M_accelerate_6lastlayer-8fold_91.67-softmax-Copy1.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": 10,
   "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": 11,
   "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": 12,
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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",
    "        self.scaleLayer= nn.Conv1d(1, 1, 1, stride=1,padding=0,groups=1,dilation=1,bias=True)\n",
    "        \n",
    "        one_conv_kernel_size = 13 # [3,7,9]\n",
    "        self.conv1D= nn.Conv1d(1, int(out_planes*0.875), one_conv_kernel_size, stride=1,padding=6,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()*1\n",
    "        \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",
    "        w=self.scaleLayer(w)\n",
    "        if a>0.37:\n",
    "            print(self.scaleLayer.weight)\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",
    "        \n",
    "        #-------------------------------------\n",
    "        w = 0.1*F.tanh(w) # [-0.5,+0.5]\n",
    "        #w=F.softmax(w,dim=2)\n",
    "        \n",
    "        if a>0.37:\n",
    "            print(self.conv1D.weight.shape)\n",
    "            print(self.conv1D.weight)\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": 13,
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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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/50000 (0%)]\tLoss: 2.310900, Accuracy: 10.55\n",
      "Train Epoch: 0 [1280/50000 (3%)]\tLoss: 2.377024, Accuracy: 11.72\n",
      "Train Epoch: 0 [2560/50000 (6%)]\tLoss: 2.342770, Accuracy: 6.64\n",
      "Train Epoch: 0 [3840/50000 (9%)]\tLoss: 2.304077, Accuracy: 10.16\n",
      "Train Epoch: 0 [5120/50000 (11%)]\tLoss: 2.238168, Accuracy: 14.45\n",
      "Train Epoch: 0 [6400/50000 (14%)]\tLoss: 2.120892, Accuracy: 20.70\n",
      "Train Epoch: 0 [7680/50000 (17%)]\tLoss: 2.119954, Accuracy: 20.31\n",
      "Train Epoch: 0 [8960/50000 (20%)]\tLoss: 2.172742, Accuracy: 16.80\n",
      "Train Epoch: 0 [10240/50000 (23%)]\tLoss: 2.068606, Accuracy: 20.31\n",
      "Train Epoch: 0 [11520/50000 (26%)]\tLoss: 2.051823, Accuracy: 22.66\n",
      "Train Epoch: 0 [12800/50000 (28%)]\tLoss: 1.970165, Accuracy: 23.05\n",
      "Train Epoch: 0 [14080/50000 (31%)]\tLoss: 2.014423, Accuracy: 21.88\n",
      "Train Epoch: 0 [15360/50000 (34%)]\tLoss: 1.884346, Accuracy: 23.83\n",
      "Train Epoch: 0 [16640/50000 (37%)]\tLoss: 1.868824, Accuracy: 23.44\n",
      "Train Epoch: 0 [17920/50000 (40%)]\tLoss: 1.891617, Accuracy: 23.83\n",
      "Train Epoch: 0 [19200/50000 (43%)]\tLoss: 1.868903, Accuracy: 25.00\n",
      "Train Epoch: 0 [20480/50000 (45%)]\tLoss: 1.885248, Accuracy: 25.00\n",
      "Train Epoch: 0 [21760/50000 (48%)]\tLoss: 1.910639, Accuracy: 28.52\n",
      "Train Epoch: 0 [23040/50000 (51%)]\tLoss: 1.912848, Accuracy: 25.39\n",
      "Train Epoch: 0 [24320/50000 (54%)]\tLoss: 1.803279, Accuracy: 30.08\n",
      "Train Epoch: 0 [25600/50000 (57%)]\tLoss: 1.818819, Accuracy: 28.91\n",
      "Train Epoch: 0 [26880/50000 (60%)]\tLoss: 1.887718, Accuracy: 26.17\n",
      "Train Epoch: 0 [28160/50000 (62%)]\tLoss: 1.875915, Accuracy: 28.91\n",
      "Train Epoch: 0 [29440/50000 (65%)]\tLoss: 1.825902, Accuracy: 26.56\n",
      "Train Epoch: 0 [30720/50000 (68%)]\tLoss: 1.818331, Accuracy: 29.69\n",
      "Train Epoch: 0 [32000/50000 (71%)]\tLoss: 1.872380, Accuracy: 28.52\n",
      "Train Epoch: 0 [33280/50000 (74%)]\tLoss: 1.770313, Accuracy: 27.34\n",
      "Train Epoch: 0 [34560/50000 (77%)]\tLoss: 1.796031, Accuracy: 30.86\n",
      "Train Epoch: 0 [35840/50000 (80%)]\tLoss: 1.779528, Accuracy: 28.12\n",
      "Train Epoch: 0 [37120/50000 (82%)]\tLoss: 1.797296, Accuracy: 28.12\n",
      "Train Epoch: 0 [38400/50000 (85%)]\tLoss: 1.767148, Accuracy: 28.91\n",
      "Train Epoch: 0 [39680/50000 (88%)]\tLoss: 1.755824, Accuracy: 33.98\n",
      "Train Epoch: 0 [40960/50000 (91%)]\tLoss: 1.702304, Accuracy: 27.73\n",
      "Train Epoch: 0 [42240/50000 (94%)]\tLoss: 1.640711, Accuracy: 37.11\n",
      "Train Epoch: 0 [43520/50000 (97%)]\tLoss: 1.717785, Accuracy: 35.55\n",
      "Train Epoch: 0 [35000/50000 (99%)]\tLoss: 1.709198, Accuracy: 36.00\n",
      "\n",
      "Validation set: Average loss: 1.7272, Accuracy: 1707/5000 (34.00%)\n",
      "\n",
      "the time of this epoch:[36.431071519851685 s]\n",
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 1.751241, Accuracy: 30.47\n",
      "Train Epoch: 1 [1280/50000 (3%)]\tLoss: 1.787766, Accuracy: 29.69\n",
      "Train Epoch: 1 [2560/50000 (6%)]\tLoss: 1.719872, Accuracy: 37.89\n",
      "Train Epoch: 1 [3840/50000 (9%)]\tLoss: 1.684180, Accuracy: 37.11\n",
      "Train Epoch: 1 [5120/50000 (11%)]\tLoss: 1.561096, Accuracy: 41.02\n",
      "Train Epoch: 1 [6400/50000 (14%)]\tLoss: 1.671927, Accuracy: 35.94\n",
      "Train Epoch: 1 [7680/50000 (17%)]\tLoss: 1.753940, Accuracy: 33.59\n",
      "Train Epoch: 1 [8960/50000 (20%)]\tLoss: 1.718541, Accuracy: 32.03\n",
      "Train Epoch: 1 [10240/50000 (23%)]\tLoss: 1.689739, Accuracy: 34.38\n",
      "Train Epoch: 1 [11520/50000 (26%)]\tLoss: 1.662891, Accuracy: 33.59\n",
      "Train Epoch: 1 [12800/50000 (28%)]\tLoss: 1.704002, Accuracy: 28.52\n",
      "Train Epoch: 1 [14080/50000 (31%)]\tLoss: 1.524882, Accuracy: 42.19\n",
      "Train Epoch: 1 [15360/50000 (34%)]\tLoss: 1.611720, Accuracy: 40.62\n",
      "Train Epoch: 1 [16640/50000 (37%)]\tLoss: 1.668101, Accuracy: 35.16\n",
      "Train Epoch: 1 [17920/50000 (40%)]\tLoss: 1.711570, Accuracy: 32.42\n",
      "Train Epoch: 1 [19200/50000 (43%)]\tLoss: 1.627091, Accuracy: 35.55\n",
      "Train Epoch: 1 [20480/50000 (45%)]\tLoss: 1.561270, Accuracy: 41.80\n",
      "Train Epoch: 1 [21760/50000 (48%)]\tLoss: 1.548926, Accuracy: 37.11\n",
      "Train Epoch: 1 [23040/50000 (51%)]\tLoss: 1.622144, Accuracy: 39.06\n",
      "Train Epoch: 1 [24320/50000 (54%)]\tLoss: 1.574570, Accuracy: 36.72\n",
      "Train Epoch: 1 [25600/50000 (57%)]\tLoss: 1.604443, Accuracy: 38.67\n",
      "Train Epoch: 1 [26880/50000 (60%)]\tLoss: 1.581749, Accuracy: 36.33\n",
      "Train Epoch: 1 [28160/50000 (62%)]\tLoss: 1.529930, Accuracy: 39.06\n",
      "Train Epoch: 1 [29440/50000 (65%)]\tLoss: 1.546022, Accuracy: 44.53\n",
      "Train Epoch: 1 [30720/50000 (68%)]\tLoss: 1.589480, Accuracy: 41.41\n",
      "Train Epoch: 1 [32000/50000 (71%)]\tLoss: 1.509350, Accuracy: 39.84\n",
      "Train Epoch: 1 [33280/50000 (74%)]\tLoss: 1.540105, Accuracy: 46.48\n",
      "Train Epoch: 1 [34560/50000 (77%)]\tLoss: 1.478274, Accuracy: 49.22\n",
      "Train Epoch: 1 [35840/50000 (80%)]\tLoss: 1.484267, Accuracy: 45.70\n",
      "Train Epoch: 1 [37120/50000 (82%)]\tLoss: 1.491550, Accuracy: 43.36\n",
      "Train Epoch: 1 [38400/50000 (85%)]\tLoss: 1.470654, Accuracy: 45.31\n",
      "Train Epoch: 1 [39680/50000 (88%)]\tLoss: 1.446312, Accuracy: 45.70\n",
      "Train Epoch: 1 [40960/50000 (91%)]\tLoss: 1.471518, Accuracy: 46.09\n",
      "Train Epoch: 1 [42240/50000 (94%)]\tLoss: 1.412366, Accuracy: 47.66\n",
      "Train Epoch: 1 [43520/50000 (97%)]\tLoss: 1.368408, Accuracy: 48.44\n",
      "Train Epoch: 1 [35000/50000 (99%)]\tLoss: 1.397900, Accuracy: 52.50\n",
      "\n",
      "Validation set: Average loss: 1.5669, Accuracy: 2177/5000 (43.00%)\n",
      "\n",
      "the time of this epoch:[36.56815052032471 s]\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.433452, Accuracy: 49.61\n",
      "Train Epoch: 2 [1280/50000 (3%)]\tLoss: 1.474970, Accuracy: 45.31\n",
      "Train Epoch: 2 [2560/50000 (6%)]\tLoss: 1.431120, Accuracy: 44.14\n",
      "Train Epoch: 2 [3840/50000 (9%)]\tLoss: 1.471304, Accuracy: 43.75\n",
      "Train Epoch: 2 [5120/50000 (11%)]\tLoss: 1.356680, Accuracy: 46.88\n",
      "Train Epoch: 2 [6400/50000 (14%)]\tLoss: 1.448870, Accuracy: 43.36\n",
      "Train Epoch: 2 [7680/50000 (17%)]\tLoss: 1.214935, Accuracy: 55.47\n",
      "Train Epoch: 2 [8960/50000 (20%)]\tLoss: 1.335444, Accuracy: 50.39\n",
      "Train Epoch: 2 [10240/50000 (23%)]\tLoss: 1.449944, Accuracy: 49.22\n",
      "Train Epoch: 2 [11520/50000 (26%)]\tLoss: 1.309692, Accuracy: 49.22\n",
      "Train Epoch: 2 [12800/50000 (28%)]\tLoss: 1.511485, Accuracy: 46.88\n",
      "Train Epoch: 2 [14080/50000 (31%)]\tLoss: 1.391011, Accuracy: 48.83\n",
      "Train Epoch: 2 [15360/50000 (34%)]\tLoss: 1.387249, Accuracy: 51.17\n",
      "Train Epoch: 2 [16640/50000 (37%)]\tLoss: 1.431410, Accuracy: 45.70\n",
      "Train Epoch: 2 [17920/50000 (40%)]\tLoss: 1.231930, Accuracy: 55.47\n",
      "Train Epoch: 2 [19200/50000 (43%)]\tLoss: 1.356108, Accuracy: 50.00\n",
      "Train Epoch: 2 [20480/50000 (45%)]\tLoss: 1.298076, Accuracy: 53.12\n",
      "Train Epoch: 2 [21760/50000 (48%)]\tLoss: 1.184779, Accuracy: 57.42\n",
      "Train Epoch: 2 [23040/50000 (51%)]\tLoss: 1.268482, Accuracy: 58.20\n",
      "Train Epoch: 2 [24320/50000 (54%)]\tLoss: 1.341293, Accuracy: 52.73\n",
      "Train Epoch: 2 [25600/50000 (57%)]\tLoss: 1.229570, Accuracy: 58.98\n",
      "Train Epoch: 2 [26880/50000 (60%)]\tLoss: 1.254431, Accuracy: 55.47\n",
      "Train Epoch: 2 [28160/50000 (62%)]\tLoss: 1.261544, Accuracy: 54.69\n",
      "Train Epoch: 2 [29440/50000 (65%)]\tLoss: 1.277382, Accuracy: 55.08\n",
      "Train Epoch: 2 [30720/50000 (68%)]\tLoss: 1.236677, Accuracy: 52.34\n",
      "Train Epoch: 2 [32000/50000 (71%)]\tLoss: 1.187981, Accuracy: 56.25\n",
      "Train Epoch: 2 [33280/50000 (74%)]\tLoss: 1.199591, Accuracy: 56.64\n",
      "Train Epoch: 2 [34560/50000 (77%)]\tLoss: 1.211907, Accuracy: 56.25\n",
      "Train Epoch: 2 [35840/50000 (80%)]\tLoss: 1.241308, Accuracy: 55.86\n",
      "Train Epoch: 2 [37120/50000 (82%)]\tLoss: 1.264875, Accuracy: 51.17\n",
      "Train Epoch: 2 [38400/50000 (85%)]\tLoss: 1.191617, Accuracy: 59.38\n",
      "Train Epoch: 2 [39680/50000 (88%)]\tLoss: 1.166205, Accuracy: 58.98\n",
      "Train Epoch: 2 [40960/50000 (91%)]\tLoss: 1.263400, Accuracy: 54.30\n",
      "Train Epoch: 2 [42240/50000 (94%)]\tLoss: 1.276522, Accuracy: 51.17\n",
      "Train Epoch: 2 [43520/50000 (97%)]\tLoss: 1.243230, Accuracy: 56.25\n",
      "Train Epoch: 2 [35000/50000 (99%)]\tLoss: 1.289375, Accuracy: 51.00\n",
      "\n",
      "Validation set: Average loss: 1.4748, Accuracy: 2377/5000 (47.00%)\n",
      "\n",
      "the time of this epoch:[36.4556200504303 s]\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.014755, Accuracy: 63.28\n",
      "Train Epoch: 3 [1280/50000 (3%)]\tLoss: 1.196937, Accuracy: 56.64\n",
      "Train Epoch: 3 [2560/50000 (6%)]\tLoss: 1.174851, Accuracy: 59.77\n",
      "Train Epoch: 3 [3840/50000 (9%)]\tLoss: 1.108582, Accuracy: 54.30\n",
      "Train Epoch: 3 [5120/50000 (11%)]\tLoss: 1.182364, Accuracy: 59.38\n",
      "Train Epoch: 3 [6400/50000 (14%)]\tLoss: 1.281377, Accuracy: 55.86\n",
      "Train Epoch: 3 [7680/50000 (17%)]\tLoss: 1.253738, Accuracy: 52.73\n",
      "Train Epoch: 3 [8960/50000 (20%)]\tLoss: 1.200232, Accuracy: 54.30\n",
      "Train Epoch: 3 [10240/50000 (23%)]\tLoss: 1.236218, Accuracy: 54.30\n",
      "Train Epoch: 3 [11520/50000 (26%)]\tLoss: 1.108685, Accuracy: 60.16\n",
      "Train Epoch: 3 [12800/50000 (28%)]\tLoss: 1.022324, Accuracy: 66.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [14080/50000 (31%)]\tLoss: 1.163964, Accuracy: 56.25\n",
      "Train Epoch: 3 [15360/50000 (34%)]\tLoss: 1.159840, Accuracy: 59.38\n",
      "Train Epoch: 3 [16640/50000 (37%)]\tLoss: 1.164303, Accuracy: 59.77\n",
      "Train Epoch: 3 [17920/50000 (40%)]\tLoss: 1.128883, Accuracy: 60.94\n",
      "Train Epoch: 3 [19200/50000 (43%)]\tLoss: 1.148498, Accuracy: 59.38\n",
      "Train Epoch: 3 [20480/50000 (45%)]\tLoss: 1.123016, Accuracy: 60.55\n",
      "Train Epoch: 3 [21760/50000 (48%)]\tLoss: 0.978111, Accuracy: 58.98\n",
      "Train Epoch: 3 [23040/50000 (51%)]\tLoss: 1.115419, Accuracy: 57.81\n",
      "Train Epoch: 3 [24320/50000 (54%)]\tLoss: 0.957566, Accuracy: 67.19\n",
      "Train Epoch: 3 [25600/50000 (57%)]\tLoss: 1.113405, Accuracy: 60.55\n",
      "Train Epoch: 3 [26880/50000 (60%)]\tLoss: 1.047447, Accuracy: 59.77\n",
      "Train Epoch: 3 [28160/50000 (62%)]\tLoss: 1.083948, Accuracy: 60.16\n",
      "Train Epoch: 3 [29440/50000 (65%)]\tLoss: 1.084501, Accuracy: 60.94\n",
      "Train Epoch: 3 [30720/50000 (68%)]\tLoss: 1.064592, Accuracy: 62.11\n",
      "Train Epoch: 3 [32000/50000 (71%)]\tLoss: 1.047152, Accuracy: 64.45\n",
      "Train Epoch: 3 [33280/50000 (74%)]\tLoss: 1.145820, Accuracy: 60.55\n",
      "Train Epoch: 3 [34560/50000 (77%)]\tLoss: 1.099306, Accuracy: 61.33\n",
      "Train Epoch: 3 [35840/50000 (80%)]\tLoss: 0.938439, Accuracy: 70.31\n",
      "Train Epoch: 3 [37120/50000 (82%)]\tLoss: 1.047283, Accuracy: 61.33\n",
      "Train Epoch: 3 [38400/50000 (85%)]\tLoss: 1.053816, Accuracy: 63.28\n",
      "Train Epoch: 3 [39680/50000 (88%)]\tLoss: 0.969516, Accuracy: 64.06\n",
      "Train Epoch: 3 [40960/50000 (91%)]\tLoss: 0.881852, Accuracy: 71.48\n",
      "Train Epoch: 3 [42240/50000 (94%)]\tLoss: 1.074314, Accuracy: 62.89\n",
      "Train Epoch: 3 [43520/50000 (97%)]\tLoss: 1.021592, Accuracy: 62.11\n",
      "Train Epoch: 3 [35000/50000 (99%)]\tLoss: 1.065796, Accuracy: 61.00\n",
      "\n",
      "Validation set: Average loss: 1.0755, Accuracy: 3088/5000 (61.00%)\n",
      "\n",
      "the time of this epoch:[36.46532154083252 s]\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 1.017222, Accuracy: 62.89\n",
      "Train Epoch: 4 [1280/50000 (3%)]\tLoss: 1.024098, Accuracy: 62.89\n",
      "Train Epoch: 4 [2560/50000 (6%)]\tLoss: 1.036285, Accuracy: 62.89\n",
      "Train Epoch: 4 [3840/50000 (9%)]\tLoss: 0.946736, Accuracy: 67.97\n",
      "Train Epoch: 4 [5120/50000 (11%)]\tLoss: 0.933203, Accuracy: 67.97\n",
      "Train Epoch: 4 [6400/50000 (14%)]\tLoss: 0.882368, Accuracy: 69.92\n",
      "Train Epoch: 4 [7680/50000 (17%)]\tLoss: 0.983649, Accuracy: 64.45\n",
      "Train Epoch: 4 [8960/50000 (20%)]\tLoss: 1.079347, Accuracy: 58.59\n",
      "Train Epoch: 4 [10240/50000 (23%)]\tLoss: 1.096812, Accuracy: 60.94\n",
      "Train Epoch: 4 [11520/50000 (26%)]\tLoss: 1.138233, Accuracy: 58.98\n",
      "Train Epoch: 4 [12800/50000 (28%)]\tLoss: 0.957670, Accuracy: 66.41\n",
      "Train Epoch: 4 [14080/50000 (31%)]\tLoss: 1.005777, Accuracy: 67.58\n",
      "Train Epoch: 4 [15360/50000 (34%)]\tLoss: 0.928819, Accuracy: 66.41\n",
      "Train Epoch: 4 [16640/50000 (37%)]\tLoss: 0.962444, Accuracy: 67.19\n",
      "torch.Size([512, 1, 1])\n",
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      "        [[-0.0236]],\n",
      "\n",
      "        [[-0.1171]],\n",
      "\n",
      "        [[ 1.2555]],\n",
      "\n",
      "        [[-0.8220]],\n",
      "\n",
      "        [[-1.0097]],\n",
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      "        [[ 0.3956]],\n",
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      "        [[ 0.9887]],\n",
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      "        [[ 0.2346]],\n",
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      "        [[-2.2925]],\n",
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      "        [[ 0.8850]],\n",
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      "        [[-1.2717]],\n",
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      "\n",
      "        [[-1.6464]],\n",
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      "        [[ 0.9740]],\n",
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      "        [[-1.9936]],\n",
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      "        [[-2.0280]],\n",
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      "        [[ 0.2479]],\n",
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      "        [[-0.4573]],\n",
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      "        [[ 0.9634]],\n",
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      "        [[-1.2746]],\n",
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      "        [[ 0.8580]],\n",
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      "        [[-0.5315]],\n",
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      "        [[ 0.7352]],\n",
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      "        [[ 1.8185]],\n",
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      "        [[-0.1973]],\n",
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      "        [[-0.0258]],\n",
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      "        [[ 1.4348]],\n",
      "\n",
      "        [[-0.7117]],\n",
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      "        [[ 1.1955]],\n",
      "\n",
      "        [[-0.1955]],\n",
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      "        [[ 0.7529]],\n",
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      "        [[-0.6935]],\n",
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      "        [[-2.6028]],\n",
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      "        [[-0.3406]],\n",
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      "        [[-0.0579]],\n",
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      "        [[ 0.2756]],\n",
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      "        [[ 0.7972]],\n",
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      "        [[-0.7825]],\n",
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      "        [[ 0.4427]],\n",
      "\n",
      "        [[ 0.4020]],\n",
      "\n",
      "        [[ 2.1030]],\n",
      "\n",
      "        [[ 1.6497]],\n",
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      "        [[ 0.6761]],\n",
      "\n",
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      "        [[-2.2749]],\n",
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      "        [[ 0.3357]],\n",
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      "        [[-0.8701]],\n",
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      "        [[ 0.8029]],\n",
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      "        [[ 0.2170]],\n",
      "\n",
      "        [[ 0.5974]],\n",
      "\n",
      "        [[-0.4939]],\n",
      "\n",
      "        [[ 0.4403]],\n",
      "\n",
      "        [[-1.4076]],\n",
      "\n",
      "        [[-1.0531]],\n",
      "\n",
      "        [[ 0.8780]],\n",
      "\n",
      "        [[-0.1967]],\n",
      "\n",
      "        [[-0.3060]],\n",
      "\n",
      "        [[-0.4598]],\n",
      "\n",
      "        [[ 0.4823]],\n",
      "\n",
      "        [[ 0.3085]],\n",
      "\n",
      "        [[ 1.1066]],\n",
      "\n",
      "        [[ 0.6814]],\n",
      "\n",
      "        [[ 0.2197]],\n",
      "\n",
      "        [[ 0.0079]],\n",
      "\n",
      "        [[-0.4038]],\n",
      "\n",
      "        [[-0.0421]],\n",
      "\n",
      "        [[ 0.5866]],\n",
      "\n",
      "        [[ 0.2131]],\n",
      "\n",
      "        [[-1.2555]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([[[-0.1203]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-0.1002,  0.1402, -0.0291,  ...,  0.1681,  0.0680,  0.0694]],\n",
      "\n",
      "        [[-0.0371,  0.0035, -0.0895,  ..., -0.0914,  0.1673, -0.0200]],\n",
      "\n",
      "        [[ 0.0789,  0.1869, -0.0186,  ..., -0.1145, -0.1779,  0.0013]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.0720,  0.0529, -0.1233,  ...,  0.0856, -0.0756, -0.0443]],\n",
      "\n",
      "        [[-0.0102, -0.1358,  0.0745,  ...,  0.1027,  0.0792,  0.0575]],\n",
      "\n",
      "        [[ 0.0364, -0.1727,  0.1520,  ...,  0.1148,  0.0768,  0.1743]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 2.3839e-02,  3.3869e-02,  2.1959e-02,  ...,  1.8363e-02,\n",
      "           1.2547e-02,  8.8946e-03],\n",
      "         [-9.0642e-03, -1.0223e-02, -6.7664e-03,  ..., -8.8955e-03,\n",
      "          -1.6956e-02, -2.0090e-02],\n",
      "         [-1.7899e-02, -2.6309e-02, -2.0292e-02,  ...,  1.5548e-03,\n",
      "           3.5494e-03,  1.3099e-02],\n",
      "         ...,\n",
      "         [-2.6634e-02, -3.3177e-02, -2.3382e-02,  ..., -2.4581e-02,\n",
      "          -2.4535e-02, -2.1513e-02],\n",
      "         [ 3.8727e-03, -2.8426e-03, -4.4022e-03,  ..., -1.2498e-02,\n",
      "          -5.9592e-03,  4.0965e-03],\n",
      "         [ 1.5580e-02,  2.7547e-02,  1.4526e-02,  ...,  3.4301e-04,\n",
      "          -1.7832e-03, -1.9916e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [17920/50000 (40%)]\tLoss: 0.883320, Accuracy: 67.58\n",
      "Train Epoch: 4 [19200/50000 (43%)]\tLoss: 0.962432, Accuracy: 67.58\n",
      "Train Epoch: 4 [20480/50000 (45%)]\tLoss: 0.939759, Accuracy: 64.84\n",
      "Train Epoch: 4 [21760/50000 (48%)]\tLoss: 1.012491, Accuracy: 62.11\n",
      "Train Epoch: 4 [23040/50000 (51%)]\tLoss: 0.887016, Accuracy: 71.09\n",
      "Train Epoch: 4 [24320/50000 (54%)]\tLoss: 0.954931, Accuracy: 67.19\n",
      "Train Epoch: 4 [25600/50000 (57%)]\tLoss: 0.837436, Accuracy: 70.70\n",
      "Train Epoch: 4 [26880/50000 (60%)]\tLoss: 0.903321, Accuracy: 66.02\n",
      "Train Epoch: 4 [28160/50000 (62%)]\tLoss: 0.993783, Accuracy: 63.67\n",
      "Train Epoch: 4 [29440/50000 (65%)]\tLoss: 0.903765, Accuracy: 65.62\n",
      "Train Epoch: 4 [30720/50000 (68%)]\tLoss: 0.919539, Accuracy: 65.62\n",
      "Train Epoch: 4 [32000/50000 (71%)]\tLoss: 0.878735, Accuracy: 67.58\n",
      "Train Epoch: 4 [33280/50000 (74%)]\tLoss: 0.949895, Accuracy: 63.67\n",
      "Train Epoch: 4 [34560/50000 (77%)]\tLoss: 0.868224, Accuracy: 67.97\n",
      "Train Epoch: 4 [35840/50000 (80%)]\tLoss: 1.011438, Accuracy: 63.67\n",
      "Train Epoch: 4 [37120/50000 (82%)]\tLoss: 0.763740, Accuracy: 73.44\n",
      "Train Epoch: 4 [38400/50000 (85%)]\tLoss: 0.915702, Accuracy: 66.41\n",
      "Train Epoch: 4 [39680/50000 (88%)]\tLoss: 0.885266, Accuracy: 67.97\n",
      "Train Epoch: 4 [40960/50000 (91%)]\tLoss: 0.901332, Accuracy: 66.80\n",
      "Train Epoch: 4 [42240/50000 (94%)]\tLoss: 0.845762, Accuracy: 70.31\n",
      "Train Epoch: 4 [43520/50000 (97%)]\tLoss: 0.906558, Accuracy: 66.80\n",
      "Train Epoch: 4 [35000/50000 (99%)]\tLoss: 0.953691, Accuracy: 66.00\n",
      "\n",
      "Validation set: Average loss: 0.9659, Accuracy: 3239/5000 (64.00%)\n",
      "\n",
      "the time of this epoch:[36.49870681762695 s]\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.842650, Accuracy: 71.09\n",
      "Train Epoch: 5 [1280/50000 (3%)]\tLoss: 0.885089, Accuracy: 64.84\n",
      "Train Epoch: 5 [2560/50000 (6%)]\tLoss: 0.833290, Accuracy: 68.36\n",
      "Train Epoch: 5 [3840/50000 (9%)]\tLoss: 0.951888, Accuracy: 64.45\n",
      "Train Epoch: 5 [5120/50000 (11%)]\tLoss: 0.866784, Accuracy: 69.92\n",
      "Train Epoch: 5 [6400/50000 (14%)]\tLoss: 0.836014, Accuracy: 72.66\n",
      "Train Epoch: 5 [7680/50000 (17%)]\tLoss: 0.734751, Accuracy: 71.88\n",
      "Train Epoch: 5 [8960/50000 (20%)]\tLoss: 0.967465, Accuracy: 68.36\n",
      "Train Epoch: 5 [10240/50000 (23%)]\tLoss: 1.031486, Accuracy: 61.33\n",
      "Train Epoch: 5 [11520/50000 (26%)]\tLoss: 0.861053, Accuracy: 68.75\n",
      "Train Epoch: 5 [12800/50000 (28%)]\tLoss: 0.944643, Accuracy: 66.80\n",
      "Train Epoch: 5 [14080/50000 (31%)]\tLoss: 0.836867, Accuracy: 70.31\n",
      "Train Epoch: 5 [15360/50000 (34%)]\tLoss: 0.841695, Accuracy: 71.09\n",
      "Train Epoch: 5 [16640/50000 (37%)]\tLoss: 0.825593, Accuracy: 69.53\n",
      "Train Epoch: 5 [17920/50000 (40%)]\tLoss: 0.814029, Accuracy: 71.09\n",
      "Train Epoch: 5 [19200/50000 (43%)]\tLoss: 0.822161, Accuracy: 71.48\n",
      "Train Epoch: 5 [20480/50000 (45%)]\tLoss: 0.722320, Accuracy: 74.22\n",
      "Train Epoch: 5 [21760/50000 (48%)]\tLoss: 0.800578, Accuracy: 71.48\n",
      "Train Epoch: 5 [23040/50000 (51%)]\tLoss: 0.856597, Accuracy: 69.53\n",
      "Train Epoch: 5 [24320/50000 (54%)]\tLoss: 0.928697, Accuracy: 67.19\n",
      "Train Epoch: 5 [25600/50000 (57%)]\tLoss: 0.807455, Accuracy: 70.70\n",
      "Train Epoch: 5 [26880/50000 (60%)]\tLoss: 0.832406, Accuracy: 72.66\n",
      "Train Epoch: 5 [28160/50000 (62%)]\tLoss: 0.680403, Accuracy: 75.78\n",
      "Train Epoch: 5 [29440/50000 (65%)]\tLoss: 0.799999, Accuracy: 69.53\n",
      "Train Epoch: 5 [30720/50000 (68%)]\tLoss: 0.685495, Accuracy: 76.17\n",
      "Train Epoch: 5 [32000/50000 (71%)]\tLoss: 0.837553, Accuracy: 71.88\n",
      "Train Epoch: 5 [33280/50000 (74%)]\tLoss: 0.782198, Accuracy: 72.66\n",
      "Train Epoch: 5 [34560/50000 (77%)]\tLoss: 0.913388, Accuracy: 66.80\n",
      "Train Epoch: 5 [35840/50000 (80%)]\tLoss: 0.774796, Accuracy: 71.88\n",
      "Train Epoch: 5 [37120/50000 (82%)]\tLoss: 0.852275, Accuracy: 71.09\n",
      "Train Epoch: 5 [38400/50000 (85%)]\tLoss: 0.867903, Accuracy: 69.92\n",
      "Train Epoch: 5 [39680/50000 (88%)]\tLoss: 0.888673, Accuracy: 68.75\n",
      "Train Epoch: 5 [40960/50000 (91%)]\tLoss: 0.886468, Accuracy: 69.14\n",
      "Train Epoch: 5 [42240/50000 (94%)]\tLoss: 0.817056, Accuracy: 71.88\n",
      "Train Epoch: 5 [43520/50000 (97%)]\tLoss: 0.727979, Accuracy: 77.34\n",
      "Train Epoch: 5 [35000/50000 (99%)]\tLoss: 0.824421, Accuracy: 71.50\n",
      "\n",
      "Validation set: Average loss: 0.9810, Accuracy: 3338/5000 (66.00%)\n",
      "\n",
      "the time of this epoch:[36.42002487182617 s]\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.695139, Accuracy: 73.83\n",
      "Train Epoch: 6 [1280/50000 (3%)]\tLoss: 0.848389, Accuracy: 70.70\n",
      "Train Epoch: 6 [2560/50000 (6%)]\tLoss: 0.755721, Accuracy: 73.44\n",
      "Train Epoch: 6 [3840/50000 (9%)]\tLoss: 0.798484, Accuracy: 72.66\n",
      "Train Epoch: 6 [5120/50000 (11%)]\tLoss: 0.784812, Accuracy: 70.31\n",
      "Train Epoch: 6 [6400/50000 (14%)]\tLoss: 0.642500, Accuracy: 74.22\n",
      "Train Epoch: 6 [7680/50000 (17%)]\tLoss: 0.777790, Accuracy: 71.88\n",
      "Train Epoch: 6 [8960/50000 (20%)]\tLoss: 0.806509, Accuracy: 71.09\n",
      "Train Epoch: 6 [10240/50000 (23%)]\tLoss: 0.662404, Accuracy: 76.56\n",
      "Train Epoch: 6 [11520/50000 (26%)]\tLoss: 0.773199, Accuracy: 73.05\n",
      "Train Epoch: 6 [12800/50000 (28%)]\tLoss: 0.713915, Accuracy: 76.17\n",
      "Train Epoch: 6 [14080/50000 (31%)]\tLoss: 0.718357, Accuracy: 75.39\n",
      "Train Epoch: 6 [15360/50000 (34%)]\tLoss: 0.717425, Accuracy: 75.39\n",
      "Train Epoch: 6 [16640/50000 (37%)]\tLoss: 0.701273, Accuracy: 75.39\n",
      "Train Epoch: 6 [17920/50000 (40%)]\tLoss: 0.610894, Accuracy: 78.52\n",
      "Train Epoch: 6 [19200/50000 (43%)]\tLoss: 0.801943, Accuracy: 72.27\n",
      "Train Epoch: 6 [20480/50000 (45%)]\tLoss: 0.891564, Accuracy: 69.92\n",
      "Train Epoch: 6 [21760/50000 (48%)]\tLoss: 0.747813, Accuracy: 74.22\n",
      "Train Epoch: 6 [23040/50000 (51%)]\tLoss: 0.758968, Accuracy: 72.27\n",
      "Train Epoch: 6 [24320/50000 (54%)]\tLoss: 0.649330, Accuracy: 76.56\n",
      "Train Epoch: 6 [25600/50000 (57%)]\tLoss: 0.754376, Accuracy: 73.44\n",
      "Train Epoch: 6 [26880/50000 (60%)]\tLoss: 0.862549, Accuracy: 69.92\n",
      "Train Epoch: 6 [28160/50000 (62%)]\tLoss: 0.867880, Accuracy: 72.27\n",
      "Train Epoch: 6 [29440/50000 (65%)]\tLoss: 0.772249, Accuracy: 75.39\n",
      "Train Epoch: 6 [30720/50000 (68%)]\tLoss: 0.719552, Accuracy: 75.00\n",
      "Train Epoch: 6 [32000/50000 (71%)]\tLoss: 0.720648, Accuracy: 75.39\n",
      "Train Epoch: 6 [33280/50000 (74%)]\tLoss: 0.620924, Accuracy: 78.91\n",
      "Train Epoch: 6 [34560/50000 (77%)]\tLoss: 0.861450, Accuracy: 70.31\n",
      "Train Epoch: 6 [35840/50000 (80%)]\tLoss: 0.700584, Accuracy: 72.66\n",
      "Train Epoch: 6 [37120/50000 (82%)]\tLoss: 0.750145, Accuracy: 72.66\n",
      "Train Epoch: 6 [38400/50000 (85%)]\tLoss: 0.872292, Accuracy: 69.53\n",
      "Train Epoch: 6 [39680/50000 (88%)]\tLoss: 0.821917, Accuracy: 69.92\n",
      "Train Epoch: 6 [40960/50000 (91%)]\tLoss: 0.670237, Accuracy: 77.34\n",
      "Train Epoch: 6 [42240/50000 (94%)]\tLoss: 0.619673, Accuracy: 78.52\n",
      "Train Epoch: 6 [43520/50000 (97%)]\tLoss: 0.682881, Accuracy: 75.39\n",
      "Train Epoch: 6 [35000/50000 (99%)]\tLoss: 0.753036, Accuracy: 73.50\n",
      "\n",
      "Validation set: Average loss: 0.7980, Accuracy: 3634/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[36.414783000946045 s]\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.718116, Accuracy: 74.61\n",
      "Train Epoch: 7 [1280/50000 (3%)]\tLoss: 0.528816, Accuracy: 83.98\n",
      "Train Epoch: 7 [2560/50000 (6%)]\tLoss: 0.784122, Accuracy: 72.27\n",
      "Train Epoch: 7 [3840/50000 (9%)]\tLoss: 0.703087, Accuracy: 75.39\n",
      "Train Epoch: 7 [5120/50000 (11%)]\tLoss: 0.664554, Accuracy: 77.73\n",
      "Train Epoch: 7 [6400/50000 (14%)]\tLoss: 0.684798, Accuracy: 78.52\n",
      "Train Epoch: 7 [7680/50000 (17%)]\tLoss: 0.648022, Accuracy: 78.12\n",
      "Train Epoch: 7 [8960/50000 (20%)]\tLoss: 0.607531, Accuracy: 77.73\n",
      "Train Epoch: 7 [10240/50000 (23%)]\tLoss: 0.741639, Accuracy: 76.56\n",
      "Train Epoch: 7 [11520/50000 (26%)]\tLoss: 0.653138, Accuracy: 78.52\n",
      "Train Epoch: 7 [12800/50000 (28%)]\tLoss: 0.686353, Accuracy: 75.39\n",
      "Train Epoch: 7 [14080/50000 (31%)]\tLoss: 0.744535, Accuracy: 75.78\n",
      "Train Epoch: 7 [15360/50000 (34%)]\tLoss: 0.705109, Accuracy: 75.78\n",
      "Train Epoch: 7 [16640/50000 (37%)]\tLoss: 0.785032, Accuracy: 74.22\n",
      "Train Epoch: 7 [17920/50000 (40%)]\tLoss: 0.657491, Accuracy: 76.17\n",
      "Train Epoch: 7 [19200/50000 (43%)]\tLoss: 0.638037, Accuracy: 76.95\n",
      "Train Epoch: 7 [20480/50000 (45%)]\tLoss: 0.587275, Accuracy: 78.52\n",
      "Train Epoch: 7 [21760/50000 (48%)]\tLoss: 0.722512, Accuracy: 74.61\n",
      "Train Epoch: 7 [23040/50000 (51%)]\tLoss: 0.622222, Accuracy: 78.52\n",
      "Train Epoch: 7 [24320/50000 (54%)]\tLoss: 0.651572, Accuracy: 77.73\n",
      "Train Epoch: 7 [25600/50000 (57%)]\tLoss: 0.625407, Accuracy: 80.08\n",
      "Train Epoch: 7 [26880/50000 (60%)]\tLoss: 0.714878, Accuracy: 73.83\n",
      "Train Epoch: 7 [28160/50000 (62%)]\tLoss: 0.629370, Accuracy: 75.39\n",
      "Train Epoch: 7 [29440/50000 (65%)]\tLoss: 0.726652, Accuracy: 74.22\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 7 [30720/50000 (68%)]\tLoss: 0.639015, Accuracy: 76.95\n",
      "Train Epoch: 7 [32000/50000 (71%)]\tLoss: 0.677657, Accuracy: 76.17\n",
      "Train Epoch: 7 [33280/50000 (74%)]\tLoss: 0.569188, Accuracy: 81.64\n",
      "Train Epoch: 7 [34560/50000 (77%)]\tLoss: 0.553536, Accuracy: 82.81\n",
      "Train Epoch: 7 [35840/50000 (80%)]\tLoss: 0.706970, Accuracy: 77.34\n",
      "Train Epoch: 7 [37120/50000 (82%)]\tLoss: 0.696520, Accuracy: 75.78\n",
      "Train Epoch: 7 [38400/50000 (85%)]\tLoss: 0.803331, Accuracy: 71.48\n",
      "Train Epoch: 7 [39680/50000 (88%)]\tLoss: 0.653795, Accuracy: 76.95\n",
      "Train Epoch: 7 [40960/50000 (91%)]\tLoss: 0.721933, Accuracy: 73.05\n",
      "Train Epoch: 7 [42240/50000 (94%)]\tLoss: 0.660897, Accuracy: 76.95\n",
      "Train Epoch: 7 [43520/50000 (97%)]\tLoss: 0.741886, Accuracy: 76.17\n",
      "Train Epoch: 7 [35000/50000 (99%)]\tLoss: 0.722037, Accuracy: 74.50\n",
      "\n",
      "Validation set: Average loss: 1.4089, Accuracy: 2779/5000 (55.00%)\n",
      "\n",
      "the time of this epoch:[36.5019428730011 s]\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 0.575518, Accuracy: 82.03\n",
      "Train Epoch: 8 [1280/50000 (3%)]\tLoss: 0.644963, Accuracy: 77.34\n",
      "Train Epoch: 8 [2560/50000 (6%)]\tLoss: 0.640971, Accuracy: 77.73\n",
      "Train Epoch: 8 [3840/50000 (9%)]\tLoss: 0.665230, Accuracy: 77.34\n",
      "Train Epoch: 8 [5120/50000 (11%)]\tLoss: 0.631417, Accuracy: 78.91\n",
      "Train Epoch: 8 [6400/50000 (14%)]\tLoss: 0.555280, Accuracy: 81.25\n",
      "Train Epoch: 8 [7680/50000 (17%)]\tLoss: 0.704594, Accuracy: 76.17\n",
      "Train Epoch: 8 [8960/50000 (20%)]\tLoss: 0.561966, Accuracy: 78.91\n",
      "Train Epoch: 8 [10240/50000 (23%)]\tLoss: 0.648513, Accuracy: 76.17\n",
      "Train Epoch: 8 [11520/50000 (26%)]\tLoss: 0.621987, Accuracy: 79.30\n",
      "Train Epoch: 8 [12800/50000 (28%)]\tLoss: 0.608789, Accuracy: 78.91\n",
      "Train Epoch: 8 [14080/50000 (31%)]\tLoss: 0.651040, Accuracy: 79.69\n",
      "Train Epoch: 8 [15360/50000 (34%)]\tLoss: 0.538582, Accuracy: 81.64\n",
      "Train Epoch: 8 [16640/50000 (37%)]\tLoss: 0.660426, Accuracy: 78.91\n",
      "Train Epoch: 8 [17920/50000 (40%)]\tLoss: 0.608398, Accuracy: 79.30\n",
      "Train Epoch: 8 [19200/50000 (43%)]\tLoss: 0.641746, Accuracy: 79.30\n",
      "Train Epoch: 8 [20480/50000 (45%)]\tLoss: 0.646901, Accuracy: 77.73\n",
      "Train Epoch: 8 [21760/50000 (48%)]\tLoss: 0.545630, Accuracy: 80.47\n",
      "Train Epoch: 8 [23040/50000 (51%)]\tLoss: 0.647823, Accuracy: 77.34\n",
      "Train Epoch: 8 [24320/50000 (54%)]\tLoss: 0.617073, Accuracy: 78.12\n",
      "Train Epoch: 8 [25600/50000 (57%)]\tLoss: 0.721150, Accuracy: 77.34\n",
      "Train Epoch: 8 [26880/50000 (60%)]\tLoss: 0.587629, Accuracy: 79.30\n",
      "Train Epoch: 8 [28160/50000 (62%)]\tLoss: 0.768980, Accuracy: 71.88\n",
      "Train Epoch: 8 [29440/50000 (65%)]\tLoss: 0.718186, Accuracy: 73.44\n",
      "Train Epoch: 8 [30720/50000 (68%)]\tLoss: 0.636883, Accuracy: 76.56\n",
      "Train Epoch: 8 [32000/50000 (71%)]\tLoss: 0.662453, Accuracy: 76.95\n",
      "Train Epoch: 8 [33280/50000 (74%)]\tLoss: 0.615551, Accuracy: 79.69\n",
