M_accelerate_6lastlayer-8fold_91.67-softmax-Copy4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>MobileNet - Pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 1: Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MobileNet-Pytorch\n",
    "import argparse \n",
    "import torch\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from sklearn.metrics import accuracy_score\n",
    "#from mobilenets import mobilenet\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "use_cudause_cud  = torch.cuda.is_available()\n",
    "dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Train, Validate, Test. Heavily inspired by Kevinzakka https://github.com/kevinzakka/DenseNet/blob/master/data_loader.py\n",
    "\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "\n",
    "valid_size=0.1\n",
    "\n",
    "# define transforms\n",
    "valid_transform = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        normalize\n",
    "])\n",
    "\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])\n",
    "\n",
    "\n",
    "# load the dataset\n",
    "train_dataset = datasets.CIFAR10(root=\"data\", train=True, \n",
    "            download=True, transform=train_transform)\n",
    "\n",
    "valid_dataset = datasets.CIFAR10(root=\"data\", train=True, \n",
    "            download=True, transform=valid_transform)\n",
    "\n",
    "num_train = len(train_dataset)\n",
    "indices = list(range(num_train))\n",
    "split = int(np.floor(valid_size * num_train)) #5w张图片的10%用来当做验证集\n",
    "\n",
    "\n",
    "np.random.seed(42)# 42\n",
    "np.random.shuffle(indices) # 随机乱序[0,1,...,49999]\n",
    "\n",
    "train_idx, valid_idx = indices[split:], indices[:split]\n",
    "\n",
    "\n",
    "train_sampler = SubsetRandomSampler(train_idx) # 这个很有意思\n",
    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "###################################################################################\n",
    "# ------------------------- 使用不同的批次大小 ------------------------------------\n",
    "###################################################################################\n",
    "\n",
    "show_step=2  # 批次大，show_step就小点\n",
    "max_epoch=80  # 训练最大epoch数目\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, \n",
    "                batch_size=256, sampler=train_sampler)\n",
    "\n",
    "valid_loader = torch.utils.data.DataLoader(valid_dataset, \n",
    "                batch_size=256, sampler=valid_sampler)\n",
    "\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(), normalize\n",
    "])\n",
    "\n",
    "test_dataset = datasets.CIFAR10(root=\"data\", \n",
    "                                train=False, \n",
    "                                download=True,transform=test_transform)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, \n",
    "                                          batch_size=256, \n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Model Config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 32  缩放5次到 1x1@1024 \n",
    "# From https://github.com/kuangliu/pytorch-cifar \n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Block(nn.Module):\n",
    "    '''Depthwise conv + Pointwise conv'''\n",
    "    def __init__(self, in_planes, out_planes, stride=1):\n",
    "        super(Block, self).__init__()\n",
    "        \n",
    "        # 分组卷积数=输入通道数\n",
    "        self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm2d(in_planes)\n",
    "        \n",
    "        \n",
    "        #self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        one_conv_kernel_size = 3\n",
    "        self.conv1D= nn.Conv1d(1, out_planes, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1,bias=False) # 在__init__初始化        \n",
    "        \n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        \n",
    "        # -------------------------- Attention -----------------------\n",
    "        w = F.avg_pool2d(x,x.shape[-1])  #最好在初始化层定义好\n",
    "        #print(w.shape)\n",
    "        # [bs,in_Channel,1,1]\n",
    "        w = w.view(w.shape[0],1,w.shape[1])\n",
    "        # [bs,1,in_Channel]\n",
    "        # one_conv_filter = nn.Conv1d(1, out_channel, one_conv_kernel_size, stride=1,padding=1,groups=1,dilation=1) # 在__init__初始化\n",
    "        # [bs,out_channel,in_Channel]\n",
    "        w = self.conv1D(w)\n",
    "        w = 0.5*F.tanh(w) # [-0.5,+0.5]\n",
    "        # -------------- softmax ---------------------------\n",
    "        #print(w.shape)\n",
    "        w = w.view(w.shape[0],w.shape[1],w.shape[2],1,1)\n",
    "        #print(w.shape)\n",
    "        \n",
    "        # ------------------------- fusion --------------------------\n",
    "        out=out.view(out.shape[0],1,out.shape[1],out.shape[2],out.shape[3])\n",
    "        #print(\"x size:\",out.shape)\n",
    "        \n",
    "        out=out*w\n",
    "        #print(\"after fusion x size:\",out.shape)\n",
    "        out=out.sum(dim=2)\n",
    "        \n",
    "        out = F.relu(self.bn2(out))\n",
    "        \n",
    "        return out\n",
    "\n",
    "\n",
    "class MobileNet(nn.Module):\n",
    "    # (128,2) means conv planes=128, conv stride=2, by default conv stride=1\n",
    "    cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), 1024]\n",
    "\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(MobileNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm2d(32)\n",
    "        self.layers = self._make_layers(in_planes=32) # 自动化构建层\n",
    "        self.linear = nn.Linear(1024, num_classes)\n",
    "\n",
    "    def _make_layers(self, in_planes):\n",
    "        layers = []\n",
    "        for x in self.cfg:\n",
    "            out_planes = x if isinstance(x, int) else x[0]\n",
    "            stride = 1 if isinstance(x, int) else x[1]\n",
    "            layers.append(Block(in_planes, out_planes, stride))\n",
    "            in_planes = out_planes\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.layers(out)\n",
    "        out = F.avg_pool2d(out, 2)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
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   "cell_type": "code",
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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=False) # 在__init__初始化        \n",
    "        \n",
    "        #------------------------------------------------------------\n",
    "        self.bn2 = nn.BatchNorm2d(out_planes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        out = F.relu6(self.bn1(self.conv1(x)))\n",
    "        \n",
    "        # -------------------------- Attention -----------------------\n",
    "        w = F.avg_pool2d(x,x.shape[-1])  #最好在初始化层定义好\n",
    "        #print(w.shape)\n",
    "        # [bs,in_Channel,1,1]\n",
    "        in_channel=w.shape[1]\n",
    "        #w = w.view(w.shape[0],1,w.shape[1])\n",
    "        # [bs,1,in_Channel]\n",
    "        # 对这批数据取平均 且保留第0维\n",
    "        \n",
    "        #w= w.mean(dim=0,keepdim=True)\n",
    "        \n",
    "        \n",
    "#         MAX=w.shape[0]\n",
    "#         NUM=torch.floor(MAX*torch.rand(1)).long()\n",
    "#         if NUM>=0 and NUM<MAX:\n",
    "#             w=w[NUM]\n",
    "#         else:\n",
    "#             w=w[0]\n",
    "        #w=w[0]-torch.mean(w[0])\n",
    "        w=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",
    "        \n",
    "        w=self.scaleLayer(w)  #自动缩放层\n",
    "        \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": 4,
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   "outputs": [],
   "source": [
    "# From https://github.com/Z0m6ie/CIFAR-10_PyTorch\n",
    "#model = mobilenet(num_classes=10, large_img=False)\n",
    "\n",
    "# From https://github.com/kuangliu/pytorch-cifar \n",
    "if torch.cuda.is_available():\n",
    "    model=MobileNet(10).cuda()\n",
    "else:\n",
    "    model=MobileNet(10)\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)\n",
    "#scheduler = StepLR(optimizer, step_size=70, gamma=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50,70,75,80], gamma=0.1)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Implement validation\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    #writer = SummaryWriter()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        correct = 0\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).sum()\n",
    "        \n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        accuracy = 100. * (correct.cpu().numpy()/ len(output))\n",
    "        optimizer.step()\n",
    "        if batch_idx % 5*show_step == 0:\n",
    "#             if batch_idx % 2*show_step == 0:\n",
    "#                 print(model.layers[1].conv1D.weight.shape)\n",
    "#                 print(model.layers[1].conv1D.weight[0:2][0:2])\n",
    "            \n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item(), accuracy))\n",
    "#             f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "#             f1.write(\"\\n\"+'Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, Accuracy: {:.2f}'.format(\n",
    "#                 epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "#                 100. * batch_idx / len(train_loader), loss.item(), accuracy))\n",
    "#             f1.close()\n",
    "            \n",
    "            #writer.add_scalar('Loss/Loss', loss.item(), epoch)\n",
    "            #writer.add_scalar('Accuracy/Accuracy', accuracy, epoch)\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validate(epoch):\n",
    "    model.eval()\n",
    "    #writer = SummaryWriter()\n",
    "    valid_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in valid_loader:\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        output = model(data)\n",
    "        valid_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).sum()\n",
    "\n",
    "    valid_loss /= len(valid_idx)\n",
    "    accuracy = 100. * correct.cpu().numpy() / len(valid_idx)\n",
    "    print('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        valid_loss, correct, len(valid_idx),\n",
    "        100. * correct / len(valid_idx)))\n",
    "    \n",
    "#     f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "#     f1.write('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "#         valid_loss, correct, len(valid_idx),\n",
    "#         100. * correct / len(valid_idx)))\n",
    "#     f1.close()\n",
    "    #writer.add_scalar('Loss/Validation_Loss', valid_loss, epoch)\n",
    "    #writer.add_scalar('Accuracy/Validation_Accuracy', accuracy, epoch)\n",
    "    return valid_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fix best model\n",
    "\n",
    "def test(epoch):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        if use_cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        output = model(data)\n",
    "        test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct.cpu().numpy() / len(test_loader.dataset)))\n",
    "    \n",
    "#     f1=open(\"Cifar10_INFO.txt\",\"a+\")\n",
    "#     f1.write('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "#         test_loss, correct, len(test_loader.dataset),\n",
    "#         100. * correct.cpu().numpy() / len(test_loader.dataset)))\n",
    "#     f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_best(loss, accuracy, best_loss, best_acc):\n",
    "    if best_loss == None:\n",
    "        best_loss = loss\n",
    "        best_acc = accuracy\n",
    "        file = 'saved_models/best_save_model.p'\n",
    "        torch.save(model.state_dict(), file)\n",
    "        \n",
    "    elif loss < best_loss and accuracy > best_acc:\n",
    "        best_loss = loss\n",
    "        best_acc = accuracy\n",
    "        file = 'saved_models/best_save_model.p'\n",
    "        torch.save(model.state_dict(), file)\n",
    "    return best_loss, best_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/50000 (0%)]\tLoss: 2.329112, Accuracy: 10.55\n",
      "Train Epoch: 0 [1280/50000 (3%)]\tLoss: 2.331939, Accuracy: 8.98\n",
      "Train Epoch: 0 [2560/50000 (6%)]\tLoss: 2.269483, Accuracy: 10.55\n",
      "Train Epoch: 0 [3840/50000 (9%)]\tLoss: 2.255202, Accuracy: 17.19\n",
      "Train Epoch: 0 [5120/50000 (11%)]\tLoss: 2.152086, Accuracy: 17.19\n",
      "Train Epoch: 0 [6400/50000 (14%)]\tLoss: 2.175110, Accuracy: 18.75\n",
      "Train Epoch: 0 [7680/50000 (17%)]\tLoss: 2.101811, Accuracy: 15.62\n",
      "Train Epoch: 0 [8960/50000 (20%)]\tLoss: 2.050855, Accuracy: 17.97\n",
      "Train Epoch: 0 [10240/50000 (23%)]\tLoss: 2.075396, Accuracy: 19.53\n",
      "Train Epoch: 0 [11520/50000 (26%)]\tLoss: 1.996977, Accuracy: 20.70\n",
      "Train Epoch: 0 [12800/50000 (28%)]\tLoss: 1.976524, Accuracy: 17.97\n",
      "Train Epoch: 0 [14080/50000 (31%)]\tLoss: 2.044901, Accuracy: 17.58\n",
      "Train Epoch: 0 [15360/50000 (34%)]\tLoss: 1.945777, Accuracy: 22.27\n",
      "Train Epoch: 0 [16640/50000 (37%)]\tLoss: 1.980004, Accuracy: 22.27\n",
      "Train Epoch: 0 [17920/50000 (40%)]\tLoss: 2.005622, Accuracy: 23.83\n",
      "Train Epoch: 0 [19200/50000 (43%)]\tLoss: 2.024373, Accuracy: 23.83\n",
      "Train Epoch: 0 [20480/50000 (45%)]\tLoss: 2.005608, Accuracy: 20.31\n",
      "Train Epoch: 0 [21760/50000 (48%)]\tLoss: 2.020754, Accuracy: 19.92\n",
      "Train Epoch: 0 [23040/50000 (51%)]\tLoss: 1.833707, Accuracy: 30.08\n",
      "Train Epoch: 0 [24320/50000 (54%)]\tLoss: 1.896621, Accuracy: 25.00\n",
      "Train Epoch: 0 [25600/50000 (57%)]\tLoss: 1.874320, Accuracy: 24.61\n",
      "Train Epoch: 0 [26880/50000 (60%)]\tLoss: 1.870454, Accuracy: 24.61\n",
      "Train Epoch: 0 [28160/50000 (62%)]\tLoss: 1.869583, Accuracy: 30.86\n",
      "Train Epoch: 0 [29440/50000 (65%)]\tLoss: 1.881509, Accuracy: 24.22\n",
      "Train Epoch: 0 [30720/50000 (68%)]\tLoss: 1.858979, Accuracy: 25.39\n",
      "Train Epoch: 0 [32000/50000 (71%)]\tLoss: 1.861736, Accuracy: 32.42\n",
      "Train Epoch: 0 [33280/50000 (74%)]\tLoss: 1.914840, Accuracy: 32.03\n",
      "Train Epoch: 0 [34560/50000 (77%)]\tLoss: 1.795394, Accuracy: 34.38\n",
      "Train Epoch: 0 [35840/50000 (80%)]\tLoss: 1.806949, Accuracy: 30.86\n",
      "Train Epoch: 0 [37120/50000 (82%)]\tLoss: 1.860937, Accuracy: 26.56\n",
      "Train Epoch: 0 [38400/50000 (85%)]\tLoss: 1.792870, Accuracy: 30.86\n",
      "Train Epoch: 0 [39680/50000 (88%)]\tLoss: 1.822480, Accuracy: 28.91\n",
      "Train Epoch: 0 [40960/50000 (91%)]\tLoss: 1.813227, Accuracy: 30.86\n",
      "Train Epoch: 0 [42240/50000 (94%)]\tLoss: 1.764295, Accuracy: 32.03\n",
      "Train Epoch: 0 [43520/50000 (97%)]\tLoss: 1.762419, Accuracy: 32.03\n",
      "Train Epoch: 0 [35000/50000 (99%)]\tLoss: 1.830302, Accuracy: 31.50\n",
      "\n",
      "Validation set: Average loss: 1.8612, Accuracy: 1574/5000 (31.00%)\n",
      "\n",
      "the time of this epoch:[38.03204822540283 s]\n",
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 1.785102, Accuracy: 36.72\n",
      "Train Epoch: 1 [1280/50000 (3%)]\tLoss: 1.722383, Accuracy: 34.77\n",
      "Train Epoch: 1 [2560/50000 (6%)]\tLoss: 1.844027, Accuracy: 28.52\n",
      "Train Epoch: 1 [3840/50000 (9%)]\tLoss: 1.654189, Accuracy: 37.11\n",
      "Train Epoch: 1 [5120/50000 (11%)]\tLoss: 1.706239, Accuracy: 36.72\n",
      "Train Epoch: 1 [6400/50000 (14%)]\tLoss: 1.715232, Accuracy: 35.16\n",
      "Train Epoch: 1 [7680/50000 (17%)]\tLoss: 1.703411, Accuracy: 32.81\n",
      "Train Epoch: 1 [8960/50000 (20%)]\tLoss: 1.663535, Accuracy: 38.67\n",
      "Train Epoch: 1 [10240/50000 (23%)]\tLoss: 1.649620, Accuracy: 34.77\n",
      "Train Epoch: 1 [11520/50000 (26%)]\tLoss: 1.660588, Accuracy: 33.20\n",
      "Train Epoch: 1 [12800/50000 (28%)]\tLoss: 1.635812, Accuracy: 36.33\n",
      "Train Epoch: 1 [14080/50000 (31%)]\tLoss: 1.629223, Accuracy: 38.28\n",
      "Train Epoch: 1 [15360/50000 (34%)]\tLoss: 1.668036, Accuracy: 33.20\n",
      "Train Epoch: 1 [16640/50000 (37%)]\tLoss: 1.602548, Accuracy: 39.06\n",
      "Train Epoch: 1 [17920/50000 (40%)]\tLoss: 1.642583, Accuracy: 38.67\n",
      "Train Epoch: 1 [19200/50000 (43%)]\tLoss: 1.675308, Accuracy: 31.64\n",
      "Train Epoch: 1 [20480/50000 (45%)]\tLoss: 1.625328, Accuracy: 38.28\n",
      "Train Epoch: 1 [21760/50000 (48%)]\tLoss: 1.543088, Accuracy: 41.80\n",
      "Train Epoch: 1 [23040/50000 (51%)]\tLoss: 1.580931, Accuracy: 36.72\n",
      "Train Epoch: 1 [24320/50000 (54%)]\tLoss: 1.614143, Accuracy: 39.06\n",
      "Train Epoch: 1 [25600/50000 (57%)]\tLoss: 1.668033, Accuracy: 38.28\n",
      "Train Epoch: 1 [26880/50000 (60%)]\tLoss: 1.586287, Accuracy: 42.97\n",
      "Train Epoch: 1 [28160/50000 (62%)]\tLoss: 1.607273, Accuracy: 29.30\n",
      "Train Epoch: 1 [29440/50000 (65%)]\tLoss: 1.633220, Accuracy: 38.28\n",
      "Train Epoch: 1 [30720/50000 (68%)]\tLoss: 1.533912, Accuracy: 41.41\n",
      "Train Epoch: 1 [32000/50000 (71%)]\tLoss: 1.580299, Accuracy: 38.28\n",
      "Train Epoch: 1 [33280/50000 (74%)]\tLoss: 1.628069, Accuracy: 37.50\n",
      "Train Epoch: 1 [34560/50000 (77%)]\tLoss: 1.586841, Accuracy: 43.36\n",
      "Train Epoch: 1 [35840/50000 (80%)]\tLoss: 1.506504, Accuracy: 43.36\n",
      "Train Epoch: 1 [37120/50000 (82%)]\tLoss: 1.601314, Accuracy: 35.94\n",
      "Train Epoch: 1 [38400/50000 (85%)]\tLoss: 1.485708, Accuracy: 41.41\n",
      "Train Epoch: 1 [39680/50000 (88%)]\tLoss: 1.351976, Accuracy: 47.66\n",
      "Train Epoch: 1 [40960/50000 (91%)]\tLoss: 1.405137, Accuracy: 42.97\n",
      "Train Epoch: 1 [42240/50000 (94%)]\tLoss: 1.393093, Accuracy: 48.44\n",
      "Train Epoch: 1 [43520/50000 (97%)]\tLoss: 1.478757, Accuracy: 47.66\n",
      "Train Epoch: 1 [35000/50000 (99%)]\tLoss: 1.428537, Accuracy: 46.00\n",
      "\n",
      "Validation set: Average loss: 1.8628, Accuracy: 1657/5000 (33.00%)\n",
      "\n",
      "the time of this epoch:[37.918360471725464 s]\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.506235, Accuracy: 41.41\n",
      "Train Epoch: 2 [1280/50000 (3%)]\tLoss: 1.473408, Accuracy: 44.92\n",
      "Train Epoch: 2 [2560/50000 (6%)]\tLoss: 1.407981, Accuracy: 44.53\n",
      "Train Epoch: 2 [3840/50000 (9%)]\tLoss: 1.492908, Accuracy: 46.09\n",
      "Train Epoch: 2 [5120/50000 (11%)]\tLoss: 1.351588, Accuracy: 51.56\n",
      "Train Epoch: 2 [6400/50000 (14%)]\tLoss: 1.427409, Accuracy: 45.31\n",
      "Train Epoch: 2 [7680/50000 (17%)]\tLoss: 1.393849, Accuracy: 48.44\n",
      "Train Epoch: 2 [8960/50000 (20%)]\tLoss: 1.405355, Accuracy: 48.83\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.1957]],\n",
      "\n",
      "        [[ 0.3438]],\n",
      "\n",
      "        [[ 0.2962]],\n",
      "\n",
      "        [[ 0.1322]],\n",
      "\n",
      "        [[ 0.0426]],\n",
      "\n",
      "        [[ 0.0010]],\n",
      "\n",
      "        [[ 0.0139]],\n",
      "\n",
      "        [[ 0.3217]],\n",
      "\n",
      "        [[ 0.1410]],\n",
      "\n",
      "        [[ 0.1190]],\n",
      "\n",
      "        [[ 0.0269]],\n",
      "\n",
      "        [[ 0.0435]],\n",
      "\n",
      "        [[ 0.0007]],\n",
      "\n",
      "        [[ 0.1098]],\n",
      "\n",
      "        [[ 0.0004]],\n",
      "\n",
      "        [[ 0.1254]],\n",
      "\n",
      "        [[ 0.2612]],\n",
      "\n",
      "        [[ 0.1575]],\n",
      "\n",
      "        [[ 0.1282]],\n",
      "\n",
      "        [[ 0.1894]],\n",
      "\n",
      "        [[ 0.0650]],\n",
      "\n",
      "        [[ 0.0353]],\n",
      "\n",
      "        [[ 0.0957]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.2543]],\n",
      "\n",
      "        [[ 0.0130]],\n",
      "\n",
      "        [[ 0.1154]],\n",
      "\n",
      "        [[ 0.0715]],\n",
      "\n",
      "        [[ 0.0331]],\n",
      "\n",
      "        [[ 0.0759]],\n",
      "\n",
      "        [[ 0.0740]],\n",
      "\n",
      "        [[ 0.1770]],\n",
      "\n",
      "        [[ 0.1839]],\n",
      "\n",
      "        [[ 0.2278]],\n",
      "\n",
      "        [[ 0.1813]],\n",
      "\n",
      "        [[ 0.0021]],\n",
      "\n",
      "        [[ 0.2923]],\n",
      "\n",
      "        [[ 0.0893]],\n",
      "\n",
      "        [[ 0.0171]],\n",
      "\n",
      "        [[ 0.3649]],\n",
      "\n",
      "        [[ 0.0982]],\n",
      "\n",
      "        [[ 0.0697]],\n",
      "\n",
      "        [[ 0.1399]],\n",
      "\n",
      "        [[ 0.0465]],\n",
      "\n",
      "        [[ 0.1780]],\n",
      "\n",
      "        [[ 0.2623]],\n",
      "\n",
      "        [[ 0.2621]],\n",
      "\n",
      "        [[ 0.1257]],\n",
      "\n",
      "        [[ 0.0363]],\n",
      "\n",
      "        [[ 0.1298]],\n",
      "\n",
      "        [[ 0.0816]],\n",
      "\n",
      "        [[ 0.1884]],\n",
      "\n",
      "        [[ 0.0414]],\n",
      "\n",
      "        [[ 0.2266]],\n",
      "\n",
      "        [[ 0.0148]],\n",
      "\n",
      "        [[ 0.3460]],\n",
      "\n",
      "        [[ 0.2502]],\n",
      "\n",
      "        [[ 0.2802]],\n",
      "\n",
      "        [[ 0.0138]],\n",
      "\n",
      "        [[ 0.0175]],\n",
      "\n",
      "        [[ 0.1740]],\n",
      "\n",
      "        [[ 0.1179]],\n",
      "\n",
      "        [[ 0.1608]],\n",
      "\n",
      "        [[ 0.2959]],\n",
      "\n",
      "        [[ 0.0675]],\n",
      "\n",
      "        [[ 0.1637]],\n",
      "\n",
      "        [[ 0.0631]],\n",
      "\n",
      "        [[ 0.0776]],\n",
      "\n",
      "        [[ 0.1168]],\n",
      "\n",
      "        [[ 0.2339]],\n",
      "\n",
      "        [[ 0.1214]],\n",
      "\n",
      "        [[ 0.0991]],\n",
      "\n",
      "        [[ 0.1756]],\n",
      "\n",
      "        [[ 0.0254]],\n",
      "\n",
      "        [[ 0.1291]],\n",
      "\n",
      "        [[ 0.1219]],\n",
      "\n",
      "        [[ 0.1190]],\n",
      "\n",
      "        [[ 0.2206]],\n",
      "\n",
      "        [[ 0.0998]],\n",
      "\n",
      "        [[ 0.0435]],\n",
      "\n",
      "        [[ 0.0659]],\n",
      "\n",
      "        [[ 0.1508]],\n",
      "\n",
      "        [[ 0.0234]],\n",
      "\n",
      "        [[ 0.0599]],\n",
      "\n",
      "        [[ 0.0242]],\n",
      "\n",
      "        [[ 0.2036]],\n",
      "\n",
      "        [[ 0.0019]],\n",
      "\n",
      "        [[ 0.0566]],\n",
      "\n",
      "        [[ 0.1147]],\n",
      "\n",
      "        [[ 0.0412]],\n",
      "\n",
      "        [[ 0.1530]],\n",
      "\n",
      "        [[ 0.1326]],\n",
      "\n",
      "        [[ 0.0884]],\n",
      "\n",
      "        [[ 0.0701]],\n",
      "\n",
      "        [[ 0.1266]],\n",
