0.334494, Accuracy: 87.50\n",
      "Train Epoch: 42 [7680/50000 (17%)]\tLoss: 0.361285, Accuracy: 86.72\n",
      "Train Epoch: 42 [8960/50000 (20%)]\tLoss: 0.364031, Accuracy: 87.50\n",
      "Train Epoch: 42 [10240/50000 (23%)]\tLoss: 0.375927, Accuracy: 85.94\n",
      "Train Epoch: 42 [11520/50000 (26%)]\tLoss: 0.382151, Accuracy: 87.89\n",
      "Train Epoch: 42 [12800/50000 (28%)]\tLoss: 0.315757, Accuracy: 89.45\n",
      "Train Epoch: 42 [14080/50000 (31%)]\tLoss: 0.462202, Accuracy: 85.16\n",
      "Train Epoch: 42 [15360/50000 (34%)]\tLoss: 0.353307, Accuracy: 88.67\n",
      "Train Epoch: 42 [16640/50000 (37%)]\tLoss: 0.326812, Accuracy: 89.84\n",
      "Train Epoch: 42 [17920/50000 (40%)]\tLoss: 0.455727, Accuracy: 84.38\n",
      "Train Epoch: 42 [19200/50000 (43%)]\tLoss: 0.337237, Accuracy: 87.50\n",
      "Train Epoch: 42 [20480/50000 (45%)]\tLoss: 0.440569, Accuracy: 88.28\n",
      "Train Epoch: 42 [21760/50000 (48%)]\tLoss: 0.274085, Accuracy: 92.19\n",
      "Train Epoch: 42 [23040/50000 (51%)]\tLoss: 0.389947, Accuracy: 87.50\n",
      "Train Epoch: 42 [24320/50000 (54%)]\tLoss: 0.390987, Accuracy: 86.33\n",
      "Train Epoch: 42 [25600/50000 (57%)]\tLoss: 0.392580, Accuracy: 85.16\n",
      "Train Epoch: 42 [26880/50000 (60%)]\tLoss: 0.317549, Accuracy: 89.06\n",
      "Train Epoch: 42 [28160/50000 (62%)]\tLoss: 0.358576, Accuracy: 88.28\n",
      "Train Epoch: 42 [29440/50000 (65%)]\tLoss: 0.415874, Accuracy: 84.38\n",
      "Train Epoch: 42 [30720/50000 (68%)]\tLoss: 0.374226, Accuracy: 89.84\n",
      "Train Epoch: 42 [32000/50000 (71%)]\tLoss: 0.400332, Accuracy: 85.94\n",
      "Train Epoch: 42 [33280/50000 (74%)]\tLoss: 0.308471, Accuracy: 91.02\n",
      "Train Epoch: 42 [34560/50000 (77%)]\tLoss: 0.354092, Accuracy: 89.06\n",
      "Train Epoch: 42 [35840/50000 (80%)]\tLoss: 0.322428, Accuracy: 89.45\n",
      "Train Epoch: 42 [37120/50000 (82%)]\tLoss: 0.429696, Accuracy: 83.20\n",
      "Train Epoch: 42 [38400/50000 (85%)]\tLoss: 0.336458, Accuracy: 89.45\n",
      "Train Epoch: 42 [39680/50000 (88%)]\tLoss: 0.318890, Accuracy: 89.06\n",
      "Train Epoch: 42 [40960/50000 (91%)]\tLoss: 0.317319, Accuracy: 88.67\n",
      "Train Epoch: 42 [42240/50000 (94%)]\tLoss: 0.437336, Accuracy: 84.77\n",
      "Train Epoch: 42 [43520/50000 (97%)]\tLoss: 0.383314, Accuracy: 88.28\n",
      "Train Epoch: 42 [35000/50000 (99%)]\tLoss: 0.433147, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.6656, Accuracy: 3920/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.23123359680176 s]\n",
      "\n",
      "Test set: Average loss: 0.6732, Accuracy: 7857/10000 (78.57%)\n",
      "\n",
      "Train Epoch: 43 [0/50000 (0%)]\tLoss: 0.415029, Accuracy: 85.55\n",
      "Train Epoch: 43 [1280/50000 (3%)]\tLoss: 0.322846, Accuracy: 89.84\n",
      "Train Epoch: 43 [2560/50000 (6%)]\tLoss: 0.285785, Accuracy: 90.62\n",
      "Train Epoch: 43 [3840/50000 (9%)]\tLoss: 0.326373, Accuracy: 88.28\n",
      "Train Epoch: 43 [5120/50000 (11%)]\tLoss: 0.336547, Accuracy: 89.45\n",
      "Train Epoch: 43 [6400/50000 (14%)]\tLoss: 0.343282, Accuracy: 89.45\n",
      "Train Epoch: 43 [7680/50000 (17%)]\tLoss: 0.379942, Accuracy: 83.59\n",
      "Train Epoch: 43 [8960/50000 (20%)]\tLoss: 0.320749, Accuracy: 91.02\n",
      "Train Epoch: 43 [10240/50000 (23%)]\tLoss: 0.357600, Accuracy: 85.16\n",
      "Train Epoch: 43 [11520/50000 (26%)]\tLoss: 0.455640, Accuracy: 83.98\n",
      "Train Epoch: 43 [12800/50000 (28%)]\tLoss: 0.387205, Accuracy: 86.72\n",
      "Train Epoch: 43 [14080/50000 (31%)]\tLoss: 0.414034, Accuracy: 87.89\n",
      "Train Epoch: 43 [15360/50000 (34%)]\tLoss: 0.354385, Accuracy: 87.89\n",
      "Train Epoch: 43 [16640/50000 (37%)]\tLoss: 0.381944, Accuracy: 85.16\n",
      "Train Epoch: 43 [17920/50000 (40%)]\tLoss: 0.382911, Accuracy: 86.33\n",
      "Train Epoch: 43 [19200/50000 (43%)]\tLoss: 0.365121, Accuracy: 85.94\n",
      "Train Epoch: 43 [20480/50000 (45%)]\tLoss: 0.323654, Accuracy: 88.67\n",
      "Train Epoch: 43 [21760/50000 (48%)]\tLoss: 0.375689, Accuracy: 87.11\n",
      "Train Epoch: 43 [23040/50000 (51%)]\tLoss: 0.379573, Accuracy: 87.11\n",
      "Train Epoch: 43 [24320/50000 (54%)]\tLoss: 0.365072, Accuracy: 87.89\n",
      "Train Epoch: 43 [25600/50000 (57%)]\tLoss: 0.341924, Accuracy: 87.89\n",
      "Train Epoch: 43 [26880/50000 (60%)]\tLoss: 0.373720, Accuracy: 85.94\n",
      "Train Epoch: 43 [28160/50000 (62%)]\tLoss: 0.298320, Accuracy: 90.62\n",
      "Train Epoch: 43 [29440/50000 (65%)]\tLoss: 0.387328, Accuracy: 87.11\n",
      "Train Epoch: 43 [30720/50000 (68%)]\tLoss: 0.360898, Accuracy: 86.72\n",
      "Train Epoch: 43 [32000/50000 (71%)]\tLoss: 0.396527, Accuracy: 87.89\n",
      "Train Epoch: 43 [33280/50000 (74%)]\tLoss: 0.530503, Accuracy: 81.64\n",
      "Train Epoch: 43 [34560/50000 (77%)]\tLoss: 0.324309, Accuracy: 89.06\n",
      "Train Epoch: 43 [35840/50000 (80%)]\tLoss: 0.351015, Accuracy: 89.06\n",
      "Train Epoch: 43 [37120/50000 (82%)]\tLoss: 0.418343, Accuracy: 87.89\n",
      "Train Epoch: 43 [38400/50000 (85%)]\tLoss: 0.415413, Accuracy: 85.55\n",
      "Train Epoch: 43 [39680/50000 (88%)]\tLoss: 0.379489, Accuracy: 86.72\n",
      "Train Epoch: 43 [40960/50000 (91%)]\tLoss: 0.356347, Accuracy: 89.45\n",
      "Train Epoch: 43 [42240/50000 (94%)]\tLoss: 0.359518, Accuracy: 86.33\n",
      "Train Epoch: 43 [43520/50000 (97%)]\tLoss: 0.314159, Accuracy: 89.06\n",
      "Train Epoch: 43 [35000/50000 (99%)]\tLoss: 0.317082, Accuracy: 91.50\n",
      "\n",
      "Validation set: Average loss: 0.5682, Accuracy: 4021/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[39.39905786514282 s]\n",
      "Train Epoch: 44 [0/50000 (0%)]\tLoss: 0.247665, Accuracy: 91.41\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 44 [1280/50000 (3%)]\tLoss: 0.244061, Accuracy: 91.02\n",
      "Train Epoch: 44 [2560/50000 (6%)]\tLoss: 0.416960, Accuracy: 86.33\n",
      "Train Epoch: 44 [3840/50000 (9%)]\tLoss: 0.350729, Accuracy: 88.67\n",
      "Train Epoch: 44 [5120/50000 (11%)]\tLoss: 0.280693, Accuracy: 91.41\n",
      "Train Epoch: 44 [6400/50000 (14%)]\tLoss: 0.352698, Accuracy: 88.28\n",
      "Train Epoch: 44 [7680/50000 (17%)]\tLoss: 0.327107, Accuracy: 89.06\n",
      "Train Epoch: 44 [8960/50000 (20%)]\tLoss: 0.316134, Accuracy: 89.06\n",
      "Train Epoch: 44 [10240/50000 (23%)]\tLoss: 0.413962, Accuracy: 86.33\n",
      "Train Epoch: 44 [11520/50000 (26%)]\tLoss: 0.356243, Accuracy: 88.67\n",
      "Train Epoch: 44 [12800/50000 (28%)]\tLoss: 0.432548, Accuracy: 86.72\n",
      "Train Epoch: 44 [14080/50000 (31%)]\tLoss: 0.304881, Accuracy: 89.84\n",
      "Train Epoch: 44 [15360/50000 (34%)]\tLoss: 0.361433, Accuracy: 87.50\n",
      "Train Epoch: 44 [16640/50000 (37%)]\tLoss: 0.391868, Accuracy: 87.50\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-1.7463]],\n",
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      "        [[-1.4881]],\n",
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      "        [[-0.3634]],\n",
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      "         [ 8.3001e-04,  8.2613e-04,  8.3731e-04,  ...,  8.3176e-04,\n",
      "           8.3051e-04,  8.2682e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 44 [17920/50000 (40%)]\tLoss: 0.354028, Accuracy: 88.28\n",
      "Train Epoch: 44 [19200/50000 (43%)]\tLoss: 0.380351, Accuracy: 83.98\n",
      "Train Epoch: 44 [20480/50000 (45%)]\tLoss: 0.420970, Accuracy: 85.94\n",
      "Train Epoch: 44 [21760/50000 (48%)]\tLoss: 0.313077, Accuracy: 89.45\n",
      "Train Epoch: 44 [23040/50000 (51%)]\tLoss: 0.336326, Accuracy: 89.45\n",
      "Train Epoch: 44 [24320/50000 (54%)]\tLoss: 0.412061, Accuracy: 85.55\n",
      "Train Epoch: 44 [25600/50000 (57%)]\tLoss: 0.380163, Accuracy: 86.72\n",
      "Train Epoch: 44 [26880/50000 (60%)]\tLoss: 0.354531, Accuracy: 87.50\n",
      "Train Epoch: 44 [28160/50000 (62%)]\tLoss: 0.342209, Accuracy: 87.50\n",
      "Train Epoch: 44 [29440/50000 (65%)]\tLoss: 0.356752, Accuracy: 88.67\n",
      "Train Epoch: 44 [30720/50000 (68%)]\tLoss: 0.375883, Accuracy: 85.16\n",
      "Train Epoch: 44 [32000/50000 (71%)]\tLoss: 0.394779, Accuracy: 86.33\n",
      "Train Epoch: 44 [33280/50000 (74%)]\tLoss: 0.505821, Accuracy: 83.20\n",
      "Train Epoch: 44 [34560/50000 (77%)]\tLoss: 0.346675, Accuracy: 87.89\n",
      "Train Epoch: 44 [35840/50000 (80%)]\tLoss: 0.301299, Accuracy: 90.62\n",
      "Train Epoch: 44 [37120/50000 (82%)]\tLoss: 0.502680, Accuracy: 82.42\n",
      "Train Epoch: 44 [38400/50000 (85%)]\tLoss: 0.399208, Accuracy: 85.16\n",
      "Train Epoch: 44 [39680/50000 (88%)]\tLoss: 0.428493, Accuracy: 85.94\n",
      "Train Epoch: 44 [40960/50000 (91%)]\tLoss: 0.430528, Accuracy: 85.94\n",
      "Train Epoch: 44 [42240/50000 (94%)]\tLoss: 0.352879, Accuracy: 89.06\n",
      "Train Epoch: 44 [43520/50000 (97%)]\tLoss: 0.421406, Accuracy: 86.33\n",
      "Train Epoch: 44 [35000/50000 (99%)]\tLoss: 0.484455, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.7280, Accuracy: 3817/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.25435829162598 s]\n",
      "\n",
      "Test set: Average loss: 0.7168, Accuracy: 7747/10000 (77.47%)\n",
      "\n",
      "Train Epoch: 45 [0/50000 (0%)]\tLoss: 0.375647, Accuracy: 87.89\n",
      "Train Epoch: 45 [1280/50000 (3%)]\tLoss: 0.359341, Accuracy: 88.28\n",
      "Train Epoch: 45 [2560/50000 (6%)]\tLoss: 0.276197, Accuracy: 89.84\n",
      "Train Epoch: 45 [3840/50000 (9%)]\tLoss: 0.367381, Accuracy: 86.72\n",
      "Train Epoch: 45 [5120/50000 (11%)]\tLoss: 0.353015, Accuracy: 87.11\n",
      "Train Epoch: 45 [6400/50000 (14%)]\tLoss: 0.434181, Accuracy: 86.33\n",
      "Train Epoch: 45 [7680/50000 (17%)]\tLoss: 0.243954, Accuracy: 92.58\n",
      "Train Epoch: 45 [8960/50000 (20%)]\tLoss: 0.399196, Accuracy: 86.72\n",
      "Train Epoch: 45 [10240/50000 (23%)]\tLoss: 0.370543, Accuracy: 88.67\n",
      "Train Epoch: 45 [11520/50000 (26%)]\tLoss: 0.299107, Accuracy: 89.45\n",
      "Train Epoch: 45 [12800/50000 (28%)]\tLoss: 0.390945, Accuracy: 86.72\n",
      "Train Epoch: 45 [14080/50000 (31%)]\tLoss: 0.376830, Accuracy: 85.94\n",
      "Train Epoch: 45 [15360/50000 (34%)]\tLoss: 0.323867, Accuracy: 88.28\n",
      "Train Epoch: 45 [16640/50000 (37%)]\tLoss: 0.349872, Accuracy: 87.50\n",
      "Train Epoch: 45 [17920/50000 (40%)]\tLoss: 0.365327, Accuracy: 87.11\n",
      "Train Epoch: 45 [19200/50000 (43%)]\tLoss: 0.422214, Accuracy: 83.59\n",
      "Train Epoch: 45 [20480/50000 (45%)]\tLoss: 0.352797, Accuracy: 87.11\n",
      "Train Epoch: 45 [21760/50000 (48%)]\tLoss: 0.447792, Accuracy: 86.33\n",
      "Train Epoch: 45 [23040/50000 (51%)]\tLoss: 0.422874, Accuracy: 87.50\n",
      "Train Epoch: 45 [24320/50000 (54%)]\tLoss: 0.371624, Accuracy: 87.50\n",
      "Train Epoch: 45 [25600/50000 (57%)]\tLoss: 0.408776, Accuracy: 85.16\n",
      "Train Epoch: 45 [26880/50000 (60%)]\tLoss: 0.372391, Accuracy: 87.50\n",
      "Train Epoch: 45 [28160/50000 (62%)]\tLoss: 0.312873, Accuracy: 89.06\n",
      "Train Epoch: 45 [29440/50000 (65%)]\tLoss: 0.395419, Accuracy: 86.72\n",
      "Train Epoch: 45 [30720/50000 (68%)]\tLoss: 0.415625, Accuracy: 87.11\n",
      "Train Epoch: 45 [32000/50000 (71%)]\tLoss: 0.412751, Accuracy: 87.11\n",
      "Train Epoch: 45 [33280/50000 (74%)]\tLoss: 0.428149, Accuracy: 86.33\n",
      "Train Epoch: 45 [34560/50000 (77%)]\tLoss: 0.394334, Accuracy: 86.33\n",
      "Train Epoch: 45 [35840/50000 (80%)]\tLoss: 0.509163, Accuracy: 82.03\n",
      "Train Epoch: 45 [37120/50000 (82%)]\tLoss: 0.350039, Accuracy: 89.06\n",
      "Train Epoch: 45 [38400/50000 (85%)]\tLoss: 0.323371, Accuracy: 88.67\n",
      "Train Epoch: 45 [39680/50000 (88%)]\tLoss: 0.329172, Accuracy: 88.67\n",
      "Train Epoch: 45 [40960/50000 (91%)]\tLoss: 0.316707, Accuracy: 89.06\n",
      "Train Epoch: 45 [42240/50000 (94%)]\tLoss: 0.330722, Accuracy: 89.06\n",
      "Train Epoch: 45 [43520/50000 (97%)]\tLoss: 0.281282, Accuracy: 89.84\n",
      "Train Epoch: 45 [35000/50000 (99%)]\tLoss: 0.395060, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.5816, Accuracy: 4056/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[39.38313388824463 s]\n",
      "Train Epoch: 46 [0/50000 (0%)]\tLoss: 0.366284, Accuracy: 89.45\n",
      "Train Epoch: 46 [1280/50000 (3%)]\tLoss: 0.331926, Accuracy: 88.28\n",
      "Train Epoch: 46 [2560/50000 (6%)]\tLoss: 0.311897, Accuracy: 87.50\n",
      "Train Epoch: 46 [3840/50000 (9%)]\tLoss: 0.345978, Accuracy: 88.28\n",
      "Train Epoch: 46 [5120/50000 (11%)]\tLoss: 0.329554, Accuracy: 88.67\n",
      "Train Epoch: 46 [6400/50000 (14%)]\tLoss: 0.349466, Accuracy: 88.67\n",
      "Train Epoch: 46 [7680/50000 (17%)]\tLoss: 0.414790, Accuracy: 86.33\n",
      "Train Epoch: 46 [8960/50000 (20%)]\tLoss: 0.342735, Accuracy: 89.45\n",
      "Train Epoch: 46 [10240/50000 (23%)]\tLoss: 0.348840, Accuracy: 88.28\n",
      "Train Epoch: 46 [11520/50000 (26%)]\tLoss: 0.456181, Accuracy: 85.94\n",
      "Train Epoch: 46 [12800/50000 (28%)]\tLoss: 0.414527, Accuracy: 87.50\n",
      "Train Epoch: 46 [14080/50000 (31%)]\tLoss: 0.313257, Accuracy: 89.45\n",
      "Train Epoch: 46 [15360/50000 (34%)]\tLoss: 0.381082, Accuracy: 87.50\n",
      "Train Epoch: 46 [16640/50000 (37%)]\tLoss: 0.407944, Accuracy: 85.16\n",
      "Train Epoch: 46 [17920/50000 (40%)]\tLoss: 0.447579, Accuracy: 86.33\n",
      "Train Epoch: 46 [19200/50000 (43%)]\tLoss: 0.435883, Accuracy: 85.16\n",
      "Train Epoch: 46 [20480/50000 (45%)]\tLoss: 0.362085, Accuracy: 89.06\n",
      "Train Epoch: 46 [21760/50000 (48%)]\tLoss: 0.283578, Accuracy: 89.45\n",
      "Train Epoch: 46 [23040/50000 (51%)]\tLoss: 0.389080, Accuracy: 86.72\n",
      "Train Epoch: 46 [24320/50000 (54%)]\tLoss: 0.423271, Accuracy: 88.28\n",
      "Train Epoch: 46 [25600/50000 (57%)]\tLoss: 0.520000, Accuracy: 82.03\n",
      "Train Epoch: 46 [26880/50000 (60%)]\tLoss: 0.354985, Accuracy: 87.89\n",
      "Train Epoch: 46 [28160/50000 (62%)]\tLoss: 0.447613, Accuracy: 85.16\n",
      "Train Epoch: 46 [29440/50000 (65%)]\tLoss: 0.322798, Accuracy: 89.84\n",
      "Train Epoch: 46 [30720/50000 (68%)]\tLoss: 0.391272, Accuracy: 85.55\n",
      "Train Epoch: 46 [32000/50000 (71%)]\tLoss: 0.380207, Accuracy: 87.50\n",
      "Train Epoch: 46 [33280/50000 (74%)]\tLoss: 0.344613, Accuracy: 90.23\n",
      "Train Epoch: 46 [34560/50000 (77%)]\tLoss: 0.343783, Accuracy: 90.23\n",
      "Train Epoch: 46 [35840/50000 (80%)]\tLoss: 0.424178, Accuracy: 85.16\n",
      "Train Epoch: 46 [37120/50000 (82%)]\tLoss: 0.355423, Accuracy: 85.55\n",
      "Train Epoch: 46 [38400/50000 (85%)]\tLoss: 0.392144, Accuracy: 88.28\n",
      "Train Epoch: 46 [39680/50000 (88%)]\tLoss: 0.381352, Accuracy: 87.11\n",
      "Train Epoch: 46 [40960/50000 (91%)]\tLoss: 0.417780, Accuracy: 87.11\n",
      "Train Epoch: 46 [42240/50000 (94%)]\tLoss: 0.381216, Accuracy: 87.50\n",
      "Train Epoch: 46 [43520/50000 (97%)]\tLoss: 0.271362, Accuracy: 91.02\n",
      "Train Epoch: 46 [35000/50000 (99%)]\tLoss: 0.461797, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.8526, Accuracy: 3675/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[36.24918270111084 s]\n",
      "\n",
      "Test set: Average loss: 0.8658, Accuracy: 7343/10000 (73.43%)\n",
      "\n",
      "Train Epoch: 47 [0/50000 (0%)]\tLoss: 0.311295, Accuracy: 88.28\n",
      "Train Epoch: 47 [1280/50000 (3%)]\tLoss: 0.386318, Accuracy: 87.89\n",
      "Train Epoch: 47 [2560/50000 (6%)]\tLoss: 0.380748, Accuracy: 87.11\n",
      "Train Epoch: 47 [3840/50000 (9%)]\tLoss: 0.358125, Accuracy: 89.06\n",
      "Train Epoch: 47 [5120/50000 (11%)]\tLoss: 0.304307, Accuracy: 89.45\n",
      "Train Epoch: 47 [6400/50000 (14%)]\tLoss: 0.344508, Accuracy: 87.50\n",
      "Train Epoch: 47 [7680/50000 (17%)]\tLoss: 0.442246, Accuracy: 85.55\n",
      "Train Epoch: 47 [8960/50000 (20%)]\tLoss: 0.322321, Accuracy: 89.06\n",
      "Train Epoch: 47 [10240/50000 (23%)]\tLoss: 0.310080, Accuracy: 89.45\n",
      "Train Epoch: 47 [11520/50000 (26%)]\tLoss: 0.408179, Accuracy: 84.77\n",
      "Train Epoch: 47 [12800/50000 (28%)]\tLoss: 0.407060, Accuracy: 86.72\n",
      "Train Epoch: 47 [14080/50000 (31%)]\tLoss: 0.374259, Accuracy: 89.06\n",
      "Train Epoch: 47 [15360/50000 (34%)]\tLoss: 0.379643, Accuracy: 89.45\n",
      "Train Epoch: 47 [16640/50000 (37%)]\tLoss: 0.415163, Accuracy: 85.55\n",
      "Train Epoch: 47 [17920/50000 (40%)]\tLoss: 0.448216, Accuracy: 85.55\n",
      "Train Epoch: 47 [19200/50000 (43%)]\tLoss: 0.406387, Accuracy: 85.55\n",
      "Train Epoch: 47 [20480/50000 (45%)]\tLoss: 0.313835, Accuracy: 89.06\n",
      "Train Epoch: 47 [21760/50000 (48%)]\tLoss: 0.474719, Accuracy: 82.42\n",
      "Train Epoch: 47 [23040/50000 (51%)]\tLoss: 0.421803, Accuracy: 85.55\n",
      "Train Epoch: 47 [24320/50000 (54%)]\tLoss: 0.418881, Accuracy: 85.16\n",