      "Train Epoch: 8 [34560/50000 (77%)]\tLoss: 0.611573, Accuracy: 79.69\n",
      "Train Epoch: 8 [35840/50000 (80%)]\tLoss: 0.514120, Accuracy: 83.59\n",
      "Train Epoch: 8 [37120/50000 (82%)]\tLoss: 0.557967, Accuracy: 79.69\n",
      "Train Epoch: 8 [38400/50000 (85%)]\tLoss: 0.634447, Accuracy: 78.52\n",
      "Train Epoch: 8 [39680/50000 (88%)]\tLoss: 0.576893, Accuracy: 76.17\n",
      "Train Epoch: 8 [40960/50000 (91%)]\tLoss: 0.692947, Accuracy: 79.30\n",
      "torch.Size([512, 1, 1])\n",
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      "\n",
      "        [[ 1.7400]],\n",
      "\n",
      "        [[-0.3884]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 6.0071]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 0.0672,  0.0049, -0.0689,  ...,  0.0603,  0.0374, -0.1112]],\n",
      "\n",
      "        [[ 0.0509,  0.1095,  0.0327,  ...,  0.0288,  0.0444,  0.0870]],\n",
      "\n",
      "        [[-0.1005,  0.1073, -0.0507,  ...,  0.0456, -0.0989,  0.0435]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.0133,  0.0338, -0.0786,  ...,  0.0653,  0.0140,  0.0883]],\n",
      "\n",
      "        [[-0.0033,  0.0714,  0.0082,  ...,  0.1077, -0.1158,  0.1022]],\n",
      "\n",
      "        [[ 0.0088,  0.0469, -0.0257,  ..., -0.0040,  0.1182, -0.0888]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[-4.7131e-03, -8.6692e-03, -1.0835e-02,  ..., -1.4482e-02,\n",
      "          -1.3317e-02, -1.7194e-02],\n",
      "         [ 1.1999e-02,  1.3425e-02,  1.0547e-02,  ...,  1.5702e-02,\n",
      "           1.1177e-02,  8.4036e-03],\n",
      "         [ 1.0269e-02,  1.1054e-02,  8.4249e-03,  ...,  9.0463e-03,\n",
      "           1.0920e-02,  1.5310e-02],\n",
      "         ...,\n",
      "         [ 4.8695e-03,  4.7638e-03,  3.5153e-03,  ..., -2.9620e-03,\n",
      "          -5.3921e-03, -4.0499e-03],\n",
      "         [ 1.5507e-03,  1.7888e-03, -3.5315e-03,  ...,  3.0342e-04,\n",
      "          -3.5661e-03, -2.9228e-03],\n",
      "         [ 1.8912e-03,  1.9353e-03,  3.7286e-03,  ...,  7.7447e-03,\n",
      "           3.1670e-03,  5.9269e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 8 [42240/50000 (94%)]\tLoss: 0.547898, Accuracy: 81.25\n",
      "Train Epoch: 8 [43520/50000 (97%)]\tLoss: 0.619870, Accuracy: 77.34\n",
      "Train Epoch: 8 [35000/50000 (99%)]\tLoss: 0.675455, Accuracy: 79.00\n",
      "\n",
      "Validation set: Average loss: 0.6958, Accuracy: 3786/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[36.54272413253784 s]\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 0.531935, Accuracy: 83.98\n",
      "Train Epoch: 9 [1280/50000 (3%)]\tLoss: 0.618826, Accuracy: 77.34\n",
      "Train Epoch: 9 [2560/50000 (6%)]\tLoss: 0.601243, Accuracy: 78.52\n",
      "Train Epoch: 9 [3840/50000 (9%)]\tLoss: 0.629306, Accuracy: 80.08\n",
      "Train Epoch: 9 [5120/50000 (11%)]\tLoss: 0.582955, Accuracy: 80.86\n",
      "Train Epoch: 9 [6400/50000 (14%)]\tLoss: 0.597590, Accuracy: 78.52\n",
      "Train Epoch: 9 [7680/50000 (17%)]\tLoss: 0.557599, Accuracy: 80.86\n",
      "Train Epoch: 9 [8960/50000 (20%)]\tLoss: 0.620252, Accuracy: 78.52\n",
      "Train Epoch: 9 [10240/50000 (23%)]\tLoss: 0.590183, Accuracy: 81.64\n",
      "Train Epoch: 9 [11520/50000 (26%)]\tLoss: 0.600226, Accuracy: 78.12\n",
      "Train Epoch: 9 [12800/50000 (28%)]\tLoss: 0.654503, Accuracy: 78.12\n",
      "Train Epoch: 9 [14080/50000 (31%)]\tLoss: 0.563117, Accuracy: 82.81\n",
      "Train Epoch: 9 [15360/50000 (34%)]\tLoss: 0.597145, Accuracy: 80.47\n",
      "Train Epoch: 9 [16640/50000 (37%)]\tLoss: 0.668243, Accuracy: 75.78\n",
      "Train Epoch: 9 [17920/50000 (40%)]\tLoss: 0.613555, Accuracy: 78.52\n",
      "Train Epoch: 9 [19200/50000 (43%)]\tLoss: 0.682083, Accuracy: 76.56\n",
      "Train Epoch: 9 [20480/50000 (45%)]\tLoss: 0.589346, Accuracy: 78.52\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.4542]],\n",
      "\n",
      "        [[-1.7679]],\n",
      "\n",
      "        [[-0.2504]],\n",
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      "        [[-0.8119]],\n",
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      "        [[-1.5505]],\n",
      "\n",
      "        [[-1.5842]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-04 *\n",
      "       [[[ 7.6308]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-0.1118,  0.0493, -0.0186,  ...,  0.0329, -0.0715, -0.0645]],\n",
      "\n",
      "        [[ 0.0174, -0.0729, -0.0635,  ...,  0.0643,  0.0078, -0.0440]],\n",
      "\n",
      "        [[-0.0811, -0.0464, -0.0459,  ..., -0.0361, -0.0030, -0.0144]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.0652,  0.0063,  0.0368,  ...,  0.0330, -0.0531, -0.0216]],\n",
      "\n",
      "        [[-0.0566,  0.0162, -0.0720,  ...,  0.0433,  0.1013,  0.0518]],\n",
      "\n",
      "        [[-0.0273,  0.0899,  0.0330,  ...,  0.0908,  0.0467,  0.0117]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.2501, -0.6742, -0.7953,  ..., -1.0601, -0.5483, -0.9120],\n",
      "         [-0.9212, -0.5930, -0.1372,  ..., -0.7366, -0.5814, -0.5039],\n",
      "         [ 1.0311,  1.0608,  0.6299,  ..., -0.1754, -0.4692, -0.7740],\n",
      "         ...,\n",
      "         [-1.1461, -0.9999, -0.9993,  ..., -0.5460, -0.2512, -0.1505],\n",
      "         [-0.7577, -1.0148, -0.9897,  ..., -2.4714, -1.9381, -1.3957],\n",
      "         [ 1.5307,  1.7531,  2.2261,  ...,  1.6461,  1.6705,  1.6144]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [21760/50000 (48%)]\tLoss: 0.586371, Accuracy: 81.64\n",
      "Train Epoch: 9 [23040/50000 (51%)]\tLoss: 0.669941, Accuracy: 77.73\n",
      "Train Epoch: 9 [24320/50000 (54%)]\tLoss: 0.525115, Accuracy: 82.81\n",
      "Train Epoch: 9 [25600/50000 (57%)]\tLoss: 0.543337, Accuracy: 80.08\n",
      "Train Epoch: 9 [26880/50000 (60%)]\tLoss: 0.523830, Accuracy: 81.64\n",
      "Train Epoch: 9 [28160/50000 (62%)]\tLoss: 0.663104, Accuracy: 78.52\n",
      "Train Epoch: 9 [29440/50000 (65%)]\tLoss: 0.607716, Accuracy: 80.47\n",
      "Train Epoch: 9 [30720/50000 (68%)]\tLoss: 0.578304, Accuracy: 81.25\n",
      "Train Epoch: 9 [32000/50000 (71%)]\tLoss: 0.576207, Accuracy: 80.86\n",
      "Train Epoch: 9 [33280/50000 (74%)]\tLoss: 0.671957, Accuracy: 78.91\n",
      "Train Epoch: 9 [34560/50000 (77%)]\tLoss: 0.669817, Accuracy: 78.91\n",
      "Train Epoch: 9 [35840/50000 (80%)]\tLoss: 0.515632, Accuracy: 84.77\n",
      "Train Epoch: 9 [37120/50000 (82%)]\tLoss: 0.581581, Accuracy: 79.30\n",
      "Train Epoch: 9 [38400/50000 (85%)]\tLoss: 0.511427, Accuracy: 82.42\n",
      "Train Epoch: 9 [39680/50000 (88%)]\tLoss: 0.554914, Accuracy: 81.64\n",
      "Train Epoch: 9 [40960/50000 (91%)]\tLoss: 0.576586, Accuracy: 80.86\n",
      "Train Epoch: 9 [42240/50000 (94%)]\tLoss: 0.563586, Accuracy: 81.64\n",
      "Train Epoch: 9 [43520/50000 (97%)]\tLoss: 0.616608, Accuracy: 78.52\n",
      "Train Epoch: 9 [35000/50000 (99%)]\tLoss: 0.735986, Accuracy: 78.50\n",
      "\n",
      "Validation set: Average loss: 0.8650, Accuracy: 3585/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[36.52668118476868 s]\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 0.573561, Accuracy: 80.86\n",
      "Train Epoch: 10 [1280/50000 (3%)]\tLoss: 0.608525, Accuracy: 77.73\n",
      "Train Epoch: 10 [2560/50000 (6%)]\tLoss: 0.587879, Accuracy: 82.03\n",
      "Train Epoch: 10 [3840/50000 (9%)]\tLoss: 0.543246, Accuracy: 83.98\n",
      "Train Epoch: 10 [5120/50000 (11%)]\tLoss: 0.524736, Accuracy: 85.94\n",
      "Train Epoch: 10 [6400/50000 (14%)]\tLoss: 0.570858, Accuracy: 81.25\n",
      "Train Epoch: 10 [7680/50000 (17%)]\tLoss: 0.662948, Accuracy: 76.95\n",
      "Train Epoch: 10 [8960/50000 (20%)]\tLoss: 0.580467, Accuracy: 78.52\n",
      "Train Epoch: 10 [10240/50000 (23%)]\tLoss: 0.435362, Accuracy: 85.55\n",
      "Train Epoch: 10 [11520/50000 (26%)]\tLoss: 0.724119, Accuracy: 74.22\n",
      "Train Epoch: 10 [12800/50000 (28%)]\tLoss: 0.631251, Accuracy: 78.52\n",
      "Train Epoch: 10 [14080/50000 (31%)]\tLoss: 0.612453, Accuracy: 77.73\n",
      "Train Epoch: 10 [15360/50000 (34%)]\tLoss: 0.569365, Accuracy: 81.25\n",
      "Train Epoch: 10 [16640/50000 (37%)]\tLoss: 0.504909, Accuracy: 84.38\n",
      "Train Epoch: 10 [17920/50000 (40%)]\tLoss: 0.679098, Accuracy: 75.39\n",
      "Train Epoch: 10 [19200/50000 (43%)]\tLoss: 0.531360, Accuracy: 84.38\n",
      "Train Epoch: 10 [20480/50000 (45%)]\tLoss: 0.514333, Accuracy: 84.77\n",
      "Train Epoch: 10 [21760/50000 (48%)]\tLoss: 0.535185, Accuracy: 83.98\n",
      "Train Epoch: 10 [23040/50000 (51%)]\tLoss: 0.665344, Accuracy: 78.91\n",
      "Train Epoch: 10 [24320/50000 (54%)]\tLoss: 0.584987, Accuracy: 80.47\n",
      "Train Epoch: 10 [25600/50000 (57%)]\tLoss: 0.563270, Accuracy: 80.08\n",
      "Train Epoch: 10 [26880/50000 (60%)]\tLoss: 0.477416, Accuracy: 85.94\n",
      "Train Epoch: 10 [28160/50000 (62%)]\tLoss: 0.534948, Accuracy: 82.42\n",
      "Train Epoch: 10 [29440/50000 (65%)]\tLoss: 0.707296, Accuracy: 74.22\n",
      "Train Epoch: 10 [30720/50000 (68%)]\tLoss: 0.625941, Accuracy: 82.03\n",
      "Train Epoch: 10 [32000/50000 (71%)]\tLoss: 0.581371, Accuracy: 80.86\n",
      "Train Epoch: 10 [33280/50000 (74%)]\tLoss: 0.490416, Accuracy: 83.98\n",
      "Train Epoch: 10 [34560/50000 (77%)]\tLoss: 0.561442, Accuracy: 81.64\n",
      "Train Epoch: 10 [35840/50000 (80%)]\tLoss: 0.424732, Accuracy: 84.77\n",
      "Train Epoch: 10 [37120/50000 (82%)]\tLoss: 0.588896, Accuracy: 80.47\n",
      "Train Epoch: 10 [38400/50000 (85%)]\tLoss: 0.469820, Accuracy: 83.98\n",
      "Train Epoch: 10 [39680/50000 (88%)]\tLoss: 0.579184, Accuracy: 79.30\n",
      "Train Epoch: 10 [40960/50000 (91%)]\tLoss: 0.472317, Accuracy: 83.98\n",
      "Train Epoch: 10 [42240/50000 (94%)]\tLoss: 0.527359, Accuracy: 79.69\n",
      "Train Epoch: 10 [43520/50000 (97%)]\tLoss: 0.549139, Accuracy: 79.30\n",
      "Train Epoch: 10 [35000/50000 (99%)]\tLoss: 0.485057, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6442, Accuracy: 3894/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.47461271286011 s]\n",
      "\n",
      "Test set: Average loss: 0.6906, Accuracy: 7694/10000 (76.94%)\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 0.431329, Accuracy: 85.94\n",
      "Train Epoch: 11 [1280/50000 (3%)]\tLoss: 0.557633, Accuracy: 81.64\n",
      "Train Epoch: 11 [2560/50000 (6%)]\tLoss: 0.536044, Accuracy: 82.42\n",
      "Train Epoch: 11 [3840/50000 (9%)]\tLoss: 0.500477, Accuracy: 84.38\n",
      "Train Epoch: 11 [5120/50000 (11%)]\tLoss: 0.443349, Accuracy: 87.50\n",
      "Train Epoch: 11 [6400/50000 (14%)]\tLoss: 0.456070, Accuracy: 85.55\n",
      "Train Epoch: 11 [7680/50000 (17%)]\tLoss: 0.551333, Accuracy: 83.59\n",
      "Train Epoch: 11 [8960/50000 (20%)]\tLoss: 0.541531, Accuracy: 83.20\n",
      "Train Epoch: 11 [10240/50000 (23%)]\tLoss: 0.626885, Accuracy: 79.69\n",
      "Train Epoch: 11 [11520/50000 (26%)]\tLoss: 0.556620, Accuracy: 82.03\n",
      "Train Epoch: 11 [12800/50000 (28%)]\tLoss: 0.647727, Accuracy: 77.73\n",
      "Train Epoch: 11 [14080/50000 (31%)]\tLoss: 0.522078, Accuracy: 83.59\n",
      "Train Epoch: 11 [15360/50000 (34%)]\tLoss: 0.485263, Accuracy: 82.42\n",
      "Train Epoch: 11 [16640/50000 (37%)]\tLoss: 0.623503, Accuracy: 78.52\n",
      "Train Epoch: 11 [17920/50000 (40%)]\tLoss: 0.556926, Accuracy: 81.25\n",
      "Train Epoch: 11 [19200/50000 (43%)]\tLoss: 0.609301, Accuracy: 79.30\n",
      "Train Epoch: 11 [20480/50000 (45%)]\tLoss: 0.485296, Accuracy: 82.81\n",
      "Train Epoch: 11 [21760/50000 (48%)]\tLoss: 0.564891, Accuracy: 79.69\n",
      "Train Epoch: 11 [23040/50000 (51%)]\tLoss: 0.550366, Accuracy: 81.25\n",
      "Train Epoch: 11 [24320/50000 (54%)]\tLoss: 0.416920, Accuracy: 85.16\n",
      "Train Epoch: 11 [25600/50000 (57%)]\tLoss: 0.605515, Accuracy: 78.91\n",
      "Train Epoch: 11 [26880/50000 (60%)]\tLoss: 0.577019, Accuracy: 78.52\n",
      "Train Epoch: 11 [28160/50000 (62%)]\tLoss: 0.605049, Accuracy: 78.91\n",
      "Train Epoch: 11 [29440/50000 (65%)]\tLoss: 0.569522, Accuracy: 80.47\n",
      "Train Epoch: 11 [30720/50000 (68%)]\tLoss: 0.546811, Accuracy: 79.69\n",
      "Train Epoch: 11 [32000/50000 (71%)]\tLoss: 0.658371, Accuracy: 79.30\n",
      "Train Epoch: 11 [33280/50000 (74%)]\tLoss: 0.489746, Accuracy: 82.03\n",
      "Train Epoch: 11 [34560/50000 (77%)]\tLoss: 0.505787, Accuracy: 82.42\n",
      "Train Epoch: 11 [35840/50000 (80%)]\tLoss: 0.600886, Accuracy: 80.47\n",
      "Train Epoch: 11 [37120/50000 (82%)]\tLoss: 0.558366, Accuracy: 81.64\n",
      "Train Epoch: 11 [38400/50000 (85%)]\tLoss: 0.492877, Accuracy: 82.42\n",
      "Train Epoch: 11 [39680/50000 (88%)]\tLoss: 0.493212, Accuracy: 84.38\n",
      "Train Epoch: 11 [40960/50000 (91%)]\tLoss: 0.539614, Accuracy: 82.42\n",
      "Train Epoch: 11 [42240/50000 (94%)]\tLoss: 0.463464, Accuracy: 84.77\n",
      "Train Epoch: 11 [43520/50000 (97%)]\tLoss: 0.608436, Accuracy: 79.30\n",
      "Train Epoch: 11 [35000/50000 (99%)]\tLoss: 0.550098, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 0.7297, Accuracy: 3809/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.56096625328064 s]\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 0.486477, Accuracy: 85.16\n",
      "Train Epoch: 12 [1280/50000 (3%)]\tLoss: 0.610245, Accuracy: 78.91\n",
      "Train Epoch: 12 [2560/50000 (6%)]\tLoss: 0.559639, Accuracy: 80.08\n",
      "Train Epoch: 12 [3840/50000 (9%)]\tLoss: 0.492913, Accuracy: 86.72\n",
      "Train Epoch: 12 [5120/50000 (11%)]\tLoss: 0.506034, Accuracy: 82.42\n",
      "Train Epoch: 12 [6400/50000 (14%)]\tLoss: 0.450304, Accuracy: 84.77\n",
      "Train Epoch: 12 [7680/50000 (17%)]\tLoss: 0.589792, Accuracy: 79.30\n",
      "Train Epoch: 12 [8960/50000 (20%)]\tLoss: 0.545175, Accuracy: 81.64\n",
      "Train Epoch: 12 [10240/50000 (23%)]\tLoss: 0.519418, Accuracy: 82.81\n",
      "Train Epoch: 12 [11520/50000 (26%)]\tLoss: 0.472131, Accuracy: 81.64\n",
      "Train Epoch: 12 [12800/50000 (28%)]\tLoss: 0.557771, Accuracy: 78.12\n",
      "Train Epoch: 12 [14080/50000 (31%)]\tLoss: 0.491214, Accuracy: 81.64\n",
      "Train Epoch: 12 [15360/50000 (34%)]\tLoss: 0.541253, Accuracy: 80.86\n",
      "Train Epoch: 12 [16640/50000 (37%)]\tLoss: 0.647587, Accuracy: 76.17\n",
      "Train Epoch: 12 [17920/50000 (40%)]\tLoss: 0.497093, Accuracy: 82.42\n",
      "Train Epoch: 12 [19200/50000 (43%)]\tLoss: 0.474728, Accuracy: 82.03\n",
      "Train Epoch: 12 [20480/50000 (45%)]\tLoss: 0.509987, Accuracy: 82.42\n",
      "Train Epoch: 12 [21760/50000 (48%)]\tLoss: 0.615998, Accuracy: 78.12\n",
      "Train Epoch: 12 [23040/50000 (51%)]\tLoss: 0.449647, Accuracy: 85.55\n",
      "Train Epoch: 12 [24320/50000 (54%)]\tLoss: 0.485397, Accuracy: 83.59\n",
      "Train Epoch: 12 [25600/50000 (57%)]\tLoss: 0.540370, Accuracy: 83.98\n",
      "Train Epoch: 12 [26880/50000 (60%)]\tLoss: 0.569498, Accuracy: 82.03\n",
      "Train Epoch: 12 [28160/50000 (62%)]\tLoss: 0.579579, Accuracy: 80.86\n",
      "Train Epoch: 12 [29440/50000 (65%)]\tLoss: 0.487559, Accuracy: 83.98\n",
      "Train Epoch: 12 [30720/50000 (68%)]\tLoss: 0.563693, Accuracy: 80.47\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 12 [32000/50000 (71%)]\tLoss: 0.559628, Accuracy: 83.20\n",
      "Train Epoch: 12 [33280/50000 (74%)]\tLoss: 0.574699, Accuracy: 79.30\n",
      "Train Epoch: 12 [34560/50000 (77%)]\tLoss: 0.466497, Accuracy: 84.77\n",
      "Train Epoch: 12 [35840/50000 (80%)]\tLoss: 0.526085, Accuracy: 81.64\n",
      "Train Epoch: 12 [37120/50000 (82%)]\tLoss: 0.504718, Accuracy: 82.81\n",
      "Train Epoch: 12 [38400/50000 (85%)]\tLoss: 0.580093, Accuracy: 79.69\n",
      "Train Epoch: 12 [39680/50000 (88%)]\tLoss: 0.585701, Accuracy: 79.30\n",
      "Train Epoch: 12 [40960/50000 (91%)]\tLoss: 0.512196, Accuracy: 81.25\n",
      "Train Epoch: 12 [42240/50000 (94%)]\tLoss: 0.573764, Accuracy: 80.47\n",
      "Train Epoch: 12 [43520/50000 (97%)]\tLoss: 0.451273, Accuracy: 85.16\n",
      "Train Epoch: 12 [35000/50000 (99%)]\tLoss: 0.589612, Accuracy: 80.50\n",
      "\n",
      "Validation set: Average loss: 0.7578, Accuracy: 3800/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[36.57694673538208 s]\n",
      "\n",
      "Test set: Average loss: 0.7859, Accuracy: 7497/10000 (74.97%)\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 0.465281, Accuracy: 83.59\n",
      "Train Epoch: 13 [1280/50000 (3%)]\tLoss: 0.453615, Accuracy: 84.38\n",
      "Train Epoch: 13 [2560/50000 (6%)]\tLoss: 0.551280, Accuracy: 80.47\n",
      "Train Epoch: 13 [3840/50000 (9%)]\tLoss: 0.551159, Accuracy: 81.25\n",
      "Train Epoch: 13 [5120/50000 (11%)]\tLoss: 0.427013, Accuracy: 87.89\n",
      "Train Epoch: 13 [6400/50000 (14%)]\tLoss: 0.528057, Accuracy: 83.59\n",
      "Train Epoch: 13 [7680/50000 (17%)]\tLoss: 0.463377, Accuracy: 83.98\n",
      "Train Epoch: 13 [8960/50000 (20%)]\tLoss: 0.580337, Accuracy: 81.25\n",
      "Train Epoch: 13 [10240/50000 (23%)]\tLoss: 0.443915, Accuracy: 85.16\n",
      "Train Epoch: 13 [11520/50000 (26%)]\tLoss: 0.484566, Accuracy: 83.59\n",
      "Train Epoch: 13 [12800/50000 (28%)]\tLoss: 0.613482, Accuracy: 80.08\n",
      "Train Epoch: 13 [14080/50000 (31%)]\tLoss: 0.543818, Accuracy: 81.64\n",
      "Train Epoch: 13 [15360/50000 (34%)]\tLoss: 0.501068, Accuracy: 85.55\n",
      "Train Epoch: 13 [16640/50000 (37%)]\tLoss: 0.426048, Accuracy: 86.72\n",
      "Train Epoch: 13 [17920/50000 (40%)]\tLoss: 0.500071, Accuracy: 83.98\n",
      "Train Epoch: 13 [19200/50000 (43%)]\tLoss: 0.525848, Accuracy: 82.03\n",
      "Train Epoch: 13 [20480/50000 (45%)]\tLoss: 0.472248, Accuracy: 83.20\n",
      "Train Epoch: 13 [21760/50000 (48%)]\tLoss: 0.399590, Accuracy: 86.72\n",
      "Train Epoch: 13 [23040/50000 (51%)]\tLoss: 0.422922, Accuracy: 87.50\n",
      "Train Epoch: 13 [24320/50000 (54%)]\tLoss: 0.477812, Accuracy: 85.16\n",
      "Train Epoch: 13 [25600/50000 (57%)]\tLoss: 0.641060, Accuracy: 80.08\n",
      "Train Epoch: 13 [26880/50000 (60%)]\tLoss: 0.505814, Accuracy: 81.64\n",
      "Train Epoch: 13 [28160/50000 (62%)]\tLoss: 0.422107, Accuracy: 85.16\n",
      "Train Epoch: 13 [29440/50000 (65%)]\tLoss: 0.395267, Accuracy: 85.94\n",
      "Train Epoch: 13 [30720/50000 (68%)]\tLoss: 0.479683, Accuracy: 83.59\n",
      "Train Epoch: 13 [32000/50000 (71%)]\tLoss: 0.439324, Accuracy: 84.38\n",
      "Train Epoch: 13 [33280/50000 (74%)]\tLoss: 0.454729, Accuracy: 84.38\n",
      "Train Epoch: 13 [34560/50000 (77%)]\tLoss: 0.470126, Accuracy: 82.03\n",
      "Train Epoch: 13 [35840/50000 (80%)]\tLoss: 0.380595, Accuracy: 87.11\n",
      "Train Epoch: 13 [37120/50000 (82%)]\tLoss: 0.489156, Accuracy: 84.38\n",
      "Train Epoch: 13 [38400/50000 (85%)]\tLoss: 0.542828, Accuracy: 81.25\n",
      "Train Epoch: 13 [39680/50000 (88%)]\tLoss: 0.536491, Accuracy: 83.59\n",
      "Train Epoch: 13 [40960/50000 (91%)]\tLoss: 0.470659, Accuracy: 83.98\n",
      "Train Epoch: 13 [42240/50000 (94%)]\tLoss: 0.487635, Accuracy: 86.33\n",
      "Train Epoch: 13 [43520/50000 (97%)]\tLoss: 0.542781, Accuracy: 82.42\n",
      "Train Epoch: 13 [35000/50000 (99%)]\tLoss: 0.791674, Accuracy: 74.50\n",
      "\n",
      "Validation set: Average loss: 0.9428, Accuracy: 3481/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[39.60190391540527 s]\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 0.586802, Accuracy: 78.52\n",
      "Train Epoch: 14 [1280/50000 (3%)]\tLoss: 0.483455, Accuracy: 83.20\n",
      "Train Epoch: 14 [2560/50000 (6%)]\tLoss: 0.529971, Accuracy: 82.81\n",
      "Train Epoch: 14 [3840/50000 (9%)]\tLoss: 0.500932, Accuracy: 80.86\n",
      "Train Epoch: 14 [5120/50000 (11%)]\tLoss: 0.348552, Accuracy: 88.67\n",
      "Train Epoch: 14 [6400/50000 (14%)]\tLoss: 0.651626, Accuracy: 78.91\n",
      "Train Epoch: 14 [7680/50000 (17%)]\tLoss: 0.432727, Accuracy: 87.89\n",
      "Train Epoch: 14 [8960/50000 (20%)]\tLoss: 0.568602, Accuracy: 79.69\n",
      "Train Epoch: 14 [10240/50000 (23%)]\tLoss: 0.593954, Accuracy: 78.12\n",
      "Train Epoch: 14 [11520/50000 (26%)]\tLoss: 0.378355, Accuracy: 84.38\n",
      "Train Epoch: 14 [12800/50000 (28%)]\tLoss: 0.531910, Accuracy: 80.08\n",
      "Train Epoch: 14 [14080/50000 (31%)]\tLoss: 0.404941, Accuracy: 85.94\n",
      "Train Epoch: 14 [15360/50000 (34%)]\tLoss: 0.590501, Accuracy: 78.52\n",
      "Train Epoch: 14 [16640/50000 (37%)]\tLoss: 0.564439, Accuracy: 82.81\n",
      "Train Epoch: 14 [17920/50000 (40%)]\tLoss: 0.551642, Accuracy: 82.03\n",
      "Train Epoch: 14 [19200/50000 (43%)]\tLoss: 0.507345, Accuracy: 82.81\n",
      "Train Epoch: 14 [20480/50000 (45%)]\tLoss: 0.357382, Accuracy: 88.67\n",
      "Train Epoch: 14 [21760/50000 (48%)]\tLoss: 0.498806, Accuracy: 82.81\n",
      "Train Epoch: 14 [23040/50000 (51%)]\tLoss: 0.412763, Accuracy: 86.72\n",
      "Train Epoch: 14 [24320/50000 (54%)]\tLoss: 0.602008, Accuracy: 83.59\n",
      "Train Epoch: 14 [25600/50000 (57%)]\tLoss: 0.444969, Accuracy: 82.42\n",
      "Train Epoch: 14 [26880/50000 (60%)]\tLoss: 0.452665, Accuracy: 84.38\n",
      "Train Epoch: 14 [28160/50000 (62%)]\tLoss: 0.576766, Accuracy: 80.47\n",
      "Train Epoch: 14 [29440/50000 (65%)]\tLoss: 0.369092, Accuracy: 87.89\n",
      "Train Epoch: 14 [30720/50000 (68%)]\tLoss: 0.538673, Accuracy: 82.03\n",
      "Train Epoch: 14 [32000/50000 (71%)]\tLoss: 0.507634, Accuracy: 83.59\n",
      "Train Epoch: 14 [33280/50000 (74%)]\tLoss: 0.442112, Accuracy: 83.98\n",
      "Train Epoch: 14 [34560/50000 (77%)]\tLoss: 0.483884, Accuracy: 83.20\n",
      "Train Epoch: 14 [35840/50000 (80%)]\tLoss: 0.485817, Accuracy: 81.64\n",
      "Train Epoch: 14 [37120/50000 (82%)]\tLoss: 0.478755, Accuracy: 83.20\n",
      "Train Epoch: 14 [38400/50000 (85%)]\tLoss: 0.471707, Accuracy: 84.38\n",
      "Train Epoch: 14 [39680/50000 (88%)]\tLoss: 0.360717, Accuracy: 88.67\n",
      "Train Epoch: 14 [40960/50000 (91%)]\tLoss: 0.458257, Accuracy: 81.64\n",
      "Train Epoch: 14 [42240/50000 (94%)]\tLoss: 0.502111, Accuracy: 81.64\n",
      "Train Epoch: 14 [43520/50000 (97%)]\tLoss: 0.483927, Accuracy: 83.20\n",
      "Train Epoch: 14 [35000/50000 (99%)]\tLoss: 0.463892, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.6407, Accuracy: 3894/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.50473165512085 s]\n",
      "\n",
      "Test set: Average loss: 0.6561, Accuracy: 7783/10000 (77.83%)\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 0.497037, Accuracy: 82.03\n",
      "Train Epoch: 15 [1280/50000 (3%)]\tLoss: 0.598615, Accuracy: 79.69\n",
      "Train Epoch: 15 [2560/50000 (6%)]\tLoss: 0.470638, Accuracy: 83.59\n",
      "Train Epoch: 15 [3840/50000 (9%)]\tLoss: 0.427574, Accuracy: 84.38\n",
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      "Train Epoch: 15 [8960/50000 (20%)]\tLoss: 0.494726, Accuracy: 83.98\n",
      "Train Epoch: 15 [10240/50000 (23%)]\tLoss: 0.380954, Accuracy: 85.94\n",
      "Train Epoch: 15 [11520/50000 (26%)]\tLoss: 0.509211, Accuracy: 82.42\n",
      "Train Epoch: 15 [12800/50000 (28%)]\tLoss: 0.577518, Accuracy: 78.91\n",
      "Train Epoch: 15 [14080/50000 (31%)]\tLoss: 0.505021, Accuracy: 80.47\n",
      "Train Epoch: 15 [15360/50000 (34%)]\tLoss: 0.462864, Accuracy: 85.55\n",
      "Train Epoch: 15 [16640/50000 (37%)]\tLoss: 0.446363, Accuracy: 85.16\n",
      "Train Epoch: 15 [17920/50000 (40%)]\tLoss: 0.482374, Accuracy: 83.59\n",
      "Train Epoch: 15 [19200/50000 (43%)]\tLoss: 0.508272, Accuracy: 83.59\n",
      "Train Epoch: 15 [20480/50000 (45%)]\tLoss: 0.585231, Accuracy: 78.52\n",
      "Train Epoch: 15 [21760/50000 (48%)]\tLoss: 0.497473, Accuracy: 83.98\n",
      "Train Epoch: 15 [23040/50000 (51%)]\tLoss: 0.537686, Accuracy: 83.59\n",
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      "Train Epoch: 15 [26880/50000 (60%)]\tLoss: 0.503742, Accuracy: 84.77\n",
      "Train Epoch: 15 [28160/50000 (62%)]\tLoss: 0.503521, Accuracy: 84.77\n",
      "Train Epoch: 15 [29440/50000 (65%)]\tLoss: 0.504643, Accuracy: 82.81\n",
      "Train Epoch: 15 [30720/50000 (68%)]\tLoss: 0.517580, Accuracy: 82.42\n",
      "Train Epoch: 15 [32000/50000 (71%)]\tLoss: 0.500791, Accuracy: 80.86\n",
      "Train Epoch: 15 [33280/50000 (74%)]\tLoss: 0.620750, Accuracy: 78.91\n",
      "Train Epoch: 15 [34560/50000 (77%)]\tLoss: 0.663023, Accuracy: 79.30\n",
      "Train Epoch: 15 [35840/50000 (80%)]\tLoss: 0.455909, Accuracy: 85.16\n",
      "Train Epoch: 15 [37120/50000 (82%)]\tLoss: 0.473608, Accuracy: 84.38\n",
      "Train Epoch: 15 [38400/50000 (85%)]\tLoss: 0.498031, Accuracy: 80.08\n",
      "Train Epoch: 15 [39680/50000 (88%)]\tLoss: 0.492724, Accuracy: 81.25\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 15 [40960/50000 (91%)]\tLoss: 0.457262, Accuracy: 83.98\n",
      "Train Epoch: 15 [42240/50000 (94%)]\tLoss: 0.531166, Accuracy: 82.81\n",
      "Train Epoch: 15 [43520/50000 (97%)]\tLoss: 0.469614, Accuracy: 86.72\n",
      "Train Epoch: 15 [35000/50000 (99%)]\tLoss: 0.432854, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6653, Accuracy: 3889/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[39.519659757614136 s]\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 0.498297, Accuracy: 82.42\n",
      "Train Epoch: 16 [1280/50000 (3%)]\tLoss: 0.446252, Accuracy: 84.77\n",
      "Train Epoch: 16 [2560/50000 (6%)]\tLoss: 0.399584, Accuracy: 87.11\n",
      "Train Epoch: 16 [3840/50000 (9%)]\tLoss: 0.436900, Accuracy: 85.94\n",
      "Train Epoch: 16 [5120/50000 (11%)]\tLoss: 0.464420, Accuracy: 85.55\n",
      "Train Epoch: 16 [6400/50000 (14%)]\tLoss: 0.409935, Accuracy: 84.38\n",
      "Train Epoch: 16 [7680/50000 (17%)]\tLoss: 0.421638, Accuracy: 84.77\n",
      "Train Epoch: 16 [8960/50000 (20%)]\tLoss: 0.473496, Accuracy: 85.16\n",
      "Train Epoch: 16 [10240/50000 (23%)]\tLoss: 0.527816, Accuracy: 81.25\n",
      "Train Epoch: 16 [11520/50000 (26%)]\tLoss: 0.627922, Accuracy: 75.39\n",
      "Train Epoch: 16 [12800/50000 (28%)]\tLoss: 0.444811, Accuracy: 85.55\n",
      "Train Epoch: 16 [14080/50000 (31%)]\tLoss: 0.400035, Accuracy: 87.11\n",
      "Train Epoch: 16 [15360/50000 (34%)]\tLoss: 0.540589, Accuracy: 81.64\n",
      "Train Epoch: 16 [16640/50000 (37%)]\tLoss: 0.566746, Accuracy: 81.25\n",
      "Train Epoch: 16 [17920/50000 (40%)]\tLoss: 0.541338, Accuracy: 82.42\n",
      "Train Epoch: 16 [19200/50000 (43%)]\tLoss: 0.528707, Accuracy: 82.81\n",
      "Train Epoch: 16 [20480/50000 (45%)]\tLoss: 0.535540, Accuracy: 80.47\n",
      "Train Epoch: 16 [21760/50000 (48%)]\tLoss: 0.459531, Accuracy: 84.77\n",
      "Train Epoch: 16 [23040/50000 (51%)]\tLoss: 0.516730, Accuracy: 81.25\n",
      "Train Epoch: 16 [24320/50000 (54%)]\tLoss: 0.326059, Accuracy: 88.28\n",
      "Train Epoch: 16 [25600/50000 (57%)]\tLoss: 0.502126, Accuracy: 82.42\n",
      "Train Epoch: 16 [26880/50000 (60%)]\tLoss: 0.633037, Accuracy: 79.30\n",
      "Train Epoch: 16 [28160/50000 (62%)]\tLoss: 0.584472, Accuracy: 80.86\n",
      "Train Epoch: 16 [29440/50000 (65%)]\tLoss: 0.495442, Accuracy: 83.20\n",
      "Train Epoch: 16 [30720/50000 (68%)]\tLoss: 0.452312, Accuracy: 82.81\n",
      "Train Epoch: 16 [32000/50000 (71%)]\tLoss: 0.480034, Accuracy: 83.20\n",
      "Train Epoch: 16 [33280/50000 (74%)]\tLoss: 0.471318, Accuracy: 83.98\n",
      "Train Epoch: 16 [34560/50000 (77%)]\tLoss: 0.371925, Accuracy: 89.45\n",
      "Train Epoch: 16 [35840/50000 (80%)]\tLoss: 0.445975, Accuracy: 85.55\n",
      "Train Epoch: 16 [37120/50000 (82%)]\tLoss: 0.471425, Accuracy: 82.42\n",
      "Train Epoch: 16 [38400/50000 (85%)]\tLoss: 0.511391, Accuracy: 81.25\n",
      "Train Epoch: 16 [39680/50000 (88%)]\tLoss: 0.486364, Accuracy: 82.03\n",
      "Train Epoch: 16 [40960/50000 (91%)]\tLoss: 0.354199, Accuracy: 89.06\n",
      "Train Epoch: 16 [42240/50000 (94%)]\tLoss: 0.569887, Accuracy: 80.47\n",
      "Train Epoch: 16 [43520/50000 (97%)]\tLoss: 0.489550, Accuracy: 82.42\n",
      "Train Epoch: 16 [35000/50000 (99%)]\tLoss: 0.469959, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.6814, Accuracy: 3866/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.49289345741272 s]\n",
      "\n",
      "Test set: Average loss: 0.6836, Accuracy: 7742/10000 (77.42%)\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 0.424264, Accuracy: 86.72\n",
      "Train Epoch: 17 [1280/50000 (3%)]\tLoss: 0.418339, Accuracy: 85.94\n",
      "Train Epoch: 17 [2560/50000 (6%)]\tLoss: 0.553986, Accuracy: 82.42\n",
      "Train Epoch: 17 [3840/50000 (9%)]\tLoss: 0.488367, Accuracy: 84.77\n",
      "Train Epoch: 17 [5120/50000 (11%)]\tLoss: 0.522799, Accuracy: 84.77\n",
      "Train Epoch: 17 [6400/50000 (14%)]\tLoss: 0.310344, Accuracy: 89.84\n",
      "Train Epoch: 17 [7680/50000 (17%)]\tLoss: 0.447963, Accuracy: 82.03\n",
      "Train Epoch: 17 [8960/50000 (20%)]\tLoss: 0.491018, Accuracy: 84.77\n",
      "Train Epoch: 17 [10240/50000 (23%)]\tLoss: 0.400815, Accuracy: 87.89\n",
      "Train Epoch: 17 [11520/50000 (26%)]\tLoss: 0.439572, Accuracy: 84.77\n",
      "Train Epoch: 17 [12800/50000 (28%)]\tLoss: 0.417061, Accuracy: 85.16\n",
      "Train Epoch: 17 [14080/50000 (31%)]\tLoss: 0.444977, Accuracy: 83.98\n",
      "Train Epoch: 17 [15360/50000 (34%)]\tLoss: 0.471827, Accuracy: 81.64\n",
      "Train Epoch: 17 [16640/50000 (37%)]\tLoss: 0.429586, Accuracy: 85.94\n",
      "Train Epoch: 17 [17920/50000 (40%)]\tLoss: 0.414211, Accuracy: 84.77\n",
      "Train Epoch: 17 [19200/50000 (43%)]\tLoss: 0.458261, Accuracy: 85.16\n",
      "Train Epoch: 17 [20480/50000 (45%)]\tLoss: 0.553487, Accuracy: 83.20\n",
      "Train Epoch: 17 [21760/50000 (48%)]\tLoss: 0.465016, Accuracy: 83.20\n",
      "Train Epoch: 17 [23040/50000 (51%)]\tLoss: 0.507978, Accuracy: 82.42\n",
      "Train Epoch: 17 [24320/50000 (54%)]\tLoss: 0.496213, Accuracy: 81.25\n",
      "Train Epoch: 17 [25600/50000 (57%)]\tLoss: 0.464240, Accuracy: 83.59\n",
      "Train Epoch: 17 [26880/50000 (60%)]\tLoss: 0.476852, Accuracy: 83.98\n",
      "Train Epoch: 17 [28160/50000 (62%)]\tLoss: 0.277846, Accuracy: 91.02\n",
      "Train Epoch: 17 [29440/50000 (65%)]\tLoss: 0.471109, Accuracy: 85.16\n",
      "Train Epoch: 17 [30720/50000 (68%)]\tLoss: 0.423311, Accuracy: 87.50\n",
      "Train Epoch: 17 [32000/50000 (71%)]\tLoss: 0.344243, Accuracy: 88.67\n",
      "Train Epoch: 17 [33280/50000 (74%)]\tLoss: 0.512186, Accuracy: 80.86\n",
      "Train Epoch: 17 [34560/50000 (77%)]\tLoss: 0.490090, Accuracy: 82.42\n",
      "Train Epoch: 17 [35840/50000 (80%)]\tLoss: 0.527398, Accuracy: 82.03\n",
      "Train Epoch: 17 [37120/50000 (82%)]\tLoss: 0.430716, Accuracy: 84.77\n",
      "Train Epoch: 17 [38400/50000 (85%)]\tLoss: 0.410022, Accuracy: 86.33\n",
      "Train Epoch: 17 [39680/50000 (88%)]\tLoss: 0.505677, Accuracy: 82.81\n",
      "Train Epoch: 17 [40960/50000 (91%)]\tLoss: 0.529336, Accuracy: 84.77\n",
      "Train Epoch: 17 [42240/50000 (94%)]\tLoss: 0.519666, Accuracy: 82.42\n",
      "Train Epoch: 17 [43520/50000 (97%)]\tLoss: 0.494848, Accuracy: 81.64\n",
      "Train Epoch: 17 [35000/50000 (99%)]\tLoss: 0.469037, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.6780, Accuracy: 3845/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[39.54121232032776 s]\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.501379, Accuracy: 79.30\n",
      "Train Epoch: 18 [1280/50000 (3%)]\tLoss: 0.344015, Accuracy: 89.45\n",
      "Train Epoch: 18 [2560/50000 (6%)]\tLoss: 0.408754, Accuracy: 87.50\n",
      "Train Epoch: 18 [3840/50000 (9%)]\tLoss: 0.393615, Accuracy: 87.50\n",
      "Train Epoch: 18 [5120/50000 (11%)]\tLoss: 0.414720, Accuracy: 84.38\n",
      "Train Epoch: 18 [6400/50000 (14%)]\tLoss: 0.432460, Accuracy: 84.77\n",
      "Train Epoch: 18 [7680/50000 (17%)]\tLoss: 0.508611, Accuracy: 80.47\n",
      "Train Epoch: 18 [8960/50000 (20%)]\tLoss: 0.373900, Accuracy: 88.28\n",
      "Train Epoch: 18 [10240/50000 (23%)]\tLoss: 0.354668, Accuracy: 84.38\n",
      "Train Epoch: 18 [11520/50000 (26%)]\tLoss: 0.442769, Accuracy: 85.55\n",
      "Train Epoch: 18 [12800/50000 (28%)]\tLoss: 0.386164, Accuracy: 87.50\n",
      "Train Epoch: 18 [14080/50000 (31%)]\tLoss: 0.475157, Accuracy: 81.25\n",
      "Train Epoch: 18 [15360/50000 (34%)]\tLoss: 0.474047, Accuracy: 82.81\n",
      "Train Epoch: 18 [16640/50000 (37%)]\tLoss: 0.535730, Accuracy: 81.64\n",
      "Train Epoch: 18 [17920/50000 (40%)]\tLoss: 0.525130, Accuracy: 81.64\n",
      "Train Epoch: 18 [19200/50000 (43%)]\tLoss: 0.472521, Accuracy: 84.77\n",
      "Train Epoch: 18 [20480/50000 (45%)]\tLoss: 0.417054, Accuracy: 86.72\n",
      "Train Epoch: 18 [21760/50000 (48%)]\tLoss: 0.405989, Accuracy: 87.11\n",
      "Train Epoch: 18 [23040/50000 (51%)]\tLoss: 0.499169, Accuracy: 81.64\n",
      "Train Epoch: 18 [24320/50000 (54%)]\tLoss: 0.587402, Accuracy: 78.91\n",
      "Train Epoch: 18 [25600/50000 (57%)]\tLoss: 0.458029, Accuracy: 83.98\n",
      "Train Epoch: 18 [26880/50000 (60%)]\tLoss: 0.527856, Accuracy: 82.81\n",
      "Train Epoch: 18 [28160/50000 (62%)]\tLoss: 0.375609, Accuracy: 87.89\n",
      "Train Epoch: 18 [29440/50000 (65%)]\tLoss: 0.465985, Accuracy: 82.03\n",
      "Train Epoch: 18 [30720/50000 (68%)]\tLoss: 0.410159, Accuracy: 87.11\n",
      "Train Epoch: 18 [32000/50000 (71%)]\tLoss: 0.453566, Accuracy: 83.20\n",
      "Train Epoch: 18 [33280/50000 (74%)]\tLoss: 0.428763, Accuracy: 86.72\n",
      "Train Epoch: 18 [34560/50000 (77%)]\tLoss: 0.500997, Accuracy: 82.81\n",
      "Train Epoch: 18 [35840/50000 (80%)]\tLoss: 0.551042, Accuracy: 82.42\n",