      "\n",
      "        [[ 0.0557]],\n",
      "\n",
      "        [[ 0.1110]],\n",
      "\n",
      "        [[ 0.2374]],\n",
      "\n",
      "        [[ 0.0373]],\n",
      "\n",
      "        [[ 0.1344]],\n",
      "\n",
      "        [[ 0.1673]],\n",
      "\n",
      "        [[ 0.1808]],\n",
      "\n",
      "        [[ 0.1580]],\n",
      "\n",
      "        [[ 0.2322]],\n",
      "\n",
      "        [[ 0.2349]],\n",
      "\n",
      "        [[ 0.2416]],\n",
      "\n",
      "        [[ 0.1670]],\n",
      "\n",
      "        [[ 0.1389]],\n",
      "\n",
      "        [[ 0.1532]],\n",
      "\n",
      "        [[ 0.0682]],\n",
      "\n",
      "        [[ 0.1467]],\n",
      "\n",
      "        [[ 0.1521]],\n",
      "\n",
      "        [[ 0.0449]],\n",
      "\n",
      "        [[ 0.1670]],\n",
      "\n",
      "        [[ 0.1064]],\n",
      "\n",
      "        [[ 0.0720]],\n",
      "\n",
      "        [[ 0.0301]],\n",
      "\n",
      "        [[ 0.1316]],\n",
      "\n",
      "        [[ 0.0757]],\n",
      "\n",
      "        [[ 0.1276]],\n",
      "\n",
      "        [[ 0.2009]],\n",
      "\n",
      "        [[ 0.1643]],\n",
      "\n",
      "        [[ 0.0937]],\n",
      "\n",
      "        [[ 0.0965]],\n",
      "\n",
      "        [[ 0.1090]],\n",
      "\n",
      "        [[ 0.1542]],\n",
      "\n",
      "        [[ 0.0286]],\n",
      "\n",
      "        [[ 0.0261]],\n",
      "\n",
      "        [[ 0.1357]],\n",
      "\n",
      "        [[ 0.1333]],\n",
      "\n",
      "        [[ 0.1224]],\n",
      "\n",
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      "        [[ 0.2019]],\n",
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      "        [[ 0.0780]],\n",
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      "        [[ 0.1171]],\n",
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      "        [[ 0.0584]],\n",
      "\n",
      "        [[ 0.1268]],\n",
      "\n",
      "        [[ 0.1396]],\n",
      "\n",
      "        [[ 0.0764]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 9.1229]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 1.1500e-01,  1.3611e-01,  8.6779e-02,  ...,  1.7360e-01,\n",
      "          -1.8011e-01, -1.5863e-01]],\n",
      "\n",
      "        [[ 6.0121e-03,  1.9076e-01,  1.8673e-01,  ...,  1.3942e-01,\n",
      "          -1.4053e-01, -1.2228e-01]],\n",
      "\n",
      "        [[-5.5045e-02,  1.1219e-01,  1.9456e-02,  ..., -1.2224e-01,\n",
      "           1.1436e-02,  2.1697e-01]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 2.2216e-02, -1.2028e-01, -1.0044e-01,  ...,  2.0087e-01,\n",
      "           2.0202e-01, -2.2153e-01]],\n",
      "\n",
      "        [[-8.6560e-02,  7.3462e-02,  6.9165e-02,  ..., -4.8606e-02,\n",
      "          -6.1457e-02,  2.1565e-01]],\n",
      "\n",
      "        [[ 1.7106e-02,  4.4490e-02,  2.2652e-01,  ...,  1.2259e-01,\n",
      "          -1.1368e-01, -4.7414e-02]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [10240/50000 (23%)]\tLoss: 1.323240, Accuracy: 52.34\n",
      "Train Epoch: 2 [11520/50000 (26%)]\tLoss: 1.404346, Accuracy: 48.05\n",
      "Train Epoch: 2 [12800/50000 (28%)]\tLoss: 1.379467, Accuracy: 55.47\n",
      "Train Epoch: 2 [14080/50000 (31%)]\tLoss: 1.322758, Accuracy: 51.17\n",
      "Train Epoch: 2 [15360/50000 (34%)]\tLoss: 1.404650, Accuracy: 52.73\n",
      "Train Epoch: 2 [16640/50000 (37%)]\tLoss: 1.344853, Accuracy: 50.78\n",
      "Train Epoch: 2 [17920/50000 (40%)]\tLoss: 1.416520, Accuracy: 47.27\n",
      "Train Epoch: 2 [19200/50000 (43%)]\tLoss: 1.414035, Accuracy: 46.48\n",
      "Train Epoch: 2 [20480/50000 (45%)]\tLoss: 1.358327, Accuracy: 49.61\n",
      "Train Epoch: 2 [21760/50000 (48%)]\tLoss: 1.324537, Accuracy: 46.88\n",
      "Train Epoch: 2 [23040/50000 (51%)]\tLoss: 1.318148, Accuracy: 52.34\n",
      "Train Epoch: 2 [24320/50000 (54%)]\tLoss: 1.380993, Accuracy: 48.83\n",
      "Train Epoch: 2 [25600/50000 (57%)]\tLoss: 1.386579, Accuracy: 45.70\n",
      "Train Epoch: 2 [26880/50000 (60%)]\tLoss: 1.271506, Accuracy: 53.12\n",
      "Train Epoch: 2 [28160/50000 (62%)]\tLoss: 1.274311, Accuracy: 52.34\n",
      "Train Epoch: 2 [29440/50000 (65%)]\tLoss: 1.281350, Accuracy: 53.91\n",
      "Train Epoch: 2 [30720/50000 (68%)]\tLoss: 1.390010, Accuracy: 46.48\n",
      "Train Epoch: 2 [32000/50000 (71%)]\tLoss: 1.313171, Accuracy: 55.47\n",
      "Train Epoch: 2 [33280/50000 (74%)]\tLoss: 1.237208, Accuracy: 56.25\n",
      "Train Epoch: 2 [34560/50000 (77%)]\tLoss: 1.296662, Accuracy: 51.17\n",
      "Train Epoch: 2 [35840/50000 (80%)]\tLoss: 1.334475, Accuracy: 53.91\n",
      "Train Epoch: 2 [37120/50000 (82%)]\tLoss: 1.168306, Accuracy: 55.08\n",
      "Train Epoch: 2 [38400/50000 (85%)]\tLoss: 1.350893, Accuracy: 53.52\n",
      "Train Epoch: 2 [39680/50000 (88%)]\tLoss: 1.191578, Accuracy: 55.08\n",
      "Train Epoch: 2 [40960/50000 (91%)]\tLoss: 1.419889, Accuracy: 50.78\n",
      "Train Epoch: 2 [42240/50000 (94%)]\tLoss: 1.245635, Accuracy: 56.64\n",
      "Train Epoch: 2 [43520/50000 (97%)]\tLoss: 1.365876, Accuracy: 45.70\n",
      "Train Epoch: 2 [35000/50000 (99%)]\tLoss: 1.168907, Accuracy: 57.50\n",
      "\n",
      "Validation set: Average loss: 1.7862, Accuracy: 2155/5000 (43.00%)\n",
      "\n",
      "the time of this epoch:[37.98442244529724 s]\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.141250, Accuracy: 58.20\n",
      "Train Epoch: 3 [1280/50000 (3%)]\tLoss: 1.252542, Accuracy: 54.30\n",
      "torch.Size([512, 1, 1])\n",
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      "Parameter containing:\n",
      "tensor([[[ 0.9906]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-0.1259, -0.1481, -0.1757,  ..., -0.0380,  0.0601, -0.0360]],\n",
      "\n",
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      "\n",
      "        ...,\n",
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      "\n",
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      "\n",
      "        [[ 0.1476, -0.1986,  0.1771,  ..., -0.1487,  0.0464,  0.1533]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor(1.00000e-02 *\n",
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      "         [ 0.4991,  3.6494,  8.3872,  ...,  6.7634,  3.2622, -1.4403],\n",
      "         [ 5.5831,  6.3033,  8.3534,  ...,  0.2873,  3.5842, -0.7132],\n",
      "         ...,\n",
      "         [-7.3939, -4.0662, -4.0334,  ...,  8.1351,  7.9294,  9.1696],\n",
      "         [-5.3788, -8.8987, -8.6900,  ..., -8.5107, -5.1138, -6.4859],\n",
      "         [-3.6847,  1.4885,  6.4524,  ...,  8.4169,  9.3686,  9.6395]]], device='cuda:0')\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [2560/50000 (6%)]\tLoss: 1.166486, Accuracy: 58.59\n",
      "Train Epoch: 3 [3840/50000 (9%)]\tLoss: 1.158385, Accuracy: 57.03\n",
      "Train Epoch: 3 [5120/50000 (11%)]\tLoss: 1.210713, Accuracy: 58.98\n",
      "Train Epoch: 3 [6400/50000 (14%)]\tLoss: 1.298489, Accuracy: 55.47\n",
      "Train Epoch: 3 [7680/50000 (17%)]\tLoss: 1.207046, Accuracy: 57.81\n",
      "Train Epoch: 3 [8960/50000 (20%)]\tLoss: 1.108949, Accuracy: 58.98\n",
      "Train Epoch: 3 [10240/50000 (23%)]\tLoss: 1.102285, Accuracy: 61.72\n",
      "Train Epoch: 3 [11520/50000 (26%)]\tLoss: 1.292103, Accuracy: 55.08\n",
      "Train Epoch: 3 [12800/50000 (28%)]\tLoss: 1.062541, Accuracy: 64.45\n",
      "Train Epoch: 3 [14080/50000 (31%)]\tLoss: 1.119843, Accuracy: 58.59\n",
      "Train Epoch: 3 [15360/50000 (34%)]\tLoss: 1.233345, Accuracy: 55.86\n",
      "Train Epoch: 3 [16640/50000 (37%)]\tLoss: 0.993727, Accuracy: 64.45\n",
      "Train Epoch: 3 [17920/50000 (40%)]\tLoss: 1.189023, Accuracy: 57.03\n",
      "Train Epoch: 3 [19200/50000 (43%)]\tLoss: 1.121965, Accuracy: 57.42\n",
      "Train Epoch: 3 [20480/50000 (45%)]\tLoss: 1.221964, Accuracy: 57.03\n",
      "Train Epoch: 3 [21760/50000 (48%)]\tLoss: 1.124682, Accuracy: 63.28\n",
      "Train Epoch: 3 [23040/50000 (51%)]\tLoss: 1.172548, Accuracy: 58.98\n",
      "Train Epoch: 3 [24320/50000 (54%)]\tLoss: 1.139084, Accuracy: 61.72\n",
      "Train Epoch: 3 [25600/50000 (57%)]\tLoss: 1.239222, Accuracy: 57.03\n",
      "Train Epoch: 3 [26880/50000 (60%)]\tLoss: 1.195940, Accuracy: 58.20\n",
      "Train Epoch: 3 [28160/50000 (62%)]\tLoss: 1.255276, Accuracy: 57.81\n",
      "Train Epoch: 3 [29440/50000 (65%)]\tLoss: 0.998797, Accuracy: 64.06\n",
      "Train Epoch: 3 [30720/50000 (68%)]\tLoss: 1.115158, Accuracy: 57.42\n",
      "Train Epoch: 3 [32000/50000 (71%)]\tLoss: 1.077868, Accuracy: 64.45\n",
      "Train Epoch: 3 [33280/50000 (74%)]\tLoss: 0.987228, Accuracy: 64.06\n",
      "Train Epoch: 3 [34560/50000 (77%)]\tLoss: 1.082652, Accuracy: 62.89\n",
      "Train Epoch: 3 [35840/50000 (80%)]\tLoss: 1.027145, Accuracy: 63.67\n",
      "Train Epoch: 3 [37120/50000 (82%)]\tLoss: 1.017040, Accuracy: 63.67\n",
      "Train Epoch: 3 [38400/50000 (85%)]\tLoss: 1.138888, Accuracy: 58.59\n",
      "Train Epoch: 3 [39680/50000 (88%)]\tLoss: 1.136965, Accuracy: 56.25\n",
      "Train Epoch: 3 [40960/50000 (91%)]\tLoss: 0.951208, Accuracy: 67.97\n",
      "Train Epoch: 3 [42240/50000 (94%)]\tLoss: 1.088166, Accuracy: 61.33\n",
      "Train Epoch: 3 [43520/50000 (97%)]\tLoss: 1.073768, Accuracy: 60.16\n",
      "Train Epoch: 3 [35000/50000 (99%)]\tLoss: 0.985553, Accuracy: 65.50\n",
      "\n",
      "Validation set: Average loss: 1.0614, Accuracy: 3054/5000 (61.00%)\n",
      "\n",
      "the time of this epoch:[37.962727546691895 s]\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 0.922095, Accuracy: 65.62\n",
      "Train Epoch: 4 [1280/50000 (3%)]\tLoss: 0.907836, Accuracy: 67.58\n",
      "Train Epoch: 4 [2560/50000 (6%)]\tLoss: 0.933536, Accuracy: 66.80\n",
      "Train Epoch: 4 [3840/50000 (9%)]\tLoss: 1.022062, Accuracy: 61.72\n",
      "Train Epoch: 4 [5120/50000 (11%)]\tLoss: 0.983337, Accuracy: 61.72\n",
      "Train Epoch: 4 [6400/50000 (14%)]\tLoss: 1.034796, Accuracy: 60.16\n",
      "Train Epoch: 4 [7680/50000 (17%)]\tLoss: 1.030229, Accuracy: 61.72\n",
      "Train Epoch: 4 [8960/50000 (20%)]\tLoss: 0.926938, Accuracy: 68.36\n",
      "Train Epoch: 4 [10240/50000 (23%)]\tLoss: 1.066324, Accuracy: 62.50\n",
      "Train Epoch: 4 [11520/50000 (26%)]\tLoss: 1.030707, Accuracy: 58.59\n",
      "Train Epoch: 4 [12800/50000 (28%)]\tLoss: 0.983692, Accuracy: 65.23\n",
      "Train Epoch: 4 [14080/50000 (31%)]\tLoss: 0.970433, Accuracy: 67.58\n",
      "Train Epoch: 4 [15360/50000 (34%)]\tLoss: 0.872665, Accuracy: 69.53\n",
      "Train Epoch: 4 [16640/50000 (37%)]\tLoss: 0.948037, Accuracy: 63.28\n",
      "Train Epoch: 4 [17920/50000 (40%)]\tLoss: 1.033769, Accuracy: 63.28\n",
      "Train Epoch: 4 [19200/50000 (43%)]\tLoss: 0.900539, Accuracy: 64.06\n",
      "Train Epoch: 4 [20480/50000 (45%)]\tLoss: 1.102086, Accuracy: 57.81\n",
      "Train Epoch: 4 [21760/50000 (48%)]\tLoss: 0.961954, Accuracy: 64.06\n",
      "Train Epoch: 4 [23040/50000 (51%)]\tLoss: 0.872170, Accuracy: 68.75\n",
      "Train Epoch: 4 [24320/50000 (54%)]\tLoss: 0.961241, Accuracy: 62.89\n",
      "torch.Size([512, 1, 1])\n",
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      "        [[ 0.0837]],\n",
      "\n",
      "        [[ 0.0166]],\n",
      "\n",
      "        [[ 0.0993]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.1649]],\n",
      "\n",
      "        [[ 0.2021]],\n",
      "\n",
      "        [[ 0.0446]],\n",
      "\n",
      "        [[ 0.0711]],\n",
      "\n",
      "        [[ 0.1459]],\n",
      "\n",
      "        [[ 0.1180]],\n",
      "\n",
      "        [[ 0.1681]],\n",
      "\n",
      "        [[ 0.2492]],\n",
      "\n",
      "        [[ 0.2028]],\n",
      "\n",
      "        [[ 0.1013]],\n",
      "\n",
      "        [[ 0.1132]],\n",
      "\n",
      "        [[ 0.1194]],\n",
      "\n",
      "        [[ 0.0627]],\n",
      "\n",
      "        [[ 0.1515]],\n",
      "\n",
      "        [[ 0.1402]],\n",
      "\n",
      "        [[ 0.0811]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([[[ 0.1009]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 0.0934,  0.1106,  0.0706,  ...,  0.1411, -0.1463, -0.1289]],\n",
      "\n",
      "        [[ 0.0049,  0.1550,  0.1517,  ...,  0.1133, -0.1142, -0.0993]],\n",
      "\n",
      "        [[-0.0448,  0.0911,  0.0158,  ..., -0.0993,  0.0093,  0.1764]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.0166, -0.0991, -0.0829,  ...,  0.1622,  0.1631, -0.1811]],\n",
      "\n",
      "        [[-0.0704,  0.0597,  0.0562,  ..., -0.0395, -0.0499,  0.1752]],\n",
      "\n",
      "        [[ 0.0136,  0.0359,  0.1838,  ...,  0.0993, -0.0927, -0.0388]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.3232,  0.0058, -0.5362,  ...,  1.1502,  1.0439,  1.2656],\n",
      "         [ 0.3867,  0.1681,  0.1183,  ...,  1.0630,  0.9143,  0.9263],\n",
      "         [-0.3400, -0.6721, -0.2328,  ..., -0.0354, -0.5346, -0.5109],\n",
      "         ...,\n",
      "         [ 1.3849,  1.3728,  1.0276,  ..., -0.1703, -0.7124, -0.9056],\n",
      "         [ 1.6516,  1.8371,  1.8450,  ...,  1.1252,  0.7111,  0.3275],\n",
      "         [-0.1851, -0.1235, -0.3551,  ...,  0.0923, -0.2380, -0.1587]]], device='cuda:0')\n",
      "Train Epoch: 4 [25600/50000 (57%)]\tLoss: 0.933262, Accuracy: 66.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [26880/50000 (60%)]\tLoss: 0.857586, Accuracy: 65.62\n",
      "Train Epoch: 4 [28160/50000 (62%)]\tLoss: 1.008799, Accuracy: 63.28\n",
      "Train Epoch: 4 [29440/50000 (65%)]\tLoss: 0.935740, Accuracy: 66.41\n",
      "Train Epoch: 4 [30720/50000 (68%)]\tLoss: 1.015284, Accuracy: 64.45\n",
      "Train Epoch: 4 [32000/50000 (71%)]\tLoss: 0.948221, Accuracy: 68.75\n",
      "Train Epoch: 4 [33280/50000 (74%)]\tLoss: 0.984640, Accuracy: 66.02\n",
      "Train Epoch: 4 [34560/50000 (77%)]\tLoss: 0.933013, Accuracy: 67.97\n",
      "Train Epoch: 4 [35840/50000 (80%)]\tLoss: 0.910831, Accuracy: 68.36\n",
      "Train Epoch: 4 [37120/50000 (82%)]\tLoss: 0.946458, Accuracy: 66.02\n",
      "Train Epoch: 4 [38400/50000 (85%)]\tLoss: 0.791995, Accuracy: 74.22\n",
      "Train Epoch: 4 [39680/50000 (88%)]\tLoss: 0.836429, Accuracy: 75.00\n",
      "Train Epoch: 4 [40960/50000 (91%)]\tLoss: 0.960835, Accuracy: 64.06\n",
      "Train Epoch: 4 [42240/50000 (94%)]\tLoss: 0.899545, Accuracy: 68.36\n",
      "Train Epoch: 4 [43520/50000 (97%)]\tLoss: 0.968692, Accuracy: 68.36\n",
      "Train Epoch: 4 [35000/50000 (99%)]\tLoss: 0.915120, Accuracy: 70.50\n",
      "\n",
      "Validation set: Average loss: 1.1867, Accuracy: 3005/5000 (60.00%)\n",
      "\n",
      "the time of this epoch:[37.848337173461914 s]\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.862133, Accuracy: 71.88\n",
      "Train Epoch: 5 [1280/50000 (3%)]\tLoss: 0.862104, Accuracy: 69.53\n",
      "Train Epoch: 5 [2560/50000 (6%)]\tLoss: 0.805332, Accuracy: 70.70\n",
      "Train Epoch: 5 [3840/50000 (9%)]\tLoss: 0.926387, Accuracy: 63.67\n",
      "Train Epoch: 5 [5120/50000 (11%)]\tLoss: 0.875372, Accuracy: 66.02\n",
      "Train Epoch: 5 [6400/50000 (14%)]\tLoss: 0.823089, Accuracy: 71.48\n",
      "Train Epoch: 5 [7680/50000 (17%)]\tLoss: 0.895786, Accuracy: 66.80\n",
      "Train Epoch: 5 [8960/50000 (20%)]\tLoss: 0.964540, Accuracy: 66.41\n",
      "Train Epoch: 5 [10240/50000 (23%)]\tLoss: 0.852124, Accuracy: 69.53\n",
      "Train Epoch: 5 [11520/50000 (26%)]\tLoss: 0.873399, Accuracy: 70.31\n",
      "Train Epoch: 5 [12800/50000 (28%)]\tLoss: 0.992179, Accuracy: 67.19\n",
      "Train Epoch: 5 [14080/50000 (31%)]\tLoss: 0.847402, Accuracy: 68.36\n",
      "Train Epoch: 5 [15360/50000 (34%)]\tLoss: 0.891527, Accuracy: 69.53\n",
      "Train Epoch: 5 [16640/50000 (37%)]\tLoss: 0.966264, Accuracy: 64.06\n",
      "Train Epoch: 5 [17920/50000 (40%)]\tLoss: 0.768250, Accuracy: 67.97\n",
      "Train Epoch: 5 [19200/50000 (43%)]\tLoss: 0.874613, Accuracy: 71.88\n",
      "Train Epoch: 5 [20480/50000 (45%)]\tLoss: 0.713022, Accuracy: 75.39\n",
      "Train Epoch: 5 [21760/50000 (48%)]\tLoss: 0.883613, Accuracy: 69.53\n",
      "Train Epoch: 5 [23040/50000 (51%)]\tLoss: 0.940819, Accuracy: 69.92\n",
      "Train Epoch: 5 [24320/50000 (54%)]\tLoss: 0.818955, Accuracy: 69.14\n",
      "Train Epoch: 5 [25600/50000 (57%)]\tLoss: 0.854540, Accuracy: 73.44\n",
      "Train Epoch: 5 [26880/50000 (60%)]\tLoss: 0.782380, Accuracy: 71.48\n",
      "Train Epoch: 5 [28160/50000 (62%)]\tLoss: 0.786443, Accuracy: 75.00\n",
      "Train Epoch: 5 [29440/50000 (65%)]\tLoss: 0.833240, Accuracy: 71.09\n",
      "Train Epoch: 5 [30720/50000 (68%)]\tLoss: 0.765684, Accuracy: 70.31\n",
      "Train Epoch: 5 [32000/50000 (71%)]\tLoss: 0.789969, Accuracy: 72.66\n",
      "Train Epoch: 5 [33280/50000 (74%)]\tLoss: 0.724869, Accuracy: 73.83\n",
      "Train Epoch: 5 [34560/50000 (77%)]\tLoss: 0.842445, Accuracy: 72.66\n",
      "Train Epoch: 5 [35840/50000 (80%)]\tLoss: 0.867811, Accuracy: 70.31\n",
      "Train Epoch: 5 [37120/50000 (82%)]\tLoss: 0.867876, Accuracy: 67.97\n",
      "Train Epoch: 5 [38400/50000 (85%)]\tLoss: 0.885626, Accuracy: 68.36\n",
      "Train Epoch: 5 [39680/50000 (88%)]\tLoss: 0.809387, Accuracy: 72.66\n",
      "Train Epoch: 5 [40960/50000 (91%)]\tLoss: 0.726189, Accuracy: 75.78\n",
      "Train Epoch: 5 [42240/50000 (94%)]\tLoss: 0.788144, Accuracy: 71.48\n",
      "Train Epoch: 5 [43520/50000 (97%)]\tLoss: 0.707102, Accuracy: 75.00\n",
      "Train Epoch: 5 [35000/50000 (99%)]\tLoss: 0.767035, Accuracy: 70.50\n",
      "\n",
      "Validation set: Average loss: 1.0166, Accuracy: 3265/5000 (65.00%)\n",
      "\n",
      "the time of this epoch:[37.844133615493774 s]\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.835298, Accuracy: 72.27\n",
      "Train Epoch: 6 [1280/50000 (3%)]\tLoss: 0.864752, Accuracy: 69.14\n",
      "Train Epoch: 6 [2560/50000 (6%)]\tLoss: 0.759790, Accuracy: 72.27\n",
      "Train Epoch: 6 [3840/50000 (9%)]\tLoss: 0.749831, Accuracy: 77.34\n",
      "Train Epoch: 6 [5120/50000 (11%)]\tLoss: 0.835928, Accuracy: 71.48\n",
      "Train Epoch: 6 [6400/50000 (14%)]\tLoss: 0.848398, Accuracy: 69.14\n",
      "Train Epoch: 6 [7680/50000 (17%)]\tLoss: 0.730162, Accuracy: 74.22\n",
      "Train Epoch: 6 [8960/50000 (20%)]\tLoss: 0.730350, Accuracy: 75.39\n",
      "Train Epoch: 6 [10240/50000 (23%)]\tLoss: 0.831557, Accuracy: 69.14\n",
      "Train Epoch: 6 [11520/50000 (26%)]\tLoss: 0.757056, Accuracy: 71.48\n",
      "Train Epoch: 6 [12800/50000 (28%)]\tLoss: 0.793901, Accuracy: 73.83\n",
      "Train Epoch: 6 [14080/50000 (31%)]\tLoss: 0.698727, Accuracy: 75.78\n",
      "Train Epoch: 6 [15360/50000 (34%)]\tLoss: 0.828597, Accuracy: 75.39\n",
      "Train Epoch: 6 [16640/50000 (37%)]\tLoss: 0.759356, Accuracy: 71.09\n",
      "Train Epoch: 6 [17920/50000 (40%)]\tLoss: 0.775286, Accuracy: 72.27\n",
      "Train Epoch: 6 [19200/50000 (43%)]\tLoss: 0.832846, Accuracy: 70.70\n",
      "Train Epoch: 6 [20480/50000 (45%)]\tLoss: 0.745770, Accuracy: 73.83\n",
      "Train Epoch: 6 [21760/50000 (48%)]\tLoss: 0.772916, Accuracy: 74.61\n",
      "Train Epoch: 6 [23040/50000 (51%)]\tLoss: 0.767113, Accuracy: 74.61\n",
      "Train Epoch: 6 [24320/50000 (54%)]\tLoss: 0.807180, Accuracy: 69.53\n",
      "Train Epoch: 6 [25600/50000 (57%)]\tLoss: 0.696095, Accuracy: 76.17\n",
      "Train Epoch: 6 [26880/50000 (60%)]\tLoss: 0.773414, Accuracy: 72.66\n",
      "Train Epoch: 6 [28160/50000 (62%)]\tLoss: 0.789752, Accuracy: 73.44\n",
      "Train Epoch: 6 [29440/50000 (65%)]\tLoss: 0.705784, Accuracy: 75.00\n",
      "Train Epoch: 6 [30720/50000 (68%)]\tLoss: 0.824460, Accuracy: 71.48\n",
      "Train Epoch: 6 [32000/50000 (71%)]\tLoss: 0.669515, Accuracy: 77.34\n",
      "Train Epoch: 6 [33280/50000 (74%)]\tLoss: 0.820765, Accuracy: 74.22\n",
      "Train Epoch: 6 [34560/50000 (77%)]\tLoss: 0.697088, Accuracy: 76.95\n",
      "Train Epoch: 6 [35840/50000 (80%)]\tLoss: 0.633377, Accuracy: 78.52\n",
      "Train Epoch: 6 [37120/50000 (82%)]\tLoss: 0.711981, Accuracy: 75.78\n",
      "Train Epoch: 6 [38400/50000 (85%)]\tLoss: 0.698283, Accuracy: 76.17\n",
      "Train Epoch: 6 [39680/50000 (88%)]\tLoss: 0.705374, Accuracy: 75.78\n",
      "Train Epoch: 6 [40960/50000 (91%)]\tLoss: 0.691414, Accuracy: 76.56\n",
      "Train Epoch: 6 [42240/50000 (94%)]\tLoss: 0.682824, Accuracy: 77.34\n",
      "Train Epoch: 6 [43520/50000 (97%)]\tLoss: 0.587656, Accuracy: 79.69\n",
      "Train Epoch: 6 [35000/50000 (99%)]\tLoss: 0.789005, Accuracy: 75.50\n",
      "\n",
      "Validation set: Average loss: 0.9195, Accuracy: 3411/5000 (68.00%)\n",
      "\n",
      "the time of this epoch:[37.98142862319946 s]\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.730782, Accuracy: 73.44\n",
      "Train Epoch: 7 [1280/50000 (3%)]\tLoss: 0.699482, Accuracy: 77.34\n",
      "Train Epoch: 7 [2560/50000 (6%)]\tLoss: 0.585912, Accuracy: 80.08\n",
      "Train Epoch: 7 [3840/50000 (9%)]\tLoss: 0.793611, Accuracy: 71.48\n",
      "Train Epoch: 7 [5120/50000 (11%)]\tLoss: 0.767657, Accuracy: 73.44\n",
      "Train Epoch: 7 [6400/50000 (14%)]\tLoss: 0.673497, Accuracy: 77.34\n",
      "Train Epoch: 7 [7680/50000 (17%)]\tLoss: 0.823926, Accuracy: 72.27\n",
      "Train Epoch: 7 [8960/50000 (20%)]\tLoss: 0.804425, Accuracy: 71.88\n",
      "Train Epoch: 7 [10240/50000 (23%)]\tLoss: 0.775098, Accuracy: 69.92\n",
      "Train Epoch: 7 [11520/50000 (26%)]\tLoss: 0.677925, Accuracy: 78.12\n",
      "Train Epoch: 7 [12800/50000 (28%)]\tLoss: 0.736974, Accuracy: 73.83\n",
      "Train Epoch: 7 [14080/50000 (31%)]\tLoss: 0.803231, Accuracy: 72.66\n",
      "Train Epoch: 7 [15360/50000 (34%)]\tLoss: 0.769210, Accuracy: 75.39\n",
      "Train Epoch: 7 [16640/50000 (37%)]\tLoss: 0.792032, Accuracy: 70.31\n",
      "Train Epoch: 7 [17920/50000 (40%)]\tLoss: 0.712975, Accuracy: 76.95\n",
      "Train Epoch: 7 [19200/50000 (43%)]\tLoss: 0.664797, Accuracy: 74.22\n",
      "Train Epoch: 7 [20480/50000 (45%)]\tLoss: 0.675681, Accuracy: 75.78\n",
      "Train Epoch: 7 [21760/50000 (48%)]\tLoss: 0.701204, Accuracy: 74.61\n",
      "Train Epoch: 7 [23040/50000 (51%)]\tLoss: 0.729133, Accuracy: 73.44\n",
      "Train Epoch: 7 [24320/50000 (54%)]\tLoss: 0.726375, Accuracy: 74.61\n",
      "Train Epoch: 7 [25600/50000 (57%)]\tLoss: 0.723940, Accuracy: 74.61\n",
      "Train Epoch: 7 [26880/50000 (60%)]\tLoss: 0.829588, Accuracy: 73.44\n",
      "Train Epoch: 7 [28160/50000 (62%)]\tLoss: 0.709588, Accuracy: 74.22\n",
      "Train Epoch: 7 [29440/50000 (65%)]\tLoss: 0.762639, Accuracy: 71.88\n",
      "Train Epoch: 7 [30720/50000 (68%)]\tLoss: 0.666541, Accuracy: 75.00\n",