      "Train Epoch: 47 [25600/50000 (57%)]\tLoss: 0.388159, Accuracy: 86.33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 47 [26880/50000 (60%)]\tLoss: 0.317204, Accuracy: 88.67\n",
      "Train Epoch: 47 [28160/50000 (62%)]\tLoss: 0.310041, Accuracy: 88.28\n",
      "Train Epoch: 47 [29440/50000 (65%)]\tLoss: 0.431742, Accuracy: 84.77\n",
      "Train Epoch: 47 [30720/50000 (68%)]\tLoss: 0.470243, Accuracy: 82.42\n",
      "Train Epoch: 47 [32000/50000 (71%)]\tLoss: 0.397431, Accuracy: 85.16\n",
      "Train Epoch: 47 [33280/50000 (74%)]\tLoss: 0.320095, Accuracy: 87.89\n",
      "Train Epoch: 47 [34560/50000 (77%)]\tLoss: 0.343816, Accuracy: 87.89\n",
      "Train Epoch: 47 [35840/50000 (80%)]\tLoss: 0.289495, Accuracy: 91.41\n",
      "Train Epoch: 47 [37120/50000 (82%)]\tLoss: 0.318600, Accuracy: 88.28\n",
      "Train Epoch: 47 [38400/50000 (85%)]\tLoss: 0.382570, Accuracy: 85.94\n",
      "Train Epoch: 47 [39680/50000 (88%)]\tLoss: 0.410666, Accuracy: 86.33\n",
      "Train Epoch: 47 [40960/50000 (91%)]\tLoss: 0.420128, Accuracy: 86.33\n",
      "Train Epoch: 47 [42240/50000 (94%)]\tLoss: 0.253924, Accuracy: 91.02\n",
      "Train Epoch: 47 [43520/50000 (97%)]\tLoss: 0.376117, Accuracy: 87.89\n",
      "Train Epoch: 47 [35000/50000 (99%)]\tLoss: 0.426780, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.5570, Accuracy: 4044/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[39.401164531707764 s]\n",
      "Train Epoch: 48 [0/50000 (0%)]\tLoss: 0.384097, Accuracy: 85.94\n",
      "Train Epoch: 48 [1280/50000 (3%)]\tLoss: 0.340381, Accuracy: 87.89\n",
      "Train Epoch: 48 [2560/50000 (6%)]\tLoss: 0.339744, Accuracy: 88.28\n",
      "Train Epoch: 48 [3840/50000 (9%)]\tLoss: 0.369837, Accuracy: 88.67\n",
      "Train Epoch: 48 [5120/50000 (11%)]\tLoss: 0.314809, Accuracy: 88.28\n",
      "Train Epoch: 48 [6400/50000 (14%)]\tLoss: 0.263188, Accuracy: 92.58\n",
      "Train Epoch: 48 [7680/50000 (17%)]\tLoss: 0.326532, Accuracy: 88.28\n",
      "Train Epoch: 48 [8960/50000 (20%)]\tLoss: 0.408424, Accuracy: 87.89\n",
      "Train Epoch: 48 [10240/50000 (23%)]\tLoss: 0.435892, Accuracy: 84.77\n",
      "Train Epoch: 48 [11520/50000 (26%)]\tLoss: 0.304962, Accuracy: 87.50\n",
      "Train Epoch: 48 [12800/50000 (28%)]\tLoss: 0.330955, Accuracy: 89.45\n",
      "Train Epoch: 48 [14080/50000 (31%)]\tLoss: 0.371853, Accuracy: 87.89\n",
      "Train Epoch: 48 [15360/50000 (34%)]\tLoss: 0.430020, Accuracy: 85.94\n",
      "Train Epoch: 48 [16640/50000 (37%)]\tLoss: 0.365650, Accuracy: 88.28\n",
      "Train Epoch: 48 [17920/50000 (40%)]\tLoss: 0.481663, Accuracy: 84.38\n",
      "Train Epoch: 48 [19200/50000 (43%)]\tLoss: 0.302067, Accuracy: 89.84\n",
      "Train Epoch: 48 [20480/50000 (45%)]\tLoss: 0.260718, Accuracy: 91.41\n",
      "Train Epoch: 48 [21760/50000 (48%)]\tLoss: 0.362468, Accuracy: 87.89\n",
      "Train Epoch: 48 [23040/50000 (51%)]\tLoss: 0.341198, Accuracy: 88.67\n",
      "Train Epoch: 48 [24320/50000 (54%)]\tLoss: 0.318151, Accuracy: 90.62\n",
      "Train Epoch: 48 [25600/50000 (57%)]\tLoss: 0.375551, Accuracy: 87.50\n",
      "Train Epoch: 48 [26880/50000 (60%)]\tLoss: 0.479190, Accuracy: 84.77\n",
      "Train Epoch: 48 [28160/50000 (62%)]\tLoss: 0.373364, Accuracy: 86.72\n",
      "Train Epoch: 48 [29440/50000 (65%)]\tLoss: 0.358894, Accuracy: 88.28\n",
      "Train Epoch: 48 [30720/50000 (68%)]\tLoss: 0.336947, Accuracy: 88.67\n",
      "Train Epoch: 48 [32000/50000 (71%)]\tLoss: 0.371876, Accuracy: 87.89\n",
      "Train Epoch: 48 [33280/50000 (74%)]\tLoss: 0.391884, Accuracy: 85.16\n",
      "Train Epoch: 48 [34560/50000 (77%)]\tLoss: 0.336835, Accuracy: 87.89\n",
      "Train Epoch: 48 [35840/50000 (80%)]\tLoss: 0.373926, Accuracy: 85.94\n",
      "Train Epoch: 48 [37120/50000 (82%)]\tLoss: 0.393855, Accuracy: 85.55\n",
      "Train Epoch: 48 [38400/50000 (85%)]\tLoss: 0.423158, Accuracy: 85.55\n",
      "Train Epoch: 48 [39680/50000 (88%)]\tLoss: 0.434093, Accuracy: 83.98\n",
      "Train Epoch: 48 [40960/50000 (91%)]\tLoss: 0.373595, Accuracy: 88.67\n",
      "Train Epoch: 48 [42240/50000 (94%)]\tLoss: 0.363373, Accuracy: 88.67\n",
      "Train Epoch: 48 [43520/50000 (97%)]\tLoss: 0.336555, Accuracy: 86.72\n",
      "Train Epoch: 48 [35000/50000 (99%)]\tLoss: 0.372168, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.8068, Accuracy: 3797/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[36.217252254486084 s]\n",
      "\n",
      "Test set: Average loss: 0.8312, Accuracy: 7523/10000 (75.23%)\n",
      "\n",
      "Train Epoch: 49 [0/50000 (0%)]\tLoss: 0.346263, Accuracy: 87.11\n",
      "Train Epoch: 49 [1280/50000 (3%)]\tLoss: 0.336520, Accuracy: 89.45\n",
      "Train Epoch: 49 [2560/50000 (6%)]\tLoss: 0.315728, Accuracy: 88.67\n",
      "Train Epoch: 49 [3840/50000 (9%)]\tLoss: 0.411510, Accuracy: 85.94\n",
      "Train Epoch: 49 [5120/50000 (11%)]\tLoss: 0.296903, Accuracy: 90.23\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.6936]],\n",
      "\n",
      "        [[-2.2216]],\n",
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      "        [[ 0.3510]],\n",
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      "        [[-0.6195]],\n",
      "\n",
      "        [[ 0.3190]],\n",
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      "        [[-0.1676]],\n",
      "\n",
      "        [[ 1.8318]],\n",
      "\n",
      "        [[-0.6128]],\n",
      "\n",
      "        [[ 0.9511]],\n",
      "\n",
      "        [[ 1.2850]],\n",
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      "        [[ 0.0863]],\n",
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      "        [[-1.4379]],\n",
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      "        [[ 0.4891]],\n",
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      "        [[ 0.2658]],\n",
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      "        [[-0.2035]],\n",
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      "        [[-0.0916]],\n",
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      "        [[-0.0940]],\n",
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      "        [[ 1.1008]],\n",
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      "        [[ 1.2136]],\n",
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      "        [[-1.2651]],\n",
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      "        [[-2.2726]],\n",
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      "        [[ 0.5133]],\n",
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      "        [[-0.1725]],\n",
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      "        [[-0.0553]],\n",
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      "        [[ 1.7763]],\n",
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      "        [[ 1.1427]],\n",
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      "        [[ 0.9913]],\n",
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      "        [[ 0.6492]],\n",
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      "        [[-0.1854]],\n",
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      "        [[-0.6186]],\n",
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      "        [[-0.1076]],\n",
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      "        [[ 0.1887]],\n",
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      "        [[ 1.0971]],\n",
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      "        [[-0.8728]],\n",
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      "        [[-0.0770]],\n",
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      "        [[ 0.8986]],\n",
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      "        [[ 1.9184]],\n",
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      "        [[ 0.8101]],\n",
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      "        [[-0.3005]],\n",
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      "        [[-0.5167]],\n",
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      "        [[-0.2582]],\n",
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      "        [[-2.3966]],\n",
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      "        [[ 0.4232]],\n",
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      "        [[ 0.3769]],\n",
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      "        [[-1.6961]],\n",
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      "        [[ 1.1853]],\n",
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      "        [[ 1.3040]],\n",
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      "        [[ 0.6198]],\n",
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      "        [[-1.4822]],\n",
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      "        [[-1.7519]],\n",
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      "        [[-0.1896]],\n",
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      "        [[-0.7442]],\n",
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      "        [[-0.0793]],\n",
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      "        [[-0.9162]],\n",
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      "        [[-0.0875]],\n",
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      "        [[-1.6494]],\n",
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      "        [[ 0.2382]],\n",
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      "        [[-0.9688]],\n",
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      "        [[ 0.0644]],\n",
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      "        [[-0.3606]],\n",
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      "        [[ 0.4016]],\n",
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      "        [[-0.6108]],\n",
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      "        [[ 0.7814]],\n",
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      "        [[-0.9592]],\n",
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      "        [[-1.0133]],\n",
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      "        [[-0.8848]],\n",
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      "        [[-0.5775]],\n",
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      "        [[-0.3020]],\n",
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      "        [[-2.1739]],\n",
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      "        [[ 0.9759]],\n",
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      "        [[-0.7859]],\n",
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      "        [[-0.2467]],\n",
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      "        [[ 0.1241]],\n",
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      "        [[ 0.7846]],\n",
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      "        [[ 0.1235]],\n",
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      "        [[-0.9335]],\n",
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      "        [[-2.1577]],\n",
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      "        [[-0.8966]],\n",
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      "        [[-0.5513]],\n",
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      "        [[ 0.5048]],\n",
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      "        [[ 1.7848]],\n",
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      "        [[ 2.0988]],\n",
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      "        [[-0.6960]],\n",
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      "        [[-1.4526]],\n",
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      "        [[ 0.0841]],\n",
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      "        [[-0.9460]],\n",
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      "        [[-0.5041]],\n",
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      "        [[-0.9956]],\n",
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      "        [[-1.6993]],\n",
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      "         [ 1.5793e-03,  1.5811e-03,  1.5666e-03,  ...,  1.5718e-03,\n",
      "           1.5787e-03,  1.5716e-03],\n",
      "         [-1.5794e-03, -1.5695e-03, -1.5733e-03,  ..., -1.5799e-03,\n",
      "          -1.5756e-03, -1.5869e-03],\n",
      "         [ 1.7001e-03,  1.6937e-03,  1.6982e-03,  ...,  1.7000e-03,\n",
      "           1.6939e-03,  1.6951e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 49 [6400/50000 (14%)]\tLoss: 0.305917, Accuracy: 91.02\n",
      "Train Epoch: 49 [7680/50000 (17%)]\tLoss: 0.339473, Accuracy: 89.84\n",
      "Train Epoch: 49 [8960/50000 (20%)]\tLoss: 0.355953, Accuracy: 86.33\n",
      "Train Epoch: 49 [10240/50000 (23%)]\tLoss: 0.364432, Accuracy: 87.89\n",
      "Train Epoch: 49 [11520/50000 (26%)]\tLoss: 0.310832, Accuracy: 89.45\n",
      "Train Epoch: 49 [12800/50000 (28%)]\tLoss: 0.410847, Accuracy: 85.55\n",
      "Train Epoch: 49 [14080/50000 (31%)]\tLoss: 0.458333, Accuracy: 86.33\n",
      "Train Epoch: 49 [15360/50000 (34%)]\tLoss: 0.383195, Accuracy: 86.72\n",
      "Train Epoch: 49 [16640/50000 (37%)]\tLoss: 0.357443, Accuracy: 85.94\n",
      "Train Epoch: 49 [17920/50000 (40%)]\tLoss: 0.547352, Accuracy: 82.42\n",
      "Train Epoch: 49 [19200/50000 (43%)]\tLoss: 0.394082, Accuracy: 85.55\n",
      "Train Epoch: 49 [20480/50000 (45%)]\tLoss: 0.376767, Accuracy: 88.28\n",
      "Train Epoch: 49 [21760/50000 (48%)]\tLoss: 0.404760, Accuracy: 84.77\n",
      "Train Epoch: 49 [23040/50000 (51%)]\tLoss: 0.349942, Accuracy: 87.89\n",
      "Train Epoch: 49 [24320/50000 (54%)]\tLoss: 0.319674, Accuracy: 87.89\n",
      "Train Epoch: 49 [25600/50000 (57%)]\tLoss: 0.403768, Accuracy: 87.89\n",
      "Train Epoch: 49 [26880/50000 (60%)]\tLoss: 0.378372, Accuracy: 86.33\n",
      "Train Epoch: 49 [28160/50000 (62%)]\tLoss: 0.525304, Accuracy: 82.42\n",
      "Train Epoch: 49 [29440/50000 (65%)]\tLoss: 0.342246, Accuracy: 88.28\n",
      "Train Epoch: 49 [30720/50000 (68%)]\tLoss: 0.425965, Accuracy: 85.94\n",
      "Train Epoch: 49 [32000/50000 (71%)]\tLoss: 0.359947, Accuracy: 87.50\n",
      "Train Epoch: 49 [33280/50000 (74%)]\tLoss: 0.318548, Accuracy: 89.06\n",
      "Train Epoch: 49 [34560/50000 (77%)]\tLoss: 0.366598, Accuracy: 89.45\n",
      "Train Epoch: 49 [35840/50000 (80%)]\tLoss: 0.252177, Accuracy: 93.75\n",
      "Train Epoch: 49 [37120/50000 (82%)]\tLoss: 0.500521, Accuracy: 83.59\n",
      "Train Epoch: 49 [38400/50000 (85%)]\tLoss: 0.366338, Accuracy: 87.11\n",
      "Train Epoch: 49 [39680/50000 (88%)]\tLoss: 0.439021, Accuracy: 83.98\n",
      "Train Epoch: 49 [40960/50000 (91%)]\tLoss: 0.296358, Accuracy: 90.62\n",
      "Train Epoch: 49 [42240/50000 (94%)]\tLoss: 0.346266, Accuracy: 87.11\n",
      "Train Epoch: 49 [43520/50000 (97%)]\tLoss: 0.382057, Accuracy: 88.28\n",
      "Train Epoch: 49 [35000/50000 (99%)]\tLoss: 0.463007, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.7565, Accuracy: 3812/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.36663579940796 s]\n",
      "Train Epoch: 50 [0/50000 (0%)]\tLoss: 0.299781, Accuracy: 91.02\n",
      "Train Epoch: 50 [1280/50000 (3%)]\tLoss: 0.322060, Accuracy: 89.06\n",
      "Train Epoch: 50 [2560/50000 (6%)]\tLoss: 0.358292, Accuracy: 85.55\n",
      "Train Epoch: 50 [3840/50000 (9%)]\tLoss: 0.398112, Accuracy: 87.50\n",
      "Train Epoch: 50 [5120/50000 (11%)]\tLoss: 0.396663, Accuracy: 86.72\n",
      "Train Epoch: 50 [6400/50000 (14%)]\tLoss: 0.491265, Accuracy: 87.89\n",
      "Train Epoch: 50 [7680/50000 (17%)]\tLoss: 0.283933, Accuracy: 88.67\n",
      "Train Epoch: 50 [8960/50000 (20%)]\tLoss: 0.408713, Accuracy: 84.77\n",
      "Train Epoch: 50 [10240/50000 (23%)]\tLoss: 0.407798, Accuracy: 86.33\n",
      "Train Epoch: 50 [11520/50000 (26%)]\tLoss: 0.472348, Accuracy: 84.38\n",
      "Train Epoch: 50 [12800/50000 (28%)]\tLoss: 0.381953, Accuracy: 88.28\n",
      "Train Epoch: 50 [14080/50000 (31%)]\tLoss: 0.358832, Accuracy: 88.28\n",
      "Train Epoch: 50 [15360/50000 (34%)]\tLoss: 0.365823, Accuracy: 87.89\n",
      "Train Epoch: 50 [16640/50000 (37%)]\tLoss: 0.316654, Accuracy: 89.45\n",
      "Train Epoch: 50 [17920/50000 (40%)]\tLoss: 0.322582, Accuracy: 89.06\n",
      "Train Epoch: 50 [19200/50000 (43%)]\tLoss: 0.423880, Accuracy: 84.77\n",
      "Train Epoch: 50 [20480/50000 (45%)]\tLoss: 0.393327, Accuracy: 87.11\n",
      "Train Epoch: 50 [21760/50000 (48%)]\tLoss: 0.455891, Accuracy: 84.38\n",
      "Train Epoch: 50 [23040/50000 (51%)]\tLoss: 0.339765, Accuracy: 87.11\n",
      "Train Epoch: 50 [24320/50000 (54%)]\tLoss: 0.295662, Accuracy: 89.84\n",
      "Train Epoch: 50 [25600/50000 (57%)]\tLoss: 0.335452, Accuracy: 88.28\n",