      "Train Epoch: 18 [37120/50000 (82%)]\tLoss: 0.451543, Accuracy: 82.81\n",
      "Train Epoch: 18 [38400/50000 (85%)]\tLoss: 0.549833, Accuracy: 82.81\n",
      "Train Epoch: 18 [39680/50000 (88%)]\tLoss: 0.527538, Accuracy: 83.98\n",
      "Train Epoch: 18 [40960/50000 (91%)]\tLoss: 0.459513, Accuracy: 84.38\n",
      "Train Epoch: 18 [42240/50000 (94%)]\tLoss: 0.422224, Accuracy: 85.55\n",
      "Train Epoch: 18 [43520/50000 (97%)]\tLoss: 0.542812, Accuracy: 78.91\n",
      "Train Epoch: 18 [35000/50000 (99%)]\tLoss: 0.459806, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6306, Accuracy: 3952/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.45955777168274 s]\n",
      "\n",
      "Test set: Average loss: 0.6553, Accuracy: 7810/10000 (78.10%)\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 0.423208, Accuracy: 85.55\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 19 [1280/50000 (3%)]\tLoss: 0.303516, Accuracy: 88.67\n",
      "Train Epoch: 19 [2560/50000 (6%)]\tLoss: 0.404800, Accuracy: 86.33\n",
      "Train Epoch: 19 [3840/50000 (9%)]\tLoss: 0.481455, Accuracy: 83.59\n",
      "Train Epoch: 19 [5120/50000 (11%)]\tLoss: 0.366714, Accuracy: 85.16\n",
      "Train Epoch: 19 [6400/50000 (14%)]\tLoss: 0.480183, Accuracy: 85.16\n",
      "Train Epoch: 19 [7680/50000 (17%)]\tLoss: 0.572997, Accuracy: 79.30\n",
      "Train Epoch: 19 [8960/50000 (20%)]\tLoss: 0.388126, Accuracy: 87.50\n",
      "Train Epoch: 19 [10240/50000 (23%)]\tLoss: 0.377992, Accuracy: 85.55\n",
      "Train Epoch: 19 [11520/50000 (26%)]\tLoss: 0.372192, Accuracy: 85.94\n",
      "Train Epoch: 19 [12800/50000 (28%)]\tLoss: 0.497197, Accuracy: 83.98\n",
      "Train Epoch: 19 [14080/50000 (31%)]\tLoss: 0.435955, Accuracy: 82.03\n",
      "Train Epoch: 19 [15360/50000 (34%)]\tLoss: 0.488135, Accuracy: 81.64\n",
      "Train Epoch: 19 [16640/50000 (37%)]\tLoss: 0.435938, Accuracy: 83.98\n",
      "Train Epoch: 19 [17920/50000 (40%)]\tLoss: 0.462483, Accuracy: 82.81\n",
      "Train Epoch: 19 [19200/50000 (43%)]\tLoss: 0.392035, Accuracy: 87.11\n",
      "Train Epoch: 19 [20480/50000 (45%)]\tLoss: 0.310975, Accuracy: 91.02\n",
      "Train Epoch: 19 [21760/50000 (48%)]\tLoss: 0.472575, Accuracy: 83.20\n",
      "Train Epoch: 19 [23040/50000 (51%)]\tLoss: 0.407268, Accuracy: 87.50\n",
      "Train Epoch: 19 [24320/50000 (54%)]\tLoss: 0.429499, Accuracy: 88.67\n",
      "Train Epoch: 19 [25600/50000 (57%)]\tLoss: 0.436872, Accuracy: 85.16\n",
      "Train Epoch: 19 [26880/50000 (60%)]\tLoss: 0.478054, Accuracy: 85.16\n",
      "Train Epoch: 19 [28160/50000 (62%)]\tLoss: 0.406321, Accuracy: 87.89\n",
      "Train Epoch: 19 [29440/50000 (65%)]\tLoss: 0.448543, Accuracy: 86.33\n",
      "Train Epoch: 19 [30720/50000 (68%)]\tLoss: 0.414768, Accuracy: 84.77\n",
      "Train Epoch: 19 [32000/50000 (71%)]\tLoss: 0.467598, Accuracy: 83.59\n",
      "Train Epoch: 19 [33280/50000 (74%)]\tLoss: 0.362805, Accuracy: 88.67\n",
      "Train Epoch: 19 [34560/50000 (77%)]\tLoss: 0.429554, Accuracy: 86.33\n",
      "Train Epoch: 19 [35840/50000 (80%)]\tLoss: 0.419121, Accuracy: 85.94\n",
      "Train Epoch: 19 [37120/50000 (82%)]\tLoss: 0.462836, Accuracy: 83.59\n",
      "Train Epoch: 19 [38400/50000 (85%)]\tLoss: 0.610299, Accuracy: 79.30\n",
      "Train Epoch: 19 [39680/50000 (88%)]\tLoss: 0.450483, Accuracy: 86.33\n",
      "Train Epoch: 19 [40960/50000 (91%)]\tLoss: 0.410067, Accuracy: 85.94\n",
      "Train Epoch: 19 [42240/50000 (94%)]\tLoss: 0.432880, Accuracy: 85.94\n",
      "Train Epoch: 19 [43520/50000 (97%)]\tLoss: 0.421702, Accuracy: 85.94\n",
      "Train Epoch: 19 [35000/50000 (99%)]\tLoss: 0.536223, Accuracy: 78.50\n",
      "\n",
      "Validation set: Average loss: 0.8875, Accuracy: 3640/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[39.53791856765747 s]\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 0.417835, Accuracy: 87.89\n",
      "Train Epoch: 20 [1280/50000 (3%)]\tLoss: 0.439012, Accuracy: 83.98\n",
      "Train Epoch: 20 [2560/50000 (6%)]\tLoss: 0.418298, Accuracy: 86.72\n",
      "Train Epoch: 20 [3840/50000 (9%)]\tLoss: 0.339275, Accuracy: 87.89\n",
      "Train Epoch: 20 [5120/50000 (11%)]\tLoss: 0.275740, Accuracy: 90.62\n",
      "Train Epoch: 20 [6400/50000 (14%)]\tLoss: 0.503051, Accuracy: 83.98\n",
      "Train Epoch: 20 [7680/50000 (17%)]\tLoss: 0.509249, Accuracy: 85.16\n",
      "Train Epoch: 20 [8960/50000 (20%)]\tLoss: 0.502778, Accuracy: 83.20\n",
      "Train Epoch: 20 [10240/50000 (23%)]\tLoss: 0.329234, Accuracy: 89.06\n",
      "Train Epoch: 20 [11520/50000 (26%)]\tLoss: 0.434362, Accuracy: 89.45\n",
      "Train Epoch: 20 [12800/50000 (28%)]\tLoss: 0.358728, Accuracy: 87.50\n",
      "Train Epoch: 20 [14080/50000 (31%)]\tLoss: 0.374611, Accuracy: 88.28\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor([[[ 1.4963]],\n",
      "\n",
      "        [[ 0.8749]],\n",
      "\n",
      "        [[ 0.6062]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.2973]],\n",
      "\n",
      "        [[-0.0074]],\n",
      "\n",
      "        [[ 1.3648]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 3.9630]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-1.3942e-02,  1.3219e-02,  3.5545e-02,  ..., -3.2440e-02,\n",
      "          -7.8798e-03, -4.2063e-02]],\n",
      "\n",
      "        [[ 6.2736e-03,  3.7214e-02, -4.4908e-02,  ...,  7.4494e-03,\n",
      "           1.8660e-02, -9.2964e-03]],\n",
      "\n",
      "        [[-1.9255e-02, -4.2722e-02,  9.1939e-03,  ..., -3.8168e-02,\n",
      "          -2.0916e-02,  1.4401e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 4.4991e-02, -2.0091e-02, -1.2379e-02,  ..., -3.4806e-02,\n",
      "           2.2573e-02, -2.1616e-02]],\n",
      "\n",
      "        [[-1.8910e-02,  3.1417e-02,  2.6261e-02,  ..., -1.6670e-02,\n",
      "           3.3733e-02,  3.9668e-04]],\n",
      "\n",
      "        [[ 4.1204e-02, -2.3694e-04,  1.7310e-02,  ...,  3.6590e-02,\n",
      "          -1.6240e-02,  1.1661e-02]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor([[[-7.1510e-03, -7.3658e-03, -8.2728e-03,  ..., -3.4566e-03,\n",
      "          -4.3744e-03, -4.6352e-03],\n",
      "         [-4.8032e-03, -4.7739e-03, -3.1324e-03,  ..., -5.3085e-03,\n",
      "          -4.6855e-03, -3.2626e-03],\n",
      "         [-2.5828e-03, -3.8945e-03, -4.5703e-03,  ..., -3.3045e-03,\n",
      "          -3.8111e-03, -5.5163e-03],\n",
      "         ...,\n",
      "         [ 3.2920e-03,  2.4903e-03,  2.9415e-03,  ...,  4.5989e-03,\n",
      "           3.7228e-03,  5.5286e-03],\n",
      "         [ 3.4970e-03,  2.6055e-03,  1.3975e-03,  ...,  3.5981e-05,\n",
      "           1.3990e-03,  1.3726e-03],\n",
      "         [ 8.6975e-03,  9.9658e-03,  1.0100e-02,  ...,  8.7051e-03,\n",
      "           8.0386e-03,  7.5328e-03]]], device='cuda:0')\n",
      "Train Epoch: 20 [15360/50000 (34%)]\tLoss: 0.471084, Accuracy: 84.38\n",
      "Train Epoch: 20 [16640/50000 (37%)]\tLoss: 0.444771, Accuracy: 83.59\n",
      "Train Epoch: 20 [17920/50000 (40%)]\tLoss: 0.400818, Accuracy: 85.94\n",
      "Train Epoch: 20 [19200/50000 (43%)]\tLoss: 0.458062, Accuracy: 85.16\n",
      "Train Epoch: 20 [20480/50000 (45%)]\tLoss: 0.371969, Accuracy: 87.89\n",
      "Train Epoch: 20 [21760/50000 (48%)]\tLoss: 0.608961, Accuracy: 79.69\n",
      "Train Epoch: 20 [23040/50000 (51%)]\tLoss: 0.478896, Accuracy: 80.47\n",
      "Train Epoch: 20 [24320/50000 (54%)]\tLoss: 0.334064, Accuracy: 89.84\n",
      "Train Epoch: 20 [25600/50000 (57%)]\tLoss: 0.357576, Accuracy: 90.23\n",
      "Train Epoch: 20 [26880/50000 (60%)]\tLoss: 0.388581, Accuracy: 88.67\n",
      "Train Epoch: 20 [28160/50000 (62%)]\tLoss: 0.447824, Accuracy: 85.55\n",
      "Train Epoch: 20 [29440/50000 (65%)]\tLoss: 0.453125, Accuracy: 84.38\n",
      "Train Epoch: 20 [30720/50000 (68%)]\tLoss: 0.341093, Accuracy: 89.45\n",
      "Train Epoch: 20 [32000/50000 (71%)]\tLoss: 0.513386, Accuracy: 82.81\n",
      "Train Epoch: 20 [33280/50000 (74%)]\tLoss: 0.446997, Accuracy: 85.55\n",
      "Train Epoch: 20 [34560/50000 (77%)]\tLoss: 0.571799, Accuracy: 79.30\n",
      "Train Epoch: 20 [35840/50000 (80%)]\tLoss: 0.380786, Accuracy: 89.06\n",
      "Train Epoch: 20 [37120/50000 (82%)]\tLoss: 0.500384, Accuracy: 81.64\n",
      "Train Epoch: 20 [38400/50000 (85%)]\tLoss: 0.419654, Accuracy: 85.16\n",
      "Train Epoch: 20 [39680/50000 (88%)]\tLoss: 0.488164, Accuracy: 85.55\n",
      "Train Epoch: 20 [40960/50000 (91%)]\tLoss: 0.364137, Accuracy: 86.33\n",
      "Train Epoch: 20 [42240/50000 (94%)]\tLoss: 0.429187, Accuracy: 85.16\n",
      "Train Epoch: 20 [43520/50000 (97%)]\tLoss: 0.440535, Accuracy: 85.55\n",
      "Train Epoch: 20 [35000/50000 (99%)]\tLoss: 0.502333, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.8745, Accuracy: 3657/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[36.49858117103577 s]\n",
      "\n",
      "Test set: Average loss: 0.8871, Accuracy: 7235/10000 (72.35%)\n",
      "\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 0.356319, Accuracy: 87.50\n",
      "Train Epoch: 21 [1280/50000 (3%)]\tLoss: 0.343146, Accuracy: 87.89\n",
      "Train Epoch: 21 [2560/50000 (6%)]\tLoss: 0.318729, Accuracy: 87.89\n",
      "Train Epoch: 21 [3840/50000 (9%)]\tLoss: 0.349507, Accuracy: 89.84\n",
      "Train Epoch: 21 [5120/50000 (11%)]\tLoss: 0.432581, Accuracy: 85.16\n",
      "Train Epoch: 21 [6400/50000 (14%)]\tLoss: 0.418233, Accuracy: 85.94\n",
      "Train Epoch: 21 [7680/50000 (17%)]\tLoss: 0.365110, Accuracy: 88.28\n",
      "Train Epoch: 21 [8960/50000 (20%)]\tLoss: 0.392350, Accuracy: 85.94\n",
      "Train Epoch: 21 [10240/50000 (23%)]\tLoss: 0.408788, Accuracy: 86.72\n",
      "Train Epoch: 21 [11520/50000 (26%)]\tLoss: 0.469049, Accuracy: 82.81\n",
      "Train Epoch: 21 [12800/50000 (28%)]\tLoss: 0.435063, Accuracy: 83.59\n",
      "Train Epoch: 21 [14080/50000 (31%)]\tLoss: 0.474191, Accuracy: 85.94\n",
      "Train Epoch: 21 [15360/50000 (34%)]\tLoss: 0.418022, Accuracy: 86.33\n",
      "Train Epoch: 21 [16640/50000 (37%)]\tLoss: 0.367324, Accuracy: 89.84\n",
      "Train Epoch: 21 [17920/50000 (40%)]\tLoss: 0.603051, Accuracy: 82.03\n",
      "Train Epoch: 21 [19200/50000 (43%)]\tLoss: 0.408812, Accuracy: 86.33\n",
      "Train Epoch: 21 [20480/50000 (45%)]\tLoss: 0.425570, Accuracy: 86.33\n",
      "Train Epoch: 21 [21760/50000 (48%)]\tLoss: 0.446432, Accuracy: 83.59\n",
      "Train Epoch: 21 [23040/50000 (51%)]\tLoss: 0.424654, Accuracy: 86.33\n",
      "Train Epoch: 21 [24320/50000 (54%)]\tLoss: 0.457009, Accuracy: 83.98\n",
      "Train Epoch: 21 [25600/50000 (57%)]\tLoss: 0.389600, Accuracy: 85.55\n",
      "Train Epoch: 21 [26880/50000 (60%)]\tLoss: 0.434654, Accuracy: 84.77\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 21 [28160/50000 (62%)]\tLoss: 0.506725, Accuracy: 84.38\n",
      "Train Epoch: 21 [29440/50000 (65%)]\tLoss: 0.414388, Accuracy: 85.55\n",
      "Train Epoch: 21 [30720/50000 (68%)]\tLoss: 0.442187, Accuracy: 85.94\n",
      "Train Epoch: 21 [32000/50000 (71%)]\tLoss: 0.495765, Accuracy: 82.81\n",
      "Train Epoch: 21 [33280/50000 (74%)]\tLoss: 0.425978, Accuracy: 84.38\n",
      "Train Epoch: 21 [34560/50000 (77%)]\tLoss: 0.448069, Accuracy: 84.38\n",
      "Train Epoch: 21 [35840/50000 (80%)]\tLoss: 0.507638, Accuracy: 83.59\n",
      "Train Epoch: 21 [37120/50000 (82%)]\tLoss: 0.417597, Accuracy: 88.28\n",
      "Train Epoch: 21 [38400/50000 (85%)]\tLoss: 0.503662, Accuracy: 83.20\n",
      "Train Epoch: 21 [39680/50000 (88%)]\tLoss: 0.403867, Accuracy: 85.94\n",
      "Train Epoch: 21 [40960/50000 (91%)]\tLoss: 0.383837, Accuracy: 85.94\n",
      "Train Epoch: 21 [42240/50000 (94%)]\tLoss: 0.395437, Accuracy: 86.72\n",
      "Train Epoch: 21 [43520/50000 (97%)]\tLoss: 0.593142, Accuracy: 80.08\n",
      "Train Epoch: 21 [35000/50000 (99%)]\tLoss: 0.358544, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.6782, Accuracy: 3888/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[39.54917788505554 s]\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 0.378178, Accuracy: 87.50\n",
      "Train Epoch: 22 [1280/50000 (3%)]\tLoss: 0.448465, Accuracy: 87.50\n",
      "Train Epoch: 22 [2560/50000 (6%)]\tLoss: 0.461146, Accuracy: 86.33\n",
      "Train Epoch: 22 [3840/50000 (9%)]\tLoss: 0.395554, Accuracy: 87.50\n",
      "Train Epoch: 22 [5120/50000 (11%)]\tLoss: 0.427167, Accuracy: 85.16\n",
      "Train Epoch: 22 [6400/50000 (14%)]\tLoss: 0.415904, Accuracy: 84.38\n",
      "Train Epoch: 22 [7680/50000 (17%)]\tLoss: 0.452063, Accuracy: 85.55\n",
      "Train Epoch: 22 [8960/50000 (20%)]\tLoss: 0.458665, Accuracy: 84.38\n",
      "Train Epoch: 22 [10240/50000 (23%)]\tLoss: 0.395088, Accuracy: 86.33\n",
      "Train Epoch: 22 [11520/50000 (26%)]\tLoss: 0.446029, Accuracy: 84.77\n",
      "Train Epoch: 22 [12800/50000 (28%)]\tLoss: 0.357773, Accuracy: 89.06\n",
      "Train Epoch: 22 [14080/50000 (31%)]\tLoss: 0.333220, Accuracy: 88.28\n",
      "Train Epoch: 22 [15360/50000 (34%)]\tLoss: 0.382208, Accuracy: 88.67\n",
      "Train Epoch: 22 [16640/50000 (37%)]\tLoss: 0.406848, Accuracy: 86.72\n",
      "Train Epoch: 22 [17920/50000 (40%)]\tLoss: 0.297247, Accuracy: 90.23\n",
      "Train Epoch: 22 [19200/50000 (43%)]\tLoss: 0.420668, Accuracy: 84.38\n",
      "Train Epoch: 22 [20480/50000 (45%)]\tLoss: 0.404912, Accuracy: 86.72\n",
      "Train Epoch: 22 [21760/50000 (48%)]\tLoss: 0.422997, Accuracy: 87.11\n",
      "Train Epoch: 22 [23040/50000 (51%)]\tLoss: 0.424687, Accuracy: 85.55\n",
      "Train Epoch: 22 [24320/50000 (54%)]\tLoss: 0.439884, Accuracy: 85.55\n",
      "Train Epoch: 22 [25600/50000 (57%)]\tLoss: 0.492304, Accuracy: 81.25\n",
      "Train Epoch: 22 [26880/50000 (60%)]\tLoss: 0.513336, Accuracy: 81.25\n",
      "Train Epoch: 22 [28160/50000 (62%)]\tLoss: 0.410059, Accuracy: 86.33\n",
      "Train Epoch: 22 [29440/50000 (65%)]\tLoss: 0.373592, Accuracy: 88.28\n",
      "Train Epoch: 22 [30720/50000 (68%)]\tLoss: 0.458929, Accuracy: 85.16\n",
      "Train Epoch: 22 [32000/50000 (71%)]\tLoss: 0.526512, Accuracy: 81.25\n",
      "Train Epoch: 22 [33280/50000 (74%)]\tLoss: 0.524532, Accuracy: 81.64\n",
      "Train Epoch: 22 [34560/50000 (77%)]\tLoss: 0.430023, Accuracy: 83.98\n",
      "Train Epoch: 22 [35840/50000 (80%)]\tLoss: 0.478216, Accuracy: 85.16\n",
      "Train Epoch: 22 [37120/50000 (82%)]\tLoss: 0.496026, Accuracy: 83.20\n",
      "Train Epoch: 22 [38400/50000 (85%)]\tLoss: 0.418224, Accuracy: 85.55\n",
      "Train Epoch: 22 [39680/50000 (88%)]\tLoss: 0.404138, Accuracy: 87.11\n",
      "Train Epoch: 22 [40960/50000 (91%)]\tLoss: 0.359763, Accuracy: 87.50\n",
      "Train Epoch: 22 [42240/50000 (94%)]\tLoss: 0.432943, Accuracy: 83.59\n",
      "Train Epoch: 22 [43520/50000 (97%)]\tLoss: 0.603732, Accuracy: 79.30\n",
      "Train Epoch: 22 [35000/50000 (99%)]\tLoss: 0.383768, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6160, Accuracy: 3948/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.428972005844116 s]\n",
      "\n",
      "Test set: Average loss: 0.6379, Accuracy: 7783/10000 (77.83%)\n",
      "\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.446700, Accuracy: 85.94\n",
      "Train Epoch: 23 [1280/50000 (3%)]\tLoss: 0.402976, Accuracy: 87.50\n",
      "Train Epoch: 23 [2560/50000 (6%)]\tLoss: 0.419897, Accuracy: 85.55\n",
      "Train Epoch: 23 [3840/50000 (9%)]\tLoss: 0.405141, Accuracy: 88.67\n",
      "Train Epoch: 23 [5120/50000 (11%)]\tLoss: 0.359555, Accuracy: 87.89\n",
      "Train Epoch: 23 [6400/50000 (14%)]\tLoss: 0.379639, Accuracy: 86.33\n",
      "Train Epoch: 23 [7680/50000 (17%)]\tLoss: 0.392369, Accuracy: 87.11\n",
      "Train Epoch: 23 [8960/50000 (20%)]\tLoss: 0.407184, Accuracy: 86.33\n",
      "Train Epoch: 23 [10240/50000 (23%)]\tLoss: 0.367674, Accuracy: 84.38\n",
      "Train Epoch: 23 [11520/50000 (26%)]\tLoss: 0.451730, Accuracy: 83.98\n",
      "Train Epoch: 23 [12800/50000 (28%)]\tLoss: 0.451023, Accuracy: 84.38\n",
      "Train Epoch: 23 [14080/50000 (31%)]\tLoss: 0.311710, Accuracy: 90.23\n",
      "Train Epoch: 23 [15360/50000 (34%)]\tLoss: 0.453323, Accuracy: 86.72\n",
      "Train Epoch: 23 [16640/50000 (37%)]\tLoss: 0.407369, Accuracy: 87.11\n",
      "Train Epoch: 23 [17920/50000 (40%)]\tLoss: 0.444263, Accuracy: 83.20\n",
      "Train Epoch: 23 [19200/50000 (43%)]\tLoss: 0.315025, Accuracy: 89.45\n",
      "Train Epoch: 23 [20480/50000 (45%)]\tLoss: 0.385631, Accuracy: 86.33\n",
      "Train Epoch: 23 [21760/50000 (48%)]\tLoss: 0.348502, Accuracy: 87.89\n",
      "Train Epoch: 23 [23040/50000 (51%)]\tLoss: 0.447148, Accuracy: 87.89\n",
      "Train Epoch: 23 [24320/50000 (54%)]\tLoss: 0.382583, Accuracy: 85.94\n",
      "Train Epoch: 23 [25600/50000 (57%)]\tLoss: 0.470312, Accuracy: 84.38\n",
      "Train Epoch: 23 [26880/50000 (60%)]\tLoss: 0.489969, Accuracy: 85.16\n",
      "Train Epoch: 23 [28160/50000 (62%)]\tLoss: 0.467781, Accuracy: 85.94\n",
      "Train Epoch: 23 [29440/50000 (65%)]\tLoss: 0.430334, Accuracy: 86.33\n",
      "Train Epoch: 23 [30720/50000 (68%)]\tLoss: 0.385140, Accuracy: 87.89\n",
      "Train Epoch: 23 [32000/50000 (71%)]\tLoss: 0.338156, Accuracy: 90.23\n",
      "Train Epoch: 23 [33280/50000 (74%)]\tLoss: 0.380226, Accuracy: 87.89\n",
      "Train Epoch: 23 [34560/50000 (77%)]\tLoss: 0.437020, Accuracy: 86.72\n",
      "Train Epoch: 23 [35840/50000 (80%)]\tLoss: 0.391133, Accuracy: 85.94\n",
      "Train Epoch: 23 [37120/50000 (82%)]\tLoss: 0.345030, Accuracy: 88.67\n",
      "Train Epoch: 23 [38400/50000 (85%)]\tLoss: 0.435089, Accuracy: 84.38\n",
      "Train Epoch: 23 [39680/50000 (88%)]\tLoss: 0.318443, Accuracy: 90.23\n",
      "Train Epoch: 23 [40960/50000 (91%)]\tLoss: 0.466948, Accuracy: 84.38\n",
      "Train Epoch: 23 [42240/50000 (94%)]\tLoss: 0.389827, Accuracy: 89.06\n",
      "Train Epoch: 23 [43520/50000 (97%)]\tLoss: 0.431697, Accuracy: 85.94\n",
      "Train Epoch: 23 [35000/50000 (99%)]\tLoss: 0.378199, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.6774, Accuracy: 3932/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.53919553756714 s]\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.417437, Accuracy: 85.16\n",
      "Train Epoch: 24 [1280/50000 (3%)]\tLoss: 0.514908, Accuracy: 83.20\n",
      "Train Epoch: 24 [2560/50000 (6%)]\tLoss: 0.411535, Accuracy: 86.72\n",
      "Train Epoch: 24 [3840/50000 (9%)]\tLoss: 0.405676, Accuracy: 86.72\n",
      "Train Epoch: 24 [5120/50000 (11%)]\tLoss: 0.421756, Accuracy: 85.94\n",
      "Train Epoch: 24 [6400/50000 (14%)]\tLoss: 0.469483, Accuracy: 79.69\n",
      "Train Epoch: 24 [7680/50000 (17%)]\tLoss: 0.343005, Accuracy: 87.11\n",
      "Train Epoch: 24 [8960/50000 (20%)]\tLoss: 0.448155, Accuracy: 84.38\n",
      "Train Epoch: 24 [10240/50000 (23%)]\tLoss: 0.275322, Accuracy: 90.62\n",
      "Train Epoch: 24 [11520/50000 (26%)]\tLoss: 0.396131, Accuracy: 87.11\n",
      "Train Epoch: 24 [12800/50000 (28%)]\tLoss: 0.340799, Accuracy: 89.06\n",
      "Train Epoch: 24 [14080/50000 (31%)]\tLoss: 0.448285, Accuracy: 85.94\n",
      "Train Epoch: 24 [15360/50000 (34%)]\tLoss: 0.412165, Accuracy: 83.98\n",
      "Train Epoch: 24 [16640/50000 (37%)]\tLoss: 0.436380, Accuracy: 84.77\n",
      "Train Epoch: 24 [17920/50000 (40%)]\tLoss: 0.369030, Accuracy: 86.33\n",
      "Train Epoch: 24 [19200/50000 (43%)]\tLoss: 0.380628, Accuracy: 89.06\n",
      "Train Epoch: 24 [20480/50000 (45%)]\tLoss: 0.397735, Accuracy: 83.98\n",
      "Train Epoch: 24 [21760/50000 (48%)]\tLoss: 0.452507, Accuracy: 82.81\n",
      "Train Epoch: 24 [23040/50000 (51%)]\tLoss: 0.415478, Accuracy: 86.33\n",
      "Train Epoch: 24 [24320/50000 (54%)]\tLoss: 0.451690, Accuracy: 81.25\n",
      "Train Epoch: 24 [25600/50000 (57%)]\tLoss: 0.430360, Accuracy: 82.81\n",
      "Train Epoch: 24 [26880/50000 (60%)]\tLoss: 0.372066, Accuracy: 89.06\n",
      "Train Epoch: 24 [28160/50000 (62%)]\tLoss: 0.372762, Accuracy: 89.06\n",
      "Train Epoch: 24 [29440/50000 (65%)]\tLoss: 0.458281, Accuracy: 83.20\n",
      "Train Epoch: 24 [30720/50000 (68%)]\tLoss: 0.372489, Accuracy: 87.50\n",
      "Train Epoch: 24 [32000/50000 (71%)]\tLoss: 0.460397, Accuracy: 87.50\n",
      "Train Epoch: 24 [33280/50000 (74%)]\tLoss: 0.431551, Accuracy: 84.77\n",
      "Train Epoch: 24 [34560/50000 (77%)]\tLoss: 0.541110, Accuracy: 82.42\n",
      "Train Epoch: 24 [35840/50000 (80%)]\tLoss: 0.463562, Accuracy: 82.81\n",
      "Train Epoch: 24 [37120/50000 (82%)]\tLoss: 0.432654, Accuracy: 85.55\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 24 [38400/50000 (85%)]\tLoss: 0.377491, Accuracy: 87.89\n",
      "Train Epoch: 24 [39680/50000 (88%)]\tLoss: 0.442127, Accuracy: 82.81\n",
      "Train Epoch: 24 [40960/50000 (91%)]\tLoss: 0.429015, Accuracy: 84.38\n",
      "Train Epoch: 24 [42240/50000 (94%)]\tLoss: 0.441521, Accuracy: 85.16\n",
      "Train Epoch: 24 [43520/50000 (97%)]\tLoss: 0.402316, Accuracy: 87.50\n",
      "Train Epoch: 24 [35000/50000 (99%)]\tLoss: 0.355971, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.5473, Accuracy: 4108/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[36.425700187683105 s]\n",
      "\n",
      "Test set: Average loss: 0.5595, Accuracy: 8174/10000 (81.74%)\n",
      "\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.404050, Accuracy: 87.11\n",
      "Train Epoch: 25 [1280/50000 (3%)]\tLoss: 0.354028, Accuracy: 89.45\n",
      "Train Epoch: 25 [2560/50000 (6%)]\tLoss: 0.346664, Accuracy: 90.23\n",
      "Train Epoch: 25 [3840/50000 (9%)]\tLoss: 0.392730, Accuracy: 86.33\n",
      "Train Epoch: 25 [5120/50000 (11%)]\tLoss: 0.344696, Accuracy: 88.28\n",
      "Train Epoch: 25 [6400/50000 (14%)]\tLoss: 0.396877, Accuracy: 86.33\n",
      "Train Epoch: 25 [7680/50000 (17%)]\tLoss: 0.350885, Accuracy: 89.45\n",
      "Train Epoch: 25 [8960/50000 (20%)]\tLoss: 0.361238, Accuracy: 88.67\n",
      "Train Epoch: 25 [10240/50000 (23%)]\tLoss: 0.413189, Accuracy: 85.55\n",
      "Train Epoch: 25 [11520/50000 (26%)]\tLoss: 0.346272, Accuracy: 88.67\n",
      "Train Epoch: 25 [12800/50000 (28%)]\tLoss: 0.417592, Accuracy: 85.55\n",
      "Train Epoch: 25 [14080/50000 (31%)]\tLoss: 0.424447, Accuracy: 83.98\n",
      "Train Epoch: 25 [15360/50000 (34%)]\tLoss: 0.391823, Accuracy: 84.77\n",
      "Train Epoch: 25 [16640/50000 (37%)]\tLoss: 0.475129, Accuracy: 84.77\n",
      "Train Epoch: 25 [17920/50000 (40%)]\tLoss: 0.427234, Accuracy: 85.94\n",
      "Train Epoch: 25 [19200/50000 (43%)]\tLoss: 0.442408, Accuracy: 83.98\n",
      "Train Epoch: 25 [20480/50000 (45%)]\tLoss: 0.468554, Accuracy: 86.33\n",
      "Train Epoch: 25 [21760/50000 (48%)]\tLoss: 0.422572, Accuracy: 86.33\n",
      "Train Epoch: 25 [23040/50000 (51%)]\tLoss: 0.373465, Accuracy: 86.72\n",
      "Train Epoch: 25 [24320/50000 (54%)]\tLoss: 0.511626, Accuracy: 80.47\n",
      "Train Epoch: 25 [25600/50000 (57%)]\tLoss: 0.398902, Accuracy: 85.16\n",
      "Train Epoch: 25 [26880/50000 (60%)]\tLoss: 0.406512, Accuracy: 88.67\n",
      "Train Epoch: 25 [28160/50000 (62%)]\tLoss: 0.344290, Accuracy: 89.06\n",
      "Train Epoch: 25 [29440/50000 (65%)]\tLoss: 0.449382, Accuracy: 84.77\n",
      "Train Epoch: 25 [30720/50000 (68%)]\tLoss: 0.339528, Accuracy: 87.89\n",
      "Train Epoch: 25 [32000/50000 (71%)]\tLoss: 0.591069, Accuracy: 80.08\n",
      "Train Epoch: 25 [33280/50000 (74%)]\tLoss: 0.341600, Accuracy: 88.67\n",
      "Train Epoch: 25 [34560/50000 (77%)]\tLoss: 0.400022, Accuracy: 85.16\n",
      "Train Epoch: 25 [35840/50000 (80%)]\tLoss: 0.479334, Accuracy: 85.16\n",
      "Train Epoch: 25 [37120/50000 (82%)]\tLoss: 0.405867, Accuracy: 87.50\n",
      "Train Epoch: 25 [38400/50000 (85%)]\tLoss: 0.372342, Accuracy: 87.50\n",
      "Train Epoch: 25 [39680/50000 (88%)]\tLoss: 0.513373, Accuracy: 83.59\n",
      "Train Epoch: 25 [40960/50000 (91%)]\tLoss: 0.437609, Accuracy: 84.38\n",
      "Train Epoch: 25 [42240/50000 (94%)]\tLoss: 0.470688, Accuracy: 84.38\n",
      "Train Epoch: 25 [43520/50000 (97%)]\tLoss: 0.377088, Accuracy: 84.38\n",
      "Train Epoch: 25 [35000/50000 (99%)]\tLoss: 0.362308, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6601, Accuracy: 3968/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[39.56910538673401 s]\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 0.334661, Accuracy: 88.67\n",
      "Train Epoch: 26 [1280/50000 (3%)]\tLoss: 0.407624, Accuracy: 87.50\n",
      "Train Epoch: 26 [2560/50000 (6%)]\tLoss: 0.370349, Accuracy: 89.45\n",
      "Train Epoch: 26 [3840/50000 (9%)]\tLoss: 0.389362, Accuracy: 86.72\n",
      "Train Epoch: 26 [5120/50000 (11%)]\tLoss: 0.374758, Accuracy: 87.50\n",
      "Train Epoch: 26 [6400/50000 (14%)]\tLoss: 0.418409, Accuracy: 85.16\n",
      "Train Epoch: 26 [7680/50000 (17%)]\tLoss: 0.322205, Accuracy: 89.84\n",
      "Train Epoch: 26 [8960/50000 (20%)]\tLoss: 0.359340, Accuracy: 86.33\n",
      "Train Epoch: 26 [10240/50000 (23%)]\tLoss: 0.349039, Accuracy: 90.23\n",
      "Train Epoch: 26 [11520/50000 (26%)]\tLoss: 0.453753, Accuracy: 83.98\n",
      "Train Epoch: 26 [12800/50000 (28%)]\tLoss: 0.291631, Accuracy: 91.02\n",
      "Train Epoch: 26 [14080/50000 (31%)]\tLoss: 0.380691, Accuracy: 87.89\n",
      "Train Epoch: 26 [15360/50000 (34%)]\tLoss: 0.454174, Accuracy: 83.98\n",
      "Train Epoch: 26 [16640/50000 (37%)]\tLoss: 0.457206, Accuracy: 85.55\n",
      "Train Epoch: 26 [17920/50000 (40%)]\tLoss: 0.440081, Accuracy: 86.72\n",
      "Train Epoch: 26 [19200/50000 (43%)]\tLoss: 0.470759, Accuracy: 82.42\n",
      "Train Epoch: 26 [20480/50000 (45%)]\tLoss: 0.356290, Accuracy: 86.33\n",
      "Train Epoch: 26 [21760/50000 (48%)]\tLoss: 0.367248, Accuracy: 88.28\n",
      "Train Epoch: 26 [23040/50000 (51%)]\tLoss: 0.387205, Accuracy: 85.55\n",
      "Train Epoch: 26 [24320/50000 (54%)]\tLoss: 0.383720, Accuracy: 85.16\n",
      "Train Epoch: 26 [25600/50000 (57%)]\tLoss: 0.289470, Accuracy: 90.62\n",
      "Train Epoch: 26 [26880/50000 (60%)]\tLoss: 0.401159, Accuracy: 85.94\n",
      "Train Epoch: 26 [28160/50000 (62%)]\tLoss: 0.361595, Accuracy: 87.11\n",
      "Train Epoch: 26 [29440/50000 (65%)]\tLoss: 0.432881, Accuracy: 83.98\n",
      "Train Epoch: 26 [30720/50000 (68%)]\tLoss: 0.398243, Accuracy: 87.50\n",
      "Train Epoch: 26 [32000/50000 (71%)]\tLoss: 0.395020, Accuracy: 83.20\n",
      "Train Epoch: 26 [33280/50000 (74%)]\tLoss: 0.401615, Accuracy: 87.50\n",
      "Train Epoch: 26 [34560/50000 (77%)]\tLoss: 0.365440, Accuracy: 87.89\n",
      "Train Epoch: 26 [35840/50000 (80%)]\tLoss: 0.425376, Accuracy: 84.38\n",
      "Train Epoch: 26 [37120/50000 (82%)]\tLoss: 0.429051, Accuracy: 85.16\n",
      "Train Epoch: 26 [38400/50000 (85%)]\tLoss: 0.471965, Accuracy: 83.59\n",
      "Train Epoch: 26 [39680/50000 (88%)]\tLoss: 0.499541, Accuracy: 82.81\n",
      "Train Epoch: 26 [40960/50000 (91%)]\tLoss: 0.461221, Accuracy: 84.38\n",
      "Train Epoch: 26 [42240/50000 (94%)]\tLoss: 0.472174, Accuracy: 84.38\n",
      "Train Epoch: 26 [43520/50000 (97%)]\tLoss: 0.447443, Accuracy: 84.77\n",
      "Train Epoch: 26 [35000/50000 (99%)]\tLoss: 0.489099, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.9025, Accuracy: 3622/5000 (72.00%)\n",
      "\n",
      "the time of this epoch:[36.41619348526001 s]\n",
      "\n",
      "Test set: Average loss: 0.9410, Accuracy: 7170/10000 (71.70%)\n",
      "\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.312524, Accuracy: 89.06\n",
      "Train Epoch: 27 [1280/50000 (3%)]\tLoss: 0.364960, Accuracy: 87.89\n",
      "Train Epoch: 27 [2560/50000 (6%)]\tLoss: 0.382627, Accuracy: 89.45\n",
      "Train Epoch: 27 [3840/50000 (9%)]\tLoss: 0.289915, Accuracy: 90.62\n",
      "Train Epoch: 27 [5120/50000 (11%)]\tLoss: 0.407945, Accuracy: 84.77\n",
      "Train Epoch: 27 [6400/50000 (14%)]\tLoss: 0.351435, Accuracy: 85.55\n",
      "Train Epoch: 27 [7680/50000 (17%)]\tLoss: 0.280596, Accuracy: 92.58\n",
      "Train Epoch: 27 [8960/50000 (20%)]\tLoss: 0.299529, Accuracy: 89.45\n",
      "Train Epoch: 27 [10240/50000 (23%)]\tLoss: 0.436233, Accuracy: 85.16\n",
      "Train Epoch: 27 [11520/50000 (26%)]\tLoss: 0.444967, Accuracy: 84.38\n",
      "Train Epoch: 27 [12800/50000 (28%)]\tLoss: 0.370578, Accuracy: 86.72\n",
      "Train Epoch: 27 [14080/50000 (31%)]\tLoss: 0.444977, Accuracy: 84.77\n",
      "Train Epoch: 27 [15360/50000 (34%)]\tLoss: 0.390099, Accuracy: 86.33\n",
      "Train Epoch: 27 [16640/50000 (37%)]\tLoss: 0.375327, Accuracy: 87.11\n",
      "Train Epoch: 27 [17920/50000 (40%)]\tLoss: 0.298992, Accuracy: 89.84\n",
      "Train Epoch: 27 [19200/50000 (43%)]\tLoss: 0.406893, Accuracy: 88.67\n",
      "Train Epoch: 27 [20480/50000 (45%)]\tLoss: 0.446449, Accuracy: 86.33\n",
      "Train Epoch: 27 [21760/50000 (48%)]\tLoss: 0.392791, Accuracy: 86.72\n",
      "Train Epoch: 27 [23040/50000 (51%)]\tLoss: 0.518969, Accuracy: 83.98\n",
      "Train Epoch: 27 [24320/50000 (54%)]\tLoss: 0.465796, Accuracy: 85.94\n",
      "Train Epoch: 27 [25600/50000 (57%)]\tLoss: 0.415975, Accuracy: 86.33\n",
      "Train Epoch: 27 [26880/50000 (60%)]\tLoss: 0.374057, Accuracy: 89.45\n",
      "Train Epoch: 27 [28160/50000 (62%)]\tLoss: 0.303386, Accuracy: 89.84\n",
      "Train Epoch: 27 [29440/50000 (65%)]\tLoss: 0.384904, Accuracy: 85.94\n",
      "Train Epoch: 27 [30720/50000 (68%)]\tLoss: 0.508142, Accuracy: 82.03\n",
      "Train Epoch: 27 [32000/50000 (71%)]\tLoss: 0.410840, Accuracy: 85.55\n",
      "Train Epoch: 27 [33280/50000 (74%)]\tLoss: 0.464005, Accuracy: 83.59\n",
      "Train Epoch: 27 [34560/50000 (77%)]\tLoss: 0.436119, Accuracy: 86.33\n",
      "Train Epoch: 27 [35840/50000 (80%)]\tLoss: 0.382101, Accuracy: 86.72\n",
      "Train Epoch: 27 [37120/50000 (82%)]\tLoss: 0.416746, Accuracy: 84.38\n",
      "Train Epoch: 27 [38400/50000 (85%)]\tLoss: 0.388920, Accuracy: 87.50\n",
      "Train Epoch: 27 [39680/50000 (88%)]\tLoss: 0.498719, Accuracy: 84.77\n",
      "Train Epoch: 27 [40960/50000 (91%)]\tLoss: 0.455255, Accuracy: 86.72\n",
      "Train Epoch: 27 [42240/50000 (94%)]\tLoss: 0.356591, Accuracy: 87.50\n",
      "Train Epoch: 27 [43520/50000 (97%)]\tLoss: 0.469633, Accuracy: 83.20\n",
      "Train Epoch: 27 [35000/50000 (99%)]\tLoss: 0.475743, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.6291, Accuracy: 3952/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[39.55595660209656 s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 0.334823, Accuracy: 89.84\n",
      "Train Epoch: 28 [1280/50000 (3%)]\tLoss: 0.392459, Accuracy: 88.28\n",
      "Train Epoch: 28 [2560/50000 (6%)]\tLoss: 0.441794, Accuracy: 83.98\n",
      "Train Epoch: 28 [3840/50000 (9%)]\tLoss: 0.377665, Accuracy: 85.55\n",
      "Train Epoch: 28 [5120/50000 (11%)]\tLoss: 0.332661, Accuracy: 89.45\n",
      "Train Epoch: 28 [6400/50000 (14%)]\tLoss: 0.340773, Accuracy: 88.67\n",
      "Train Epoch: 28 [7680/50000 (17%)]\tLoss: 0.370730, Accuracy: 85.55\n",
      "Train Epoch: 28 [8960/50000 (20%)]\tLoss: 0.337146, Accuracy: 89.06\n",
      "Train Epoch: 28 [10240/50000 (23%)]\tLoss: 0.380154, Accuracy: 85.94\n",
      "Train Epoch: 28 [11520/50000 (26%)]\tLoss: 0.405733, Accuracy: 87.11\n",
      "Train Epoch: 28 [12800/50000 (28%)]\tLoss: 0.444609, Accuracy: 85.16\n",
      "Train Epoch: 28 [14080/50000 (31%)]\tLoss: 0.432747, Accuracy: 85.55\n",
      "Train Epoch: 28 [15360/50000 (34%)]\tLoss: 0.344555, Accuracy: 88.28\n",
      "Train Epoch: 28 [16640/50000 (37%)]\tLoss: 0.349964, Accuracy: 87.50\n",
      "Train Epoch: 28 [17920/50000 (40%)]\tLoss: 0.280632, Accuracy: 92.19\n",
      "Train Epoch: 28 [19200/50000 (43%)]\tLoss: 0.466497, Accuracy: 82.81\n",
      "Train Epoch: 28 [20480/50000 (45%)]\tLoss: 0.332327, Accuracy: 89.45\n",
      "Train Epoch: 28 [21760/50000 (48%)]\tLoss: 0.333377, Accuracy: 89.06\n",
      "Train Epoch: 28 [23040/50000 (51%)]\tLoss: 0.396946, Accuracy: 86.72\n",
      "Train Epoch: 28 [24320/50000 (54%)]\tLoss: 0.303143, Accuracy: 91.02\n",
      "Train Epoch: 28 [25600/50000 (57%)]\tLoss: 0.371180, Accuracy: 86.72\n",
      "Train Epoch: 28 [26880/50000 (60%)]\tLoss: 0.430147, Accuracy: 82.81\n",
      "Train Epoch: 28 [28160/50000 (62%)]\tLoss: 0.406252, Accuracy: 86.72\n",
      "Train Epoch: 28 [29440/50000 (65%)]\tLoss: 0.334991, Accuracy: 87.50\n",
      "Train Epoch: 28 [30720/50000 (68%)]\tLoss: 0.373928, Accuracy: 87.50\n",
      "Train Epoch: 28 [32000/50000 (71%)]\tLoss: 0.414153, Accuracy: 86.72\n",
      "Train Epoch: 28 [33280/50000 (74%)]\tLoss: 0.420040, Accuracy: 87.50\n",
      "Train Epoch: 28 [34560/50000 (77%)]\tLoss: 0.432504, Accuracy: 82.42\n",
      "Train Epoch: 28 [35840/50000 (80%)]\tLoss: 0.364955, Accuracy: 87.11\n",
      "Train Epoch: 28 [37120/50000 (82%)]\tLoss: 0.404948, Accuracy: 84.77\n",
      "Train Epoch: 28 [38400/50000 (85%)]\tLoss: 0.428707, Accuracy: 86.72\n",
      "Train Epoch: 28 [39680/50000 (88%)]\tLoss: 0.476353, Accuracy: 83.98\n",
      "Train Epoch: 28 [40960/50000 (91%)]\tLoss: 0.493131, Accuracy: 83.20\n",
      "Train Epoch: 28 [42240/50000 (94%)]\tLoss: 0.443538, Accuracy: 85.55\n",
      "Train Epoch: 28 [43520/50000 (97%)]\tLoss: 0.323941, Accuracy: 88.67\n",
      "Train Epoch: 28 [35000/50000 (99%)]\tLoss: 0.443693, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.5717, Accuracy: 4092/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[36.460901975631714 s]\n",
      "\n",
      "Test set: Average loss: 0.6065, Accuracy: 8100/10000 (81.00%)\n",
      "\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 0.391554, Accuracy: 87.11\n",
      "Train Epoch: 29 [1280/50000 (3%)]\tLoss: 0.473777, Accuracy: 82.81\n",