      "Train Epoch: 7 [32000/50000 (71%)]\tLoss: 0.679866, Accuracy: 75.78\n",
      "Train Epoch: 7 [33280/50000 (74%)]\tLoss: 0.762534, Accuracy: 74.61\n",
      "Train Epoch: 7 [34560/50000 (77%)]\tLoss: 0.635275, Accuracy: 78.91\n",
      "Train Epoch: 7 [35840/50000 (80%)]\tLoss: 0.616188, Accuracy: 82.03\n",
      "Train Epoch: 7 [37120/50000 (82%)]\tLoss: 0.720643, Accuracy: 76.56\n",
      "Train Epoch: 7 [38400/50000 (85%)]\tLoss: 0.766332, Accuracy: 74.22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 7 [39680/50000 (88%)]\tLoss: 0.725222, Accuracy: 75.00\n",
      "Train Epoch: 7 [40960/50000 (91%)]\tLoss: 0.728713, Accuracy: 75.00\n",
      "Train Epoch: 7 [42240/50000 (94%)]\tLoss: 0.602114, Accuracy: 81.64\n",
      "Train Epoch: 7 [43520/50000 (97%)]\tLoss: 0.700164, Accuracy: 77.34\n",
      "Train Epoch: 7 [35000/50000 (99%)]\tLoss: 0.767010, Accuracy: 76.00\n",
      "\n",
      "Validation set: Average loss: 0.9946, Accuracy: 3366/5000 (67.00%)\n",
      "\n",
      "the time of this epoch:[37.81495451927185 s]\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 0.630426, Accuracy: 78.91\n",
      "Train Epoch: 8 [1280/50000 (3%)]\tLoss: 0.732682, Accuracy: 73.05\n",
      "Train Epoch: 8 [2560/50000 (6%)]\tLoss: 0.546613, Accuracy: 82.42\n",
      "Train Epoch: 8 [3840/50000 (9%)]\tLoss: 0.566824, Accuracy: 77.73\n",
      "Train Epoch: 8 [5120/50000 (11%)]\tLoss: 0.643367, Accuracy: 78.91\n",
      "Train Epoch: 8 [6400/50000 (14%)]\tLoss: 0.601966, Accuracy: 78.52\n",
      "Train Epoch: 8 [7680/50000 (17%)]\tLoss: 0.583344, Accuracy: 76.56\n",
      "Train Epoch: 8 [8960/50000 (20%)]\tLoss: 0.800645, Accuracy: 72.66\n",
      "Train Epoch: 8 [10240/50000 (23%)]\tLoss: 0.602157, Accuracy: 79.30\n",
      "Train Epoch: 8 [11520/50000 (26%)]\tLoss: 0.669826, Accuracy: 76.95\n",
      "Train Epoch: 8 [12800/50000 (28%)]\tLoss: 0.616221, Accuracy: 80.86\n",
      "Train Epoch: 8 [14080/50000 (31%)]\tLoss: 0.603771, Accuracy: 79.30\n",
      "Train Epoch: 8 [15360/50000 (34%)]\tLoss: 0.740466, Accuracy: 75.00\n",
      "Train Epoch: 8 [16640/50000 (37%)]\tLoss: 0.668442, Accuracy: 75.00\n",
      "Train Epoch: 8 [17920/50000 (40%)]\tLoss: 0.631046, Accuracy: 80.08\n",
      "Train Epoch: 8 [19200/50000 (43%)]\tLoss: 0.720596, Accuracy: 75.39\n",
      "Train Epoch: 8 [20480/50000 (45%)]\tLoss: 0.692649, Accuracy: 79.30\n",
      "Train Epoch: 8 [21760/50000 (48%)]\tLoss: 0.586101, Accuracy: 81.25\n",
      "Train Epoch: 8 [23040/50000 (51%)]\tLoss: 0.666870, Accuracy: 77.73\n",
      "Train Epoch: 8 [24320/50000 (54%)]\tLoss: 0.696221, Accuracy: 75.78\n",
      "Train Epoch: 8 [25600/50000 (57%)]\tLoss: 0.673926, Accuracy: 75.78\n",
      "Train Epoch: 8 [26880/50000 (60%)]\tLoss: 0.676468, Accuracy: 75.00\n",
      "Train Epoch: 8 [28160/50000 (62%)]\tLoss: 0.750909, Accuracy: 73.44\n",
      "Train Epoch: 8 [29440/50000 (65%)]\tLoss: 0.643387, Accuracy: 77.73\n",
      "Train Epoch: 8 [30720/50000 (68%)]\tLoss: 0.626000, Accuracy: 80.86\n",
      "Train Epoch: 8 [32000/50000 (71%)]\tLoss: 0.710031, Accuracy: 76.17\n",
      "Train Epoch: 8 [33280/50000 (74%)]\tLoss: 0.653024, Accuracy: 76.56\n",
      "Train Epoch: 8 [34560/50000 (77%)]\tLoss: 0.594610, Accuracy: 80.47\n",
      "Train Epoch: 8 [35840/50000 (80%)]\tLoss: 0.556943, Accuracy: 80.08\n",
      "Train Epoch: 8 [37120/50000 (82%)]\tLoss: 0.646715, Accuracy: 80.08\n",
      "Train Epoch: 8 [38400/50000 (85%)]\tLoss: 0.572464, Accuracy: 78.52\n",
      "Train Epoch: 8 [39680/50000 (88%)]\tLoss: 0.693722, Accuracy: 77.73\n",
      "Train Epoch: 8 [40960/50000 (91%)]\tLoss: 0.661426, Accuracy: 76.17\n",
      "Train Epoch: 8 [42240/50000 (94%)]\tLoss: 0.631802, Accuracy: 78.12\n",
      "Train Epoch: 8 [43520/50000 (97%)]\tLoss: 0.768076, Accuracy: 72.27\n",
      "Train Epoch: 8 [35000/50000 (99%)]\tLoss: 0.591656, Accuracy: 80.50\n",
      "\n",
      "Validation set: Average loss: 0.8726, Accuracy: 3571/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[37.79910111427307 s]\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 0.547116, Accuracy: 79.69\n",
      "Train Epoch: 9 [1280/50000 (3%)]\tLoss: 0.729350, Accuracy: 74.61\n",
      "Train Epoch: 9 [2560/50000 (6%)]\tLoss: 0.650696, Accuracy: 75.00\n",
      "Train Epoch: 9 [3840/50000 (9%)]\tLoss: 0.663293, Accuracy: 78.52\n",
      "Train Epoch: 9 [5120/50000 (11%)]\tLoss: 0.706125, Accuracy: 76.56\n",
      "Train Epoch: 9 [6400/50000 (14%)]\tLoss: 0.531091, Accuracy: 82.81\n",
      "Train Epoch: 9 [7680/50000 (17%)]\tLoss: 0.517987, Accuracy: 80.86\n",
      "Train Epoch: 9 [8960/50000 (20%)]\tLoss: 0.662973, Accuracy: 79.69\n",
      "Train Epoch: 9 [10240/50000 (23%)]\tLoss: 0.547615, Accuracy: 81.25\n",
      "Train Epoch: 9 [11520/50000 (26%)]\tLoss: 0.689688, Accuracy: 78.52\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.0487]],\n",
      "\n",
      "        [[ 0.0063]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.2114]],\n",
      "\n",
      "        [[ 0.0429]],\n",
      "\n",
      "        [[ 0.0180]],\n",
      "\n",
      "        [[ 0.0524]],\n",
      "\n",
      "        [[ 0.0696]],\n",
      "\n",
      "        [[ 0.0716]],\n",
      "\n",
      "        [[ 0.0012]],\n",
      "\n",
      "        [[ 0.3011]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0234]],\n",
      "\n",
      "        [[ 0.0631]],\n",
      "\n",
      "        [[ 0.0758]],\n",
      "\n",
      "        [[ 0.0246]],\n",
      "\n",
      "        [[ 0.0006]],\n",
      "\n",
      "        [[ 0.2267]],\n",
      "\n",
      "        [[ 0.1710]],\n",
      "\n",
      "        [[ 0.0000]],\n",
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      "\n",
      "        [[ 0.1412]],\n",
      "\n",
      "        [[ 0.0040]],\n",
      "\n",
      "        [[ 0.1510]],\n",
      "\n",
      "        [[ 0.0814]],\n",
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      "        [[ 0.0279]],\n",
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      "        [[ 0.0110]],\n",
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      "        [[ 0.1198]],\n",
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      "        [[ 0.0134]],\n",
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      "        [[ 0.0382]],\n",
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      "        [[ 0.0457]],\n",
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      "        [[ 0.2340]],\n",
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      "        [[ 0.0170]],\n",
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      "        [[ 0.0009]],\n",
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      "        [[ 0.0779]],\n",
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      "        [[ 0.0578]],\n",
      "\n",
      "        [[ 0.0038]],\n",
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      "        [[ 0.0411]],\n",
      "\n",
      "        [[ 0.0173]],\n",
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      "        [[ 0.0737]],\n",
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      "        [[ 0.1260]],\n",
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      "        [[ 0.1041]],\n",
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      "        [[ 0.1494]],\n",
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      "        [[ 0.0000]],\n",
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      "        [[ 0.0651]],\n",
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      "        [[ 0.0420]],\n",
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      "        [[ 0.0944]],\n",
      "\n",
      "        [[ 0.0234]],\n",
      "\n",
      "        [[ 0.0019]],\n",
      "\n",
      "        [[ 0.0653]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([[[ 0.5709]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-7.2816e-02, -8.5506e-02, -1.0138e-01,  ..., -2.1130e-02,\n",
      "           3.5338e-02, -2.0099e-02]],\n",
      "\n",
      "        [[-6.2633e-02, -8.8890e-03,  2.5962e-03,  ..., -8.4606e-02,\n",
      "           1.0115e-01, -7.8445e-02]],\n",
      "\n",
      "        [[ 4.8728e-02, -2.7778e-03,  6.6525e-02,  ..., -1.6938e-02,\n",
      "           9.3994e-02,  4.8607e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 6.3376e-03,  9.1902e-02,  8.1488e-02,  ...,  4.1469e-02,\n",
      "           2.8900e-02, -1.0171e-01]],\n",
      "\n",
      "        [[-5.2638e-02, -2.3016e-02, -1.9589e-02,  ...,  9.2307e-02,\n",
      "          -6.9858e-02,  1.2975e-02]],\n",
      "\n",
      "        [[ 8.4657e-02, -1.1488e-01,  1.0144e-01,  ..., -8.7271e-02,\n",
      "           2.5431e-02,  8.6623e-02]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-3.2045, -4.8199, -6.1300,  ..., -8.2957, -7.7629, -7.0815],\n",
      "         [ 0.3649,  1.5610,  3.7263,  ...,  2.6844,  1.2034, -0.6056],\n",
      "         [ 2.0973,  2.5655,  3.9525,  ...,  0.7919,  2.0116,  0.1757],\n",
      "         ...,\n",
      "         [-2.9466, -1.6494, -2.0268,  ...,  3.3599,  3.0273,  4.6242],\n",
      "         [-2.1096, -4.1919, -3.7700,  ..., -4.3804, -2.1693, -2.8290],\n",
      "         [-1.5904, -0.1989,  2.0811,  ...,  3.4481,  5.0914,  5.4377]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [12800/50000 (28%)]\tLoss: 0.588500, Accuracy: 81.25\n",
      "Train Epoch: 9 [14080/50000 (31%)]\tLoss: 0.617249, Accuracy: 79.30\n",
      "Train Epoch: 9 [15360/50000 (34%)]\tLoss: 0.614006, Accuracy: 80.08\n",
      "Train Epoch: 9 [16640/50000 (37%)]\tLoss: 0.569773, Accuracy: 82.42\n",
      "Train Epoch: 9 [17920/50000 (40%)]\tLoss: 0.543308, Accuracy: 80.47\n",
      "Train Epoch: 9 [19200/50000 (43%)]\tLoss: 0.592120, Accuracy: 80.86\n",
      "Train Epoch: 9 [20480/50000 (45%)]\tLoss: 0.630556, Accuracy: 76.95\n",
      "Train Epoch: 9 [21760/50000 (48%)]\tLoss: 0.601600, Accuracy: 79.30\n",
      "Train Epoch: 9 [23040/50000 (51%)]\tLoss: 0.533292, Accuracy: 81.25\n",
      "Train Epoch: 9 [24320/50000 (54%)]\tLoss: 0.601883, Accuracy: 78.12\n",
      "Train Epoch: 9 [25600/50000 (57%)]\tLoss: 0.655627, Accuracy: 77.73\n",
      "Train Epoch: 9 [26880/50000 (60%)]\tLoss: 0.604590, Accuracy: 78.52\n",
      "Train Epoch: 9 [28160/50000 (62%)]\tLoss: 0.631581, Accuracy: 78.12\n",
      "Train Epoch: 9 [29440/50000 (65%)]\tLoss: 0.631826, Accuracy: 78.91\n",
      "Train Epoch: 9 [30720/50000 (68%)]\tLoss: 0.623317, Accuracy: 80.86\n",
      "Train Epoch: 9 [32000/50000 (71%)]\tLoss: 0.618959, Accuracy: 77.73\n",
      "Train Epoch: 9 [33280/50000 (74%)]\tLoss: 0.542294, Accuracy: 80.47\n",
      "Train Epoch: 9 [34560/50000 (77%)]\tLoss: 0.582941, Accuracy: 79.30\n",
      "Train Epoch: 9 [35840/50000 (80%)]\tLoss: 0.622769, Accuracy: 79.69\n",
      "Train Epoch: 9 [37120/50000 (82%)]\tLoss: 0.595400, Accuracy: 78.91\n",
      "Train Epoch: 9 [38400/50000 (85%)]\tLoss: 0.633833, Accuracy: 80.08\n",
      "Train Epoch: 9 [39680/50000 (88%)]\tLoss: 0.677641, Accuracy: 78.52\n",
      "Train Epoch: 9 [40960/50000 (91%)]\tLoss: 0.685792, Accuracy: 77.34\n",
      "Train Epoch: 9 [42240/50000 (94%)]\tLoss: 0.677693, Accuracy: 76.56\n",
      "Train Epoch: 9 [43520/50000 (97%)]\tLoss: 0.711467, Accuracy: 80.08\n",
      "Train Epoch: 9 [35000/50000 (99%)]\tLoss: 0.729518, Accuracy: 72.50\n",
      "\n",
      "Validation set: Average loss: 1.1515, Accuracy: 3218/5000 (64.00%)\n",
      "\n",
      "the time of this epoch:[37.865211963653564 s]\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 0.574333, Accuracy: 80.47\n",
      "Train Epoch: 10 [1280/50000 (3%)]\tLoss: 0.561446, Accuracy: 77.73\n",
      "Train Epoch: 10 [2560/50000 (6%)]\tLoss: 0.648353, Accuracy: 77.34\n",
      "Train Epoch: 10 [3840/50000 (9%)]\tLoss: 0.642244, Accuracy: 75.39\n",
      "Train Epoch: 10 [5120/50000 (11%)]\tLoss: 0.662613, Accuracy: 78.91\n",
      "Train Epoch: 10 [6400/50000 (14%)]\tLoss: 0.590565, Accuracy: 82.03\n",
      "Train Epoch: 10 [7680/50000 (17%)]\tLoss: 0.566451, Accuracy: 77.73\n",
      "Train Epoch: 10 [8960/50000 (20%)]\tLoss: 0.550417, Accuracy: 79.69\n",
      "Train Epoch: 10 [10240/50000 (23%)]\tLoss: 0.487948, Accuracy: 84.38\n",
      "Train Epoch: 10 [11520/50000 (26%)]\tLoss: 0.517182, Accuracy: 83.20\n",
      "Train Epoch: 10 [12800/50000 (28%)]\tLoss: 0.555051, Accuracy: 81.64\n",
      "Train Epoch: 10 [14080/50000 (31%)]\tLoss: 0.570731, Accuracy: 78.91\n",
      "Train Epoch: 10 [15360/50000 (34%)]\tLoss: 0.619743, Accuracy: 78.52\n",
      "Train Epoch: 10 [16640/50000 (37%)]\tLoss: 0.650735, Accuracy: 76.95\n",
      "Train Epoch: 10 [17920/50000 (40%)]\tLoss: 0.570090, Accuracy: 78.12\n",
      "Train Epoch: 10 [19200/50000 (43%)]\tLoss: 0.621826, Accuracy: 78.91\n",
      "Train Epoch: 10 [20480/50000 (45%)]\tLoss: 0.561922, Accuracy: 79.69\n",
      "Train Epoch: 10 [21760/50000 (48%)]\tLoss: 0.560449, Accuracy: 81.25\n",
      "Train Epoch: 10 [23040/50000 (51%)]\tLoss: 0.617529, Accuracy: 78.52\n",
      "Train Epoch: 10 [24320/50000 (54%)]\tLoss: 0.599301, Accuracy: 76.56\n",
      "Train Epoch: 10 [25600/50000 (57%)]\tLoss: 0.511008, Accuracy: 83.59\n",
      "Train Epoch: 10 [26880/50000 (60%)]\tLoss: 0.487162, Accuracy: 84.38\n",
      "Train Epoch: 10 [28160/50000 (62%)]\tLoss: 0.698692, Accuracy: 77.34\n",
      "Train Epoch: 10 [29440/50000 (65%)]\tLoss: 0.588439, Accuracy: 82.81\n",
      "Train Epoch: 10 [30720/50000 (68%)]\tLoss: 0.515416, Accuracy: 83.20\n",
      "Train Epoch: 10 [32000/50000 (71%)]\tLoss: 0.469132, Accuracy: 82.81\n",
      "Train Epoch: 10 [33280/50000 (74%)]\tLoss: 0.562728, Accuracy: 82.03\n",
      "Train Epoch: 10 [34560/50000 (77%)]\tLoss: 0.589880, Accuracy: 79.69\n",
      "Train Epoch: 10 [35840/50000 (80%)]\tLoss: 0.696422, Accuracy: 75.39\n",
      "Train Epoch: 10 [37120/50000 (82%)]\tLoss: 0.520022, Accuracy: 81.25\n",
      "Train Epoch: 10 [38400/50000 (85%)]\tLoss: 0.536157, Accuracy: 82.81\n",
      "Train Epoch: 10 [39680/50000 (88%)]\tLoss: 0.630788, Accuracy: 79.30\n",
      "Train Epoch: 10 [40960/50000 (91%)]\tLoss: 0.589626, Accuracy: 83.20\n",
      "Train Epoch: 10 [42240/50000 (94%)]\tLoss: 0.615971, Accuracy: 78.91\n",
      "Train Epoch: 10 [43520/50000 (97%)]\tLoss: 0.628691, Accuracy: 76.56\n",
      "Train Epoch: 10 [35000/50000 (99%)]\tLoss: 0.623896, Accuracy: 74.50\n",
      "\n",
      "Validation set: Average loss: 0.9153, Accuracy: 3552/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[37.76519179344177 s]\n",
      "\n",
      "Test set: Average loss: 0.9203, Accuracy: 7098/10000 (70.98%)\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 0.506454, Accuracy: 82.03\n",
      "Train Epoch: 11 [1280/50000 (3%)]\tLoss: 0.480204, Accuracy: 84.77\n",
      "Train Epoch: 11 [2560/50000 (6%)]\tLoss: 0.625527, Accuracy: 77.34\n",
      "Train Epoch: 11 [3840/50000 (9%)]\tLoss: 0.620033, Accuracy: 80.47\n",
      "Train Epoch: 11 [5120/50000 (11%)]\tLoss: 0.564619, Accuracy: 81.64\n",
      "Train Epoch: 11 [6400/50000 (14%)]\tLoss: 0.635968, Accuracy: 81.25\n",
      "Train Epoch: 11 [7680/50000 (17%)]\tLoss: 0.553364, Accuracy: 82.42\n",
      "Train Epoch: 11 [8960/50000 (20%)]\tLoss: 0.534227, Accuracy: 82.81\n",
      "Train Epoch: 11 [10240/50000 (23%)]\tLoss: 0.591453, Accuracy: 80.86\n",
      "Train Epoch: 11 [11520/50000 (26%)]\tLoss: 0.632712, Accuracy: 79.69\n",
      "Train Epoch: 11 [12800/50000 (28%)]\tLoss: 0.531245, Accuracy: 82.03\n",
      "Train Epoch: 11 [14080/50000 (31%)]\tLoss: 0.531640, Accuracy: 82.81\n",
      "Train Epoch: 11 [15360/50000 (34%)]\tLoss: 0.556888, Accuracy: 80.08\n",
      "Train Epoch: 11 [16640/50000 (37%)]\tLoss: 0.482183, Accuracy: 83.59\n",
      "Train Epoch: 11 [17920/50000 (40%)]\tLoss: 0.542321, Accuracy: 79.30\n",
      "Train Epoch: 11 [19200/50000 (43%)]\tLoss: 0.560827, Accuracy: 80.86\n",
      "Train Epoch: 11 [20480/50000 (45%)]\tLoss: 0.641579, Accuracy: 78.12\n",
      "Train Epoch: 11 [21760/50000 (48%)]\tLoss: 0.526332, Accuracy: 79.69\n",
      "Train Epoch: 11 [23040/50000 (51%)]\tLoss: 0.568408, Accuracy: 80.08\n",
      "Train Epoch: 11 [24320/50000 (54%)]\tLoss: 0.619179, Accuracy: 78.52\n",
      "Train Epoch: 11 [25600/50000 (57%)]\tLoss: 0.631004, Accuracy: 77.34\n",
      "Train Epoch: 11 [26880/50000 (60%)]\tLoss: 0.754827, Accuracy: 73.83\n",
      "Train Epoch: 11 [28160/50000 (62%)]\tLoss: 0.541331, Accuracy: 80.47\n",
      "Train Epoch: 11 [29440/50000 (65%)]\tLoss: 0.609924, Accuracy: 80.08\n",
      "Train Epoch: 11 [30720/50000 (68%)]\tLoss: 0.592759, Accuracy: 80.86\n",
      "Train Epoch: 11 [32000/50000 (71%)]\tLoss: 0.572104, Accuracy: 76.95\n",
      "Train Epoch: 11 [33280/50000 (74%)]\tLoss: 0.573175, Accuracy: 80.47\n",
      "Train Epoch: 11 [34560/50000 (77%)]\tLoss: 0.506181, Accuracy: 85.16\n",
      "Train Epoch: 11 [35840/50000 (80%)]\tLoss: 0.538727, Accuracy: 80.47\n",
      "Train Epoch: 11 [37120/50000 (82%)]\tLoss: 0.624558, Accuracy: 80.08\n",
      "Train Epoch: 11 [38400/50000 (85%)]\tLoss: 0.555202, Accuracy: 83.20\n",
      "Train Epoch: 11 [39680/50000 (88%)]\tLoss: 0.657363, Accuracy: 78.12\n",
      "Train Epoch: 11 [40960/50000 (91%)]\tLoss: 0.565918, Accuracy: 79.69\n",
      "Train Epoch: 11 [42240/50000 (94%)]\tLoss: 0.531172, Accuracy: 79.30\n",
      "Train Epoch: 11 [43520/50000 (97%)]\tLoss: 0.561838, Accuracy: 81.64\n",
      "Train Epoch: 11 [35000/50000 (99%)]\tLoss: 0.634741, Accuracy: 75.50\n",
      "\n",
      "Validation set: Average loss: 0.6510, Accuracy: 3915/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[40.9571259021759 s]\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 0.469392, Accuracy: 85.94\n",
      "Train Epoch: 12 [1280/50000 (3%)]\tLoss: 0.558984, Accuracy: 80.08\n",
      "Train Epoch: 12 [2560/50000 (6%)]\tLoss: 0.681240, Accuracy: 77.34\n",
      "Train Epoch: 12 [3840/50000 (9%)]\tLoss: 0.529922, Accuracy: 83.20\n",
      "Train Epoch: 12 [5120/50000 (11%)]\tLoss: 0.595667, Accuracy: 79.69\n",
      "Train Epoch: 12 [6400/50000 (14%)]\tLoss: 0.544360, Accuracy: 83.59\n",
      "Train Epoch: 12 [7680/50000 (17%)]\tLoss: 0.531709, Accuracy: 84.77\n",
      "Train Epoch: 12 [8960/50000 (20%)]\tLoss: 0.442984, Accuracy: 84.38\n",
      "Train Epoch: 12 [10240/50000 (23%)]\tLoss: 0.533980, Accuracy: 82.03\n",
      "Train Epoch: 12 [11520/50000 (26%)]\tLoss: 0.566423, Accuracy: 80.47\n",
      "Train Epoch: 12 [12800/50000 (28%)]\tLoss: 0.580049, Accuracy: 79.30\n",
      "Train Epoch: 12 [14080/50000 (31%)]\tLoss: 0.531312, Accuracy: 79.30\n",
      "Train Epoch: 12 [15360/50000 (34%)]\tLoss: 0.563179, Accuracy: 82.42\n",
      "Train Epoch: 12 [16640/50000 (37%)]\tLoss: 0.567661, Accuracy: 82.42\n",
      "Train Epoch: 12 [17920/50000 (40%)]\tLoss: 0.569097, Accuracy: 80.47\n",
      "Train Epoch: 12 [19200/50000 (43%)]\tLoss: 0.522040, Accuracy: 84.77\n",
      "Train Epoch: 12 [20480/50000 (45%)]\tLoss: 0.512488, Accuracy: 82.42\n",
      "Train Epoch: 12 [21760/50000 (48%)]\tLoss: 0.431425, Accuracy: 83.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 12 [23040/50000 (51%)]\tLoss: 0.519777, Accuracy: 82.03\n",
      "Train Epoch: 12 [24320/50000 (54%)]\tLoss: 0.521879, Accuracy: 81.25\n",
      "Train Epoch: 12 [25600/50000 (57%)]\tLoss: 0.499494, Accuracy: 83.98\n",
      "Train Epoch: 12 [26880/50000 (60%)]\tLoss: 0.460083, Accuracy: 85.55\n",
      "Train Epoch: 12 [28160/50000 (62%)]\tLoss: 0.604296, Accuracy: 78.12\n",
      "Train Epoch: 12 [29440/50000 (65%)]\tLoss: 0.650593, Accuracy: 80.08\n",
      "Train Epoch: 12 [30720/50000 (68%)]\tLoss: 0.484433, Accuracy: 84.38\n",
      "Train Epoch: 12 [32000/50000 (71%)]\tLoss: 0.522679, Accuracy: 84.77\n",
      "Train Epoch: 12 [33280/50000 (74%)]\tLoss: 0.448516, Accuracy: 84.77\n",
      "Train Epoch: 12 [34560/50000 (77%)]\tLoss: 0.596749, Accuracy: 80.08\n",
      "Train Epoch: 12 [35840/50000 (80%)]\tLoss: 0.540413, Accuracy: 81.25\n",
      "Train Epoch: 12 [37120/50000 (82%)]\tLoss: 0.577040, Accuracy: 77.34\n",
      "Train Epoch: 12 [38400/50000 (85%)]\tLoss: 0.615830, Accuracy: 79.69\n",
      "Train Epoch: 12 [39680/50000 (88%)]\tLoss: 0.431170, Accuracy: 85.16\n",
      "Train Epoch: 12 [40960/50000 (91%)]\tLoss: 0.596468, Accuracy: 76.95\n",
      "Train Epoch: 12 [42240/50000 (94%)]\tLoss: 0.587514, Accuracy: 81.64\n",
      "Train Epoch: 12 [43520/50000 (97%)]\tLoss: 0.559113, Accuracy: 81.64\n",
      "Train Epoch: 12 [35000/50000 (99%)]\tLoss: 0.492680, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.8357, Accuracy: 3662/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[37.817572593688965 s]\n",
      "\n",
      "Test set: Average loss: 0.8608, Accuracy: 7213/10000 (72.13%)\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 0.516032, Accuracy: 83.59\n",
      "Train Epoch: 13 [1280/50000 (3%)]\tLoss: 0.500682, Accuracy: 83.98\n",
      "Train Epoch: 13 [2560/50000 (6%)]\tLoss: 0.593968, Accuracy: 81.25\n",
      "Train Epoch: 13 [3840/50000 (9%)]\tLoss: 0.393759, Accuracy: 85.16\n",
      "Train Epoch: 13 [5120/50000 (11%)]\tLoss: 0.561760, Accuracy: 81.64\n",
      "Train Epoch: 13 [6400/50000 (14%)]\tLoss: 0.549562, Accuracy: 80.86\n",
      "Train Epoch: 13 [7680/50000 (17%)]\tLoss: 0.486412, Accuracy: 84.38\n",
      "Train Epoch: 13 [8960/50000 (20%)]\tLoss: 0.460215, Accuracy: 86.33\n",
      "Train Epoch: 13 [10240/50000 (23%)]\tLoss: 0.386187, Accuracy: 89.45\n",
      "Train Epoch: 13 [11520/50000 (26%)]\tLoss: 0.527155, Accuracy: 81.25\n",
      "Train Epoch: 13 [12800/50000 (28%)]\tLoss: 0.519522, Accuracy: 80.86\n",
      "Train Epoch: 13 [14080/50000 (31%)]\tLoss: 0.446174, Accuracy: 83.20\n",
      "Train Epoch: 13 [15360/50000 (34%)]\tLoss: 0.586698, Accuracy: 79.30\n",
      "Train Epoch: 13 [16640/50000 (37%)]\tLoss: 0.526093, Accuracy: 79.69\n",
      "Train Epoch: 13 [17920/50000 (40%)]\tLoss: 0.516004, Accuracy: 82.81\n",
      "Train Epoch: 13 [19200/50000 (43%)]\tLoss: 0.583423, Accuracy: 80.47\n",
      "Train Epoch: 13 [20480/50000 (45%)]\tLoss: 0.645033, Accuracy: 77.73\n",
      "Train Epoch: 13 [21760/50000 (48%)]\tLoss: 0.487327, Accuracy: 82.03\n",
      "Train Epoch: 13 [23040/50000 (51%)]\tLoss: 0.457459, Accuracy: 85.94\n",
      "Train Epoch: 13 [24320/50000 (54%)]\tLoss: 0.551434, Accuracy: 79.69\n",
      "Train Epoch: 13 [25600/50000 (57%)]\tLoss: 0.503922, Accuracy: 82.03\n",
      "Train Epoch: 13 [26880/50000 (60%)]\tLoss: 0.631528, Accuracy: 78.12\n",
      "Train Epoch: 13 [28160/50000 (62%)]\tLoss: 0.431720, Accuracy: 83.20\n",
      "Train Epoch: 13 [29440/50000 (65%)]\tLoss: 0.521810, Accuracy: 79.69\n",