      "Train Epoch: 50 [26880/50000 (60%)]\tLoss: 0.291526, Accuracy: 89.06\n",
      "Train Epoch: 50 [28160/50000 (62%)]\tLoss: 0.317584, Accuracy: 87.50\n",
      "Train Epoch: 50 [29440/50000 (65%)]\tLoss: 0.430288, Accuracy: 85.16\n",
      "Train Epoch: 50 [30720/50000 (68%)]\tLoss: 0.347011, Accuracy: 86.72\n",
      "Train Epoch: 50 [32000/50000 (71%)]\tLoss: 0.321572, Accuracy: 89.45\n",
      "Train Epoch: 50 [33280/50000 (74%)]\tLoss: 0.415727, Accuracy: 85.16\n",
      "Train Epoch: 50 [34560/50000 (77%)]\tLoss: 0.325297, Accuracy: 85.94\n",
      "Train Epoch: 50 [35840/50000 (80%)]\tLoss: 0.357558, Accuracy: 86.33\n",
      "Train Epoch: 50 [37120/50000 (82%)]\tLoss: 0.398074, Accuracy: 85.94\n",
      "Train Epoch: 50 [38400/50000 (85%)]\tLoss: 0.436280, Accuracy: 85.16\n",
      "Train Epoch: 50 [39680/50000 (88%)]\tLoss: 0.347731, Accuracy: 88.28\n",
      "Train Epoch: 50 [40960/50000 (91%)]\tLoss: 0.385474, Accuracy: 89.84\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.6285]],\n",
      "\n",
      "        [[-0.6913]],\n",
      "\n",
      "        [[-1.3839]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.8640]],\n",
      "\n",
      "        [[-2.9726]],\n",
      "\n",
      "        [[ 0.3533]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor([[[ 2.5030e-03,  2.5158e-03,  2.5099e-03,  ...,  2.5127e-03,\n",
      "           2.5052e-03,  2.5057e-03],\n",
      "         [ 2.2330e-03,  2.2276e-03,  2.2318e-03,  ...,  2.2405e-03,\n",
      "           2.2412e-03,  2.2306e-03],\n",
      "         [ 2.8192e-03,  2.8323e-03,  2.8269e-03,  ...,  2.8317e-03,\n",
      "           2.8242e-03,  2.8264e-03],\n",
      "         ...,\n",
      "         [ 2.3811e-03,  2.3774e-03,  2.3732e-03,  ...,  2.3682e-03,\n",
      "           2.3617e-03,  2.3791e-03],\n",
      "         [ 3.0769e-03,  3.0837e-03,  3.0668e-03,  ...,  3.0795e-03,\n",
      "           3.0723e-03,  3.0786e-03],\n",
      "         [ 1.0945e-03,  1.0854e-03,  1.0969e-03,  ...,  1.0942e-03,\n",
      "           1.0957e-03,  1.0871e-03]]], device='cuda:0')\n",
      "Train Epoch: 50 [42240/50000 (94%)]\tLoss: 0.321948, Accuracy: 89.45\n",
      "Train Epoch: 50 [43520/50000 (97%)]\tLoss: 0.338919, Accuracy: 90.23\n",
      "Train Epoch: 50 [35000/50000 (99%)]\tLoss: 0.419490, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.9481, Accuracy: 3631/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[36.264384269714355 s]\n",
      "\n",
      "Test set: Average loss: 0.9732, Accuracy: 7165/10000 (71.65%)\n",
      "\n",
      "Train Epoch: 51 [0/50000 (0%)]\tLoss: 0.336267, Accuracy: 87.89\n",
      "Train Epoch: 51 [1280/50000 (3%)]\tLoss: 0.287338, Accuracy: 88.67\n",
      "Train Epoch: 51 [2560/50000 (6%)]\tLoss: 0.300417, Accuracy: 91.02\n",
      "Train Epoch: 51 [3840/50000 (9%)]\tLoss: 0.296539, Accuracy: 89.84\n",
      "Train Epoch: 51 [5120/50000 (11%)]\tLoss: 0.289203, Accuracy: 89.06\n",
      "Train Epoch: 51 [6400/50000 (14%)]\tLoss: 0.351315, Accuracy: 87.50\n",
      "Train Epoch: 51 [7680/50000 (17%)]\tLoss: 0.266843, Accuracy: 90.23\n",
      "Train Epoch: 51 [8960/50000 (20%)]\tLoss: 0.381742, Accuracy: 89.45\n",
      "Train Epoch: 51 [10240/50000 (23%)]\tLoss: 0.226112, Accuracy: 92.97\n",
      "Train Epoch: 51 [11520/50000 (26%)]\tLoss: 0.218653, Accuracy: 91.80\n",
      "Train Epoch: 51 [12800/50000 (28%)]\tLoss: 0.224200, Accuracy: 92.19\n",
      "Train Epoch: 51 [14080/50000 (31%)]\tLoss: 0.338222, Accuracy: 88.28\n",
      "Train Epoch: 51 [15360/50000 (34%)]\tLoss: 0.204245, Accuracy: 91.80\n",
      "Train Epoch: 51 [16640/50000 (37%)]\tLoss: 0.200061, Accuracy: 93.75\n",
      "Train Epoch: 51 [17920/50000 (40%)]\tLoss: 0.186398, Accuracy: 95.31\n",
      "Train Epoch: 51 [19200/50000 (43%)]\tLoss: 0.244157, Accuracy: 91.41\n",
      "Train Epoch: 51 [20480/50000 (45%)]\tLoss: 0.239714, Accuracy: 91.02\n",
      "Train Epoch: 51 [21760/50000 (48%)]\tLoss: 0.214462, Accuracy: 92.58\n",
      "Train Epoch: 51 [23040/50000 (51%)]\tLoss: 0.189997, Accuracy: 94.14\n",
      "Train Epoch: 51 [24320/50000 (54%)]\tLoss: 0.285158, Accuracy: 89.84\n",
      "Train Epoch: 51 [25600/50000 (57%)]\tLoss: 0.294481, Accuracy: 89.84\n",
      "Train Epoch: 51 [26880/50000 (60%)]\tLoss: 0.231951, Accuracy: 91.80\n",
      "Train Epoch: 51 [28160/50000 (62%)]\tLoss: 0.246588, Accuracy: 91.41\n",
      "Train Epoch: 51 [29440/50000 (65%)]\tLoss: 0.203844, Accuracy: 91.80\n",
      "Train Epoch: 51 [30720/50000 (68%)]\tLoss: 0.268893, Accuracy: 92.58\n",
      "Train Epoch: 51 [32000/50000 (71%)]\tLoss: 0.210260, Accuracy: 93.75\n",
      "Train Epoch: 51 [33280/50000 (74%)]\tLoss: 0.232212, Accuracy: 91.80\n",
      "Train Epoch: 51 [34560/50000 (77%)]\tLoss: 0.276789, Accuracy: 90.23\n",
      "Train Epoch: 51 [35840/50000 (80%)]\tLoss: 0.292129, Accuracy: 91.41\n",
      "Train Epoch: 51 [37120/50000 (82%)]\tLoss: 0.193932, Accuracy: 94.14\n",
      "Train Epoch: 51 [38400/50000 (85%)]\tLoss: 0.228225, Accuracy: 92.19\n",
      "Train Epoch: 51 [39680/50000 (88%)]\tLoss: 0.228971, Accuracy: 94.53\n",
      "Train Epoch: 51 [40960/50000 (91%)]\tLoss: 0.201540, Accuracy: 93.75\n",
      "Train Epoch: 51 [42240/50000 (94%)]\tLoss: 0.207364, Accuracy: 94.14\n",
      "Train Epoch: 51 [43520/50000 (97%)]\tLoss: 0.255136, Accuracy: 91.02\n",
      "Train Epoch: 51 [35000/50000 (99%)]\tLoss: 0.212632, Accuracy: 92.00\n",
      "\n",
      "Validation set: Average loss: 0.2756, Accuracy: 4536/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.379082918167114 s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 52 [0/50000 (0%)]\tLoss: 0.222598, Accuracy: 94.53\n",
      "Train Epoch: 52 [1280/50000 (3%)]\tLoss: 0.177667, Accuracy: 94.53\n",
      "Train Epoch: 52 [2560/50000 (6%)]\tLoss: 0.206838, Accuracy: 94.53\n",
      "Train Epoch: 52 [3840/50000 (9%)]\tLoss: 0.211075, Accuracy: 92.19\n",
      "Train Epoch: 52 [5120/50000 (11%)]\tLoss: 0.258330, Accuracy: 93.36\n",
      "Train Epoch: 52 [6400/50000 (14%)]\tLoss: 0.232898, Accuracy: 91.41\n",
      "Train Epoch: 52 [7680/50000 (17%)]\tLoss: 0.246294, Accuracy: 90.62\n",
      "Train Epoch: 52 [8960/50000 (20%)]\tLoss: 0.203024, Accuracy: 92.58\n",
      "Train Epoch: 52 [10240/50000 (23%)]\tLoss: 0.180972, Accuracy: 92.19\n",
      "Train Epoch: 52 [11520/50000 (26%)]\tLoss: 0.197220, Accuracy: 92.19\n",
      "Train Epoch: 52 [12800/50000 (28%)]\tLoss: 0.176596, Accuracy: 95.70\n",
      "Train Epoch: 52 [14080/50000 (31%)]\tLoss: 0.203454, Accuracy: 92.58\n",
      "Train Epoch: 52 [15360/50000 (34%)]\tLoss: 0.164606, Accuracy: 94.92\n",
      "Train Epoch: 52 [16640/50000 (37%)]\tLoss: 0.165068, Accuracy: 94.92\n",
      "Train Epoch: 52 [17920/50000 (40%)]\tLoss: 0.119501, Accuracy: 97.27\n",
      "Train Epoch: 52 [19200/50000 (43%)]\tLoss: 0.174960, Accuracy: 95.70\n",
      "Train Epoch: 52 [20480/50000 (45%)]\tLoss: 0.165922, Accuracy: 93.75\n",
      "Train Epoch: 52 [21760/50000 (48%)]\tLoss: 0.265270, Accuracy: 89.45\n",
      "Train Epoch: 52 [23040/50000 (51%)]\tLoss: 0.213403, Accuracy: 92.97\n",
      "Train Epoch: 52 [24320/50000 (54%)]\tLoss: 0.151121, Accuracy: 93.36\n",
      "Train Epoch: 52 [25600/50000 (57%)]\tLoss: 0.166081, Accuracy: 95.70\n",
      "Train Epoch: 52 [26880/50000 (60%)]\tLoss: 0.240218, Accuracy: 90.62\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-1.8596]],\n",
      "\n",
      "        [[ 0.0567]],\n",
      "\n",
      "        [[ 0.3178]],\n",
      "\n",
      "        [[ 1.0825]],\n",
      "\n",
      "        [[ 0.2246]],\n",
      "\n",
      "        [[ 0.7013]],\n",
      "\n",
      "        [[ 1.6890]],\n",
      "\n",
      "        [[-0.7990]],\n",
      "\n",
      "        [[-1.0300]],\n",
      "\n",
      "        [[-0.0103]],\n",
      "\n",
      "        [[ 0.8122]],\n",
      "\n",
      "        [[ 0.9451]],\n",
      "\n",
      "        [[-1.1854]],\n",
      "\n",
      "        [[ 1.0077]],\n",
      "\n",
      "        [[ 1.1340]],\n",
      "\n",
      "        [[ 0.3676]],\n",
      "\n",
      "        [[-0.1757]],\n",
      "\n",
      "        [[-1.0067]],\n",
      "\n",
      "        [[-0.4729]],\n",
      "\n",
      "        [[ 0.1348]],\n",
      "\n",
      "        [[-0.7629]],\n",
      "\n",
      "        [[ 0.6406]],\n",
      "\n",
      "        [[-0.9371]],\n",
      "\n",
      "        [[ 0.4502]],\n",
      "\n",
      "        [[ 0.0750]],\n",
      "\n",
      "        [[ 0.2353]],\n",
      "\n",
      "        [[-2.0889]],\n",
      "\n",
      "        [[ 0.9609]],\n",
      "\n",
      "        [[ 0.5617]],\n",
      "\n",
      "        [[ 1.6741]],\n",
      "\n",
      "        [[ 0.3012]],\n",
      "\n",
      "        [[ 0.5489]],\n",
      "\n",
      "        [[ 0.4098]],\n",
      "\n",
      "        [[ 0.7756]],\n",
      "\n",
      "        [[-0.0769]],\n",
      "\n",
      "        [[ 1.5680]],\n",
      "\n",
      "        [[-0.0666]],\n",
      "\n",
      "        [[-0.4417]],\n",
      "\n",
      "        [[-0.2650]],\n",
      "\n",
      "        [[ 2.2745]],\n",
      "\n",
      "        [[ 0.5402]],\n",
      "\n",
      "        [[-0.1246]],\n",
      "\n",
      "        [[ 0.4087]],\n",
      "\n",
      "        [[-0.2028]],\n",
      "\n",
      "        [[-1.4715]],\n",
      "\n",
      "        [[ 0.1140]],\n",
      "\n",
      "        [[-0.6953]],\n",
      "\n",
      "        [[ 0.4823]],\n",
      "\n",
      "        [[-0.1370]],\n",
      "\n",
      "        [[-0.3830]],\n",
      "\n",
      "        [[-0.4611]],\n",
      "\n",
      "        [[ 0.1523]],\n",
      "\n",
      "        [[-0.1909]],\n",
      "\n",
      "        [[ 0.2475]],\n",
      "\n",
      "        [[ 0.6489]],\n",
      "\n",
      "        [[-0.4136]],\n",
      "\n",
      "        [[-1.1104]],\n",
      "\n",
      "        [[-0.6307]],\n",
      "\n",
      "        [[-0.7720]],\n",
      "\n",
      "        [[ 0.5376]],\n",
      "\n",
      "        [[-0.5971]],\n",
      "\n",
      "        [[-0.4296]],\n",
      "\n",
      "        [[ 0.7883]],\n",
      "\n",
      "        [[-0.7029]],\n",
      "\n",
      "        [[ 0.8440]],\n",
      "\n",
      "        [[ 1.9183]],\n",
      "\n",
      "        [[ 0.1235]],\n",
      "\n",
      "        [[-0.3994]],\n",
      "\n",
      "        [[ 1.9492]],\n",
      "\n",
      "        [[-0.2692]],\n",
      "\n",
      "        [[-0.7432]],\n",
      "\n",
      "        [[ 0.2967]],\n",
      "\n",
      "        [[-0.8190]],\n",
      "\n",
      "        [[ 1.2383]],\n",
      "\n",
      "        [[ 0.9705]],\n",
      "\n",
      "        [[ 1.4041]],\n",
      "\n",
      "        [[-0.4435]],\n",
      "\n",
      "        [[-1.0414]],\n",
      "\n",
      "        [[ 1.0190]],\n",
      "\n",
      "        [[ 0.0666]],\n",
      "\n",
      "        [[-0.8233]],\n",
      "\n",
      "        [[ 0.5374]],\n",
      "\n",
      "        [[-0.0725]],\n",
      "\n",
      "        [[-0.6204]],\n",
      "\n",
      "        [[-0.7577]],\n",
      "\n",
      "        [[-0.7677]],\n",
      "\n",
      "        [[-1.6895]],\n",
      "\n",
      "        [[ 1.1928]],\n",
      "\n",
      "        [[-0.2928]],\n",
      "\n",
      "        [[ 1.1552]],\n",
      "\n",
      "        [[-2.3978]],\n",
      "\n",
      "        [[-1.2172]],\n",
      "\n",
      "        [[-0.3495]],\n",
      "\n",
      "        [[-0.3711]],\n",
      "\n",
      "        [[-0.8957]],\n",
      "\n",
      "        [[ 0.7216]],\n",
      "\n",
      "        [[-0.1651]],\n",
      "\n",
      "        [[ 0.5835]],\n",
      "\n",
      "        [[-1.2235]],\n",
      "\n",
      "        [[-0.4833]],\n",
      "\n",
      "        [[ 0.6241]],\n",
      "\n",
      "        [[-0.9781]],\n",
      "\n",
      "        [[ 0.6254]],\n",
      "\n",
      "        [[ 0.6718]],\n",
      "\n",
      "        [[-0.5942]],\n",
      "\n",
      "        [[ 1.0127]],\n",
      "\n",
      "        [[-0.4123]],\n",
      "\n",
      "        [[-0.2479]],\n",
      "\n",
      "        [[-0.2848]],\n",
      "\n",
      "        [[-0.8043]],\n",
      "\n",
      "        [[ 0.5985]],\n",
      "\n",
      "        [[-1.7584]],\n",
      "\n",
      "        [[-0.3084]],\n",
      "\n",
      "        [[ 1.0044]],\n",
      "\n",
      "        [[-0.9306]],\n",
      "\n",
      "        [[-1.1837]],\n",
      "\n",
      "        [[-0.1677]],\n",
      "\n",
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      "tensor([[[-4.5377e-05, -4.2487e-05, -3.6881e-05,  ..., -3.4320e-05,\n",
      "          -3.6400e-05, -3.3959e-05],\n",
      "         [-7.0475e-04, -7.0026e-04, -6.9550e-04,  ..., -6.9875e-04,\n",
      "          -6.9879e-04, -6.9215e-04],\n",
      "         [ 2.2484e-03,  2.2470e-03,  2.2478e-03,  ...,  2.2510e-03,\n",
      "           2.2615e-03,  2.2536e-03],\n",
      "         ...,\n",
      "         [-3.8880e-05, -4.4466e-05, -4.8653e-05,  ..., -4.6282e-05,\n",
      "          -4.5077e-05, -4.9621e-05],\n",
      "         [-5.9647e-04, -6.0113e-04, -6.1093e-04,  ..., -5.9235e-04,\n",
      "          -5.9907e-04, -6.0018e-04],\n",
      "         [-1.3920e-04, -1.4445e-04, -1.4428e-04,  ..., -1.3863e-04,\n",
      "          -1.4770e-04, -1.4429e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 52 [28160/50000 (62%)]\tLoss: 0.143575, Accuracy: 95.31\n",
      "Train Epoch: 52 [29440/50000 (65%)]\tLoss: 0.187122, Accuracy: 94.92\n",
      "Train Epoch: 52 [30720/50000 (68%)]\tLoss: 0.138403, Accuracy: 94.92\n",
      "Train Epoch: 52 [32000/50000 (71%)]\tLoss: 0.198952, Accuracy: 91.80\n",
      "Train Epoch: 52 [33280/50000 (74%)]\tLoss: 0.206495, Accuracy: 94.53\n",
      "Train Epoch: 52 [34560/50000 (77%)]\tLoss: 0.204260, Accuracy: 92.58\n",
      "Train Epoch: 52 [35840/50000 (80%)]\tLoss: 0.138287, Accuracy: 96.09\n",
      "Train Epoch: 52 [37120/50000 (82%)]\tLoss: 0.125252, Accuracy: 95.31\n",
      "Train Epoch: 52 [38400/50000 (85%)]\tLoss: 0.195272, Accuracy: 92.97\n",
      "Train Epoch: 52 [39680/50000 (88%)]\tLoss: 0.136558, Accuracy: 94.53\n",
      "Train Epoch: 52 [40960/50000 (91%)]\tLoss: 0.220269, Accuracy: 92.97\n",
      "Train Epoch: 52 [42240/50000 (94%)]\tLoss: 0.208675, Accuracy: 93.36\n",
      "Train Epoch: 52 [43520/50000 (97%)]\tLoss: 0.267932, Accuracy: 91.80\n",
      "Train Epoch: 52 [35000/50000 (99%)]\tLoss: 0.181334, Accuracy: 92.50\n",
      "\n",
      "Validation set: Average loss: 0.2652, Accuracy: 4567/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.26800608634949 s]\n",
      "\n",
      "Test set: Average loss: 0.2818, Accuracy: 9080/10000 (90.80%)\n",
      "\n",
      "Train Epoch: 53 [0/50000 (0%)]\tLoss: 0.142338, Accuracy: 96.88\n",
      "Train Epoch: 53 [1280/50000 (3%)]\tLoss: 0.209031, Accuracy: 92.58\n",
      "Train Epoch: 53 [2560/50000 (6%)]\tLoss: 0.153501, Accuracy: 93.75\n",
      "Train Epoch: 53 [3840/50000 (9%)]\tLoss: 0.238452, Accuracy: 92.19\n",
      "Train Epoch: 53 [5120/50000 (11%)]\tLoss: 0.170668, Accuracy: 93.75\n",
      "Train Epoch: 53 [6400/50000 (14%)]\tLoss: 0.176292, Accuracy: 94.92\n",
      "Train Epoch: 53 [7680/50000 (17%)]\tLoss: 0.197092, Accuracy: 94.14\n",
      "Train Epoch: 53 [8960/50000 (20%)]\tLoss: 0.239604, Accuracy: 92.58\n",
      "Train Epoch: 53 [10240/50000 (23%)]\tLoss: 0.126083, Accuracy: 96.09\n",
      "Train Epoch: 53 [11520/50000 (26%)]\tLoss: 0.150408, Accuracy: 95.70\n",
      "Train Epoch: 53 [12800/50000 (28%)]\tLoss: 0.116609, Accuracy: 96.48\n",
      "Train Epoch: 53 [14080/50000 (31%)]\tLoss: 0.186507, Accuracy: 92.58\n",
      "Train Epoch: 53 [15360/50000 (34%)]\tLoss: 0.195866, Accuracy: 94.14\n",
      "Train Epoch: 53 [16640/50000 (37%)]\tLoss: 0.174732, Accuracy: 93.36\n",
      "Train Epoch: 53 [17920/50000 (40%)]\tLoss: 0.142688, Accuracy: 94.53\n",
      "Train Epoch: 53 [19200/50000 (43%)]\tLoss: 0.179733, Accuracy: 93.75\n",
      "Train Epoch: 53 [20480/50000 (45%)]\tLoss: 0.284912, Accuracy: 89.45\n",
      "Train Epoch: 53 [21760/50000 (48%)]\tLoss: 0.138862, Accuracy: 96.09\n",
      "Train Epoch: 53 [23040/50000 (51%)]\tLoss: 0.207764, Accuracy: 91.41\n",
      "Train Epoch: 53 [24320/50000 (54%)]\tLoss: 0.167121, Accuracy: 95.31\n",
      "Train Epoch: 53 [25600/50000 (57%)]\tLoss: 0.116175, Accuracy: 95.31\n",
      "Train Epoch: 53 [26880/50000 (60%)]\tLoss: 0.152838, Accuracy: 95.31\n",
      "Train Epoch: 53 [28160/50000 (62%)]\tLoss: 0.149756, Accuracy: 95.70\n",
      "Train Epoch: 53 [29440/50000 (65%)]\tLoss: 0.202745, Accuracy: 92.97\n",
      "Train Epoch: 53 [30720/50000 (68%)]\tLoss: 0.174005, Accuracy: 94.14\n",
      "Train Epoch: 53 [32000/50000 (71%)]\tLoss: 0.286780, Accuracy: 90.62\n",
      "Train Epoch: 53 [33280/50000 (74%)]\tLoss: 0.167284, Accuracy: 93.36\n",