      "Train Epoch: 29 [2560/50000 (6%)]\tLoss: 0.358939, Accuracy: 87.89\n",
      "Train Epoch: 29 [3840/50000 (9%)]\tLoss: 0.436087, Accuracy: 86.33\n",
      "Train Epoch: 29 [5120/50000 (11%)]\tLoss: 0.400342, Accuracy: 86.33\n",
      "Train Epoch: 29 [6400/50000 (14%)]\tLoss: 0.399757, Accuracy: 85.55\n",
      "Train Epoch: 29 [7680/50000 (17%)]\tLoss: 0.455321, Accuracy: 84.38\n",
      "Train Epoch: 29 [8960/50000 (20%)]\tLoss: 0.470730, Accuracy: 86.33\n",
      "Train Epoch: 29 [10240/50000 (23%)]\tLoss: 0.452393, Accuracy: 84.38\n",
      "Train Epoch: 29 [11520/50000 (26%)]\tLoss: 0.307570, Accuracy: 87.89\n",
      "Train Epoch: 29 [12800/50000 (28%)]\tLoss: 0.327153, Accuracy: 88.67\n",
      "Train Epoch: 29 [14080/50000 (31%)]\tLoss: 0.483883, Accuracy: 85.55\n",
      "Train Epoch: 29 [15360/50000 (34%)]\tLoss: 0.371005, Accuracy: 84.77\n",
      "Train Epoch: 29 [16640/50000 (37%)]\tLoss: 0.414842, Accuracy: 87.11\n",
      "Train Epoch: 29 [17920/50000 (40%)]\tLoss: 0.367911, Accuracy: 89.06\n",
      "Train Epoch: 29 [19200/50000 (43%)]\tLoss: 0.305635, Accuracy: 89.45\n",
      "Train Epoch: 29 [20480/50000 (45%)]\tLoss: 0.444034, Accuracy: 85.16\n",
      "Train Epoch: 29 [21760/50000 (48%)]\tLoss: 0.446321, Accuracy: 86.33\n",
      "Train Epoch: 29 [23040/50000 (51%)]\tLoss: 0.419493, Accuracy: 86.33\n",
      "Train Epoch: 29 [24320/50000 (54%)]\tLoss: 0.410777, Accuracy: 85.55\n",
      "Train Epoch: 29 [25600/50000 (57%)]\tLoss: 0.356104, Accuracy: 86.72\n",
      "Train Epoch: 29 [26880/50000 (60%)]\tLoss: 0.441844, Accuracy: 87.50\n",
      "Train Epoch: 29 [28160/50000 (62%)]\tLoss: 0.343038, Accuracy: 87.11\n",
      "Train Epoch: 29 [29440/50000 (65%)]\tLoss: 0.486480, Accuracy: 83.98\n",
      "Train Epoch: 29 [30720/50000 (68%)]\tLoss: 0.513591, Accuracy: 81.64\n",
      "Train Epoch: 29 [32000/50000 (71%)]\tLoss: 0.435942, Accuracy: 85.55\n",
      "Train Epoch: 29 [33280/50000 (74%)]\tLoss: 0.458535, Accuracy: 85.16\n",
      "Train Epoch: 29 [34560/50000 (77%)]\tLoss: 0.376693, Accuracy: 87.89\n",
      "Train Epoch: 29 [35840/50000 (80%)]\tLoss: 0.373113, Accuracy: 88.67\n",
      "Train Epoch: 29 [37120/50000 (82%)]\tLoss: 0.397397, Accuracy: 86.72\n",
      "Train Epoch: 29 [38400/50000 (85%)]\tLoss: 0.359633, Accuracy: 89.06\n",
      "Train Epoch: 29 [39680/50000 (88%)]\tLoss: 0.408988, Accuracy: 85.16\n",
      "Train Epoch: 29 [40960/50000 (91%)]\tLoss: 0.467290, Accuracy: 85.94\n",
      "Train Epoch: 29 [42240/50000 (94%)]\tLoss: 0.379883, Accuracy: 86.33\n",
      "Train Epoch: 29 [43520/50000 (97%)]\tLoss: 0.399145, Accuracy: 86.72\n",
      "Train Epoch: 29 [35000/50000 (99%)]\tLoss: 0.441458, Accuracy: 85.50\n",
      "\n",
      "Validation set: Average loss: 0.5646, Accuracy: 4060/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[39.54996180534363 s]\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 0.374943, Accuracy: 86.72\n",
      "Train Epoch: 30 [1280/50000 (3%)]\tLoss: 0.444022, Accuracy: 84.38\n",
      "Train Epoch: 30 [2560/50000 (6%)]\tLoss: 0.440102, Accuracy: 83.20\n",
      "Train Epoch: 30 [3840/50000 (9%)]\tLoss: 0.377799, Accuracy: 87.50\n",
      "Train Epoch: 30 [5120/50000 (11%)]\tLoss: 0.339573, Accuracy: 87.89\n",
      "Train Epoch: 30 [6400/50000 (14%)]\tLoss: 0.364848, Accuracy: 85.94\n",
      "Train Epoch: 30 [7680/50000 (17%)]\tLoss: 0.350427, Accuracy: 87.50\n",
      "Train Epoch: 30 [8960/50000 (20%)]\tLoss: 0.367162, Accuracy: 86.72\n",
      "Train Epoch: 30 [10240/50000 (23%)]\tLoss: 0.300661, Accuracy: 89.45\n",
      "Train Epoch: 30 [11520/50000 (26%)]\tLoss: 0.371511, Accuracy: 85.55\n",
      "Train Epoch: 30 [12800/50000 (28%)]\tLoss: 0.267504, Accuracy: 90.23\n",
      "Train Epoch: 30 [14080/50000 (31%)]\tLoss: 0.436846, Accuracy: 85.16\n",
      "Train Epoch: 30 [15360/50000 (34%)]\tLoss: 0.357425, Accuracy: 90.23\n",
      "Train Epoch: 30 [16640/50000 (37%)]\tLoss: 0.376686, Accuracy: 85.55\n",
      "Train Epoch: 30 [17920/50000 (40%)]\tLoss: 0.447426, Accuracy: 85.94\n",
      "Train Epoch: 30 [19200/50000 (43%)]\tLoss: 0.301249, Accuracy: 90.23\n",
      "Train Epoch: 30 [20480/50000 (45%)]\tLoss: 0.406268, Accuracy: 85.16\n",
      "Train Epoch: 30 [21760/50000 (48%)]\tLoss: 0.392348, Accuracy: 86.33\n",
      "Train Epoch: 30 [23040/50000 (51%)]\tLoss: 0.419867, Accuracy: 85.94\n",
      "Train Epoch: 30 [24320/50000 (54%)]\tLoss: 0.424904, Accuracy: 88.67\n",
      "Train Epoch: 30 [25600/50000 (57%)]\tLoss: 0.367930, Accuracy: 86.72\n",
      "Train Epoch: 30 [26880/50000 (60%)]\tLoss: 0.468108, Accuracy: 84.38\n",
      "Train Epoch: 30 [28160/50000 (62%)]\tLoss: 0.345910, Accuracy: 88.67\n",
      "Train Epoch: 30 [29440/50000 (65%)]\tLoss: 0.365843, Accuracy: 88.28\n",
      "Train Epoch: 30 [30720/50000 (68%)]\tLoss: 0.433634, Accuracy: 86.33\n",
      "Train Epoch: 30 [32000/50000 (71%)]\tLoss: 0.437881, Accuracy: 83.59\n",
      "Train Epoch: 30 [33280/50000 (74%)]\tLoss: 0.356560, Accuracy: 88.67\n",
      "Train Epoch: 30 [34560/50000 (77%)]\tLoss: 0.405682, Accuracy: 85.55\n",
      "Train Epoch: 30 [35840/50000 (80%)]\tLoss: 0.372899, Accuracy: 85.16\n",
      "Train Epoch: 30 [37120/50000 (82%)]\tLoss: 0.353743, Accuracy: 86.33\n",
      "Train Epoch: 30 [38400/50000 (85%)]\tLoss: 0.387535, Accuracy: 88.67\n",
      "Train Epoch: 30 [39680/50000 (88%)]\tLoss: 0.331023, Accuracy: 88.28\n",
      "Train Epoch: 30 [40960/50000 (91%)]\tLoss: 0.403645, Accuracy: 86.72\n",
      "Train Epoch: 30 [42240/50000 (94%)]\tLoss: 0.445513, Accuracy: 85.55\n",
      "Train Epoch: 30 [43520/50000 (97%)]\tLoss: 0.322803, Accuracy: 89.45\n",
      "Train Epoch: 30 [35000/50000 (99%)]\tLoss: 0.446643, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.6410, Accuracy: 3982/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.374213457107544 s]\n",
      "\n",
      "Test set: Average loss: 0.6415, Accuracy: 7977/10000 (79.77%)\n",
      "\n",
      "Train Epoch: 31 [0/50000 (0%)]\tLoss: 0.327106, Accuracy: 91.02\n",
      "Train Epoch: 31 [1280/50000 (3%)]\tLoss: 0.478582, Accuracy: 83.59\n",
      "Train Epoch: 31 [2560/50000 (6%)]\tLoss: 0.283607, Accuracy: 88.67\n",
      "Train Epoch: 31 [3840/50000 (9%)]\tLoss: 0.296783, Accuracy: 89.84\n",
      "Train Epoch: 31 [5120/50000 (11%)]\tLoss: 0.324058, Accuracy: 89.06\n",
      "Train Epoch: 31 [6400/50000 (14%)]\tLoss: 0.394607, Accuracy: 87.11\n",
      "Train Epoch: 31 [7680/50000 (17%)]\tLoss: 0.449555, Accuracy: 83.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 31 [8960/50000 (20%)]\tLoss: 0.456091, Accuracy: 84.38\n",
      "Train Epoch: 31 [10240/50000 (23%)]\tLoss: 0.381817, Accuracy: 87.11\n",
      "Train Epoch: 31 [11520/50000 (26%)]\tLoss: 0.431832, Accuracy: 85.16\n",
      "Train Epoch: 31 [12800/50000 (28%)]\tLoss: 0.368304, Accuracy: 89.45\n",
      "Train Epoch: 31 [14080/50000 (31%)]\tLoss: 0.489602, Accuracy: 83.20\n",
      "Train Epoch: 31 [15360/50000 (34%)]\tLoss: 0.405825, Accuracy: 85.94\n",
      "Train Epoch: 31 [16640/50000 (37%)]\tLoss: 0.342292, Accuracy: 86.72\n",
      "Train Epoch: 31 [17920/50000 (40%)]\tLoss: 0.397902, Accuracy: 86.72\n",
      "Train Epoch: 31 [19200/50000 (43%)]\tLoss: 0.337789, Accuracy: 89.06\n",
      "Train Epoch: 31 [20480/50000 (45%)]\tLoss: 0.454677, Accuracy: 83.20\n",
      "Train Epoch: 31 [21760/50000 (48%)]\tLoss: 0.282119, Accuracy: 91.02\n",
      "Train Epoch: 31 [23040/50000 (51%)]\tLoss: 0.296477, Accuracy: 89.06\n",
      "Train Epoch: 31 [24320/50000 (54%)]\tLoss: 0.502219, Accuracy: 80.86\n",
      "Train Epoch: 31 [25600/50000 (57%)]\tLoss: 0.481564, Accuracy: 82.42\n",
      "Train Epoch: 31 [26880/50000 (60%)]\tLoss: 0.435803, Accuracy: 85.16\n",
      "Train Epoch: 31 [28160/50000 (62%)]\tLoss: 0.397815, Accuracy: 87.89\n",
      "Train Epoch: 31 [29440/50000 (65%)]\tLoss: 0.404070, Accuracy: 87.50\n",
      "Train Epoch: 31 [30720/50000 (68%)]\tLoss: 0.467602, Accuracy: 85.94\n",
      "Train Epoch: 31 [32000/50000 (71%)]\tLoss: 0.284128, Accuracy: 86.72\n",
      "Train Epoch: 31 [33280/50000 (74%)]\tLoss: 0.468025, Accuracy: 82.81\n",
      "Train Epoch: 31 [34560/50000 (77%)]\tLoss: 0.394755, Accuracy: 88.28\n",
      "Train Epoch: 31 [35840/50000 (80%)]\tLoss: 0.336459, Accuracy: 88.67\n",
      "Train Epoch: 31 [37120/50000 (82%)]\tLoss: 0.407253, Accuracy: 87.50\n",
      "Train Epoch: 31 [38400/50000 (85%)]\tLoss: 0.394816, Accuracy: 85.94\n",
      "Train Epoch: 31 [39680/50000 (88%)]\tLoss: 0.423913, Accuracy: 87.11\n",
      "Train Epoch: 31 [40960/50000 (91%)]\tLoss: 0.379220, Accuracy: 89.06\n",
      "Train Epoch: 31 [42240/50000 (94%)]\tLoss: 0.495406, Accuracy: 80.86\n",
      "Train Epoch: 31 [43520/50000 (97%)]\tLoss: 0.340027, Accuracy: 89.06\n",
      "Train Epoch: 31 [35000/50000 (99%)]\tLoss: 0.405014, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.7615, Accuracy: 3884/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[39.45819163322449 s]\n",
      "Train Epoch: 32 [0/50000 (0%)]\tLoss: 0.343144, Accuracy: 87.89\n",
      "Train Epoch: 32 [1280/50000 (3%)]\tLoss: 0.299017, Accuracy: 91.02\n",
      "Train Epoch: 32 [2560/50000 (6%)]\tLoss: 0.344849, Accuracy: 89.06\n",
      "Train Epoch: 32 [3840/50000 (9%)]\tLoss: 0.433341, Accuracy: 85.16\n",
      "Train Epoch: 32 [5120/50000 (11%)]\tLoss: 0.424656, Accuracy: 84.77\n",
      "Train Epoch: 32 [6400/50000 (14%)]\tLoss: 0.324377, Accuracy: 87.11\n",
      "Train Epoch: 32 [7680/50000 (17%)]\tLoss: 0.309797, Accuracy: 88.67\n",
      "Train Epoch: 32 [8960/50000 (20%)]\tLoss: 0.366579, Accuracy: 87.11\n",
      "Train Epoch: 32 [10240/50000 (23%)]\tLoss: 0.437225, Accuracy: 85.55\n",
      "Train Epoch: 32 [11520/50000 (26%)]\tLoss: 0.337527, Accuracy: 89.45\n",
      "Train Epoch: 32 [12800/50000 (28%)]\tLoss: 0.391582, Accuracy: 86.33\n",
      "Train Epoch: 32 [14080/50000 (31%)]\tLoss: 0.446197, Accuracy: 84.77\n",
      "Train Epoch: 32 [15360/50000 (34%)]\tLoss: 0.478332, Accuracy: 85.16\n",
      "Train Epoch: 32 [16640/50000 (37%)]\tLoss: 0.374506, Accuracy: 86.72\n",
      "Train Epoch: 32 [17920/50000 (40%)]\tLoss: 0.297350, Accuracy: 90.62\n",
      "Train Epoch: 32 [19200/50000 (43%)]\tLoss: 0.375090, Accuracy: 86.72\n",
      "Train Epoch: 32 [20480/50000 (45%)]\tLoss: 0.536163, Accuracy: 82.81\n",
      "Train Epoch: 32 [21760/50000 (48%)]\tLoss: 0.363388, Accuracy: 88.67\n",
      "Train Epoch: 32 [23040/50000 (51%)]\tLoss: 0.433169, Accuracy: 86.72\n",
      "Train Epoch: 32 [24320/50000 (54%)]\tLoss: 0.430643, Accuracy: 84.38\n",
      "Train Epoch: 32 [25600/50000 (57%)]\tLoss: 0.313161, Accuracy: 90.62\n",
      "Train Epoch: 32 [26880/50000 (60%)]\tLoss: 0.419132, Accuracy: 84.77\n",
      "Train Epoch: 32 [28160/50000 (62%)]\tLoss: 0.385855, Accuracy: 85.16\n",
      "Train Epoch: 32 [29440/50000 (65%)]\tLoss: 0.312111, Accuracy: 90.23\n",
      "Train Epoch: 32 [30720/50000 (68%)]\tLoss: 0.477654, Accuracy: 84.38\n",
      "Train Epoch: 32 [32000/50000 (71%)]\tLoss: 0.390760, Accuracy: 88.67\n",
      "Train Epoch: 32 [33280/50000 (74%)]\tLoss: 0.403922, Accuracy: 85.16\n",
      "Train Epoch: 32 [34560/50000 (77%)]\tLoss: 0.382807, Accuracy: 84.38\n",
      "Train Epoch: 32 [35840/50000 (80%)]\tLoss: 0.301828, Accuracy: 90.23\n",
      "Train Epoch: 32 [37120/50000 (82%)]\tLoss: 0.402348, Accuracy: 87.11\n",
      "Train Epoch: 32 [38400/50000 (85%)]\tLoss: 0.314161, Accuracy: 91.02\n",
      "Train Epoch: 32 [39680/50000 (88%)]\tLoss: 0.414932, Accuracy: 84.38\n",
      "Train Epoch: 32 [40960/50000 (91%)]\tLoss: 0.403294, Accuracy: 87.50\n",
      "Train Epoch: 32 [42240/50000 (94%)]\tLoss: 0.331412, Accuracy: 87.50\n",
      "Train Epoch: 32 [43520/50000 (97%)]\tLoss: 0.435122, Accuracy: 83.98\n",
      "Train Epoch: 32 [35000/50000 (99%)]\tLoss: 0.358826, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.5729, Accuracy: 4043/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[36.42407560348511 s]\n",
      "\n",
      "Test set: Average loss: 0.5818, Accuracy: 8098/10000 (80.98%)\n",
      "\n",
      "Train Epoch: 33 [0/50000 (0%)]\tLoss: 0.329939, Accuracy: 89.84\n",
      "Train Epoch: 33 [1280/50000 (3%)]\tLoss: 0.301590, Accuracy: 89.06\n",
      "Train Epoch: 33 [2560/50000 (6%)]\tLoss: 0.361357, Accuracy: 89.06\n",
      "Train Epoch: 33 [3840/50000 (9%)]\tLoss: 0.370581, Accuracy: 89.45\n",
      "Train Epoch: 33 [5120/50000 (11%)]\tLoss: 0.343482, Accuracy: 86.72\n",
      "Train Epoch: 33 [6400/50000 (14%)]\tLoss: 0.352224, Accuracy: 87.89\n",
      "Train Epoch: 33 [7680/50000 (17%)]\tLoss: 0.316431, Accuracy: 88.67\n",
      "Train Epoch: 33 [8960/50000 (20%)]\tLoss: 0.330560, Accuracy: 89.06\n",
      "Train Epoch: 33 [10240/50000 (23%)]\tLoss: 0.328534, Accuracy: 88.67\n",
      "Train Epoch: 33 [11520/50000 (26%)]\tLoss: 0.318980, Accuracy: 89.45\n",
      "Train Epoch: 33 [12800/50000 (28%)]\tLoss: 0.377656, Accuracy: 84.77\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.1333]],\n",
      "\n",
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      "\n",
      "        [[-1.3297]],\n",
      "\n",
      "        [[-0.9407]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 2.1229]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-7.7778e-03,  1.0877e-02, -2.2580e-03,  ...,  1.3039e-02,\n",
      "           5.2846e-03,  5.3842e-03]],\n",
      "\n",
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      "           1.2980e-02, -1.5522e-03]],\n",
      "\n",
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      "          -1.5881e-02,  1.7819e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 9.4890e-03,  1.4946e-02, -4.3340e-03,  ...,  1.2295e-02,\n",
      "          -2.8803e-03,  2.7692e-03]],\n",
      "\n",
      "        [[-7.2071e-04, -1.0450e-02,  5.8550e-03,  ...,  8.0871e-03,\n",
      "           6.2262e-03,  4.4733e-03]],\n",
      "\n",
      "        [[ 2.4265e-03, -1.2654e-02,  1.1861e-02,  ...,  9.3396e-03,\n",
      "           7.9420e-03,  1.3857e-02]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 1.5450e-03,  1.5519e-03,  1.4377e-03,  ...,  1.2085e-03,\n",
      "           1.1717e-03,  1.0985e-03],\n",
      "         [-8.5824e-04, -9.8705e-04, -7.5956e-04,  ..., -8.2065e-04,\n",
      "          -1.0110e-03, -1.0492e-03],\n",
      "         [ 1.9409e-03,  1.9440e-03,  2.1945e-03,  ...,  2.7926e-03,\n",
      "           2.7038e-03,  2.7770e-03],\n",
      "         ...,\n",
      "         [-1.9710e-03, -2.1621e-03, -1.9920e-03,  ..., -1.8317e-03,\n",
      "          -1.8353e-03, -2.0401e-03],\n",
      "         [ 4.8103e-04,  2.5781e-04,  1.0177e-04,  ...,  8.9448e-05,\n",
      "           3.0602e-04,  4.7915e-04],\n",
      "         [ 9.2693e-04,  1.0630e-03,  9.0270e-04,  ...,  4.6273e-04,\n",
      "           3.6492e-04,  4.9734e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 33 [14080/50000 (31%)]\tLoss: 0.449456, Accuracy: 84.38\n",
      "Train Epoch: 33 [15360/50000 (34%)]\tLoss: 0.366138, Accuracy: 87.50\n",
      "Train Epoch: 33 [16640/50000 (37%)]\tLoss: 0.405760, Accuracy: 84.38\n",
      "Train Epoch: 33 [17920/50000 (40%)]\tLoss: 0.552573, Accuracy: 80.86\n",
      "Train Epoch: 33 [19200/50000 (43%)]\tLoss: 0.482858, Accuracy: 85.16\n",
      "Train Epoch: 33 [20480/50000 (45%)]\tLoss: 0.483496, Accuracy: 84.77\n",
      "Train Epoch: 33 [21760/50000 (48%)]\tLoss: 0.367880, Accuracy: 87.89\n",
      "Train Epoch: 33 [23040/50000 (51%)]\tLoss: 0.469100, Accuracy: 84.38\n",
      "Train Epoch: 33 [24320/50000 (54%)]\tLoss: 0.383574, Accuracy: 85.94\n",
      "Train Epoch: 33 [25600/50000 (57%)]\tLoss: 0.276581, Accuracy: 91.41\n",
      "Train Epoch: 33 [26880/50000 (60%)]\tLoss: 0.444129, Accuracy: 85.16\n",
      "Train Epoch: 33 [28160/50000 (62%)]\tLoss: 0.275956, Accuracy: 90.62\n",
      "Train Epoch: 33 [29440/50000 (65%)]\tLoss: 0.417237, Accuracy: 88.67\n",
      "Train Epoch: 33 [30720/50000 (68%)]\tLoss: 0.516331, Accuracy: 81.25\n",
      "Train Epoch: 33 [32000/50000 (71%)]\tLoss: 0.386265, Accuracy: 87.89\n",
      "Train Epoch: 33 [33280/50000 (74%)]\tLoss: 0.319738, Accuracy: 88.67\n",
      "Train Epoch: 33 [34560/50000 (77%)]\tLoss: 0.413225, Accuracy: 87.89\n",
      "Train Epoch: 33 [35840/50000 (80%)]\tLoss: 0.446244, Accuracy: 86.72\n",
      "Train Epoch: 33 [37120/50000 (82%)]\tLoss: 0.381903, Accuracy: 87.11\n",
      "Train Epoch: 33 [38400/50000 (85%)]\tLoss: 0.478098, Accuracy: 83.59\n",
      "Train Epoch: 33 [39680/50000 (88%)]\tLoss: 0.398968, Accuracy: 84.77\n",
      "Train Epoch: 33 [40960/50000 (91%)]\tLoss: 0.506968, Accuracy: 81.25\n",
      "Train Epoch: 33 [42240/50000 (94%)]\tLoss: 0.442257, Accuracy: 85.55\n",
      "Train Epoch: 33 [43520/50000 (97%)]\tLoss: 0.372604, Accuracy: 87.50\n",
      "Train Epoch: 33 [35000/50000 (99%)]\tLoss: 0.293654, Accuracy: 89.00\n",
      "\n",
      "Validation set: Average loss: 0.5704, Accuracy: 4074/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[39.57111978530884 s]\n",
      "Train Epoch: 34 [0/50000 (0%)]\tLoss: 0.526785, Accuracy: 84.77\n",
      "Train Epoch: 34 [1280/50000 (3%)]\tLoss: 0.325791, Accuracy: 90.62\n",
      "Train Epoch: 34 [2560/50000 (6%)]\tLoss: 0.386650, Accuracy: 85.55\n",
      "Train Epoch: 34 [3840/50000 (9%)]\tLoss: 0.417754, Accuracy: 87.11\n",
      "Train Epoch: 34 [5120/50000 (11%)]\tLoss: 0.441138, Accuracy: 85.55\n",
      "Train Epoch: 34 [6400/50000 (14%)]\tLoss: 0.382595, Accuracy: 89.45\n",
      "Train Epoch: 34 [7680/50000 (17%)]\tLoss: 0.319581, Accuracy: 89.06\n",
      "Train Epoch: 34 [8960/50000 (20%)]\tLoss: 0.488968, Accuracy: 80.86\n",
      "Train Epoch: 34 [10240/50000 (23%)]\tLoss: 0.473319, Accuracy: 86.72\n",
      "Train Epoch: 34 [11520/50000 (26%)]\tLoss: 0.383870, Accuracy: 88.28\n",
      "Train Epoch: 34 [12800/50000 (28%)]\tLoss: 0.315269, Accuracy: 89.06\n",
      "Train Epoch: 34 [14080/50000 (31%)]\tLoss: 0.465243, Accuracy: 83.59\n",
      "Train Epoch: 34 [15360/50000 (34%)]\tLoss: 0.333143, Accuracy: 90.62\n",
      "Train Epoch: 34 [16640/50000 (37%)]\tLoss: 0.335329, Accuracy: 90.62\n",
      "Train Epoch: 34 [17920/50000 (40%)]\tLoss: 0.393031, Accuracy: 86.33\n",
      "Train Epoch: 34 [19200/50000 (43%)]\tLoss: 0.499342, Accuracy: 84.38\n",
      "Train Epoch: 34 [20480/50000 (45%)]\tLoss: 0.374900, Accuracy: 84.38\n",
      "Train Epoch: 34 [21760/50000 (48%)]\tLoss: 0.347275, Accuracy: 88.28\n",
      "Train Epoch: 34 [23040/50000 (51%)]\tLoss: 0.326812, Accuracy: 88.28\n",
      "Train Epoch: 34 [24320/50000 (54%)]\tLoss: 0.351724, Accuracy: 86.72\n",
      "Train Epoch: 34 [25600/50000 (57%)]\tLoss: 0.416854, Accuracy: 83.98\n",
      "Train Epoch: 34 [26880/50000 (60%)]\tLoss: 0.403078, Accuracy: 87.50\n",
      "Train Epoch: 34 [28160/50000 (62%)]\tLoss: 0.372042, Accuracy: 88.28\n",
      "Train Epoch: 34 [29440/50000 (65%)]\tLoss: 0.298148, Accuracy: 89.45\n",
      "Train Epoch: 34 [30720/50000 (68%)]\tLoss: 0.437654, Accuracy: 83.98\n",
      "Train Epoch: 34 [32000/50000 (71%)]\tLoss: 0.374254, Accuracy: 86.72\n",
      "Train Epoch: 34 [33280/50000 (74%)]\tLoss: 0.432810, Accuracy: 87.89\n",
      "Train Epoch: 34 [34560/50000 (77%)]\tLoss: 0.434965, Accuracy: 84.77\n",
      "Train Epoch: 34 [35840/50000 (80%)]\tLoss: 0.474499, Accuracy: 85.16\n",
      "Train Epoch: 34 [37120/50000 (82%)]\tLoss: 0.375230, Accuracy: 85.55\n",
      "Train Epoch: 34 [38400/50000 (85%)]\tLoss: 0.376539, Accuracy: 87.11\n",
      "Train Epoch: 34 [39680/50000 (88%)]\tLoss: 0.316820, Accuracy: 89.84\n",
      "Train Epoch: 34 [40960/50000 (91%)]\tLoss: 0.395863, Accuracy: 86.33\n",
      "Train Epoch: 34 [42240/50000 (94%)]\tLoss: 0.362740, Accuracy: 89.45\n",
      "Train Epoch: 34 [43520/50000 (97%)]\tLoss: 0.443498, Accuracy: 87.11\n",
      "Train Epoch: 34 [35000/50000 (99%)]\tLoss: 0.397318, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.7170, Accuracy: 3883/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[36.55269169807434 s]\n",
      "\n",
      "Test set: Average loss: 0.7198, Accuracy: 7734/10000 (77.34%)\n",
      "\n",
      "Train Epoch: 35 [0/50000 (0%)]\tLoss: 0.325933, Accuracy: 89.84\n",
      "Train Epoch: 35 [1280/50000 (3%)]\tLoss: 0.323232, Accuracy: 88.28\n",
      "Train Epoch: 35 [2560/50000 (6%)]\tLoss: 0.360144, Accuracy: 88.28\n",
      "Train Epoch: 35 [3840/50000 (9%)]\tLoss: 0.285059, Accuracy: 92.19\n",
      "Train Epoch: 35 [5120/50000 (11%)]\tLoss: 0.300348, Accuracy: 91.02\n",
      "Train Epoch: 35 [6400/50000 (14%)]\tLoss: 0.363672, Accuracy: 88.67\n",
      "Train Epoch: 35 [7680/50000 (17%)]\tLoss: 0.305020, Accuracy: 88.67\n",
      "Train Epoch: 35 [8960/50000 (20%)]\tLoss: 0.400514, Accuracy: 87.11\n",
      "Train Epoch: 35 [10240/50000 (23%)]\tLoss: 0.392119, Accuracy: 87.50\n",
      "Train Epoch: 35 [11520/50000 (26%)]\tLoss: 0.344629, Accuracy: 87.11\n",
      "Train Epoch: 35 [12800/50000 (28%)]\tLoss: 0.348331, Accuracy: 88.67\n",
      "Train Epoch: 35 [14080/50000 (31%)]\tLoss: 0.393886, Accuracy: 85.94\n",
      "Train Epoch: 35 [15360/50000 (34%)]\tLoss: 0.360544, Accuracy: 87.50\n",
      "Train Epoch: 35 [16640/50000 (37%)]\tLoss: 0.448507, Accuracy: 85.16\n",
      "Train Epoch: 35 [17920/50000 (40%)]\tLoss: 0.373991, Accuracy: 87.11\n",
      "Train Epoch: 35 [19200/50000 (43%)]\tLoss: 0.392201, Accuracy: 87.50\n",
      "Train Epoch: 35 [20480/50000 (45%)]\tLoss: 0.473297, Accuracy: 85.16\n",
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      "Train Epoch: 35 [23040/50000 (51%)]\tLoss: 0.309380, Accuracy: 87.89\n",
      "Train Epoch: 35 [24320/50000 (54%)]\tLoss: 0.355816, Accuracy: 89.06\n",
      "Train Epoch: 35 [25600/50000 (57%)]\tLoss: 0.390001, Accuracy: 85.16\n",
      "Train Epoch: 35 [26880/50000 (60%)]\tLoss: 0.434506, Accuracy: 86.72\n",
      "Train Epoch: 35 [28160/50000 (62%)]\tLoss: 0.481154, Accuracy: 84.77\n",
      "Train Epoch: 35 [29440/50000 (65%)]\tLoss: 0.287411, Accuracy: 89.84\n",
      "Train Epoch: 35 [30720/50000 (68%)]\tLoss: 0.346403, Accuracy: 87.89\n",
      "Train Epoch: 35 [32000/50000 (71%)]\tLoss: 0.479000, Accuracy: 83.59\n",
      "Train Epoch: 35 [33280/50000 (74%)]\tLoss: 0.377794, Accuracy: 87.89\n",
      "Train Epoch: 35 [34560/50000 (77%)]\tLoss: 0.300251, Accuracy: 89.45\n",
      "Train Epoch: 35 [35840/50000 (80%)]\tLoss: 0.372658, Accuracy: 85.55\n",
      "Train Epoch: 35 [37120/50000 (82%)]\tLoss: 0.360122, Accuracy: 90.23\n",
      "Train Epoch: 35 [38400/50000 (85%)]\tLoss: 0.368390, Accuracy: 86.72\n",
      "Train Epoch: 35 [39680/50000 (88%)]\tLoss: 0.495052, Accuracy: 83.98\n",
      "Train Epoch: 35 [40960/50000 (91%)]\tLoss: 0.404795, Accuracy: 85.55\n",
      "Train Epoch: 35 [42240/50000 (94%)]\tLoss: 0.378003, Accuracy: 88.28\n",
      "Train Epoch: 35 [43520/50000 (97%)]\tLoss: 0.496457, Accuracy: 82.42\n",
      "Train Epoch: 35 [35000/50000 (99%)]\tLoss: 0.355908, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.5910, Accuracy: 4042/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[39.58226680755615 s]\n",
      "Train Epoch: 36 [0/50000 (0%)]\tLoss: 0.351028, Accuracy: 85.94\n",
      "Train Epoch: 36 [1280/50000 (3%)]\tLoss: 0.349507, Accuracy: 86.72\n",
      "Train Epoch: 36 [2560/50000 (6%)]\tLoss: 0.457985, Accuracy: 80.86\n",
      "Train Epoch: 36 [3840/50000 (9%)]\tLoss: 0.369549, Accuracy: 86.33\n",
      "Train Epoch: 36 [5120/50000 (11%)]\tLoss: 0.388166, Accuracy: 86.33\n",
      "Train Epoch: 36 [6400/50000 (14%)]\tLoss: 0.290006, Accuracy: 90.23\n",
      "Train Epoch: 36 [7680/50000 (17%)]\tLoss: 0.369736, Accuracy: 87.50\n",
      "Train Epoch: 36 [8960/50000 (20%)]\tLoss: 0.393121, Accuracy: 85.55\n",
      "Train Epoch: 36 [10240/50000 (23%)]\tLoss: 0.395745, Accuracy: 85.94\n",
      "Train Epoch: 36 [11520/50000 (26%)]\tLoss: 0.393714, Accuracy: 86.33\n",
      "Train Epoch: 36 [12800/50000 (28%)]\tLoss: 0.321659, Accuracy: 90.23\n",
      "Train Epoch: 36 [14080/50000 (31%)]\tLoss: 0.441895, Accuracy: 85.94\n",
      "Train Epoch: 36 [15360/50000 (34%)]\tLoss: 0.379919, Accuracy: 87.89\n",
      "Train Epoch: 36 [16640/50000 (37%)]\tLoss: 0.376827, Accuracy: 89.06\n",
      "Train Epoch: 36 [17920/50000 (40%)]\tLoss: 0.359530, Accuracy: 86.72\n",
      "Train Epoch: 36 [19200/50000 (43%)]\tLoss: 0.287636, Accuracy: 90.23\n",
      "Train Epoch: 36 [20480/50000 (45%)]\tLoss: 0.296881, Accuracy: 89.84\n",
      "Train Epoch: 36 [21760/50000 (48%)]\tLoss: 0.325225, Accuracy: 87.11\n",
      "Train Epoch: 36 [23040/50000 (51%)]\tLoss: 0.306024, Accuracy: 88.28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 36 [24320/50000 (54%)]\tLoss: 0.358888, Accuracy: 85.16\n",
      "Train Epoch: 36 [25600/50000 (57%)]\tLoss: 0.316192, Accuracy: 89.45\n",
      "Train Epoch: 36 [26880/50000 (60%)]\tLoss: 0.388665, Accuracy: 87.50\n",
      "Train Epoch: 36 [28160/50000 (62%)]\tLoss: 0.409102, Accuracy: 87.11\n",
      "Train Epoch: 36 [29440/50000 (65%)]\tLoss: 0.348941, Accuracy: 87.50\n",
      "Train Epoch: 36 [30720/50000 (68%)]\tLoss: 0.388118, Accuracy: 85.94\n",
      "Train Epoch: 36 [32000/50000 (71%)]\tLoss: 0.381455, Accuracy: 85.55\n",
      "Train Epoch: 36 [33280/50000 (74%)]\tLoss: 0.409503, Accuracy: 84.77\n",
      "Train Epoch: 36 [34560/50000 (77%)]\tLoss: 0.443904, Accuracy: 86.72\n",
      "Train Epoch: 36 [35840/50000 (80%)]\tLoss: 0.371955, Accuracy: 86.72\n",
      "Train Epoch: 36 [37120/50000 (82%)]\tLoss: 0.394165, Accuracy: 87.50\n",
      "Train Epoch: 36 [38400/50000 (85%)]\tLoss: 0.408001, Accuracy: 85.94\n",
      "Train Epoch: 36 [39680/50000 (88%)]\tLoss: 0.507856, Accuracy: 80.08\n",
      "Train Epoch: 36 [40960/50000 (91%)]\tLoss: 0.483972, Accuracy: 82.42\n",
      "Train Epoch: 36 [42240/50000 (94%)]\tLoss: 0.423196, Accuracy: 86.33\n",
      "Train Epoch: 36 [43520/50000 (97%)]\tLoss: 0.384213, Accuracy: 85.55\n",
      "Train Epoch: 36 [35000/50000 (99%)]\tLoss: 0.293865, Accuracy: 89.50\n",
      "\n",
      "Validation set: Average loss: 0.7123, Accuracy: 3917/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.46024513244629 s]\n",
      "\n",
      "Test set: Average loss: 0.7077, Accuracy: 7822/10000 (78.22%)\n",
      "\n",
      "Train Epoch: 37 [0/50000 (0%)]\tLoss: 0.388440, Accuracy: 85.16\n",
      "Train Epoch: 37 [1280/50000 (3%)]\tLoss: 0.375343, Accuracy: 86.72\n",
      "Train Epoch: 37 [2560/50000 (6%)]\tLoss: 0.266670, Accuracy: 91.80\n",
      "Train Epoch: 37 [3840/50000 (9%)]\tLoss: 0.271423, Accuracy: 91.41\n",
      "Train Epoch: 37 [5120/50000 (11%)]\tLoss: 0.333073, Accuracy: 87.50\n",
      "Train Epoch: 37 [6400/50000 (14%)]\tLoss: 0.230848, Accuracy: 91.80\n",
      "Train Epoch: 37 [7680/50000 (17%)]\tLoss: 0.393350, Accuracy: 85.94\n",
      "Train Epoch: 37 [8960/50000 (20%)]\tLoss: 0.398419, Accuracy: 86.33\n",
      "Train Epoch: 37 [10240/50000 (23%)]\tLoss: 0.354065, Accuracy: 89.06\n",
      "Train Epoch: 37 [11520/50000 (26%)]\tLoss: 0.415118, Accuracy: 88.28\n",
      "Train Epoch: 37 [12800/50000 (28%)]\tLoss: 0.389290, Accuracy: 85.16\n",
      "Train Epoch: 37 [14080/50000 (31%)]\tLoss: 0.365945, Accuracy: 89.84\n",
      "Train Epoch: 37 [15360/50000 (34%)]\tLoss: 0.422319, Accuracy: 83.98\n",
      "Train Epoch: 37 [16640/50000 (37%)]\tLoss: 0.370913, Accuracy: 87.89\n",
      "Train Epoch: 37 [17920/50000 (40%)]\tLoss: 0.346462, Accuracy: 87.89\n",
      "Train Epoch: 37 [19200/50000 (43%)]\tLoss: 0.360243, Accuracy: 88.67\n",
      "Train Epoch: 37 [20480/50000 (45%)]\tLoss: 0.289404, Accuracy: 90.62\n",
      "Train Epoch: 37 [21760/50000 (48%)]\tLoss: 0.363927, Accuracy: 87.50\n",
      "Train Epoch: 37 [23040/50000 (51%)]\tLoss: 0.295727, Accuracy: 91.80\n",
      "Train Epoch: 37 [24320/50000 (54%)]\tLoss: 0.360135, Accuracy: 88.28\n",
      "Train Epoch: 37 [25600/50000 (57%)]\tLoss: 0.373193, Accuracy: 88.67\n",
      "Train Epoch: 37 [26880/50000 (60%)]\tLoss: 0.363332, Accuracy: 86.72\n",
      "Train Epoch: 37 [28160/50000 (62%)]\tLoss: 0.344342, Accuracy: 87.11\n",
      "Train Epoch: 37 [29440/50000 (65%)]\tLoss: 0.409401, Accuracy: 88.67\n",
      "Train Epoch: 37 [30720/50000 (68%)]\tLoss: 0.347807, Accuracy: 88.28\n",
      "Train Epoch: 37 [32000/50000 (71%)]\tLoss: 0.292216, Accuracy: 89.06\n",
      "Train Epoch: 37 [33280/50000 (74%)]\tLoss: 0.381277, Accuracy: 84.38\n",
      "Train Epoch: 37 [34560/50000 (77%)]\tLoss: 0.424651, Accuracy: 85.94\n",
      "Train Epoch: 37 [35840/50000 (80%)]\tLoss: 0.514459, Accuracy: 81.25\n",
      "Train Epoch: 37 [37120/50000 (82%)]\tLoss: 0.498966, Accuracy: 80.47\n",
      "Train Epoch: 37 [38400/50000 (85%)]\tLoss: 0.387365, Accuracy: 88.67\n",
      "Train Epoch: 37 [39680/50000 (88%)]\tLoss: 0.364463, Accuracy: 87.50\n",
      "Train Epoch: 37 [40960/50000 (91%)]\tLoss: 0.413787, Accuracy: 85.94\n",
      "Train Epoch: 37 [42240/50000 (94%)]\tLoss: 0.331525, Accuracy: 89.06\n",
      "Train Epoch: 37 [43520/50000 (97%)]\tLoss: 0.453299, Accuracy: 84.77\n",
      "Train Epoch: 37 [35000/50000 (99%)]\tLoss: 0.313298, Accuracy: 91.00\n",
      "\n",
      "Validation set: Average loss: 0.6489, Accuracy: 3946/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.47083353996277 s]\n",
      "Train Epoch: 38 [0/50000 (0%)]\tLoss: 0.365725, Accuracy: 87.50\n",
      "Train Epoch: 38 [1280/50000 (3%)]\tLoss: 0.506722, Accuracy: 79.69\n",
      "Train Epoch: 38 [2560/50000 (6%)]\tLoss: 0.356328, Accuracy: 87.50\n",
      "Train Epoch: 38 [3840/50000 (9%)]\tLoss: 0.288174, Accuracy: 90.23\n",
      "Train Epoch: 38 [5120/50000 (11%)]\tLoss: 0.449771, Accuracy: 87.89\n",
      "Train Epoch: 38 [6400/50000 (14%)]\tLoss: 0.417934, Accuracy: 86.72\n",
      "Train Epoch: 38 [7680/50000 (17%)]\tLoss: 0.369030, Accuracy: 87.89\n",
      "Train Epoch: 38 [8960/50000 (20%)]\tLoss: 0.418342, Accuracy: 84.38\n",
      "Train Epoch: 38 [10240/50000 (23%)]\tLoss: 0.333515, Accuracy: 89.06\n",
      "Train Epoch: 38 [11520/50000 (26%)]\tLoss: 0.440299, Accuracy: 85.55\n",
      "Train Epoch: 38 [12800/50000 (28%)]\tLoss: 0.408350, Accuracy: 86.72\n",
      "Train Epoch: 38 [14080/50000 (31%)]\tLoss: 0.401217, Accuracy: 87.50\n",
      "Train Epoch: 38 [15360/50000 (34%)]\tLoss: 0.414537, Accuracy: 85.55\n",
      "Train Epoch: 38 [16640/50000 (37%)]\tLoss: 0.329715, Accuracy: 88.67\n",
      "Train Epoch: 38 [17920/50000 (40%)]\tLoss: 0.388573, Accuracy: 86.33\n",
      "Train Epoch: 38 [19200/50000 (43%)]\tLoss: 0.313865, Accuracy: 88.28\n",
      "Train Epoch: 38 [20480/50000 (45%)]\tLoss: 0.362248, Accuracy: 89.45\n",
      "Train Epoch: 38 [21760/50000 (48%)]\tLoss: 0.433836, Accuracy: 86.72\n",
      "Train Epoch: 38 [23040/50000 (51%)]\tLoss: 0.341428, Accuracy: 89.06\n",
      "Train Epoch: 38 [24320/50000 (54%)]\tLoss: 0.515135, Accuracy: 85.16\n",
      "Train Epoch: 38 [25600/50000 (57%)]\tLoss: 0.397570, Accuracy: 87.11\n",
      "Train Epoch: 38 [26880/50000 (60%)]\tLoss: 0.329902, Accuracy: 88.67\n",
      "Train Epoch: 38 [28160/50000 (62%)]\tLoss: 0.371140, Accuracy: 87.11\n",
      "Train Epoch: 38 [29440/50000 (65%)]\tLoss: 0.398547, Accuracy: 87.11\n",
      "Train Epoch: 38 [30720/50000 (68%)]\tLoss: 0.390699, Accuracy: 87.11\n",
      "Train Epoch: 38 [32000/50000 (71%)]\tLoss: 0.430147, Accuracy: 83.98\n",
      "Train Epoch: 38 [33280/50000 (74%)]\tLoss: 0.404147, Accuracy: 84.77\n",
      "Train Epoch: 38 [34560/50000 (77%)]\tLoss: 0.302059, Accuracy: 88.67\n",
      "Train Epoch: 38 [35840/50000 (80%)]\tLoss: 0.423566, Accuracy: 85.55\n",
      "Train Epoch: 38 [37120/50000 (82%)]\tLoss: 0.430419, Accuracy: 85.16\n",
      "Train Epoch: 38 [38400/50000 (85%)]\tLoss: 0.424790, Accuracy: 84.77\n",
      "Train Epoch: 38 [39680/50000 (88%)]\tLoss: 0.351359, Accuracy: 87.89\n",
      "Train Epoch: 38 [40960/50000 (91%)]\tLoss: 0.372493, Accuracy: 87.11\n",
      "Train Epoch: 38 [42240/50000 (94%)]\tLoss: 0.401661, Accuracy: 86.72\n",
      "Train Epoch: 38 [43520/50000 (97%)]\tLoss: 0.422138, Accuracy: 83.98\n",
      "Train Epoch: 38 [35000/50000 (99%)]\tLoss: 0.335806, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.5985, Accuracy: 3980/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.4444260597229 s]\n",
      "\n",
      "Test set: Average loss: 0.6040, Accuracy: 8044/10000 (80.44%)\n",
      "\n",
      "Train Epoch: 39 [0/50000 (0%)]\tLoss: 0.343050, Accuracy: 89.06\n",
      "Train Epoch: 39 [1280/50000 (3%)]\tLoss: 0.377511, Accuracy: 87.89\n",
      "Train Epoch: 39 [2560/50000 (6%)]\tLoss: 0.391192, Accuracy: 86.33\n",
      "Train Epoch: 39 [3840/50000 (9%)]\tLoss: 0.382988, Accuracy: 88.28\n",
      "Train Epoch: 39 [5120/50000 (11%)]\tLoss: 0.380347, Accuracy: 85.16\n",
      "Train Epoch: 39 [6400/50000 (14%)]\tLoss: 0.337499, Accuracy: 87.11\n",
      "Train Epoch: 39 [7680/50000 (17%)]\tLoss: 0.420795, Accuracy: 85.94\n",
      "Train Epoch: 39 [8960/50000 (20%)]\tLoss: 0.405089, Accuracy: 85.94\n",
      "Train Epoch: 39 [10240/50000 (23%)]\tLoss: 0.440961, Accuracy: 85.16\n",
      "Train Epoch: 39 [11520/50000 (26%)]\tLoss: 0.329213, Accuracy: 88.67\n",
      "Train Epoch: 39 [12800/50000 (28%)]\tLoss: 0.410052, Accuracy: 85.16\n",
      "Train Epoch: 39 [14080/50000 (31%)]\tLoss: 0.359958, Accuracy: 89.84\n",
      "Train Epoch: 39 [15360/50000 (34%)]\tLoss: 0.267253, Accuracy: 91.80\n",
      "Train Epoch: 39 [16640/50000 (37%)]\tLoss: 0.361777, Accuracy: 89.06\n",
      "Train Epoch: 39 [17920/50000 (40%)]\tLoss: 0.356445, Accuracy: 87.89\n",
      "Train Epoch: 39 [19200/50000 (43%)]\tLoss: 0.383224, Accuracy: 88.67\n",