      "Train Epoch: 13 [30720/50000 (68%)]\tLoss: 0.552266, Accuracy: 80.86\n",
      "Train Epoch: 13 [32000/50000 (71%)]\tLoss: 0.522039, Accuracy: 81.25\n",
      "Train Epoch: 13 [33280/50000 (74%)]\tLoss: 0.511180, Accuracy: 84.38\n",
      "Train Epoch: 13 [34560/50000 (77%)]\tLoss: 0.436507, Accuracy: 86.33\n",
      "Train Epoch: 13 [35840/50000 (80%)]\tLoss: 0.432572, Accuracy: 86.72\n",
      "Train Epoch: 13 [37120/50000 (82%)]\tLoss: 0.543152, Accuracy: 83.98\n",
      "Train Epoch: 13 [38400/50000 (85%)]\tLoss: 0.464506, Accuracy: 85.55\n",
      "Train Epoch: 13 [39680/50000 (88%)]\tLoss: 0.477790, Accuracy: 85.16\n",
      "Train Epoch: 13 [40960/50000 (91%)]\tLoss: 0.589675, Accuracy: 77.73\n",
      "Train Epoch: 13 [42240/50000 (94%)]\tLoss: 0.603978, Accuracy: 79.69\n",
      "Train Epoch: 13 [43520/50000 (97%)]\tLoss: 0.648318, Accuracy: 76.56\n",
      "Train Epoch: 13 [35000/50000 (99%)]\tLoss: 0.576784, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.8779, Accuracy: 3555/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[40.983506202697754 s]\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 0.496297, Accuracy: 84.38\n",
      "Train Epoch: 14 [1280/50000 (3%)]\tLoss: 0.575853, Accuracy: 82.42\n",
      "Train Epoch: 14 [2560/50000 (6%)]\tLoss: 0.462607, Accuracy: 84.38\n",
      "Train Epoch: 14 [3840/50000 (9%)]\tLoss: 0.501752, Accuracy: 82.03\n",
      "Train Epoch: 14 [5120/50000 (11%)]\tLoss: 0.557469, Accuracy: 81.64\n",
      "Train Epoch: 14 [6400/50000 (14%)]\tLoss: 0.491177, Accuracy: 83.20\n",
      "Train Epoch: 14 [7680/50000 (17%)]\tLoss: 0.549249, Accuracy: 79.30\n",
      "Train Epoch: 14 [8960/50000 (20%)]\tLoss: 0.484422, Accuracy: 85.16\n",
      "Train Epoch: 14 [10240/50000 (23%)]\tLoss: 0.506621, Accuracy: 82.81\n",
      "Train Epoch: 14 [11520/50000 (26%)]\tLoss: 0.572009, Accuracy: 83.20\n",
      "Train Epoch: 14 [12800/50000 (28%)]\tLoss: 0.492752, Accuracy: 82.81\n",
      "Train Epoch: 14 [14080/50000 (31%)]\tLoss: 0.548715, Accuracy: 80.86\n",
      "Train Epoch: 14 [15360/50000 (34%)]\tLoss: 0.414589, Accuracy: 87.89\n",
      "Train Epoch: 14 [16640/50000 (37%)]\tLoss: 0.439118, Accuracy: 85.55\n",
      "Train Epoch: 14 [17920/50000 (40%)]\tLoss: 0.511409, Accuracy: 82.42\n",
      "Train Epoch: 14 [19200/50000 (43%)]\tLoss: 0.587335, Accuracy: 82.42\n",
      "Train Epoch: 14 [20480/50000 (45%)]\tLoss: 0.662647, Accuracy: 79.69\n",
      "Train Epoch: 14 [21760/50000 (48%)]\tLoss: 0.488745, Accuracy: 83.98\n",
      "Train Epoch: 14 [23040/50000 (51%)]\tLoss: 0.453766, Accuracy: 84.38\n",
      "Train Epoch: 14 [24320/50000 (54%)]\tLoss: 0.446635, Accuracy: 85.16\n",
      "Train Epoch: 14 [25600/50000 (57%)]\tLoss: 0.585970, Accuracy: 80.47\n",
      "Train Epoch: 14 [26880/50000 (60%)]\tLoss: 0.434316, Accuracy: 86.33\n",
      "Train Epoch: 14 [28160/50000 (62%)]\tLoss: 0.453430, Accuracy: 84.77\n",
      "Train Epoch: 14 [29440/50000 (65%)]\tLoss: 0.455298, Accuracy: 85.16\n",
      "Train Epoch: 14 [30720/50000 (68%)]\tLoss: 0.622116, Accuracy: 79.30\n",
      "Train Epoch: 14 [32000/50000 (71%)]\tLoss: 0.519684, Accuracy: 80.86\n",
      "Train Epoch: 14 [33280/50000 (74%)]\tLoss: 0.538346, Accuracy: 81.64\n",
      "Train Epoch: 14 [34560/50000 (77%)]\tLoss: 0.495248, Accuracy: 82.03\n",
      "Train Epoch: 14 [35840/50000 (80%)]\tLoss: 0.486112, Accuracy: 83.98\n",
      "Train Epoch: 14 [37120/50000 (82%)]\tLoss: 0.468623, Accuracy: 83.98\n",
      "Train Epoch: 14 [38400/50000 (85%)]\tLoss: 0.524002, Accuracy: 83.98\n",
      "Train Epoch: 14 [39680/50000 (88%)]\tLoss: 0.618418, Accuracy: 75.39\n",
      "Train Epoch: 14 [40960/50000 (91%)]\tLoss: 0.505644, Accuracy: 80.47\n",
      "Train Epoch: 14 [42240/50000 (94%)]\tLoss: 0.498577, Accuracy: 83.20\n",
      "Train Epoch: 14 [43520/50000 (97%)]\tLoss: 0.531373, Accuracy: 83.20\n",
      "Train Epoch: 14 [35000/50000 (99%)]\tLoss: 0.588958, Accuracy: 78.00\n",
      "\n",
      "Validation set: Average loss: 1.0983, Accuracy: 3338/5000 (66.00%)\n",
      "\n",
      "the time of this epoch:[37.86682200431824 s]\n",
      "\n",
      "Test set: Average loss: 1.0730, Accuracy: 6749/10000 (67.49%)\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 0.464749, Accuracy: 82.81\n",
      "Train Epoch: 15 [1280/50000 (3%)]\tLoss: 0.582608, Accuracy: 81.25\n",
      "Train Epoch: 15 [2560/50000 (6%)]\tLoss: 0.622101, Accuracy: 79.30\n",
      "Train Epoch: 15 [3840/50000 (9%)]\tLoss: 0.445277, Accuracy: 86.33\n",
      "Train Epoch: 15 [5120/50000 (11%)]\tLoss: 0.354657, Accuracy: 88.28\n",
      "Train Epoch: 15 [6400/50000 (14%)]\tLoss: 0.537929, Accuracy: 82.03\n",
      "Train Epoch: 15 [7680/50000 (17%)]\tLoss: 0.446187, Accuracy: 84.77\n",
      "Train Epoch: 15 [8960/50000 (20%)]\tLoss: 0.476905, Accuracy: 84.77\n",
      "Train Epoch: 15 [10240/50000 (23%)]\tLoss: 0.526649, Accuracy: 81.64\n",
      "Train Epoch: 15 [11520/50000 (26%)]\tLoss: 0.589556, Accuracy: 79.69\n",
      "Train Epoch: 15 [12800/50000 (28%)]\tLoss: 0.408983, Accuracy: 84.77\n",
      "Train Epoch: 15 [14080/50000 (31%)]\tLoss: 0.596852, Accuracy: 80.08\n",
      "Train Epoch: 15 [15360/50000 (34%)]\tLoss: 0.420041, Accuracy: 85.94\n",
      "Train Epoch: 15 [16640/50000 (37%)]\tLoss: 0.475398, Accuracy: 82.81\n",
      "Train Epoch: 15 [17920/50000 (40%)]\tLoss: 0.513168, Accuracy: 81.64\n",
      "Train Epoch: 15 [19200/50000 (43%)]\tLoss: 0.492783, Accuracy: 84.38\n",
      "Train Epoch: 15 [20480/50000 (45%)]\tLoss: 0.535873, Accuracy: 83.20\n",
      "Train Epoch: 15 [21760/50000 (48%)]\tLoss: 0.460980, Accuracy: 83.20\n",
      "Train Epoch: 15 [23040/50000 (51%)]\tLoss: 0.517986, Accuracy: 83.98\n",
      "Train Epoch: 15 [24320/50000 (54%)]\tLoss: 0.380421, Accuracy: 87.50\n",
      "Train Epoch: 15 [25600/50000 (57%)]\tLoss: 0.431452, Accuracy: 87.11\n",
      "Train Epoch: 15 [26880/50000 (60%)]\tLoss: 0.588058, Accuracy: 79.69\n",
      "Train Epoch: 15 [28160/50000 (62%)]\tLoss: 0.418359, Accuracy: 86.72\n",
      "Train Epoch: 15 [29440/50000 (65%)]\tLoss: 0.529199, Accuracy: 82.42\n",
      "Train Epoch: 15 [30720/50000 (68%)]\tLoss: 0.493181, Accuracy: 85.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 15 [32000/50000 (71%)]\tLoss: 0.417776, Accuracy: 83.59\n",
      "Train Epoch: 15 [33280/50000 (74%)]\tLoss: 0.423125, Accuracy: 85.16\n",
      "Train Epoch: 15 [34560/50000 (77%)]\tLoss: 0.516814, Accuracy: 83.59\n",
      "Train Epoch: 15 [35840/50000 (80%)]\tLoss: 0.603676, Accuracy: 80.08\n",
      "Train Epoch: 15 [37120/50000 (82%)]\tLoss: 0.484484, Accuracy: 82.81\n",
      "Train Epoch: 15 [38400/50000 (85%)]\tLoss: 0.430166, Accuracy: 85.94\n",
      "Train Epoch: 15 [39680/50000 (88%)]\tLoss: 0.481169, Accuracy: 83.98\n",
      "Train Epoch: 15 [40960/50000 (91%)]\tLoss: 0.448453, Accuracy: 86.33\n",
      "Train Epoch: 15 [42240/50000 (94%)]\tLoss: 0.536660, Accuracy: 83.59\n",
      "Train Epoch: 15 [43520/50000 (97%)]\tLoss: 0.561242, Accuracy: 82.03\n",
      "Train Epoch: 15 [35000/50000 (99%)]\tLoss: 0.485575, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.7601, Accuracy: 3805/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[41.00613451004028 s]\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 0.416676, Accuracy: 86.72\n",
      "Train Epoch: 16 [1280/50000 (3%)]\tLoss: 0.417915, Accuracy: 86.72\n",
      "Train Epoch: 16 [2560/50000 (6%)]\tLoss: 0.568439, Accuracy: 81.25\n",
      "Train Epoch: 16 [3840/50000 (9%)]\tLoss: 0.507414, Accuracy: 83.59\n",
      "Train Epoch: 16 [5120/50000 (11%)]\tLoss: 0.535795, Accuracy: 81.25\n",
      "Train Epoch: 16 [6400/50000 (14%)]\tLoss: 0.527367, Accuracy: 82.42\n",
      "Train Epoch: 16 [7680/50000 (17%)]\tLoss: 0.370029, Accuracy: 86.72\n",
      "Train Epoch: 16 [8960/50000 (20%)]\tLoss: 0.430200, Accuracy: 85.55\n",
      "Train Epoch: 16 [10240/50000 (23%)]\tLoss: 0.381337, Accuracy: 87.11\n",
      "Train Epoch: 16 [11520/50000 (26%)]\tLoss: 0.505695, Accuracy: 81.25\n",
      "Train Epoch: 16 [12800/50000 (28%)]\tLoss: 0.439858, Accuracy: 82.42\n",
      "Train Epoch: 16 [14080/50000 (31%)]\tLoss: 0.462267, Accuracy: 84.38\n",
      "Train Epoch: 16 [15360/50000 (34%)]\tLoss: 0.423433, Accuracy: 85.55\n",
      "Train Epoch: 16 [16640/50000 (37%)]\tLoss: 0.503737, Accuracy: 84.77\n",
      "Train Epoch: 16 [17920/50000 (40%)]\tLoss: 0.628581, Accuracy: 78.52\n",
      "Train Epoch: 16 [19200/50000 (43%)]\tLoss: 0.586129, Accuracy: 77.34\n",
      "Train Epoch: 16 [20480/50000 (45%)]\tLoss: 0.481488, Accuracy: 83.98\n",
      "Train Epoch: 16 [21760/50000 (48%)]\tLoss: 0.475239, Accuracy: 83.59\n",
      "Train Epoch: 16 [23040/50000 (51%)]\tLoss: 0.561640, Accuracy: 80.08\n",
      "Train Epoch: 16 [24320/50000 (54%)]\tLoss: 0.551166, Accuracy: 80.86\n",
      "Train Epoch: 16 [25600/50000 (57%)]\tLoss: 0.444011, Accuracy: 85.16\n",
      "Train Epoch: 16 [26880/50000 (60%)]\tLoss: 0.492327, Accuracy: 83.98\n",
      "Train Epoch: 16 [28160/50000 (62%)]\tLoss: 0.481170, Accuracy: 85.55\n",
      "Train Epoch: 16 [29440/50000 (65%)]\tLoss: 0.466647, Accuracy: 82.42\n",
      "Train Epoch: 16 [30720/50000 (68%)]\tLoss: 0.505814, Accuracy: 82.81\n",
      "Train Epoch: 16 [32000/50000 (71%)]\tLoss: 0.481749, Accuracy: 85.94\n",
      "Train Epoch: 16 [33280/50000 (74%)]\tLoss: 0.435826, Accuracy: 84.77\n",
      "Train Epoch: 16 [34560/50000 (77%)]\tLoss: 0.473535, Accuracy: 82.42\n",
      "Train Epoch: 16 [35840/50000 (80%)]\tLoss: 0.573871, Accuracy: 80.47\n",
      "Train Epoch: 16 [37120/50000 (82%)]\tLoss: 0.402920, Accuracy: 88.28\n",
      "Train Epoch: 16 [38400/50000 (85%)]\tLoss: 0.523861, Accuracy: 83.98\n",
      "Train Epoch: 16 [39680/50000 (88%)]\tLoss: 0.492672, Accuracy: 82.42\n",
      "Train Epoch: 16 [40960/50000 (91%)]\tLoss: 0.433045, Accuracy: 87.50\n",
      "Train Epoch: 16 [42240/50000 (94%)]\tLoss: 0.486317, Accuracy: 83.59\n",
      "Train Epoch: 16 [43520/50000 (97%)]\tLoss: 0.449071, Accuracy: 82.81\n",
      "Train Epoch: 16 [35000/50000 (99%)]\tLoss: 0.519748, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.6252, Accuracy: 3971/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[37.804603576660156 s]\n",
      "\n",
      "Test set: Average loss: 0.6321, Accuracy: 7933/10000 (79.33%)\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 0.416004, Accuracy: 86.72\n",
      "Train Epoch: 17 [1280/50000 (3%)]\tLoss: 0.436581, Accuracy: 83.98\n",
      "Train Epoch: 17 [2560/50000 (6%)]\tLoss: 0.368679, Accuracy: 88.67\n",
      "Train Epoch: 17 [3840/50000 (9%)]\tLoss: 0.451445, Accuracy: 82.81\n",
      "Train Epoch: 17 [5120/50000 (11%)]\tLoss: 0.476977, Accuracy: 86.33\n",
      "Train Epoch: 17 [6400/50000 (14%)]\tLoss: 0.588899, Accuracy: 78.91\n",
      "Train Epoch: 17 [7680/50000 (17%)]\tLoss: 0.512592, Accuracy: 82.42\n",
      "Train Epoch: 17 [8960/50000 (20%)]\tLoss: 0.547789, Accuracy: 82.42\n",
      "Train Epoch: 17 [10240/50000 (23%)]\tLoss: 0.536618, Accuracy: 83.98\n",
      "Train Epoch: 17 [11520/50000 (26%)]\tLoss: 0.552387, Accuracy: 82.03\n",
      "Train Epoch: 17 [12800/50000 (28%)]\tLoss: 0.423417, Accuracy: 87.11\n",
      "Train Epoch: 17 [14080/50000 (31%)]\tLoss: 0.410374, Accuracy: 85.55\n",
      "Train Epoch: 17 [15360/50000 (34%)]\tLoss: 0.370761, Accuracy: 86.72\n",
      "Train Epoch: 17 [16640/50000 (37%)]\tLoss: 0.468784, Accuracy: 85.55\n",
      "Train Epoch: 17 [17920/50000 (40%)]\tLoss: 0.526807, Accuracy: 80.08\n",
      "Train Epoch: 17 [19200/50000 (43%)]\tLoss: 0.446849, Accuracy: 87.11\n",
      "Train Epoch: 17 [20480/50000 (45%)]\tLoss: 0.541757, Accuracy: 80.86\n",
      "Train Epoch: 17 [21760/50000 (48%)]\tLoss: 0.531438, Accuracy: 81.25\n",
      "Train Epoch: 17 [23040/50000 (51%)]\tLoss: 0.525137, Accuracy: 79.69\n",
      "Train Epoch: 17 [24320/50000 (54%)]\tLoss: 0.481576, Accuracy: 83.98\n",
      "Train Epoch: 17 [25600/50000 (57%)]\tLoss: 0.544153, Accuracy: 83.59\n",
      "Train Epoch: 17 [26880/50000 (60%)]\tLoss: 0.353535, Accuracy: 88.67\n",
      "Train Epoch: 17 [28160/50000 (62%)]\tLoss: 0.402576, Accuracy: 88.28\n",
      "Train Epoch: 17 [29440/50000 (65%)]\tLoss: 0.582779, Accuracy: 81.25\n",
      "Train Epoch: 17 [30720/50000 (68%)]\tLoss: 0.494011, Accuracy: 82.81\n",
      "Train Epoch: 17 [32000/50000 (71%)]\tLoss: 0.472191, Accuracy: 81.64\n",
      "Train Epoch: 17 [33280/50000 (74%)]\tLoss: 0.556578, Accuracy: 79.69\n",
      "Train Epoch: 17 [34560/50000 (77%)]\tLoss: 0.489185, Accuracy: 84.38\n",
      "Train Epoch: 17 [35840/50000 (80%)]\tLoss: 0.509879, Accuracy: 83.98\n",
      "Train Epoch: 17 [37120/50000 (82%)]\tLoss: 0.480327, Accuracy: 85.55\n",
      "Train Epoch: 17 [38400/50000 (85%)]\tLoss: 0.443206, Accuracy: 83.59\n",
      "Train Epoch: 17 [39680/50000 (88%)]\tLoss: 0.453173, Accuracy: 85.94\n",
      "Train Epoch: 17 [40960/50000 (91%)]\tLoss: 0.563099, Accuracy: 81.64\n",
      "Train Epoch: 17 [42240/50000 (94%)]\tLoss: 0.489304, Accuracy: 83.59\n",
      "Train Epoch: 17 [43520/50000 (97%)]\tLoss: 0.551408, Accuracy: 82.42\n",
      "Train Epoch: 17 [35000/50000 (99%)]\tLoss: 0.653310, Accuracy: 78.00\n",
      "\n",
      "Validation set: Average loss: 0.8029, Accuracy: 3688/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[40.97099566459656 s]\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.420050, Accuracy: 84.77\n",
      "Train Epoch: 18 [1280/50000 (3%)]\tLoss: 0.458911, Accuracy: 84.38\n",
      "Train Epoch: 18 [2560/50000 (6%)]\tLoss: 0.454798, Accuracy: 85.55\n",
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      "Train Epoch: 18 [8960/50000 (20%)]\tLoss: 0.572575, Accuracy: 81.64\n",
      "Train Epoch: 18 [10240/50000 (23%)]\tLoss: 0.455309, Accuracy: 83.98\n",
      "Train Epoch: 18 [11520/50000 (26%)]\tLoss: 0.443741, Accuracy: 83.98\n",
      "Train Epoch: 18 [12800/50000 (28%)]\tLoss: 0.493543, Accuracy: 81.64\n",
      "Train Epoch: 18 [14080/50000 (31%)]\tLoss: 0.537705, Accuracy: 80.86\n",
      "Train Epoch: 18 [15360/50000 (34%)]\tLoss: 0.451223, Accuracy: 84.77\n",
      "Train Epoch: 18 [16640/50000 (37%)]\tLoss: 0.465810, Accuracy: 86.33\n",
      "Train Epoch: 18 [17920/50000 (40%)]\tLoss: 0.506851, Accuracy: 80.47\n",
      "Train Epoch: 18 [19200/50000 (43%)]\tLoss: 0.495221, Accuracy: 83.20\n",
      "Train Epoch: 18 [20480/50000 (45%)]\tLoss: 0.522407, Accuracy: 81.64\n",
      "Train Epoch: 18 [21760/50000 (48%)]\tLoss: 0.614917, Accuracy: 79.30\n",
      "Train Epoch: 18 [23040/50000 (51%)]\tLoss: 0.451620, Accuracy: 84.38\n",
      "Train Epoch: 18 [24320/50000 (54%)]\tLoss: 0.413021, Accuracy: 85.55\n",
      "Train Epoch: 18 [25600/50000 (57%)]\tLoss: 0.417592, Accuracy: 85.55\n",
      "Train Epoch: 18 [26880/50000 (60%)]\tLoss: 0.396369, Accuracy: 86.72\n",
      "Train Epoch: 18 [28160/50000 (62%)]\tLoss: 0.514341, Accuracy: 82.03\n",
      "Train Epoch: 18 [29440/50000 (65%)]\tLoss: 0.455175, Accuracy: 85.16\n",
      "Train Epoch: 18 [30720/50000 (68%)]\tLoss: 0.494719, Accuracy: 83.20\n",
      "Train Epoch: 18 [32000/50000 (71%)]\tLoss: 0.525918, Accuracy: 78.91\n",
      "Train Epoch: 18 [33280/50000 (74%)]\tLoss: 0.548551, Accuracy: 78.91\n",
      "Train Epoch: 18 [34560/50000 (77%)]\tLoss: 0.527044, Accuracy: 81.64\n",
      "Train Epoch: 18 [35840/50000 (80%)]\tLoss: 0.460366, Accuracy: 84.38\n",
      "Train Epoch: 18 [37120/50000 (82%)]\tLoss: 0.429843, Accuracy: 86.72\n",
      "Train Epoch: 18 [38400/50000 (85%)]\tLoss: 0.535443, Accuracy: 83.20\n",
      "Train Epoch: 18 [39680/50000 (88%)]\tLoss: 0.631343, Accuracy: 77.34\n",
      "Train Epoch: 18 [40960/50000 (91%)]\tLoss: 0.646284, Accuracy: 76.17\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 18 [42240/50000 (94%)]\tLoss: 0.453429, Accuracy: 82.81\n",
      "Train Epoch: 18 [43520/50000 (97%)]\tLoss: 0.524916, Accuracy: 81.25\n",
      "Train Epoch: 18 [35000/50000 (99%)]\tLoss: 0.422331, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.6301, Accuracy: 3942/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[37.87766480445862 s]\n",
      "\n",
      "Test set: Average loss: 0.6601, Accuracy: 7788/10000 (77.88%)\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 0.429320, Accuracy: 85.55\n",
      "Train Epoch: 19 [1280/50000 (3%)]\tLoss: 0.468809, Accuracy: 83.20\n",
      "Train Epoch: 19 [2560/50000 (6%)]\tLoss: 0.555179, Accuracy: 82.42\n",
      "Train Epoch: 19 [3840/50000 (9%)]\tLoss: 0.370538, Accuracy: 89.06\n",
      "Train Epoch: 19 [5120/50000 (11%)]\tLoss: 0.451959, Accuracy: 84.38\n",
      "Train Epoch: 19 [6400/50000 (14%)]\tLoss: 0.394976, Accuracy: 85.94\n",
      "Train Epoch: 19 [7680/50000 (17%)]\tLoss: 0.460754, Accuracy: 84.38\n",
      "Train Epoch: 19 [8960/50000 (20%)]\tLoss: 0.425294, Accuracy: 85.55\n",
      "Train Epoch: 19 [10240/50000 (23%)]\tLoss: 0.385480, Accuracy: 85.94\n",
      "Train Epoch: 19 [11520/50000 (26%)]\tLoss: 0.405294, Accuracy: 86.72\n",
      "Train Epoch: 19 [12800/50000 (28%)]\tLoss: 0.409695, Accuracy: 85.94\n",
      "Train Epoch: 19 [14080/50000 (31%)]\tLoss: 0.553768, Accuracy: 80.08\n",
      "Train Epoch: 19 [15360/50000 (34%)]\tLoss: 0.388883, Accuracy: 88.28\n",
      "Train Epoch: 19 [16640/50000 (37%)]\tLoss: 0.451503, Accuracy: 83.20\n",
      "Train Epoch: 19 [17920/50000 (40%)]\tLoss: 0.454962, Accuracy: 85.94\n",
      "Train Epoch: 19 [19200/50000 (43%)]\tLoss: 0.362847, Accuracy: 89.45\n",
      "Train Epoch: 19 [20480/50000 (45%)]\tLoss: 0.503207, Accuracy: 83.20\n",
      "Train Epoch: 19 [21760/50000 (48%)]\tLoss: 0.516007, Accuracy: 83.59\n",
      "Train Epoch: 19 [23040/50000 (51%)]\tLoss: 0.517549, Accuracy: 81.64\n",
      "Train Epoch: 19 [24320/50000 (54%)]\tLoss: 0.462657, Accuracy: 85.94\n",
      "Train Epoch: 19 [25600/50000 (57%)]\tLoss: 0.498226, Accuracy: 83.59\n",
      "Train Epoch: 19 [26880/50000 (60%)]\tLoss: 0.406098, Accuracy: 87.50\n",
      "Train Epoch: 19 [28160/50000 (62%)]\tLoss: 0.519747, Accuracy: 80.08\n",
      "Train Epoch: 19 [29440/50000 (65%)]\tLoss: 0.493750, Accuracy: 83.20\n",
      "Train Epoch: 19 [30720/50000 (68%)]\tLoss: 0.471882, Accuracy: 85.94\n",
      "Train Epoch: 19 [32000/50000 (71%)]\tLoss: 0.541639, Accuracy: 80.86\n",
      "Train Epoch: 19 [33280/50000 (74%)]\tLoss: 0.480728, Accuracy: 83.20\n",
      "Train Epoch: 19 [34560/50000 (77%)]\tLoss: 0.468614, Accuracy: 83.98\n",
      "Train Epoch: 19 [35840/50000 (80%)]\tLoss: 0.409281, Accuracy: 84.38\n",
      "Train Epoch: 19 [37120/50000 (82%)]\tLoss: 0.477787, Accuracy: 84.38\n",
      "Train Epoch: 19 [38400/50000 (85%)]\tLoss: 0.371434, Accuracy: 87.50\n",
      "Train Epoch: 19 [39680/50000 (88%)]\tLoss: 0.425377, Accuracy: 85.55\n",
      "Train Epoch: 19 [40960/50000 (91%)]\tLoss: 0.536099, Accuracy: 80.86\n",
      "Train Epoch: 19 [42240/50000 (94%)]\tLoss: 0.452371, Accuracy: 83.59\n",
      "Train Epoch: 19 [43520/50000 (97%)]\tLoss: 0.407126, Accuracy: 85.94\n",
      "Train Epoch: 19 [35000/50000 (99%)]\tLoss: 0.559781, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.6417, Accuracy: 3948/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[41.011088848114014 s]\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 0.313661, Accuracy: 91.02\n",
      "Train Epoch: 20 [1280/50000 (3%)]\tLoss: 0.341065, Accuracy: 87.50\n",
      "Train Epoch: 20 [2560/50000 (6%)]\tLoss: 0.483775, Accuracy: 83.98\n",
      "Train Epoch: 20 [3840/50000 (9%)]\tLoss: 0.393304, Accuracy: 85.94\n",
      "Train Epoch: 20 [5120/50000 (11%)]\tLoss: 0.319925, Accuracy: 89.45\n",
      "Train Epoch: 20 [6400/50000 (14%)]\tLoss: 0.357765, Accuracy: 88.67\n",
      "Train Epoch: 20 [7680/50000 (17%)]\tLoss: 0.334536, Accuracy: 87.89\n",
      "Train Epoch: 20 [8960/50000 (20%)]\tLoss: 0.501072, Accuracy: 83.98\n",
      "Train Epoch: 20 [10240/50000 (23%)]\tLoss: 0.371642, Accuracy: 86.72\n",
      "Train Epoch: 20 [11520/50000 (26%)]\tLoss: 0.520873, Accuracy: 82.42\n",
      "Train Epoch: 20 [12800/50000 (28%)]\tLoss: 0.501824, Accuracy: 81.64\n",
      "Train Epoch: 20 [14080/50000 (31%)]\tLoss: 0.420595, Accuracy: 85.16\n",
      "Train Epoch: 20 [15360/50000 (34%)]\tLoss: 0.445747, Accuracy: 83.20\n",
      "Train Epoch: 20 [16640/50000 (37%)]\tLoss: 0.444646, Accuracy: 85.55\n",
      "Train Epoch: 20 [17920/50000 (40%)]\tLoss: 0.530215, Accuracy: 81.64\n",
      "Train Epoch: 20 [19200/50000 (43%)]\tLoss: 0.473203, Accuracy: 86.72\n",
      "Train Epoch: 20 [20480/50000 (45%)]\tLoss: 0.517413, Accuracy: 82.42\n",
      "Train Epoch: 20 [21760/50000 (48%)]\tLoss: 0.524963, Accuracy: 83.59\n",
      "Train Epoch: 20 [23040/50000 (51%)]\tLoss: 0.451319, Accuracy: 84.77\n",
      "Train Epoch: 20 [24320/50000 (54%)]\tLoss: 0.530275, Accuracy: 82.03\n",
      "Train Epoch: 20 [25600/50000 (57%)]\tLoss: 0.412431, Accuracy: 87.50\n",
      "Train Epoch: 20 [26880/50000 (60%)]\tLoss: 0.399763, Accuracy: 85.94\n",
      "Train Epoch: 20 [28160/50000 (62%)]\tLoss: 0.460166, Accuracy: 85.16\n",
      "Train Epoch: 20 [29440/50000 (65%)]\tLoss: 0.474043, Accuracy: 85.55\n",
      "Train Epoch: 20 [30720/50000 (68%)]\tLoss: 0.431607, Accuracy: 85.16\n",
      "Train Epoch: 20 [32000/50000 (71%)]\tLoss: 0.472254, Accuracy: 84.38\n",
      "Train Epoch: 20 [33280/50000 (74%)]\tLoss: 0.426062, Accuracy: 86.33\n",
      "Train Epoch: 20 [34560/50000 (77%)]\tLoss: 0.447056, Accuracy: 83.59\n",
      "Train Epoch: 20 [35840/50000 (80%)]\tLoss: 0.439668, Accuracy: 84.38\n",
      "Train Epoch: 20 [37120/50000 (82%)]\tLoss: 0.459844, Accuracy: 86.33\n",
      "Train Epoch: 20 [38400/50000 (85%)]\tLoss: 0.430135, Accuracy: 85.16\n",
      "Train Epoch: 20 [39680/50000 (88%)]\tLoss: 0.493743, Accuracy: 84.77\n",
      "Train Epoch: 20 [40960/50000 (91%)]\tLoss: 0.435794, Accuracy: 84.77\n",
      "Train Epoch: 20 [42240/50000 (94%)]\tLoss: 0.526979, Accuracy: 82.81\n",
      "Train Epoch: 20 [43520/50000 (97%)]\tLoss: 0.421937, Accuracy: 85.94\n",
      "Train Epoch: 20 [35000/50000 (99%)]\tLoss: 0.510719, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.8655, Accuracy: 3676/5000 (73.00%)\n",
      "\n",
      "the time of this epoch:[37.83809208869934 s]\n",
      "\n",
      "Test set: Average loss: 0.8851, Accuracy: 7292/10000 (72.92%)\n",
      "\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 0.481822, Accuracy: 82.81\n",
      "Train Epoch: 21 [1280/50000 (3%)]\tLoss: 0.489801, Accuracy: 80.08\n",
      "Train Epoch: 21 [2560/50000 (6%)]\tLoss: 0.425476, Accuracy: 86.72\n",
      "Train Epoch: 21 [3840/50000 (9%)]\tLoss: 0.443942, Accuracy: 86.33\n",
      "Train Epoch: 21 [5120/50000 (11%)]\tLoss: 0.451289, Accuracy: 85.94\n",