      "Train Epoch: 53 [34560/50000 (77%)]\tLoss: 0.196276, Accuracy: 92.97\n",
      "Train Epoch: 53 [35840/50000 (80%)]\tLoss: 0.122505, Accuracy: 95.31\n",
      "Train Epoch: 53 [37120/50000 (82%)]\tLoss: 0.141682, Accuracy: 95.31\n",
      "Train Epoch: 53 [38400/50000 (85%)]\tLoss: 0.125248, Accuracy: 96.88\n",
      "Train Epoch: 53 [39680/50000 (88%)]\tLoss: 0.196504, Accuracy: 92.97\n",
      "Train Epoch: 53 [40960/50000 (91%)]\tLoss: 0.090190, Accuracy: 97.27\n",
      "Train Epoch: 53 [42240/50000 (94%)]\tLoss: 0.239020, Accuracy: 92.19\n",
      "Train Epoch: 53 [43520/50000 (97%)]\tLoss: 0.125179, Accuracy: 95.70\n",
      "Train Epoch: 53 [35000/50000 (99%)]\tLoss: 0.262943, Accuracy: 92.00\n",
      "\n",
      "Validation set: Average loss: 0.2548, Accuracy: 4591/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.38224697113037 s]\n",
      "Train Epoch: 54 [0/50000 (0%)]\tLoss: 0.155861, Accuracy: 95.31\n",
      "Train Epoch: 54 [1280/50000 (3%)]\tLoss: 0.175745, Accuracy: 93.75\n",
      "Train Epoch: 54 [2560/50000 (6%)]\tLoss: 0.163780, Accuracy: 94.92\n",
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      "Train Epoch: 54 [15360/50000 (34%)]\tLoss: 0.139847, Accuracy: 94.53\n",
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      "Train Epoch: 54 [17920/50000 (40%)]\tLoss: 0.114277, Accuracy: 96.48\n",
      "Train Epoch: 54 [19200/50000 (43%)]\tLoss: 0.144098, Accuracy: 93.75\n",
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      "Train Epoch: 54 [21760/50000 (48%)]\tLoss: 0.142204, Accuracy: 95.70\n",
      "Train Epoch: 54 [23040/50000 (51%)]\tLoss: 0.221836, Accuracy: 91.80\n",
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      "Train Epoch: 54 [25600/50000 (57%)]\tLoss: 0.143342, Accuracy: 95.31\n",
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      "Train Epoch: 54 [28160/50000 (62%)]\tLoss: 0.100696, Accuracy: 97.66\n",
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      "Train Epoch: 54 [32000/50000 (71%)]\tLoss: 0.093373, Accuracy: 96.09\n",
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      "Train Epoch: 54 [38400/50000 (85%)]\tLoss: 0.152743, Accuracy: 93.75\n",
      "Train Epoch: 54 [39680/50000 (88%)]\tLoss: 0.169398, Accuracy: 94.14\n",
      "Train Epoch: 54 [40960/50000 (91%)]\tLoss: 0.222839, Accuracy: 91.80\n",
      "Train Epoch: 54 [42240/50000 (94%)]\tLoss: 0.203137, Accuracy: 92.97\n",
      "Train Epoch: 54 [43520/50000 (97%)]\tLoss: 0.131351, Accuracy: 95.31\n",
      "Train Epoch: 54 [35000/50000 (99%)]\tLoss: 0.138119, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2596, Accuracy: 4599/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.27162790298462 s]\n",
      "\n",
      "Test set: Average loss: 0.2815, Accuracy: 9092/10000 (90.92%)\n",
      "\n",
      "Train Epoch: 55 [0/50000 (0%)]\tLoss: 0.113418, Accuracy: 96.09\n",
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      "Train Epoch: 55 [12800/50000 (28%)]\tLoss: 0.116982, Accuracy: 95.70\n",
      "Train Epoch: 55 [14080/50000 (31%)]\tLoss: 0.077647, Accuracy: 98.44\n",
      "Train Epoch: 55 [15360/50000 (34%)]\tLoss: 0.137804, Accuracy: 95.70\n",
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      "Train Epoch: 55 [23040/50000 (51%)]\tLoss: 0.114163, Accuracy: 96.88\n",
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      "Train Epoch: 55 [25600/50000 (57%)]\tLoss: 0.120645, Accuracy: 96.48\n",
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      "Train Epoch: 55 [28160/50000 (62%)]\tLoss: 0.168266, Accuracy: 92.58\n",
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      "Train Epoch: 55 [32000/50000 (71%)]\tLoss: 0.119565, Accuracy: 95.70\n",
      "Train Epoch: 55 [33280/50000 (74%)]\tLoss: 0.109112, Accuracy: 96.48\n",
      "Train Epoch: 55 [34560/50000 (77%)]\tLoss: 0.164996, Accuracy: 93.36\n",
      "Train Epoch: 55 [35840/50000 (80%)]\tLoss: 0.172486, Accuracy: 94.92\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 55 [37120/50000 (82%)]\tLoss: 0.140284, Accuracy: 96.48\n",
      "Train Epoch: 55 [38400/50000 (85%)]\tLoss: 0.130737, Accuracy: 95.70\n",
      "Train Epoch: 55 [39680/50000 (88%)]\tLoss: 0.120617, Accuracy: 95.31\n",
      "Train Epoch: 55 [40960/50000 (91%)]\tLoss: 0.173335, Accuracy: 93.36\n",
      "Train Epoch: 55 [42240/50000 (94%)]\tLoss: 0.167544, Accuracy: 96.09\n",
      "Train Epoch: 55 [43520/50000 (97%)]\tLoss: 0.245086, Accuracy: 93.75\n",
      "Train Epoch: 55 [35000/50000 (99%)]\tLoss: 0.096915, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2589, Accuracy: 4585/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.39658045768738 s]\n",
      "Train Epoch: 56 [0/50000 (0%)]\tLoss: 0.132677, Accuracy: 94.92\n",
      "Train Epoch: 56 [1280/50000 (3%)]\tLoss: 0.143619, Accuracy: 94.14\n",
      "Train Epoch: 56 [2560/50000 (6%)]\tLoss: 0.097417, Accuracy: 97.27\n",
      "Train Epoch: 56 [3840/50000 (9%)]\tLoss: 0.181232, Accuracy: 94.53\n",
      "Train Epoch: 56 [5120/50000 (11%)]\tLoss: 0.088498, Accuracy: 96.09\n",
      "Train Epoch: 56 [6400/50000 (14%)]\tLoss: 0.164110, Accuracy: 95.31\n",
      "Train Epoch: 56 [7680/50000 (17%)]\tLoss: 0.165511, Accuracy: 95.31\n",
      "Train Epoch: 56 [8960/50000 (20%)]\tLoss: 0.157585, Accuracy: 95.70\n",
      "Train Epoch: 56 [10240/50000 (23%)]\tLoss: 0.140777, Accuracy: 94.53\n",
      "Train Epoch: 56 [11520/50000 (26%)]\tLoss: 0.149941, Accuracy: 94.14\n",
      "Train Epoch: 56 [12800/50000 (28%)]\tLoss: 0.110193, Accuracy: 96.09\n",
      "Train Epoch: 56 [14080/50000 (31%)]\tLoss: 0.131434, Accuracy: 95.70\n",
      "Train Epoch: 56 [15360/50000 (34%)]\tLoss: 0.174952, Accuracy: 94.53\n",
      "Train Epoch: 56 [16640/50000 (37%)]\tLoss: 0.101712, Accuracy: 97.27\n",
      "Train Epoch: 56 [17920/50000 (40%)]\tLoss: 0.141220, Accuracy: 94.14\n",
      "Train Epoch: 56 [19200/50000 (43%)]\tLoss: 0.133787, Accuracy: 96.88\n",
      "Train Epoch: 56 [20480/50000 (45%)]\tLoss: 0.148646, Accuracy: 94.92\n",
      "Train Epoch: 56 [21760/50000 (48%)]\tLoss: 0.250425, Accuracy: 91.02\n",
      "Train Epoch: 56 [23040/50000 (51%)]\tLoss: 0.144347, Accuracy: 94.92\n",
      "Train Epoch: 56 [24320/50000 (54%)]\tLoss: 0.139500, Accuracy: 96.48\n",
      "Train Epoch: 56 [25600/50000 (57%)]\tLoss: 0.154341, Accuracy: 95.31\n",
      "Train Epoch: 56 [26880/50000 (60%)]\tLoss: 0.137368, Accuracy: 95.31\n",
      "Train Epoch: 56 [28160/50000 (62%)]\tLoss: 0.136997, Accuracy: 96.09\n",
      "Train Epoch: 56 [29440/50000 (65%)]\tLoss: 0.136828, Accuracy: 94.92\n",
      "Train Epoch: 56 [30720/50000 (68%)]\tLoss: 0.118216, Accuracy: 95.70\n",
      "Train Epoch: 56 [32000/50000 (71%)]\tLoss: 0.169348, Accuracy: 93.75\n",
      "Train Epoch: 56 [33280/50000 (74%)]\tLoss: 0.164017, Accuracy: 95.31\n",
      "Train Epoch: 56 [34560/50000 (77%)]\tLoss: 0.099615, Accuracy: 96.48\n",
      "Train Epoch: 56 [35840/50000 (80%)]\tLoss: 0.111146, Accuracy: 96.48\n",
      "Train Epoch: 56 [37120/50000 (82%)]\tLoss: 0.126835, Accuracy: 94.92\n",
      "Train Epoch: 56 [38400/50000 (85%)]\tLoss: 0.159192, Accuracy: 95.31\n",
      "Train Epoch: 56 [39680/50000 (88%)]\tLoss: 0.193447, Accuracy: 92.97\n",
      "Train Epoch: 56 [40960/50000 (91%)]\tLoss: 0.143744, Accuracy: 96.48\n",
      "Train Epoch: 56 [42240/50000 (94%)]\tLoss: 0.176849, Accuracy: 94.92\n",
      "Train Epoch: 56 [43520/50000 (97%)]\tLoss: 0.149446, Accuracy: 96.09\n",
      "Train Epoch: 56 [35000/50000 (99%)]\tLoss: 0.174020, Accuracy: 93.00\n",
      "\n",
      "Validation set: Average loss: 0.2586, Accuracy: 4582/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.26436948776245 s]\n",
      "\n",
      "Test set: Average loss: 0.2870, Accuracy: 9100/10000 (91.00%)\n",
      "\n",
      "Train Epoch: 57 [0/50000 (0%)]\tLoss: 0.131095, Accuracy: 94.92\n",
      "Train Epoch: 57 [1280/50000 (3%)]\tLoss: 0.081492, Accuracy: 96.48\n",
      "Train Epoch: 57 [2560/50000 (6%)]\tLoss: 0.120533, Accuracy: 95.70\n",
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      "Train Epoch: 57 [5120/50000 (11%)]\tLoss: 0.161947, Accuracy: 94.14\n",
      "Train Epoch: 57 [6400/50000 (14%)]\tLoss: 0.111446, Accuracy: 96.09\n",
      "Train Epoch: 57 [7680/50000 (17%)]\tLoss: 0.142473, Accuracy: 95.70\n",
      "Train Epoch: 57 [8960/50000 (20%)]\tLoss: 0.085284, Accuracy: 97.66\n",
      "Train Epoch: 57 [10240/50000 (23%)]\tLoss: 0.100614, Accuracy: 95.31\n",
      "Train Epoch: 57 [11520/50000 (26%)]\tLoss: 0.080849, Accuracy: 96.09\n",
      "Train Epoch: 57 [12800/50000 (28%)]\tLoss: 0.188555, Accuracy: 93.75\n",
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      "Train Epoch: 57 [15360/50000 (34%)]\tLoss: 0.068870, Accuracy: 98.05\n",
      "Train Epoch: 57 [16640/50000 (37%)]\tLoss: 0.141473, Accuracy: 95.31\n",
      "Train Epoch: 57 [17920/50000 (40%)]\tLoss: 0.094302, Accuracy: 97.27\n",
      "Train Epoch: 57 [19200/50000 (43%)]\tLoss: 0.128125, Accuracy: 96.09\n",
      "Train Epoch: 57 [20480/50000 (45%)]\tLoss: 0.134044, Accuracy: 95.31\n",
      "Train Epoch: 57 [21760/50000 (48%)]\tLoss: 0.172532, Accuracy: 93.36\n",
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      "Train Epoch: 57 [24320/50000 (54%)]\tLoss: 0.171862, Accuracy: 93.36\n",
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      "Train Epoch: 57 [26880/50000 (60%)]\tLoss: 0.141177, Accuracy: 94.92\n",
      "Train Epoch: 57 [28160/50000 (62%)]\tLoss: 0.142581, Accuracy: 95.31\n",
      "Train Epoch: 57 [29440/50000 (65%)]\tLoss: 0.128153, Accuracy: 95.31\n",
      "Train Epoch: 57 [30720/50000 (68%)]\tLoss: 0.078302, Accuracy: 96.88\n",
      "Train Epoch: 57 [32000/50000 (71%)]\tLoss: 0.104081, Accuracy: 96.48\n",
      "Train Epoch: 57 [33280/50000 (74%)]\tLoss: 0.109274, Accuracy: 94.92\n",
      "Train Epoch: 57 [34560/50000 (77%)]\tLoss: 0.139623, Accuracy: 94.92\n",
      "Train Epoch: 57 [35840/50000 (80%)]\tLoss: 0.119286, Accuracy: 96.09\n",
      "Train Epoch: 57 [37120/50000 (82%)]\tLoss: 0.159854, Accuracy: 93.75\n",
      "Train Epoch: 57 [38400/50000 (85%)]\tLoss: 0.116033, Accuracy: 96.48\n",
      "Train Epoch: 57 [39680/50000 (88%)]\tLoss: 0.152490, Accuracy: 93.75\n",
      "Train Epoch: 57 [40960/50000 (91%)]\tLoss: 0.117172, Accuracy: 95.31\n",
      "Train Epoch: 57 [42240/50000 (94%)]\tLoss: 0.160449, Accuracy: 94.92\n",
      "Train Epoch: 57 [43520/50000 (97%)]\tLoss: 0.095266, Accuracy: 96.09\n",
      "Train Epoch: 57 [35000/50000 (99%)]\tLoss: 0.123162, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2567, Accuracy: 4585/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.417720556259155 s]\n",
      "Train Epoch: 58 [0/50000 (0%)]\tLoss: 0.086688, Accuracy: 98.05\n",
      "Train Epoch: 58 [1280/50000 (3%)]\tLoss: 0.125351, Accuracy: 96.09\n",
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      "Train Epoch: 58 [21760/50000 (48%)]\tLoss: 0.160740, Accuracy: 94.14\n",
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      "Train Epoch: 58 [24320/50000 (54%)]\tLoss: 0.096024, Accuracy: 95.70\n",
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      "Train Epoch: 58 [26880/50000 (60%)]\tLoss: 0.142025, Accuracy: 96.48\n",
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      "Train Epoch: 58 [35840/50000 (80%)]\tLoss: 0.106780, Accuracy: 96.09\n",
      "Train Epoch: 58 [37120/50000 (82%)]\tLoss: 0.176368, Accuracy: 94.14\n",
      "Train Epoch: 58 [38400/50000 (85%)]\tLoss: 0.066867, Accuracy: 98.44\n",
      "Train Epoch: 58 [39680/50000 (88%)]\tLoss: 0.082252, Accuracy: 96.88\n",
      "Train Epoch: 58 [40960/50000 (91%)]\tLoss: 0.120464, Accuracy: 96.09\n",
      "Train Epoch: 58 [42240/50000 (94%)]\tLoss: 0.112184, Accuracy: 96.88\n",
      "Train Epoch: 58 [43520/50000 (97%)]\tLoss: 0.097709, Accuracy: 96.88\n",
      "Train Epoch: 58 [35000/50000 (99%)]\tLoss: 0.142803, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2602, Accuracy: 4592/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.24973940849304 s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.2853, Accuracy: 9153/10000 (91.53%)\n",
      "\n",
      "Train Epoch: 59 [0/50000 (0%)]\tLoss: 0.104094, Accuracy: 97.66\n",
      "Train Epoch: 59 [1280/50000 (3%)]\tLoss: 0.089778, Accuracy: 97.27\n",
      "Train Epoch: 59 [2560/50000 (6%)]\tLoss: 0.160420, Accuracy: 93.36\n",
      "Train Epoch: 59 [3840/50000 (9%)]\tLoss: 0.168892, Accuracy: 94.92\n",
      "Train Epoch: 59 [5120/50000 (11%)]\tLoss: 0.105577, Accuracy: 97.27\n",
      "Train Epoch: 59 [6400/50000 (14%)]\tLoss: 0.174609, Accuracy: 94.92\n",
      "Train Epoch: 59 [7680/50000 (17%)]\tLoss: 0.133445, Accuracy: 94.92\n",
      "Train Epoch: 59 [8960/50000 (20%)]\tLoss: 0.099358, Accuracy: 96.48\n",
      "Train Epoch: 59 [10240/50000 (23%)]\tLoss: 0.127114, Accuracy: 95.31\n",
      "Train Epoch: 59 [11520/50000 (26%)]\tLoss: 0.107221, Accuracy: 96.48\n",
      "Train Epoch: 59 [12800/50000 (28%)]\tLoss: 0.087771, Accuracy: 96.88\n",
      "Train Epoch: 59 [14080/50000 (31%)]\tLoss: 0.108344, Accuracy: 97.27\n",
      "Train Epoch: 59 [15360/50000 (34%)]\tLoss: 0.132771, Accuracy: 94.92\n",
      "Train Epoch: 59 [16640/50000 (37%)]\tLoss: 0.132355, Accuracy: 96.88\n",
      "Train Epoch: 59 [17920/50000 (40%)]\tLoss: 0.098213, Accuracy: 96.09\n",
      "Train Epoch: 59 [19200/50000 (43%)]\tLoss: 0.088622, Accuracy: 97.27\n",
      "Train Epoch: 59 [20480/50000 (45%)]\tLoss: 0.126354, Accuracy: 95.70\n",
      "Train Epoch: 59 [21760/50000 (48%)]\tLoss: 0.120410, Accuracy: 96.09\n",
      "Train Epoch: 59 [23040/50000 (51%)]\tLoss: 0.122362, Accuracy: 95.70\n",
      "Train Epoch: 59 [24320/50000 (54%)]\tLoss: 0.080602, Accuracy: 98.05\n",
      "Train Epoch: 59 [25600/50000 (57%)]\tLoss: 0.099713, Accuracy: 96.48\n",
      "Train Epoch: 59 [26880/50000 (60%)]\tLoss: 0.102574, Accuracy: 96.48\n",
      "Train Epoch: 59 [28160/50000 (62%)]\tLoss: 0.127298, Accuracy: 95.31\n",
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      "Train Epoch: 59 [30720/50000 (68%)]\tLoss: 0.076052, Accuracy: 98.05\n",
      "Train Epoch: 59 [32000/50000 (71%)]\tLoss: 0.111571, Accuracy: 97.27\n",
      "Train Epoch: 59 [33280/50000 (74%)]\tLoss: 0.132950, Accuracy: 95.31\n",
      "Train Epoch: 59 [34560/50000 (77%)]\tLoss: 0.144860, Accuracy: 96.09\n",
      "Train Epoch: 59 [35840/50000 (80%)]\tLoss: 0.141869, Accuracy: 96.09\n",
      "Train Epoch: 59 [37120/50000 (82%)]\tLoss: 0.181846, Accuracy: 94.53\n",
      "Train Epoch: 59 [38400/50000 (85%)]\tLoss: 0.156882, Accuracy: 94.92\n",
      "Train Epoch: 59 [39680/50000 (88%)]\tLoss: 0.079465, Accuracy: 98.05\n",
      "Train Epoch: 59 [40960/50000 (91%)]\tLoss: 0.129155, Accuracy: 94.92\n",
      "Train Epoch: 59 [42240/50000 (94%)]\tLoss: 0.127187, Accuracy: 96.48\n",
      "Train Epoch: 59 [43520/50000 (97%)]\tLoss: 0.136720, Accuracy: 95.70\n",
      "Train Epoch: 59 [35000/50000 (99%)]\tLoss: 0.114847, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2695, Accuracy: 4576/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.353663206100464 s]\n",
      "Train Epoch: 60 [0/50000 (0%)]\tLoss: 0.099888, Accuracy: 97.66\n",
      "Train Epoch: 60 [1280/50000 (3%)]\tLoss: 0.093730, Accuracy: 95.70\n",
      "Train Epoch: 60 [2560/50000 (6%)]\tLoss: 0.109008, Accuracy: 96.88\n",
      "Train Epoch: 60 [3840/50000 (9%)]\tLoss: 0.105862, Accuracy: 96.09\n",
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      "Train Epoch: 60 [11520/50000 (26%)]\tLoss: 0.093444, Accuracy: 96.48\n",
      "Train Epoch: 60 [12800/50000 (28%)]\tLoss: 0.144876, Accuracy: 95.31\n",
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      "Train Epoch: 60 [15360/50000 (34%)]\tLoss: 0.120389, Accuracy: 96.09\n",
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      "Train Epoch: 60 [17920/50000 (40%)]\tLoss: 0.146415, Accuracy: 93.75\n",
      "Train Epoch: 60 [19200/50000 (43%)]\tLoss: 0.152181, Accuracy: 95.31\n",
      "Train Epoch: 60 [20480/50000 (45%)]\tLoss: 0.118246, Accuracy: 96.48\n",
      "Train Epoch: 60 [21760/50000 (48%)]\tLoss: 0.086913, Accuracy: 98.05\n",
      "Train Epoch: 60 [23040/50000 (51%)]\tLoss: 0.070131, Accuracy: 98.44\n",
      "Train Epoch: 60 [24320/50000 (54%)]\tLoss: 0.112111, Accuracy: 96.48\n",
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      "Train Epoch: 60 [29440/50000 (65%)]\tLoss: 0.109794, Accuracy: 97.27\n",
      "Train Epoch: 60 [30720/50000 (68%)]\tLoss: 0.190360, Accuracy: 93.36\n",
      "Train Epoch: 60 [32000/50000 (71%)]\tLoss: 0.099376, Accuracy: 96.48\n",
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      "Train Epoch: 60 [38400/50000 (85%)]\tLoss: 0.060551, Accuracy: 97.66\n",
      "Train Epoch: 60 [39680/50000 (88%)]\tLoss: 0.094336, Accuracy: 96.48\n",
      "Train Epoch: 60 [40960/50000 (91%)]\tLoss: 0.093093, Accuracy: 96.09\n",