      "Train Epoch: 39 [20480/50000 (45%)]\tLoss: 0.367045, Accuracy: 87.89\n",
      "Train Epoch: 39 [21760/50000 (48%)]\tLoss: 0.411880, Accuracy: 85.16\n",
      "Train Epoch: 39 [23040/50000 (51%)]\tLoss: 0.493626, Accuracy: 84.77\n",
      "Train Epoch: 39 [24320/50000 (54%)]\tLoss: 0.322741, Accuracy: 90.62\n",
      "Train Epoch: 39 [25600/50000 (57%)]\tLoss: 0.349543, Accuracy: 87.89\n",
      "Train Epoch: 39 [26880/50000 (60%)]\tLoss: 0.357117, Accuracy: 88.67\n",
      "Train Epoch: 39 [28160/50000 (62%)]\tLoss: 0.341354, Accuracy: 89.84\n",
      "Train Epoch: 39 [29440/50000 (65%)]\tLoss: 0.479635, Accuracy: 83.98\n",
      "Train Epoch: 39 [30720/50000 (68%)]\tLoss: 0.378705, Accuracy: 86.33\n",
      "Train Epoch: 39 [32000/50000 (71%)]\tLoss: 0.307231, Accuracy: 89.45\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 39 [33280/50000 (74%)]\tLoss: 0.336927, Accuracy: 88.67\n",
      "Train Epoch: 39 [34560/50000 (77%)]\tLoss: 0.348759, Accuracy: 87.89\n",
      "Train Epoch: 39 [35840/50000 (80%)]\tLoss: 0.323871, Accuracy: 88.28\n",
      "Train Epoch: 39 [37120/50000 (82%)]\tLoss: 0.319662, Accuracy: 87.89\n",
      "Train Epoch: 39 [38400/50000 (85%)]\tLoss: 0.493438, Accuracy: 83.98\n",
      "Train Epoch: 39 [39680/50000 (88%)]\tLoss: 0.424155, Accuracy: 86.33\n",
      "Train Epoch: 39 [40960/50000 (91%)]\tLoss: 0.376007, Accuracy: 87.89\n",
      "Train Epoch: 39 [42240/50000 (94%)]\tLoss: 0.268446, Accuracy: 91.02\n",
      "Train Epoch: 39 [43520/50000 (97%)]\tLoss: 0.326964, Accuracy: 88.67\n",
      "Train Epoch: 39 [35000/50000 (99%)]\tLoss: 0.432811, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.6260, Accuracy: 3986/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[39.43285012245178 s]\n",
      "Train Epoch: 40 [0/50000 (0%)]\tLoss: 0.306331, Accuracy: 87.11\n",
      "Train Epoch: 40 [1280/50000 (3%)]\tLoss: 0.337611, Accuracy: 87.50\n",
      "Train Epoch: 40 [2560/50000 (6%)]\tLoss: 0.388340, Accuracy: 85.55\n",
      "Train Epoch: 40 [3840/50000 (9%)]\tLoss: 0.410040, Accuracy: 84.77\n",
      "Train Epoch: 40 [5120/50000 (11%)]\tLoss: 0.442086, Accuracy: 85.94\n",
      "Train Epoch: 40 [6400/50000 (14%)]\tLoss: 0.347732, Accuracy: 88.28\n",
      "Train Epoch: 40 [7680/50000 (17%)]\tLoss: 0.391737, Accuracy: 88.28\n",
      "Train Epoch: 40 [8960/50000 (20%)]\tLoss: 0.352086, Accuracy: 90.23\n",
      "Train Epoch: 40 [10240/50000 (23%)]\tLoss: 0.418122, Accuracy: 86.33\n",
      "Train Epoch: 40 [11520/50000 (26%)]\tLoss: 0.339910, Accuracy: 87.89\n",
      "Train Epoch: 40 [12800/50000 (28%)]\tLoss: 0.398986, Accuracy: 85.55\n",
      "Train Epoch: 40 [14080/50000 (31%)]\tLoss: 0.393527, Accuracy: 87.11\n",
      "Train Epoch: 40 [15360/50000 (34%)]\tLoss: 0.370256, Accuracy: 87.50\n",
      "Train Epoch: 40 [16640/50000 (37%)]\tLoss: 0.392551, Accuracy: 86.72\n",
      "Train Epoch: 40 [17920/50000 (40%)]\tLoss: 0.271310, Accuracy: 88.67\n",
      "Train Epoch: 40 [19200/50000 (43%)]\tLoss: 0.424248, Accuracy: 85.16\n",
      "Train Epoch: 40 [20480/50000 (45%)]\tLoss: 0.305541, Accuracy: 89.45\n",
      "Train Epoch: 40 [21760/50000 (48%)]\tLoss: 0.362584, Accuracy: 87.89\n",
      "Train Epoch: 40 [23040/50000 (51%)]\tLoss: 0.356514, Accuracy: 84.38\n",
      "Train Epoch: 40 [24320/50000 (54%)]\tLoss: 0.396213, Accuracy: 89.06\n",
      "Train Epoch: 40 [25600/50000 (57%)]\tLoss: 0.429059, Accuracy: 85.16\n",
      "Train Epoch: 40 [26880/50000 (60%)]\tLoss: 0.352846, Accuracy: 87.11\n",
      "Train Epoch: 40 [28160/50000 (62%)]\tLoss: 0.365787, Accuracy: 85.16\n",
      "Train Epoch: 40 [29440/50000 (65%)]\tLoss: 0.380185, Accuracy: 86.72\n",
      "Train Epoch: 40 [30720/50000 (68%)]\tLoss: 0.405051, Accuracy: 86.33\n",
      "Train Epoch: 40 [32000/50000 (71%)]\tLoss: 0.382107, Accuracy: 85.55\n",
      "Train Epoch: 40 [33280/50000 (74%)]\tLoss: 0.534114, Accuracy: 80.86\n",
      "Train Epoch: 40 [34560/50000 (77%)]\tLoss: 0.354168, Accuracy: 88.28\n",
      "Train Epoch: 40 [35840/50000 (80%)]\tLoss: 0.332608, Accuracy: 89.06\n",
      "Train Epoch: 40 [37120/50000 (82%)]\tLoss: 0.308440, Accuracy: 89.84\n",
      "Train Epoch: 40 [38400/50000 (85%)]\tLoss: 0.412457, Accuracy: 87.11\n",
      "Train Epoch: 40 [39680/50000 (88%)]\tLoss: 0.285848, Accuracy: 89.45\n",
      "Train Epoch: 40 [40960/50000 (91%)]\tLoss: 0.390141, Accuracy: 86.33\n",
      "Train Epoch: 40 [42240/50000 (94%)]\tLoss: 0.398643, Accuracy: 88.28\n",
      "Train Epoch: 40 [43520/50000 (97%)]\tLoss: 0.317947, Accuracy: 89.84\n",
      "Train Epoch: 40 [35000/50000 (99%)]\tLoss: 0.485167, Accuracy: 79.50\n",
      "\n",
      "Validation set: Average loss: 0.5395, Accuracy: 4105/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[36.3279492855072 s]\n",
      "\n",
      "Test set: Average loss: 0.5503, Accuracy: 8250/10000 (82.50%)\n",
      "\n",
      "Train Epoch: 41 [0/50000 (0%)]\tLoss: 0.377500, Accuracy: 88.28\n",
      "Train Epoch: 41 [1280/50000 (3%)]\tLoss: 0.320256, Accuracy: 88.67\n",
      "Train Epoch: 41 [2560/50000 (6%)]\tLoss: 0.251905, Accuracy: 90.23\n",
      "Train Epoch: 41 [3840/50000 (9%)]\tLoss: 0.264433, Accuracy: 91.02\n",
      "Train Epoch: 41 [5120/50000 (11%)]\tLoss: 0.492317, Accuracy: 81.64\n",
      "Train Epoch: 41 [6400/50000 (14%)]\tLoss: 0.367879, Accuracy: 88.67\n",
      "Train Epoch: 41 [7680/50000 (17%)]\tLoss: 0.306081, Accuracy: 88.67\n",
      "Train Epoch: 41 [8960/50000 (20%)]\tLoss: 0.339460, Accuracy: 87.50\n",
      "Train Epoch: 41 [10240/50000 (23%)]\tLoss: 0.368115, Accuracy: 88.28\n",
      "Train Epoch: 41 [11520/50000 (26%)]\tLoss: 0.316536, Accuracy: 89.45\n",
      "Train Epoch: 41 [12800/50000 (28%)]\tLoss: 0.406692, Accuracy: 84.77\n",
      "Train Epoch: 41 [14080/50000 (31%)]\tLoss: 0.384687, Accuracy: 87.89\n",
      "Train Epoch: 41 [15360/50000 (34%)]\tLoss: 0.379809, Accuracy: 87.11\n",
      "Train Epoch: 41 [16640/50000 (37%)]\tLoss: 0.428434, Accuracy: 85.16\n",
      "Train Epoch: 41 [17920/50000 (40%)]\tLoss: 0.292462, Accuracy: 88.28\n",
      "Train Epoch: 41 [19200/50000 (43%)]\tLoss: 0.304908, Accuracy: 88.67\n",
      "Train Epoch: 41 [20480/50000 (45%)]\tLoss: 0.390873, Accuracy: 86.72\n",
      "Train Epoch: 41 [21760/50000 (48%)]\tLoss: 0.442945, Accuracy: 83.59\n",
      "Train Epoch: 41 [23040/50000 (51%)]\tLoss: 0.362616, Accuracy: 87.50\n",
      "Train Epoch: 41 [24320/50000 (54%)]\tLoss: 0.321948, Accuracy: 90.62\n",
      "Train Epoch: 41 [25600/50000 (57%)]\tLoss: 0.476535, Accuracy: 85.55\n",
      "Train Epoch: 41 [26880/50000 (60%)]\tLoss: 0.385537, Accuracy: 86.72\n",
      "Train Epoch: 41 [28160/50000 (62%)]\tLoss: 0.404712, Accuracy: 86.33\n",
      "Train Epoch: 41 [29440/50000 (65%)]\tLoss: 0.304290, Accuracy: 89.06\n",
      "Train Epoch: 41 [30720/50000 (68%)]\tLoss: 0.391782, Accuracy: 84.38\n",
      "Train Epoch: 41 [32000/50000 (71%)]\tLoss: 0.399870, Accuracy: 87.89\n",
      "Train Epoch: 41 [33280/50000 (74%)]\tLoss: 0.383043, Accuracy: 84.77\n",
      "Train Epoch: 41 [34560/50000 (77%)]\tLoss: 0.349216, Accuracy: 89.06\n",
      "Train Epoch: 41 [35840/50000 (80%)]\tLoss: 0.353285, Accuracy: 87.11\n",
      "Train Epoch: 41 [37120/50000 (82%)]\tLoss: 0.379557, Accuracy: 89.06\n",
      "Train Epoch: 41 [38400/50000 (85%)]\tLoss: 0.320826, Accuracy: 88.28\n",
      "Train Epoch: 41 [39680/50000 (88%)]\tLoss: 0.331171, Accuracy: 87.11\n",
      "Train Epoch: 41 [40960/50000 (91%)]\tLoss: 0.463372, Accuracy: 83.98\n",
      "Train Epoch: 41 [42240/50000 (94%)]\tLoss: 0.276097, Accuracy: 90.62\n",
      "Train Epoch: 41 [43520/50000 (97%)]\tLoss: 0.393544, Accuracy: 85.55\n",
      "Train Epoch: 41 [35000/50000 (99%)]\tLoss: 0.360816, Accuracy: 88.50\n",
      "\n",
      "Validation set: Average loss: 0.5036, Accuracy: 4151/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[39.53043246269226 s]\n",
      "Train Epoch: 42 [0/50000 (0%)]\tLoss: 0.333146, Accuracy: 89.84\n",
      "Train Epoch: 42 [1280/50000 (3%)]\tLoss: 0.340872, Accuracy: 88.28\n",
      "Train Epoch: 42 [2560/50000 (6%)]\tLoss: 0.262857, Accuracy: 92.58\n",
      "Train Epoch: 42 [3840/50000 (9%)]\tLoss: 0.337841, Accuracy: 88.67\n",
      "Train Epoch: 42 [5120/50000 (11%)]\tLoss: 0.312915, Accuracy: 89.84\n",
      "Train Epoch: 42 [6400/50000 (14%)]\tLoss: 0.371931, Accuracy: 87.11\n",
      "Train Epoch: 42 [7680/50000 (17%)]\tLoss: 0.359155, Accuracy: 88.67\n",
      "Train Epoch: 42 [8960/50000 (20%)]\tLoss: 0.330444, Accuracy: 87.50\n",
      "Train Epoch: 42 [10240/50000 (23%)]\tLoss: 0.398803, Accuracy: 86.72\n",
      "Train Epoch: 42 [11520/50000 (26%)]\tLoss: 0.367153, Accuracy: 87.11\n",
      "Train Epoch: 42 [12800/50000 (28%)]\tLoss: 0.364329, Accuracy: 89.45\n",
      "Train Epoch: 42 [14080/50000 (31%)]\tLoss: 0.290762, Accuracy: 90.23\n",
      "Train Epoch: 42 [15360/50000 (34%)]\tLoss: 0.303081, Accuracy: 89.45\n",
      "Train Epoch: 42 [16640/50000 (37%)]\tLoss: 0.369340, Accuracy: 88.28\n",
      "Train Epoch: 42 [17920/50000 (40%)]\tLoss: 0.350831, Accuracy: 88.67\n",
      "Train Epoch: 42 [19200/50000 (43%)]\tLoss: 0.446840, Accuracy: 84.38\n",
      "Train Epoch: 42 [20480/50000 (45%)]\tLoss: 0.351498, Accuracy: 87.50\n",
      "Train Epoch: 42 [21760/50000 (48%)]\tLoss: 0.404161, Accuracy: 86.33\n",
      "Train Epoch: 42 [23040/50000 (51%)]\tLoss: 0.358684, Accuracy: 87.89\n",
      "Train Epoch: 42 [24320/50000 (54%)]\tLoss: 0.328742, Accuracy: 88.28\n",
      "Train Epoch: 42 [25600/50000 (57%)]\tLoss: 0.298685, Accuracy: 89.84\n",
      "Train Epoch: 42 [26880/50000 (60%)]\tLoss: 0.428965, Accuracy: 84.77\n",
      "Train Epoch: 42 [28160/50000 (62%)]\tLoss: 0.362582, Accuracy: 87.89\n",
      "Train Epoch: 42 [29440/50000 (65%)]\tLoss: 0.472998, Accuracy: 82.42\n",
      "Train Epoch: 42 [30720/50000 (68%)]\tLoss: 0.482855, Accuracy: 84.38\n",
      "Train Epoch: 42 [32000/50000 (71%)]\tLoss: 0.455026, Accuracy: 83.98\n",
      "Train Epoch: 42 [33280/50000 (74%)]\tLoss: 0.279375, Accuracy: 91.41\n",
      "Train Epoch: 42 [34560/50000 (77%)]\tLoss: 0.376299, Accuracy: 86.33\n",
      "Train Epoch: 42 [35840/50000 (80%)]\tLoss: 0.312266, Accuracy: 92.97\n",
      "Train Epoch: 42 [37120/50000 (82%)]\tLoss: 0.309482, Accuracy: 88.67\n",
      "Train Epoch: 42 [38400/50000 (85%)]\tLoss: 0.308766, Accuracy: 91.02\n",
      "Train Epoch: 42 [39680/50000 (88%)]\tLoss: 0.487195, Accuracy: 83.20\n",
      "Train Epoch: 42 [40960/50000 (91%)]\tLoss: 0.470117, Accuracy: 83.98\n",
      "Train Epoch: 42 [42240/50000 (94%)]\tLoss: 0.393365, Accuracy: 86.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 42 [43520/50000 (97%)]\tLoss: 0.384873, Accuracy: 87.50\n",
      "Train Epoch: 42 [35000/50000 (99%)]\tLoss: 0.359269, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6499, Accuracy: 3932/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.333221673965454 s]\n",
      "\n",
      "Test set: Average loss: 0.6656, Accuracy: 7847/10000 (78.47%)\n",
      "\n",
      "Train Epoch: 43 [0/50000 (0%)]\tLoss: 0.375959, Accuracy: 87.50\n",
      "Train Epoch: 43 [1280/50000 (3%)]\tLoss: 0.276516, Accuracy: 91.41\n",
      "Train Epoch: 43 [2560/50000 (6%)]\tLoss: 0.300378, Accuracy: 89.84\n",
      "Train Epoch: 43 [3840/50000 (9%)]\tLoss: 0.305303, Accuracy: 88.67\n",
      "Train Epoch: 43 [5120/50000 (11%)]\tLoss: 0.385824, Accuracy: 84.77\n",
      "Train Epoch: 43 [6400/50000 (14%)]\tLoss: 0.253335, Accuracy: 91.80\n",
      "Train Epoch: 43 [7680/50000 (17%)]\tLoss: 0.349547, Accuracy: 87.50\n",
      "Train Epoch: 43 [8960/50000 (20%)]\tLoss: 0.349754, Accuracy: 86.72\n",
      "Train Epoch: 43 [10240/50000 (23%)]\tLoss: 0.403730, Accuracy: 88.28\n",
      "Train Epoch: 43 [11520/50000 (26%)]\tLoss: 0.350306, Accuracy: 89.84\n",
      "Train Epoch: 43 [12800/50000 (28%)]\tLoss: 0.353925, Accuracy: 87.50\n",
      "Train Epoch: 43 [14080/50000 (31%)]\tLoss: 0.360369, Accuracy: 88.28\n",
      "Train Epoch: 43 [15360/50000 (34%)]\tLoss: 0.372587, Accuracy: 87.50\n",
      "Train Epoch: 43 [16640/50000 (37%)]\tLoss: 0.403752, Accuracy: 85.94\n",
      "Train Epoch: 43 [17920/50000 (40%)]\tLoss: 0.338547, Accuracy: 87.11\n",
      "Train Epoch: 43 [19200/50000 (43%)]\tLoss: 0.360603, Accuracy: 87.50\n",
      "Train Epoch: 43 [20480/50000 (45%)]\tLoss: 0.282011, Accuracy: 90.23\n",
      "Train Epoch: 43 [21760/50000 (48%)]\tLoss: 0.400105, Accuracy: 87.50\n",
      "Train Epoch: 43 [23040/50000 (51%)]\tLoss: 0.303963, Accuracy: 89.06\n",
      "Train Epoch: 43 [24320/50000 (54%)]\tLoss: 0.345895, Accuracy: 89.45\n",
      "Train Epoch: 43 [25600/50000 (57%)]\tLoss: 0.394748, Accuracy: 85.94\n",
      "Train Epoch: 43 [26880/50000 (60%)]\tLoss: 0.280171, Accuracy: 90.62\n",
      "Train Epoch: 43 [28160/50000 (62%)]\tLoss: 0.339622, Accuracy: 87.89\n",
      "Train Epoch: 43 [29440/50000 (65%)]\tLoss: 0.414597, Accuracy: 85.16\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[-0.3113]],\n",
      "\n",
      "        [[-0.0320]],\n",
      "\n",
      "        [[-1.2516]],\n",
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      "        [[-0.3511]],\n",
      "\n",
      "        [[ 1.2370]],\n",
      "\n",
      "        [[ 0.2260]],\n",
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      "        [[-0.4710]],\n",
      "\n",
      "        [[-0.8383]],\n",
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      "        [[-0.3657]],\n",
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      "        [[ 2.5513]],\n",
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      "        [[-0.1866]],\n",
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      "        [[-2.5850]],\n",
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      "        [[-1.1911]],\n",
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      "        [[-1.3641]],\n",
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      "        [[ 0.6540]],\n",
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      "        [[ 1.5983]],\n",
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      "        [[ 0.1156]],\n",
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      "        [[-0.7454]],\n",
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      "        [[-0.6127]],\n",
      "\n",
      "        [[-0.0020]],\n",
      "\n",
      "        [[-1.0161]],\n",
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      "        [[-0.5788]],\n",
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      "        [[ 1.4102]],\n",
      "\n",
      "        [[-1.0016]],\n",
      "\n",
      "        [[ 2.2482]],\n",
      "\n",
      "        [[-1.3571]],\n",
      "\n",
      "        [[-0.8063]],\n",
      "\n",
      "        [[-1.2332]],\n",
      "\n",
      "        [[-0.4156]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-7.0053]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-5.4347e-03,  2.3963e-03, -9.0589e-04,  ...,  1.5995e-03,\n",
      "          -3.4779e-03, -3.1348e-03]],\n",
      "\n",
      "        [[ 8.4782e-04, -3.5416e-03, -3.0855e-03,  ...,  3.1269e-03,\n",
      "           3.7787e-04, -2.1389e-03]],\n",
      "\n",
      "        [[-3.0177e-03, -1.3449e-03, -1.2555e-03,  ..., -9.0815e-04,\n",
      "           7.5600e-04,  7.2890e-05]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-3.8716e-03, -3.0427e-04,  1.0825e-03,  ...,  1.7366e-03,\n",
      "          -2.4754e-03, -9.4097e-04]],\n",
      "\n",
      "        [[-1.5695e-02, -1.0626e-02, -1.6322e-02,  ...,  3.2977e-03,\n",
      "           5.6537e-03,  3.4890e-03]],\n",
      "\n",
      "        [[ 7.5675e-03,  1.2470e-02,  1.0834e-02,  ...,  4.8121e-03,\n",
      "           1.5697e-03,  9.6960e-04]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[-1.4213e-04, -2.7869e-04, -2.2287e-04,  ..., -3.4847e-04,\n",
      "          -2.6509e-04, -2.3807e-04],\n",
      "         [-4.1995e-04, -2.4710e-04, -8.2110e-05,  ..., -3.5596e-04,\n",
      "          -1.2786e-04, -1.9611e-04],\n",
      "         [ 8.6427e-04,  9.0166e-04,  7.2968e-04,  ...,  6.0916e-04,\n",
      "           4.2483e-04,  3.0416e-04],\n",
      "         ...,\n",
      "         [-2.9948e-04, -2.0923e-04, -2.4026e-04,  ..., -2.2013e-04,\n",
      "          -1.3479e-04, -2.2776e-05],\n",
      "         [ 1.4482e-04,  7.5491e-05,  1.3245e-04,  ..., -1.8187e-03,\n",
      "          -1.6350e-03, -1.2342e-03],\n",
      "         [ 2.8203e-04,  4.2879e-04,  6.2874e-04,  ...,  1.1988e-03,\n",
      "           1.4043e-03,  1.2060e-03]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 43 [30720/50000 (68%)]\tLoss: 0.326146, Accuracy: 89.45\n",
      "Train Epoch: 43 [32000/50000 (71%)]\tLoss: 0.322451, Accuracy: 89.06\n",
      "Train Epoch: 43 [33280/50000 (74%)]\tLoss: 0.362874, Accuracy: 87.50\n",
      "Train Epoch: 43 [34560/50000 (77%)]\tLoss: 0.392246, Accuracy: 86.72\n",
      "Train Epoch: 43 [35840/50000 (80%)]\tLoss: 0.372240, Accuracy: 87.89\n",
      "Train Epoch: 43 [37120/50000 (82%)]\tLoss: 0.276837, Accuracy: 89.84\n",
      "Train Epoch: 43 [38400/50000 (85%)]\tLoss: 0.431726, Accuracy: 83.98\n",
      "Train Epoch: 43 [39680/50000 (88%)]\tLoss: 0.383415, Accuracy: 87.11\n",
      "Train Epoch: 43 [40960/50000 (91%)]\tLoss: 0.337995, Accuracy: 88.67\n",
      "Train Epoch: 43 [42240/50000 (94%)]\tLoss: 0.415402, Accuracy: 85.16\n",
      "Train Epoch: 43 [43520/50000 (97%)]\tLoss: 0.409236, Accuracy: 84.38\n",
      "Train Epoch: 43 [35000/50000 (99%)]\tLoss: 0.339342, Accuracy: 89.00\n",
      "\n",
      "Validation set: Average loss: 0.5087, Accuracy: 4161/5000 (83.00%)\n",
      "\n",
      "the time of this epoch:[39.50084948539734 s]\n",
      "Train Epoch: 44 [0/50000 (0%)]\tLoss: 0.283574, Accuracy: 91.02\n",
      "Train Epoch: 44 [1280/50000 (3%)]\tLoss: 0.386983, Accuracy: 85.55\n",
      "Train Epoch: 44 [2560/50000 (6%)]\tLoss: 0.261780, Accuracy: 90.62\n",
      "Train Epoch: 44 [3840/50000 (9%)]\tLoss: 0.346861, Accuracy: 87.11\n",
      "Train Epoch: 44 [5120/50000 (11%)]\tLoss: 0.423029, Accuracy: 83.20\n",
      "Train Epoch: 44 [6400/50000 (14%)]\tLoss: 0.279644, Accuracy: 89.45\n",
      "Train Epoch: 44 [7680/50000 (17%)]\tLoss: 0.335351, Accuracy: 88.28\n",
      "Train Epoch: 44 [8960/50000 (20%)]\tLoss: 0.307884, Accuracy: 89.06\n",
      "Train Epoch: 44 [10240/50000 (23%)]\tLoss: 0.302374, Accuracy: 89.06\n",
      "Train Epoch: 44 [11520/50000 (26%)]\tLoss: 0.354287, Accuracy: 88.28\n",
      "Train Epoch: 44 [12800/50000 (28%)]\tLoss: 0.337808, Accuracy: 90.62\n",
      "Train Epoch: 44 [14080/50000 (31%)]\tLoss: 0.363266, Accuracy: 86.72\n",
      "Train Epoch: 44 [15360/50000 (34%)]\tLoss: 0.353614, Accuracy: 88.28\n",
      "Train Epoch: 44 [16640/50000 (37%)]\tLoss: 0.331619, Accuracy: 89.06\n",
      "Train Epoch: 44 [17920/50000 (40%)]\tLoss: 0.387846, Accuracy: 86.72\n",
      "Train Epoch: 44 [19200/50000 (43%)]\tLoss: 0.347475, Accuracy: 87.11\n",
      "Train Epoch: 44 [20480/50000 (45%)]\tLoss: 0.382982, Accuracy: 90.23\n",
      "Train Epoch: 44 [21760/50000 (48%)]\tLoss: 0.361795, Accuracy: 87.11\n",
      "Train Epoch: 44 [23040/50000 (51%)]\tLoss: 0.347102, Accuracy: 87.11\n",
      "Train Epoch: 44 [24320/50000 (54%)]\tLoss: 0.388441, Accuracy: 87.50\n",
      "Train Epoch: 44 [25600/50000 (57%)]\tLoss: 0.410086, Accuracy: 85.94\n",
      "Train Epoch: 44 [26880/50000 (60%)]\tLoss: 0.494925, Accuracy: 81.64\n",
      "Train Epoch: 44 [28160/50000 (62%)]\tLoss: 0.382542, Accuracy: 88.28\n",
      "Train Epoch: 44 [29440/50000 (65%)]\tLoss: 0.329806, Accuracy: 90.62\n",
      "Train Epoch: 44 [30720/50000 (68%)]\tLoss: 0.320372, Accuracy: 89.84\n",
      "Train Epoch: 44 [32000/50000 (71%)]\tLoss: 0.464172, Accuracy: 84.77\n",
      "Train Epoch: 44 [33280/50000 (74%)]\tLoss: 0.377655, Accuracy: 85.55\n",
      "Train Epoch: 44 [34560/50000 (77%)]\tLoss: 0.315817, Accuracy: 88.28\n",
      "Train Epoch: 44 [35840/50000 (80%)]\tLoss: 0.349208, Accuracy: 89.06\n",
      "Train Epoch: 44 [37120/50000 (82%)]\tLoss: 0.362026, Accuracy: 86.72\n",
      "Train Epoch: 44 [38400/50000 (85%)]\tLoss: 0.364773, Accuracy: 88.28\n",
      "Train Epoch: 44 [39680/50000 (88%)]\tLoss: 0.286141, Accuracy: 91.02\n",
      "Train Epoch: 44 [40960/50000 (91%)]\tLoss: 0.368223, Accuracy: 87.89\n",
      "Train Epoch: 44 [42240/50000 (94%)]\tLoss: 0.379011, Accuracy: 84.77\n",
      "Train Epoch: 44 [43520/50000 (97%)]\tLoss: 0.481621, Accuracy: 87.11\n",
      "Train Epoch: 44 [35000/50000 (99%)]\tLoss: 0.376130, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6516, Accuracy: 3946/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[36.40138339996338 s]\n",
      "\n",
      "Test set: Average loss: 0.6661, Accuracy: 7907/10000 (79.07%)\n",
      "\n",
      "Train Epoch: 45 [0/50000 (0%)]\tLoss: 0.332713, Accuracy: 88.28\n",
      "Train Epoch: 45 [1280/50000 (3%)]\tLoss: 0.412959, Accuracy: 86.72\n",
      "Train Epoch: 45 [2560/50000 (6%)]\tLoss: 0.321286, Accuracy: 89.84\n",
      "Train Epoch: 45 [3840/50000 (9%)]\tLoss: 0.336603, Accuracy: 89.06\n",
      "Train Epoch: 45 [5120/50000 (11%)]\tLoss: 0.394608, Accuracy: 84.77\n",
      "Train Epoch: 45 [6400/50000 (14%)]\tLoss: 0.334514, Accuracy: 89.06\n",
      "Train Epoch: 45 [7680/50000 (17%)]\tLoss: 0.376131, Accuracy: 86.72\n",
      "Train Epoch: 45 [8960/50000 (20%)]\tLoss: 0.320807, Accuracy: 88.67\n",
      "Train Epoch: 45 [10240/50000 (23%)]\tLoss: 0.328749, Accuracy: 89.06\n",
      "Train Epoch: 45 [11520/50000 (26%)]\tLoss: 0.287083, Accuracy: 91.80\n",
      "Train Epoch: 45 [12800/50000 (28%)]\tLoss: 0.377570, Accuracy: 87.89\n",
      "Train Epoch: 45 [14080/50000 (31%)]\tLoss: 0.538400, Accuracy: 82.03\n",
      "Train Epoch: 45 [15360/50000 (34%)]\tLoss: 0.364628, Accuracy: 87.50\n",
      "Train Epoch: 45 [16640/50000 (37%)]\tLoss: 0.406676, Accuracy: 86.72\n",
      "Train Epoch: 45 [17920/50000 (40%)]\tLoss: 0.381755, Accuracy: 85.55\n",
      "Train Epoch: 45 [19200/50000 (43%)]\tLoss: 0.417757, Accuracy: 83.59\n",
      "Train Epoch: 45 [20480/50000 (45%)]\tLoss: 0.308336, Accuracy: 88.67\n",
      "Train Epoch: 45 [21760/50000 (48%)]\tLoss: 0.450767, Accuracy: 83.98\n",
      "Train Epoch: 45 [23040/50000 (51%)]\tLoss: 0.376566, Accuracy: 87.50\n",
      "Train Epoch: 45 [24320/50000 (54%)]\tLoss: 0.433905, Accuracy: 84.77\n",
      "Train Epoch: 45 [25600/50000 (57%)]\tLoss: 0.427718, Accuracy: 86.33\n",
      "Train Epoch: 45 [26880/50000 (60%)]\tLoss: 0.327267, Accuracy: 88.67\n",
      "Train Epoch: 45 [28160/50000 (62%)]\tLoss: 0.405284, Accuracy: 83.98\n",
      "Train Epoch: 45 [29440/50000 (65%)]\tLoss: 0.423863, Accuracy: 87.11\n",
      "Train Epoch: 45 [30720/50000 (68%)]\tLoss: 0.476027, Accuracy: 85.55\n",
      "Train Epoch: 45 [32000/50000 (71%)]\tLoss: 0.399983, Accuracy: 87.89\n",
      "Train Epoch: 45 [33280/50000 (74%)]\tLoss: 0.473608, Accuracy: 83.20\n",
      "Train Epoch: 45 [34560/50000 (77%)]\tLoss: 0.421812, Accuracy: 83.98\n",
      "Train Epoch: 45 [35840/50000 (80%)]\tLoss: 0.341760, Accuracy: 88.67\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor([[[ 0.7721]],\n",
      "\n",
      "        [[ 0.2174]],\n",
      "\n",
      "        [[-1.7129]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.8622]],\n",
      "\n",
      "        [[-0.0478]],\n",
      "\n",
      "        [[ 1.6477]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 5.5959]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-2.2954e-03,  1.0235e-03,  3.4399e-03,  ..., -4.3227e-03,\n",
      "          -2.0834e-03, -5.5079e-03]],\n",
      "\n",
      "        [[ 4.8293e-04,  3.7507e-03, -4.8788e-03,  ...,  5.9910e-04,\n",
      "           1.7655e-03, -1.1666e-03]],\n",
      "\n",
      "        [[-2.4430e-03, -4.8268e-03,  6.4074e-04,  ..., -4.4510e-03,\n",
      "          -2.6939e-03,  1.0482e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 6.8156e-03,  1.3023e-05,  7.9172e-04,  ..., -1.8061e-03,\n",
      "           4.2606e-03, -4.1945e-04]],\n",
      "\n",
      "        [[-1.8204e-03,  3.4694e-03,  2.9268e-03,  ..., -1.5845e-03,\n",
      "           3.7124e-03,  2.0814e-04]],\n",
      "\n",
      "        [[ 7.8624e-03,  3.6879e-03,  5.4110e-03,  ...,  6.2172e-03,\n",
      "           9.0377e-04,  3.6300e-03]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor([[[-1.2677e-03, -1.2457e-03, -1.4379e-03,  ..., -8.4339e-04,\n",
      "          -9.6123e-04, -9.4766e-04],\n",
      "         [-6.0182e-04, -5.6884e-04, -4.8372e-04,  ..., -6.5434e-04,\n",
      "          -6.0300e-04, -4.4841e-04],\n",
      "         [-4.8965e-04, -7.2677e-04, -6.0495e-04,  ..., -6.7027e-04,\n",
      "          -6.5560e-04, -7.8179e-04],\n",
      "         ...,\n",
      "         [ 1.4123e-03,  1.3405e-03,  1.4322e-03,  ...,  1.7066e-03,\n",
      "           1.5988e-03,  1.7021e-03],\n",
      "         [ 5.5387e-04,  2.9111e-04,  2.6518e-04,  ...,  8.1855e-05,\n",
      "           1.8079e-04,  2.0135e-04],\n",
      "         [ 2.3089e-03,  2.5972e-03,  2.7641e-03,  ...,  2.8423e-03,\n",
      "           2.7445e-03,  2.4841e-03]]], device='cuda:0')\n",
      "Train Epoch: 45 [37120/50000 (82%)]\tLoss: 0.412698, Accuracy: 87.11\n",
      "Train Epoch: 45 [38400/50000 (85%)]\tLoss: 0.327749, Accuracy: 89.84\n",
      "Train Epoch: 45 [39680/50000 (88%)]\tLoss: 0.336112, Accuracy: 87.89\n",
      "Train Epoch: 45 [40960/50000 (91%)]\tLoss: 0.401305, Accuracy: 88.28\n",
      "Train Epoch: 45 [42240/50000 (94%)]\tLoss: 0.437842, Accuracy: 85.16\n",
      "Train Epoch: 45 [43520/50000 (97%)]\tLoss: 0.300773, Accuracy: 90.23\n",
      "Train Epoch: 45 [35000/50000 (99%)]\tLoss: 0.353658, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.6817, Accuracy: 3979/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[39.54407501220703 s]\n",
      "Train Epoch: 46 [0/50000 (0%)]\tLoss: 0.360007, Accuracy: 88.28\n",
      "Train Epoch: 46 [1280/50000 (3%)]\tLoss: 0.298271, Accuracy: 90.62\n",
      "Train Epoch: 46 [2560/50000 (6%)]\tLoss: 0.337058, Accuracy: 89.84\n",
      "Train Epoch: 46 [3840/50000 (9%)]\tLoss: 0.309563, Accuracy: 87.89\n",
      "Train Epoch: 46 [5120/50000 (11%)]\tLoss: 0.256265, Accuracy: 89.06\n",
      "Train Epoch: 46 [6400/50000 (14%)]\tLoss: 0.325044, Accuracy: 89.84\n",
      "Train Epoch: 46 [7680/50000 (17%)]\tLoss: 0.321106, Accuracy: 92.19\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 46 [8960/50000 (20%)]\tLoss: 0.337003, Accuracy: 89.84\n",
      "Train Epoch: 46 [10240/50000 (23%)]\tLoss: 0.418447, Accuracy: 87.11\n",
      "Train Epoch: 46 [11520/50000 (26%)]\tLoss: 0.392455, Accuracy: 86.33\n",
      "Train Epoch: 46 [12800/50000 (28%)]\tLoss: 0.362729, Accuracy: 89.45\n",
      "Train Epoch: 46 [14080/50000 (31%)]\tLoss: 0.396994, Accuracy: 85.16\n",
      "Train Epoch: 46 [15360/50000 (34%)]\tLoss: 0.302520, Accuracy: 88.28\n",
      "Train Epoch: 46 [16640/50000 (37%)]\tLoss: 0.298301, Accuracy: 88.67\n",
      "Train Epoch: 46 [17920/50000 (40%)]\tLoss: 0.383832, Accuracy: 88.28\n",
      "Train Epoch: 46 [19200/50000 (43%)]\tLoss: 0.356554, Accuracy: 85.55\n",
      "Train Epoch: 46 [20480/50000 (45%)]\tLoss: 0.377429, Accuracy: 85.16\n",
      "Train Epoch: 46 [21760/50000 (48%)]\tLoss: 0.341904, Accuracy: 89.06\n",
      "Train Epoch: 46 [23040/50000 (51%)]\tLoss: 0.409942, Accuracy: 87.50\n",
      "Train Epoch: 46 [24320/50000 (54%)]\tLoss: 0.358303, Accuracy: 89.06\n",
      "Train Epoch: 46 [25600/50000 (57%)]\tLoss: 0.484423, Accuracy: 80.47\n",
      "Train Epoch: 46 [26880/50000 (60%)]\tLoss: 0.420126, Accuracy: 86.72\n",
      "Train Epoch: 46 [28160/50000 (62%)]\tLoss: 0.330807, Accuracy: 88.67\n",
      "Train Epoch: 46 [29440/50000 (65%)]\tLoss: 0.455550, Accuracy: 86.72\n",
      "Train Epoch: 46 [30720/50000 (68%)]\tLoss: 0.418966, Accuracy: 86.72\n",
      "Train Epoch: 46 [32000/50000 (71%)]\tLoss: 0.358328, Accuracy: 88.67\n",
      "Train Epoch: 46 [33280/50000 (74%)]\tLoss: 0.451355, Accuracy: 83.59\n",
      "Train Epoch: 46 [34560/50000 (77%)]\tLoss: 0.379579, Accuracy: 86.72\n",
      "Train Epoch: 46 [35840/50000 (80%)]\tLoss: 0.332707, Accuracy: 86.33\n",
      "Train Epoch: 46 [37120/50000 (82%)]\tLoss: 0.401694, Accuracy: 86.72\n",
      "Train Epoch: 46 [38400/50000 (85%)]\tLoss: 0.412910, Accuracy: 85.16\n",
      "Train Epoch: 46 [39680/50000 (88%)]\tLoss: 0.379270, Accuracy: 86.72\n",
      "Train Epoch: 46 [40960/50000 (91%)]\tLoss: 0.399922, Accuracy: 86.33\n",
      "Train Epoch: 46 [42240/50000 (94%)]\tLoss: 0.406929, Accuracy: 85.16\n",
      "Train Epoch: 46 [43520/50000 (97%)]\tLoss: 0.359037, Accuracy: 88.67\n",
      "Train Epoch: 46 [35000/50000 (99%)]\tLoss: 0.291437, Accuracy: 91.00\n",
      "\n",
      "Validation set: Average loss: 0.6618, Accuracy: 3952/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[36.33471441268921 s]\n",
      "\n",
      "Test set: Average loss: 0.6872, Accuracy: 7796/10000 (77.96%)\n",
      "\n",
      "Train Epoch: 47 [0/50000 (0%)]\tLoss: 0.380697, Accuracy: 87.11\n",
      "Train Epoch: 47 [1280/50000 (3%)]\tLoss: 0.384781, Accuracy: 87.89\n",
      "Train Epoch: 47 [2560/50000 (6%)]\tLoss: 0.396241, Accuracy: 86.72\n",
      "Train Epoch: 47 [3840/50000 (9%)]\tLoss: 0.284036, Accuracy: 89.45\n",
      "Train Epoch: 47 [5120/50000 (11%)]\tLoss: 0.389131, Accuracy: 86.72\n",
      "Train Epoch: 47 [6400/50000 (14%)]\tLoss: 0.345033, Accuracy: 88.67\n",
      "Train Epoch: 47 [7680/50000 (17%)]\tLoss: 0.468535, Accuracy: 84.38\n",
      "Train Epoch: 47 [8960/50000 (20%)]\tLoss: 0.360422, Accuracy: 87.89\n",
      "Train Epoch: 47 [10240/50000 (23%)]\tLoss: 0.315212, Accuracy: 89.45\n",
      "Train Epoch: 47 [11520/50000 (26%)]\tLoss: 0.454418, Accuracy: 85.55\n",
      "Train Epoch: 47 [12800/50000 (28%)]\tLoss: 0.368755, Accuracy: 86.72\n",
      "Train Epoch: 47 [14080/50000 (31%)]\tLoss: 0.303936, Accuracy: 91.41\n",
      "Train Epoch: 47 [15360/50000 (34%)]\tLoss: 0.440332, Accuracy: 84.38\n",
      "Train Epoch: 47 [16640/50000 (37%)]\tLoss: 0.439853, Accuracy: 85.16\n",
      "Train Epoch: 47 [17920/50000 (40%)]\tLoss: 0.390294, Accuracy: 86.72\n",
      "Train Epoch: 47 [19200/50000 (43%)]\tLoss: 0.332525, Accuracy: 89.45\n",
      "Train Epoch: 47 [20480/50000 (45%)]\tLoss: 0.422200, Accuracy: 87.11\n",
      "Train Epoch: 47 [21760/50000 (48%)]\tLoss: 0.417592, Accuracy: 85.55\n",
      "Train Epoch: 47 [23040/50000 (51%)]\tLoss: 0.408764, Accuracy: 86.33\n",
      "Train Epoch: 47 [24320/50000 (54%)]\tLoss: 0.400017, Accuracy: 85.16\n",
      "Train Epoch: 47 [25600/50000 (57%)]\tLoss: 0.335415, Accuracy: 87.89\n",
      "Train Epoch: 47 [26880/50000 (60%)]\tLoss: 0.368481, Accuracy: 86.33\n",
      "Train Epoch: 47 [28160/50000 (62%)]\tLoss: 0.458729, Accuracy: 85.16\n",
      "Train Epoch: 47 [29440/50000 (65%)]\tLoss: 0.399813, Accuracy: 85.55\n",
      "Train Epoch: 47 [30720/50000 (68%)]\tLoss: 0.444629, Accuracy: 86.33\n",
      "Train Epoch: 47 [32000/50000 (71%)]\tLoss: 0.379333, Accuracy: 87.11\n",
      "Train Epoch: 47 [33280/50000 (74%)]\tLoss: 0.365740, Accuracy: 90.23\n",
      "Train Epoch: 47 [34560/50000 (77%)]\tLoss: 0.384493, Accuracy: 87.11\n",
      "Train Epoch: 47 [35840/50000 (80%)]\tLoss: 0.395919, Accuracy: 85.94\n",
      "Train Epoch: 47 [37120/50000 (82%)]\tLoss: 0.444391, Accuracy: 84.77\n",
      "Train Epoch: 47 [38400/50000 (85%)]\tLoss: 0.455040, Accuracy: 85.16\n",
      "Train Epoch: 47 [39680/50000 (88%)]\tLoss: 0.372968, Accuracy: 87.11\n",
      "Train Epoch: 47 [40960/50000 (91%)]\tLoss: 0.350938, Accuracy: 87.11\n",
      "Train Epoch: 47 [42240/50000 (94%)]\tLoss: 0.390213, Accuracy: 87.50\n",
      "Train Epoch: 47 [43520/50000 (97%)]\tLoss: 0.287724, Accuracy: 91.02\n",
      "Train Epoch: 47 [35000/50000 (99%)]\tLoss: 0.443996, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.6997, Accuracy: 3900/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[39.48348379135132 s]\n",
      "Train Epoch: 48 [0/50000 (0%)]\tLoss: 0.284540, Accuracy: 89.84\n",
      "Train Epoch: 48 [1280/50000 (3%)]\tLoss: 0.293632, Accuracy: 89.45\n",
      "Train Epoch: 48 [2560/50000 (6%)]\tLoss: 0.233674, Accuracy: 93.36\n",
      "Train Epoch: 48 [3840/50000 (9%)]\tLoss: 0.324067, Accuracy: 87.89\n",
      "Train Epoch: 48 [5120/50000 (11%)]\tLoss: 0.436711, Accuracy: 87.89\n",
      "Train Epoch: 48 [6400/50000 (14%)]\tLoss: 0.407079, Accuracy: 86.33\n",
      "Train Epoch: 48 [7680/50000 (17%)]\tLoss: 0.365179, Accuracy: 87.89\n",
      "Train Epoch: 48 [8960/50000 (20%)]\tLoss: 0.384953, Accuracy: 84.77\n",
      "Train Epoch: 48 [10240/50000 (23%)]\tLoss: 0.319961, Accuracy: 89.84\n",
      "Train Epoch: 48 [11520/50000 (26%)]\tLoss: 0.329626, Accuracy: 87.89\n",
      "Train Epoch: 48 [12800/50000 (28%)]\tLoss: 0.357270, Accuracy: 87.89\n",
      "Train Epoch: 48 [14080/50000 (31%)]\tLoss: 0.313948, Accuracy: 89.45\n",
      "Train Epoch: 48 [15360/50000 (34%)]\tLoss: 0.393914, Accuracy: 85.94\n",
      "Train Epoch: 48 [16640/50000 (37%)]\tLoss: 0.359460, Accuracy: 87.89\n",
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      "Train Epoch: 48 [19200/50000 (43%)]\tLoss: 0.438448, Accuracy: 85.16\n",
      "Train Epoch: 48 [20480/50000 (45%)]\tLoss: 0.297660, Accuracy: 90.23\n",
      "Train Epoch: 48 [21760/50000 (48%)]\tLoss: 0.328669, Accuracy: 89.06\n",
      "Train Epoch: 48 [23040/50000 (51%)]\tLoss: 0.370300, Accuracy: 86.72\n",
      "Train Epoch: 48 [24320/50000 (54%)]\tLoss: 0.272220, Accuracy: 92.19\n",