      "Train Epoch: 21 [6400/50000 (14%)]\tLoss: 0.360405, Accuracy: 87.50\n",
      "Train Epoch: 21 [7680/50000 (17%)]\tLoss: 0.378239, Accuracy: 87.50\n",
      "Train Epoch: 21 [8960/50000 (20%)]\tLoss: 0.358352, Accuracy: 90.23\n",
      "Train Epoch: 21 [10240/50000 (23%)]\tLoss: 0.484225, Accuracy: 83.59\n",
      "Train Epoch: 21 [11520/50000 (26%)]\tLoss: 0.469503, Accuracy: 83.59\n",
      "Train Epoch: 21 [12800/50000 (28%)]\tLoss: 0.476520, Accuracy: 83.59\n",
      "Train Epoch: 21 [14080/50000 (31%)]\tLoss: 0.470533, Accuracy: 84.38\n",
      "Train Epoch: 21 [15360/50000 (34%)]\tLoss: 0.349953, Accuracy: 87.89\n",
      "Train Epoch: 21 [16640/50000 (37%)]\tLoss: 0.416754, Accuracy: 84.77\n",
      "Train Epoch: 21 [17920/50000 (40%)]\tLoss: 0.491180, Accuracy: 83.20\n",
      "Train Epoch: 21 [19200/50000 (43%)]\tLoss: 0.495273, Accuracy: 84.38\n",
      "Train Epoch: 21 [20480/50000 (45%)]\tLoss: 0.356562, Accuracy: 88.28\n",
      "Train Epoch: 21 [21760/50000 (48%)]\tLoss: 0.439161, Accuracy: 87.50\n",
      "Train Epoch: 21 [23040/50000 (51%)]\tLoss: 0.486147, Accuracy: 80.47\n",
      "Train Epoch: 21 [24320/50000 (54%)]\tLoss: 0.430102, Accuracy: 89.45\n",
      "Train Epoch: 21 [25600/50000 (57%)]\tLoss: 0.471209, Accuracy: 85.16\n",
      "Train Epoch: 21 [26880/50000 (60%)]\tLoss: 0.385185, Accuracy: 85.16\n",
      "Train Epoch: 21 [28160/50000 (62%)]\tLoss: 0.517488, Accuracy: 79.69\n",
      "Train Epoch: 21 [29440/50000 (65%)]\tLoss: 0.621530, Accuracy: 80.08\n",
      "Train Epoch: 21 [30720/50000 (68%)]\tLoss: 0.471648, Accuracy: 83.98\n",
      "Train Epoch: 21 [32000/50000 (71%)]\tLoss: 0.472314, Accuracy: 81.64\n",
      "Train Epoch: 21 [33280/50000 (74%)]\tLoss: 0.399490, Accuracy: 86.72\n",
      "Train Epoch: 21 [34560/50000 (77%)]\tLoss: 0.454689, Accuracy: 85.55\n",
      "Train Epoch: 21 [35840/50000 (80%)]\tLoss: 0.487899, Accuracy: 83.98\n",
      "Train Epoch: 21 [37120/50000 (82%)]\tLoss: 0.331266, Accuracy: 87.11\n",
      "Train Epoch: 21 [38400/50000 (85%)]\tLoss: 0.545211, Accuracy: 80.47\n",
      "Train Epoch: 21 [39680/50000 (88%)]\tLoss: 0.546204, Accuracy: 82.03\n",
      "Train Epoch: 21 [40960/50000 (91%)]\tLoss: 0.457281, Accuracy: 83.59\n",
      "Train Epoch: 21 [42240/50000 (94%)]\tLoss: 0.495576, Accuracy: 81.64\n",
      "Train Epoch: 21 [43520/50000 (97%)]\tLoss: 0.381201, Accuracy: 87.50\n",
      "Train Epoch: 21 [35000/50000 (99%)]\tLoss: 0.474089, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.6612, Accuracy: 3882/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[40.999431848526 s]\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 0.406934, Accuracy: 87.11\n",
      "Train Epoch: 22 [1280/50000 (3%)]\tLoss: 0.466793, Accuracy: 86.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 22 [2560/50000 (6%)]\tLoss: 0.436366, Accuracy: 86.33\n",
      "Train Epoch: 22 [3840/50000 (9%)]\tLoss: 0.430877, Accuracy: 83.98\n",
      "Train Epoch: 22 [5120/50000 (11%)]\tLoss: 0.312550, Accuracy: 88.28\n",
      "Train Epoch: 22 [6400/50000 (14%)]\tLoss: 0.477294, Accuracy: 84.38\n",
      "Train Epoch: 22 [7680/50000 (17%)]\tLoss: 0.435417, Accuracy: 85.94\n",
      "Train Epoch: 22 [8960/50000 (20%)]\tLoss: 0.438722, Accuracy: 86.72\n",
      "Train Epoch: 22 [10240/50000 (23%)]\tLoss: 0.535277, Accuracy: 86.33\n",
      "Train Epoch: 22 [11520/50000 (26%)]\tLoss: 0.496989, Accuracy: 83.20\n",
      "Train Epoch: 22 [12800/50000 (28%)]\tLoss: 0.451695, Accuracy: 87.11\n",
      "Train Epoch: 22 [14080/50000 (31%)]\tLoss: 0.473109, Accuracy: 83.59\n",
      "Train Epoch: 22 [15360/50000 (34%)]\tLoss: 0.412281, Accuracy: 85.16\n",
      "Train Epoch: 22 [16640/50000 (37%)]\tLoss: 0.462456, Accuracy: 86.33\n",
      "Train Epoch: 22 [17920/50000 (40%)]\tLoss: 0.331487, Accuracy: 88.28\n",
      "Train Epoch: 22 [19200/50000 (43%)]\tLoss: 0.545769, Accuracy: 82.42\n",
      "Train Epoch: 22 [20480/50000 (45%)]\tLoss: 0.490031, Accuracy: 84.38\n",
      "Train Epoch: 22 [21760/50000 (48%)]\tLoss: 0.451726, Accuracy: 88.28\n",
      "Train Epoch: 22 [23040/50000 (51%)]\tLoss: 0.463250, Accuracy: 83.59\n",
      "Train Epoch: 22 [24320/50000 (54%)]\tLoss: 0.458030, Accuracy: 85.16\n",
      "Train Epoch: 22 [25600/50000 (57%)]\tLoss: 0.465395, Accuracy: 82.81\n",
      "Train Epoch: 22 [26880/50000 (60%)]\tLoss: 0.452743, Accuracy: 85.16\n",
      "Train Epoch: 22 [28160/50000 (62%)]\tLoss: 0.404489, Accuracy: 85.94\n",
      "Train Epoch: 22 [29440/50000 (65%)]\tLoss: 0.408976, Accuracy: 88.28\n",
      "Train Epoch: 22 [30720/50000 (68%)]\tLoss: 0.391421, Accuracy: 85.94\n",
      "Train Epoch: 22 [32000/50000 (71%)]\tLoss: 0.426151, Accuracy: 85.94\n",
      "Train Epoch: 22 [33280/50000 (74%)]\tLoss: 0.491973, Accuracy: 82.81\n",
      "Train Epoch: 22 [34560/50000 (77%)]\tLoss: 0.392773, Accuracy: 85.94\n",
      "Train Epoch: 22 [35840/50000 (80%)]\tLoss: 0.445706, Accuracy: 85.94\n",
      "Train Epoch: 22 [37120/50000 (82%)]\tLoss: 0.395472, Accuracy: 85.94\n",
      "Train Epoch: 22 [38400/50000 (85%)]\tLoss: 0.516471, Accuracy: 82.81\n",
      "Train Epoch: 22 [39680/50000 (88%)]\tLoss: 0.369512, Accuracy: 88.67\n",
      "Train Epoch: 22 [40960/50000 (91%)]\tLoss: 0.431029, Accuracy: 83.59\n",
      "Train Epoch: 22 [42240/50000 (94%)]\tLoss: 0.499815, Accuracy: 84.77\n",
      "Train Epoch: 22 [43520/50000 (97%)]\tLoss: 0.375821, Accuracy: 88.67\n",
      "Train Epoch: 22 [35000/50000 (99%)]\tLoss: 0.483722, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.9360, Accuracy: 3489/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[37.81572198867798 s]\n",
      "\n",
      "Test set: Average loss: 0.9568, Accuracy: 6952/10000 (69.52%)\n",
      "\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.508015, Accuracy: 81.64\n",
      "Train Epoch: 23 [1280/50000 (3%)]\tLoss: 0.592008, Accuracy: 79.69\n",
      "Train Epoch: 23 [2560/50000 (6%)]\tLoss: 0.337727, Accuracy: 87.89\n",
      "Train Epoch: 23 [3840/50000 (9%)]\tLoss: 0.478401, Accuracy: 85.94\n",
      "Train Epoch: 23 [5120/50000 (11%)]\tLoss: 0.332662, Accuracy: 87.50\n",
      "Train Epoch: 23 [6400/50000 (14%)]\tLoss: 0.489273, Accuracy: 84.38\n",
      "Train Epoch: 23 [7680/50000 (17%)]\tLoss: 0.530635, Accuracy: 82.81\n",
      "Train Epoch: 23 [8960/50000 (20%)]\tLoss: 0.482006, Accuracy: 83.98\n",
      "Train Epoch: 23 [10240/50000 (23%)]\tLoss: 0.393568, Accuracy: 85.94\n",
      "Train Epoch: 23 [11520/50000 (26%)]\tLoss: 0.436438, Accuracy: 83.20\n",
      "Train Epoch: 23 [12800/50000 (28%)]\tLoss: 0.329843, Accuracy: 88.67\n",
      "Train Epoch: 23 [14080/50000 (31%)]\tLoss: 0.288022, Accuracy: 89.06\n",
      "Train Epoch: 23 [15360/50000 (34%)]\tLoss: 0.460071, Accuracy: 85.55\n",
      "Train Epoch: 23 [16640/50000 (37%)]\tLoss: 0.391336, Accuracy: 87.11\n",
      "Train Epoch: 23 [17920/50000 (40%)]\tLoss: 0.525307, Accuracy: 81.64\n",
      "Train Epoch: 23 [19200/50000 (43%)]\tLoss: 0.417458, Accuracy: 85.16\n",
      "Train Epoch: 23 [20480/50000 (45%)]\tLoss: 0.467411, Accuracy: 85.16\n",
      "Train Epoch: 23 [21760/50000 (48%)]\tLoss: 0.494029, Accuracy: 82.42\n",
      "Train Epoch: 23 [23040/50000 (51%)]\tLoss: 0.402552, Accuracy: 83.59\n",
      "Train Epoch: 23 [24320/50000 (54%)]\tLoss: 0.480941, Accuracy: 85.55\n",
      "Train Epoch: 23 [25600/50000 (57%)]\tLoss: 0.410400, Accuracy: 86.72\n",
      "Train Epoch: 23 [26880/50000 (60%)]\tLoss: 0.457401, Accuracy: 83.59\n",
      "Train Epoch: 23 [28160/50000 (62%)]\tLoss: 0.530572, Accuracy: 82.42\n",
      "Train Epoch: 23 [29440/50000 (65%)]\tLoss: 0.457244, Accuracy: 84.38\n",
      "Train Epoch: 23 [30720/50000 (68%)]\tLoss: 0.370621, Accuracy: 87.50\n",
      "Train Epoch: 23 [32000/50000 (71%)]\tLoss: 0.463058, Accuracy: 83.98\n",
      "Train Epoch: 23 [33280/50000 (74%)]\tLoss: 0.538369, Accuracy: 82.03\n",
      "Train Epoch: 23 [34560/50000 (77%)]\tLoss: 0.324684, Accuracy: 87.89\n",
      "Train Epoch: 23 [35840/50000 (80%)]\tLoss: 0.373891, Accuracy: 86.72\n",
      "Train Epoch: 23 [37120/50000 (82%)]\tLoss: 0.396831, Accuracy: 87.89\n",
      "Train Epoch: 23 [38400/50000 (85%)]\tLoss: 0.485350, Accuracy: 82.03\n",
      "Train Epoch: 23 [39680/50000 (88%)]\tLoss: 0.412813, Accuracy: 87.50\n",
      "Train Epoch: 23 [40960/50000 (91%)]\tLoss: 0.431785, Accuracy: 85.94\n",
      "Train Epoch: 23 [42240/50000 (94%)]\tLoss: 0.399391, Accuracy: 86.72\n",
      "Train Epoch: 23 [43520/50000 (97%)]\tLoss: 0.389621, Accuracy: 86.72\n",
      "Train Epoch: 23 [35000/50000 (99%)]\tLoss: 0.456118, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.7497, Accuracy: 3809/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[41.09088921546936 s]\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.362822, Accuracy: 89.45\n",
      "Train Epoch: 24 [1280/50000 (3%)]\tLoss: 0.432283, Accuracy: 89.06\n",
      "Train Epoch: 24 [2560/50000 (6%)]\tLoss: 0.538449, Accuracy: 82.03\n",
      "Train Epoch: 24 [3840/50000 (9%)]\tLoss: 0.581330, Accuracy: 81.64\n",
      "Train Epoch: 24 [5120/50000 (11%)]\tLoss: 0.462326, Accuracy: 84.38\n",
      "Train Epoch: 24 [6400/50000 (14%)]\tLoss: 0.354415, Accuracy: 90.23\n",
      "Train Epoch: 24 [7680/50000 (17%)]\tLoss: 0.514173, Accuracy: 83.98\n",
      "Train Epoch: 24 [8960/50000 (20%)]\tLoss: 0.311932, Accuracy: 90.62\n",
      "Train Epoch: 24 [10240/50000 (23%)]\tLoss: 0.345883, Accuracy: 87.50\n",
      "Train Epoch: 24 [11520/50000 (26%)]\tLoss: 0.335167, Accuracy: 87.50\n",
      "Train Epoch: 24 [12800/50000 (28%)]\tLoss: 0.373282, Accuracy: 88.67\n",
      "Train Epoch: 24 [14080/50000 (31%)]\tLoss: 0.425529, Accuracy: 85.16\n",
      "Train Epoch: 24 [15360/50000 (34%)]\tLoss: 0.459244, Accuracy: 83.98\n",
      "Train Epoch: 24 [16640/50000 (37%)]\tLoss: 0.515515, Accuracy: 79.30\n",
      "Train Epoch: 24 [17920/50000 (40%)]\tLoss: 0.429797, Accuracy: 84.38\n",
      "Train Epoch: 24 [19200/50000 (43%)]\tLoss: 0.353263, Accuracy: 89.06\n",
      "Train Epoch: 24 [20480/50000 (45%)]\tLoss: 0.287560, Accuracy: 90.23\n",
      "Train Epoch: 24 [21760/50000 (48%)]\tLoss: 0.412554, Accuracy: 83.98\n",
      "Train Epoch: 24 [23040/50000 (51%)]\tLoss: 0.375289, Accuracy: 88.28\n",
      "Train Epoch: 24 [24320/50000 (54%)]\tLoss: 0.410339, Accuracy: 83.98\n",
      "Train Epoch: 24 [25600/50000 (57%)]\tLoss: 0.440833, Accuracy: 85.16\n",
      "Train Epoch: 24 [26880/50000 (60%)]\tLoss: 0.471339, Accuracy: 85.94\n",
      "Train Epoch: 24 [28160/50000 (62%)]\tLoss: 0.418616, Accuracy: 84.38\n",
      "Train Epoch: 24 [29440/50000 (65%)]\tLoss: 0.423180, Accuracy: 86.33\n",
      "Train Epoch: 24 [30720/50000 (68%)]\tLoss: 0.554895, Accuracy: 79.69\n",
      "Train Epoch: 24 [32000/50000 (71%)]\tLoss: 0.496964, Accuracy: 81.64\n",
      "Train Epoch: 24 [33280/50000 (74%)]\tLoss: 0.420596, Accuracy: 85.94\n",
      "Train Epoch: 24 [34560/50000 (77%)]\tLoss: 0.445203, Accuracy: 85.55\n",
      "Train Epoch: 24 [35840/50000 (80%)]\tLoss: 0.443523, Accuracy: 84.38\n",
      "Train Epoch: 24 [37120/50000 (82%)]\tLoss: 0.501350, Accuracy: 83.20\n",
      "Train Epoch: 24 [38400/50000 (85%)]\tLoss: 0.506352, Accuracy: 82.03\n",
      "Train Epoch: 24 [39680/50000 (88%)]\tLoss: 0.483823, Accuracy: 86.33\n",
      "Train Epoch: 24 [40960/50000 (91%)]\tLoss: 0.453735, Accuracy: 85.55\n",
      "Train Epoch: 24 [42240/50000 (94%)]\tLoss: 0.568277, Accuracy: 80.08\n",
      "Train Epoch: 24 [43520/50000 (97%)]\tLoss: 0.500186, Accuracy: 84.77\n",
      "Train Epoch: 24 [35000/50000 (99%)]\tLoss: 0.559165, Accuracy: 79.50\n",
      "\n",
      "Validation set: Average loss: 0.6001, Accuracy: 3951/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[37.82691240310669 s]\n",
      "\n",
      "Test set: Average loss: 0.6142, Accuracy: 7978/10000 (79.78%)\n",
      "\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.359171, Accuracy: 88.28\n",
      "Train Epoch: 25 [1280/50000 (3%)]\tLoss: 0.366775, Accuracy: 87.89\n",
      "Train Epoch: 25 [2560/50000 (6%)]\tLoss: 0.381652, Accuracy: 86.33\n",
      "Train Epoch: 25 [3840/50000 (9%)]\tLoss: 0.443208, Accuracy: 85.16\n",
      "Train Epoch: 25 [5120/50000 (11%)]\tLoss: 0.436152, Accuracy: 83.98\n",
      "Train Epoch: 25 [6400/50000 (14%)]\tLoss: 0.373395, Accuracy: 86.72\n",
      "Train Epoch: 25 [7680/50000 (17%)]\tLoss: 0.408029, Accuracy: 87.89\n",
      "Train Epoch: 25 [8960/50000 (20%)]\tLoss: 0.358867, Accuracy: 89.06\n",
      "Train Epoch: 25 [10240/50000 (23%)]\tLoss: 0.341113, Accuracy: 88.67\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 25 [11520/50000 (26%)]\tLoss: 0.419424, Accuracy: 86.33\n",
      "Train Epoch: 25 [12800/50000 (28%)]\tLoss: 0.512115, Accuracy: 82.03\n",
      "Train Epoch: 25 [14080/50000 (31%)]\tLoss: 0.455962, Accuracy: 82.81\n",
      "Train Epoch: 25 [15360/50000 (34%)]\tLoss: 0.408195, Accuracy: 85.94\n",
      "Train Epoch: 25 [16640/50000 (37%)]\tLoss: 0.473434, Accuracy: 84.38\n",
      "Train Epoch: 25 [17920/50000 (40%)]\tLoss: 0.408133, Accuracy: 87.50\n",
      "Train Epoch: 25 [19200/50000 (43%)]\tLoss: 0.365833, Accuracy: 87.89\n",
      "Train Epoch: 25 [20480/50000 (45%)]\tLoss: 0.444146, Accuracy: 85.94\n",
      "Train Epoch: 25 [21760/50000 (48%)]\tLoss: 0.466606, Accuracy: 83.59\n",
      "Train Epoch: 25 [23040/50000 (51%)]\tLoss: 0.415174, Accuracy: 85.16\n",
      "Train Epoch: 25 [24320/50000 (54%)]\tLoss: 0.412840, Accuracy: 84.38\n",
      "Train Epoch: 25 [25600/50000 (57%)]\tLoss: 0.470307, Accuracy: 84.38\n",
      "Train Epoch: 25 [26880/50000 (60%)]\tLoss: 0.432900, Accuracy: 83.98\n",
      "Train Epoch: 25 [28160/50000 (62%)]\tLoss: 0.403374, Accuracy: 87.50\n",
      "Train Epoch: 25 [29440/50000 (65%)]\tLoss: 0.465924, Accuracy: 83.59\n",
      "Train Epoch: 25 [30720/50000 (68%)]\tLoss: 0.455205, Accuracy: 84.77\n",
      "Train Epoch: 25 [32000/50000 (71%)]\tLoss: 0.560000, Accuracy: 82.81\n",
      "Train Epoch: 25 [33280/50000 (74%)]\tLoss: 0.351422, Accuracy: 87.11\n",
      "Train Epoch: 25 [34560/50000 (77%)]\tLoss: 0.517771, Accuracy: 82.81\n",
      "Train Epoch: 25 [35840/50000 (80%)]\tLoss: 0.427681, Accuracy: 84.77\n",
      "Train Epoch: 25 [37120/50000 (82%)]\tLoss: 0.423997, Accuracy: 84.38\n",
      "Train Epoch: 25 [38400/50000 (85%)]\tLoss: 0.396168, Accuracy: 86.72\n",
      "Train Epoch: 25 [39680/50000 (88%)]\tLoss: 0.444922, Accuracy: 85.16\n",
      "Train Epoch: 25 [40960/50000 (91%)]\tLoss: 0.469067, Accuracy: 83.20\n",
      "Train Epoch: 25 [42240/50000 (94%)]\tLoss: 0.560434, Accuracy: 82.42\n",
      "Train Epoch: 25 [43520/50000 (97%)]\tLoss: 0.435232, Accuracy: 85.16\n",
      "Train Epoch: 25 [35000/50000 (99%)]\tLoss: 0.488899, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.6409, Accuracy: 3986/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[40.95026898384094 s]\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 0.470973, Accuracy: 83.98\n",
      "Train Epoch: 26 [1280/50000 (3%)]\tLoss: 0.452865, Accuracy: 84.77\n",
      "Train Epoch: 26 [2560/50000 (6%)]\tLoss: 0.474505, Accuracy: 85.55\n",
      "Train Epoch: 26 [3840/50000 (9%)]\tLoss: 0.344723, Accuracy: 87.89\n",
      "Train Epoch: 26 [5120/50000 (11%)]\tLoss: 0.348712, Accuracy: 88.67\n",
      "Train Epoch: 26 [6400/50000 (14%)]\tLoss: 0.352483, Accuracy: 88.28\n",
      "Train Epoch: 26 [7680/50000 (17%)]\tLoss: 0.475988, Accuracy: 82.42\n",
      "Train Epoch: 26 [8960/50000 (20%)]\tLoss: 0.384132, Accuracy: 86.72\n",
      "Train Epoch: 26 [10240/50000 (23%)]\tLoss: 0.386063, Accuracy: 86.33\n",
      "Train Epoch: 26 [11520/50000 (26%)]\tLoss: 0.460793, Accuracy: 82.03\n",
      "Train Epoch: 26 [12800/50000 (28%)]\tLoss: 0.348034, Accuracy: 88.28\n",
      "Train Epoch: 26 [14080/50000 (31%)]\tLoss: 0.455479, Accuracy: 83.20\n",
      "Train Epoch: 26 [15360/50000 (34%)]\tLoss: 0.445197, Accuracy: 83.59\n",
      "Train Epoch: 26 [16640/50000 (37%)]\tLoss: 0.387217, Accuracy: 86.33\n",
      "Train Epoch: 26 [17920/50000 (40%)]\tLoss: 0.421627, Accuracy: 85.94\n",
      "Train Epoch: 26 [19200/50000 (43%)]\tLoss: 0.407069, Accuracy: 86.33\n",
      "Train Epoch: 26 [20480/50000 (45%)]\tLoss: 0.526356, Accuracy: 82.42\n",
      "Train Epoch: 26 [21760/50000 (48%)]\tLoss: 0.393842, Accuracy: 87.50\n",
      "Train Epoch: 26 [23040/50000 (51%)]\tLoss: 0.396930, Accuracy: 85.16\n",
      "Train Epoch: 26 [24320/50000 (54%)]\tLoss: 0.504366, Accuracy: 81.25\n",
      "Train Epoch: 26 [25600/50000 (57%)]\tLoss: 0.402236, Accuracy: 88.67\n",
      "Train Epoch: 26 [26880/50000 (60%)]\tLoss: 0.378867, Accuracy: 85.94\n",
      "Train Epoch: 26 [28160/50000 (62%)]\tLoss: 0.436517, Accuracy: 85.94\n",
      "Train Epoch: 26 [29440/50000 (65%)]\tLoss: 0.548327, Accuracy: 81.25\n",
      "Train Epoch: 26 [30720/50000 (68%)]\tLoss: 0.428353, Accuracy: 83.98\n",
      "Train Epoch: 26 [32000/50000 (71%)]\tLoss: 0.405458, Accuracy: 85.55\n",
      "Train Epoch: 26 [33280/50000 (74%)]\tLoss: 0.364675, Accuracy: 88.67\n",
      "Train Epoch: 26 [34560/50000 (77%)]\tLoss: 0.317777, Accuracy: 91.41\n",
      "Train Epoch: 26 [35840/50000 (80%)]\tLoss: 0.445158, Accuracy: 84.38\n",
      "Train Epoch: 26 [37120/50000 (82%)]\tLoss: 0.418168, Accuracy: 85.55\n",
      "Train Epoch: 26 [38400/50000 (85%)]\tLoss: 0.494041, Accuracy: 83.98\n",
      "Train Epoch: 26 [39680/50000 (88%)]\tLoss: 0.424490, Accuracy: 85.94\n",
      "Train Epoch: 26 [40960/50000 (91%)]\tLoss: 0.400154, Accuracy: 89.06\n",
      "Train Epoch: 26 [42240/50000 (94%)]\tLoss: 0.434694, Accuracy: 84.77\n",
      "Train Epoch: 26 [43520/50000 (97%)]\tLoss: 0.459914, Accuracy: 83.59\n",
      "Train Epoch: 26 [35000/50000 (99%)]\tLoss: 0.512051, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.5295, Accuracy: 4117/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[37.830265522003174 s]\n",
      "\n",
      "Test set: Average loss: 0.5657, Accuracy: 8131/10000 (81.31%)\n",
      "\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.445365, Accuracy: 83.20\n",
      "Train Epoch: 27 [1280/50000 (3%)]\tLoss: 0.330937, Accuracy: 89.06\n",
      "Train Epoch: 27 [2560/50000 (6%)]\tLoss: 0.392407, Accuracy: 86.72\n",
      "Train Epoch: 27 [3840/50000 (9%)]\tLoss: 0.401427, Accuracy: 87.11\n",
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      "Train Epoch: 27 [7680/50000 (17%)]\tLoss: 0.328962, Accuracy: 87.11\n",
      "Train Epoch: 27 [8960/50000 (20%)]\tLoss: 0.403751, Accuracy: 86.72\n",
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      "Train Epoch: 27 [12800/50000 (28%)]\tLoss: 0.333004, Accuracy: 89.06\n",
      "Train Epoch: 27 [14080/50000 (31%)]\tLoss: 0.348467, Accuracy: 87.89\n",
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      "Train Epoch: 27 [24320/50000 (54%)]\tLoss: 0.329082, Accuracy: 89.06\n",
      "Train Epoch: 27 [25600/50000 (57%)]\tLoss: 0.412519, Accuracy: 87.11\n",
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      "Train Epoch: 27 [28160/50000 (62%)]\tLoss: 0.505955, Accuracy: 80.86\n",
      "Train Epoch: 27 [29440/50000 (65%)]\tLoss: 0.602427, Accuracy: 80.47\n",
      "Train Epoch: 27 [30720/50000 (68%)]\tLoss: 0.414049, Accuracy: 87.11\n",
      "Train Epoch: 27 [32000/50000 (71%)]\tLoss: 0.426936, Accuracy: 87.11\n",
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      "Train Epoch: 27 [35840/50000 (80%)]\tLoss: 0.439462, Accuracy: 83.59\n",
      "Train Epoch: 27 [37120/50000 (82%)]\tLoss: 0.377516, Accuracy: 86.33\n",
      "Train Epoch: 27 [38400/50000 (85%)]\tLoss: 0.545127, Accuracy: 82.03\n",
      "Train Epoch: 27 [39680/50000 (88%)]\tLoss: 0.419615, Accuracy: 85.55\n",
      "Train Epoch: 27 [40960/50000 (91%)]\tLoss: 0.454889, Accuracy: 83.59\n",
      "Train Epoch: 27 [42240/50000 (94%)]\tLoss: 0.506963, Accuracy: 81.25\n",
      "Train Epoch: 27 [43520/50000 (97%)]\tLoss: 0.365162, Accuracy: 87.50\n",
      "Train Epoch: 27 [35000/50000 (99%)]\tLoss: 0.454410, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.6917, Accuracy: 3875/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[40.945881843566895 s]\n",
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 0.416936, Accuracy: 84.38\n",
      "Train Epoch: 28 [1280/50000 (3%)]\tLoss: 0.433953, Accuracy: 83.98\n",
      "Train Epoch: 28 [2560/50000 (6%)]\tLoss: 0.373047, Accuracy: 87.11\n",
      "Train Epoch: 28 [3840/50000 (9%)]\tLoss: 0.338754, Accuracy: 88.67\n",
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      "Train Epoch: 28 [7680/50000 (17%)]\tLoss: 0.475193, Accuracy: 83.98\n",
      "Train Epoch: 28 [8960/50000 (20%)]\tLoss: 0.372101, Accuracy: 86.72\n",
      "Train Epoch: 28 [10240/50000 (23%)]\tLoss: 0.476132, Accuracy: 85.55\n",
      "Train Epoch: 28 [11520/50000 (26%)]\tLoss: 0.418419, Accuracy: 85.55\n",
      "Train Epoch: 28 [12800/50000 (28%)]\tLoss: 0.474570, Accuracy: 85.16\n",
      "Train Epoch: 28 [14080/50000 (31%)]\tLoss: 0.389841, Accuracy: 85.94\n",
      "Train Epoch: 28 [15360/50000 (34%)]\tLoss: 0.580796, Accuracy: 80.86\n",
      "Train Epoch: 28 [16640/50000 (37%)]\tLoss: 0.402970, Accuracy: 85.94\n",
      "Train Epoch: 28 [17920/50000 (40%)]\tLoss: 0.534164, Accuracy: 81.25\n",
      "Train Epoch: 28 [19200/50000 (43%)]\tLoss: 0.364791, Accuracy: 87.11\n",
      "Train Epoch: 28 [20480/50000 (45%)]\tLoss: 0.442603, Accuracy: 85.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 28 [21760/50000 (48%)]\tLoss: 0.412190, Accuracy: 86.72\n",
      "Train Epoch: 28 [23040/50000 (51%)]\tLoss: 0.389039, Accuracy: 86.33\n",
      "Train Epoch: 28 [24320/50000 (54%)]\tLoss: 0.424749, Accuracy: 86.72\n",
      "Train Epoch: 28 [25600/50000 (57%)]\tLoss: 0.435146, Accuracy: 86.33\n",
      "Train Epoch: 28 [26880/50000 (60%)]\tLoss: 0.432342, Accuracy: 86.33\n",
      "Train Epoch: 28 [28160/50000 (62%)]\tLoss: 0.491350, Accuracy: 83.20\n",
      "Train Epoch: 28 [29440/50000 (65%)]\tLoss: 0.398932, Accuracy: 87.11\n",
      "Train Epoch: 28 [30720/50000 (68%)]\tLoss: 0.470973, Accuracy: 83.20\n",
      "Train Epoch: 28 [32000/50000 (71%)]\tLoss: 0.438170, Accuracy: 83.98\n",
      "Train Epoch: 28 [33280/50000 (74%)]\tLoss: 0.374023, Accuracy: 86.72\n",
      "Train Epoch: 28 [34560/50000 (77%)]\tLoss: 0.359260, Accuracy: 87.50\n",
      "Train Epoch: 28 [35840/50000 (80%)]\tLoss: 0.367135, Accuracy: 87.50\n",
      "Train Epoch: 28 [37120/50000 (82%)]\tLoss: 0.480883, Accuracy: 82.81\n",
      "Train Epoch: 28 [38400/50000 (85%)]\tLoss: 0.360678, Accuracy: 86.33\n",
      "Train Epoch: 28 [39680/50000 (88%)]\tLoss: 0.431541, Accuracy: 85.16\n",
      "Train Epoch: 28 [40960/50000 (91%)]\tLoss: 0.480796, Accuracy: 83.59\n",
      "Train Epoch: 28 [42240/50000 (94%)]\tLoss: 0.405787, Accuracy: 86.33\n",