      "Train Epoch: 60 [42240/50000 (94%)]\tLoss: 0.136394, Accuracy: 94.92\n",
      "Train Epoch: 60 [43520/50000 (97%)]\tLoss: 0.088072, Accuracy: 96.48\n",
      "Train Epoch: 60 [35000/50000 (99%)]\tLoss: 0.130084, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2672, Accuracy: 4599/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.21900773048401 s]\n",
      "\n",
      "Test set: Average loss: 0.2876, Accuracy: 9127/10000 (91.27%)\n",
      "\n",
      "Train Epoch: 61 [0/50000 (0%)]\tLoss: 0.086531, Accuracy: 96.48\n",
      "Train Epoch: 61 [1280/50000 (3%)]\tLoss: 0.108140, Accuracy: 96.88\n",
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      "Train Epoch: 61 [8960/50000 (20%)]\tLoss: 0.046965, Accuracy: 99.22\n",
      "Train Epoch: 61 [10240/50000 (23%)]\tLoss: 0.123314, Accuracy: 96.09\n",
      "Train Epoch: 61 [11520/50000 (26%)]\tLoss: 0.133146, Accuracy: 95.31\n",
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      "Train Epoch: 61 [30720/50000 (68%)]\tLoss: 0.133135, Accuracy: 95.70\n",
      "Train Epoch: 61 [32000/50000 (71%)]\tLoss: 0.108936, Accuracy: 96.48\n",
      "Train Epoch: 61 [33280/50000 (74%)]\tLoss: 0.133205, Accuracy: 95.70\n",
      "Train Epoch: 61 [34560/50000 (77%)]\tLoss: 0.117842, Accuracy: 95.31\n",
      "Train Epoch: 61 [35840/50000 (80%)]\tLoss: 0.116008, Accuracy: 95.70\n",
      "Train Epoch: 61 [37120/50000 (82%)]\tLoss: 0.098707, Accuracy: 96.88\n",
      "Train Epoch: 61 [38400/50000 (85%)]\tLoss: 0.114814, Accuracy: 95.70\n",
      "Train Epoch: 61 [39680/50000 (88%)]\tLoss: 0.149125, Accuracy: 95.70\n",
      "Train Epoch: 61 [40960/50000 (91%)]\tLoss: 0.152800, Accuracy: 92.97\n",
      "Train Epoch: 61 [42240/50000 (94%)]\tLoss: 0.065570, Accuracy: 98.05\n",
      "Train Epoch: 61 [43520/50000 (97%)]\tLoss: 0.108354, Accuracy: 95.70\n",
      "Train Epoch: 61 [35000/50000 (99%)]\tLoss: 0.165294, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2754, Accuracy: 4584/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.35874319076538 s]\n",
      "Train Epoch: 62 [0/50000 (0%)]\tLoss: 0.175212, Accuracy: 94.92\n",
      "Train Epoch: 62 [1280/50000 (3%)]\tLoss: 0.064210, Accuracy: 97.27\n",
      "Train Epoch: 62 [2560/50000 (6%)]\tLoss: 0.064957, Accuracy: 99.22\n",
      "Train Epoch: 62 [3840/50000 (9%)]\tLoss: 0.092565, Accuracy: 97.27\n",
      "Train Epoch: 62 [5120/50000 (11%)]\tLoss: 0.097745, Accuracy: 95.70\n",
      "Train Epoch: 62 [6400/50000 (14%)]\tLoss: 0.076647, Accuracy: 98.44\n",
      "Train Epoch: 62 [7680/50000 (17%)]\tLoss: 0.071715, Accuracy: 97.27\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 62 [8960/50000 (20%)]\tLoss: 0.107166, Accuracy: 96.48\n",
      "Train Epoch: 62 [10240/50000 (23%)]\tLoss: 0.103107, Accuracy: 96.48\n",
      "Train Epoch: 62 [11520/50000 (26%)]\tLoss: 0.116667, Accuracy: 96.88\n",
      "Train Epoch: 62 [12800/50000 (28%)]\tLoss: 0.086037, Accuracy: 96.88\n",
      "Train Epoch: 62 [14080/50000 (31%)]\tLoss: 0.166201, Accuracy: 93.75\n",
      "Train Epoch: 62 [15360/50000 (34%)]\tLoss: 0.048882, Accuracy: 98.44\n",
      "Train Epoch: 62 [16640/50000 (37%)]\tLoss: 0.184632, Accuracy: 95.70\n",
      "Train Epoch: 62 [17920/50000 (40%)]\tLoss: 0.099150, Accuracy: 96.48\n",
      "Train Epoch: 62 [19200/50000 (43%)]\tLoss: 0.098970, Accuracy: 98.05\n",
      "Train Epoch: 62 [20480/50000 (45%)]\tLoss: 0.108768, Accuracy: 95.70\n",
      "Train Epoch: 62 [21760/50000 (48%)]\tLoss: 0.164413, Accuracy: 95.70\n",
      "Train Epoch: 62 [23040/50000 (51%)]\tLoss: 0.080185, Accuracy: 97.27\n",
      "Train Epoch: 62 [24320/50000 (54%)]\tLoss: 0.060522, Accuracy: 98.44\n",
      "Train Epoch: 62 [25600/50000 (57%)]\tLoss: 0.069299, Accuracy: 97.27\n",
      "Train Epoch: 62 [26880/50000 (60%)]\tLoss: 0.099225, Accuracy: 95.70\n",
      "Train Epoch: 62 [28160/50000 (62%)]\tLoss: 0.082945, Accuracy: 98.05\n",
      "Train Epoch: 62 [29440/50000 (65%)]\tLoss: 0.095043, Accuracy: 97.27\n",
      "Train Epoch: 62 [30720/50000 (68%)]\tLoss: 0.070410, Accuracy: 98.05\n",
      "Train Epoch: 62 [32000/50000 (71%)]\tLoss: 0.089493, Accuracy: 96.48\n",
      "Train Epoch: 62 [33280/50000 (74%)]\tLoss: 0.131886, Accuracy: 95.70\n",
      "Train Epoch: 62 [34560/50000 (77%)]\tLoss: 0.114066, Accuracy: 96.88\n",
      "Train Epoch: 62 [35840/50000 (80%)]\tLoss: 0.111040, Accuracy: 96.88\n",
      "Train Epoch: 62 [37120/50000 (82%)]\tLoss: 0.143112, Accuracy: 94.14\n",
      "Train Epoch: 62 [38400/50000 (85%)]\tLoss: 0.127485, Accuracy: 94.53\n",
      "Train Epoch: 62 [39680/50000 (88%)]\tLoss: 0.115055, Accuracy: 94.92\n",
      "Train Epoch: 62 [40960/50000 (91%)]\tLoss: 0.095787, Accuracy: 96.09\n",
      "Train Epoch: 62 [42240/50000 (94%)]\tLoss: 0.041649, Accuracy: 99.22\n",
      "Train Epoch: 62 [43520/50000 (97%)]\tLoss: 0.105833, Accuracy: 96.88\n",
      "Train Epoch: 62 [35000/50000 (99%)]\tLoss: 0.151841, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2755, Accuracy: 4571/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.296236753463745 s]\n",
      "\n",
      "Test set: Average loss: 0.3058, Accuracy: 9098/10000 (90.98%)\n",
      "\n",
      "Train Epoch: 63 [0/50000 (0%)]\tLoss: 0.122659, Accuracy: 96.48\n",
      "Train Epoch: 63 [1280/50000 (3%)]\tLoss: 0.054343, Accuracy: 98.83\n",
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      "Train Epoch: 63 [11520/50000 (26%)]\tLoss: 0.088295, Accuracy: 96.48\n",
      "Train Epoch: 63 [12800/50000 (28%)]\tLoss: 0.077669, Accuracy: 97.66\n",
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      "Train Epoch: 63 [15360/50000 (34%)]\tLoss: 0.105030, Accuracy: 96.88\n",
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      "Train Epoch: 63 [20480/50000 (45%)]\tLoss: 0.076443, Accuracy: 97.27\n",
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      "Train Epoch: 63 [32000/50000 (71%)]\tLoss: 0.100802, Accuracy: 96.88\n",
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      "Train Epoch: 63 [34560/50000 (77%)]\tLoss: 0.063343, Accuracy: 97.66\n",
      "Train Epoch: 63 [35840/50000 (80%)]\tLoss: 0.143155, Accuracy: 94.53\n",
      "Train Epoch: 63 [37120/50000 (82%)]\tLoss: 0.167796, Accuracy: 92.97\n",
      "Train Epoch: 63 [38400/50000 (85%)]\tLoss: 0.119774, Accuracy: 96.09\n",
      "Train Epoch: 63 [39680/50000 (88%)]\tLoss: 0.096069, Accuracy: 96.88\n",
      "Train Epoch: 63 [40960/50000 (91%)]\tLoss: 0.088917, Accuracy: 96.88\n",
      "Train Epoch: 63 [42240/50000 (94%)]\tLoss: 0.102745, Accuracy: 96.88\n",
      "Train Epoch: 63 [43520/50000 (97%)]\tLoss: 0.112573, Accuracy: 94.92\n",
      "Train Epoch: 63 [35000/50000 (99%)]\tLoss: 0.128555, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2972, Accuracy: 4567/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.50840759277344 s]\n",
      "Train Epoch: 64 [0/50000 (0%)]\tLoss: 0.088412, Accuracy: 96.88\n",
      "Train Epoch: 64 [1280/50000 (3%)]\tLoss: 0.109606, Accuracy: 96.09\n",
      "Train Epoch: 64 [2560/50000 (6%)]\tLoss: 0.060457, Accuracy: 98.05\n",
      "Train Epoch: 64 [3840/50000 (9%)]\tLoss: 0.066419, Accuracy: 97.27\n",
      "Train Epoch: 64 [5120/50000 (11%)]\tLoss: 0.102494, Accuracy: 96.48\n",
      "Train Epoch: 64 [6400/50000 (14%)]\tLoss: 0.092902, Accuracy: 96.48\n",
      "Train Epoch: 64 [7680/50000 (17%)]\tLoss: 0.132885, Accuracy: 94.92\n",
      "Train Epoch: 64 [8960/50000 (20%)]\tLoss: 0.103063, Accuracy: 97.27\n",
      "Train Epoch: 64 [10240/50000 (23%)]\tLoss: 0.066450, Accuracy: 97.66\n",
      "Train Epoch: 64 [11520/50000 (26%)]\tLoss: 0.072552, Accuracy: 97.66\n",
      "Train Epoch: 64 [12800/50000 (28%)]\tLoss: 0.058829, Accuracy: 96.48\n",
      "Train Epoch: 64 [14080/50000 (31%)]\tLoss: 0.059857, Accuracy: 98.05\n",
      "Train Epoch: 64 [15360/50000 (34%)]\tLoss: 0.076948, Accuracy: 98.05\n",
      "Train Epoch: 64 [16640/50000 (37%)]\tLoss: 0.137572, Accuracy: 94.92\n",
      "Train Epoch: 64 [17920/50000 (40%)]\tLoss: 0.109357, Accuracy: 96.09\n",
      "Train Epoch: 64 [19200/50000 (43%)]\tLoss: 0.095409, Accuracy: 97.27\n",
      "Train Epoch: 64 [20480/50000 (45%)]\tLoss: 0.079345, Accuracy: 96.48\n",
      "Train Epoch: 64 [21760/50000 (48%)]\tLoss: 0.099983, Accuracy: 96.48\n",
      "Train Epoch: 64 [23040/50000 (51%)]\tLoss: 0.138258, Accuracy: 94.14\n",
      "Train Epoch: 64 [24320/50000 (54%)]\tLoss: 0.109438, Accuracy: 96.09\n",
      "Train Epoch: 64 [25600/50000 (57%)]\tLoss: 0.105897, Accuracy: 96.48\n",
      "Train Epoch: 64 [26880/50000 (60%)]\tLoss: 0.158171, Accuracy: 94.92\n",
      "Train Epoch: 64 [28160/50000 (62%)]\tLoss: 0.034156, Accuracy: 99.61\n",
      "Train Epoch: 64 [29440/50000 (65%)]\tLoss: 0.066346, Accuracy: 98.44\n",
      "Train Epoch: 64 [30720/50000 (68%)]\tLoss: 0.082759, Accuracy: 97.27\n",
      "Train Epoch: 64 [32000/50000 (71%)]\tLoss: 0.150605, Accuracy: 96.09\n",
      "Train Epoch: 64 [33280/50000 (74%)]\tLoss: 0.057450, Accuracy: 97.66\n",
      "Train Epoch: 64 [34560/50000 (77%)]\tLoss: 0.048400, Accuracy: 98.44\n",
      "Train Epoch: 64 [35840/50000 (80%)]\tLoss: 0.077017, Accuracy: 97.66\n",
      "Train Epoch: 64 [37120/50000 (82%)]\tLoss: 0.137567, Accuracy: 92.97\n",
      "Train Epoch: 64 [38400/50000 (85%)]\tLoss: 0.149363, Accuracy: 95.70\n",
      "Train Epoch: 64 [39680/50000 (88%)]\tLoss: 0.106277, Accuracy: 96.88\n",
      "Train Epoch: 64 [40960/50000 (91%)]\tLoss: 0.065408, Accuracy: 97.66\n",
      "Train Epoch: 64 [42240/50000 (94%)]\tLoss: 0.097996, Accuracy: 96.48\n",
      "Train Epoch: 64 [43520/50000 (97%)]\tLoss: 0.103990, Accuracy: 96.48\n",
      "Train Epoch: 64 [35000/50000 (99%)]\tLoss: 0.093434, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2826, Accuracy: 4580/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.27074694633484 s]\n",
      "\n",
      "Test set: Average loss: 0.3068, Accuracy: 9129/10000 (91.29%)\n",
      "\n",
      "Train Epoch: 65 [0/50000 (0%)]\tLoss: 0.034779, Accuracy: 98.83\n",
      "Train Epoch: 65 [1280/50000 (3%)]\tLoss: 0.102485, Accuracy: 95.70\n",
      "Train Epoch: 65 [2560/50000 (6%)]\tLoss: 0.079932, Accuracy: 97.27\n",
      "Train Epoch: 65 [3840/50000 (9%)]\tLoss: 0.049582, Accuracy: 98.83\n",
      "Train Epoch: 65 [5120/50000 (11%)]\tLoss: 0.054565, Accuracy: 98.05\n",
      "Train Epoch: 65 [6400/50000 (14%)]\tLoss: 0.154282, Accuracy: 94.92\n",
      "Train Epoch: 65 [7680/50000 (17%)]\tLoss: 0.114807, Accuracy: 97.27\n",
      "Train Epoch: 65 [8960/50000 (20%)]\tLoss: 0.123464, Accuracy: 96.48\n",
      "Train Epoch: 65 [10240/50000 (23%)]\tLoss: 0.106345, Accuracy: 95.70\n",
      "Train Epoch: 65 [11520/50000 (26%)]\tLoss: 0.067169, Accuracy: 98.44\n",
      "Train Epoch: 65 [12800/50000 (28%)]\tLoss: 0.057658, Accuracy: 98.05\n",
      "Train Epoch: 65 [14080/50000 (31%)]\tLoss: 0.087638, Accuracy: 97.27\n",
      "Train Epoch: 65 [15360/50000 (34%)]\tLoss: 0.087725, Accuracy: 96.09\n",
      "Train Epoch: 65 [16640/50000 (37%)]\tLoss: 0.056807, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 65 [17920/50000 (40%)]\tLoss: 0.162671, Accuracy: 94.53\n",
      "Train Epoch: 65 [19200/50000 (43%)]\tLoss: 0.065574, Accuracy: 97.27\n",
      "Train Epoch: 65 [20480/50000 (45%)]\tLoss: 0.055517, Accuracy: 97.66\n",
      "Train Epoch: 65 [21760/50000 (48%)]\tLoss: 0.142025, Accuracy: 95.31\n",
      "Train Epoch: 65 [23040/50000 (51%)]\tLoss: 0.075409, Accuracy: 97.27\n",
      "Train Epoch: 65 [24320/50000 (54%)]\tLoss: 0.096024, Accuracy: 95.31\n",
      "Train Epoch: 65 [25600/50000 (57%)]\tLoss: 0.099894, Accuracy: 96.09\n",
      "Train Epoch: 65 [26880/50000 (60%)]\tLoss: 0.060311, Accuracy: 98.44\n",
      "Train Epoch: 65 [28160/50000 (62%)]\tLoss: 0.059621, Accuracy: 97.66\n",
      "Train Epoch: 65 [29440/50000 (65%)]\tLoss: 0.117440, Accuracy: 94.92\n",
      "Train Epoch: 65 [30720/50000 (68%)]\tLoss: 0.101182, Accuracy: 95.70\n",
      "Train Epoch: 65 [32000/50000 (71%)]\tLoss: 0.160679, Accuracy: 94.92\n",
      "Train Epoch: 65 [33280/50000 (74%)]\tLoss: 0.097309, Accuracy: 95.70\n",
      "Train Epoch: 65 [34560/50000 (77%)]\tLoss: 0.139825, Accuracy: 96.09\n",
      "Train Epoch: 65 [35840/50000 (80%)]\tLoss: 0.134425, Accuracy: 95.70\n",
      "Train Epoch: 65 [37120/50000 (82%)]\tLoss: 0.123186, Accuracy: 95.31\n",
      "Train Epoch: 65 [38400/50000 (85%)]\tLoss: 0.057488, Accuracy: 98.05\n",
      "Train Epoch: 65 [39680/50000 (88%)]\tLoss: 0.075642, Accuracy: 96.48\n",
      "Train Epoch: 65 [40960/50000 (91%)]\tLoss: 0.115524, Accuracy: 94.92\n",
      "Train Epoch: 65 [42240/50000 (94%)]\tLoss: 0.102780, Accuracy: 95.70\n",
      "Train Epoch: 65 [43520/50000 (97%)]\tLoss: 0.124511, Accuracy: 95.31\n",
      "Train Epoch: 65 [35000/50000 (99%)]\tLoss: 0.114678, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.3087, Accuracy: 4544/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.44288754463196 s]\n",
      "Train Epoch: 66 [0/50000 (0%)]\tLoss: 0.078748, Accuracy: 97.27\n",
      "Train Epoch: 66 [1280/50000 (3%)]\tLoss: 0.094024, Accuracy: 97.27\n",
      "Train Epoch: 66 [2560/50000 (6%)]\tLoss: 0.078863, Accuracy: 97.66\n",
      "Train Epoch: 66 [3840/50000 (9%)]\tLoss: 0.091587, Accuracy: 96.88\n",
      "Train Epoch: 66 [5120/50000 (11%)]\tLoss: 0.068754, Accuracy: 97.66\n",
      "Train Epoch: 66 [6400/50000 (14%)]\tLoss: 0.055251, Accuracy: 98.83\n",
      "Train Epoch: 66 [7680/50000 (17%)]\tLoss: 0.070048, Accuracy: 97.27\n",
      "Train Epoch: 66 [8960/50000 (20%)]\tLoss: 0.096170, Accuracy: 96.09\n",
      "Train Epoch: 66 [10240/50000 (23%)]\tLoss: 0.090156, Accuracy: 96.48\n",
      "Train Epoch: 66 [11520/50000 (26%)]\tLoss: 0.056733, Accuracy: 98.44\n",
      "Train Epoch: 66 [12800/50000 (28%)]\tLoss: 0.093680, Accuracy: 97.27\n",
      "Train Epoch: 66 [14080/50000 (31%)]\tLoss: 0.093565, Accuracy: 96.09\n",
      "Train Epoch: 66 [15360/50000 (34%)]\tLoss: 0.086609, Accuracy: 98.05\n",
      "Train Epoch: 66 [16640/50000 (37%)]\tLoss: 0.087445, Accuracy: 98.05\n",
      "Train Epoch: 66 [17920/50000 (40%)]\tLoss: 0.132684, Accuracy: 96.09\n",
      "Train Epoch: 66 [19200/50000 (43%)]\tLoss: 0.124649, Accuracy: 96.88\n",
      "Train Epoch: 66 [20480/50000 (45%)]\tLoss: 0.063265, Accuracy: 97.66\n",
      "Train Epoch: 66 [21760/50000 (48%)]\tLoss: 0.089743, Accuracy: 97.66\n",
      "Train Epoch: 66 [23040/50000 (51%)]\tLoss: 0.204127, Accuracy: 94.14\n",
      "Train Epoch: 66 [24320/50000 (54%)]\tLoss: 0.074855, Accuracy: 96.48\n",
      "Train Epoch: 66 [25600/50000 (57%)]\tLoss: 0.109730, Accuracy: 96.09\n",
      "Train Epoch: 66 [26880/50000 (60%)]\tLoss: 0.082705, Accuracy: 96.48\n",
      "Train Epoch: 66 [28160/50000 (62%)]\tLoss: 0.135980, Accuracy: 96.48\n",
      "Train Epoch: 66 [29440/50000 (65%)]\tLoss: 0.056262, Accuracy: 98.05\n",
      "Train Epoch: 66 [30720/50000 (68%)]\tLoss: 0.112454, Accuracy: 96.09\n",
      "Train Epoch: 66 [32000/50000 (71%)]\tLoss: 0.123243, Accuracy: 96.09\n",
      "Train Epoch: 66 [33280/50000 (74%)]\tLoss: 0.102138, Accuracy: 96.48\n",
      "Train Epoch: 66 [34560/50000 (77%)]\tLoss: 0.146198, Accuracy: 94.92\n",
      "Train Epoch: 66 [35840/50000 (80%)]\tLoss: 0.045385, Accuracy: 98.83\n",
      "Train Epoch: 66 [37120/50000 (82%)]\tLoss: 0.073741, Accuracy: 97.27\n",
      "Train Epoch: 66 [38400/50000 (85%)]\tLoss: 0.071851, Accuracy: 97.66\n",
      "Train Epoch: 66 [39680/50000 (88%)]\tLoss: 0.066313, Accuracy: 97.66\n",
      "Train Epoch: 66 [40960/50000 (91%)]\tLoss: 0.114191, Accuracy: 96.48\n",
      "Train Epoch: 66 [42240/50000 (94%)]\tLoss: 0.074470, Accuracy: 97.27\n",
      "Train Epoch: 66 [43520/50000 (97%)]\tLoss: 0.105918, Accuracy: 96.88\n",
      "Train Epoch: 66 [35000/50000 (99%)]\tLoss: 0.054171, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.2910, Accuracy: 4576/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.20758652687073 s]\n",
      "\n",
      "Test set: Average loss: 0.3139, Accuracy: 9113/10000 (91.13%)\n",
      "\n",
      "Train Epoch: 67 [0/50000 (0%)]\tLoss: 0.086778, Accuracy: 97.66\n",
      "Train Epoch: 67 [1280/50000 (3%)]\tLoss: 0.063984, Accuracy: 97.27\n",
      "Train Epoch: 67 [2560/50000 (6%)]\tLoss: 0.082754, Accuracy: 96.88\n",
      "Train Epoch: 67 [3840/50000 (9%)]\tLoss: 0.091555, Accuracy: 95.70\n",
      "Train Epoch: 67 [5120/50000 (11%)]\tLoss: 0.126253, Accuracy: 97.27\n",
      "Train Epoch: 67 [6400/50000 (14%)]\tLoss: 0.074457, Accuracy: 97.66\n",
      "Train Epoch: 67 [7680/50000 (17%)]\tLoss: 0.179818, Accuracy: 94.53\n",
      "Train Epoch: 67 [8960/50000 (20%)]\tLoss: 0.092280, Accuracy: 96.09\n",
      "Train Epoch: 67 [10240/50000 (23%)]\tLoss: 0.032996, Accuracy: 98.83\n",
      "Train Epoch: 67 [11520/50000 (26%)]\tLoss: 0.066102, Accuracy: 98.05\n",
      "Train Epoch: 67 [12800/50000 (28%)]\tLoss: 0.065890, Accuracy: 97.66\n",