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      "Train Epoch: 48 [28160/50000 (62%)]\tLoss: 0.415720, Accuracy: 85.94\n",
      "Train Epoch: 48 [29440/50000 (65%)]\tLoss: 0.354926, Accuracy: 86.72\n",
      "Train Epoch: 48 [30720/50000 (68%)]\tLoss: 0.378157, Accuracy: 87.11\n",
      "Train Epoch: 48 [32000/50000 (71%)]\tLoss: 0.405179, Accuracy: 85.94\n",
      "Train Epoch: 48 [33280/50000 (74%)]\tLoss: 0.280835, Accuracy: 91.80\n",
      "Train Epoch: 48 [34560/50000 (77%)]\tLoss: 0.374045, Accuracy: 85.94\n",
      "Train Epoch: 48 [35840/50000 (80%)]\tLoss: 0.383416, Accuracy: 85.55\n",
      "Train Epoch: 48 [37120/50000 (82%)]\tLoss: 0.394181, Accuracy: 86.33\n",
      "Train Epoch: 48 [38400/50000 (85%)]\tLoss: 0.355702, Accuracy: 87.11\n",
      "Train Epoch: 48 [39680/50000 (88%)]\tLoss: 0.320259, Accuracy: 89.84\n",
      "Train Epoch: 48 [40960/50000 (91%)]\tLoss: 0.296625, Accuracy: 90.62\n",
      "Train Epoch: 48 [42240/50000 (94%)]\tLoss: 0.445993, Accuracy: 84.77\n",
      "Train Epoch: 48 [43520/50000 (97%)]\tLoss: 0.396456, Accuracy: 87.11\n",
      "Train Epoch: 48 [35000/50000 (99%)]\tLoss: 0.565946, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 0.5914, Accuracy: 4091/5000 (81.00%)\n",
      "\n",
      "the time of this epoch:[36.359134674072266 s]\n",
      "\n",
      "Test set: Average loss: 0.6250, Accuracy: 8104/10000 (81.04%)\n",
      "\n",
      "Train Epoch: 49 [0/50000 (0%)]\tLoss: 0.360239, Accuracy: 87.11\n",
      "Train Epoch: 49 [1280/50000 (3%)]\tLoss: 0.314206, Accuracy: 89.45\n",
      "Train Epoch: 49 [2560/50000 (6%)]\tLoss: 0.321901, Accuracy: 87.89\n",
      "Train Epoch: 49 [3840/50000 (9%)]\tLoss: 0.331159, Accuracy: 88.28\n",
      "Train Epoch: 49 [5120/50000 (11%)]\tLoss: 0.424422, Accuracy: 85.16\n",
      "Train Epoch: 49 [6400/50000 (14%)]\tLoss: 0.334443, Accuracy: 89.06\n",
      "Train Epoch: 49 [7680/50000 (17%)]\tLoss: 0.295894, Accuracy: 90.62\n",
      "Train Epoch: 49 [8960/50000 (20%)]\tLoss: 0.383204, Accuracy: 85.94\n",
      "Train Epoch: 49 [10240/50000 (23%)]\tLoss: 0.357179, Accuracy: 89.06\n",
      "Train Epoch: 49 [11520/50000 (26%)]\tLoss: 0.419726, Accuracy: 86.72\n",
      "Train Epoch: 49 [12800/50000 (28%)]\tLoss: 0.360996, Accuracy: 87.11\n",
      "Train Epoch: 49 [14080/50000 (31%)]\tLoss: 0.379504, Accuracy: 87.11\n",
      "Train Epoch: 49 [15360/50000 (34%)]\tLoss: 0.328846, Accuracy: 91.02\n",
      "Train Epoch: 49 [16640/50000 (37%)]\tLoss: 0.319216, Accuracy: 87.50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 49 [17920/50000 (40%)]\tLoss: 0.373125, Accuracy: 86.72\n",
      "Train Epoch: 49 [19200/50000 (43%)]\tLoss: 0.287595, Accuracy: 90.23\n",
      "Train Epoch: 49 [20480/50000 (45%)]\tLoss: 0.368193, Accuracy: 87.89\n",
      "Train Epoch: 49 [21760/50000 (48%)]\tLoss: 0.283674, Accuracy: 89.45\n",
      "Train Epoch: 49 [23040/50000 (51%)]\tLoss: 0.409757, Accuracy: 86.72\n",
      "Train Epoch: 49 [24320/50000 (54%)]\tLoss: 0.320184, Accuracy: 90.62\n",
      "Train Epoch: 49 [25600/50000 (57%)]\tLoss: 0.456032, Accuracy: 85.55\n",
      "Train Epoch: 49 [26880/50000 (60%)]\tLoss: 0.325609, Accuracy: 89.45\n",
      "Train Epoch: 49 [28160/50000 (62%)]\tLoss: 0.431726, Accuracy: 86.33\n",
      "Train Epoch: 49 [29440/50000 (65%)]\tLoss: 0.414544, Accuracy: 84.38\n",
      "Train Epoch: 49 [30720/50000 (68%)]\tLoss: 0.434764, Accuracy: 84.77\n",
      "Train Epoch: 49 [32000/50000 (71%)]\tLoss: 0.335162, Accuracy: 88.28\n",
      "Train Epoch: 49 [33280/50000 (74%)]\tLoss: 0.500769, Accuracy: 85.55\n",
      "Train Epoch: 49 [34560/50000 (77%)]\tLoss: 0.392891, Accuracy: 86.33\n",
      "Train Epoch: 49 [35840/50000 (80%)]\tLoss: 0.300729, Accuracy: 89.06\n",
      "Train Epoch: 49 [37120/50000 (82%)]\tLoss: 0.318480, Accuracy: 89.45\n",
      "Train Epoch: 49 [38400/50000 (85%)]\tLoss: 0.435178, Accuracy: 85.16\n",
      "Train Epoch: 49 [39680/50000 (88%)]\tLoss: 0.375178, Accuracy: 87.11\n",
      "Train Epoch: 49 [40960/50000 (91%)]\tLoss: 0.278900, Accuracy: 90.23\n",
      "Train Epoch: 49 [42240/50000 (94%)]\tLoss: 0.294427, Accuracy: 90.62\n",
      "Train Epoch: 49 [43520/50000 (97%)]\tLoss: 0.426522, Accuracy: 86.33\n",
      "Train Epoch: 49 [35000/50000 (99%)]\tLoss: 0.361455, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.9808, Accuracy: 3570/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[39.47541356086731 s]\n",
      "Train Epoch: 50 [0/50000 (0%)]\tLoss: 0.359825, Accuracy: 87.89\n",
      "Train Epoch: 50 [1280/50000 (3%)]\tLoss: 0.267363, Accuracy: 91.41\n",
      "Train Epoch: 50 [2560/50000 (6%)]\tLoss: 0.324535, Accuracy: 89.06\n",
      "Train Epoch: 50 [3840/50000 (9%)]\tLoss: 0.435258, Accuracy: 83.20\n",
      "Train Epoch: 50 [5120/50000 (11%)]\tLoss: 0.246617, Accuracy: 91.02\n",
      "Train Epoch: 50 [6400/50000 (14%)]\tLoss: 0.431064, Accuracy: 85.94\n",
      "Train Epoch: 50 [7680/50000 (17%)]\tLoss: 0.394264, Accuracy: 87.50\n",
      "Train Epoch: 50 [8960/50000 (20%)]\tLoss: 0.402229, Accuracy: 85.55\n",
      "Train Epoch: 50 [10240/50000 (23%)]\tLoss: 0.368741, Accuracy: 86.72\n",
      "Train Epoch: 50 [11520/50000 (26%)]\tLoss: 0.262280, Accuracy: 90.23\n",
      "Train Epoch: 50 [12800/50000 (28%)]\tLoss: 0.380371, Accuracy: 87.89\n",
      "Train Epoch: 50 [14080/50000 (31%)]\tLoss: 0.243859, Accuracy: 92.58\n",
      "Train Epoch: 50 [15360/50000 (34%)]\tLoss: 0.282320, Accuracy: 91.41\n",
      "Train Epoch: 50 [16640/50000 (37%)]\tLoss: 0.390227, Accuracy: 86.33\n",
      "Train Epoch: 50 [17920/50000 (40%)]\tLoss: 0.337529, Accuracy: 90.62\n",
      "Train Epoch: 50 [19200/50000 (43%)]\tLoss: 0.394970, Accuracy: 85.16\n",
      "Train Epoch: 50 [20480/50000 (45%)]\tLoss: 0.292834, Accuracy: 90.62\n",
      "Train Epoch: 50 [21760/50000 (48%)]\tLoss: 0.373351, Accuracy: 89.45\n",
      "Train Epoch: 50 [23040/50000 (51%)]\tLoss: 0.369790, Accuracy: 87.89\n",
      "Train Epoch: 50 [24320/50000 (54%)]\tLoss: 0.350151, Accuracy: 87.89\n",
      "Train Epoch: 50 [25600/50000 (57%)]\tLoss: 0.250981, Accuracy: 91.02\n",
      "Train Epoch: 50 [26880/50000 (60%)]\tLoss: 0.447208, Accuracy: 84.77\n",
      "Train Epoch: 50 [28160/50000 (62%)]\tLoss: 0.417049, Accuracy: 85.55\n",
      "Train Epoch: 50 [29440/50000 (65%)]\tLoss: 0.507137, Accuracy: 81.64\n",
      "Train Epoch: 50 [30720/50000 (68%)]\tLoss: 0.366006, Accuracy: 86.72\n",
      "Train Epoch: 50 [32000/50000 (71%)]\tLoss: 0.472486, Accuracy: 83.98\n",
      "Train Epoch: 50 [33280/50000 (74%)]\tLoss: 0.362443, Accuracy: 90.23\n",
      "Train Epoch: 50 [34560/50000 (77%)]\tLoss: 0.327746, Accuracy: 89.45\n",
      "Train Epoch: 50 [35840/50000 (80%)]\tLoss: 0.466417, Accuracy: 85.55\n",
      "Train Epoch: 50 [37120/50000 (82%)]\tLoss: 0.331351, Accuracy: 88.67\n",
      "Train Epoch: 50 [38400/50000 (85%)]\tLoss: 0.353480, Accuracy: 88.28\n",
      "Train Epoch: 50 [39680/50000 (88%)]\tLoss: 0.484207, Accuracy: 83.98\n",
      "Train Epoch: 50 [40960/50000 (91%)]\tLoss: 0.293662, Accuracy: 89.45\n",
      "Train Epoch: 50 [42240/50000 (94%)]\tLoss: 0.370824, Accuracy: 86.72\n",
      "Train Epoch: 50 [43520/50000 (97%)]\tLoss: 0.484619, Accuracy: 82.81\n",
      "Train Epoch: 50 [35000/50000 (99%)]\tLoss: 0.394926, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.5342, Accuracy: 4122/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[36.307957887649536 s]\n",
      "\n",
      "Test set: Average loss: 0.5299, Accuracy: 8229/10000 (82.29%)\n",
      "\n",
      "Train Epoch: 51 [0/50000 (0%)]\tLoss: 0.346227, Accuracy: 89.45\n",
      "Train Epoch: 51 [1280/50000 (3%)]\tLoss: 0.326084, Accuracy: 89.06\n",
      "Train Epoch: 51 [2560/50000 (6%)]\tLoss: 0.261867, Accuracy: 91.02\n",
      "Train Epoch: 51 [3840/50000 (9%)]\tLoss: 0.294960, Accuracy: 90.62\n",
      "Train Epoch: 51 [5120/50000 (11%)]\tLoss: 0.232388, Accuracy: 91.41\n",
      "Train Epoch: 51 [6400/50000 (14%)]\tLoss: 0.267222, Accuracy: 90.23\n",
      "Train Epoch: 51 [7680/50000 (17%)]\tLoss: 0.340657, Accuracy: 88.67\n",
      "Train Epoch: 51 [8960/50000 (20%)]\tLoss: 0.350306, Accuracy: 86.72\n",
      "Train Epoch: 51 [10240/50000 (23%)]\tLoss: 0.198574, Accuracy: 94.14\n",
      "Train Epoch: 51 [11520/50000 (26%)]\tLoss: 0.256541, Accuracy: 90.23\n",
      "Train Epoch: 51 [12800/50000 (28%)]\tLoss: 0.266410, Accuracy: 91.02\n",
      "Train Epoch: 51 [14080/50000 (31%)]\tLoss: 0.271515, Accuracy: 90.62\n",
      "Train Epoch: 51 [15360/50000 (34%)]\tLoss: 0.263167, Accuracy: 93.75\n",
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      "Train Epoch: 51 [17920/50000 (40%)]\tLoss: 0.231603, Accuracy: 93.36\n",
      "Train Epoch: 51 [19200/50000 (43%)]\tLoss: 0.217463, Accuracy: 94.14\n",
      "Train Epoch: 51 [20480/50000 (45%)]\tLoss: 0.312749, Accuracy: 91.02\n",
      "Train Epoch: 51 [21760/50000 (48%)]\tLoss: 0.227757, Accuracy: 92.97\n",
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      "Train Epoch: 51 [24320/50000 (54%)]\tLoss: 0.193257, Accuracy: 92.19\n",
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      "Train Epoch: 51 [26880/50000 (60%)]\tLoss: 0.185308, Accuracy: 92.58\n",
      "Train Epoch: 51 [28160/50000 (62%)]\tLoss: 0.248247, Accuracy: 92.97\n",
      "Train Epoch: 51 [29440/50000 (65%)]\tLoss: 0.218834, Accuracy: 91.80\n",
      "Train Epoch: 51 [30720/50000 (68%)]\tLoss: 0.181375, Accuracy: 94.92\n",
      "Train Epoch: 51 [32000/50000 (71%)]\tLoss: 0.185823, Accuracy: 95.70\n",
      "Train Epoch: 51 [33280/50000 (74%)]\tLoss: 0.184499, Accuracy: 92.97\n",
      "Train Epoch: 51 [34560/50000 (77%)]\tLoss: 0.253463, Accuracy: 92.97\n",
      "Train Epoch: 51 [35840/50000 (80%)]\tLoss: 0.164934, Accuracy: 95.31\n",
      "Train Epoch: 51 [37120/50000 (82%)]\tLoss: 0.184361, Accuracy: 93.36\n",
      "Train Epoch: 51 [38400/50000 (85%)]\tLoss: 0.194488, Accuracy: 93.75\n",
      "Train Epoch: 51 [39680/50000 (88%)]\tLoss: 0.183486, Accuracy: 92.58\n",
      "Train Epoch: 51 [40960/50000 (91%)]\tLoss: 0.169196, Accuracy: 92.97\n",
      "Train Epoch: 51 [42240/50000 (94%)]\tLoss: 0.220040, Accuracy: 93.36\n",
      "Train Epoch: 51 [43520/50000 (97%)]\tLoss: 0.224561, Accuracy: 92.97\n",
      "Train Epoch: 51 [35000/50000 (99%)]\tLoss: 0.187009, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2821, Accuracy: 4531/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.479063749313354 s]\n",
      "Train Epoch: 52 [0/50000 (0%)]\tLoss: 0.232170, Accuracy: 93.75\n",
      "Train Epoch: 52 [1280/50000 (3%)]\tLoss: 0.099043, Accuracy: 97.27\n",
      "Train Epoch: 52 [2560/50000 (6%)]\tLoss: 0.167270, Accuracy: 95.70\n",
      "Train Epoch: 52 [3840/50000 (9%)]\tLoss: 0.175262, Accuracy: 93.75\n",
      "Train Epoch: 52 [5120/50000 (11%)]\tLoss: 0.285324, Accuracy: 89.84\n",
      "Train Epoch: 52 [6400/50000 (14%)]\tLoss: 0.137772, Accuracy: 94.92\n",
      "Train Epoch: 52 [7680/50000 (17%)]\tLoss: 0.180948, Accuracy: 94.53\n",
      "Train Epoch: 52 [8960/50000 (20%)]\tLoss: 0.145704, Accuracy: 96.48\n",
      "Train Epoch: 52 [10240/50000 (23%)]\tLoss: 0.127978, Accuracy: 95.31\n",
      "Train Epoch: 52 [11520/50000 (26%)]\tLoss: 0.225635, Accuracy: 93.75\n",
      "Train Epoch: 52 [12800/50000 (28%)]\tLoss: 0.194916, Accuracy: 94.14\n",
      "Train Epoch: 52 [14080/50000 (31%)]\tLoss: 0.128718, Accuracy: 96.88\n",
      "Train Epoch: 52 [15360/50000 (34%)]\tLoss: 0.218794, Accuracy: 92.58\n",
      "Train Epoch: 52 [16640/50000 (37%)]\tLoss: 0.145063, Accuracy: 95.70\n",
      "Train Epoch: 52 [17920/50000 (40%)]\tLoss: 0.263705, Accuracy: 91.02\n",
      "Train Epoch: 52 [19200/50000 (43%)]\tLoss: 0.207519, Accuracy: 93.36\n",
      "Train Epoch: 52 [20480/50000 (45%)]\tLoss: 0.164662, Accuracy: 94.53\n",
      "Train Epoch: 52 [21760/50000 (48%)]\tLoss: 0.184951, Accuracy: 94.14\n",
      "Train Epoch: 52 [23040/50000 (51%)]\tLoss: 0.144734, Accuracy: 96.09\n",
      "Train Epoch: 52 [24320/50000 (54%)]\tLoss: 0.239399, Accuracy: 91.80\n",
      "Train Epoch: 52 [25600/50000 (57%)]\tLoss: 0.194924, Accuracy: 92.97\n",
      "Train Epoch: 52 [26880/50000 (60%)]\tLoss: 0.158284, Accuracy: 96.48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 52 [28160/50000 (62%)]\tLoss: 0.148689, Accuracy: 95.31\n",
      "Train Epoch: 52 [29440/50000 (65%)]\tLoss: 0.149352, Accuracy: 95.70\n",
      "Train Epoch: 52 [30720/50000 (68%)]\tLoss: 0.177288, Accuracy: 93.75\n",
      "Train Epoch: 52 [32000/50000 (71%)]\tLoss: 0.154380, Accuracy: 95.31\n",
      "Train Epoch: 52 [33280/50000 (74%)]\tLoss: 0.152414, Accuracy: 93.36\n",
      "Train Epoch: 52 [34560/50000 (77%)]\tLoss: 0.201453, Accuracy: 93.36\n",
      "Train Epoch: 52 [35840/50000 (80%)]\tLoss: 0.158774, Accuracy: 93.75\n",
      "Train Epoch: 52 [37120/50000 (82%)]\tLoss: 0.233230, Accuracy: 92.19\n",
      "Train Epoch: 52 [38400/50000 (85%)]\tLoss: 0.261551, Accuracy: 90.23\n",
      "Train Epoch: 52 [39680/50000 (88%)]\tLoss: 0.167610, Accuracy: 93.75\n",
      "Train Epoch: 52 [40960/50000 (91%)]\tLoss: 0.126188, Accuracy: 96.48\n",
      "Train Epoch: 52 [42240/50000 (94%)]\tLoss: 0.236609, Accuracy: 90.23\n",
      "Train Epoch: 52 [43520/50000 (97%)]\tLoss: 0.173595, Accuracy: 93.75\n",
      "Train Epoch: 52 [35000/50000 (99%)]\tLoss: 0.197805, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2735, Accuracy: 4535/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[36.338600873947144 s]\n",
      "\n",
      "Test set: Average loss: 0.2878, Accuracy: 9070/10000 (90.70%)\n",
      "\n",
      "Train Epoch: 53 [0/50000 (0%)]\tLoss: 0.189765, Accuracy: 93.75\n",
      "Train Epoch: 53 [1280/50000 (3%)]\tLoss: 0.148451, Accuracy: 94.92\n",
      "Train Epoch: 53 [2560/50000 (6%)]\tLoss: 0.109836, Accuracy: 96.88\n",
      "Train Epoch: 53 [3840/50000 (9%)]\tLoss: 0.156181, Accuracy: 95.70\n",
      "Train Epoch: 53 [5120/50000 (11%)]\tLoss: 0.123105, Accuracy: 95.70\n",
      "Train Epoch: 53 [6400/50000 (14%)]\tLoss: 0.150621, Accuracy: 95.31\n",
      "Train Epoch: 53 [7680/50000 (17%)]\tLoss: 0.233792, Accuracy: 92.97\n",
      "Train Epoch: 53 [8960/50000 (20%)]\tLoss: 0.154094, Accuracy: 94.92\n",
      "Train Epoch: 53 [10240/50000 (23%)]\tLoss: 0.185424, Accuracy: 93.36\n",
      "Train Epoch: 53 [11520/50000 (26%)]\tLoss: 0.135556, Accuracy: 95.70\n",
      "Train Epoch: 53 [12800/50000 (28%)]\tLoss: 0.213038, Accuracy: 92.19\n",
      "Train Epoch: 53 [14080/50000 (31%)]\tLoss: 0.145138, Accuracy: 93.75\n",
      "Train Epoch: 53 [15360/50000 (34%)]\tLoss: 0.165582, Accuracy: 94.53\n",
      "Train Epoch: 53 [16640/50000 (37%)]\tLoss: 0.123703, Accuracy: 96.88\n",
      "Train Epoch: 53 [17920/50000 (40%)]\tLoss: 0.229302, Accuracy: 91.80\n",
      "Train Epoch: 53 [19200/50000 (43%)]\tLoss: 0.171169, Accuracy: 92.58\n",
      "Train Epoch: 53 [20480/50000 (45%)]\tLoss: 0.153440, Accuracy: 93.75\n",
      "Train Epoch: 53 [21760/50000 (48%)]\tLoss: 0.225067, Accuracy: 91.41\n",
      "Train Epoch: 53 [23040/50000 (51%)]\tLoss: 0.116288, Accuracy: 95.70\n",
      "Train Epoch: 53 [24320/50000 (54%)]\tLoss: 0.127335, Accuracy: 95.70\n",
      "Train Epoch: 53 [25600/50000 (57%)]\tLoss: 0.214108, Accuracy: 94.53\n",
      "Train Epoch: 53 [26880/50000 (60%)]\tLoss: 0.133151, Accuracy: 95.31\n",
      "Train Epoch: 53 [28160/50000 (62%)]\tLoss: 0.105466, Accuracy: 96.48\n",
      "Train Epoch: 53 [29440/50000 (65%)]\tLoss: 0.151145, Accuracy: 96.48\n",
      "Train Epoch: 53 [30720/50000 (68%)]\tLoss: 0.176155, Accuracy: 94.92\n",
      "Train Epoch: 53 [32000/50000 (71%)]\tLoss: 0.141632, Accuracy: 95.31\n",
      "Train Epoch: 53 [33280/50000 (74%)]\tLoss: 0.191003, Accuracy: 93.75\n",
      "Train Epoch: 53 [34560/50000 (77%)]\tLoss: 0.114979, Accuracy: 96.48\n",
      "Train Epoch: 53 [35840/50000 (80%)]\tLoss: 0.176610, Accuracy: 94.92\n",
      "Train Epoch: 53 [37120/50000 (82%)]\tLoss: 0.204296, Accuracy: 94.14\n",
      "Train Epoch: 53 [38400/50000 (85%)]\tLoss: 0.161172, Accuracy: 94.53\n",
      "Train Epoch: 53 [39680/50000 (88%)]\tLoss: 0.154540, Accuracy: 94.92\n",
      "Train Epoch: 53 [40960/50000 (91%)]\tLoss: 0.203613, Accuracy: 92.58\n",
      "Train Epoch: 53 [42240/50000 (94%)]\tLoss: 0.161210, Accuracy: 95.31\n",
      "Train Epoch: 53 [43520/50000 (97%)]\tLoss: 0.198701, Accuracy: 94.14\n",
      "Train Epoch: 53 [35000/50000 (99%)]\tLoss: 0.139587, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2693, Accuracy: 4564/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.46935510635376 s]\n",
      "Train Epoch: 54 [0/50000 (0%)]\tLoss: 0.117202, Accuracy: 96.09\n",
      "Train Epoch: 54 [1280/50000 (3%)]\tLoss: 0.106045, Accuracy: 98.05\n",
      "Train Epoch: 54 [2560/50000 (6%)]\tLoss: 0.177581, Accuracy: 94.14\n",
      "Train Epoch: 54 [3840/50000 (9%)]\tLoss: 0.130744, Accuracy: 97.27\n",
      "Train Epoch: 54 [5120/50000 (11%)]\tLoss: 0.138116, Accuracy: 94.92\n",
      "Train Epoch: 54 [6400/50000 (14%)]\tLoss: 0.178849, Accuracy: 95.31\n",
      "Train Epoch: 54 [7680/50000 (17%)]\tLoss: 0.122647, Accuracy: 95.31\n",
      "Train Epoch: 54 [8960/50000 (20%)]\tLoss: 0.172194, Accuracy: 94.53\n",
      "Train Epoch: 54 [10240/50000 (23%)]\tLoss: 0.154828, Accuracy: 94.53\n",
      "Train Epoch: 54 [11520/50000 (26%)]\tLoss: 0.154701, Accuracy: 94.92\n",
      "Train Epoch: 54 [12800/50000 (28%)]\tLoss: 0.175913, Accuracy: 94.14\n",
      "Train Epoch: 54 [14080/50000 (31%)]\tLoss: 0.203185, Accuracy: 92.97\n",
      "Train Epoch: 54 [15360/50000 (34%)]\tLoss: 0.177816, Accuracy: 94.14\n",
      "Train Epoch: 54 [16640/50000 (37%)]\tLoss: 0.177993, Accuracy: 93.75\n",
      "Train Epoch: 54 [17920/50000 (40%)]\tLoss: 0.169307, Accuracy: 92.97\n",
      "Train Epoch: 54 [19200/50000 (43%)]\tLoss: 0.131014, Accuracy: 95.31\n",
      "Train Epoch: 54 [20480/50000 (45%)]\tLoss: 0.164311, Accuracy: 94.92\n",
      "Train Epoch: 54 [21760/50000 (48%)]\tLoss: 0.141434, Accuracy: 95.70\n",
      "Train Epoch: 54 [23040/50000 (51%)]\tLoss: 0.157073, Accuracy: 95.70\n",
      "Train Epoch: 54 [24320/50000 (54%)]\tLoss: 0.141099, Accuracy: 95.31\n",
      "Train Epoch: 54 [25600/50000 (57%)]\tLoss: 0.142997, Accuracy: 95.70\n",
      "Train Epoch: 54 [26880/50000 (60%)]\tLoss: 0.274529, Accuracy: 89.45\n",
      "Train Epoch: 54 [28160/50000 (62%)]\tLoss: 0.148013, Accuracy: 93.75\n",
      "Train Epoch: 54 [29440/50000 (65%)]\tLoss: 0.198734, Accuracy: 92.58\n",
      "Train Epoch: 54 [30720/50000 (68%)]\tLoss: 0.164867, Accuracy: 94.14\n",
      "Train Epoch: 54 [32000/50000 (71%)]\tLoss: 0.109689, Accuracy: 96.09\n",
      "Train Epoch: 54 [33280/50000 (74%)]\tLoss: 0.170517, Accuracy: 93.75\n",
      "Train Epoch: 54 [34560/50000 (77%)]\tLoss: 0.096650, Accuracy: 96.88\n",
      "Train Epoch: 54 [35840/50000 (80%)]\tLoss: 0.149468, Accuracy: 95.70\n",
      "Train Epoch: 54 [37120/50000 (82%)]\tLoss: 0.200899, Accuracy: 92.19\n",
      "Train Epoch: 54 [38400/50000 (85%)]\tLoss: 0.119106, Accuracy: 95.70\n",
      "Train Epoch: 54 [39680/50000 (88%)]\tLoss: 0.174228, Accuracy: 92.97\n",
      "Train Epoch: 54 [40960/50000 (91%)]\tLoss: 0.158033, Accuracy: 93.75\n",
      "Train Epoch: 54 [42240/50000 (94%)]\tLoss: 0.176798, Accuracy: 94.53\n",
      "Train Epoch: 54 [43520/50000 (97%)]\tLoss: 0.140386, Accuracy: 94.53\n",
      "Train Epoch: 54 [35000/50000 (99%)]\tLoss: 0.165501, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2709, Accuracy: 4562/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.36135411262512 s]\n",
      "\n",
      "Test set: Average loss: 0.2796, Accuracy: 9120/10000 (91.20%)\n",
      "\n",
      "Train Epoch: 55 [0/50000 (0%)]\tLoss: 0.164774, Accuracy: 94.14\n",
      "Train Epoch: 55 [1280/50000 (3%)]\tLoss: 0.173525, Accuracy: 94.53\n",
      "Train Epoch: 55 [2560/50000 (6%)]\tLoss: 0.132368, Accuracy: 95.31\n",
      "Train Epoch: 55 [3840/50000 (9%)]\tLoss: 0.139104, Accuracy: 96.48\n",
      "Train Epoch: 55 [5120/50000 (11%)]\tLoss: 0.089727, Accuracy: 98.05\n",
      "Train Epoch: 55 [6400/50000 (14%)]\tLoss: 0.126256, Accuracy: 96.48\n",
      "Train Epoch: 55 [7680/50000 (17%)]\tLoss: 0.153158, Accuracy: 95.70\n",
      "Train Epoch: 55 [8960/50000 (20%)]\tLoss: 0.089463, Accuracy: 98.05\n",
      "Train Epoch: 55 [10240/50000 (23%)]\tLoss: 0.100993, Accuracy: 96.48\n",
      "Train Epoch: 55 [11520/50000 (26%)]\tLoss: 0.102981, Accuracy: 95.70\n",
      "Train Epoch: 55 [12800/50000 (28%)]\tLoss: 0.105585, Accuracy: 96.09\n",
      "Train Epoch: 55 [14080/50000 (31%)]\tLoss: 0.154916, Accuracy: 95.70\n",
      "Train Epoch: 55 [15360/50000 (34%)]\tLoss: 0.130522, Accuracy: 96.09\n",
      "Train Epoch: 55 [16640/50000 (37%)]\tLoss: 0.184658, Accuracy: 93.75\n",
      "Train Epoch: 55 [17920/50000 (40%)]\tLoss: 0.243747, Accuracy: 91.02\n",
      "Train Epoch: 55 [19200/50000 (43%)]\tLoss: 0.140593, Accuracy: 95.31\n",
      "Train Epoch: 55 [20480/50000 (45%)]\tLoss: 0.104165, Accuracy: 96.48\n",
      "Train Epoch: 55 [21760/50000 (48%)]\tLoss: 0.130189, Accuracy: 95.31\n",
      "Train Epoch: 55 [23040/50000 (51%)]\tLoss: 0.147812, Accuracy: 95.70\n",
      "Train Epoch: 55 [24320/50000 (54%)]\tLoss: 0.087158, Accuracy: 96.88\n",
      "Train Epoch: 55 [25600/50000 (57%)]\tLoss: 0.082040, Accuracy: 96.88\n",
      "Train Epoch: 55 [26880/50000 (60%)]\tLoss: 0.149007, Accuracy: 94.53\n",
      "Train Epoch: 55 [28160/50000 (62%)]\tLoss: 0.127012, Accuracy: 96.48\n",
      "Train Epoch: 55 [29440/50000 (65%)]\tLoss: 0.135959, Accuracy: 95.70\n",
      "Train Epoch: 55 [30720/50000 (68%)]\tLoss: 0.157936, Accuracy: 96.09\n",
      "Train Epoch: 55 [32000/50000 (71%)]\tLoss: 0.179752, Accuracy: 92.58\n",
      "Train Epoch: 55 [33280/50000 (74%)]\tLoss: 0.142156, Accuracy: 96.09\n",
      "Train Epoch: 55 [34560/50000 (77%)]\tLoss: 0.149148, Accuracy: 96.48\n",
      "Train Epoch: 55 [35840/50000 (80%)]\tLoss: 0.113152, Accuracy: 95.70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 55 [37120/50000 (82%)]\tLoss: 0.182856, Accuracy: 94.92\n",
      "Train Epoch: 55 [38400/50000 (85%)]\tLoss: 0.158243, Accuracy: 94.14\n",
      "Train Epoch: 55 [39680/50000 (88%)]\tLoss: 0.118338, Accuracy: 95.70\n",
      "Train Epoch: 55 [40960/50000 (91%)]\tLoss: 0.103431, Accuracy: 96.88\n",
      "Train Epoch: 55 [42240/50000 (94%)]\tLoss: 0.169642, Accuracy: 94.92\n",
      "Train Epoch: 55 [43520/50000 (97%)]\tLoss: 0.094659, Accuracy: 96.09\n",
      "Train Epoch: 55 [35000/50000 (99%)]\tLoss: 0.165495, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2748, Accuracy: 4559/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.4622004032135 s]\n",
      "Train Epoch: 56 [0/50000 (0%)]\tLoss: 0.144905, Accuracy: 94.14\n",
      "Train Epoch: 56 [1280/50000 (3%)]\tLoss: 0.147496, Accuracy: 93.75\n",
      "Train Epoch: 56 [2560/50000 (6%)]\tLoss: 0.180216, Accuracy: 92.58\n",
      "Train Epoch: 56 [3840/50000 (9%)]\tLoss: 0.162246, Accuracy: 95.31\n",
      "Train Epoch: 56 [5120/50000 (11%)]\tLoss: 0.089177, Accuracy: 96.88\n",
      "Train Epoch: 56 [6400/50000 (14%)]\tLoss: 0.097014, Accuracy: 96.09\n",
      "Train Epoch: 56 [7680/50000 (17%)]\tLoss: 0.156385, Accuracy: 94.53\n",
      "Train Epoch: 56 [8960/50000 (20%)]\tLoss: 0.121242, Accuracy: 95.70\n",
      "Train Epoch: 56 [10240/50000 (23%)]\tLoss: 0.107695, Accuracy: 95.31\n",
      "Train Epoch: 56 [11520/50000 (26%)]\tLoss: 0.171129, Accuracy: 93.75\n",
      "Train Epoch: 56 [12800/50000 (28%)]\tLoss: 0.186878, Accuracy: 94.14\n",
      "Train Epoch: 56 [14080/50000 (31%)]\tLoss: 0.181422, Accuracy: 94.53\n",
      "Train Epoch: 56 [15360/50000 (34%)]\tLoss: 0.135991, Accuracy: 96.09\n",
      "Train Epoch: 56 [16640/50000 (37%)]\tLoss: 0.159802, Accuracy: 93.75\n",
      "Train Epoch: 56 [17920/50000 (40%)]\tLoss: 0.179862, Accuracy: 95.31\n",
      "Train Epoch: 56 [19200/50000 (43%)]\tLoss: 0.147685, Accuracy: 94.53\n",
      "Train Epoch: 56 [20480/50000 (45%)]\tLoss: 0.121824, Accuracy: 95.31\n",
      "Train Epoch: 56 [21760/50000 (48%)]\tLoss: 0.114865, Accuracy: 96.09\n",
      "Train Epoch: 56 [23040/50000 (51%)]\tLoss: 0.151356, Accuracy: 93.75\n",
      "Train Epoch: 56 [24320/50000 (54%)]\tLoss: 0.174671, Accuracy: 94.53\n",
      "Train Epoch: 56 [25600/50000 (57%)]\tLoss: 0.148607, Accuracy: 93.75\n",
      "Train Epoch: 56 [26880/50000 (60%)]\tLoss: 0.168543, Accuracy: 94.92\n",
      "Train Epoch: 56 [28160/50000 (62%)]\tLoss: 0.142141, Accuracy: 94.92\n",
      "Train Epoch: 56 [29440/50000 (65%)]\tLoss: 0.160852, Accuracy: 94.14\n",
      "Train Epoch: 56 [30720/50000 (68%)]\tLoss: 0.144367, Accuracy: 96.09\n",
      "Train Epoch: 56 [32000/50000 (71%)]\tLoss: 0.111025, Accuracy: 96.48\n",
      "Train Epoch: 56 [33280/50000 (74%)]\tLoss: 0.123250, Accuracy: 96.88\n",
      "Train Epoch: 56 [34560/50000 (77%)]\tLoss: 0.117241, Accuracy: 97.27\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.8381]],\n",
      "\n",
      "        [[ 0.1589]],\n",
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      "\n",
      "        [[ 0.4702]],\n",
      "\n",
      "        [[ 0.9989]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-04 *\n",
      "       [[[-5.1227]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.0806,  0.1803,  0.2954,  ...,  0.1913, -0.0354,  0.0529]],\n",
      "\n",
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      "\n",
      "        [[ 0.0396,  0.1563,  0.1579,  ...,  0.0203, -0.2600,  0.0847]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.2893, -0.2363, -0.3411,  ..., -0.1153, -0.1479, -0.4033]],\n",
      "\n",
      "        [[-0.7420, -0.5166, -0.6941,  ..., -0.0005, -0.0810, -0.6433]],\n",
      "\n",
      "        [[ 0.0866,  0.0379, -0.0887,  ...,  0.2809,  0.1082, -0.1969]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 7.8244e-04,  8.3018e-04,  8.5034e-04,  ...,  9.4897e-04,\n",
      "           9.4766e-04,  9.1129e-04],\n",
      "         [ 7.1037e-06,  9.1968e-06,  4.7187e-05,  ...,  1.3980e-04,\n",
      "           1.1515e-04,  7.0540e-05],\n",
      "         [ 2.7459e-04,  2.4735e-04,  2.0914e-04,  ...,  2.3845e-04,\n",
      "           2.0597e-04,  2.0199e-04],\n",
      "         ...,\n",
      "         [-1.2920e-03, -1.3424e-03, -1.3487e-03,  ..., -1.3283e-03,\n",
      "          -1.3355e-03, -1.3096e-03],\n",
      "         [-1.3851e-03, -1.4084e-03, -1.3936e-03,  ..., -1.5820e-03,\n",
      "          -1.5545e-03, -1.4975e-03],\n",
      "         [ 2.7364e-04,  2.3734e-04,  2.1866e-04,  ...,  2.2077e-04,\n",
      "           2.5452e-04,  2.3086e-04]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 56 [35840/50000 (80%)]\tLoss: 0.135170, Accuracy: 94.53\n",
      "Train Epoch: 56 [37120/50000 (82%)]\tLoss: 0.095254, Accuracy: 97.66\n",
      "Train Epoch: 56 [38400/50000 (85%)]\tLoss: 0.158042, Accuracy: 94.14\n",
      "Train Epoch: 56 [39680/50000 (88%)]\tLoss: 0.124289, Accuracy: 96.48\n",
      "Train Epoch: 56 [40960/50000 (91%)]\tLoss: 0.176328, Accuracy: 94.53\n",
      "Train Epoch: 56 [42240/50000 (94%)]\tLoss: 0.122854, Accuracy: 94.92\n",
      "Train Epoch: 56 [43520/50000 (97%)]\tLoss: 0.087762, Accuracy: 96.88\n",
      "Train Epoch: 56 [35000/50000 (99%)]\tLoss: 0.184509, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2714, Accuracy: 4558/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.419878244400024 s]\n",
      "\n",
      "Test set: Average loss: 0.2860, Accuracy: 9122/10000 (91.22%)\n",
      "\n",
      "Train Epoch: 57 [0/50000 (0%)]\tLoss: 0.132418, Accuracy: 95.70\n",
      "Train Epoch: 57 [1280/50000 (3%)]\tLoss: 0.111636, Accuracy: 96.88\n",
      "Train Epoch: 57 [2560/50000 (6%)]\tLoss: 0.124809, Accuracy: 96.09\n",
      "Train Epoch: 57 [3840/50000 (9%)]\tLoss: 0.131699, Accuracy: 94.53\n",
      "Train Epoch: 57 [5120/50000 (11%)]\tLoss: 0.124647, Accuracy: 96.48\n",
      "Train Epoch: 57 [6400/50000 (14%)]\tLoss: 0.086652, Accuracy: 97.66\n",
      "Train Epoch: 57 [7680/50000 (17%)]\tLoss: 0.114500, Accuracy: 96.48\n",
      "Train Epoch: 57 [8960/50000 (20%)]\tLoss: 0.105299, Accuracy: 96.48\n",
      "Train Epoch: 57 [10240/50000 (23%)]\tLoss: 0.146914, Accuracy: 94.92\n",
      "Train Epoch: 57 [11520/50000 (26%)]\tLoss: 0.146930, Accuracy: 94.14\n",
      "Train Epoch: 57 [12800/50000 (28%)]\tLoss: 0.097148, Accuracy: 96.09\n",
      "Train Epoch: 57 [14080/50000 (31%)]\tLoss: 0.140495, Accuracy: 94.53\n",
      "Train Epoch: 57 [15360/50000 (34%)]\tLoss: 0.154295, Accuracy: 94.14\n",
      "Train Epoch: 57 [16640/50000 (37%)]\tLoss: 0.097559, Accuracy: 98.05\n",
      "Train Epoch: 57 [17920/50000 (40%)]\tLoss: 0.165050, Accuracy: 95.70\n",
      "Train Epoch: 57 [19200/50000 (43%)]\tLoss: 0.136821, Accuracy: 95.70\n",
      "Train Epoch: 57 [20480/50000 (45%)]\tLoss: 0.095345, Accuracy: 97.66\n",
      "Train Epoch: 57 [21760/50000 (48%)]\tLoss: 0.135174, Accuracy: 94.92\n",
      "Train Epoch: 57 [23040/50000 (51%)]\tLoss: 0.140321, Accuracy: 96.48\n",
      "Train Epoch: 57 [24320/50000 (54%)]\tLoss: 0.096344, Accuracy: 97.27\n",
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      "Train Epoch: 57 [26880/50000 (60%)]\tLoss: 0.120217, Accuracy: 95.31\n",
      "Train Epoch: 57 [28160/50000 (62%)]\tLoss: 0.169625, Accuracy: 94.53\n",
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      "Train Epoch: 57 [30720/50000 (68%)]\tLoss: 0.170165, Accuracy: 92.58\n",
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      "Train Epoch: 57 [33280/50000 (74%)]\tLoss: 0.076855, Accuracy: 97.66\n",
      "Train Epoch: 57 [34560/50000 (77%)]\tLoss: 0.124617, Accuracy: 95.70\n",
      "Train Epoch: 57 [35840/50000 (80%)]\tLoss: 0.124570, Accuracy: 94.92\n",
      "Train Epoch: 57 [37120/50000 (82%)]\tLoss: 0.070497, Accuracy: 98.05\n",
      "Train Epoch: 57 [38400/50000 (85%)]\tLoss: 0.150849, Accuracy: 94.14\n",
      "Train Epoch: 57 [39680/50000 (88%)]\tLoss: 0.111492, Accuracy: 96.48\n",
      "Train Epoch: 57 [40960/50000 (91%)]\tLoss: 0.082731, Accuracy: 97.27\n",
      "Train Epoch: 57 [42240/50000 (94%)]\tLoss: 0.164674, Accuracy: 93.36\n",
      "Train Epoch: 57 [43520/50000 (97%)]\tLoss: 0.115973, Accuracy: 94.53\n",
      "Train Epoch: 57 [35000/50000 (99%)]\tLoss: 0.142537, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2752, Accuracy: 4564/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.41637897491455 s]\n",
      "Train Epoch: 58 [0/50000 (0%)]\tLoss: 0.127731, Accuracy: 96.88\n",
      "Train Epoch: 58 [1280/50000 (3%)]\tLoss: 0.114954, Accuracy: 96.48\n",
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      "Train Epoch: 58 [5120/50000 (11%)]\tLoss: 0.086678, Accuracy: 97.66\n",
      "Train Epoch: 58 [6400/50000 (14%)]\tLoss: 0.128849, Accuracy: 96.09\n",
      "Train Epoch: 58 [7680/50000 (17%)]\tLoss: 0.089425, Accuracy: 97.66\n",
      "Train Epoch: 58 [8960/50000 (20%)]\tLoss: 0.120431, Accuracy: 96.88\n",
      "Train Epoch: 58 [10240/50000 (23%)]\tLoss: 0.089619, Accuracy: 96.48\n",
      "Train Epoch: 58 [11520/50000 (26%)]\tLoss: 0.126383, Accuracy: 95.31\n",
      "Train Epoch: 58 [12800/50000 (28%)]\tLoss: 0.140280, Accuracy: 95.31\n",
      "Train Epoch: 58 [14080/50000 (31%)]\tLoss: 0.085357, Accuracy: 96.48\n",
      "Train Epoch: 58 [15360/50000 (34%)]\tLoss: 0.129847, Accuracy: 95.31\n",
      "Train Epoch: 58 [16640/50000 (37%)]\tLoss: 0.103034, Accuracy: 97.66\n",
      "Train Epoch: 58 [17920/50000 (40%)]\tLoss: 0.083808, Accuracy: 96.88\n",
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      "Train Epoch: 58 [24320/50000 (54%)]\tLoss: 0.198799, Accuracy: 94.53\n",
      "Train Epoch: 58 [25600/50000 (57%)]\tLoss: 0.082299, Accuracy: 97.66\n",
      "Train Epoch: 58 [26880/50000 (60%)]\tLoss: 0.135679, Accuracy: 95.70\n",
      "Train Epoch: 58 [28160/50000 (62%)]\tLoss: 0.126336, Accuracy: 95.31\n",
      "Train Epoch: 58 [29440/50000 (65%)]\tLoss: 0.051617, Accuracy: 98.83\n",
      "Train Epoch: 58 [30720/50000 (68%)]\tLoss: 0.088110, Accuracy: 96.09\n",
      "Train Epoch: 58 [32000/50000 (71%)]\tLoss: 0.105787, Accuracy: 96.48\n",
      "Train Epoch: 58 [33280/50000 (74%)]\tLoss: 0.074245, Accuracy: 97.66\n",
      "Train Epoch: 58 [34560/50000 (77%)]\tLoss: 0.105768, Accuracy: 96.48\n",
      "Train Epoch: 58 [35840/50000 (80%)]\tLoss: 0.081678, Accuracy: 96.09\n",
      "Train Epoch: 58 [37120/50000 (82%)]\tLoss: 0.143893, Accuracy: 94.14\n",
      "Train Epoch: 58 [38400/50000 (85%)]\tLoss: 0.115405, Accuracy: 95.70\n",
      "Train Epoch: 58 [39680/50000 (88%)]\tLoss: 0.196188, Accuracy: 92.97\n",
      "Train Epoch: 58 [40960/50000 (91%)]\tLoss: 0.101648, Accuracy: 97.66\n",
      "Train Epoch: 58 [42240/50000 (94%)]\tLoss: 0.114121, Accuracy: 96.88\n",
      "Train Epoch: 58 [43520/50000 (97%)]\tLoss: 0.110571, Accuracy: 96.48\n",
      "Train Epoch: 58 [35000/50000 (99%)]\tLoss: 0.099772, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2717, Accuracy: 4569/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.40226697921753 s]\n",
      "\n",
      "Test set: Average loss: 0.2952, Accuracy: 9108/10000 (91.08%)\n",
      "\n",
      "Train Epoch: 59 [0/50000 (0%)]\tLoss: 0.106928, Accuracy: 96.09\n",
      "Train Epoch: 59 [1280/50000 (3%)]\tLoss: 0.073096, Accuracy: 96.48\n",
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      "Train Epoch: 59 [10240/50000 (23%)]\tLoss: 0.101151, Accuracy: 96.88\n",
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      "Train Epoch: 59 [12800/50000 (28%)]\tLoss: 0.097573, Accuracy: 96.48\n",
      "Train Epoch: 59 [14080/50000 (31%)]\tLoss: 0.065188, Accuracy: 98.83\n",