      "Train Epoch: 28 [43520/50000 (97%)]\tLoss: 0.472145, Accuracy: 82.42\n",
      "Train Epoch: 28 [35000/50000 (99%)]\tLoss: 0.531779, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 0.8856, Accuracy: 3573/5000 (71.00%)\n",
      "\n",
      "the time of this epoch:[37.760611057281494 s]\n",
      "\n",
      "Test set: Average loss: 0.9013, Accuracy: 7120/10000 (71.20%)\n",
      "\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 0.365770, Accuracy: 89.06\n",
      "Train Epoch: 29 [1280/50000 (3%)]\tLoss: 0.321385, Accuracy: 89.45\n",
      "Train Epoch: 29 [2560/50000 (6%)]\tLoss: 0.263837, Accuracy: 92.19\n",
      "Train Epoch: 29 [3840/50000 (9%)]\tLoss: 0.355266, Accuracy: 85.55\n",
      "Train Epoch: 29 [5120/50000 (11%)]\tLoss: 0.381943, Accuracy: 86.72\n",
      "Train Epoch: 29 [6400/50000 (14%)]\tLoss: 0.388507, Accuracy: 88.28\n",
      "Train Epoch: 29 [7680/50000 (17%)]\tLoss: 0.374141, Accuracy: 88.67\n",
      "Train Epoch: 29 [8960/50000 (20%)]\tLoss: 0.366256, Accuracy: 86.72\n",
      "Train Epoch: 29 [10240/50000 (23%)]\tLoss: 0.429727, Accuracy: 85.55\n",
      "Train Epoch: 29 [11520/50000 (26%)]\tLoss: 0.499153, Accuracy: 84.77\n",
      "Train Epoch: 29 [12800/50000 (28%)]\tLoss: 0.460279, Accuracy: 83.20\n",
      "Train Epoch: 29 [14080/50000 (31%)]\tLoss: 0.417289, Accuracy: 85.55\n",
      "Train Epoch: 29 [15360/50000 (34%)]\tLoss: 0.420577, Accuracy: 86.72\n",
      "Train Epoch: 29 [16640/50000 (37%)]\tLoss: 0.424956, Accuracy: 85.16\n",
      "Train Epoch: 29 [17920/50000 (40%)]\tLoss: 0.326711, Accuracy: 87.50\n",
      "Train Epoch: 29 [19200/50000 (43%)]\tLoss: 0.374366, Accuracy: 87.89\n",
      "Train Epoch: 29 [20480/50000 (45%)]\tLoss: 0.441188, Accuracy: 86.33\n",
      "Train Epoch: 29 [21760/50000 (48%)]\tLoss: 0.386935, Accuracy: 85.16\n",
      "Train Epoch: 29 [23040/50000 (51%)]\tLoss: 0.448869, Accuracy: 81.25\n",
      "Train Epoch: 29 [24320/50000 (54%)]\tLoss: 0.546722, Accuracy: 81.25\n",
      "Train Epoch: 29 [25600/50000 (57%)]\tLoss: 0.438942, Accuracy: 83.98\n",
      "Train Epoch: 29 [26880/50000 (60%)]\tLoss: 0.442187, Accuracy: 85.94\n",
      "Train Epoch: 29 [28160/50000 (62%)]\tLoss: 0.453309, Accuracy: 84.77\n",
      "Train Epoch: 29 [29440/50000 (65%)]\tLoss: 0.397244, Accuracy: 85.55\n",
      "Train Epoch: 29 [30720/50000 (68%)]\tLoss: 0.413742, Accuracy: 84.77\n",
      "Train Epoch: 29 [32000/50000 (71%)]\tLoss: 0.404416, Accuracy: 87.50\n",
      "Train Epoch: 29 [33280/50000 (74%)]\tLoss: 0.434234, Accuracy: 82.03\n",
      "Train Epoch: 29 [34560/50000 (77%)]\tLoss: 0.491315, Accuracy: 81.25\n",
      "Train Epoch: 29 [35840/50000 (80%)]\tLoss: 0.414924, Accuracy: 86.33\n",
      "Train Epoch: 29 [37120/50000 (82%)]\tLoss: 0.427809, Accuracy: 83.59\n",
      "Train Epoch: 29 [38400/50000 (85%)]\tLoss: 0.546741, Accuracy: 83.20\n",
      "Train Epoch: 29 [39680/50000 (88%)]\tLoss: 0.476901, Accuracy: 82.81\n",
      "Train Epoch: 29 [40960/50000 (91%)]\tLoss: 0.366946, Accuracy: 86.72\n",
      "Train Epoch: 29 [42240/50000 (94%)]\tLoss: 0.361052, Accuracy: 89.45\n",
      "Train Epoch: 29 [43520/50000 (97%)]\tLoss: 0.259838, Accuracy: 91.02\n",
      "Train Epoch: 29 [35000/50000 (99%)]\tLoss: 0.334585, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.6461, Accuracy: 3925/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[40.95147347450256 s]\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 0.360738, Accuracy: 86.72\n",
      "Train Epoch: 30 [1280/50000 (3%)]\tLoss: 0.285737, Accuracy: 90.62\n",
      "Train Epoch: 30 [2560/50000 (6%)]\tLoss: 0.368418, Accuracy: 89.06\n",
      "Train Epoch: 30 [3840/50000 (9%)]\tLoss: 0.454588, Accuracy: 85.55\n",
      "Train Epoch: 30 [5120/50000 (11%)]\tLoss: 0.429033, Accuracy: 83.59\n",
      "Train Epoch: 30 [6400/50000 (14%)]\tLoss: 0.482978, Accuracy: 82.81\n",
      "Train Epoch: 30 [7680/50000 (17%)]\tLoss: 0.339557, Accuracy: 89.84\n",
      "Train Epoch: 30 [8960/50000 (20%)]\tLoss: 0.357812, Accuracy: 87.11\n",
      "Train Epoch: 30 [10240/50000 (23%)]\tLoss: 0.362010, Accuracy: 87.89\n",
      "Train Epoch: 30 [11520/50000 (26%)]\tLoss: 0.328008, Accuracy: 89.45\n",
      "Train Epoch: 30 [12800/50000 (28%)]\tLoss: 0.395376, Accuracy: 88.28\n",
      "Train Epoch: 30 [14080/50000 (31%)]\tLoss: 0.521500, Accuracy: 81.25\n",
      "Train Epoch: 30 [15360/50000 (34%)]\tLoss: 0.480566, Accuracy: 83.59\n",
      "Train Epoch: 30 [16640/50000 (37%)]\tLoss: 0.424658, Accuracy: 86.33\n",
      "Train Epoch: 30 [17920/50000 (40%)]\tLoss: 0.364136, Accuracy: 88.67\n",
      "Train Epoch: 30 [19200/50000 (43%)]\tLoss: 0.564881, Accuracy: 82.03\n",
      "Train Epoch: 30 [20480/50000 (45%)]\tLoss: 0.365560, Accuracy: 89.84\n",
      "Train Epoch: 30 [21760/50000 (48%)]\tLoss: 0.457711, Accuracy: 84.38\n",
      "Train Epoch: 30 [23040/50000 (51%)]\tLoss: 0.467044, Accuracy: 83.98\n",
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      "Train Epoch: 30 [28160/50000 (62%)]\tLoss: 0.336215, Accuracy: 89.45\n",
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      "Train Epoch: 30 [32000/50000 (71%)]\tLoss: 0.425241, Accuracy: 86.33\n",
      "Train Epoch: 30 [33280/50000 (74%)]\tLoss: 0.356928, Accuracy: 87.50\n",
      "Train Epoch: 30 [34560/50000 (77%)]\tLoss: 0.555743, Accuracy: 78.12\n",
      "Train Epoch: 30 [35840/50000 (80%)]\tLoss: 0.298669, Accuracy: 90.23\n",
      "Train Epoch: 30 [37120/50000 (82%)]\tLoss: 0.391150, Accuracy: 87.11\n",
      "Train Epoch: 30 [38400/50000 (85%)]\tLoss: 0.330402, Accuracy: 89.84\n",
      "Train Epoch: 30 [39680/50000 (88%)]\tLoss: 0.428812, Accuracy: 85.94\n",
      "Train Epoch: 30 [40960/50000 (91%)]\tLoss: 0.470065, Accuracy: 83.98\n",
      "Train Epoch: 30 [42240/50000 (94%)]\tLoss: 0.459519, Accuracy: 84.77\n",
      "Train Epoch: 30 [43520/50000 (97%)]\tLoss: 0.474075, Accuracy: 82.81\n",
      "Train Epoch: 30 [35000/50000 (99%)]\tLoss: 0.441290, Accuracy: 84.50\n",
      "\n",
      "Validation set: Average loss: 0.7336, Accuracy: 3854/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[37.77818727493286 s]\n",
      "\n",
      "Test set: Average loss: 0.7197, Accuracy: 7783/10000 (77.83%)\n",
      "\n",
      "Train Epoch: 31 [0/50000 (0%)]\tLoss: 0.449003, Accuracy: 84.77\n",
      "Train Epoch: 31 [1280/50000 (3%)]\tLoss: 0.460426, Accuracy: 83.20\n",
      "Train Epoch: 31 [2560/50000 (6%)]\tLoss: 0.359108, Accuracy: 87.50\n",
      "Train Epoch: 31 [3840/50000 (9%)]\tLoss: 0.331377, Accuracy: 88.67\n",
      "Train Epoch: 31 [5120/50000 (11%)]\tLoss: 0.386426, Accuracy: 85.55\n",
      "Train Epoch: 31 [6400/50000 (14%)]\tLoss: 0.503392, Accuracy: 83.20\n",
      "Train Epoch: 31 [7680/50000 (17%)]\tLoss: 0.333536, Accuracy: 89.06\n",
      "Train Epoch: 31 [8960/50000 (20%)]\tLoss: 0.421791, Accuracy: 83.20\n",
      "Train Epoch: 31 [10240/50000 (23%)]\tLoss: 0.419151, Accuracy: 86.33\n",
      "Train Epoch: 31 [11520/50000 (26%)]\tLoss: 0.415695, Accuracy: 86.72\n",
      "Train Epoch: 31 [12800/50000 (28%)]\tLoss: 0.341278, Accuracy: 87.50\n",
      "Train Epoch: 31 [14080/50000 (31%)]\tLoss: 0.399156, Accuracy: 85.55\n",
      "Train Epoch: 31 [15360/50000 (34%)]\tLoss: 0.367966, Accuracy: 89.84\n",
      "Train Epoch: 31 [16640/50000 (37%)]\tLoss: 0.406784, Accuracy: 88.67\n",
      "Train Epoch: 31 [17920/50000 (40%)]\tLoss: 0.323981, Accuracy: 88.28\n",
      "Train Epoch: 31 [19200/50000 (43%)]\tLoss: 0.357895, Accuracy: 90.62\n",
      "Train Epoch: 31 [20480/50000 (45%)]\tLoss: 0.482660, Accuracy: 84.77\n",
      "Train Epoch: 31 [21760/50000 (48%)]\tLoss: 0.366736, Accuracy: 86.72\n",
      "Train Epoch: 31 [23040/50000 (51%)]\tLoss: 0.391799, Accuracy: 87.50\n",
      "Train Epoch: 31 [24320/50000 (54%)]\tLoss: 0.369605, Accuracy: 86.72\n",
      "Train Epoch: 31 [25600/50000 (57%)]\tLoss: 0.484492, Accuracy: 83.59\n",
      "Train Epoch: 31 [26880/50000 (60%)]\tLoss: 0.285465, Accuracy: 90.23\n",
      "Train Epoch: 31 [28160/50000 (62%)]\tLoss: 0.440171, Accuracy: 87.11\n",
      "Train Epoch: 31 [29440/50000 (65%)]\tLoss: 0.340927, Accuracy: 86.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 31 [30720/50000 (68%)]\tLoss: 0.445071, Accuracy: 83.59\n",
      "Train Epoch: 31 [32000/50000 (71%)]\tLoss: 0.442206, Accuracy: 84.38\n",
      "Train Epoch: 31 [33280/50000 (74%)]\tLoss: 0.360281, Accuracy: 85.16\n",
      "Train Epoch: 31 [34560/50000 (77%)]\tLoss: 0.373220, Accuracy: 86.33\n",
      "Train Epoch: 31 [35840/50000 (80%)]\tLoss: 0.326877, Accuracy: 90.62\n",
      "Train Epoch: 31 [37120/50000 (82%)]\tLoss: 0.295791, Accuracy: 89.06\n",
      "Train Epoch: 31 [38400/50000 (85%)]\tLoss: 0.462912, Accuracy: 86.72\n",
      "Train Epoch: 31 [39680/50000 (88%)]\tLoss: 0.412932, Accuracy: 86.33\n",
      "Train Epoch: 31 [40960/50000 (91%)]\tLoss: 0.346409, Accuracy: 88.67\n",
      "Train Epoch: 31 [42240/50000 (94%)]\tLoss: 0.442931, Accuracy: 83.59\n",
      "Train Epoch: 31 [43520/50000 (97%)]\tLoss: 0.461277, Accuracy: 82.03\n",
      "Train Epoch: 31 [35000/50000 (99%)]\tLoss: 0.536782, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.5921, Accuracy: 3988/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[40.910672187805176 s]\n",
      "Train Epoch: 32 [0/50000 (0%)]\tLoss: 0.321166, Accuracy: 90.23\n",
      "Train Epoch: 32 [1280/50000 (3%)]\tLoss: 0.343144, Accuracy: 88.67\n",
      "Train Epoch: 32 [2560/50000 (6%)]\tLoss: 0.414308, Accuracy: 84.38\n",
      "Train Epoch: 32 [3840/50000 (9%)]\tLoss: 0.412375, Accuracy: 86.72\n",
      "Train Epoch: 32 [5120/50000 (11%)]\tLoss: 0.335919, Accuracy: 89.06\n",
      "Train Epoch: 32 [6400/50000 (14%)]\tLoss: 0.370905, Accuracy: 89.45\n",
      "Train Epoch: 32 [7680/50000 (17%)]\tLoss: 0.405109, Accuracy: 86.72\n",
      "Train Epoch: 32 [8960/50000 (20%)]\tLoss: 0.372340, Accuracy: 85.94\n",
      "Train Epoch: 32 [10240/50000 (23%)]\tLoss: 0.270419, Accuracy: 92.58\n",
      "Train Epoch: 32 [11520/50000 (26%)]\tLoss: 0.410438, Accuracy: 87.11\n",
      "Train Epoch: 32 [12800/50000 (28%)]\tLoss: 0.356143, Accuracy: 87.50\n",
      "Train Epoch: 32 [14080/50000 (31%)]\tLoss: 0.343336, Accuracy: 89.06\n",
      "Train Epoch: 32 [15360/50000 (34%)]\tLoss: 0.385553, Accuracy: 88.28\n",
      "Train Epoch: 32 [16640/50000 (37%)]\tLoss: 0.420087, Accuracy: 84.77\n",
      "Train Epoch: 32 [17920/50000 (40%)]\tLoss: 0.416957, Accuracy: 86.33\n",
      "Train Epoch: 32 [19200/50000 (43%)]\tLoss: 0.409414, Accuracy: 85.16\n",
      "Train Epoch: 32 [20480/50000 (45%)]\tLoss: 0.376258, Accuracy: 86.33\n",
      "Train Epoch: 32 [21760/50000 (48%)]\tLoss: 0.423317, Accuracy: 85.55\n",
      "Train Epoch: 32 [23040/50000 (51%)]\tLoss: 0.270809, Accuracy: 91.41\n",
      "Train Epoch: 32 [24320/50000 (54%)]\tLoss: 0.350108, Accuracy: 89.84\n",
      "Train Epoch: 32 [25600/50000 (57%)]\tLoss: 0.481945, Accuracy: 83.59\n",
      "Train Epoch: 32 [26880/50000 (60%)]\tLoss: 0.420747, Accuracy: 88.67\n",
      "Train Epoch: 32 [28160/50000 (62%)]\tLoss: 0.396127, Accuracy: 86.33\n",
      "Train Epoch: 32 [29440/50000 (65%)]\tLoss: 0.375621, Accuracy: 87.11\n",
      "Train Epoch: 32 [30720/50000 (68%)]\tLoss: 0.429959, Accuracy: 83.98\n",
      "Train Epoch: 32 [32000/50000 (71%)]\tLoss: 0.369821, Accuracy: 89.84\n",
      "Train Epoch: 32 [33280/50000 (74%)]\tLoss: 0.450787, Accuracy: 83.98\n",
      "Train Epoch: 32 [34560/50000 (77%)]\tLoss: 0.557140, Accuracy: 81.25\n",
      "Train Epoch: 32 [35840/50000 (80%)]\tLoss: 0.349609, Accuracy: 88.28\n",
      "Train Epoch: 32 [37120/50000 (82%)]\tLoss: 0.370960, Accuracy: 84.77\n",
      "Train Epoch: 32 [38400/50000 (85%)]\tLoss: 0.395071, Accuracy: 85.55\n",
      "Train Epoch: 32 [39680/50000 (88%)]\tLoss: 0.405856, Accuracy: 86.33\n",
      "Train Epoch: 32 [40960/50000 (91%)]\tLoss: 0.437115, Accuracy: 82.81\n",
      "Train Epoch: 32 [42240/50000 (94%)]\tLoss: 0.378928, Accuracy: 87.11\n",
      "Train Epoch: 32 [43520/50000 (97%)]\tLoss: 0.404310, Accuracy: 85.94\n",
      "Train Epoch: 32 [35000/50000 (99%)]\tLoss: 0.312019, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7773, Accuracy: 3788/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[37.73925423622131 s]\n",
      "\n",
      "Test set: Average loss: 0.8249, Accuracy: 7520/10000 (75.20%)\n",
      "\n",
      "Train Epoch: 33 [0/50000 (0%)]\tLoss: 0.461318, Accuracy: 83.98\n",
      "Train Epoch: 33 [1280/50000 (3%)]\tLoss: 0.359024, Accuracy: 87.50\n",
      "Train Epoch: 33 [2560/50000 (6%)]\tLoss: 0.318987, Accuracy: 90.62\n",
      "Train Epoch: 33 [3840/50000 (9%)]\tLoss: 0.404192, Accuracy: 85.55\n",
      "Train Epoch: 33 [5120/50000 (11%)]\tLoss: 0.441901, Accuracy: 83.98\n",
      "Train Epoch: 33 [6400/50000 (14%)]\tLoss: 0.326885, Accuracy: 89.84\n",
      "Train Epoch: 33 [7680/50000 (17%)]\tLoss: 0.468732, Accuracy: 83.20\n",
      "Train Epoch: 33 [8960/50000 (20%)]\tLoss: 0.376156, Accuracy: 85.94\n",
      "Train Epoch: 33 [10240/50000 (23%)]\tLoss: 0.473430, Accuracy: 86.33\n",
      "Train Epoch: 33 [11520/50000 (26%)]\tLoss: 0.462531, Accuracy: 85.16\n",
      "Train Epoch: 33 [12800/50000 (28%)]\tLoss: 0.505745, Accuracy: 82.03\n",
      "Train Epoch: 33 [14080/50000 (31%)]\tLoss: 0.292632, Accuracy: 87.50\n",
      "Train Epoch: 33 [15360/50000 (34%)]\tLoss: 0.372640, Accuracy: 87.11\n",
      "Train Epoch: 33 [16640/50000 (37%)]\tLoss: 0.346401, Accuracy: 89.06\n",
      "Train Epoch: 33 [17920/50000 (40%)]\tLoss: 0.428239, Accuracy: 87.11\n",
      "Train Epoch: 33 [19200/50000 (43%)]\tLoss: 0.452808, Accuracy: 84.77\n",
      "Train Epoch: 33 [20480/50000 (45%)]\tLoss: 0.376861, Accuracy: 88.28\n",
      "Train Epoch: 33 [21760/50000 (48%)]\tLoss: 0.343388, Accuracy: 88.28\n",
      "Train Epoch: 33 [23040/50000 (51%)]\tLoss: 0.470597, Accuracy: 84.77\n",
      "Train Epoch: 33 [24320/50000 (54%)]\tLoss: 0.441408, Accuracy: 85.16\n",
      "Train Epoch: 33 [25600/50000 (57%)]\tLoss: 0.357499, Accuracy: 87.89\n",
      "Train Epoch: 33 [26880/50000 (60%)]\tLoss: 0.354544, Accuracy: 88.67\n",
      "Train Epoch: 33 [28160/50000 (62%)]\tLoss: 0.406600, Accuracy: 85.94\n",
      "Train Epoch: 33 [29440/50000 (65%)]\tLoss: 0.406125, Accuracy: 87.11\n",
      "Train Epoch: 33 [30720/50000 (68%)]\tLoss: 0.399229, Accuracy: 85.55\n",
      "Train Epoch: 33 [32000/50000 (71%)]\tLoss: 0.404854, Accuracy: 83.98\n",
      "Train Epoch: 33 [33280/50000 (74%)]\tLoss: 0.376114, Accuracy: 88.28\n",
      "Train Epoch: 33 [34560/50000 (77%)]\tLoss: 0.414806, Accuracy: 85.16\n",
      "Train Epoch: 33 [35840/50000 (80%)]\tLoss: 0.316879, Accuracy: 88.67\n",
      "Train Epoch: 33 [37120/50000 (82%)]\tLoss: 0.358840, Accuracy: 89.45\n",
      "Train Epoch: 33 [38400/50000 (85%)]\tLoss: 0.487372, Accuracy: 83.98\n",
      "Train Epoch: 33 [39680/50000 (88%)]\tLoss: 0.406237, Accuracy: 87.11\n",
      "Train Epoch: 33 [40960/50000 (91%)]\tLoss: 0.404344, Accuracy: 85.94\n",
      "Train Epoch: 33 [42240/50000 (94%)]\tLoss: 0.393758, Accuracy: 86.72\n",
      "Train Epoch: 33 [43520/50000 (97%)]\tLoss: 0.395254, Accuracy: 89.06\n",
      "Train Epoch: 33 [35000/50000 (99%)]\tLoss: 0.374855, Accuracy: 87.50\n",
      "\n",
      "Validation set: Average loss: 0.7540, Accuracy: 3816/5000 (76.00%)\n",
      "\n",
      "the time of this epoch:[40.77960777282715 s]\n",
      "Train Epoch: 34 [0/50000 (0%)]\tLoss: 0.354910, Accuracy: 88.67\n",
      "Train Epoch: 34 [1280/50000 (3%)]\tLoss: 0.394111, Accuracy: 85.94\n",
      "Train Epoch: 34 [2560/50000 (6%)]\tLoss: 0.326897, Accuracy: 88.28\n",
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      "Train Epoch: 34 [10240/50000 (23%)]\tLoss: 0.318792, Accuracy: 88.28\n",
      "Train Epoch: 34 [11520/50000 (26%)]\tLoss: 0.474613, Accuracy: 83.98\n",
      "Train Epoch: 34 [12800/50000 (28%)]\tLoss: 0.325116, Accuracy: 91.80\n",
      "Train Epoch: 34 [14080/50000 (31%)]\tLoss: 0.431804, Accuracy: 86.72\n",
      "Train Epoch: 34 [15360/50000 (34%)]\tLoss: 0.474446, Accuracy: 84.77\n",
      "Train Epoch: 34 [16640/50000 (37%)]\tLoss: 0.369059, Accuracy: 85.55\n",
      "Train Epoch: 34 [17920/50000 (40%)]\tLoss: 0.410469, Accuracy: 85.55\n",
      "Train Epoch: 34 [19200/50000 (43%)]\tLoss: 0.352132, Accuracy: 87.89\n",
      "Train Epoch: 34 [20480/50000 (45%)]\tLoss: 0.435893, Accuracy: 86.33\n",
      "Train Epoch: 34 [21760/50000 (48%)]\tLoss: 0.405142, Accuracy: 87.11\n",
      "Train Epoch: 34 [23040/50000 (51%)]\tLoss: 0.534543, Accuracy: 82.03\n",
      "Train Epoch: 34 [24320/50000 (54%)]\tLoss: 0.459542, Accuracy: 85.16\n",
      "Train Epoch: 34 [25600/50000 (57%)]\tLoss: 0.435116, Accuracy: 85.94\n",
      "Train Epoch: 34 [26880/50000 (60%)]\tLoss: 0.497269, Accuracy: 83.98\n",
      "Train Epoch: 34 [28160/50000 (62%)]\tLoss: 0.415643, Accuracy: 85.55\n",
      "Train Epoch: 34 [29440/50000 (65%)]\tLoss: 0.516004, Accuracy: 83.59\n",
      "Train Epoch: 34 [30720/50000 (68%)]\tLoss: 0.348963, Accuracy: 87.11\n",
      "Train Epoch: 34 [32000/50000 (71%)]\tLoss: 0.457639, Accuracy: 84.77\n",
      "Train Epoch: 34 [33280/50000 (74%)]\tLoss: 0.281744, Accuracy: 92.58\n",
      "Train Epoch: 34 [34560/50000 (77%)]\tLoss: 0.335760, Accuracy: 89.06\n",
      "Train Epoch: 34 [35840/50000 (80%)]\tLoss: 0.367934, Accuracy: 87.50\n",
      "Train Epoch: 34 [37120/50000 (82%)]\tLoss: 0.312865, Accuracy: 89.45\n",
      "Train Epoch: 34 [38400/50000 (85%)]\tLoss: 0.402037, Accuracy: 86.33\n",
      "Train Epoch: 34 [39680/50000 (88%)]\tLoss: 0.529803, Accuracy: 80.47\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 34 [40960/50000 (91%)]\tLoss: 0.446214, Accuracy: 86.33\n",
      "Train Epoch: 34 [42240/50000 (94%)]\tLoss: 0.341663, Accuracy: 87.50\n",
      "Train Epoch: 34 [43520/50000 (97%)]\tLoss: 0.387499, Accuracy: 85.94\n",
      "Train Epoch: 34 [35000/50000 (99%)]\tLoss: 0.387970, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.7461, Accuracy: 3779/5000 (75.00%)\n",
      "\n",
      "the time of this epoch:[37.681721925735474 s]\n",
      "\n",
      "Test set: Average loss: 0.7722, Accuracy: 7495/10000 (74.95%)\n",
      "\n",
      "Train Epoch: 35 [0/50000 (0%)]\tLoss: 0.385906, Accuracy: 85.55\n",
      "Train Epoch: 35 [1280/50000 (3%)]\tLoss: 0.340975, Accuracy: 86.72\n",
      "Train Epoch: 35 [2560/50000 (6%)]\tLoss: 0.355732, Accuracy: 89.84\n",
      "Train Epoch: 35 [3840/50000 (9%)]\tLoss: 0.385215, Accuracy: 87.89\n",
      "Train Epoch: 35 [5120/50000 (11%)]\tLoss: 0.374375, Accuracy: 87.50\n",
      "Train Epoch: 35 [6400/50000 (14%)]\tLoss: 0.356995, Accuracy: 86.72\n",
      "Train Epoch: 35 [7680/50000 (17%)]\tLoss: 0.347757, Accuracy: 87.50\n",
      "Train Epoch: 35 [8960/50000 (20%)]\tLoss: 0.391565, Accuracy: 86.72\n",
      "Train Epoch: 35 [10240/50000 (23%)]\tLoss: 0.406695, Accuracy: 85.94\n",
      "Train Epoch: 35 [11520/50000 (26%)]\tLoss: 0.403557, Accuracy: 86.33\n",
      "Train Epoch: 35 [12800/50000 (28%)]\tLoss: 0.339656, Accuracy: 87.89\n",
      "Train Epoch: 35 [14080/50000 (31%)]\tLoss: 0.456260, Accuracy: 83.98\n",
      "Train Epoch: 35 [15360/50000 (34%)]\tLoss: 0.342331, Accuracy: 87.89\n",
      "Train Epoch: 35 [16640/50000 (37%)]\tLoss: 0.373827, Accuracy: 89.06\n",
      "Train Epoch: 35 [17920/50000 (40%)]\tLoss: 0.363935, Accuracy: 88.67\n",
      "Train Epoch: 35 [19200/50000 (43%)]\tLoss: 0.420501, Accuracy: 86.72\n",
      "Train Epoch: 35 [20480/50000 (45%)]\tLoss: 0.450072, Accuracy: 82.03\n",
      "Train Epoch: 35 [21760/50000 (48%)]\tLoss: 0.423645, Accuracy: 86.33\n",
      "Train Epoch: 35 [23040/50000 (51%)]\tLoss: 0.387403, Accuracy: 86.33\n",
      "Train Epoch: 35 [24320/50000 (54%)]\tLoss: 0.585747, Accuracy: 78.91\n",
      "Train Epoch: 35 [25600/50000 (57%)]\tLoss: 0.344562, Accuracy: 91.02\n",
      "Train Epoch: 35 [26880/50000 (60%)]\tLoss: 0.398834, Accuracy: 85.16\n",
      "Train Epoch: 35 [28160/50000 (62%)]\tLoss: 0.372153, Accuracy: 88.67\n",
      "Train Epoch: 35 [29440/50000 (65%)]\tLoss: 0.406018, Accuracy: 84.77\n",
      "Train Epoch: 35 [30720/50000 (68%)]\tLoss: 0.263275, Accuracy: 91.80\n",
      "Train Epoch: 35 [32000/50000 (71%)]\tLoss: 0.413831, Accuracy: 83.98\n",
      "Train Epoch: 35 [33280/50000 (74%)]\tLoss: 0.409198, Accuracy: 84.38\n",
      "Train Epoch: 35 [34560/50000 (77%)]\tLoss: 0.396419, Accuracy: 86.33\n",
      "Train Epoch: 35 [35840/50000 (80%)]\tLoss: 0.414882, Accuracy: 87.11\n",
      "Train Epoch: 35 [37120/50000 (82%)]\tLoss: 0.423379, Accuracy: 85.55\n",
      "Train Epoch: 35 [38400/50000 (85%)]\tLoss: 0.568106, Accuracy: 79.69\n",
      "Train Epoch: 35 [39680/50000 (88%)]\tLoss: 0.445369, Accuracy: 83.98\n",
      "Train Epoch: 35 [40960/50000 (91%)]\tLoss: 0.287518, Accuracy: 91.02\n",
      "Train Epoch: 35 [42240/50000 (94%)]\tLoss: 0.294157, Accuracy: 89.06\n",
      "Train Epoch: 35 [43520/50000 (97%)]\tLoss: 0.381729, Accuracy: 87.50\n",
      "Train Epoch: 35 [35000/50000 (99%)]\tLoss: 0.365214, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.6064, Accuracy: 4023/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[40.7927610874176 s]\n",
      "Train Epoch: 36 [0/50000 (0%)]\tLoss: 0.398301, Accuracy: 85.55\n",
      "Train Epoch: 36 [1280/50000 (3%)]\tLoss: 0.352989, Accuracy: 87.50\n",
      "Train Epoch: 36 [2560/50000 (6%)]\tLoss: 0.487173, Accuracy: 83.59\n",
      "Train Epoch: 36 [3840/50000 (9%)]\tLoss: 0.318830, Accuracy: 88.67\n",
      "Train Epoch: 36 [5120/50000 (11%)]\tLoss: 0.467666, Accuracy: 83.98\n",
      "Train Epoch: 36 [6400/50000 (14%)]\tLoss: 0.381993, Accuracy: 88.67\n",
      "Train Epoch: 36 [7680/50000 (17%)]\tLoss: 0.378998, Accuracy: 88.28\n",
      "Train Epoch: 36 [8960/50000 (20%)]\tLoss: 0.405256, Accuracy: 88.28\n",
      "Train Epoch: 36 [10240/50000 (23%)]\tLoss: 0.422343, Accuracy: 84.38\n",
      "Train Epoch: 36 [11520/50000 (26%)]\tLoss: 0.425649, Accuracy: 85.94\n",
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      "Train Epoch: 36 [15360/50000 (34%)]\tLoss: 0.348557, Accuracy: 87.50\n",
      "Train Epoch: 36 [16640/50000 (37%)]\tLoss: 0.470452, Accuracy: 82.42\n",
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      "Train Epoch: 36 [19200/50000 (43%)]\tLoss: 0.407872, Accuracy: 84.77\n",
      "Train Epoch: 36 [20480/50000 (45%)]\tLoss: 0.339942, Accuracy: 89.06\n",
      "Train Epoch: 36 [21760/50000 (48%)]\tLoss: 0.519442, Accuracy: 84.38\n",
      "Train Epoch: 36 [23040/50000 (51%)]\tLoss: 0.380599, Accuracy: 87.11\n",
      "Train Epoch: 36 [24320/50000 (54%)]\tLoss: 0.401125, Accuracy: 85.94\n",
      "Train Epoch: 36 [25600/50000 (57%)]\tLoss: 0.364870, Accuracy: 85.94\n",