      "Train Epoch: 67 [14080/50000 (31%)]\tLoss: 0.144223, Accuracy: 95.31\n",
      "Train Epoch: 67 [15360/50000 (34%)]\tLoss: 0.052195, Accuracy: 98.05\n",
      "Train Epoch: 67 [16640/50000 (37%)]\tLoss: 0.069338, Accuracy: 98.44\n",
      "Train Epoch: 67 [17920/50000 (40%)]\tLoss: 0.055789, Accuracy: 98.44\n",
      "Train Epoch: 67 [19200/50000 (43%)]\tLoss: 0.068142, Accuracy: 97.27\n",
      "Train Epoch: 67 [20480/50000 (45%)]\tLoss: 0.095319, Accuracy: 97.66\n",
      "Train Epoch: 67 [21760/50000 (48%)]\tLoss: 0.061096, Accuracy: 98.05\n",
      "Train Epoch: 67 [23040/50000 (51%)]\tLoss: 0.086122, Accuracy: 96.88\n",
      "Train Epoch: 67 [24320/50000 (54%)]\tLoss: 0.124088, Accuracy: 96.09\n",
      "Train Epoch: 67 [25600/50000 (57%)]\tLoss: 0.126642, Accuracy: 94.92\n",
      "Train Epoch: 67 [26880/50000 (60%)]\tLoss: 0.114338, Accuracy: 96.09\n",
      "Train Epoch: 67 [28160/50000 (62%)]\tLoss: 0.074699, Accuracy: 96.88\n",
      "Train Epoch: 67 [29440/50000 (65%)]\tLoss: 0.048526, Accuracy: 99.22\n",
      "Train Epoch: 67 [30720/50000 (68%)]\tLoss: 0.029610, Accuracy: 99.61\n",
      "Train Epoch: 67 [32000/50000 (71%)]\tLoss: 0.106603, Accuracy: 97.27\n",
      "Train Epoch: 67 [33280/50000 (74%)]\tLoss: 0.056961, Accuracy: 98.44\n",
      "Train Epoch: 67 [34560/50000 (77%)]\tLoss: 0.060602, Accuracy: 97.27\n",
      "Train Epoch: 67 [35840/50000 (80%)]\tLoss: 0.079340, Accuracy: 96.88\n",
      "Train Epoch: 67 [37120/50000 (82%)]\tLoss: 0.098318, Accuracy: 96.48\n",
      "Train Epoch: 67 [38400/50000 (85%)]\tLoss: 0.039568, Accuracy: 98.83\n",
      "Train Epoch: 67 [39680/50000 (88%)]\tLoss: 0.063603, Accuracy: 97.27\n",
      "Train Epoch: 67 [40960/50000 (91%)]\tLoss: 0.066404, Accuracy: 98.05\n",
      "Train Epoch: 67 [42240/50000 (94%)]\tLoss: 0.074779, Accuracy: 97.27\n",
      "Train Epoch: 67 [43520/50000 (97%)]\tLoss: 0.055686, Accuracy: 98.44\n",
      "Train Epoch: 67 [35000/50000 (99%)]\tLoss: 0.122510, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.2913, Accuracy: 4584/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.37866997718811 s]\n",
      "Train Epoch: 68 [0/50000 (0%)]\tLoss: 0.071487, Accuracy: 96.88\n",
      "Train Epoch: 68 [1280/50000 (3%)]\tLoss: 0.075962, Accuracy: 97.27\n",
      "Train Epoch: 68 [2560/50000 (6%)]\tLoss: 0.042844, Accuracy: 98.83\n",
      "Train Epoch: 68 [3840/50000 (9%)]\tLoss: 0.024959, Accuracy: 99.61\n",
      "Train Epoch: 68 [5120/50000 (11%)]\tLoss: 0.105463, Accuracy: 97.66\n",
      "Train Epoch: 68 [6400/50000 (14%)]\tLoss: 0.125739, Accuracy: 94.92\n",
      "Train Epoch: 68 [7680/50000 (17%)]\tLoss: 0.112777, Accuracy: 96.88\n",
      "Train Epoch: 68 [8960/50000 (20%)]\tLoss: 0.073800, Accuracy: 97.27\n",
      "Train Epoch: 68 [10240/50000 (23%)]\tLoss: 0.085550, Accuracy: 98.05\n",
      "Train Epoch: 68 [11520/50000 (26%)]\tLoss: 0.095457, Accuracy: 96.88\n",
      "Train Epoch: 68 [12800/50000 (28%)]\tLoss: 0.050164, Accuracy: 98.44\n",
      "Train Epoch: 68 [14080/50000 (31%)]\tLoss: 0.104082, Accuracy: 96.48\n",
      "Train Epoch: 68 [15360/50000 (34%)]\tLoss: 0.072536, Accuracy: 97.27\n",
      "Train Epoch: 68 [16640/50000 (37%)]\tLoss: 0.054456, Accuracy: 98.05\n",
      "Train Epoch: 68 [17920/50000 (40%)]\tLoss: 0.115938, Accuracy: 96.88\n",
      "Train Epoch: 68 [19200/50000 (43%)]\tLoss: 0.156706, Accuracy: 94.14\n",
      "Train Epoch: 68 [20480/50000 (45%)]\tLoss: 0.083874, Accuracy: 96.48\n",
      "Train Epoch: 68 [21760/50000 (48%)]\tLoss: 0.088187, Accuracy: 97.66\n",
      "Train Epoch: 68 [23040/50000 (51%)]\tLoss: 0.078997, Accuracy: 96.88\n",
      "Train Epoch: 68 [24320/50000 (54%)]\tLoss: 0.099687, Accuracy: 96.09\n",
      "Train Epoch: 68 [25600/50000 (57%)]\tLoss: 0.062797, Accuracy: 97.27\n",
      "Train Epoch: 68 [26880/50000 (60%)]\tLoss: 0.090677, Accuracy: 97.27\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 68 [28160/50000 (62%)]\tLoss: 0.118905, Accuracy: 97.27\n",
      "Train Epoch: 68 [29440/50000 (65%)]\tLoss: 0.067781, Accuracy: 97.66\n",
      "Train Epoch: 68 [30720/50000 (68%)]\tLoss: 0.087421, Accuracy: 97.27\n",
      "Train Epoch: 68 [32000/50000 (71%)]\tLoss: 0.059672, Accuracy: 97.27\n",
      "Train Epoch: 68 [33280/50000 (74%)]\tLoss: 0.090382, Accuracy: 96.09\n",
      "Train Epoch: 68 [34560/50000 (77%)]\tLoss: 0.099821, Accuracy: 96.48\n",
      "Train Epoch: 68 [35840/50000 (80%)]\tLoss: 0.072513, Accuracy: 98.44\n",
      "Train Epoch: 68 [37120/50000 (82%)]\tLoss: 0.061625, Accuracy: 98.05\n",
      "Train Epoch: 68 [38400/50000 (85%)]\tLoss: 0.118621, Accuracy: 95.70\n",
      "Train Epoch: 68 [39680/50000 (88%)]\tLoss: 0.110346, Accuracy: 97.27\n",
      "Train Epoch: 68 [40960/50000 (91%)]\tLoss: 0.088210, Accuracy: 97.66\n",
      "Train Epoch: 68 [42240/50000 (94%)]\tLoss: 0.051032, Accuracy: 98.05\n",
      "Train Epoch: 68 [43520/50000 (97%)]\tLoss: 0.072652, Accuracy: 98.44\n",
      "Train Epoch: 68 [35000/50000 (99%)]\tLoss: 0.137066, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2923, Accuracy: 4588/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.261279821395874 s]\n",
      "\n",
      "Test set: Average loss: 0.3258, Accuracy: 9082/10000 (90.82%)\n",
      "\n",
      "Train Epoch: 69 [0/50000 (0%)]\tLoss: 0.084967, Accuracy: 97.66\n",
      "Train Epoch: 69 [1280/50000 (3%)]\tLoss: 0.073653, Accuracy: 98.05\n",
      "Train Epoch: 69 [2560/50000 (6%)]\tLoss: 0.063869, Accuracy: 97.66\n",
      "Train Epoch: 69 [3840/50000 (9%)]\tLoss: 0.077204, Accuracy: 98.05\n",
      "Train Epoch: 69 [5120/50000 (11%)]\tLoss: 0.092812, Accuracy: 95.70\n",
      "Train Epoch: 69 [6400/50000 (14%)]\tLoss: 0.068632, Accuracy: 98.05\n",
      "Train Epoch: 69 [7680/50000 (17%)]\tLoss: 0.092226, Accuracy: 96.09\n",
      "Train Epoch: 69 [8960/50000 (20%)]\tLoss: 0.125138, Accuracy: 95.70\n",
      "Train Epoch: 69 [10240/50000 (23%)]\tLoss: 0.072560, Accuracy: 98.05\n",
      "Train Epoch: 69 [11520/50000 (26%)]\tLoss: 0.059612, Accuracy: 96.48\n",
      "Train Epoch: 69 [12800/50000 (28%)]\tLoss: 0.093243, Accuracy: 95.31\n",
      "Train Epoch: 69 [14080/50000 (31%)]\tLoss: 0.042406, Accuracy: 98.05\n",
      "Train Epoch: 69 [15360/50000 (34%)]\tLoss: 0.051402, Accuracy: 98.44\n",
      "Train Epoch: 69 [16640/50000 (37%)]\tLoss: 0.075185, Accuracy: 98.05\n",
      "Train Epoch: 69 [17920/50000 (40%)]\tLoss: 0.047279, Accuracy: 98.05\n",
      "Train Epoch: 69 [19200/50000 (43%)]\tLoss: 0.135413, Accuracy: 95.70\n",
      "Train Epoch: 69 [20480/50000 (45%)]\tLoss: 0.041195, Accuracy: 98.44\n",
      "Train Epoch: 69 [21760/50000 (48%)]\tLoss: 0.073646, Accuracy: 97.27\n",
      "Train Epoch: 69 [23040/50000 (51%)]\tLoss: 0.098471, Accuracy: 96.09\n",
      "Train Epoch: 69 [24320/50000 (54%)]\tLoss: 0.072886, Accuracy: 97.27\n",
      "Train Epoch: 69 [25600/50000 (57%)]\tLoss: 0.099957, Accuracy: 97.27\n",
      "Train Epoch: 69 [26880/50000 (60%)]\tLoss: 0.063990, Accuracy: 97.27\n",
      "Train Epoch: 69 [28160/50000 (62%)]\tLoss: 0.075637, Accuracy: 96.48\n",
      "Train Epoch: 69 [29440/50000 (65%)]\tLoss: 0.050177, Accuracy: 97.66\n",
      "Train Epoch: 69 [30720/50000 (68%)]\tLoss: 0.098189, Accuracy: 97.66\n",
      "Train Epoch: 69 [32000/50000 (71%)]\tLoss: 0.091618, Accuracy: 95.70\n",
      "Train Epoch: 69 [33280/50000 (74%)]\tLoss: 0.133137, Accuracy: 94.53\n",
      "Train Epoch: 69 [34560/50000 (77%)]\tLoss: 0.109502, Accuracy: 95.70\n",
      "Train Epoch: 69 [35840/50000 (80%)]\tLoss: 0.105256, Accuracy: 97.66\n",
      "Train Epoch: 69 [37120/50000 (82%)]\tLoss: 0.088894, Accuracy: 96.48\n",
      "Train Epoch: 69 [38400/50000 (85%)]\tLoss: 0.040287, Accuracy: 98.05\n",
      "Train Epoch: 69 [39680/50000 (88%)]\tLoss: 0.075372, Accuracy: 98.05\n",
      "Train Epoch: 69 [40960/50000 (91%)]\tLoss: 0.104648, Accuracy: 96.48\n",
      "Train Epoch: 69 [42240/50000 (94%)]\tLoss: 0.081382, Accuracy: 96.48\n",
      "Train Epoch: 69 [43520/50000 (97%)]\tLoss: 0.120015, Accuracy: 96.48\n",
      "Train Epoch: 69 [35000/50000 (99%)]\tLoss: 0.114879, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.3037, Accuracy: 4553/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.3694851398468 s]\n",
      "Train Epoch: 70 [0/50000 (0%)]\tLoss: 0.055033, Accuracy: 97.27\n",
      "Train Epoch: 70 [1280/50000 (3%)]\tLoss: 0.065994, Accuracy: 97.27\n",
      "Train Epoch: 70 [2560/50000 (6%)]\tLoss: 0.092112, Accuracy: 97.27\n",
      "Train Epoch: 70 [3840/50000 (9%)]\tLoss: 0.071467, Accuracy: 96.88\n",
      "Train Epoch: 70 [5120/50000 (11%)]\tLoss: 0.072880, Accuracy: 98.05\n",
      "Train Epoch: 70 [6400/50000 (14%)]\tLoss: 0.057690, Accuracy: 98.05\n",
      "Train Epoch: 70 [7680/50000 (17%)]\tLoss: 0.056341, Accuracy: 98.83\n",
      "Train Epoch: 70 [8960/50000 (20%)]\tLoss: 0.059551, Accuracy: 97.27\n",
      "Train Epoch: 70 [10240/50000 (23%)]\tLoss: 0.102361, Accuracy: 96.09\n",
      "Train Epoch: 70 [11520/50000 (26%)]\tLoss: 0.066671, Accuracy: 97.27\n",
      "Train Epoch: 70 [12800/50000 (28%)]\tLoss: 0.066308, Accuracy: 97.66\n",
      "Train Epoch: 70 [14080/50000 (31%)]\tLoss: 0.065363, Accuracy: 98.44\n",
      "Train Epoch: 70 [15360/50000 (34%)]\tLoss: 0.052035, Accuracy: 98.44\n",
      "Train Epoch: 70 [16640/50000 (37%)]\tLoss: 0.079408, Accuracy: 96.88\n",
      "Train Epoch: 70 [17920/50000 (40%)]\tLoss: 0.081604, Accuracy: 97.66\n",
      "Train Epoch: 70 [19200/50000 (43%)]\tLoss: 0.090135, Accuracy: 96.48\n",
      "Train Epoch: 70 [20480/50000 (45%)]\tLoss: 0.078141, Accuracy: 97.27\n",
      "Train Epoch: 70 [21760/50000 (48%)]\tLoss: 0.081563, Accuracy: 97.66\n",
      "Train Epoch: 70 [23040/50000 (51%)]\tLoss: 0.051853, Accuracy: 97.27\n",
      "Train Epoch: 70 [24320/50000 (54%)]\tLoss: 0.102700, Accuracy: 96.88\n",
      "Train Epoch: 70 [25600/50000 (57%)]\tLoss: 0.099461, Accuracy: 95.31\n",
      "Train Epoch: 70 [26880/50000 (60%)]\tLoss: 0.056454, Accuracy: 97.27\n",
      "Train Epoch: 70 [28160/50000 (62%)]\tLoss: 0.055923, Accuracy: 97.66\n",
      "Train Epoch: 70 [29440/50000 (65%)]\tLoss: 0.098645, Accuracy: 96.48\n",
      "Train Epoch: 70 [30720/50000 (68%)]\tLoss: 0.056537, Accuracy: 98.44\n",
      "Train Epoch: 70 [32000/50000 (71%)]\tLoss: 0.084327, Accuracy: 97.66\n",
      "Train Epoch: 70 [33280/50000 (74%)]\tLoss: 0.098087, Accuracy: 95.70\n",
      "Train Epoch: 70 [34560/50000 (77%)]\tLoss: 0.075169, Accuracy: 96.88\n",
      "Train Epoch: 70 [35840/50000 (80%)]\tLoss: 0.076303, Accuracy: 96.88\n",
      "Train Epoch: 70 [37120/50000 (82%)]\tLoss: 0.125729, Accuracy: 95.31\n",
      "Train Epoch: 70 [38400/50000 (85%)]\tLoss: 0.071754, Accuracy: 97.66\n",
      "Train Epoch: 70 [39680/50000 (88%)]\tLoss: 0.119207, Accuracy: 96.09\n",
      "Train Epoch: 70 [40960/50000 (91%)]\tLoss: 0.086297, Accuracy: 96.09\n",
      "Train Epoch: 70 [42240/50000 (94%)]\tLoss: 0.082701, Accuracy: 96.88\n",
      "Train Epoch: 70 [43520/50000 (97%)]\tLoss: 0.152444, Accuracy: 94.14\n",
      "Train Epoch: 70 [35000/50000 (99%)]\tLoss: 0.100833, Accuracy: 96.00\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.8581]],\n",
      "\n",
      "        [[-1.0176]],\n",
      "\n",
      "        [[-0.0620]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.0335]],\n",
      "\n",
      "        [[ 0.1129]],\n",
      "\n",
      "        [[-1.5742]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor([[[ 2.6683e-03,  2.6675e-03,  2.6697e-03,  ...,  2.6697e-03,\n",
      "           2.6663e-03,  2.6665e-03],\n",
      "         [ 2.3694e-03,  2.3683e-03,  2.3689e-03,  ...,  2.3608e-03,\n",
      "           2.3768e-03,  2.3767e-03],\n",
      "         [ 3.0142e-03,  3.0093e-03,  3.0163e-03,  ...,  3.0076e-03,\n",
      "           3.0177e-03,  3.0170e-03],\n",
      "         ...,\n",
      "         [ 2.5177e-03,  2.5160e-03,  2.5213e-03,  ...,  2.5255e-03,\n",
      "           2.5142e-03,  2.5083e-03],\n",
      "         [ 3.3020e-03,  3.3006e-03,  3.2991e-03,  ...,  3.2930e-03,\n",
      "           3.3057e-03,  3.2983e-03],\n",
      "         [ 1.1361e-03,  1.1404e-03,  1.1375e-03,  ...,  1.1359e-03,\n",
      "           1.1390e-03,  1.1447e-03]]], device='cuda:0')\n",
      "\n",
      "Validation set: Average loss: 0.3096, Accuracy: 4566/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.26441192626953 s]\n",
      "\n",
      "Test set: Average loss: 0.3238, Accuracy: 9111/10000 (91.11%)\n",
      "\n",
      "Train Epoch: 71 [0/50000 (0%)]\tLoss: 0.066608, Accuracy: 98.05\n",
      "Train Epoch: 71 [1280/50000 (3%)]\tLoss: 0.056036, Accuracy: 98.05\n",
      "Train Epoch: 71 [2560/50000 (6%)]\tLoss: 0.049643, Accuracy: 97.66\n",
      "Train Epoch: 71 [3840/50000 (9%)]\tLoss: 0.043274, Accuracy: 98.83\n",
      "Train Epoch: 71 [5120/50000 (11%)]\tLoss: 0.115016, Accuracy: 96.88\n",
      "Train Epoch: 71 [6400/50000 (14%)]\tLoss: 0.075549, Accuracy: 98.05\n",
      "Train Epoch: 71 [7680/50000 (17%)]\tLoss: 0.071853, Accuracy: 96.88\n",
      "Train Epoch: 71 [8960/50000 (20%)]\tLoss: 0.069631, Accuracy: 97.66\n",
      "Train Epoch: 71 [10240/50000 (23%)]\tLoss: 0.054497, Accuracy: 98.05\n",
      "Train Epoch: 71 [11520/50000 (26%)]\tLoss: 0.035690, Accuracy: 98.83\n",
      "Train Epoch: 71 [12800/50000 (28%)]\tLoss: 0.043665, Accuracy: 99.22\n",
      "Train Epoch: 71 [14080/50000 (31%)]\tLoss: 0.068183, Accuracy: 98.44\n",
      "Train Epoch: 71 [15360/50000 (34%)]\tLoss: 0.093320, Accuracy: 97.27\n",
      "Train Epoch: 71 [16640/50000 (37%)]\tLoss: 0.028810, Accuracy: 99.22\n",
      "Train Epoch: 71 [17920/50000 (40%)]\tLoss: 0.080760, Accuracy: 96.48\n",
      "Train Epoch: 71 [19200/50000 (43%)]\tLoss: 0.074598, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 71 [20480/50000 (45%)]\tLoss: 0.054899, Accuracy: 98.05\n",
      "Train Epoch: 71 [21760/50000 (48%)]\tLoss: 0.051002, Accuracy: 98.05\n",
      "Train Epoch: 71 [23040/50000 (51%)]\tLoss: 0.048953, Accuracy: 98.44\n",
      "Train Epoch: 71 [24320/50000 (54%)]\tLoss: 0.076432, Accuracy: 98.05\n",
      "Train Epoch: 71 [25600/50000 (57%)]\tLoss: 0.093565, Accuracy: 96.09\n",
      "Train Epoch: 71 [26880/50000 (60%)]\tLoss: 0.070733, Accuracy: 96.48\n",
      "Train Epoch: 71 [28160/50000 (62%)]\tLoss: 0.036215, Accuracy: 99.61\n",
      "Train Epoch: 71 [29440/50000 (65%)]\tLoss: 0.033157, Accuracy: 98.83\n",
      "Train Epoch: 71 [30720/50000 (68%)]\tLoss: 0.063617, Accuracy: 98.44\n",
      "Train Epoch: 71 [32000/50000 (71%)]\tLoss: 0.077315, Accuracy: 97.27\n",
      "Train Epoch: 71 [33280/50000 (74%)]\tLoss: 0.057879, Accuracy: 98.05\n",
      "Train Epoch: 71 [34560/50000 (77%)]\tLoss: 0.040454, Accuracy: 98.44\n",
      "Train Epoch: 71 [35840/50000 (80%)]\tLoss: 0.070357, Accuracy: 97.27\n",
      "Train Epoch: 71 [37120/50000 (82%)]\tLoss: 0.070800, Accuracy: 98.05\n",
      "Train Epoch: 71 [38400/50000 (85%)]\tLoss: 0.044398, Accuracy: 98.83\n",
      "Train Epoch: 71 [39680/50000 (88%)]\tLoss: 0.075239, Accuracy: 96.48\n",
      "Train Epoch: 71 [40960/50000 (91%)]\tLoss: 0.039883, Accuracy: 98.44\n",
      "Train Epoch: 71 [42240/50000 (94%)]\tLoss: 0.040096, Accuracy: 99.22\n",
      "Train Epoch: 71 [43520/50000 (97%)]\tLoss: 0.075234, Accuracy: 97.66\n",
      "Train Epoch: 71 [35000/50000 (99%)]\tLoss: 0.054163, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2911, Accuracy: 4576/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.37734627723694 s]\n",
      "Train Epoch: 72 [0/50000 (0%)]\tLoss: 0.042886, Accuracy: 98.83\n",
      "Train Epoch: 72 [1280/50000 (3%)]\tLoss: 0.042062, Accuracy: 98.83\n",
      "Train Epoch: 72 [2560/50000 (6%)]\tLoss: 0.051196, Accuracy: 98.05\n",
      "Train Epoch: 72 [3840/50000 (9%)]\tLoss: 0.059455, Accuracy: 98.83\n",
      "Train Epoch: 72 [5120/50000 (11%)]\tLoss: 0.043324, Accuracy: 98.05\n",
      "Train Epoch: 72 [6400/50000 (14%)]\tLoss: 0.063856, Accuracy: 98.05\n",
      "Train Epoch: 72 [7680/50000 (17%)]\tLoss: 0.035478, Accuracy: 98.44\n",
      "Train Epoch: 72 [8960/50000 (20%)]\tLoss: 0.044506, Accuracy: 98.83\n",
      "Train Epoch: 72 [10240/50000 (23%)]\tLoss: 0.045834, Accuracy: 98.83\n",
      "Train Epoch: 72 [11520/50000 (26%)]\tLoss: 0.078717, Accuracy: 96.88\n",
      "Train Epoch: 72 [12800/50000 (28%)]\tLoss: 0.057627, Accuracy: 98.05\n",
      "Train Epoch: 72 [14080/50000 (31%)]\tLoss: 0.055488, Accuracy: 98.44\n",
      "Train Epoch: 72 [15360/50000 (34%)]\tLoss: 0.059596, Accuracy: 97.66\n",
      "Train Epoch: 72 [16640/50000 (37%)]\tLoss: 0.057972, Accuracy: 98.44\n",
      "Train Epoch: 72 [17920/50000 (40%)]\tLoss: 0.024848, Accuracy: 99.61\n",
      "Train Epoch: 72 [19200/50000 (43%)]\tLoss: 0.056478, Accuracy: 98.05\n",
      "Train Epoch: 72 [20480/50000 (45%)]\tLoss: 0.061065, Accuracy: 98.05\n",
      "Train Epoch: 72 [21760/50000 (48%)]\tLoss: 0.047049, Accuracy: 98.83\n",