      "Train Epoch: 59 [15360/50000 (34%)]\tLoss: 0.061176, Accuracy: 97.66\n",
      "Train Epoch: 59 [16640/50000 (37%)]\tLoss: 0.089704, Accuracy: 97.66\n",
      "Train Epoch: 59 [17920/50000 (40%)]\tLoss: 0.110857, Accuracy: 96.88\n",
      "Train Epoch: 59 [19200/50000 (43%)]\tLoss: 0.185273, Accuracy: 95.31\n",
      "Train Epoch: 59 [20480/50000 (45%)]\tLoss: 0.077282, Accuracy: 96.88\n",
      "Train Epoch: 59 [21760/50000 (48%)]\tLoss: 0.119883, Accuracy: 97.27\n",
      "Train Epoch: 59 [23040/50000 (51%)]\tLoss: 0.123843, Accuracy: 96.09\n",
      "Train Epoch: 59 [24320/50000 (54%)]\tLoss: 0.073979, Accuracy: 97.66\n",
      "Train Epoch: 59 [25600/50000 (57%)]\tLoss: 0.095765, Accuracy: 95.70\n",
      "Train Epoch: 59 [26880/50000 (60%)]\tLoss: 0.140394, Accuracy: 94.92\n",
      "Train Epoch: 59 [28160/50000 (62%)]\tLoss: 0.096554, Accuracy: 96.88\n",
      "Train Epoch: 59 [29440/50000 (65%)]\tLoss: 0.103992, Accuracy: 96.88\n",
      "Train Epoch: 59 [30720/50000 (68%)]\tLoss: 0.084618, Accuracy: 96.88\n",
      "Train Epoch: 59 [32000/50000 (71%)]\tLoss: 0.097338, Accuracy: 96.09\n",
      "Train Epoch: 59 [33280/50000 (74%)]\tLoss: 0.129075, Accuracy: 96.48\n",
      "Train Epoch: 59 [34560/50000 (77%)]\tLoss: 0.069733, Accuracy: 97.66\n",
      "Train Epoch: 59 [35840/50000 (80%)]\tLoss: 0.135475, Accuracy: 95.70\n",
      "Train Epoch: 59 [37120/50000 (82%)]\tLoss: 0.100008, Accuracy: 96.88\n",
      "Train Epoch: 59 [38400/50000 (85%)]\tLoss: 0.105794, Accuracy: 97.27\n",
      "Train Epoch: 59 [39680/50000 (88%)]\tLoss: 0.096419, Accuracy: 97.27\n",
      "Train Epoch: 59 [40960/50000 (91%)]\tLoss: 0.083610, Accuracy: 98.44\n",
      "Train Epoch: 59 [42240/50000 (94%)]\tLoss: 0.163863, Accuracy: 93.36\n",
      "Train Epoch: 59 [43520/50000 (97%)]\tLoss: 0.097887, Accuracy: 96.48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 59 [35000/50000 (99%)]\tLoss: 0.081096, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2872, Accuracy: 4560/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.4444146156311 s]\n",
      "Train Epoch: 60 [0/50000 (0%)]\tLoss: 0.133842, Accuracy: 95.70\n",
      "Train Epoch: 60 [1280/50000 (3%)]\tLoss: 0.062287, Accuracy: 98.44\n",
      "Train Epoch: 60 [2560/50000 (6%)]\tLoss: 0.077883, Accuracy: 97.27\n",
      "Train Epoch: 60 [3840/50000 (9%)]\tLoss: 0.092335, Accuracy: 97.66\n",
      "Train Epoch: 60 [5120/50000 (11%)]\tLoss: 0.116334, Accuracy: 95.31\n",
      "Train Epoch: 60 [6400/50000 (14%)]\tLoss: 0.112788, Accuracy: 95.70\n",
      "Train Epoch: 60 [7680/50000 (17%)]\tLoss: 0.106642, Accuracy: 96.88\n",
      "Train Epoch: 60 [8960/50000 (20%)]\tLoss: 0.067465, Accuracy: 98.05\n",
      "Train Epoch: 60 [10240/50000 (23%)]\tLoss: 0.137618, Accuracy: 95.70\n",
      "Train Epoch: 60 [11520/50000 (26%)]\tLoss: 0.085368, Accuracy: 97.66\n",
      "Train Epoch: 60 [12800/50000 (28%)]\tLoss: 0.078788, Accuracy: 96.88\n",
      "Train Epoch: 60 [14080/50000 (31%)]\tLoss: 0.145988, Accuracy: 95.70\n",
      "Train Epoch: 60 [15360/50000 (34%)]\tLoss: 0.111809, Accuracy: 95.70\n",
      "Train Epoch: 60 [16640/50000 (37%)]\tLoss: 0.131179, Accuracy: 95.31\n",
      "Train Epoch: 60 [17920/50000 (40%)]\tLoss: 0.086808, Accuracy: 97.27\n",
      "Train Epoch: 60 [19200/50000 (43%)]\tLoss: 0.079732, Accuracy: 97.27\n",
      "Train Epoch: 60 [20480/50000 (45%)]\tLoss: 0.085073, Accuracy: 96.48\n",
      "Train Epoch: 60 [21760/50000 (48%)]\tLoss: 0.084682, Accuracy: 96.88\n",
      "Train Epoch: 60 [23040/50000 (51%)]\tLoss: 0.069835, Accuracy: 98.05\n",
      "Train Epoch: 60 [24320/50000 (54%)]\tLoss: 0.083277, Accuracy: 96.48\n",
      "Train Epoch: 60 [25600/50000 (57%)]\tLoss: 0.109570, Accuracy: 95.70\n",
      "Train Epoch: 60 [26880/50000 (60%)]\tLoss: 0.055839, Accuracy: 98.05\n",
      "Train Epoch: 60 [28160/50000 (62%)]\tLoss: 0.072365, Accuracy: 97.66\n",
      "Train Epoch: 60 [29440/50000 (65%)]\tLoss: 0.123358, Accuracy: 96.48\n",
      "Train Epoch: 60 [30720/50000 (68%)]\tLoss: 0.144582, Accuracy: 96.48\n",
      "Train Epoch: 60 [32000/50000 (71%)]\tLoss: 0.089441, Accuracy: 96.88\n",
      "Train Epoch: 60 [33280/50000 (74%)]\tLoss: 0.118287, Accuracy: 95.70\n",
      "Train Epoch: 60 [34560/50000 (77%)]\tLoss: 0.107502, Accuracy: 96.09\n",
      "Train Epoch: 60 [35840/50000 (80%)]\tLoss: 0.097243, Accuracy: 96.09\n",
      "Train Epoch: 60 [37120/50000 (82%)]\tLoss: 0.125344, Accuracy: 95.31\n",
      "Train Epoch: 60 [38400/50000 (85%)]\tLoss: 0.083963, Accuracy: 97.66\n",
      "Train Epoch: 60 [39680/50000 (88%)]\tLoss: 0.081382, Accuracy: 97.27\n",
      "Train Epoch: 60 [40960/50000 (91%)]\tLoss: 0.071751, Accuracy: 98.05\n",
      "Train Epoch: 60 [42240/50000 (94%)]\tLoss: 0.097013, Accuracy: 96.09\n",
      "Train Epoch: 60 [43520/50000 (97%)]\tLoss: 0.118242, Accuracy: 96.48\n",
      "Train Epoch: 60 [35000/50000 (99%)]\tLoss: 0.097400, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2873, Accuracy: 4560/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.37907648086548 s]\n",
      "\n",
      "Test set: Average loss: 0.2995, Accuracy: 9124/10000 (91.24%)\n",
      "\n",
      "Train Epoch: 61 [0/50000 (0%)]\tLoss: 0.095333, Accuracy: 98.05\n",
      "Train Epoch: 61 [1280/50000 (3%)]\tLoss: 0.042283, Accuracy: 98.83\n",
      "Train Epoch: 61 [2560/50000 (6%)]\tLoss: 0.076695, Accuracy: 96.88\n",
      "Train Epoch: 61 [3840/50000 (9%)]\tLoss: 0.118373, Accuracy: 95.70\n",
      "Train Epoch: 61 [5120/50000 (11%)]\tLoss: 0.107005, Accuracy: 96.48\n",
      "Train Epoch: 61 [6400/50000 (14%)]\tLoss: 0.158152, Accuracy: 94.92\n",
      "Train Epoch: 61 [7680/50000 (17%)]\tLoss: 0.151116, Accuracy: 95.70\n",
      "Train Epoch: 61 [8960/50000 (20%)]\tLoss: 0.119321, Accuracy: 96.09\n",
      "Train Epoch: 61 [10240/50000 (23%)]\tLoss: 0.087169, Accuracy: 97.27\n",
      "Train Epoch: 61 [11520/50000 (26%)]\tLoss: 0.072420, Accuracy: 97.66\n",
      "Train Epoch: 61 [12800/50000 (28%)]\tLoss: 0.116531, Accuracy: 95.70\n",
      "Train Epoch: 61 [14080/50000 (31%)]\tLoss: 0.183162, Accuracy: 95.31\n",
      "Train Epoch: 61 [15360/50000 (34%)]\tLoss: 0.079036, Accuracy: 97.27\n",
      "Train Epoch: 61 [16640/50000 (37%)]\tLoss: 0.069960, Accuracy: 96.88\n",
      "Train Epoch: 61 [17920/50000 (40%)]\tLoss: 0.090570, Accuracy: 96.48\n",
      "Train Epoch: 61 [19200/50000 (43%)]\tLoss: 0.140155, Accuracy: 94.53\n",
      "Train Epoch: 61 [20480/50000 (45%)]\tLoss: 0.103200, Accuracy: 96.88\n",
      "Train Epoch: 61 [21760/50000 (48%)]\tLoss: 0.107838, Accuracy: 96.09\n",
      "Train Epoch: 61 [23040/50000 (51%)]\tLoss: 0.111661, Accuracy: 96.48\n",
      "Train Epoch: 61 [24320/50000 (54%)]\tLoss: 0.093136, Accuracy: 97.27\n",
      "Train Epoch: 61 [25600/50000 (57%)]\tLoss: 0.132418, Accuracy: 96.09\n",
      "Train Epoch: 61 [26880/50000 (60%)]\tLoss: 0.139190, Accuracy: 96.09\n",
      "Train Epoch: 61 [28160/50000 (62%)]\tLoss: 0.084593, Accuracy: 97.27\n",
      "Train Epoch: 61 [29440/50000 (65%)]\tLoss: 0.056602, Accuracy: 97.27\n",
      "Train Epoch: 61 [30720/50000 (68%)]\tLoss: 0.131907, Accuracy: 94.92\n",
      "Train Epoch: 61 [32000/50000 (71%)]\tLoss: 0.108709, Accuracy: 96.88\n",
      "Train Epoch: 61 [33280/50000 (74%)]\tLoss: 0.077120, Accuracy: 97.27\n",
      "Train Epoch: 61 [34560/50000 (77%)]\tLoss: 0.125784, Accuracy: 95.70\n",
      "Train Epoch: 61 [35840/50000 (80%)]\tLoss: 0.057507, Accuracy: 98.05\n",
      "Train Epoch: 61 [37120/50000 (82%)]\tLoss: 0.075085, Accuracy: 97.27\n",
      "Train Epoch: 61 [38400/50000 (85%)]\tLoss: 0.085465, Accuracy: 97.66\n",
      "Train Epoch: 61 [39680/50000 (88%)]\tLoss: 0.121160, Accuracy: 95.31\n",
      "Train Epoch: 61 [40960/50000 (91%)]\tLoss: 0.059426, Accuracy: 98.83\n",
      "Train Epoch: 61 [42240/50000 (94%)]\tLoss: 0.074378, Accuracy: 97.27\n",
      "Train Epoch: 61 [43520/50000 (97%)]\tLoss: 0.144477, Accuracy: 93.75\n",
      "Train Epoch: 61 [35000/50000 (99%)]\tLoss: 0.050056, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2802, Accuracy: 4571/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.46058392524719 s]\n",
      "Train Epoch: 62 [0/50000 (0%)]\tLoss: 0.089552, Accuracy: 97.27\n",
      "Train Epoch: 62 [1280/50000 (3%)]\tLoss: 0.101454, Accuracy: 96.09\n",
      "Train Epoch: 62 [2560/50000 (6%)]\tLoss: 0.125530, Accuracy: 94.92\n",
      "Train Epoch: 62 [3840/50000 (9%)]\tLoss: 0.078027, Accuracy: 96.88\n",
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      "Train Epoch: 62 [6400/50000 (14%)]\tLoss: 0.083075, Accuracy: 97.27\n",
      "Train Epoch: 62 [7680/50000 (17%)]\tLoss: 0.086432, Accuracy: 96.88\n",
      "Train Epoch: 62 [8960/50000 (20%)]\tLoss: 0.090017, Accuracy: 97.66\n",
      "Train Epoch: 62 [10240/50000 (23%)]\tLoss: 0.106266, Accuracy: 96.48\n",
      "Train Epoch: 62 [11520/50000 (26%)]\tLoss: 0.083893, Accuracy: 98.05\n",
      "Train Epoch: 62 [12800/50000 (28%)]\tLoss: 0.068779, Accuracy: 98.05\n",
      "Train Epoch: 62 [14080/50000 (31%)]\tLoss: 0.099883, Accuracy: 97.66\n",
      "Train Epoch: 62 [15360/50000 (34%)]\tLoss: 0.081116, Accuracy: 97.27\n",
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      "Train Epoch: 62 [21760/50000 (48%)]\tLoss: 0.053965, Accuracy: 99.22\n",
      "Train Epoch: 62 [23040/50000 (51%)]\tLoss: 0.104854, Accuracy: 96.48\n",
      "Train Epoch: 62 [24320/50000 (54%)]\tLoss: 0.093727, Accuracy: 97.27\n",
      "Train Epoch: 62 [25600/50000 (57%)]\tLoss: 0.113734, Accuracy: 96.09\n",
      "Train Epoch: 62 [26880/50000 (60%)]\tLoss: 0.106914, Accuracy: 95.70\n",
      "Train Epoch: 62 [28160/50000 (62%)]\tLoss: 0.128321, Accuracy: 95.31\n",
      "Train Epoch: 62 [29440/50000 (65%)]\tLoss: 0.137892, Accuracy: 96.48\n",
      "Train Epoch: 62 [30720/50000 (68%)]\tLoss: 0.060427, Accuracy: 98.05\n",
      "Train Epoch: 62 [32000/50000 (71%)]\tLoss: 0.089929, Accuracy: 98.44\n",
      "Train Epoch: 62 [33280/50000 (74%)]\tLoss: 0.112923, Accuracy: 96.09\n",
      "Train Epoch: 62 [34560/50000 (77%)]\tLoss: 0.097012, Accuracy: 96.88\n",
      "Train Epoch: 62 [35840/50000 (80%)]\tLoss: 0.137603, Accuracy: 94.92\n",
      "Train Epoch: 62 [37120/50000 (82%)]\tLoss: 0.095733, Accuracy: 97.66\n",
      "Train Epoch: 62 [38400/50000 (85%)]\tLoss: 0.080958, Accuracy: 96.88\n",
      "Train Epoch: 62 [39680/50000 (88%)]\tLoss: 0.118169, Accuracy: 95.31\n",
      "Train Epoch: 62 [40960/50000 (91%)]\tLoss: 0.078396, Accuracy: 97.66\n",
      "Train Epoch: 62 [42240/50000 (94%)]\tLoss: 0.099345, Accuracy: 96.48\n",
      "Train Epoch: 62 [43520/50000 (97%)]\tLoss: 0.150379, Accuracy: 93.36\n",
      "Train Epoch: 62 [35000/50000 (99%)]\tLoss: 0.138219, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2843, Accuracy: 4580/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.355661392211914 s]\n",
      "\n",
      "Test set: Average loss: 0.3113, Accuracy: 9106/10000 (91.06%)\n",
      "\n",
      "Train Epoch: 63 [0/50000 (0%)]\tLoss: 0.082713, Accuracy: 97.66\n",
      "Train Epoch: 63 [1280/50000 (3%)]\tLoss: 0.121631, Accuracy: 94.92\n",
      "Train Epoch: 63 [2560/50000 (6%)]\tLoss: 0.107816, Accuracy: 96.48\n",
      "Train Epoch: 63 [3840/50000 (9%)]\tLoss: 0.095632, Accuracy: 97.27\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 63 [5120/50000 (11%)]\tLoss: 0.085273, Accuracy: 97.66\n",
      "Train Epoch: 63 [6400/50000 (14%)]\tLoss: 0.071794, Accuracy: 97.66\n",
      "Train Epoch: 63 [7680/50000 (17%)]\tLoss: 0.141761, Accuracy: 94.92\n",
      "Train Epoch: 63 [8960/50000 (20%)]\tLoss: 0.110244, Accuracy: 96.88\n",
      "Train Epoch: 63 [10240/50000 (23%)]\tLoss: 0.077753, Accuracy: 96.48\n",
      "Train Epoch: 63 [11520/50000 (26%)]\tLoss: 0.058224, Accuracy: 98.83\n",
      "Train Epoch: 63 [12800/50000 (28%)]\tLoss: 0.120865, Accuracy: 96.09\n",
      "Train Epoch: 63 [14080/50000 (31%)]\tLoss: 0.147077, Accuracy: 94.53\n",
      "Train Epoch: 63 [15360/50000 (34%)]\tLoss: 0.079106, Accuracy: 97.27\n",
      "Train Epoch: 63 [16640/50000 (37%)]\tLoss: 0.080920, Accuracy: 97.27\n",
      "Train Epoch: 63 [17920/50000 (40%)]\tLoss: 0.102893, Accuracy: 96.48\n",
      "Train Epoch: 63 [19200/50000 (43%)]\tLoss: 0.075793, Accuracy: 97.66\n",
      "Train Epoch: 63 [20480/50000 (45%)]\tLoss: 0.067543, Accuracy: 98.44\n",
      "Train Epoch: 63 [21760/50000 (48%)]\tLoss: 0.065837, Accuracy: 98.05\n",
      "Train Epoch: 63 [23040/50000 (51%)]\tLoss: 0.079203, Accuracy: 97.27\n",
      "Train Epoch: 63 [24320/50000 (54%)]\tLoss: 0.125283, Accuracy: 96.09\n",
      "Train Epoch: 63 [25600/50000 (57%)]\tLoss: 0.071277, Accuracy: 97.27\n",
      "Train Epoch: 63 [26880/50000 (60%)]\tLoss: 0.064669, Accuracy: 98.05\n",
      "Train Epoch: 63 [28160/50000 (62%)]\tLoss: 0.074337, Accuracy: 98.05\n",
      "Train Epoch: 63 [29440/50000 (65%)]\tLoss: 0.064483, Accuracy: 97.66\n",
      "Train Epoch: 63 [30720/50000 (68%)]\tLoss: 0.113802, Accuracy: 96.09\n",
      "Train Epoch: 63 [32000/50000 (71%)]\tLoss: 0.106321, Accuracy: 94.92\n",
      "Train Epoch: 63 [33280/50000 (74%)]\tLoss: 0.059556, Accuracy: 98.44\n",
      "Train Epoch: 63 [34560/50000 (77%)]\tLoss: 0.077765, Accuracy: 97.27\n",
      "Train Epoch: 63 [35840/50000 (80%)]\tLoss: 0.074972, Accuracy: 98.05\n",
      "Train Epoch: 63 [37120/50000 (82%)]\tLoss: 0.062846, Accuracy: 97.27\n",
      "Train Epoch: 63 [38400/50000 (85%)]\tLoss: 0.130640, Accuracy: 94.92\n",
      "Train Epoch: 63 [39680/50000 (88%)]\tLoss: 0.104338, Accuracy: 96.09\n",
      "Train Epoch: 63 [40960/50000 (91%)]\tLoss: 0.098124, Accuracy: 97.27\n",
      "Train Epoch: 63 [42240/50000 (94%)]\tLoss: 0.140018, Accuracy: 95.70\n",
      "Train Epoch: 63 [43520/50000 (97%)]\tLoss: 0.073147, Accuracy: 96.48\n",
      "Train Epoch: 63 [35000/50000 (99%)]\tLoss: 0.116181, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.2955, Accuracy: 4545/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.50617480278015 s]\n",
      "Train Epoch: 64 [0/50000 (0%)]\tLoss: 0.051769, Accuracy: 98.05\n",
      "Train Epoch: 64 [1280/50000 (3%)]\tLoss: 0.093911, Accuracy: 97.27\n",
      "Train Epoch: 64 [2560/50000 (6%)]\tLoss: 0.105815, Accuracy: 96.88\n",
      "Train Epoch: 64 [3840/50000 (9%)]\tLoss: 0.054862, Accuracy: 98.83\n",
      "Train Epoch: 64 [5120/50000 (11%)]\tLoss: 0.056055, Accuracy: 99.22\n",
      "Train Epoch: 64 [6400/50000 (14%)]\tLoss: 0.085962, Accuracy: 97.27\n",
      "Train Epoch: 64 [7680/50000 (17%)]\tLoss: 0.103348, Accuracy: 97.27\n",
      "Train Epoch: 64 [8960/50000 (20%)]\tLoss: 0.063309, Accuracy: 98.05\n",
      "Train Epoch: 64 [10240/50000 (23%)]\tLoss: 0.091553, Accuracy: 96.09\n",
      "Train Epoch: 64 [11520/50000 (26%)]\tLoss: 0.093413, Accuracy: 96.88\n",
      "Train Epoch: 64 [12800/50000 (28%)]\tLoss: 0.092390, Accuracy: 96.88\n",
      "Train Epoch: 64 [14080/50000 (31%)]\tLoss: 0.040639, Accuracy: 99.22\n",
      "Train Epoch: 64 [15360/50000 (34%)]\tLoss: 0.051601, Accuracy: 98.05\n",
      "Train Epoch: 64 [16640/50000 (37%)]\tLoss: 0.096324, Accuracy: 95.70\n",
      "Train Epoch: 64 [17920/50000 (40%)]\tLoss: 0.063643, Accuracy: 97.27\n",
      "Train Epoch: 64 [19200/50000 (43%)]\tLoss: 0.080531, Accuracy: 97.66\n",
      "Train Epoch: 64 [20480/50000 (45%)]\tLoss: 0.058267, Accuracy: 99.22\n",
      "Train Epoch: 64 [21760/50000 (48%)]\tLoss: 0.069364, Accuracy: 98.05\n",
      "Train Epoch: 64 [23040/50000 (51%)]\tLoss: 0.091047, Accuracy: 95.31\n",
      "Train Epoch: 64 [24320/50000 (54%)]\tLoss: 0.043806, Accuracy: 98.44\n",
      "Train Epoch: 64 [25600/50000 (57%)]\tLoss: 0.069505, Accuracy: 97.27\n",
      "Train Epoch: 64 [26880/50000 (60%)]\tLoss: 0.103396, Accuracy: 96.48\n",
      "Train Epoch: 64 [28160/50000 (62%)]\tLoss: 0.116580, Accuracy: 96.48\n",
      "Train Epoch: 64 [29440/50000 (65%)]\tLoss: 0.077145, Accuracy: 98.05\n",
      "Train Epoch: 64 [30720/50000 (68%)]\tLoss: 0.068231, Accuracy: 97.66\n",
      "Train Epoch: 64 [32000/50000 (71%)]\tLoss: 0.115362, Accuracy: 96.48\n",
      "Train Epoch: 64 [33280/50000 (74%)]\tLoss: 0.078803, Accuracy: 97.27\n",
      "Train Epoch: 64 [34560/50000 (77%)]\tLoss: 0.094686, Accuracy: 97.66\n",
      "Train Epoch: 64 [35840/50000 (80%)]\tLoss: 0.075805, Accuracy: 97.27\n",
      "Train Epoch: 64 [37120/50000 (82%)]\tLoss: 0.103915, Accuracy: 96.88\n",
      "Train Epoch: 64 [38400/50000 (85%)]\tLoss: 0.089517, Accuracy: 96.09\n",
      "Train Epoch: 64 [39680/50000 (88%)]\tLoss: 0.061081, Accuracy: 97.27\n",
      "Train Epoch: 64 [40960/50000 (91%)]\tLoss: 0.139253, Accuracy: 94.53\n",
      "Train Epoch: 64 [42240/50000 (94%)]\tLoss: 0.066582, Accuracy: 98.05\n",
      "Train Epoch: 64 [43520/50000 (97%)]\tLoss: 0.103695, Accuracy: 95.70\n",
      "Train Epoch: 64 [35000/50000 (99%)]\tLoss: 0.123842, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.2880, Accuracy: 4558/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.37177085876465 s]\n",
      "\n",
      "Test set: Average loss: 0.3182, Accuracy: 9112/10000 (91.12%)\n",
      "\n",
      "Train Epoch: 65 [0/50000 (0%)]\tLoss: 0.059599, Accuracy: 98.05\n",
      "Train Epoch: 65 [1280/50000 (3%)]\tLoss: 0.066633, Accuracy: 98.44\n",
      "Train Epoch: 65 [2560/50000 (6%)]\tLoss: 0.043549, Accuracy: 99.22\n",
      "Train Epoch: 65 [3840/50000 (9%)]\tLoss: 0.101208, Accuracy: 96.48\n",
      "Train Epoch: 65 [5120/50000 (11%)]\tLoss: 0.109909, Accuracy: 96.88\n",
      "Train Epoch: 65 [6400/50000 (14%)]\tLoss: 0.075627, Accuracy: 97.66\n",
      "Train Epoch: 65 [7680/50000 (17%)]\tLoss: 0.057353, Accuracy: 98.44\n",
      "Train Epoch: 65 [8960/50000 (20%)]\tLoss: 0.063667, Accuracy: 98.44\n",
      "Train Epoch: 65 [10240/50000 (23%)]\tLoss: 0.073580, Accuracy: 98.44\n",
      "Train Epoch: 65 [11520/50000 (26%)]\tLoss: 0.060684, Accuracy: 98.44\n",
      "Train Epoch: 65 [12800/50000 (28%)]\tLoss: 0.107738, Accuracy: 96.09\n",
      "Train Epoch: 65 [14080/50000 (31%)]\tLoss: 0.059182, Accuracy: 98.05\n",
      "Train Epoch: 65 [15360/50000 (34%)]\tLoss: 0.056242, Accuracy: 97.66\n",
      "Train Epoch: 65 [16640/50000 (37%)]\tLoss: 0.061234, Accuracy: 98.05\n",
      "Train Epoch: 65 [17920/50000 (40%)]\tLoss: 0.058630, Accuracy: 98.44\n",
      "Train Epoch: 65 [19200/50000 (43%)]\tLoss: 0.095802, Accuracy: 96.88\n",
      "Train Epoch: 65 [20480/50000 (45%)]\tLoss: 0.058997, Accuracy: 98.05\n",
      "Train Epoch: 65 [21760/50000 (48%)]\tLoss: 0.092135, Accuracy: 97.27\n",
      "Train Epoch: 65 [23040/50000 (51%)]\tLoss: 0.154769, Accuracy: 95.31\n",
      "Train Epoch: 65 [24320/50000 (54%)]\tLoss: 0.116407, Accuracy: 96.48\n",
      "Train Epoch: 65 [25600/50000 (57%)]\tLoss: 0.062134, Accuracy: 97.66\n",
      "Train Epoch: 65 [26880/50000 (60%)]\tLoss: 0.087397, Accuracy: 96.48\n",
      "Train Epoch: 65 [28160/50000 (62%)]\tLoss: 0.076517, Accuracy: 98.05\n",
      "Train Epoch: 65 [29440/50000 (65%)]\tLoss: 0.053959, Accuracy: 97.27\n",
      "Train Epoch: 65 [30720/50000 (68%)]\tLoss: 0.075806, Accuracy: 97.66\n",
      "Train Epoch: 65 [32000/50000 (71%)]\tLoss: 0.097743, Accuracy: 96.48\n",
      "Train Epoch: 65 [33280/50000 (74%)]\tLoss: 0.084377, Accuracy: 96.88\n",
      "Train Epoch: 65 [34560/50000 (77%)]\tLoss: 0.093726, Accuracy: 97.66\n",
      "Train Epoch: 65 [35840/50000 (80%)]\tLoss: 0.045312, Accuracy: 98.83\n",
      "Train Epoch: 65 [37120/50000 (82%)]\tLoss: 0.096040, Accuracy: 97.66\n",
      "Train Epoch: 65 [38400/50000 (85%)]\tLoss: 0.046459, Accuracy: 98.44\n",
      "Train Epoch: 65 [39680/50000 (88%)]\tLoss: 0.089008, Accuracy: 97.66\n",
      "Train Epoch: 65 [40960/50000 (91%)]\tLoss: 0.116685, Accuracy: 95.31\n",
      "Train Epoch: 65 [42240/50000 (94%)]\tLoss: 0.096390, Accuracy: 96.88\n",
      "Train Epoch: 65 [43520/50000 (97%)]\tLoss: 0.071315, Accuracy: 97.27\n",
      "Train Epoch: 65 [35000/50000 (99%)]\tLoss: 0.085731, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.3131, Accuracy: 4547/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.53204131126404 s]\n",
      "Train Epoch: 66 [0/50000 (0%)]\tLoss: 0.082787, Accuracy: 96.88\n",
      "Train Epoch: 66 [1280/50000 (3%)]\tLoss: 0.046336, Accuracy: 98.05\n",
      "Train Epoch: 66 [2560/50000 (6%)]\tLoss: 0.081448, Accuracy: 97.66\n",
      "Train Epoch: 66 [3840/50000 (9%)]\tLoss: 0.104189, Accuracy: 96.48\n",
      "Train Epoch: 66 [5120/50000 (11%)]\tLoss: 0.108035, Accuracy: 96.09\n",
      "Train Epoch: 66 [6400/50000 (14%)]\tLoss: 0.114161, Accuracy: 96.48\n",
      "Train Epoch: 66 [7680/50000 (17%)]\tLoss: 0.049384, Accuracy: 97.66\n",
      "Train Epoch: 66 [8960/50000 (20%)]\tLoss: 0.049947, Accuracy: 98.44\n",
      "Train Epoch: 66 [10240/50000 (23%)]\tLoss: 0.108909, Accuracy: 96.48\n",
      "Train Epoch: 66 [11520/50000 (26%)]\tLoss: 0.065396, Accuracy: 97.27\n",
      "Train Epoch: 66 [12800/50000 (28%)]\tLoss: 0.061647, Accuracy: 97.27\n",
      "Train Epoch: 66 [14080/50000 (31%)]\tLoss: 0.053675, Accuracy: 99.22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 66 [15360/50000 (34%)]\tLoss: 0.080492, Accuracy: 98.05\n",
      "Train Epoch: 66 [16640/50000 (37%)]\tLoss: 0.076735, Accuracy: 96.09\n",
      "Train Epoch: 66 [17920/50000 (40%)]\tLoss: 0.076251, Accuracy: 97.27\n",
      "Train Epoch: 66 [19200/50000 (43%)]\tLoss: 0.053076, Accuracy: 98.05\n",
      "Train Epoch: 66 [20480/50000 (45%)]\tLoss: 0.089640, Accuracy: 96.48\n",
      "Train Epoch: 66 [21760/50000 (48%)]\tLoss: 0.053051, Accuracy: 97.66\n",
      "Train Epoch: 66 [23040/50000 (51%)]\tLoss: 0.098058, Accuracy: 96.48\n",
      "Train Epoch: 66 [24320/50000 (54%)]\tLoss: 0.106651, Accuracy: 96.48\n",
      "Train Epoch: 66 [25600/50000 (57%)]\tLoss: 0.057839, Accuracy: 98.05\n",
      "Train Epoch: 66 [26880/50000 (60%)]\tLoss: 0.085727, Accuracy: 97.27\n",
      "Train Epoch: 66 [28160/50000 (62%)]\tLoss: 0.102253, Accuracy: 97.66\n",
      "Train Epoch: 66 [29440/50000 (65%)]\tLoss: 0.097777, Accuracy: 96.88\n",
      "Train Epoch: 66 [30720/50000 (68%)]\tLoss: 0.059367, Accuracy: 97.66\n",
      "Train Epoch: 66 [32000/50000 (71%)]\tLoss: 0.082005, Accuracy: 97.27\n",
      "Train Epoch: 66 [33280/50000 (74%)]\tLoss: 0.089050, Accuracy: 97.27\n",
      "Train Epoch: 66 [34560/50000 (77%)]\tLoss: 0.093780, Accuracy: 97.27\n",
      "Train Epoch: 66 [35840/50000 (80%)]\tLoss: 0.060369, Accuracy: 97.27\n",
      "Train Epoch: 66 [37120/50000 (82%)]\tLoss: 0.085118, Accuracy: 97.66\n",
      "Train Epoch: 66 [38400/50000 (85%)]\tLoss: 0.043355, Accuracy: 98.05\n",
      "Train Epoch: 66 [39680/50000 (88%)]\tLoss: 0.097338, Accuracy: 97.27\n",
      "Train Epoch: 66 [40960/50000 (91%)]\tLoss: 0.075442, Accuracy: 97.66\n",
      "Train Epoch: 66 [42240/50000 (94%)]\tLoss: 0.093331, Accuracy: 98.05\n",
      "Train Epoch: 66 [43520/50000 (97%)]\tLoss: 0.105191, Accuracy: 96.09\n",
      "Train Epoch: 66 [35000/50000 (99%)]\tLoss: 0.069215, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.3091, Accuracy: 4562/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.342841386795044 s]\n",
      "\n",
      "Test set: Average loss: 0.3330, Accuracy: 9095/10000 (90.95%)\n",
      "\n",
      "Train Epoch: 67 [0/50000 (0%)]\tLoss: 0.045039, Accuracy: 98.44\n",
      "Train Epoch: 67 [1280/50000 (3%)]\tLoss: 0.101547, Accuracy: 97.27\n",
      "Train Epoch: 67 [2560/50000 (6%)]\tLoss: 0.044394, Accuracy: 99.22\n",
      "Train Epoch: 67 [3840/50000 (9%)]\tLoss: 0.078794, Accuracy: 97.66\n",
      "Train Epoch: 67 [5120/50000 (11%)]\tLoss: 0.056248, Accuracy: 98.44\n",
      "Train Epoch: 67 [6400/50000 (14%)]\tLoss: 0.095225, Accuracy: 96.88\n",
      "Train Epoch: 67 [7680/50000 (17%)]\tLoss: 0.101136, Accuracy: 96.88\n",
      "Train Epoch: 67 [8960/50000 (20%)]\tLoss: 0.050672, Accuracy: 98.44\n",
      "Train Epoch: 67 [10240/50000 (23%)]\tLoss: 0.090047, Accuracy: 97.66\n",
      "Train Epoch: 67 [11520/50000 (26%)]\tLoss: 0.063517, Accuracy: 97.66\n",
      "Train Epoch: 67 [12800/50000 (28%)]\tLoss: 0.054149, Accuracy: 98.83\n",
      "Train Epoch: 67 [14080/50000 (31%)]\tLoss: 0.066967, Accuracy: 97.27\n",
      "Train Epoch: 67 [15360/50000 (34%)]\tLoss: 0.087428, Accuracy: 96.09\n",
      "Train Epoch: 67 [16640/50000 (37%)]\tLoss: 0.087775, Accuracy: 97.27\n",
      "Train Epoch: 67 [17920/50000 (40%)]\tLoss: 0.069042, Accuracy: 97.66\n",
      "Train Epoch: 67 [19200/50000 (43%)]\tLoss: 0.058255, Accuracy: 97.27\n",
      "Train Epoch: 67 [20480/50000 (45%)]\tLoss: 0.090371, Accuracy: 97.66\n",
      "Train Epoch: 67 [21760/50000 (48%)]\tLoss: 0.066416, Accuracy: 97.27\n",
      "Train Epoch: 67 [23040/50000 (51%)]\tLoss: 0.092670, Accuracy: 96.48\n",
      "Train Epoch: 67 [24320/50000 (54%)]\tLoss: 0.065480, Accuracy: 97.66\n",
      "Train Epoch: 67 [25600/50000 (57%)]\tLoss: 0.102978, Accuracy: 95.70\n",
      "Train Epoch: 67 [26880/50000 (60%)]\tLoss: 0.054028, Accuracy: 98.44\n",
      "Train Epoch: 67 [28160/50000 (62%)]\tLoss: 0.067150, Accuracy: 97.66\n",
      "Train Epoch: 67 [29440/50000 (65%)]\tLoss: 0.102280, Accuracy: 96.88\n",
      "Train Epoch: 67 [30720/50000 (68%)]\tLoss: 0.061469, Accuracy: 97.66\n",
      "Train Epoch: 67 [32000/50000 (71%)]\tLoss: 0.114120, Accuracy: 96.09\n",
      "Train Epoch: 67 [33280/50000 (74%)]\tLoss: 0.104364, Accuracy: 96.48\n",
      "Train Epoch: 67 [34560/50000 (77%)]\tLoss: 0.079710, Accuracy: 96.88\n",
      "Train Epoch: 67 [35840/50000 (80%)]\tLoss: 0.083743, Accuracy: 97.66\n",
      "Train Epoch: 67 [37120/50000 (82%)]\tLoss: 0.061682, Accuracy: 98.05\n",
      "Train Epoch: 67 [38400/50000 (85%)]\tLoss: 0.124016, Accuracy: 96.48\n",
      "Train Epoch: 67 [39680/50000 (88%)]\tLoss: 0.091294, Accuracy: 97.27\n",
      "Train Epoch: 67 [40960/50000 (91%)]\tLoss: 0.095971, Accuracy: 96.48\n",
      "Train Epoch: 67 [42240/50000 (94%)]\tLoss: 0.083518, Accuracy: 96.88\n",
      "Train Epoch: 67 [43520/50000 (97%)]\tLoss: 0.059700, Accuracy: 97.66\n",
      "Train Epoch: 67 [35000/50000 (99%)]\tLoss: 0.122201, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.3169, Accuracy: 4553/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.50813674926758 s]\n",
      "Train Epoch: 68 [0/50000 (0%)]\tLoss: 0.061340, Accuracy: 98.05\n",
      "Train Epoch: 68 [1280/50000 (3%)]\tLoss: 0.079072, Accuracy: 97.66\n",
      "Train Epoch: 68 [2560/50000 (6%)]\tLoss: 0.076348, Accuracy: 98.44\n",
      "Train Epoch: 68 [3840/50000 (9%)]\tLoss: 0.079782, Accuracy: 96.88\n",
      "Train Epoch: 68 [5120/50000 (11%)]\tLoss: 0.034971, Accuracy: 99.22\n",
      "Train Epoch: 68 [6400/50000 (14%)]\tLoss: 0.049696, Accuracy: 98.44\n",
      "Train Epoch: 68 [7680/50000 (17%)]\tLoss: 0.120042, Accuracy: 96.09\n",
      "Train Epoch: 68 [8960/50000 (20%)]\tLoss: 0.061620, Accuracy: 97.66\n",
      "Train Epoch: 68 [10240/50000 (23%)]\tLoss: 0.064703, Accuracy: 96.48\n",
      "Train Epoch: 68 [11520/50000 (26%)]\tLoss: 0.052989, Accuracy: 98.05\n",
      "Train Epoch: 68 [12800/50000 (28%)]\tLoss: 0.071027, Accuracy: 97.66\n",
      "Train Epoch: 68 [14080/50000 (31%)]\tLoss: 0.091252, Accuracy: 97.27\n",
      "Train Epoch: 68 [15360/50000 (34%)]\tLoss: 0.052467, Accuracy: 98.05\n",
      "Train Epoch: 68 [16640/50000 (37%)]\tLoss: 0.042451, Accuracy: 98.44\n",
      "Train Epoch: 68 [17920/50000 (40%)]\tLoss: 0.047621, Accuracy: 98.44\n",
      "Train Epoch: 68 [19200/50000 (43%)]\tLoss: 0.033886, Accuracy: 99.22\n",
      "Train Epoch: 68 [20480/50000 (45%)]\tLoss: 0.075998, Accuracy: 97.66\n",
      "Train Epoch: 68 [21760/50000 (48%)]\tLoss: 0.122390, Accuracy: 94.53\n",
      "Train Epoch: 68 [23040/50000 (51%)]\tLoss: 0.115108, Accuracy: 96.88\n",
      "Train Epoch: 68 [24320/50000 (54%)]\tLoss: 0.067616, Accuracy: 98.05\n",
      "Train Epoch: 68 [25600/50000 (57%)]\tLoss: 0.125926, Accuracy: 95.70\n",
      "Train Epoch: 68 [26880/50000 (60%)]\tLoss: 0.061639, Accuracy: 97.66\n",
      "Train Epoch: 68 [28160/50000 (62%)]\tLoss: 0.097370, Accuracy: 96.48\n",
      "Train Epoch: 68 [29440/50000 (65%)]\tLoss: 0.071870, Accuracy: 97.66\n",
      "Train Epoch: 68 [30720/50000 (68%)]\tLoss: 0.052054, Accuracy: 97.66\n",
      "Train Epoch: 68 [32000/50000 (71%)]\tLoss: 0.104136, Accuracy: 94.92\n",
      "Train Epoch: 68 [33280/50000 (74%)]\tLoss: 0.058610, Accuracy: 98.05\n",
      "Train Epoch: 68 [34560/50000 (77%)]\tLoss: 0.107387, Accuracy: 96.09\n",
      "Train Epoch: 68 [35840/50000 (80%)]\tLoss: 0.093234, Accuracy: 95.70\n",
      "Train Epoch: 68 [37120/50000 (82%)]\tLoss: 0.064435, Accuracy: 98.05\n",
      "Train Epoch: 68 [38400/50000 (85%)]\tLoss: 0.051835, Accuracy: 98.83\n",
      "Train Epoch: 68 [39680/50000 (88%)]\tLoss: 0.063271, Accuracy: 98.83\n",
      "Train Epoch: 68 [40960/50000 (91%)]\tLoss: 0.088096, Accuracy: 96.88\n",
      "Train Epoch: 68 [42240/50000 (94%)]\tLoss: 0.088708, Accuracy: 98.05\n",
      "Train Epoch: 68 [43520/50000 (97%)]\tLoss: 0.068280, Accuracy: 98.44\n",
      "Train Epoch: 68 [35000/50000 (99%)]\tLoss: 0.096929, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.3154, Accuracy: 4553/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.380995750427246 s]\n",
      "\n",
      "Test set: Average loss: 0.3400, Accuracy: 9074/10000 (90.74%)\n",
      "\n",
      "Train Epoch: 69 [0/50000 (0%)]\tLoss: 0.093128, Accuracy: 96.48\n",
      "Train Epoch: 69 [1280/50000 (3%)]\tLoss: 0.057300, Accuracy: 98.44\n",
      "Train Epoch: 69 [2560/50000 (6%)]\tLoss: 0.076724, Accuracy: 97.27\n",
      "Train Epoch: 69 [3840/50000 (9%)]\tLoss: 0.065666, Accuracy: 97.27\n",
      "Train Epoch: 69 [5120/50000 (11%)]\tLoss: 0.059494, Accuracy: 97.27\n",
      "Train Epoch: 69 [6400/50000 (14%)]\tLoss: 0.044420, Accuracy: 98.83\n",
      "Train Epoch: 69 [7680/50000 (17%)]\tLoss: 0.054468, Accuracy: 98.44\n",
      "Train Epoch: 69 [8960/50000 (20%)]\tLoss: 0.066575, Accuracy: 98.44\n",
      "Train Epoch: 69 [10240/50000 (23%)]\tLoss: 0.043484, Accuracy: 99.22\n",
      "Train Epoch: 69 [11520/50000 (26%)]\tLoss: 0.053500, Accuracy: 98.05\n",
      "Train Epoch: 69 [12800/50000 (28%)]\tLoss: 0.063937, Accuracy: 98.05\n",
      "Train Epoch: 69 [14080/50000 (31%)]\tLoss: 0.082411, Accuracy: 96.88\n",
      "Train Epoch: 69 [15360/50000 (34%)]\tLoss: 0.084364, Accuracy: 98.44\n",
      "Train Epoch: 69 [16640/50000 (37%)]\tLoss: 0.080521, Accuracy: 98.05\n",
      "Train Epoch: 69 [17920/50000 (40%)]\tLoss: 0.045938, Accuracy: 98.05\n",
      "Train Epoch: 69 [19200/50000 (43%)]\tLoss: 0.077309, Accuracy: 97.66\n",
      "Train Epoch: 69 [20480/50000 (45%)]\tLoss: 0.062683, Accuracy: 98.05\n",
      "Train Epoch: 69 [21760/50000 (48%)]\tLoss: 0.061435, Accuracy: 98.05\n",
      "Train Epoch: 69 [23040/50000 (51%)]\tLoss: 0.066092, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 69 [24320/50000 (54%)]\tLoss: 0.099935, Accuracy: 96.48\n",
      "Train Epoch: 69 [25600/50000 (57%)]\tLoss: 0.072100, Accuracy: 97.66\n",
      "Train Epoch: 69 [26880/50000 (60%)]\tLoss: 0.058124, Accuracy: 98.05\n",
      "Train Epoch: 69 [28160/50000 (62%)]\tLoss: 0.085906, Accuracy: 96.48\n",
      "Train Epoch: 69 [29440/50000 (65%)]\tLoss: 0.106157, Accuracy: 95.70\n",
      "Train Epoch: 69 [30720/50000 (68%)]\tLoss: 0.039690, Accuracy: 98.44\n",
      "Train Epoch: 69 [32000/50000 (71%)]\tLoss: 0.065347, Accuracy: 97.66\n",
      "Train Epoch: 69 [33280/50000 (74%)]\tLoss: 0.118109, Accuracy: 95.70\n",
      "Train Epoch: 69 [34560/50000 (77%)]\tLoss: 0.076430, Accuracy: 98.44\n",
      "Train Epoch: 69 [35840/50000 (80%)]\tLoss: 0.049414, Accuracy: 98.05\n",
      "Train Epoch: 69 [37120/50000 (82%)]\tLoss: 0.080377, Accuracy: 97.66\n",
      "Train Epoch: 69 [38400/50000 (85%)]\tLoss: 0.078500, Accuracy: 97.27\n",
      "Train Epoch: 69 [39680/50000 (88%)]\tLoss: 0.075703, Accuracy: 97.66\n",
      "Train Epoch: 69 [40960/50000 (91%)]\tLoss: 0.051561, Accuracy: 98.05\n",
      "Train Epoch: 69 [42240/50000 (94%)]\tLoss: 0.054110, Accuracy: 98.05\n",
      "Train Epoch: 69 [43520/50000 (97%)]\tLoss: 0.092802, Accuracy: 96.48\n",
      "Train Epoch: 69 [35000/50000 (99%)]\tLoss: 0.123116, Accuracy: 95.50\n",
      "\n",
      "Validation set: Average loss: 0.3404, Accuracy: 4541/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[39.52068114280701 s]\n",
      "Train Epoch: 70 [0/50000 (0%)]\tLoss: 0.040840, Accuracy: 98.83\n",
      "Train Epoch: 70 [1280/50000 (3%)]\tLoss: 0.076958, Accuracy: 97.66\n",
      "torch.Size([512, 1, 1])\n",
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      "\n",
      "        [[ 1.5658]],\n",
      "\n",
      "        [[-0.7472]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-03 *\n",
      "       [[[ 6.1025]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.0873,  0.1442,  0.2470,  ...,  0.1545, -0.0479,  0.0303]],\n",
      "\n",
      "        [[ 0.1804, -0.1455,  0.0994,  ..., -0.2120, -0.0964,  0.0968]],\n",
      "\n",
      "        [[ 0.0353,  0.1390,  0.1405,  ...,  0.0181, -0.2314,  0.0753]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.2526, -0.2048, -0.3042,  ..., -0.1037, -0.1326, -0.3590]],\n",
      "\n",
      "        [[-0.6569, -0.4571, -0.6235,  ..., -0.0119, -0.0842, -0.5834]],\n",
      "\n",
      "        [[ 0.0770,  0.0337, -0.0789,  ...,  0.2499,  0.0963, -0.1751]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 5.5322e-04,  5.8894e-04,  6.0002e-04,  ...,  6.8342e-04,\n",
      "           6.8598e-04,  6.6424e-04],\n",
      "         [ 3.9567e-07,  8.7384e-06,  3.5636e-05,  ...,  1.1475e-04,\n",
      "           8.9879e-05,  5.7387e-05],\n",