      "Train Epoch: 36 [26880/50000 (60%)]\tLoss: 0.303161, Accuracy: 88.67\n",
      "Train Epoch: 36 [28160/50000 (62%)]\tLoss: 0.457146, Accuracy: 85.55\n",
      "Train Epoch: 36 [29440/50000 (65%)]\tLoss: 0.413140, Accuracy: 86.72\n",
      "Train Epoch: 36 [30720/50000 (68%)]\tLoss: 0.481289, Accuracy: 84.38\n",
      "Train Epoch: 36 [32000/50000 (71%)]\tLoss: 0.378762, Accuracy: 86.33\n",
      "Train Epoch: 36 [33280/50000 (74%)]\tLoss: 0.393012, Accuracy: 85.16\n",
      "Train Epoch: 36 [34560/50000 (77%)]\tLoss: 0.411662, Accuracy: 86.72\n",
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      "\n",
      "        [[ 0.0712]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 4.7689]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.7505, -0.8725, -0.9305,  ..., -0.5225,  0.0939, -0.5424]],\n",
      "\n",
      "        [[-0.2811,  0.3061,  0.4203,  ..., -0.0569,  1.6733, -0.0488]],\n",
      "\n",
      "        [[ 0.6395,  0.1125,  0.7266,  ...,  0.2563,  1.2095,  0.3095]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.4297,  1.1054,  1.0031,  ...,  0.6200,  0.4790, -0.7022]],\n",
      "\n",
      "        [[-0.4690, -0.2087, -0.1748,  ...,  0.8048, -0.6079,  0.1146]],\n",
      "\n",
      "        [[ 0.0832, -1.6837,  0.2015,  ..., -1.6401, -0.6399, -0.1136]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[-9.2604e-04, -1.2466e-03, -1.5381e-03,  ..., -1.9165e-03,\n",
      "          -1.6769e-03, -1.4498e-03],\n",
      "         [ 1.0560e-03,  1.2032e-03,  1.4767e-03,  ...,  8.8743e-04,\n",
      "           5.8345e-04,  2.9490e-04],\n",
      "         [ 8.1471e-04,  9.4979e-04,  1.2025e-03,  ...,  6.2997e-04,\n",
      "           7.1621e-04,  4.5063e-04],\n",
      "         ...,\n",
      "         [ 4.6364e-06,  2.1589e-04,  2.5135e-04,  ...,  9.7274e-04,\n",
      "           8.9971e-04,  1.0762e-03],\n",
      "         [-2.4482e-04, -4.9891e-04, -4.7717e-04,  ..., -5.5475e-04,\n",
      "          -2.7358e-04, -3.5818e-04],\n",
      "         [-1.7334e-03, -1.7686e-03, -1.6964e-03,  ..., -1.3342e-03,\n",
      "          -8.8624e-04, -5.9570e-04]]], device='cuda:0')\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 36 [40960/50000 (91%)]\tLoss: 0.418225, Accuracy: 86.33\n",
      "Train Epoch: 36 [42240/50000 (94%)]\tLoss: 0.323748, Accuracy: 89.06\n",
      "Train Epoch: 36 [43520/50000 (97%)]\tLoss: 0.333089, Accuracy: 87.50\n",
      "Train Epoch: 36 [35000/50000 (99%)]\tLoss: 0.419230, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.5907, Accuracy: 4008/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[37.688355684280396 s]\n",
      "\n",
      "Test set: Average loss: 0.6083, Accuracy: 7982/10000 (79.82%)\n",
      "\n",
      "Train Epoch: 37 [0/50000 (0%)]\tLoss: 0.378109, Accuracy: 86.72\n",
      "Train Epoch: 37 [1280/50000 (3%)]\tLoss: 0.316631, Accuracy: 90.23\n",
      "Train Epoch: 37 [2560/50000 (6%)]\tLoss: 0.368457, Accuracy: 88.67\n",
      "Train Epoch: 37 [3840/50000 (9%)]\tLoss: 0.444406, Accuracy: 85.55\n",
      "Train Epoch: 37 [5120/50000 (11%)]\tLoss: 0.472705, Accuracy: 80.86\n",
      "Train Epoch: 37 [6400/50000 (14%)]\tLoss: 0.482735, Accuracy: 82.81\n",
      "Train Epoch: 37 [7680/50000 (17%)]\tLoss: 0.411793, Accuracy: 85.55\n",
      "Train Epoch: 37 [8960/50000 (20%)]\tLoss: 0.476794, Accuracy: 83.59\n",
      "Train Epoch: 37 [10240/50000 (23%)]\tLoss: 0.424203, Accuracy: 86.72\n",
      "Train Epoch: 37 [11520/50000 (26%)]\tLoss: 0.374245, Accuracy: 88.28\n",
      "Train Epoch: 37 [12800/50000 (28%)]\tLoss: 0.408850, Accuracy: 87.11\n",
      "Train Epoch: 37 [14080/50000 (31%)]\tLoss: 0.464946, Accuracy: 82.81\n",
      "Train Epoch: 37 [15360/50000 (34%)]\tLoss: 0.467385, Accuracy: 85.16\n",
      "Train Epoch: 37 [16640/50000 (37%)]\tLoss: 0.410888, Accuracy: 86.33\n",
      "Train Epoch: 37 [17920/50000 (40%)]\tLoss: 0.384135, Accuracy: 86.72\n",
      "Train Epoch: 37 [19200/50000 (43%)]\tLoss: 0.375163, Accuracy: 88.28\n",
      "Train Epoch: 37 [20480/50000 (45%)]\tLoss: 0.388918, Accuracy: 85.55\n",
      "Train Epoch: 37 [21760/50000 (48%)]\tLoss: 0.290999, Accuracy: 90.62\n",
      "Train Epoch: 37 [23040/50000 (51%)]\tLoss: 0.404027, Accuracy: 87.50\n",
      "Train Epoch: 37 [24320/50000 (54%)]\tLoss: 0.323063, Accuracy: 88.28\n",
      "Train Epoch: 37 [25600/50000 (57%)]\tLoss: 0.390235, Accuracy: 87.89\n",
      "Train Epoch: 37 [26880/50000 (60%)]\tLoss: 0.505462, Accuracy: 83.20\n",
      "Train Epoch: 37 [28160/50000 (62%)]\tLoss: 0.281980, Accuracy: 89.45\n",
      "Train Epoch: 37 [29440/50000 (65%)]\tLoss: 0.362909, Accuracy: 87.50\n",
      "Train Epoch: 37 [30720/50000 (68%)]\tLoss: 0.356434, Accuracy: 86.33\n",
      "Train Epoch: 37 [32000/50000 (71%)]\tLoss: 0.407199, Accuracy: 85.55\n",
      "Train Epoch: 37 [33280/50000 (74%)]\tLoss: 0.342581, Accuracy: 87.89\n",
      "Train Epoch: 37 [34560/50000 (77%)]\tLoss: 0.408987, Accuracy: 86.72\n",
      "Train Epoch: 37 [35840/50000 (80%)]\tLoss: 0.340009, Accuracy: 89.45\n",
      "Train Epoch: 37 [37120/50000 (82%)]\tLoss: 0.389819, Accuracy: 86.33\n",
      "Train Epoch: 37 [38400/50000 (85%)]\tLoss: 0.379643, Accuracy: 87.50\n",
      "Train Epoch: 37 [39680/50000 (88%)]\tLoss: 0.418718, Accuracy: 85.16\n",
      "Train Epoch: 37 [40960/50000 (91%)]\tLoss: 0.426445, Accuracy: 86.33\n",
      "Train Epoch: 37 [42240/50000 (94%)]\tLoss: 0.293946, Accuracy: 90.62\n",
      "Train Epoch: 37 [43520/50000 (97%)]\tLoss: 0.399220, Accuracy: 88.28\n",
      "Train Epoch: 37 [35000/50000 (99%)]\tLoss: 0.331147, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.7108, Accuracy: 3868/5000 (77.00%)\n",
      "\n",
      "the time of this epoch:[40.77769756317139 s]\n",
      "Train Epoch: 38 [0/50000 (0%)]\tLoss: 0.288481, Accuracy: 90.62\n",
      "Train Epoch: 38 [1280/50000 (3%)]\tLoss: 0.453796, Accuracy: 83.98\n",
      "Train Epoch: 38 [2560/50000 (6%)]\tLoss: 0.343762, Accuracy: 88.67\n",
      "Train Epoch: 38 [3840/50000 (9%)]\tLoss: 0.345509, Accuracy: 88.28\n",
      "Train Epoch: 38 [5120/50000 (11%)]\tLoss: 0.284396, Accuracy: 92.19\n",
      "Train Epoch: 38 [6400/50000 (14%)]\tLoss: 0.311779, Accuracy: 88.28\n",
      "Train Epoch: 38 [7680/50000 (17%)]\tLoss: 0.337464, Accuracy: 89.45\n",
      "Train Epoch: 38 [8960/50000 (20%)]\tLoss: 0.320964, Accuracy: 89.06\n",
      "Train Epoch: 38 [10240/50000 (23%)]\tLoss: 0.410739, Accuracy: 85.94\n",
      "Train Epoch: 38 [11520/50000 (26%)]\tLoss: 0.324795, Accuracy: 88.67\n",
      "Train Epoch: 38 [12800/50000 (28%)]\tLoss: 0.423762, Accuracy: 87.89\n",
      "Train Epoch: 38 [14080/50000 (31%)]\tLoss: 0.405533, Accuracy: 85.16\n",
      "Train Epoch: 38 [15360/50000 (34%)]\tLoss: 0.344257, Accuracy: 87.89\n",
      "Train Epoch: 38 [16640/50000 (37%)]\tLoss: 0.431518, Accuracy: 88.28\n",
      "Train Epoch: 38 [17920/50000 (40%)]\tLoss: 0.323575, Accuracy: 88.28\n",
      "Train Epoch: 38 [19200/50000 (43%)]\tLoss: 0.371215, Accuracy: 87.89\n",
      "Train Epoch: 38 [20480/50000 (45%)]\tLoss: 0.398638, Accuracy: 85.94\n",
      "Train Epoch: 38 [21760/50000 (48%)]\tLoss: 0.384575, Accuracy: 87.50\n",
      "Train Epoch: 38 [23040/50000 (51%)]\tLoss: 0.423859, Accuracy: 85.55\n",
      "Train Epoch: 38 [24320/50000 (54%)]\tLoss: 0.350523, Accuracy: 88.28\n",
      "Train Epoch: 38 [25600/50000 (57%)]\tLoss: 0.285165, Accuracy: 90.62\n",
      "Train Epoch: 38 [26880/50000 (60%)]\tLoss: 0.399219, Accuracy: 86.33\n",
      "Train Epoch: 38 [28160/50000 (62%)]\tLoss: 0.283576, Accuracy: 90.23\n",
      "Train Epoch: 38 [29440/50000 (65%)]\tLoss: 0.449318, Accuracy: 85.94\n",
      "Train Epoch: 38 [30720/50000 (68%)]\tLoss: 0.469631, Accuracy: 81.64\n",
      "Train Epoch: 38 [32000/50000 (71%)]\tLoss: 0.372009, Accuracy: 87.50\n",
      "Train Epoch: 38 [33280/50000 (74%)]\tLoss: 0.371482, Accuracy: 89.45\n",
      "Train Epoch: 38 [34560/50000 (77%)]\tLoss: 0.352148, Accuracy: 88.28\n",
      "Train Epoch: 38 [35840/50000 (80%)]\tLoss: 0.396482, Accuracy: 87.11\n",
      "Train Epoch: 38 [37120/50000 (82%)]\tLoss: 0.344383, Accuracy: 85.55\n",
      "Train Epoch: 38 [38400/50000 (85%)]\tLoss: 0.281451, Accuracy: 89.84\n",
      "Train Epoch: 38 [39680/50000 (88%)]\tLoss: 0.397010, Accuracy: 87.11\n",
      "Train Epoch: 38 [40960/50000 (91%)]\tLoss: 0.310145, Accuracy: 89.84\n",
      "Train Epoch: 38 [42240/50000 (94%)]\tLoss: 0.377645, Accuracy: 85.55\n",
      "Train Epoch: 38 [43520/50000 (97%)]\tLoss: 0.418808, Accuracy: 87.11\n",
      "Train Epoch: 38 [35000/50000 (99%)]\tLoss: 0.384037, Accuracy: 88.00\n",
      "\n",
      "Validation set: Average loss: 0.5863, Accuracy: 4011/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[37.636515617370605 s]\n",
      "\n",
      "Test set: Average loss: 0.5970, Accuracy: 8053/10000 (80.53%)\n",
      "\n",
      "Train Epoch: 39 [0/50000 (0%)]\tLoss: 0.397852, Accuracy: 85.16\n",
      "Train Epoch: 39 [1280/50000 (3%)]\tLoss: 0.340871, Accuracy: 88.28\n",
      "Train Epoch: 39 [2560/50000 (6%)]\tLoss: 0.342505, Accuracy: 87.89\n",
      "Train Epoch: 39 [3840/50000 (9%)]\tLoss: 0.309231, Accuracy: 90.23\n",
      "Train Epoch: 39 [5120/50000 (11%)]\tLoss: 0.341817, Accuracy: 87.11\n",
      "Train Epoch: 39 [6400/50000 (14%)]\tLoss: 0.352534, Accuracy: 87.89\n",
      "Train Epoch: 39 [7680/50000 (17%)]\tLoss: 0.399374, Accuracy: 84.38\n",
      "Train Epoch: 39 [8960/50000 (20%)]\tLoss: 0.368213, Accuracy: 87.50\n",
      "Train Epoch: 39 [10240/50000 (23%)]\tLoss: 0.350520, Accuracy: 88.28\n",
      "Train Epoch: 39 [11520/50000 (26%)]\tLoss: 0.412894, Accuracy: 85.16\n",
      "Train Epoch: 39 [12800/50000 (28%)]\tLoss: 0.340824, Accuracy: 87.89\n",
      "Train Epoch: 39 [14080/50000 (31%)]\tLoss: 0.338144, Accuracy: 86.72\n",
      "Train Epoch: 39 [15360/50000 (34%)]\tLoss: 0.365815, Accuracy: 86.72\n",
      "Train Epoch: 39 [16640/50000 (37%)]\tLoss: 0.327476, Accuracy: 89.45\n",
      "Train Epoch: 39 [17920/50000 (40%)]\tLoss: 0.343432, Accuracy: 87.89\n",
      "Train Epoch: 39 [19200/50000 (43%)]\tLoss: 0.351072, Accuracy: 87.11\n",
      "Train Epoch: 39 [20480/50000 (45%)]\tLoss: 0.376120, Accuracy: 85.16\n",
      "Train Epoch: 39 [21760/50000 (48%)]\tLoss: 0.421364, Accuracy: 86.33\n",
      "Train Epoch: 39 [23040/50000 (51%)]\tLoss: 0.380970, Accuracy: 85.94\n",
      "Train Epoch: 39 [24320/50000 (54%)]\tLoss: 0.420217, Accuracy: 87.89\n",
      "Train Epoch: 39 [25600/50000 (57%)]\tLoss: 0.353190, Accuracy: 87.11\n",
      "Train Epoch: 39 [26880/50000 (60%)]\tLoss: 0.347728, Accuracy: 87.11\n",
      "Train Epoch: 39 [28160/50000 (62%)]\tLoss: 0.418826, Accuracy: 86.33\n",
      "Train Epoch: 39 [29440/50000 (65%)]\tLoss: 0.303147, Accuracy: 88.67\n",
      "Train Epoch: 39 [30720/50000 (68%)]\tLoss: 0.389368, Accuracy: 86.72\n",
      "Train Epoch: 39 [32000/50000 (71%)]\tLoss: 0.357974, Accuracy: 86.72\n",
      "Train Epoch: 39 [33280/50000 (74%)]\tLoss: 0.415934, Accuracy: 85.55\n",
      "Train Epoch: 39 [34560/50000 (77%)]\tLoss: 0.310905, Accuracy: 91.02\n",
      "Train Epoch: 39 [35840/50000 (80%)]\tLoss: 0.451584, Accuracy: 84.77\n",
      "Train Epoch: 39 [37120/50000 (82%)]\tLoss: 0.418764, Accuracy: 85.16\n",
      "Train Epoch: 39 [38400/50000 (85%)]\tLoss: 0.378360, Accuracy: 87.11\n",
      "Train Epoch: 39 [39680/50000 (88%)]\tLoss: 0.337011, Accuracy: 86.33\n",
      "Train Epoch: 39 [40960/50000 (91%)]\tLoss: 0.344524, Accuracy: 91.80\n",
      "Train Epoch: 39 [42240/50000 (94%)]\tLoss: 0.535978, Accuracy: 82.42\n",
      "Train Epoch: 39 [43520/50000 (97%)]\tLoss: 0.379890, Accuracy: 87.89\n",
      "Train Epoch: 39 [35000/50000 (99%)]\tLoss: 0.484925, Accuracy: 83.00\n",
      "\n",
      "Validation set: Average loss: 0.9655, Accuracy: 3481/5000 (69.00%)\n",
      "\n",
      "the time of this epoch:[40.789780139923096 s]\n",
      "Train Epoch: 40 [0/50000 (0%)]\tLoss: 0.352140, Accuracy: 88.28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 40 [1280/50000 (3%)]\tLoss: 0.302167, Accuracy: 90.62\n",
      "Train Epoch: 40 [2560/50000 (6%)]\tLoss: 0.430871, Accuracy: 88.28\n",
      "Train Epoch: 40 [3840/50000 (9%)]\tLoss: 0.413056, Accuracy: 84.38\n",
      "Train Epoch: 40 [5120/50000 (11%)]\tLoss: 0.302301, Accuracy: 88.28\n",
      "Train Epoch: 40 [6400/50000 (14%)]\tLoss: 0.376794, Accuracy: 89.45\n",
      "Train Epoch: 40 [7680/50000 (17%)]\tLoss: 0.358910, Accuracy: 85.16\n",
      "Train Epoch: 40 [8960/50000 (20%)]\tLoss: 0.354233, Accuracy: 87.11\n",
      "Train Epoch: 40 [10240/50000 (23%)]\tLoss: 0.439632, Accuracy: 83.98\n",
      "Train Epoch: 40 [11520/50000 (26%)]\tLoss: 0.242870, Accuracy: 92.58\n",
      "Train Epoch: 40 [12800/50000 (28%)]\tLoss: 0.339692, Accuracy: 87.50\n",
      "Train Epoch: 40 [14080/50000 (31%)]\tLoss: 0.388117, Accuracy: 86.72\n",
      "Train Epoch: 40 [15360/50000 (34%)]\tLoss: 0.447051, Accuracy: 85.55\n",
      "Train Epoch: 40 [16640/50000 (37%)]\tLoss: 0.382162, Accuracy: 87.50\n",
      "Train Epoch: 40 [17920/50000 (40%)]\tLoss: 0.395624, Accuracy: 85.55\n",
      "Train Epoch: 40 [19200/50000 (43%)]\tLoss: 0.333206, Accuracy: 88.67\n",
      "Train Epoch: 40 [20480/50000 (45%)]\tLoss: 0.381486, Accuracy: 87.50\n",
      "Train Epoch: 40 [21760/50000 (48%)]\tLoss: 0.345675, Accuracy: 88.67\n",
      "Train Epoch: 40 [23040/50000 (51%)]\tLoss: 0.441872, Accuracy: 83.98\n",
      "Train Epoch: 40 [24320/50000 (54%)]\tLoss: 0.427405, Accuracy: 86.72\n",
      "Train Epoch: 40 [25600/50000 (57%)]\tLoss: 0.369298, Accuracy: 87.89\n",
      "Train Epoch: 40 [26880/50000 (60%)]\tLoss: 0.408260, Accuracy: 85.16\n",
      "Train Epoch: 40 [28160/50000 (62%)]\tLoss: 0.348413, Accuracy: 86.33\n",
      "Train Epoch: 40 [29440/50000 (65%)]\tLoss: 0.443814, Accuracy: 83.98\n",
      "Train Epoch: 40 [30720/50000 (68%)]\tLoss: 0.321680, Accuracy: 88.28\n",
      "Train Epoch: 40 [32000/50000 (71%)]\tLoss: 0.402701, Accuracy: 83.59\n",
      "Train Epoch: 40 [33280/50000 (74%)]\tLoss: 0.361914, Accuracy: 89.84\n",
      "Train Epoch: 40 [34560/50000 (77%)]\tLoss: 0.496714, Accuracy: 83.98\n",
      "Train Epoch: 40 [35840/50000 (80%)]\tLoss: 0.453136, Accuracy: 86.72\n",
      "Train Epoch: 40 [37120/50000 (82%)]\tLoss: 0.446816, Accuracy: 85.55\n",
      "Train Epoch: 40 [38400/50000 (85%)]\tLoss: 0.331111, Accuracy: 89.06\n",
      "Train Epoch: 40 [39680/50000 (88%)]\tLoss: 0.433808, Accuracy: 83.59\n",
      "Train Epoch: 40 [40960/50000 (91%)]\tLoss: 0.388677, Accuracy: 85.94\n",
      "Train Epoch: 40 [42240/50000 (94%)]\tLoss: 0.453197, Accuracy: 82.42\n",
      "Train Epoch: 40 [43520/50000 (97%)]\tLoss: 0.325987, Accuracy: 88.28\n",
      "Train Epoch: 40 [35000/50000 (99%)]\tLoss: 0.432649, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.6460, Accuracy: 3967/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[37.63287663459778 s]\n",
      "\n",
      "Test set: Average loss: 0.6365, Accuracy: 7902/10000 (79.02%)\n",
      "\n",
      "Train Epoch: 41 [0/50000 (0%)]\tLoss: 0.306161, Accuracy: 89.45\n",
      "Train Epoch: 41 [1280/50000 (3%)]\tLoss: 0.334903, Accuracy: 88.28\n",
      "Train Epoch: 41 [2560/50000 (6%)]\tLoss: 0.360258, Accuracy: 86.72\n",
      "Train Epoch: 41 [3840/50000 (9%)]\tLoss: 0.350916, Accuracy: 88.67\n",
      "Train Epoch: 41 [5120/50000 (11%)]\tLoss: 0.233971, Accuracy: 92.97\n",
      "Train Epoch: 41 [6400/50000 (14%)]\tLoss: 0.263662, Accuracy: 91.41\n",
      "Train Epoch: 41 [7680/50000 (17%)]\tLoss: 0.319844, Accuracy: 86.72\n",
      "Train Epoch: 41 [8960/50000 (20%)]\tLoss: 0.348545, Accuracy: 88.28\n",
      "Train Epoch: 41 [10240/50000 (23%)]\tLoss: 0.504979, Accuracy: 83.98\n",
      "Train Epoch: 41 [11520/50000 (26%)]\tLoss: 0.426665, Accuracy: 83.98\n",
      "Train Epoch: 41 [12800/50000 (28%)]\tLoss: 0.312243, Accuracy: 91.02\n",
      "Train Epoch: 41 [14080/50000 (31%)]\tLoss: 0.321634, Accuracy: 90.23\n",
      "Train Epoch: 41 [15360/50000 (34%)]\tLoss: 0.475900, Accuracy: 83.59\n",
      "Train Epoch: 41 [16640/50000 (37%)]\tLoss: 0.304980, Accuracy: 90.23\n",
      "Train Epoch: 41 [17920/50000 (40%)]\tLoss: 0.444877, Accuracy: 84.77\n",
      "Train Epoch: 41 [19200/50000 (43%)]\tLoss: 0.369696, Accuracy: 87.50\n",
      "Train Epoch: 41 [20480/50000 (45%)]\tLoss: 0.416206, Accuracy: 83.98\n",
      "Train Epoch: 41 [21760/50000 (48%)]\tLoss: 0.382833, Accuracy: 86.72\n",
      "Train Epoch: 41 [23040/50000 (51%)]\tLoss: 0.421909, Accuracy: 85.55\n",
      "Train Epoch: 41 [24320/50000 (54%)]\tLoss: 0.421805, Accuracy: 85.55\n",
      "Train Epoch: 41 [25600/50000 (57%)]\tLoss: 0.368297, Accuracy: 87.11\n",
      "Train Epoch: 41 [26880/50000 (60%)]\tLoss: 0.317603, Accuracy: 88.67\n",
      "Train Epoch: 41 [28160/50000 (62%)]\tLoss: 0.397277, Accuracy: 85.55\n",
      "Train Epoch: 41 [29440/50000 (65%)]\tLoss: 0.310091, Accuracy: 89.45\n",
      "Train Epoch: 41 [30720/50000 (68%)]\tLoss: 0.400702, Accuracy: 85.94\n",
      "Train Epoch: 41 [32000/50000 (71%)]\tLoss: 0.477274, Accuracy: 82.81\n",
      "Train Epoch: 41 [33280/50000 (74%)]\tLoss: 0.400204, Accuracy: 87.11\n",
      "Train Epoch: 41 [34560/50000 (77%)]\tLoss: 0.360890, Accuracy: 88.28\n",
      "Train Epoch: 41 [35840/50000 (80%)]\tLoss: 0.381493, Accuracy: 86.33\n",
      "Train Epoch: 41 [37120/50000 (82%)]\tLoss: 0.429787, Accuracy: 87.11\n",
      "Train Epoch: 41 [38400/50000 (85%)]\tLoss: 0.324673, Accuracy: 87.89\n",
      "Train Epoch: 41 [39680/50000 (88%)]\tLoss: 0.359225, Accuracy: 87.89\n",
      "Train Epoch: 41 [40960/50000 (91%)]\tLoss: 0.366212, Accuracy: 87.11\n",
      "Train Epoch: 41 [42240/50000 (94%)]\tLoss: 0.373291, Accuracy: 88.28\n",
      "Train Epoch: 41 [43520/50000 (97%)]\tLoss: 0.460774, Accuracy: 86.33\n",
      "Train Epoch: 41 [35000/50000 (99%)]\tLoss: 0.463103, Accuracy: 82.00\n",
      "\n",
      "Validation set: Average loss: 1.0126, Accuracy: 3518/5000 (70.00%)\n",
      "\n",
      "the time of this epoch:[40.78106141090393 s]\n",
      "Train Epoch: 42 [0/50000 (0%)]\tLoss: 0.353699, Accuracy: 87.89\n",
      "Train Epoch: 42 [1280/50000 (3%)]\tLoss: 0.350234, Accuracy: 89.06\n",
      "Train Epoch: 42 [2560/50000 (6%)]\tLoss: 0.398986, Accuracy: 87.89\n",
      "Train Epoch: 42 [3840/50000 (9%)]\tLoss: 0.385909, Accuracy: 87.50\n",
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      "Train Epoch: 42 [12800/50000 (28%)]\tLoss: 0.364210, Accuracy: 89.06\n",
      "Train Epoch: 42 [14080/50000 (31%)]\tLoss: 0.402863, Accuracy: 85.55\n",
      "Train Epoch: 42 [15360/50000 (34%)]\tLoss: 0.360976, Accuracy: 87.89\n",
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      "Train Epoch: 42 [19200/50000 (43%)]\tLoss: 0.340305, Accuracy: 88.67\n",
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      "Train Epoch: 42 [23040/50000 (51%)]\tLoss: 0.457224, Accuracy: 83.98\n",
      "Train Epoch: 42 [24320/50000 (54%)]\tLoss: 0.286659, Accuracy: 91.41\n",
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      "Train Epoch: 42 [28160/50000 (62%)]\tLoss: 0.355223, Accuracy: 86.33\n",
      "Train Epoch: 42 [29440/50000 (65%)]\tLoss: 0.372818, Accuracy: 88.28\n",
      "Train Epoch: 42 [30720/50000 (68%)]\tLoss: 0.482318, Accuracy: 82.81\n",
      "Train Epoch: 42 [32000/50000 (71%)]\tLoss: 0.346766, Accuracy: 87.11\n",
      "Train Epoch: 42 [33280/50000 (74%)]\tLoss: 0.328586, Accuracy: 88.28\n",
      "Train Epoch: 42 [34560/50000 (77%)]\tLoss: 0.307067, Accuracy: 91.02\n",
      "Train Epoch: 42 [35840/50000 (80%)]\tLoss: 0.379333, Accuracy: 87.11\n",
      "Train Epoch: 42 [37120/50000 (82%)]\tLoss: 0.517586, Accuracy: 81.25\n",
      "Train Epoch: 42 [38400/50000 (85%)]\tLoss: 0.367991, Accuracy: 89.06\n",
      "Train Epoch: 42 [39680/50000 (88%)]\tLoss: 0.440159, Accuracy: 86.33\n",
      "Train Epoch: 42 [40960/50000 (91%)]\tLoss: 0.486302, Accuracy: 82.81\n",
      "Train Epoch: 42 [42240/50000 (94%)]\tLoss: 0.390304, Accuracy: 85.16\n",
      "Train Epoch: 42 [43520/50000 (97%)]\tLoss: 0.414840, Accuracy: 85.55\n",
      "Train Epoch: 42 [35000/50000 (99%)]\tLoss: 0.296490, Accuracy: 89.50\n",
      "\n",
      "Validation set: Average loss: 0.6480, Accuracy: 3965/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[37.63804769515991 s]\n",
      "\n",
      "Test set: Average loss: 0.6612, Accuracy: 7893/10000 (78.93%)\n",
      "\n",
      "Train Epoch: 43 [0/50000 (0%)]\tLoss: 0.390561, Accuracy: 88.67\n",
      "Train Epoch: 43 [1280/50000 (3%)]\tLoss: 0.360161, Accuracy: 88.28\n",
      "Train Epoch: 43 [2560/50000 (6%)]\tLoss: 0.322967, Accuracy: 89.45\n",
      "Train Epoch: 43 [3840/50000 (9%)]\tLoss: 0.440279, Accuracy: 86.72\n",
      "Train Epoch: 43 [5120/50000 (11%)]\tLoss: 0.376072, Accuracy: 88.28\n",
      "Train Epoch: 43 [6400/50000 (14%)]\tLoss: 0.377704, Accuracy: 86.33\n",
      "Train Epoch: 43 [7680/50000 (17%)]\tLoss: 0.413406, Accuracy: 85.55\n",
      "Train Epoch: 43 [8960/50000 (20%)]\tLoss: 0.305713, Accuracy: 91.41\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 43 [10240/50000 (23%)]\tLoss: 0.313768, Accuracy: 89.84\n",
      "Train Epoch: 43 [11520/50000 (26%)]\tLoss: 0.364052, Accuracy: 89.06\n",
      "Train Epoch: 43 [12800/50000 (28%)]\tLoss: 0.349397, Accuracy: 86.72\n",
      "Train Epoch: 43 [14080/50000 (31%)]\tLoss: 0.290933, Accuracy: 91.41\n",
      "Train Epoch: 43 [15360/50000 (34%)]\tLoss: 0.315711, Accuracy: 88.28\n",
      "Train Epoch: 43 [16640/50000 (37%)]\tLoss: 0.386867, Accuracy: 86.72\n",
      "Train Epoch: 43 [17920/50000 (40%)]\tLoss: 0.339686, Accuracy: 87.89\n",
      "Train Epoch: 43 [19200/50000 (43%)]\tLoss: 0.443950, Accuracy: 83.98\n",
      "Train Epoch: 43 [20480/50000 (45%)]\tLoss: 0.389626, Accuracy: 88.28\n",
      "Train Epoch: 43 [21760/50000 (48%)]\tLoss: 0.470288, Accuracy: 83.98\n",
      "Train Epoch: 43 [23040/50000 (51%)]\tLoss: 0.376949, Accuracy: 87.11\n",
      "Train Epoch: 43 [24320/50000 (54%)]\tLoss: 0.340871, Accuracy: 89.45\n",
      "Train Epoch: 43 [25600/50000 (57%)]\tLoss: 0.386807, Accuracy: 88.28\n",
      "Train Epoch: 43 [26880/50000 (60%)]\tLoss: 0.345890, Accuracy: 88.28\n",
      "Train Epoch: 43 [28160/50000 (62%)]\tLoss: 0.329117, Accuracy: 89.45\n",
      "Train Epoch: 43 [29440/50000 (65%)]\tLoss: 0.362713, Accuracy: 87.50\n",
      "Train Epoch: 43 [30720/50000 (68%)]\tLoss: 0.332405, Accuracy: 89.84\n",
      "Train Epoch: 43 [32000/50000 (71%)]\tLoss: 0.303522, Accuracy: 91.02\n",
      "Train Epoch: 43 [33280/50000 (74%)]\tLoss: 0.339582, Accuracy: 87.50\n",
      "Train Epoch: 43 [34560/50000 (77%)]\tLoss: 0.425654, Accuracy: 85.55\n",