      "Train Epoch: 72 [23040/50000 (51%)]\tLoss: 0.050724, Accuracy: 98.44\n",
      "Train Epoch: 72 [24320/50000 (54%)]\tLoss: 0.063384, Accuracy: 98.83\n",
      "Train Epoch: 72 [25600/50000 (57%)]\tLoss: 0.057759, Accuracy: 97.66\n",
      "Train Epoch: 72 [26880/50000 (60%)]\tLoss: 0.058049, Accuracy: 98.05\n",
      "Train Epoch: 72 [28160/50000 (62%)]\tLoss: 0.053498, Accuracy: 97.66\n",
      "Train Epoch: 72 [29440/50000 (65%)]\tLoss: 0.071500, Accuracy: 97.66\n",
      "Train Epoch: 72 [30720/50000 (68%)]\tLoss: 0.053972, Accuracy: 98.83\n",
      "Train Epoch: 72 [32000/50000 (71%)]\tLoss: 0.067815, Accuracy: 98.05\n",
      "Train Epoch: 72 [33280/50000 (74%)]\tLoss: 0.082737, Accuracy: 96.48\n",
      "Train Epoch: 72 [34560/50000 (77%)]\tLoss: 0.052561, Accuracy: 98.05\n",
      "Train Epoch: 72 [35840/50000 (80%)]\tLoss: 0.042916, Accuracy: 98.44\n",
      "Train Epoch: 72 [37120/50000 (82%)]\tLoss: 0.032031, Accuracy: 98.83\n",
      "Train Epoch: 72 [38400/50000 (85%)]\tLoss: 0.077729, Accuracy: 97.27\n",
      "Train Epoch: 72 [39680/50000 (88%)]\tLoss: 0.036732, Accuracy: 99.22\n",
      "Train Epoch: 72 [40960/50000 (91%)]\tLoss: 0.065020, Accuracy: 97.66\n",
      "Train Epoch: 72 [42240/50000 (94%)]\tLoss: 0.053928, Accuracy: 98.05\n",
      "Train Epoch: 72 [43520/50000 (97%)]\tLoss: 0.036073, Accuracy: 98.83\n",
      "Train Epoch: 72 [35000/50000 (99%)]\tLoss: 0.083866, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2927, Accuracy: 4586/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.214460372924805 s]\n",
      "\n",
      "Test set: Average loss: 0.3035, Accuracy: 9158/10000 (91.58%)\n",
      "\n",
      "Train Epoch: 73 [0/50000 (0%)]\tLoss: 0.036448, Accuracy: 98.44\n",
      "Train Epoch: 73 [1280/50000 (3%)]\tLoss: 0.050065, Accuracy: 98.83\n",
      "Train Epoch: 73 [2560/50000 (6%)]\tLoss: 0.041440, Accuracy: 98.83\n",
      "Train Epoch: 73 [3840/50000 (9%)]\tLoss: 0.049681, Accuracy: 98.44\n",
      "Train Epoch: 73 [5120/50000 (11%)]\tLoss: 0.062627, Accuracy: 98.83\n",
      "Train Epoch: 73 [6400/50000 (14%)]\tLoss: 0.047803, Accuracy: 98.44\n",
      "Train Epoch: 73 [7680/50000 (17%)]\tLoss: 0.073865, Accuracy: 96.09\n",
      "Train Epoch: 73 [8960/50000 (20%)]\tLoss: 0.055846, Accuracy: 97.27\n",
      "Train Epoch: 73 [10240/50000 (23%)]\tLoss: 0.026653, Accuracy: 99.61\n",
      "Train Epoch: 73 [11520/50000 (26%)]\tLoss: 0.043122, Accuracy: 98.05\n",
      "Train Epoch: 73 [12800/50000 (28%)]\tLoss: 0.052425, Accuracy: 98.83\n",
      "Train Epoch: 73 [14080/50000 (31%)]\tLoss: 0.079349, Accuracy: 96.48\n",
      "Train Epoch: 73 [15360/50000 (34%)]\tLoss: 0.032231, Accuracy: 99.22\n",
      "Train Epoch: 73 [16640/50000 (37%)]\tLoss: 0.041711, Accuracy: 98.44\n",
      "Train Epoch: 73 [17920/50000 (40%)]\tLoss: 0.062882, Accuracy: 97.66\n",
      "Train Epoch: 73 [19200/50000 (43%)]\tLoss: 0.022363, Accuracy: 99.61\n",
      "Train Epoch: 73 [20480/50000 (45%)]\tLoss: 0.032005, Accuracy: 99.61\n",
      "Train Epoch: 73 [21760/50000 (48%)]\tLoss: 0.034654, Accuracy: 99.22\n",
      "Train Epoch: 73 [23040/50000 (51%)]\tLoss: 0.054511, Accuracy: 98.83\n",
      "Train Epoch: 73 [24320/50000 (54%)]\tLoss: 0.041460, Accuracy: 98.83\n",
      "Train Epoch: 73 [25600/50000 (57%)]\tLoss: 0.057465, Accuracy: 96.88\n",
      "Train Epoch: 73 [26880/50000 (60%)]\tLoss: 0.071364, Accuracy: 98.83\n",
      "Train Epoch: 73 [28160/50000 (62%)]\tLoss: 0.077180, Accuracy: 96.88\n",
      "Train Epoch: 73 [29440/50000 (65%)]\tLoss: 0.058993, Accuracy: 98.44\n",
      "Train Epoch: 73 [30720/50000 (68%)]\tLoss: 0.031441, Accuracy: 99.61\n",
      "Train Epoch: 73 [32000/50000 (71%)]\tLoss: 0.027792, Accuracy: 99.22\n",
      "Train Epoch: 73 [33280/50000 (74%)]\tLoss: 0.038977, Accuracy: 98.44\n",
      "Train Epoch: 73 [34560/50000 (77%)]\tLoss: 0.054901, Accuracy: 97.27\n",
      "Train Epoch: 73 [35840/50000 (80%)]\tLoss: 0.045807, Accuracy: 98.05\n",
      "Train Epoch: 73 [37120/50000 (82%)]\tLoss: 0.109663, Accuracy: 96.09\n",
      "Train Epoch: 73 [38400/50000 (85%)]\tLoss: 0.059528, Accuracy: 97.66\n",
      "Train Epoch: 73 [39680/50000 (88%)]\tLoss: 0.056120, Accuracy: 97.66\n",
      "Train Epoch: 73 [40960/50000 (91%)]\tLoss: 0.044219, Accuracy: 98.44\n",
      "Train Epoch: 73 [42240/50000 (94%)]\tLoss: 0.091509, Accuracy: 96.48\n",
      "Train Epoch: 73 [43520/50000 (97%)]\tLoss: 0.075050, Accuracy: 97.66\n",
      "Train Epoch: 73 [35000/50000 (99%)]\tLoss: 0.035084, Accuracy: 98.50\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.6900]],\n",
      "\n",
      "        [[-0.5053]],\n",
      "\n",
      "        [[-0.6684]],\n",
      "\n",
      "        [[-0.8389]],\n",
      "\n",
      "        [[-1.1542]],\n",
      "\n",
      "        [[ 0.7582]],\n",
      "\n",
      "        [[ 0.5343]],\n",
      "\n",
      "        [[-0.4801]],\n",
      "\n",
      "        [[ 0.2456]],\n",
      "\n",
      "        [[ 0.5921]],\n",
      "\n",
      "        [[-0.4268]],\n",
      "\n",
      "        [[-2.9130]],\n",
      "\n",
      "        [[ 0.6031]],\n",
      "\n",
      "        [[-1.4647]],\n",
      "\n",
      "        [[ 0.9472]],\n",
      "\n",
      "        [[ 0.0573]],\n",
      "\n",
      "        [[ 0.5615]],\n",
      "\n",
      "        [[-0.1111]],\n",
      "\n",
      "        [[-0.9475]],\n",
      "\n",
      "        [[-0.5578]],\n",
      "\n",
      "        [[ 2.0783]],\n",
      "\n",
      "        [[-0.4413]],\n",
      "\n",
      "        [[ 0.6329]],\n",
      "\n",
      "        [[ 0.5123]],\n",
      "\n",
      "        [[-1.7813]],\n",
      "\n",
      "        [[-0.3826]],\n",
      "\n",
      "        [[-0.8311]],\n",
      "\n",
      "        [[ 1.5517]],\n",
      "\n",
      "        [[ 0.9691]],\n",
      "\n",
      "        [[-0.0151]],\n",
      "\n",
      "        [[-0.3028]],\n",
      "\n",
      "        [[ 0.6239]],\n",
      "\n",
      "        [[-1.9614]],\n",
      "\n",
      "        [[ 0.7006]],\n",
      "\n",
      "        [[-1.0526]],\n",
      "\n",
      "        [[ 0.1698]],\n",
      "\n",
      "        [[ 1.1091]],\n",
      "\n",
      "        [[ 0.3523]],\n",
      "\n",
      "        [[ 0.7592]],\n",
      "\n",
      "        [[ 0.4495]],\n",
      "\n",
      "        [[-1.0239]],\n",
      "\n",
      "        [[-0.1848]],\n",
      "\n",
      "        [[ 0.2832]],\n",
      "\n",
      "        [[ 1.2697]],\n",
      "\n",
      "        [[ 0.8874]],\n",
      "\n",
      "        [[-0.6456]],\n",
      "\n",
      "        [[-0.3445]],\n",
      "\n",
      "        [[-0.2051]],\n",
      "\n",
      "        [[ 1.3875]],\n",
      "\n",
      "        [[ 2.0194]],\n",
      "\n",
      "        [[-1.2570]],\n",
      "\n",
      "        [[-0.6472]],\n",
      "\n",
      "        [[ 0.6673]],\n",
      "\n",
      "        [[ 0.1945]],\n",
      "\n",
      "        [[ 0.7102]],\n",
      "\n",
      "        [[ 1.0811]],\n",
      "\n",
      "        [[-0.1148]],\n",
      "\n",
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      "        [[ 1.0853]],\n",
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      "\n",
      "        [[-0.0799]],\n",
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      "        [[-0.1960]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 7.8687e-06,  5.4694e-06,  3.8416e-06,  ...,  8.3896e-06,\n",
      "           4.5282e-06,  7.9345e-06],\n",
      "         [ 1.2145e-03,  1.2168e-03,  1.2138e-03,  ...,  1.2153e-03,\n",
      "           1.2160e-03,  1.2141e-03],\n",
      "         [-9.6588e-05, -9.3070e-05, -9.3168e-05,  ..., -9.4345e-05,\n",
      "          -9.0982e-05, -9.5790e-05],\n",
      "         ...,\n",
      "         [-1.9683e-03, -1.9717e-03, -1.9719e-03,  ..., -1.9702e-03,\n",
      "          -1.9707e-03, -1.9705e-03],\n",
      "         [ 1.7951e-04,  1.8040e-04,  1.7978e-04,  ...,  1.7791e-04,\n",
      "           1.8029e-04,  1.7687e-04],\n",
      "         [ 4.5914e-04,  4.5621e-04,  4.5643e-04,  ...,  4.5626e-04,\n",
      "           4.5605e-04,  4.6053e-04]]], device='cuda:0')\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 0.2931, Accuracy: 4596/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.38265013694763 s]\n",
      "Train Epoch: 74 [0/50000 (0%)]\tLoss: 0.052950, Accuracy: 98.05\n",
      "Train Epoch: 74 [1280/50000 (3%)]\tLoss: 0.051158, Accuracy: 97.66\n",
      "Train Epoch: 74 [2560/50000 (6%)]\tLoss: 0.032703, Accuracy: 98.83\n",
      "Train Epoch: 74 [3840/50000 (9%)]\tLoss: 0.026261, Accuracy: 99.22\n",
      "Train Epoch: 74 [5120/50000 (11%)]\tLoss: 0.060556, Accuracy: 96.88\n",
      "Train Epoch: 74 [6400/50000 (14%)]\tLoss: 0.059662, Accuracy: 97.66\n",
      "Train Epoch: 74 [7680/50000 (17%)]\tLoss: 0.045139, Accuracy: 98.44\n",
      "Train Epoch: 74 [8960/50000 (20%)]\tLoss: 0.034688, Accuracy: 98.44\n",
      "Train Epoch: 74 [10240/50000 (23%)]\tLoss: 0.035404, Accuracy: 99.22\n",
      "Train Epoch: 74 [11520/50000 (26%)]\tLoss: 0.036031, Accuracy: 99.61\n",
      "Train Epoch: 74 [12800/50000 (28%)]\tLoss: 0.079088, Accuracy: 97.27\n",
      "Train Epoch: 74 [14080/50000 (31%)]\tLoss: 0.068985, Accuracy: 97.66\n",
      "Train Epoch: 74 [15360/50000 (34%)]\tLoss: 0.073773, Accuracy: 97.66\n",
      "Train Epoch: 74 [16640/50000 (37%)]\tLoss: 0.054066, Accuracy: 98.44\n",
      "Train Epoch: 74 [17920/50000 (40%)]\tLoss: 0.041363, Accuracy: 98.44\n",
      "Train Epoch: 74 [19200/50000 (43%)]\tLoss: 0.051840, Accuracy: 98.05\n",
      "Train Epoch: 74 [20480/50000 (45%)]\tLoss: 0.055841, Accuracy: 98.05\n",
      "Train Epoch: 74 [21760/50000 (48%)]\tLoss: 0.107323, Accuracy: 96.88\n",
      "Train Epoch: 74 [23040/50000 (51%)]\tLoss: 0.057911, Accuracy: 98.05\n",
      "Train Epoch: 74 [24320/50000 (54%)]\tLoss: 0.062470, Accuracy: 97.66\n",
      "Train Epoch: 74 [25600/50000 (57%)]\tLoss: 0.046727, Accuracy: 98.05\n",
      "Train Epoch: 74 [26880/50000 (60%)]\tLoss: 0.048355, Accuracy: 98.83\n",
      "Train Epoch: 74 [28160/50000 (62%)]\tLoss: 0.026625, Accuracy: 99.22\n",
      "Train Epoch: 74 [29440/50000 (65%)]\tLoss: 0.031555, Accuracy: 98.44\n",
      "Train Epoch: 74 [30720/50000 (68%)]\tLoss: 0.061136, Accuracy: 97.66\n",
      "Train Epoch: 74 [32000/50000 (71%)]\tLoss: 0.037713, Accuracy: 99.22\n",
      "Train Epoch: 74 [33280/50000 (74%)]\tLoss: 0.038022, Accuracy: 98.83\n",
      "Train Epoch: 74 [34560/50000 (77%)]\tLoss: 0.055369, Accuracy: 98.44\n",
      "Train Epoch: 74 [35840/50000 (80%)]\tLoss: 0.038235, Accuracy: 98.83\n",
      "Train Epoch: 74 [37120/50000 (82%)]\tLoss: 0.044152, Accuracy: 99.22\n",
      "Train Epoch: 74 [38400/50000 (85%)]\tLoss: 0.021541, Accuracy: 100.00\n",
      "Train Epoch: 74 [39680/50000 (88%)]\tLoss: 0.021559, Accuracy: 100.00\n",
      "Train Epoch: 74 [40960/50000 (91%)]\tLoss: 0.024683, Accuracy: 99.22\n",
      "Train Epoch: 74 [42240/50000 (94%)]\tLoss: 0.039085, Accuracy: 99.22\n",
      "Train Epoch: 74 [43520/50000 (97%)]\tLoss: 0.049142, Accuracy: 98.83\n",
      "Train Epoch: 74 [35000/50000 (99%)]\tLoss: 0.051925, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2917, Accuracy: 4603/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[36.27755880355835 s]\n",
      "\n",
      "Test set: Average loss: 0.3061, Accuracy: 9156/10000 (91.56%)\n",
      "\n",
      "Train Epoch: 75 [0/50000 (0%)]\tLoss: 0.061629, Accuracy: 98.05\n",
      "Train Epoch: 75 [1280/50000 (3%)]\tLoss: 0.047207, Accuracy: 98.44\n",
      "Train Epoch: 75 [2560/50000 (6%)]\tLoss: 0.052884, Accuracy: 98.05\n",
      "Train Epoch: 75 [3840/50000 (9%)]\tLoss: 0.064318, Accuracy: 98.05\n",
      "Train Epoch: 75 [5120/50000 (11%)]\tLoss: 0.064432, Accuracy: 98.44\n",
      "Train Epoch: 75 [6400/50000 (14%)]\tLoss: 0.036324, Accuracy: 98.83\n",
      "Train Epoch: 75 [7680/50000 (17%)]\tLoss: 0.043712, Accuracy: 98.05\n",
      "Train Epoch: 75 [8960/50000 (20%)]\tLoss: 0.039687, Accuracy: 99.22\n",
      "Train Epoch: 75 [10240/50000 (23%)]\tLoss: 0.064291, Accuracy: 97.66\n",
      "Train Epoch: 75 [11520/50000 (26%)]\tLoss: 0.038658, Accuracy: 98.83\n",
      "Train Epoch: 75 [12800/50000 (28%)]\tLoss: 0.054392, Accuracy: 97.66\n",
      "Train Epoch: 75 [14080/50000 (31%)]\tLoss: 0.040211, Accuracy: 99.22\n",
      "Train Epoch: 75 [15360/50000 (34%)]\tLoss: 0.035147, Accuracy: 99.22\n",
      "Train Epoch: 75 [16640/50000 (37%)]\tLoss: 0.058398, Accuracy: 98.05\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.3388]],\n",
      "\n",
      "        [[-0.1281]],\n",
      "\n",
      "        [[ 0.4186]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.9977]],\n",
      "\n",
      "        [[ 1.1017]],\n",
      "\n",
      "        [[ 1.3087]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[ 2.6741,  2.6695,  2.6703,  ...,  2.6642,  2.6620,  2.6723],\n",
      "         [ 2.3677,  2.3732,  2.3749,  ...,  2.3711,  2.3764,  2.3644],\n",
      "         [ 3.0201,  3.0143,  3.0156,  ...,  3.0227,  3.0148,  3.0136],\n",
      "         ...,\n",
      "         [ 2.5170,  2.5120,  2.5195,  ...,  2.5260,  2.5173,  2.5205],\n",
      "         [ 3.3058,  3.2990,  3.3084,  ...,  3.2973,  3.3058,  3.3103],\n",
      "         [ 1.1321,  1.1400,  1.1390,  ...,  1.1376,  1.1393,  1.1346]]], device='cuda:0')\n",
      "Train Epoch: 75 [17920/50000 (40%)]\tLoss: 0.035781, Accuracy: 98.44\n",
      "Train Epoch: 75 [19200/50000 (43%)]\tLoss: 0.074314, Accuracy: 98.05\n",
      "Train Epoch: 75 [20480/50000 (45%)]\tLoss: 0.047071, Accuracy: 98.83\n",
      "Train Epoch: 75 [21760/50000 (48%)]\tLoss: 0.058600, Accuracy: 97.66\n",
      "Train Epoch: 75 [23040/50000 (51%)]\tLoss: 0.063827, Accuracy: 98.44\n",
      "Train Epoch: 75 [24320/50000 (54%)]\tLoss: 0.034683, Accuracy: 99.22\n",
      "Train Epoch: 75 [25600/50000 (57%)]\tLoss: 0.058448, Accuracy: 98.44\n",
      "Train Epoch: 75 [26880/50000 (60%)]\tLoss: 0.037031, Accuracy: 99.22\n",
      "Train Epoch: 75 [28160/50000 (62%)]\tLoss: 0.029152, Accuracy: 99.22\n",
      "Train Epoch: 75 [29440/50000 (65%)]\tLoss: 0.016069, Accuracy: 100.00\n",
      "Train Epoch: 75 [30720/50000 (68%)]\tLoss: 0.035078, Accuracy: 99.22\n",
      "Train Epoch: 75 [32000/50000 (71%)]\tLoss: 0.043127, Accuracy: 98.44\n",
      "Train Epoch: 75 [33280/50000 (74%)]\tLoss: 0.054453, Accuracy: 98.05\n",
      "Train Epoch: 75 [34560/50000 (77%)]\tLoss: 0.059201, Accuracy: 98.83\n",
      "Train Epoch: 75 [35840/50000 (80%)]\tLoss: 0.039090, Accuracy: 98.44\n",
      "Train Epoch: 75 [37120/50000 (82%)]\tLoss: 0.053749, Accuracy: 98.05\n",
      "Train Epoch: 75 [38400/50000 (85%)]\tLoss: 0.053839, Accuracy: 98.44\n",
      "Train Epoch: 75 [39680/50000 (88%)]\tLoss: 0.035660, Accuracy: 99.61\n",
      "Train Epoch: 75 [40960/50000 (91%)]\tLoss: 0.072346, Accuracy: 97.66\n",
      "Train Epoch: 75 [42240/50000 (94%)]\tLoss: 0.045797, Accuracy: 98.83\n",
      "Train Epoch: 75 [43520/50000 (97%)]\tLoss: 0.061019, Accuracy: 98.83\n",
      "Train Epoch: 75 [35000/50000 (99%)]\tLoss: 0.086928, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2936, Accuracy: 4602/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[39.46998190879822 s]\n",
      "Train Epoch: 76 [0/50000 (0%)]\tLoss: 0.063273, Accuracy: 97.66\n",
      "Train Epoch: 76 [1280/50000 (3%)]\tLoss: 0.073397, Accuracy: 98.05\n",
      "Train Epoch: 76 [2560/50000 (6%)]\tLoss: 0.044059, Accuracy: 98.83\n",
      "Train Epoch: 76 [3840/50000 (9%)]\tLoss: 0.031915, Accuracy: 99.22\n",
      "Train Epoch: 76 [5120/50000 (11%)]\tLoss: 0.036447, Accuracy: 98.83\n",
      "Train Epoch: 76 [6400/50000 (14%)]\tLoss: 0.028767, Accuracy: 99.61\n",
      "Train Epoch: 76 [7680/50000 (17%)]\tLoss: 0.053100, Accuracy: 98.05\n",
      "Train Epoch: 76 [8960/50000 (20%)]\tLoss: 0.056100, Accuracy: 98.44\n",
      "Train Epoch: 76 [10240/50000 (23%)]\tLoss: 0.057516, Accuracy: 98.05\n",
      "Train Epoch: 76 [11520/50000 (26%)]\tLoss: 0.023165, Accuracy: 100.00\n",
      "Train Epoch: 76 [12800/50000 (28%)]\tLoss: 0.048565, Accuracy: 98.83\n",
      "Train Epoch: 76 [14080/50000 (31%)]\tLoss: 0.030830, Accuracy: 98.44\n",
      "Train Epoch: 76 [15360/50000 (34%)]\tLoss: 0.074136, Accuracy: 96.88\n",
      "Train Epoch: 76 [16640/50000 (37%)]\tLoss: 0.049604, Accuracy: 98.05\n",
      "Train Epoch: 76 [17920/50000 (40%)]\tLoss: 0.038216, Accuracy: 98.83\n",
      "Train Epoch: 76 [19200/50000 (43%)]\tLoss: 0.026253, Accuracy: 99.22\n",
      "Train Epoch: 76 [20480/50000 (45%)]\tLoss: 0.039498, Accuracy: 98.44\n",
      "Train Epoch: 76 [21760/50000 (48%)]\tLoss: 0.030229, Accuracy: 98.83\n",
      "Train Epoch: 76 [23040/50000 (51%)]\tLoss: 0.016747, Accuracy: 100.00\n",
      "Train Epoch: 76 [24320/50000 (54%)]\tLoss: 0.055067, Accuracy: 98.83\n",
      "Train Epoch: 76 [25600/50000 (57%)]\tLoss: 0.024042, Accuracy: 99.61\n",