      "         [ 2.4174e-04,  2.2284e-04,  1.9159e-04,  ...,  2.0782e-04,\n",
      "           1.8240e-04,  1.7989e-04],\n",
      "         ...,\n",
      "         [-1.1168e-03, -1.1565e-03, -1.1559e-03,  ..., -1.1502e-03,\n",
      "          -1.1569e-03, -1.1422e-03],\n",
      "         [-1.2758e-03, -1.3039e-03, -1.2933e-03,  ..., -1.4477e-03,\n",
      "          -1.4247e-03, -1.3798e-03],\n",
      "         [ 2.3782e-04,  2.1124e-04,  2.0208e-04,  ...,  2.0156e-04,\n",
      "           2.2409e-04,  2.0272e-04]]], device='cuda:0')\n",
      "Train Epoch: 70 [2560/50000 (6%)]\tLoss: 0.032520, Accuracy: 99.22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 70 [3840/50000 (9%)]\tLoss: 0.049216, Accuracy: 99.22\n",
      "Train Epoch: 70 [5120/50000 (11%)]\tLoss: 0.094194, Accuracy: 95.31\n",
      "Train Epoch: 70 [6400/50000 (14%)]\tLoss: 0.030015, Accuracy: 99.22\n",
      "Train Epoch: 70 [7680/50000 (17%)]\tLoss: 0.056348, Accuracy: 97.66\n",
      "Train Epoch: 70 [8960/50000 (20%)]\tLoss: 0.045297, Accuracy: 98.05\n",
      "Train Epoch: 70 [10240/50000 (23%)]\tLoss: 0.080459, Accuracy: 97.27\n",
      "Train Epoch: 70 [11520/50000 (26%)]\tLoss: 0.048158, Accuracy: 98.05\n",
      "Train Epoch: 70 [12800/50000 (28%)]\tLoss: 0.043449, Accuracy: 98.83\n",
      "Train Epoch: 70 [14080/50000 (31%)]\tLoss: 0.059662, Accuracy: 97.66\n",
      "Train Epoch: 70 [15360/50000 (34%)]\tLoss: 0.066772, Accuracy: 98.05\n",
      "Train Epoch: 70 [16640/50000 (37%)]\tLoss: 0.078154, Accuracy: 96.88\n",
      "Train Epoch: 70 [17920/50000 (40%)]\tLoss: 0.040623, Accuracy: 98.83\n",
      "Train Epoch: 70 [19200/50000 (43%)]\tLoss: 0.064982, Accuracy: 97.66\n",
      "Train Epoch: 70 [20480/50000 (45%)]\tLoss: 0.048590, Accuracy: 98.83\n",
      "Train Epoch: 70 [21760/50000 (48%)]\tLoss: 0.081161, Accuracy: 98.05\n",
      "Train Epoch: 70 [23040/50000 (51%)]\tLoss: 0.029753, Accuracy: 99.61\n",
      "Train Epoch: 70 [24320/50000 (54%)]\tLoss: 0.058922, Accuracy: 98.05\n",
      "Train Epoch: 70 [25600/50000 (57%)]\tLoss: 0.097607, Accuracy: 96.88\n",
      "Train Epoch: 70 [26880/50000 (60%)]\tLoss: 0.060966, Accuracy: 98.05\n",
      "Train Epoch: 70 [28160/50000 (62%)]\tLoss: 0.092738, Accuracy: 96.09\n",
      "Train Epoch: 70 [29440/50000 (65%)]\tLoss: 0.063257, Accuracy: 97.66\n",
      "Train Epoch: 70 [30720/50000 (68%)]\tLoss: 0.045873, Accuracy: 97.66\n",
      "Train Epoch: 70 [32000/50000 (71%)]\tLoss: 0.115732, Accuracy: 96.09\n",
      "Train Epoch: 70 [33280/50000 (74%)]\tLoss: 0.091371, Accuracy: 96.88\n",
      "Train Epoch: 70 [34560/50000 (77%)]\tLoss: 0.043251, Accuracy: 98.83\n",
      "Train Epoch: 70 [35840/50000 (80%)]\tLoss: 0.089209, Accuracy: 97.66\n",
      "Train Epoch: 70 [37120/50000 (82%)]\tLoss: 0.041969, Accuracy: 99.22\n",
      "Train Epoch: 70 [38400/50000 (85%)]\tLoss: 0.072284, Accuracy: 97.66\n",
      "Train Epoch: 70 [39680/50000 (88%)]\tLoss: 0.098087, Accuracy: 98.05\n",
      "Train Epoch: 70 [40960/50000 (91%)]\tLoss: 0.079266, Accuracy: 96.48\n",
      "Train Epoch: 70 [42240/50000 (94%)]\tLoss: 0.064028, Accuracy: 98.05\n",
      "Train Epoch: 70 [43520/50000 (97%)]\tLoss: 0.065178, Accuracy: 97.66\n",
      "Train Epoch: 70 [35000/50000 (99%)]\tLoss: 0.063842, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.3157, Accuracy: 4551/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.46074938774109 s]\n",
      "\n",
      "Test set: Average loss: 0.3437, Accuracy: 9094/10000 (90.94%)\n",
      "\n",
      "Train Epoch: 71 [0/50000 (0%)]\tLoss: 0.083957, Accuracy: 97.66\n",
      "Train Epoch: 71 [1280/50000 (3%)]\tLoss: 0.057806, Accuracy: 98.05\n",
      "Train Epoch: 71 [2560/50000 (6%)]\tLoss: 0.074120, Accuracy: 97.27\n",
      "Train Epoch: 71 [3840/50000 (9%)]\tLoss: 0.077131, Accuracy: 97.27\n",
      "Train Epoch: 71 [5120/50000 (11%)]\tLoss: 0.045360, Accuracy: 98.44\n",
      "Train Epoch: 71 [6400/50000 (14%)]\tLoss: 0.091791, Accuracy: 97.66\n",
      "Train Epoch: 71 [7680/50000 (17%)]\tLoss: 0.037415, Accuracy: 98.83\n",
      "Train Epoch: 71 [8960/50000 (20%)]\tLoss: 0.077751, Accuracy: 97.27\n",
      "Train Epoch: 71 [10240/50000 (23%)]\tLoss: 0.058816, Accuracy: 98.05\n",
      "Train Epoch: 71 [11520/50000 (26%)]\tLoss: 0.033155, Accuracy: 99.61\n",
      "Train Epoch: 71 [12800/50000 (28%)]\tLoss: 0.070599, Accuracy: 96.48\n",
      "Train Epoch: 71 [14080/50000 (31%)]\tLoss: 0.040330, Accuracy: 98.83\n",
      "Train Epoch: 71 [15360/50000 (34%)]\tLoss: 0.058629, Accuracy: 97.27\n",
      "Train Epoch: 71 [16640/50000 (37%)]\tLoss: 0.048408, Accuracy: 97.66\n",
      "Train Epoch: 71 [17920/50000 (40%)]\tLoss: 0.074283, Accuracy: 97.27\n",
      "Train Epoch: 71 [19200/50000 (43%)]\tLoss: 0.094262, Accuracy: 96.88\n",
      "Train Epoch: 71 [20480/50000 (45%)]\tLoss: 0.079063, Accuracy: 98.05\n",
      "Train Epoch: 71 [21760/50000 (48%)]\tLoss: 0.067524, Accuracy: 98.05\n",
      "Train Epoch: 71 [23040/50000 (51%)]\tLoss: 0.057128, Accuracy: 97.66\n",
      "Train Epoch: 71 [24320/50000 (54%)]\tLoss: 0.044660, Accuracy: 98.44\n",
      "Train Epoch: 71 [25600/50000 (57%)]\tLoss: 0.071195, Accuracy: 97.66\n",
      "Train Epoch: 71 [26880/50000 (60%)]\tLoss: 0.035960, Accuracy: 98.83\n",
      "Train Epoch: 71 [28160/50000 (62%)]\tLoss: 0.047590, Accuracy: 98.83\n",
      "Train Epoch: 71 [29440/50000 (65%)]\tLoss: 0.046656, Accuracy: 98.44\n",
      "Train Epoch: 71 [30720/50000 (68%)]\tLoss: 0.065214, Accuracy: 97.66\n",
      "Train Epoch: 71 [32000/50000 (71%)]\tLoss: 0.079961, Accuracy: 96.48\n",
      "Train Epoch: 71 [33280/50000 (74%)]\tLoss: 0.053778, Accuracy: 98.83\n",
      "Train Epoch: 71 [34560/50000 (77%)]\tLoss: 0.045785, Accuracy: 98.05\n",
      "Train Epoch: 71 [35840/50000 (80%)]\tLoss: 0.066805, Accuracy: 98.05\n",
      "Train Epoch: 71 [37120/50000 (82%)]\tLoss: 0.041472, Accuracy: 99.22\n",
      "Train Epoch: 71 [38400/50000 (85%)]\tLoss: 0.070596, Accuracy: 98.44\n",
      "Train Epoch: 71 [39680/50000 (88%)]\tLoss: 0.057356, Accuracy: 97.66\n",
      "Train Epoch: 71 [40960/50000 (91%)]\tLoss: 0.037388, Accuracy: 98.83\n",
      "Train Epoch: 71 [42240/50000 (94%)]\tLoss: 0.068673, Accuracy: 97.27\n",
      "Train Epoch: 71 [43520/50000 (97%)]\tLoss: 0.042520, Accuracy: 98.83\n",
      "Train Epoch: 71 [35000/50000 (99%)]\tLoss: 0.051593, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2959, Accuracy: 4597/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.631471395492554 s]\n",
      "Train Epoch: 72 [0/50000 (0%)]\tLoss: 0.030755, Accuracy: 99.22\n",
      "Train Epoch: 72 [1280/50000 (3%)]\tLoss: 0.038681, Accuracy: 98.05\n",
      "Train Epoch: 72 [2560/50000 (6%)]\tLoss: 0.044368, Accuracy: 98.44\n",
      "Train Epoch: 72 [3840/50000 (9%)]\tLoss: 0.051574, Accuracy: 98.83\n",
      "Train Epoch: 72 [5120/50000 (11%)]\tLoss: 0.045237, Accuracy: 98.44\n",
      "Train Epoch: 72 [6400/50000 (14%)]\tLoss: 0.043649, Accuracy: 98.44\n",
      "Train Epoch: 72 [7680/50000 (17%)]\tLoss: 0.072578, Accuracy: 97.27\n",
      "Train Epoch: 72 [8960/50000 (20%)]\tLoss: 0.071161, Accuracy: 97.66\n",
      "Train Epoch: 72 [10240/50000 (23%)]\tLoss: 0.041645, Accuracy: 98.83\n",
      "Train Epoch: 72 [11520/50000 (26%)]\tLoss: 0.020035, Accuracy: 99.61\n",
      "Train Epoch: 72 [12800/50000 (28%)]\tLoss: 0.025650, Accuracy: 99.22\n",
      "Train Epoch: 72 [14080/50000 (31%)]\tLoss: 0.073074, Accuracy: 97.27\n",
      "Train Epoch: 72 [15360/50000 (34%)]\tLoss: 0.035142, Accuracy: 99.22\n",
      "Train Epoch: 72 [16640/50000 (37%)]\tLoss: 0.072337, Accuracy: 98.05\n",
      "Train Epoch: 72 [17920/50000 (40%)]\tLoss: 0.041464, Accuracy: 98.83\n",
      "Train Epoch: 72 [19200/50000 (43%)]\tLoss: 0.066344, Accuracy: 98.05\n",
      "Train Epoch: 72 [20480/50000 (45%)]\tLoss: 0.050484, Accuracy: 98.44\n",
      "Train Epoch: 72 [21760/50000 (48%)]\tLoss: 0.078668, Accuracy: 98.05\n",
      "Train Epoch: 72 [23040/50000 (51%)]\tLoss: 0.075682, Accuracy: 96.48\n",
      "Train Epoch: 72 [24320/50000 (54%)]\tLoss: 0.064091, Accuracy: 98.44\n",
      "Train Epoch: 72 [25600/50000 (57%)]\tLoss: 0.047826, Accuracy: 98.83\n",
      "Train Epoch: 72 [26880/50000 (60%)]\tLoss: 0.034882, Accuracy: 99.61\n",
      "Train Epoch: 72 [28160/50000 (62%)]\tLoss: 0.070893, Accuracy: 98.05\n",
      "Train Epoch: 72 [29440/50000 (65%)]\tLoss: 0.035631, Accuracy: 98.83\n",
      "Train Epoch: 72 [30720/50000 (68%)]\tLoss: 0.042425, Accuracy: 98.44\n",
      "Train Epoch: 72 [32000/50000 (71%)]\tLoss: 0.055405, Accuracy: 97.66\n",
      "Train Epoch: 72 [33280/50000 (74%)]\tLoss: 0.050805, Accuracy: 98.44\n",
      "Train Epoch: 72 [34560/50000 (77%)]\tLoss: 0.043877, Accuracy: 98.05\n",
      "Train Epoch: 72 [35840/50000 (80%)]\tLoss: 0.063981, Accuracy: 97.27\n",
      "Train Epoch: 72 [37120/50000 (82%)]\tLoss: 0.033151, Accuracy: 99.22\n",
      "Train Epoch: 72 [38400/50000 (85%)]\tLoss: 0.067320, Accuracy: 98.83\n",
      "Train Epoch: 72 [39680/50000 (88%)]\tLoss: 0.116747, Accuracy: 96.88\n",
      "Train Epoch: 72 [40960/50000 (91%)]\tLoss: 0.070999, Accuracy: 98.05\n",
      "Train Epoch: 72 [42240/50000 (94%)]\tLoss: 0.059098, Accuracy: 97.27\n",
      "Train Epoch: 72 [43520/50000 (97%)]\tLoss: 0.070438, Accuracy: 98.05\n",
      "Train Epoch: 72 [35000/50000 (99%)]\tLoss: 0.051878, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2974, Accuracy: 4593/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.473859786987305 s]\n",
      "\n",
      "Test set: Average loss: 0.3249, Accuracy: 9153/10000 (91.53%)\n",
      "\n",
      "Train Epoch: 73 [0/50000 (0%)]\tLoss: 0.029237, Accuracy: 99.61\n",
      "Train Epoch: 73 [1280/50000 (3%)]\tLoss: 0.065812, Accuracy: 98.05\n",
      "Train Epoch: 73 [2560/50000 (6%)]\tLoss: 0.045371, Accuracy: 98.44\n",
      "Train Epoch: 73 [3840/50000 (9%)]\tLoss: 0.027056, Accuracy: 99.61\n",
      "Train Epoch: 73 [5120/50000 (11%)]\tLoss: 0.065783, Accuracy: 98.05\n",
      "Train Epoch: 73 [6400/50000 (14%)]\tLoss: 0.040725, Accuracy: 98.83\n",
      "Train Epoch: 73 [7680/50000 (17%)]\tLoss: 0.024231, Accuracy: 99.61\n",
      "Train Epoch: 73 [8960/50000 (20%)]\tLoss: 0.042852, Accuracy: 98.83\n",
      "Train Epoch: 73 [10240/50000 (23%)]\tLoss: 0.099075, Accuracy: 97.27\n",
      "Train Epoch: 73 [11520/50000 (26%)]\tLoss: 0.084535, Accuracy: 97.27\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 73 [12800/50000 (28%)]\tLoss: 0.025390, Accuracy: 99.22\n",
      "Train Epoch: 73 [14080/50000 (31%)]\tLoss: 0.060756, Accuracy: 98.44\n",
      "Train Epoch: 73 [15360/50000 (34%)]\tLoss: 0.059629, Accuracy: 97.66\n",
      "Train Epoch: 73 [16640/50000 (37%)]\tLoss: 0.057304, Accuracy: 98.05\n",
      "Train Epoch: 73 [17920/50000 (40%)]\tLoss: 0.037233, Accuracy: 99.61\n",
      "Train Epoch: 73 [19200/50000 (43%)]\tLoss: 0.045629, Accuracy: 98.05\n",
      "Train Epoch: 73 [20480/50000 (45%)]\tLoss: 0.032523, Accuracy: 98.83\n",
      "Train Epoch: 73 [21760/50000 (48%)]\tLoss: 0.031536, Accuracy: 99.22\n",
      "Train Epoch: 73 [23040/50000 (51%)]\tLoss: 0.030808, Accuracy: 99.22\n",
      "Train Epoch: 73 [24320/50000 (54%)]\tLoss: 0.041841, Accuracy: 98.83\n",
      "Train Epoch: 73 [25600/50000 (57%)]\tLoss: 0.022959, Accuracy: 99.61\n",
      "Train Epoch: 73 [26880/50000 (60%)]\tLoss: 0.049924, Accuracy: 99.22\n",
      "Train Epoch: 73 [28160/50000 (62%)]\tLoss: 0.038577, Accuracy: 98.83\n",
      "Train Epoch: 73 [29440/50000 (65%)]\tLoss: 0.036003, Accuracy: 99.22\n",
      "Train Epoch: 73 [30720/50000 (68%)]\tLoss: 0.040273, Accuracy: 98.44\n",
      "Train Epoch: 73 [32000/50000 (71%)]\tLoss: 0.061519, Accuracy: 98.05\n",
      "Train Epoch: 73 [33280/50000 (74%)]\tLoss: 0.047237, Accuracy: 98.83\n",
      "Train Epoch: 73 [34560/50000 (77%)]\tLoss: 0.062668, Accuracy: 98.44\n",
      "Train Epoch: 73 [35840/50000 (80%)]\tLoss: 0.055389, Accuracy: 97.66\n",
      "Train Epoch: 73 [37120/50000 (82%)]\tLoss: 0.046848, Accuracy: 98.83\n",
      "Train Epoch: 73 [38400/50000 (85%)]\tLoss: 0.026134, Accuracy: 99.22\n",
      "Train Epoch: 73 [39680/50000 (88%)]\tLoss: 0.032501, Accuracy: 99.22\n",
      "Train Epoch: 73 [40960/50000 (91%)]\tLoss: 0.035987, Accuracy: 98.83\n",
      "Train Epoch: 73 [42240/50000 (94%)]\tLoss: 0.047504, Accuracy: 99.61\n",
      "Train Epoch: 73 [43520/50000 (97%)]\tLoss: 0.057106, Accuracy: 98.44\n",
      "Train Epoch: 73 [35000/50000 (99%)]\tLoss: 0.044132, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.3009, Accuracy: 4597/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.58095669746399 s]\n",
      "Train Epoch: 74 [0/50000 (0%)]\tLoss: 0.041077, Accuracy: 98.83\n",
      "Train Epoch: 74 [1280/50000 (3%)]\tLoss: 0.025610, Accuracy: 99.61\n",
      "Train Epoch: 74 [2560/50000 (6%)]\tLoss: 0.036964, Accuracy: 98.83\n",
      "Train Epoch: 74 [3840/50000 (9%)]\tLoss: 0.068722, Accuracy: 98.05\n",
      "Train Epoch: 74 [5120/50000 (11%)]\tLoss: 0.044827, Accuracy: 98.83\n",
      "Train Epoch: 74 [6400/50000 (14%)]\tLoss: 0.022659, Accuracy: 99.61\n",
      "Train Epoch: 74 [7680/50000 (17%)]\tLoss: 0.041169, Accuracy: 99.22\n",
      "Train Epoch: 74 [8960/50000 (20%)]\tLoss: 0.041524, Accuracy: 98.83\n",
      "Train Epoch: 74 [10240/50000 (23%)]\tLoss: 0.049890, Accuracy: 98.44\n",
      "Train Epoch: 74 [11520/50000 (26%)]\tLoss: 0.039348, Accuracy: 98.83\n",
      "Train Epoch: 74 [12800/50000 (28%)]\tLoss: 0.049855, Accuracy: 98.44\n",
      "Train Epoch: 74 [14080/50000 (31%)]\tLoss: 0.044581, Accuracy: 99.22\n",
      "Train Epoch: 74 [15360/50000 (34%)]\tLoss: 0.023341, Accuracy: 99.22\n",
      "Train Epoch: 74 [16640/50000 (37%)]\tLoss: 0.049964, Accuracy: 98.44\n",
      "Train Epoch: 74 [17920/50000 (40%)]\tLoss: 0.035737, Accuracy: 98.83\n",
      "Train Epoch: 74 [19200/50000 (43%)]\tLoss: 0.059934, Accuracy: 98.05\n",
      "Train Epoch: 74 [20480/50000 (45%)]\tLoss: 0.032759, Accuracy: 99.22\n",
      "Train Epoch: 74 [21760/50000 (48%)]\tLoss: 0.076047, Accuracy: 97.27\n",
      "Train Epoch: 74 [23040/50000 (51%)]\tLoss: 0.033590, Accuracy: 98.83\n",
      "Train Epoch: 74 [24320/50000 (54%)]\tLoss: 0.047758, Accuracy: 98.83\n",
      "Train Epoch: 74 [25600/50000 (57%)]\tLoss: 0.041695, Accuracy: 98.83\n",
      "Train Epoch: 74 [26880/50000 (60%)]\tLoss: 0.076566, Accuracy: 97.27\n",
      "Train Epoch: 74 [28160/50000 (62%)]\tLoss: 0.021160, Accuracy: 99.22\n",
      "Train Epoch: 74 [29440/50000 (65%)]\tLoss: 0.070628, Accuracy: 97.27\n",
      "Train Epoch: 74 [30720/50000 (68%)]\tLoss: 0.068542, Accuracy: 97.66\n",
      "Train Epoch: 74 [32000/50000 (71%)]\tLoss: 0.017962, Accuracy: 99.61\n",
      "Train Epoch: 74 [33280/50000 (74%)]\tLoss: 0.041705, Accuracy: 98.83\n",
      "Train Epoch: 74 [34560/50000 (77%)]\tLoss: 0.018624, Accuracy: 100.00\n",
      "Train Epoch: 74 [35840/50000 (80%)]\tLoss: 0.070935, Accuracy: 97.66\n",
      "Train Epoch: 74 [37120/50000 (82%)]\tLoss: 0.034051, Accuracy: 98.44\n",
      "Train Epoch: 74 [38400/50000 (85%)]\tLoss: 0.037153, Accuracy: 99.22\n",
      "Train Epoch: 74 [39680/50000 (88%)]\tLoss: 0.038488, Accuracy: 98.83\n",
      "Train Epoch: 74 [40960/50000 (91%)]\tLoss: 0.069790, Accuracy: 98.05\n",
      "Train Epoch: 74 [42240/50000 (94%)]\tLoss: 0.045482, Accuracy: 98.44\n",
      "Train Epoch: 74 [43520/50000 (97%)]\tLoss: 0.020935, Accuracy: 100.00\n",
      "Train Epoch: 74 [35000/50000 (99%)]\tLoss: 0.052867, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2992, Accuracy: 4599/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.356783390045166 s]\n",
      "\n",
      "Test set: Average loss: 0.3289, Accuracy: 9160/10000 (91.60%)\n",
      "\n",
      "Train Epoch: 75 [0/50000 (0%)]\tLoss: 0.026713, Accuracy: 99.22\n",
      "Train Epoch: 75 [1280/50000 (3%)]\tLoss: 0.040924, Accuracy: 99.22\n",
      "Train Epoch: 75 [2560/50000 (6%)]\tLoss: 0.043707, Accuracy: 98.44\n",
      "Train Epoch: 75 [3840/50000 (9%)]\tLoss: 0.032885, Accuracy: 98.83\n",
      "Train Epoch: 75 [5120/50000 (11%)]\tLoss: 0.088350, Accuracy: 96.88\n",
      "Train Epoch: 75 [6400/50000 (14%)]\tLoss: 0.070039, Accuracy: 96.09\n",
      "Train Epoch: 75 [7680/50000 (17%)]\tLoss: 0.069527, Accuracy: 97.66\n",
      "Train Epoch: 75 [8960/50000 (20%)]\tLoss: 0.069956, Accuracy: 98.05\n",
      "Train Epoch: 75 [10240/50000 (23%)]\tLoss: 0.058623, Accuracy: 98.05\n",
      "Train Epoch: 75 [11520/50000 (26%)]\tLoss: 0.031234, Accuracy: 99.22\n",
      "Train Epoch: 75 [12800/50000 (28%)]\tLoss: 0.032181, Accuracy: 99.22\n",
      "Train Epoch: 75 [14080/50000 (31%)]\tLoss: 0.028910, Accuracy: 99.61\n",
      "Train Epoch: 75 [15360/50000 (34%)]\tLoss: 0.057269, Accuracy: 98.05\n",
      "Train Epoch: 75 [16640/50000 (37%)]\tLoss: 0.089360, Accuracy: 97.66\n",
      "Train Epoch: 75 [17920/50000 (40%)]\tLoss: 0.050221, Accuracy: 97.27\n",
      "Train Epoch: 75 [19200/50000 (43%)]\tLoss: 0.052195, Accuracy: 97.27\n",
      "Train Epoch: 75 [20480/50000 (45%)]\tLoss: 0.048102, Accuracy: 98.83\n",
      "Train Epoch: 75 [21760/50000 (48%)]\tLoss: 0.015123, Accuracy: 100.00\n",
      "Train Epoch: 75 [23040/50000 (51%)]\tLoss: 0.072658, Accuracy: 97.27\n",
      "Train Epoch: 75 [24320/50000 (54%)]\tLoss: 0.042469, Accuracy: 98.83\n",
      "Train Epoch: 75 [25600/50000 (57%)]\tLoss: 0.061364, Accuracy: 98.05\n",
      "Train Epoch: 75 [26880/50000 (60%)]\tLoss: 0.052510, Accuracy: 98.44\n",
      "Train Epoch: 75 [28160/50000 (62%)]\tLoss: 0.027772, Accuracy: 99.61\n",
      "Train Epoch: 75 [29440/50000 (65%)]\tLoss: 0.052246, Accuracy: 99.22\n",
      "Train Epoch: 75 [30720/50000 (68%)]\tLoss: 0.045607, Accuracy: 98.44\n",
      "Train Epoch: 75 [32000/50000 (71%)]\tLoss: 0.035448, Accuracy: 98.83\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[-0.6683]],\n",
      "\n",
      "        [[-0.6736]],\n",
      "\n",
      "        [[-1.1044]],\n",
      "\n",
      "        [[-1.7372]],\n",
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      "        [[-0.0656]],\n",
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      "        [[-0.3679]],\n",
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      "        [[ 1.9746]],\n",
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      "        [[ 0.7329]],\n",
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      "\n",
      "        [[ 1.5427]],\n",
      "\n",
      "        [[-0.4395]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 2.1889]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 1.4440e-04, -1.9558e-04, -2.4742e-03,  ...,  1.9645e-03,\n",
      "          -4.0512e-04, -1.7462e-03]],\n",
      "\n",
      "        [[-2.5848e-03, -2.3014e-04,  2.3800e-04,  ..., -1.5945e-03,\n",
      "           3.5026e-04,  1.7076e-04]],\n",
      "\n",
      "        [[ 8.2074e-04,  1.6197e-03,  1.0704e-04,  ..., -2.6753e-04,\n",
      "          -2.1273e-03, -9.2597e-04]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-9.5147e-04, -1.6592e-03, -7.4553e-04,  ...,  1.6887e-03,\n",
      "          -1.0057e-03, -1.7218e-03]],\n",
      "\n",
      "        [[ 2.2226e-04,  1.8346e-03,  1.5730e-03,  ...,  6.8570e-05,\n",
      "          -1.8059e-03,  2.1354e-03]],\n",
      "\n",
      "        [[ 2.2240e-03, -6.4936e-04,  1.0984e-03,  ...,  8.5670e-04,\n",
      "           7.2250e-04, -2.2809e-04]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
      "tensor([[[-2.2044e-04, -2.1153e-04, -1.8261e-04,  ..., -2.5884e-04,\n",
      "          -2.5890e-04, -2.3986e-04],\n",
      "         [-2.4315e-04, -2.5287e-04, -2.6991e-04,  ..., -3.1337e-04,\n",
      "          -2.8714e-04, -2.7922e-04],\n",
      "         [-3.3298e-04, -3.1634e-04, -3.1018e-04,  ..., -2.1036e-04,\n",
      "          -1.7061e-04, -1.4237e-04],\n",
      "         ...,\n",
      "         [-1.5046e-04, -1.3662e-04, -1.1822e-04,  ..., -1.5130e-04,\n",
      "          -1.2751e-04, -9.9625e-05],\n",
      "         [ 2.0337e-04,  2.1069e-04,  2.3225e-04,  ...,  2.5464e-04,\n",
      "           2.4708e-04,  2.5201e-04],\n",
      "         [ 9.9791e-04,  1.0399e-03,  1.0478e-03,  ...,  1.0519e-03,\n",
      "           1.0530e-03,  1.0277e-03]]], device='cuda:0')\n",
      "Train Epoch: 75 [33280/50000 (74%)]\tLoss: 0.042659, Accuracy: 98.44\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 75 [34560/50000 (77%)]\tLoss: 0.064587, Accuracy: 97.66\n",
      "Train Epoch: 75 [35840/50000 (80%)]\tLoss: 0.056830, Accuracy: 98.05\n",
      "Train Epoch: 75 [37120/50000 (82%)]\tLoss: 0.031353, Accuracy: 98.83\n",
      "Train Epoch: 75 [38400/50000 (85%)]\tLoss: 0.043830, Accuracy: 99.22\n",
      "Train Epoch: 75 [39680/50000 (88%)]\tLoss: 0.040056, Accuracy: 98.44\n",
      "Train Epoch: 75 [40960/50000 (91%)]\tLoss: 0.050957, Accuracy: 98.44\n",
      "Train Epoch: 75 [42240/50000 (94%)]\tLoss: 0.046400, Accuracy: 98.05\n",
      "Train Epoch: 75 [43520/50000 (97%)]\tLoss: 0.052941, Accuracy: 98.05\n",
      "Train Epoch: 75 [35000/50000 (99%)]\tLoss: 0.018897, Accuracy: 99.50\n",
      "\n",
      "Validation set: Average loss: 0.3047, Accuracy: 4601/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[39.46972990036011 s]\n",
      "Train Epoch: 76 [0/50000 (0%)]\tLoss: 0.044921, Accuracy: 98.05\n",
      "Train Epoch: 76 [1280/50000 (3%)]\tLoss: 0.046098, Accuracy: 98.44\n",
      "Train Epoch: 76 [2560/50000 (6%)]\tLoss: 0.040493, Accuracy: 99.22\n",
      "Train Epoch: 76 [3840/50000 (9%)]\tLoss: 0.037072, Accuracy: 98.83\n",
      "Train Epoch: 76 [5120/50000 (11%)]\tLoss: 0.028638, Accuracy: 99.22\n",
      "Train Epoch: 76 [6400/50000 (14%)]\tLoss: 0.071611, Accuracy: 97.27\n",
      "Train Epoch: 76 [7680/50000 (17%)]\tLoss: 0.036733, Accuracy: 98.83\n",
      "Train Epoch: 76 [8960/50000 (20%)]\tLoss: 0.038654, Accuracy: 98.44\n",
      "Train Epoch: 76 [10240/50000 (23%)]\tLoss: 0.027081, Accuracy: 99.22\n",
      "Train Epoch: 76 [11520/50000 (26%)]\tLoss: 0.023618, Accuracy: 99.61\n",
      "Train Epoch: 76 [12800/50000 (28%)]\tLoss: 0.038429, Accuracy: 98.44\n",
      "Train Epoch: 76 [14080/50000 (31%)]\tLoss: 0.032020, Accuracy: 99.22\n",
      "Train Epoch: 76 [15360/50000 (34%)]\tLoss: 0.036220, Accuracy: 98.44\n",
      "Train Epoch: 76 [16640/50000 (37%)]\tLoss: 0.047600, Accuracy: 98.05\n",
      "Train Epoch: 76 [17920/50000 (40%)]\tLoss: 0.047104, Accuracy: 98.83\n",
      "Train Epoch: 76 [19200/50000 (43%)]\tLoss: 0.028576, Accuracy: 99.61\n",
      "Train Epoch: 76 [20480/50000 (45%)]\tLoss: 0.040938, Accuracy: 98.83\n",
      "Train Epoch: 76 [21760/50000 (48%)]\tLoss: 0.044589, Accuracy: 98.05\n",
      "Train Epoch: 76 [23040/50000 (51%)]\tLoss: 0.033322, Accuracy: 99.22\n",
      "Train Epoch: 76 [24320/50000 (54%)]\tLoss: 0.041213, Accuracy: 99.22\n",
      "Train Epoch: 76 [25600/50000 (57%)]\tLoss: 0.036276, Accuracy: 98.83\n",
      "Train Epoch: 76 [26880/50000 (60%)]\tLoss: 0.028119, Accuracy: 99.22\n",
      "Train Epoch: 76 [28160/50000 (62%)]\tLoss: 0.065344, Accuracy: 97.66\n",
      "Train Epoch: 76 [29440/50000 (65%)]\tLoss: 0.074527, Accuracy: 97.27\n",
      "Train Epoch: 76 [30720/50000 (68%)]\tLoss: 0.054673, Accuracy: 99.22\n",
      "Train Epoch: 76 [32000/50000 (71%)]\tLoss: 0.019824, Accuracy: 99.61\n",
      "Train Epoch: 76 [33280/50000 (74%)]\tLoss: 0.044354, Accuracy: 98.44\n",
      "Train Epoch: 76 [34560/50000 (77%)]\tLoss: 0.032704, Accuracy: 99.22\n",
      "Train Epoch: 76 [35840/50000 (80%)]\tLoss: 0.042447, Accuracy: 98.05\n",
      "Train Epoch: 76 [37120/50000 (82%)]\tLoss: 0.034335, Accuracy: 98.44\n",
      "Train Epoch: 76 [38400/50000 (85%)]\tLoss: 0.076786, Accuracy: 97.66\n",
      "Train Epoch: 76 [39680/50000 (88%)]\tLoss: 0.046578, Accuracy: 98.44\n",
      "Train Epoch: 76 [40960/50000 (91%)]\tLoss: 0.043553, Accuracy: 98.83\n",
      "Train Epoch: 76 [42240/50000 (94%)]\tLoss: 0.047174, Accuracy: 98.83\n",
      "Train Epoch: 76 [43520/50000 (97%)]\tLoss: 0.027262, Accuracy: 99.22\n",
      "Train Epoch: 76 [35000/50000 (99%)]\tLoss: 0.040958, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.3045, Accuracy: 4594/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.447036266326904 s]\n",
      "\n",
      "Test set: Average loss: 0.3309, Accuracy: 9150/10000 (91.50%)\n",
      "\n",
      "Train Epoch: 77 [0/50000 (0%)]\tLoss: 0.012484, Accuracy: 100.00\n",
      "Train Epoch: 77 [1280/50000 (3%)]\tLoss: 0.065920, Accuracy: 97.66\n",
      "Train Epoch: 77 [2560/50000 (6%)]\tLoss: 0.042531, Accuracy: 98.83\n",
      "Train Epoch: 77 [3840/50000 (9%)]\tLoss: 0.031616, Accuracy: 99.61\n",
      "Train Epoch: 77 [5120/50000 (11%)]\tLoss: 0.057159, Accuracy: 97.27\n",
      "Train Epoch: 77 [6400/50000 (14%)]\tLoss: 0.064494, Accuracy: 98.44\n",
      "Train Epoch: 77 [7680/50000 (17%)]\tLoss: 0.037275, Accuracy: 98.44\n",
      "Train Epoch: 77 [8960/50000 (20%)]\tLoss: 0.079019, Accuracy: 96.88\n",
      "Train Epoch: 77 [10240/50000 (23%)]\tLoss: 0.056679, Accuracy: 98.44\n",
      "Train Epoch: 77 [11520/50000 (26%)]\tLoss: 0.061251, Accuracy: 97.66\n",
      "Train Epoch: 77 [12800/50000 (28%)]\tLoss: 0.042719, Accuracy: 98.83\n",
      "Train Epoch: 77 [14080/50000 (31%)]\tLoss: 0.037218, Accuracy: 99.22\n",
      "Train Epoch: 77 [15360/50000 (34%)]\tLoss: 0.042511, Accuracy: 98.44\n",
      "Train Epoch: 77 [16640/50000 (37%)]\tLoss: 0.024721, Accuracy: 100.00\n",
      "Train Epoch: 77 [17920/50000 (40%)]\tLoss: 0.018679, Accuracy: 99.61\n",
      "Train Epoch: 77 [19200/50000 (43%)]\tLoss: 0.049118, Accuracy: 99.22\n",
      "Train Epoch: 77 [20480/50000 (45%)]\tLoss: 0.061786, Accuracy: 98.44\n",
      "Train Epoch: 77 [21760/50000 (48%)]\tLoss: 0.027639, Accuracy: 99.22\n",
      "Train Epoch: 77 [23040/50000 (51%)]\tLoss: 0.059410, Accuracy: 98.44\n",
      "Train Epoch: 77 [24320/50000 (54%)]\tLoss: 0.061899, Accuracy: 98.05\n",
      "Train Epoch: 77 [25600/50000 (57%)]\tLoss: 0.022170, Accuracy: 99.61\n",
      "Train Epoch: 77 [26880/50000 (60%)]\tLoss: 0.026443, Accuracy: 99.22\n",
      "Train Epoch: 77 [28160/50000 (62%)]\tLoss: 0.069520, Accuracy: 98.05\n",
      "Train Epoch: 77 [29440/50000 (65%)]\tLoss: 0.040668, Accuracy: 98.44\n",
      "Train Epoch: 77 [30720/50000 (68%)]\tLoss: 0.035515, Accuracy: 99.22\n",
      "Train Epoch: 77 [32000/50000 (71%)]\tLoss: 0.051670, Accuracy: 99.22\n",
      "Train Epoch: 77 [33280/50000 (74%)]\tLoss: 0.030780, Accuracy: 98.83\n",
      "Train Epoch: 77 [34560/50000 (77%)]\tLoss: 0.037791, Accuracy: 98.44\n",
      "Train Epoch: 77 [35840/50000 (80%)]\tLoss: 0.017446, Accuracy: 100.00\n",
      "Train Epoch: 77 [37120/50000 (82%)]\tLoss: 0.066034, Accuracy: 97.27\n",
      "Train Epoch: 77 [38400/50000 (85%)]\tLoss: 0.013832, Accuracy: 99.61\n",
      "Train Epoch: 77 [39680/50000 (88%)]\tLoss: 0.094682, Accuracy: 97.27\n",
      "Train Epoch: 77 [40960/50000 (91%)]\tLoss: 0.031168, Accuracy: 99.61\n",
      "Train Epoch: 77 [42240/50000 (94%)]\tLoss: 0.039275, Accuracy: 99.22\n",
      "Train Epoch: 77 [43520/50000 (97%)]\tLoss: 0.061854, Accuracy: 98.05\n",
      "Train Epoch: 77 [35000/50000 (99%)]\tLoss: 0.051695, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.3029, Accuracy: 4593/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.56803488731384 s]\n",
      "Train Epoch: 78 [0/50000 (0%)]\tLoss: 0.027257, Accuracy: 99.22\n",
      "Train Epoch: 78 [1280/50000 (3%)]\tLoss: 0.061797, Accuracy: 97.66\n",
      "Train Epoch: 78 [2560/50000 (6%)]\tLoss: 0.050964, Accuracy: 98.83\n",
      "Train Epoch: 78 [3840/50000 (9%)]\tLoss: 0.027077, Accuracy: 99.61\n",
      "Train Epoch: 78 [5120/50000 (11%)]\tLoss: 0.059366, Accuracy: 98.05\n",
      "Train Epoch: 78 [6400/50000 (14%)]\tLoss: 0.062890, Accuracy: 98.44\n",
      "Train Epoch: 78 [7680/50000 (17%)]\tLoss: 0.035387, Accuracy: 98.83\n",
      "Train Epoch: 78 [8960/50000 (20%)]\tLoss: 0.078189, Accuracy: 97.66\n",
      "Train Epoch: 78 [10240/50000 (23%)]\tLoss: 0.026873, Accuracy: 98.44\n",
      "Train Epoch: 78 [11520/50000 (26%)]\tLoss: 0.079126, Accuracy: 98.44\n",
      "Train Epoch: 78 [12800/50000 (28%)]\tLoss: 0.027180, Accuracy: 99.61\n",
      "Train Epoch: 78 [14080/50000 (31%)]\tLoss: 0.068507, Accuracy: 96.88\n",
      "Train Epoch: 78 [15360/50000 (34%)]\tLoss: 0.042841, Accuracy: 98.83\n",
      "Train Epoch: 78 [16640/50000 (37%)]\tLoss: 0.060152, Accuracy: 97.66\n",
      "Train Epoch: 78 [17920/50000 (40%)]\tLoss: 0.054767, Accuracy: 98.44\n",
      "Train Epoch: 78 [19200/50000 (43%)]\tLoss: 0.027001, Accuracy: 98.83\n",
      "Train Epoch: 78 [20480/50000 (45%)]\tLoss: 0.022184, Accuracy: 99.61\n",
      "Train Epoch: 78 [21760/50000 (48%)]\tLoss: 0.036852, Accuracy: 99.22\n",
      "Train Epoch: 78 [23040/50000 (51%)]\tLoss: 0.046195, Accuracy: 98.83\n",
      "Train Epoch: 78 [24320/50000 (54%)]\tLoss: 0.063052, Accuracy: 97.66\n",
      "Train Epoch: 78 [25600/50000 (57%)]\tLoss: 0.036286, Accuracy: 98.83\n",
      "Train Epoch: 78 [26880/50000 (60%)]\tLoss: 0.058503, Accuracy: 98.05\n",
      "Train Epoch: 78 [28160/50000 (62%)]\tLoss: 0.022446, Accuracy: 99.61\n",
      "Train Epoch: 78 [29440/50000 (65%)]\tLoss: 0.060776, Accuracy: 98.44\n",
      "Train Epoch: 78 [30720/50000 (68%)]\tLoss: 0.033329, Accuracy: 99.22\n",
      "Train Epoch: 78 [32000/50000 (71%)]\tLoss: 0.034361, Accuracy: 99.61\n",
      "Train Epoch: 78 [33280/50000 (74%)]\tLoss: 0.044758, Accuracy: 98.44\n",
      "Train Epoch: 78 [34560/50000 (77%)]\tLoss: 0.052964, Accuracy: 98.83\n",
      "Train Epoch: 78 [35840/50000 (80%)]\tLoss: 0.059121, Accuracy: 98.05\n",
      "Train Epoch: 78 [37120/50000 (82%)]\tLoss: 0.056814, Accuracy: 98.05\n",
      "Train Epoch: 78 [38400/50000 (85%)]\tLoss: 0.040203, Accuracy: 99.22\n",
      "Train Epoch: 78 [39680/50000 (88%)]\tLoss: 0.016079, Accuracy: 100.00\n",
      "Train Epoch: 78 [40960/50000 (91%)]\tLoss: 0.031726, Accuracy: 99.22\n",
      "Train Epoch: 78 [42240/50000 (94%)]\tLoss: 0.037335, Accuracy: 98.44\n",
      "Train Epoch: 78 [43520/50000 (97%)]\tLoss: 0.067185, Accuracy: 97.66\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 78 [35000/50000 (99%)]\tLoss: 0.080777, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.3053, Accuracy: 4596/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[36.39461660385132 s]\n",
      "\n",
      "Test set: Average loss: 0.3305, Accuracy: 9161/10000 (91.61%)\n",
      "\n",
      "Train Epoch: 79 [0/50000 (0%)]\tLoss: 0.043962, Accuracy: 98.44\n",
      "Train Epoch: 79 [1280/50000 (3%)]\tLoss: 0.023410, Accuracy: 99.22\n",
      "Train Epoch: 79 [2560/50000 (6%)]\tLoss: 0.067380, Accuracy: 99.22\n",
      "Train Epoch: 79 [3840/50000 (9%)]\tLoss: 0.027039, Accuracy: 99.61\n",
      "Train Epoch: 79 [5120/50000 (11%)]\tLoss: 0.016871, Accuracy: 100.00\n",
      "Train Epoch: 79 [6400/50000 (14%)]\tLoss: 0.029363, Accuracy: 98.83\n",
      "Train Epoch: 79 [7680/50000 (17%)]\tLoss: 0.039668, Accuracy: 98.83\n",
      "Train Epoch: 79 [8960/50000 (20%)]\tLoss: 0.023678, Accuracy: 100.00\n",
      "Train Epoch: 79 [10240/50000 (23%)]\tLoss: 0.010688, Accuracy: 100.00\n",
      "Train Epoch: 79 [11520/50000 (26%)]\tLoss: 0.026978, Accuracy: 98.83\n",
      "Train Epoch: 79 [12800/50000 (28%)]\tLoss: 0.041501, Accuracy: 98.44\n",
      "Train Epoch: 79 [14080/50000 (31%)]\tLoss: 0.024901, Accuracy: 100.00\n",
      "Train Epoch: 79 [15360/50000 (34%)]\tLoss: 0.038160, Accuracy: 98.05\n",
      "Train Epoch: 79 [16640/50000 (37%)]\tLoss: 0.035879, Accuracy: 98.44\n",
      "Train Epoch: 79 [17920/50000 (40%)]\tLoss: 0.048410, Accuracy: 98.05\n",
      "Train Epoch: 79 [19200/50000 (43%)]\tLoss: 0.041054, Accuracy: 98.44\n",
      "Train Epoch: 79 [20480/50000 (45%)]\tLoss: 0.049782, Accuracy: 98.83\n",
      "Train Epoch: 79 [21760/50000 (48%)]\tLoss: 0.091894, Accuracy: 97.27\n",
      "Train Epoch: 79 [23040/50000 (51%)]\tLoss: 0.031760, Accuracy: 99.22\n",
      "Train Epoch: 79 [24320/50000 (54%)]\tLoss: 0.030333, Accuracy: 99.61\n",
      "Train Epoch: 79 [25600/50000 (57%)]\tLoss: 0.019252, Accuracy: 100.00\n",
      "Train Epoch: 79 [26880/50000 (60%)]\tLoss: 0.044228, Accuracy: 98.83\n",
      "Train Epoch: 79 [28160/50000 (62%)]\tLoss: 0.035951, Accuracy: 98.83\n",
      "Train Epoch: 79 [29440/50000 (65%)]\tLoss: 0.018601, Accuracy: 100.00\n",
      "Train Epoch: 79 [30720/50000 (68%)]\tLoss: 0.031650, Accuracy: 99.22\n",
      "Train Epoch: 79 [32000/50000 (71%)]\tLoss: 0.037602, Accuracy: 98.83\n",
      "Train Epoch: 79 [33280/50000 (74%)]\tLoss: 0.065382, Accuracy: 98.44\n",
      "Train Epoch: 79 [34560/50000 (77%)]\tLoss: 0.036180, Accuracy: 99.22\n",
      "Train Epoch: 79 [35840/50000 (80%)]\tLoss: 0.044783, Accuracy: 98.83\n",
      "Train Epoch: 79 [37120/50000 (82%)]\tLoss: 0.030017, Accuracy: 98.44\n",
      "Train Epoch: 79 [38400/50000 (85%)]\tLoss: 0.042665, Accuracy: 98.44\n",
      "Train Epoch: 79 [39680/50000 (88%)]\tLoss: 0.038726, Accuracy: 99.22\n",
      "Train Epoch: 79 [40960/50000 (91%)]\tLoss: 0.034345, Accuracy: 98.83\n",
      "Train Epoch: 79 [42240/50000 (94%)]\tLoss: 0.045934, Accuracy: 99.22\n",
      "Train Epoch: 79 [43520/50000 (97%)]\tLoss: 0.022654, Accuracy: 99.61\n",
      "Train Epoch: 79 [35000/50000 (99%)]\tLoss: 0.029056, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.3033, Accuracy: 4592/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[39.42583727836609 s]\n",
      "\n",
      "Test set: Average loss: 0.3333, Accuracy: 9155/10000 (91.55%)\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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