      "Train Epoch: 76 [26880/50000 (60%)]\tLoss: 0.055630, Accuracy: 98.83\n",
      "Train Epoch: 76 [28160/50000 (62%)]\tLoss: 0.050296, Accuracy: 98.44\n",
      "Train Epoch: 76 [29440/50000 (65%)]\tLoss: 0.046547, Accuracy: 98.83\n",
      "Train Epoch: 76 [30720/50000 (68%)]\tLoss: 0.038733, Accuracy: 98.83\n",
      "Train Epoch: 76 [32000/50000 (71%)]\tLoss: 0.055732, Accuracy: 98.83\n",
      "Train Epoch: 76 [33280/50000 (74%)]\tLoss: 0.029849, Accuracy: 99.22\n",
      "Train Epoch: 76 [34560/50000 (77%)]\tLoss: 0.030303, Accuracy: 99.61\n",
      "Train Epoch: 76 [35840/50000 (80%)]\tLoss: 0.030159, Accuracy: 98.44\n",
      "Train Epoch: 76 [37120/50000 (82%)]\tLoss: 0.068329, Accuracy: 97.27\n",
      "Train Epoch: 76 [38400/50000 (85%)]\tLoss: 0.062992, Accuracy: 98.05\n",
      "Train Epoch: 76 [39680/50000 (88%)]\tLoss: 0.021298, Accuracy: 99.22\n",
      "Train Epoch: 76 [40960/50000 (91%)]\tLoss: 0.066145, Accuracy: 97.66\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 76 [42240/50000 (94%)]\tLoss: 0.052394, Accuracy: 98.44\n",
      "Train Epoch: 76 [43520/50000 (97%)]\tLoss: 0.030497, Accuracy: 98.83\n",
      "Train Epoch: 76 [35000/50000 (99%)]\tLoss: 0.010749, Accuracy: 100.00\n",
      "\n",
      "Validation set: Average loss: 0.2981, Accuracy: 4587/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.269925355911255 s]\n",
      "\n",
      "Test set: Average loss: 0.3124, Accuracy: 9143/10000 (91.43%)\n",
      "\n",
      "Train Epoch: 77 [0/50000 (0%)]\tLoss: 0.036767, Accuracy: 98.44\n",
      "Train Epoch: 77 [1280/50000 (3%)]\tLoss: 0.052402, Accuracy: 98.44\n",
      "Train Epoch: 77 [2560/50000 (6%)]\tLoss: 0.067100, Accuracy: 97.27\n",
      "Train Epoch: 77 [3840/50000 (9%)]\tLoss: 0.035428, Accuracy: 98.83\n",
      "Train Epoch: 77 [5120/50000 (11%)]\tLoss: 0.066208, Accuracy: 97.66\n",
      "Train Epoch: 77 [6400/50000 (14%)]\tLoss: 0.057308, Accuracy: 97.66\n",
      "Train Epoch: 77 [7680/50000 (17%)]\tLoss: 0.032179, Accuracy: 98.44\n",
      "Train Epoch: 77 [8960/50000 (20%)]\tLoss: 0.042231, Accuracy: 98.05\n",
      "Train Epoch: 77 [10240/50000 (23%)]\tLoss: 0.029690, Accuracy: 99.22\n",
      "Train Epoch: 77 [11520/50000 (26%)]\tLoss: 0.131301, Accuracy: 96.09\n",
      "Train Epoch: 77 [12800/50000 (28%)]\tLoss: 0.037764, Accuracy: 98.44\n",
      "Train Epoch: 77 [14080/50000 (31%)]\tLoss: 0.045390, Accuracy: 98.44\n",
      "Train Epoch: 77 [15360/50000 (34%)]\tLoss: 0.038062, Accuracy: 98.83\n",
      "Train Epoch: 77 [16640/50000 (37%)]\tLoss: 0.048809, Accuracy: 98.44\n",
      "Train Epoch: 77 [17920/50000 (40%)]\tLoss: 0.017126, Accuracy: 99.61\n",
      "Train Epoch: 77 [19200/50000 (43%)]\tLoss: 0.059323, Accuracy: 98.44\n",
      "Train Epoch: 77 [20480/50000 (45%)]\tLoss: 0.046158, Accuracy: 97.66\n",
      "Train Epoch: 77 [21760/50000 (48%)]\tLoss: 0.025114, Accuracy: 99.22\n",
      "Train Epoch: 77 [23040/50000 (51%)]\tLoss: 0.033369, Accuracy: 98.83\n",
      "Train Epoch: 77 [24320/50000 (54%)]\tLoss: 0.033375, Accuracy: 98.44\n",
      "Train Epoch: 77 [25600/50000 (57%)]\tLoss: 0.023751, Accuracy: 99.61\n",
      "Train Epoch: 77 [26880/50000 (60%)]\tLoss: 0.029836, Accuracy: 98.83\n",
      "Train Epoch: 77 [28160/50000 (62%)]\tLoss: 0.058921, Accuracy: 97.27\n",
      "Train Epoch: 77 [29440/50000 (65%)]\tLoss: 0.077062, Accuracy: 98.44\n",
      "Train Epoch: 77 [30720/50000 (68%)]\tLoss: 0.048256, Accuracy: 98.44\n",
      "Train Epoch: 77 [32000/50000 (71%)]\tLoss: 0.035411, Accuracy: 99.22\n",
      "Train Epoch: 77 [33280/50000 (74%)]\tLoss: 0.059273, Accuracy: 97.27\n",
      "Train Epoch: 77 [34560/50000 (77%)]\tLoss: 0.065290, Accuracy: 98.44\n",
      "Train Epoch: 77 [35840/50000 (80%)]\tLoss: 0.039246, Accuracy: 98.83\n",
      "Train Epoch: 77 [37120/50000 (82%)]\tLoss: 0.053842, Accuracy: 97.66\n",
      "Train Epoch: 77 [38400/50000 (85%)]\tLoss: 0.068003, Accuracy: 98.44\n",
      "Train Epoch: 77 [39680/50000 (88%)]\tLoss: 0.057108, Accuracy: 98.44\n",
      "Train Epoch: 77 [40960/50000 (91%)]\tLoss: 0.030496, Accuracy: 98.83\n",
      "Train Epoch: 77 [42240/50000 (94%)]\tLoss: 0.030274, Accuracy: 98.83\n",
      "Train Epoch: 77 [43520/50000 (97%)]\tLoss: 0.056949, Accuracy: 98.83\n",
      "Train Epoch: 77 [35000/50000 (99%)]\tLoss: 0.028225, Accuracy: 99.50\n",
      "torch.Size([512, 1, 1])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 0.6903]],\n",
      "\n",
      "        [[ 0.4932]],\n",
      "\n",
      "        [[ 0.0146]],\n",
      "\n",
      "        [[-0.4143]],\n",
      "\n",
      "        [[ 0.2941]],\n",
      "\n",
      "        [[ 0.6327]],\n",
      "\n",
      "        [[-1.5548]],\n",
      "\n",
      "        [[-2.4896]],\n",
      "\n",
      "        [[-1.7916]],\n",
      "\n",
      "        [[ 0.7251]],\n",
      "\n",
      "        [[ 1.6805]],\n",
      "\n",
      "        [[ 0.6556]],\n",
      "\n",
      "        [[-0.7184]],\n",
      "\n",
      "        [[-0.3004]],\n",
      "\n",
      "        [[-1.1200]],\n",
      "\n",
      "        [[ 1.8085]],\n",
      "\n",
      "        [[-0.0178]],\n",
      "\n",
      "        [[-0.2172]],\n",
      "\n",
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      "        [[ 1.4544]],\n",
      "\n",
      "        [[-2.2937]],\n",
      "\n",
      "        [[-0.3783]],\n",
      "\n",
      "        [[-0.7334]],\n",
      "\n",
      "        [[ 0.8100]],\n",
      "\n",
      "        [[-0.1462]],\n",
      "\n",
      "        [[ 0.1148]],\n",
      "\n",
      "        [[-0.6766]],\n",
      "\n",
      "        [[-0.2324]],\n",
      "\n",
      "        [[ 0.4728]],\n",
      "\n",
      "        [[ 1.6912]],\n",
      "\n",
      "        [[ 0.9026]],\n",
      "\n",
      "        [[ 0.2659]],\n",
      "\n",
      "        [[ 0.1416]],\n",
      "\n",
      "        [[ 1.0860]],\n",
      "\n",
      "        [[ 2.3002]],\n",
      "\n",
      "        [[ 0.3816]],\n",
      "\n",
      "        [[ 1.0111]],\n",
      "\n",
      "        [[ 1.9196]],\n",
      "\n",
      "        [[ 1.0497]],\n",
      "\n",
      "        [[-0.5567]],\n",
      "\n",
      "        [[-0.8166]],\n",
      "\n",
      "        [[-0.0954]],\n",
      "\n",
      "        [[ 0.6848]],\n",
      "\n",
      "        [[-0.4913]],\n",
      "\n",
      "        [[-1.0478]],\n",
      "\n",
      "        [[-0.2776]],\n",
      "\n",
      "        [[-0.9951]],\n",
      "\n",
      "        [[ 0.5735]],\n",
      "\n",
      "        [[-0.4625]],\n",
      "\n",
      "        [[ 0.0898]],\n",
      "\n",
      "        [[ 0.2956]],\n",
      "\n",
      "        [[-0.5879]],\n",
      "\n",
      "        [[ 0.9673]],\n",
      "\n",
      "        [[-0.4689]],\n",
      "\n",
      "        [[ 1.0367]],\n",
      "\n",
      "        [[ 1.1562]],\n",
      "\n",
      "        [[-1.7183]],\n",
      "\n",
      "        [[-1.1105]],\n",
      "\n",
      "        [[-0.2096]],\n",
      "\n",
      "        [[ 1.3923]],\n",
      "\n",
      "        [[ 0.4083]],\n",
      "\n",
      "        [[-0.7703]],\n",
      "\n",
      "        [[ 1.6895]],\n",
      "\n",
      "        [[-0.5251]],\n",
      "\n",
      "        [[ 0.4164]],\n",
      "\n",
      "        [[-0.0820]],\n",
      "\n",
      "        [[-0.0682]],\n",
      "\n",
      "        [[-1.3992]],\n",
      "\n",
      "        [[ 0.6740]],\n",
      "\n",
      "        [[-0.1331]],\n",
      "\n",
      "        [[ 0.9084]],\n",
      "\n",
      "        [[ 0.6470]],\n",
      "\n",
      "        [[-1.8795]],\n",
      "\n",
      "        [[ 0.4730]],\n",
      "\n",
      "        [[ 0.8461]],\n",
      "\n",
      "        [[ 1.1858]],\n",
      "\n",
      "        [[ 0.2799]],\n",
      "\n",
      "        [[ 1.0992]],\n",
      "\n",
      "        [[-1.5535]],\n",
      "\n",
      "        [[ 1.4969]],\n",
      "\n",
      "        [[-0.7247]],\n",
      "\n",
      "        [[ 0.0103]],\n",
      "\n",
      "        [[ 1.1082]],\n",
      "\n",
      "        [[ 0.0646]],\n",
      "\n",
      "        [[ 0.6670]],\n",
      "\n",
      "        [[-1.7180]],\n",
      "\n",
      "        [[ 0.8782]],\n",
      "\n",
      "        [[ 0.3921]],\n",
      "\n",
      "        [[ 0.6657]],\n",
      "\n",
      "        [[-1.2275]],\n",
      "\n",
      "        [[ 0.4452]],\n",
      "\n",
      "        [[-0.9047]],\n",
      "\n",
      "        [[-0.4876]],\n",
      "\n",
      "        [[-0.8666]],\n",
      "\n",
      "        [[-1.2766]],\n",
      "\n",
      "        [[ 1.0025]],\n",
      "\n",
      "        [[ 2.0610]],\n",
      "\n",
      "        [[ 0.4634]],\n",
      "\n",
      "        [[-0.0320]],\n",
      "\n",
      "        [[ 1.5262]],\n",
      "\n",
      "        [[ 0.2118]],\n",
      "\n",
      "        [[ 0.6712]],\n",
      "\n",
      "        [[ 0.6404]],\n",
      "\n",
      "        [[-0.0060]],\n",
      "\n",
      "        [[ 0.6936]],\n",
      "\n",
      "        [[-2.5701]],\n",
      "\n",
      "        [[ 1.1145]],\n",
      "\n",
      "        [[ 1.4400]],\n",
      "\n",
      "        [[ 0.0595]],\n",
      "\n",
      "        [[ 0.8379]],\n",
      "\n",
      "        [[-0.8509]],\n",
      "\n",
      "        [[ 1.7668]],\n",
      "\n",
      "        [[ 1.1245]],\n",
      "\n",
      "        [[-2.7353]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 6.2080e-04,  6.1703e-04,  6.1975e-04,  ...,  6.1920e-04,\n",
      "           6.1672e-04,  6.2305e-04],\n",
      "         [-2.0742e-04, -2.0060e-04, -1.9693e-04,  ..., -1.9918e-04,\n",
      "          -2.0413e-04, -1.9007e-04],\n",
      "         [-1.7795e-03, -1.7774e-03, -1.7835e-03,  ..., -1.7727e-03,\n",
      "          -1.7911e-03, -1.7910e-03],\n",
      "         ...,\n",
      "         [ 1.5737e-03,  1.5743e-03,  1.5794e-03,  ...,  1.5635e-03,\n",
      "           1.5768e-03,  1.5777e-03],\n",
      "         [-1.3577e-03, -1.3511e-03, -1.3533e-03,  ..., -1.3443e-03,\n",
      "          -1.3571e-03, -1.3463e-03],\n",
      "         [ 1.6859e-03,  1.6842e-03,  1.6853e-03,  ...,  1.6808e-03,\n",
      "           1.6891e-03,  1.6815e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 0.3003, Accuracy: 4590/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.424253940582275 s]\n",
      "Train Epoch: 78 [0/50000 (0%)]\tLoss: 0.058554, Accuracy: 98.05\n",
      "Train Epoch: 78 [1280/50000 (3%)]\tLoss: 0.013412, Accuracy: 99.61\n",
      "Train Epoch: 78 [2560/50000 (6%)]\tLoss: 0.039223, Accuracy: 98.83\n",
      "Train Epoch: 78 [3840/50000 (9%)]\tLoss: 0.040144, Accuracy: 98.83\n",
      "Train Epoch: 78 [5120/50000 (11%)]\tLoss: 0.033537, Accuracy: 99.22\n",
      "Train Epoch: 78 [6400/50000 (14%)]\tLoss: 0.040510, Accuracy: 99.61\n",
      "Train Epoch: 78 [7680/50000 (17%)]\tLoss: 0.028650, Accuracy: 99.61\n",
      "Train Epoch: 78 [8960/50000 (20%)]\tLoss: 0.032155, Accuracy: 99.61\n",
      "Train Epoch: 78 [10240/50000 (23%)]\tLoss: 0.027928, Accuracy: 99.22\n",
      "Train Epoch: 78 [11520/50000 (26%)]\tLoss: 0.077667, Accuracy: 96.48\n",
      "Train Epoch: 78 [12800/50000 (28%)]\tLoss: 0.041064, Accuracy: 98.83\n",
      "Train Epoch: 78 [14080/50000 (31%)]\tLoss: 0.056708, Accuracy: 98.05\n",
      "Train Epoch: 78 [15360/50000 (34%)]\tLoss: 0.030753, Accuracy: 98.83\n",
      "Train Epoch: 78 [16640/50000 (37%)]\tLoss: 0.050411, Accuracy: 98.05\n",
      "Train Epoch: 78 [17920/50000 (40%)]\tLoss: 0.031120, Accuracy: 98.83\n",
      "Train Epoch: 78 [19200/50000 (43%)]\tLoss: 0.037271, Accuracy: 99.22\n",
      "Train Epoch: 78 [20480/50000 (45%)]\tLoss: 0.041253, Accuracy: 98.44\n",
      "Train Epoch: 78 [21760/50000 (48%)]\tLoss: 0.034590, Accuracy: 98.44\n",
      "Train Epoch: 78 [23040/50000 (51%)]\tLoss: 0.083368, Accuracy: 96.88\n",
      "Train Epoch: 78 [24320/50000 (54%)]\tLoss: 0.051573, Accuracy: 98.83\n",
      "Train Epoch: 78 [25600/50000 (57%)]\tLoss: 0.068259, Accuracy: 96.88\n",
      "Train Epoch: 78 [26880/50000 (60%)]\tLoss: 0.051204, Accuracy: 98.05\n",
      "Train Epoch: 78 [28160/50000 (62%)]\tLoss: 0.045862, Accuracy: 99.22\n",
      "Train Epoch: 78 [29440/50000 (65%)]\tLoss: 0.030489, Accuracy: 98.44\n",
      "Train Epoch: 78 [30720/50000 (68%)]\tLoss: 0.071774, Accuracy: 96.88\n",
      "Train Epoch: 78 [32000/50000 (71%)]\tLoss: 0.028073, Accuracy: 98.83\n",
      "Train Epoch: 78 [33280/50000 (74%)]\tLoss: 0.054870, Accuracy: 99.22\n",
      "Train Epoch: 78 [34560/50000 (77%)]\tLoss: 0.029737, Accuracy: 99.22\n",
      "Train Epoch: 78 [35840/50000 (80%)]\tLoss: 0.044050, Accuracy: 98.83\n",
      "Train Epoch: 78 [37120/50000 (82%)]\tLoss: 0.038695, Accuracy: 98.83\n",
      "Train Epoch: 78 [38400/50000 (85%)]\tLoss: 0.072140, Accuracy: 97.27\n",
      "Train Epoch: 78 [39680/50000 (88%)]\tLoss: 0.064385, Accuracy: 97.27\n",
      "Train Epoch: 78 [40960/50000 (91%)]\tLoss: 0.029297, Accuracy: 99.22\n",
      "Train Epoch: 78 [42240/50000 (94%)]\tLoss: 0.038508, Accuracy: 98.05\n",
      "Train Epoch: 78 [43520/50000 (97%)]\tLoss: 0.026464, Accuracy: 99.61\n",
      "Train Epoch: 78 [35000/50000 (99%)]\tLoss: 0.046323, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.2971, Accuracy: 4588/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.24169087409973 s]\n",
      "\n",
      "Test set: Average loss: 0.3111, Accuracy: 9164/10000 (91.64%)\n",
      "\n",
      "Train Epoch: 79 [0/50000 (0%)]\tLoss: 0.030013, Accuracy: 98.44\n",
      "Train Epoch: 79 [1280/50000 (3%)]\tLoss: 0.038769, Accuracy: 98.83\n",
      "Train Epoch: 79 [2560/50000 (6%)]\tLoss: 0.057108, Accuracy: 98.44\n",
      "Train Epoch: 79 [3840/50000 (9%)]\tLoss: 0.083348, Accuracy: 97.66\n",
      "Train Epoch: 79 [5120/50000 (11%)]\tLoss: 0.042046, Accuracy: 98.83\n",
      "Train Epoch: 79 [6400/50000 (14%)]\tLoss: 0.056052, Accuracy: 98.44\n",
      "Train Epoch: 79 [7680/50000 (17%)]\tLoss: 0.053548, Accuracy: 98.05\n",
      "Train Epoch: 79 [8960/50000 (20%)]\tLoss: 0.043148, Accuracy: 98.83\n",
      "Train Epoch: 79 [10240/50000 (23%)]\tLoss: 0.030874, Accuracy: 98.83\n",
      "Train Epoch: 79 [11520/50000 (26%)]\tLoss: 0.047343, Accuracy: 98.44\n",
      "Train Epoch: 79 [12800/50000 (28%)]\tLoss: 0.050693, Accuracy: 97.66\n",
      "Train Epoch: 79 [14080/50000 (31%)]\tLoss: 0.053947, Accuracy: 97.66\n",
      "Train Epoch: 79 [15360/50000 (34%)]\tLoss: 0.061402, Accuracy: 98.83\n",
      "Train Epoch: 79 [16640/50000 (37%)]\tLoss: 0.041268, Accuracy: 98.05\n",
      "Train Epoch: 79 [17920/50000 (40%)]\tLoss: 0.081757, Accuracy: 96.48\n",
      "Train Epoch: 79 [19200/50000 (43%)]\tLoss: 0.030383, Accuracy: 99.22\n",
      "Train Epoch: 79 [20480/50000 (45%)]\tLoss: 0.057874, Accuracy: 98.05\n",
      "Train Epoch: 79 [21760/50000 (48%)]\tLoss: 0.048384, Accuracy: 98.44\n",
      "Train Epoch: 79 [23040/50000 (51%)]\tLoss: 0.034703, Accuracy: 99.22\n",
      "Train Epoch: 79 [24320/50000 (54%)]\tLoss: 0.023850, Accuracy: 99.61\n",
      "Train Epoch: 79 [25600/50000 (57%)]\tLoss: 0.030049, Accuracy: 99.22\n",
      "Train Epoch: 79 [26880/50000 (60%)]\tLoss: 0.050895, Accuracy: 98.44\n",
      "Train Epoch: 79 [28160/50000 (62%)]\tLoss: 0.037888, Accuracy: 98.83\n",
      "Train Epoch: 79 [29440/50000 (65%)]\tLoss: 0.063792, Accuracy: 97.27\n",
      "Train Epoch: 79 [30720/50000 (68%)]\tLoss: 0.038050, Accuracy: 98.83\n",
      "Train Epoch: 79 [32000/50000 (71%)]\tLoss: 0.062378, Accuracy: 97.27\n",
      "Train Epoch: 79 [33280/50000 (74%)]\tLoss: 0.037322, Accuracy: 98.44\n",
      "Train Epoch: 79 [34560/50000 (77%)]\tLoss: 0.046268, Accuracy: 98.44\n",
      "Train Epoch: 79 [35840/50000 (80%)]\tLoss: 0.049049, Accuracy: 98.44\n",
      "Train Epoch: 79 [37120/50000 (82%)]\tLoss: 0.031145, Accuracy: 99.22\n",
      "Train Epoch: 79 [38400/50000 (85%)]\tLoss: 0.062916, Accuracy: 97.27\n",
      "Train Epoch: 79 [39680/50000 (88%)]\tLoss: 0.049122, Accuracy: 98.44\n",
      "Train Epoch: 79 [40960/50000 (91%)]\tLoss: 0.037056, Accuracy: 99.22\n",
      "Train Epoch: 79 [42240/50000 (94%)]\tLoss: 0.028364, Accuracy: 99.22\n",
      "Train Epoch: 79 [43520/50000 (97%)]\tLoss: 0.061145, Accuracy: 98.83\n",
      "Train Epoch: 79 [35000/50000 (99%)]\tLoss: 0.072103, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2944, Accuracy: 4599/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.355761766433716 s]\n",
      "\n",
      "Test set: Average loss: 0.3100, Accuracy: 9144/10000 (91.44%)\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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\naRBY0BmGYRqE/w/U/zxNDJVCvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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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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