      "Train Epoch: 43 [35840/50000 (80%)]\tLoss: 0.521492, Accuracy: 82.42\n",
      "Train Epoch: 43 [37120/50000 (82%)]\tLoss: 0.346063, Accuracy: 87.89\n",
      "Train Epoch: 43 [38400/50000 (85%)]\tLoss: 0.357629, Accuracy: 88.67\n",
      "Train Epoch: 43 [39680/50000 (88%)]\tLoss: 0.338282, Accuracy: 88.28\n",
      "Train Epoch: 43 [40960/50000 (91%)]\tLoss: 0.405128, Accuracy: 83.59\n",
      "Train Epoch: 43 [42240/50000 (94%)]\tLoss: 0.402867, Accuracy: 84.77\n",
      "Train Epoch: 43 [43520/50000 (97%)]\tLoss: 0.374294, Accuracy: 87.50\n",
      "Train Epoch: 43 [35000/50000 (99%)]\tLoss: 0.449751, Accuracy: 86.50\n",
      "\n",
      "Validation set: Average loss: 0.6314, Accuracy: 4023/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[40.83401155471802 s]\n",
      "Train Epoch: 44 [0/50000 (0%)]\tLoss: 0.325777, Accuracy: 89.45\n",
      "Train Epoch: 44 [1280/50000 (3%)]\tLoss: 0.344493, Accuracy: 87.11\n",
      "Train Epoch: 44 [2560/50000 (6%)]\tLoss: 0.309472, Accuracy: 89.06\n",
      "Train Epoch: 44 [3840/50000 (9%)]\tLoss: 0.323951, Accuracy: 88.67\n",
      "Train Epoch: 44 [5120/50000 (11%)]\tLoss: 0.366122, Accuracy: 87.50\n",
      "Train Epoch: 44 [6400/50000 (14%)]\tLoss: 0.382228, Accuracy: 86.72\n",
      "Train Epoch: 44 [7680/50000 (17%)]\tLoss: 0.380426, Accuracy: 84.77\n",
      "Train Epoch: 44 [8960/50000 (20%)]\tLoss: 0.298258, Accuracy: 89.84\n",
      "Train Epoch: 44 [10240/50000 (23%)]\tLoss: 0.386531, Accuracy: 86.33\n",
      "Train Epoch: 44 [11520/50000 (26%)]\tLoss: 0.357755, Accuracy: 88.28\n",
      "Train Epoch: 44 [12800/50000 (28%)]\tLoss: 0.356308, Accuracy: 87.50\n",
      "Train Epoch: 44 [14080/50000 (31%)]\tLoss: 0.289780, Accuracy: 88.67\n",
      "Train Epoch: 44 [15360/50000 (34%)]\tLoss: 0.354476, Accuracy: 86.72\n",
      "Train Epoch: 44 [16640/50000 (37%)]\tLoss: 0.350388, Accuracy: 87.89\n",
      "Train Epoch: 44 [17920/50000 (40%)]\tLoss: 0.431184, Accuracy: 86.72\n",
      "Train Epoch: 44 [19200/50000 (43%)]\tLoss: 0.350603, Accuracy: 88.28\n",
      "Train Epoch: 44 [20480/50000 (45%)]\tLoss: 0.433052, Accuracy: 83.20\n",
      "Train Epoch: 44 [21760/50000 (48%)]\tLoss: 0.252913, Accuracy: 89.84\n",
      "Train Epoch: 44 [23040/50000 (51%)]\tLoss: 0.395577, Accuracy: 86.72\n",
      "Train Epoch: 44 [24320/50000 (54%)]\tLoss: 0.353918, Accuracy: 87.50\n",
      "Train Epoch: 44 [25600/50000 (57%)]\tLoss: 0.450404, Accuracy: 83.59\n",
      "Train Epoch: 44 [26880/50000 (60%)]\tLoss: 0.411285, Accuracy: 87.11\n",
      "Train Epoch: 44 [28160/50000 (62%)]\tLoss: 0.419007, Accuracy: 85.94\n",
      "Train Epoch: 44 [29440/50000 (65%)]\tLoss: 0.314148, Accuracy: 88.28\n",
      "Train Epoch: 44 [30720/50000 (68%)]\tLoss: 0.404221, Accuracy: 85.94\n",
      "Train Epoch: 44 [32000/50000 (71%)]\tLoss: 0.409169, Accuracy: 85.94\n",
      "Train Epoch: 44 [33280/50000 (74%)]\tLoss: 0.334903, Accuracy: 86.33\n",
      "Train Epoch: 44 [34560/50000 (77%)]\tLoss: 0.363907, Accuracy: 88.67\n",
      "Train Epoch: 44 [35840/50000 (80%)]\tLoss: 0.405551, Accuracy: 86.72\n",
      "Train Epoch: 44 [37120/50000 (82%)]\tLoss: 0.438373, Accuracy: 85.94\n",
      "Train Epoch: 44 [38400/50000 (85%)]\tLoss: 0.373104, Accuracy: 87.11\n",
      "Train Epoch: 44 [39680/50000 (88%)]\tLoss: 0.368271, Accuracy: 87.50\n",
      "Train Epoch: 44 [40960/50000 (91%)]\tLoss: 0.361336, Accuracy: 87.11\n",
      "Train Epoch: 44 [42240/50000 (94%)]\tLoss: 0.360721, Accuracy: 87.50\n",
      "Train Epoch: 44 [43520/50000 (97%)]\tLoss: 0.334364, Accuracy: 88.28\n",
      "Train Epoch: 44 [35000/50000 (99%)]\tLoss: 0.354482, Accuracy: 90.00\n",
      "\n",
      "Validation set: Average loss: 0.5724, Accuracy: 4036/5000 (80.00%)\n",
      "\n",
      "the time of this epoch:[37.71672821044922 s]\n",
      "\n",
      "Test set: Average loss: 0.5920, Accuracy: 8134/10000 (81.34%)\n",
      "\n",
      "Train Epoch: 45 [0/50000 (0%)]\tLoss: 0.339945, Accuracy: 89.84\n",
      "Train Epoch: 45 [1280/50000 (3%)]\tLoss: 0.397671, Accuracy: 85.16\n",
      "Train Epoch: 45 [2560/50000 (6%)]\tLoss: 0.321028, Accuracy: 88.67\n",
      "Train Epoch: 45 [3840/50000 (9%)]\tLoss: 0.335562, Accuracy: 88.28\n",
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      "Train Epoch: 45 [8960/50000 (20%)]\tLoss: 0.317369, Accuracy: 91.41\n",
      "Train Epoch: 45 [10240/50000 (23%)]\tLoss: 0.409872, Accuracy: 84.38\n",
      "Train Epoch: 45 [11520/50000 (26%)]\tLoss: 0.575368, Accuracy: 81.25\n",
      "Train Epoch: 45 [12800/50000 (28%)]\tLoss: 0.361586, Accuracy: 85.94\n",
      "Train Epoch: 45 [14080/50000 (31%)]\tLoss: 0.250232, Accuracy: 92.19\n",
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      "Train Epoch: 45 [16640/50000 (37%)]\tLoss: 0.388499, Accuracy: 87.11\n",
      "Train Epoch: 45 [17920/50000 (40%)]\tLoss: 0.433630, Accuracy: 83.98\n",
      "Train Epoch: 45 [19200/50000 (43%)]\tLoss: 0.381342, Accuracy: 87.50\n",
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      "Train Epoch: 45 [23040/50000 (51%)]\tLoss: 0.287481, Accuracy: 90.23\n",
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      "Train Epoch: 45 [25600/50000 (57%)]\tLoss: 0.344557, Accuracy: 89.45\n",
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      "Train Epoch: 45 [28160/50000 (62%)]\tLoss: 0.425398, Accuracy: 84.38\n",
      "Train Epoch: 45 [29440/50000 (65%)]\tLoss: 0.355258, Accuracy: 86.33\n",
      "Train Epoch: 45 [30720/50000 (68%)]\tLoss: 0.458926, Accuracy: 83.59\n",
      "Train Epoch: 45 [32000/50000 (71%)]\tLoss: 0.317521, Accuracy: 89.06\n",
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      "Train Epoch: 45 [34560/50000 (77%)]\tLoss: 0.433152, Accuracy: 84.77\n",
      "Train Epoch: 45 [35840/50000 (80%)]\tLoss: 0.413193, Accuracy: 87.89\n",
      "Train Epoch: 45 [37120/50000 (82%)]\tLoss: 0.312037, Accuracy: 88.67\n",
      "Train Epoch: 45 [38400/50000 (85%)]\tLoss: 0.401868, Accuracy: 85.94\n",
      "Train Epoch: 45 [39680/50000 (88%)]\tLoss: 0.422617, Accuracy: 85.94\n",
      "Train Epoch: 45 [40960/50000 (91%)]\tLoss: 0.253915, Accuracy: 90.23\n",
      "Train Epoch: 45 [42240/50000 (94%)]\tLoss: 0.309388, Accuracy: 89.45\n",
      "Train Epoch: 45 [43520/50000 (97%)]\tLoss: 0.289099, Accuracy: 89.06\n",
      "Train Epoch: 45 [35000/50000 (99%)]\tLoss: 0.441453, Accuracy: 81.50\n",
      "\n",
      "Validation set: Average loss: 0.9097, Accuracy: 3738/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[40.81479620933533 s]\n",
      "Train Epoch: 46 [0/50000 (0%)]\tLoss: 0.344506, Accuracy: 88.67\n",
      "Train Epoch: 46 [1280/50000 (3%)]\tLoss: 0.341672, Accuracy: 89.45\n",
      "Train Epoch: 46 [2560/50000 (6%)]\tLoss: 0.334794, Accuracy: 90.62\n",
      "Train Epoch: 46 [3840/50000 (9%)]\tLoss: 0.381215, Accuracy: 86.72\n",
      "Train Epoch: 46 [5120/50000 (11%)]\tLoss: 0.285827, Accuracy: 89.45\n",
      "Train Epoch: 46 [6400/50000 (14%)]\tLoss: 0.357318, Accuracy: 88.67\n",
      "Train Epoch: 46 [7680/50000 (17%)]\tLoss: 0.293076, Accuracy: 90.23\n",
      "Train Epoch: 46 [8960/50000 (20%)]\tLoss: 0.395223, Accuracy: 86.33\n",
      "Train Epoch: 46 [10240/50000 (23%)]\tLoss: 0.354655, Accuracy: 87.11\n",
      "Train Epoch: 46 [11520/50000 (26%)]\tLoss: 0.363701, Accuracy: 87.11\n",
      "Train Epoch: 46 [12800/50000 (28%)]\tLoss: 0.383206, Accuracy: 86.72\n",
      "Train Epoch: 46 [14080/50000 (31%)]\tLoss: 0.295139, Accuracy: 91.02\n",
      "Train Epoch: 46 [15360/50000 (34%)]\tLoss: 0.317966, Accuracy: 88.28\n",
      "Train Epoch: 46 [16640/50000 (37%)]\tLoss: 0.270430, Accuracy: 92.19\n",
      "Train Epoch: 46 [17920/50000 (40%)]\tLoss: 0.356768, Accuracy: 87.50\n",
      "Train Epoch: 46 [19200/50000 (43%)]\tLoss: 0.298767, Accuracy: 90.23\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 46 [20480/50000 (45%)]\tLoss: 0.346731, Accuracy: 87.11\n",
      "Train Epoch: 46 [21760/50000 (48%)]\tLoss: 0.358525, Accuracy: 88.67\n",
      "Train Epoch: 46 [23040/50000 (51%)]\tLoss: 0.273283, Accuracy: 91.41\n",
      "Train Epoch: 46 [24320/50000 (54%)]\tLoss: 0.340715, Accuracy: 88.28\n",
      "Train Epoch: 46 [25600/50000 (57%)]\tLoss: 0.334041, Accuracy: 89.84\n",
      "Train Epoch: 46 [26880/50000 (60%)]\tLoss: 0.420720, Accuracy: 84.77\n",
      "Train Epoch: 46 [28160/50000 (62%)]\tLoss: 0.376696, Accuracy: 87.11\n",
      "Train Epoch: 46 [29440/50000 (65%)]\tLoss: 0.369283, Accuracy: 86.72\n",
      "Train Epoch: 46 [30720/50000 (68%)]\tLoss: 0.453044, Accuracy: 85.55\n",
      "Train Epoch: 46 [32000/50000 (71%)]\tLoss: 0.382484, Accuracy: 89.45\n",
      "Train Epoch: 46 [33280/50000 (74%)]\tLoss: 0.429405, Accuracy: 82.42\n",
      "Train Epoch: 46 [34560/50000 (77%)]\tLoss: 0.395682, Accuracy: 87.11\n",
      "Train Epoch: 46 [35840/50000 (80%)]\tLoss: 0.425256, Accuracy: 86.33\n",
      "Train Epoch: 46 [37120/50000 (82%)]\tLoss: 0.296044, Accuracy: 87.89\n",
      "Train Epoch: 46 [38400/50000 (85%)]\tLoss: 0.456842, Accuracy: 86.72\n",
      "Train Epoch: 46 [39680/50000 (88%)]\tLoss: 0.397748, Accuracy: 85.94\n",
      "Train Epoch: 46 [40960/50000 (91%)]\tLoss: 0.269552, Accuracy: 89.84\n",
      "Train Epoch: 46 [42240/50000 (94%)]\tLoss: 0.350779, Accuracy: 87.11\n",
      "Train Epoch: 46 [43520/50000 (97%)]\tLoss: 0.344407, Accuracy: 90.23\n",
      "Train Epoch: 46 [35000/50000 (99%)]\tLoss: 0.468822, Accuracy: 82.50\n",
      "\n",
      "Validation set: Average loss: 0.8461, Accuracy: 3737/5000 (74.00%)\n",
      "\n",
      "the time of this epoch:[37.6405303478241 s]\n",
      "\n",
      "Test set: Average loss: 0.8408, Accuracy: 7464/10000 (74.64%)\n",
      "\n",
      "Train Epoch: 47 [0/50000 (0%)]\tLoss: 0.393681, Accuracy: 85.55\n",
      "Train Epoch: 47 [1280/50000 (3%)]\tLoss: 0.465391, Accuracy: 80.47\n",
      "Train Epoch: 47 [2560/50000 (6%)]\tLoss: 0.331343, Accuracy: 90.23\n",
      "Train Epoch: 47 [3840/50000 (9%)]\tLoss: 0.399117, Accuracy: 86.72\n",
      "Train Epoch: 47 [5120/50000 (11%)]\tLoss: 0.330210, Accuracy: 86.72\n",
      "Train Epoch: 47 [6400/50000 (14%)]\tLoss: 0.303437, Accuracy: 88.67\n",
      "Train Epoch: 47 [7680/50000 (17%)]\tLoss: 0.346261, Accuracy: 89.06\n",
      "Train Epoch: 47 [8960/50000 (20%)]\tLoss: 0.276587, Accuracy: 89.45\n",
      "Train Epoch: 47 [10240/50000 (23%)]\tLoss: 0.412627, Accuracy: 86.72\n",
      "Train Epoch: 47 [11520/50000 (26%)]\tLoss: 0.312722, Accuracy: 88.28\n",
      "Train Epoch: 47 [12800/50000 (28%)]\tLoss: 0.325297, Accuracy: 91.41\n",
      "Train Epoch: 47 [14080/50000 (31%)]\tLoss: 0.321556, Accuracy: 90.23\n",
      "Train Epoch: 47 [15360/50000 (34%)]\tLoss: 0.339647, Accuracy: 86.72\n",
      "Train Epoch: 47 [16640/50000 (37%)]\tLoss: 0.313185, Accuracy: 91.80\n",
      "Train Epoch: 47 [17920/50000 (40%)]\tLoss: 0.346396, Accuracy: 88.67\n",
      "Train Epoch: 47 [19200/50000 (43%)]\tLoss: 0.390125, Accuracy: 86.72\n",
      "Train Epoch: 47 [20480/50000 (45%)]\tLoss: 0.389117, Accuracy: 87.89\n",
      "Train Epoch: 47 [21760/50000 (48%)]\tLoss: 0.318887, Accuracy: 89.84\n",
      "Train Epoch: 47 [23040/50000 (51%)]\tLoss: 0.338953, Accuracy: 87.50\n",
      "Train Epoch: 47 [24320/50000 (54%)]\tLoss: 0.336216, Accuracy: 88.28\n",
      "Train Epoch: 47 [25600/50000 (57%)]\tLoss: 0.415281, Accuracy: 87.50\n",
      "Train Epoch: 47 [26880/50000 (60%)]\tLoss: 0.284239, Accuracy: 91.02\n",
      "Train Epoch: 47 [28160/50000 (62%)]\tLoss: 0.347700, Accuracy: 87.50\n",
      "Train Epoch: 47 [29440/50000 (65%)]\tLoss: 0.398088, Accuracy: 84.38\n",
      "Train Epoch: 47 [30720/50000 (68%)]\tLoss: 0.473390, Accuracy: 83.59\n",
      "Train Epoch: 47 [32000/50000 (71%)]\tLoss: 0.341063, Accuracy: 88.67\n",
      "Train Epoch: 47 [33280/50000 (74%)]\tLoss: 0.401563, Accuracy: 83.98\n",
      "Train Epoch: 47 [34560/50000 (77%)]\tLoss: 0.387712, Accuracy: 83.98\n",
      "Train Epoch: 47 [35840/50000 (80%)]\tLoss: 0.406109, Accuracy: 86.33\n",
      "Train Epoch: 47 [37120/50000 (82%)]\tLoss: 0.454732, Accuracy: 87.11\n",
      "Train Epoch: 47 [38400/50000 (85%)]\tLoss: 0.350999, Accuracy: 87.89\n",
      "Train Epoch: 47 [39680/50000 (88%)]\tLoss: 0.350393, Accuracy: 89.45\n",
      "Train Epoch: 47 [40960/50000 (91%)]\tLoss: 0.483477, Accuracy: 83.98\n",
      "Train Epoch: 47 [42240/50000 (94%)]\tLoss: 0.389376, Accuracy: 86.72\n",
      "Train Epoch: 47 [43520/50000 (97%)]\tLoss: 0.417020, Accuracy: 87.89\n",
      "Train Epoch: 47 [35000/50000 (99%)]\tLoss: 0.454821, Accuracy: 85.00\n",
      "\n",
      "Validation set: Average loss: 0.6796, Accuracy: 3902/5000 (78.00%)\n",
      "\n",
      "the time of this epoch:[40.794533252716064 s]\n",
      "Train Epoch: 48 [0/50000 (0%)]\tLoss: 0.403222, Accuracy: 87.11\n",
      "Train Epoch: 48 [1280/50000 (3%)]\tLoss: 0.369543, Accuracy: 87.11\n",
      "Train Epoch: 48 [2560/50000 (6%)]\tLoss: 0.302701, Accuracy: 89.84\n",
      "torch.Size([512, 1, 1])\n",
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      "tensor(1.00000e-02 *\n",
      "       [[[ 5.5506]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
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      "\n",
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      "torch.Size([1, 448, 512])\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 48 [3840/50000 (9%)]\tLoss: 0.426536, Accuracy: 85.55\n",
      "Train Epoch: 48 [5120/50000 (11%)]\tLoss: 0.286717, Accuracy: 91.80\n",
      "Train Epoch: 48 [6400/50000 (14%)]\tLoss: 0.379401, Accuracy: 87.89\n",
      "Train Epoch: 48 [7680/50000 (17%)]\tLoss: 0.378148, Accuracy: 88.28\n",
      "Train Epoch: 48 [8960/50000 (20%)]\tLoss: 0.308394, Accuracy: 90.23\n",
      "Train Epoch: 48 [10240/50000 (23%)]\tLoss: 0.418849, Accuracy: 85.94\n",
      "Train Epoch: 48 [11520/50000 (26%)]\tLoss: 0.394378, Accuracy: 87.11\n",
      "Train Epoch: 48 [12800/50000 (28%)]\tLoss: 0.393830, Accuracy: 86.72\n",
      "Train Epoch: 48 [14080/50000 (31%)]\tLoss: 0.303740, Accuracy: 91.41\n",
      "Train Epoch: 48 [15360/50000 (34%)]\tLoss: 0.393903, Accuracy: 87.11\n",
      "Train Epoch: 48 [16640/50000 (37%)]\tLoss: 0.350420, Accuracy: 87.11\n",
      "Train Epoch: 48 [17920/50000 (40%)]\tLoss: 0.285813, Accuracy: 91.02\n",
      "Train Epoch: 48 [19200/50000 (43%)]\tLoss: 0.389188, Accuracy: 86.72\n",
      "Train Epoch: 48 [20480/50000 (45%)]\tLoss: 0.505334, Accuracy: 80.08\n",
      "Train Epoch: 48 [21760/50000 (48%)]\tLoss: 0.450127, Accuracy: 83.59\n",
      "Train Epoch: 48 [23040/50000 (51%)]\tLoss: 0.370645, Accuracy: 87.89\n",
      "Train Epoch: 48 [24320/50000 (54%)]\tLoss: 0.432142, Accuracy: 85.55\n",
      "Train Epoch: 48 [25600/50000 (57%)]\tLoss: 0.335247, Accuracy: 89.45\n",
      "Train Epoch: 48 [26880/50000 (60%)]\tLoss: 0.425015, Accuracy: 85.94\n",
      "Train Epoch: 48 [28160/50000 (62%)]\tLoss: 0.474568, Accuracy: 83.59\n",
      "Train Epoch: 48 [29440/50000 (65%)]\tLoss: 0.423597, Accuracy: 84.77\n",
      "Train Epoch: 48 [30720/50000 (68%)]\tLoss: 0.293684, Accuracy: 90.23\n",
      "Train Epoch: 48 [32000/50000 (71%)]\tLoss: 0.390014, Accuracy: 88.67\n",
      "Train Epoch: 48 [33280/50000 (74%)]\tLoss: 0.369458, Accuracy: 88.28\n",
      "Train Epoch: 48 [34560/50000 (77%)]\tLoss: 0.320005, Accuracy: 90.62\n",
      "Train Epoch: 48 [35840/50000 (80%)]\tLoss: 0.411474, Accuracy: 87.11\n",
      "Train Epoch: 48 [37120/50000 (82%)]\tLoss: 0.437012, Accuracy: 85.55\n",
      "Train Epoch: 48 [38400/50000 (85%)]\tLoss: 0.446915, Accuracy: 84.77\n",
      "Train Epoch: 48 [39680/50000 (88%)]\tLoss: 0.423353, Accuracy: 85.55\n",
      "Train Epoch: 48 [40960/50000 (91%)]\tLoss: 0.382553, Accuracy: 87.11\n",
      "Train Epoch: 48 [42240/50000 (94%)]\tLoss: 0.386145, Accuracy: 85.94\n",
      "Train Epoch: 48 [43520/50000 (97%)]\tLoss: 0.346121, Accuracy: 88.28\n",
      "Train Epoch: 48 [35000/50000 (99%)]\tLoss: 0.423901, Accuracy: 87.00\n",
      "\n",
      "Validation set: Average loss: 0.5357, Accuracy: 4112/5000 (82.00%)\n",
      "\n",
      "the time of this epoch:[37.7296257019043 s]\n",
      "\n",
      "Test set: Average loss: 0.5613, Accuracy: 8173/10000 (81.73%)\n",
      "\n",
      "Train Epoch: 49 [0/50000 (0%)]\tLoss: 0.321971, Accuracy: 89.84\n",
      "Train Epoch: 49 [1280/50000 (3%)]\tLoss: 0.314067, Accuracy: 90.23\n",
      "Train Epoch: 49 [2560/50000 (6%)]\tLoss: 0.441599, Accuracy: 85.16\n",
      "Train Epoch: 49 [3840/50000 (9%)]\tLoss: 0.206256, Accuracy: 94.14\n",
      "Train Epoch: 49 [5120/50000 (11%)]\tLoss: 0.409537, Accuracy: 85.16\n",
      "Train Epoch: 49 [6400/50000 (14%)]\tLoss: 0.316644, Accuracy: 88.28\n",
      "Train Epoch: 49 [7680/50000 (17%)]\tLoss: 0.427111, Accuracy: 85.16\n",
      "Train Epoch: 49 [8960/50000 (20%)]\tLoss: 0.338626, Accuracy: 89.06\n",
      "Train Epoch: 49 [10240/50000 (23%)]\tLoss: 0.409884, Accuracy: 85.94\n",
      "Train Epoch: 49 [11520/50000 (26%)]\tLoss: 0.443346, Accuracy: 86.33\n",
      "Train Epoch: 49 [12800/50000 (28%)]\tLoss: 0.369751, Accuracy: 89.06\n",
      "Train Epoch: 49 [14080/50000 (31%)]\tLoss: 0.278298, Accuracy: 91.80\n",
      "Train Epoch: 49 [15360/50000 (34%)]\tLoss: 0.361230, Accuracy: 87.11\n",
      "Train Epoch: 49 [16640/50000 (37%)]\tLoss: 0.328649, Accuracy: 87.89\n",
      "Train Epoch: 49 [17920/50000 (40%)]\tLoss: 0.355466, Accuracy: 88.28\n",
      "Train Epoch: 49 [19200/50000 (43%)]\tLoss: 0.267881, Accuracy: 90.62\n",
      "Train Epoch: 49 [20480/50000 (45%)]\tLoss: 0.325356, Accuracy: 88.28\n",
      "Train Epoch: 49 [21760/50000 (48%)]\tLoss: 0.351180, Accuracy: 86.72\n",
      "Train Epoch: 49 [23040/50000 (51%)]\tLoss: 0.390719, Accuracy: 85.55\n",
      "Train Epoch: 49 [24320/50000 (54%)]\tLoss: 0.397702, Accuracy: 84.77\n",
      "Train Epoch: 49 [25600/50000 (57%)]\tLoss: 0.295060, Accuracy: 90.23\n",
      "Train Epoch: 49 [26880/50000 (60%)]\tLoss: 0.450850, Accuracy: 83.98\n",
      "Train Epoch: 49 [28160/50000 (62%)]\tLoss: 0.347420, Accuracy: 89.06\n",
      "Train Epoch: 49 [29440/50000 (65%)]\tLoss: 0.375353, Accuracy: 86.72\n",
      "Train Epoch: 49 [30720/50000 (68%)]\tLoss: 0.292378, Accuracy: 89.45\n",
      "Train Epoch: 49 [32000/50000 (71%)]\tLoss: 0.403990, Accuracy: 86.72\n",
      "Train Epoch: 49 [33280/50000 (74%)]\tLoss: 0.413095, Accuracy: 83.98\n",
      "Train Epoch: 49 [34560/50000 (77%)]\tLoss: 0.328693, Accuracy: 88.67\n",
      "Train Epoch: 49 [35840/50000 (80%)]\tLoss: 0.316605, Accuracy: 89.06\n",
      "Train Epoch: 49 [37120/50000 (82%)]\tLoss: 0.300485, Accuracy: 90.23\n",
      "Train Epoch: 49 [38400/50000 (85%)]\tLoss: 0.371640, Accuracy: 85.94\n",
      "Train Epoch: 49 [39680/50000 (88%)]\tLoss: 0.494472, Accuracy: 82.81\n",
      "Train Epoch: 49 [40960/50000 (91%)]\tLoss: 0.418448, Accuracy: 85.16\n",
      "Train Epoch: 49 [42240/50000 (94%)]\tLoss: 0.385363, Accuracy: 87.11\n",
      "Train Epoch: 49 [43520/50000 (97%)]\tLoss: 0.581155, Accuracy: 79.30\n",
      "Train Epoch: 49 [35000/50000 (99%)]\tLoss: 0.498739, Accuracy: 84.00\n",
      "\n",
      "Validation set: Average loss: 0.6502, Accuracy: 3956/5000 (79.00%)\n",
      "\n",
      "the time of this epoch:[40.85223889350891 s]\n",
      "Train Epoch: 50 [0/50000 (0%)]\tLoss: 0.378624, Accuracy: 86.72\n",
      "Train Epoch: 50 [1280/50000 (3%)]\tLoss: 0.378043, Accuracy: 87.89\n",
      "Train Epoch: 50 [2560/50000 (6%)]\tLoss: 0.376309, Accuracy: 89.45\n",
      "Train Epoch: 50 [3840/50000 (9%)]\tLoss: 0.298769, Accuracy: 89.45\n",
      "Train Epoch: 50 [5120/50000 (11%)]\tLoss: 0.363265, Accuracy: 87.89\n",
      "Train Epoch: 50 [6400/50000 (14%)]\tLoss: 0.311227, Accuracy: 89.45\n",
      "Train Epoch: 50 [7680/50000 (17%)]\tLoss: 0.295428, Accuracy: 89.06\n",
      "Train Epoch: 50 [8960/50000 (20%)]\tLoss: 0.405351, Accuracy: 87.11\n",
      "Train Epoch: 50 [10240/50000 (23%)]\tLoss: 0.366699, Accuracy: 85.94\n",
      "Train Epoch: 50 [11520/50000 (26%)]\tLoss: 0.304141, Accuracy: 89.45\n",
      "Train Epoch: 50 [12800/50000 (28%)]\tLoss: 0.368240, Accuracy: 87.50\n",
      "Train Epoch: 50 [14080/50000 (31%)]\tLoss: 0.343419, Accuracy: 88.28\n",
      "Train Epoch: 50 [15360/50000 (34%)]\tLoss: 0.442821, Accuracy: 83.59\n",
      "Train Epoch: 50 [16640/50000 (37%)]\tLoss: 0.439668, Accuracy: 85.94\n",
      "Train Epoch: 50 [17920/50000 (40%)]\tLoss: 0.402834, Accuracy: 86.72\n",
      "Train Epoch: 50 [19200/50000 (43%)]\tLoss: 0.402844, Accuracy: 85.55\n",
      "Train Epoch: 50 [20480/50000 (45%)]\tLoss: 0.366319, Accuracy: 88.28\n",
      "Train Epoch: 50 [21760/50000 (48%)]\tLoss: 0.408625, Accuracy: 85.55\n",
      "Train Epoch: 50 [23040/50000 (51%)]\tLoss: 0.258621, Accuracy: 91.02\n",
      "Train Epoch: 50 [24320/50000 (54%)]\tLoss: 0.297024, Accuracy: 89.84\n",
      "Train Epoch: 50 [25600/50000 (57%)]\tLoss: 0.393088, Accuracy: 86.33\n",
      "Train Epoch: 50 [26880/50000 (60%)]\tLoss: 0.371152, Accuracy: 87.50\n",
      "Train Epoch: 50 [28160/50000 (62%)]\tLoss: 0.316139, Accuracy: 91.02\n",
      "Train Epoch: 50 [29440/50000 (65%)]\tLoss: 0.406070, Accuracy: 86.33\n",
      "Train Epoch: 50 [30720/50000 (68%)]\tLoss: 0.297528, Accuracy: 90.23\n",
      "Train Epoch: 50 [32000/50000 (71%)]\tLoss: 0.336320, Accuracy: 89.45\n",
      "Train Epoch: 50 [33280/50000 (74%)]\tLoss: 0.295459, Accuracy: 90.23\n",
      "Train Epoch: 50 [34560/50000 (77%)]\tLoss: 0.345227, Accuracy: 88.67\n",
      "Train Epoch: 50 [35840/50000 (80%)]\tLoss: 0.369675, Accuracy: 87.50\n",
      "Train Epoch: 50 [37120/50000 (82%)]\tLoss: 0.428238, Accuracy: 88.28\n",
      "Train Epoch: 50 [38400/50000 (85%)]\tLoss: 0.404962, Accuracy: 86.33\n",
      "Train Epoch: 50 [39680/50000 (88%)]\tLoss: 0.363658, Accuracy: 86.33\n",
      "Train Epoch: 50 [40960/50000 (91%)]\tLoss: 0.385112, Accuracy: 87.50\n",
      "Train Epoch: 50 [42240/50000 (94%)]\tLoss: 0.381033, Accuracy: 87.89\n",
      "Train Epoch: 50 [43520/50000 (97%)]\tLoss: 0.405644, Accuracy: 87.50\n",
      "Train Epoch: 50 [35000/50000 (99%)]\tLoss: 0.321023, Accuracy: 86.00\n",
      "\n",
      "Validation set: Average loss: 0.9474, Accuracy: 3546/5000 (70.00%)\n",
      "\n",
      "the time of this epoch:[37.65465545654297 s]\n",
      "\n",
      "Test set: Average loss: 0.9456, Accuracy: 7081/10000 (70.81%)\n",
      "\n",
      "Train Epoch: 51 [0/50000 (0%)]\tLoss: 0.345599, Accuracy: 87.50\n",
      "Train Epoch: 51 [1280/50000 (3%)]\tLoss: 0.313664, Accuracy: 87.50\n",
      "Train Epoch: 51 [2560/50000 (6%)]\tLoss: 0.390611, Accuracy: 86.33\n",
      "Train Epoch: 51 [3840/50000 (9%)]\tLoss: 0.322824, Accuracy: 89.06\n",
      "Train Epoch: 51 [5120/50000 (11%)]\tLoss: 0.317677, Accuracy: 90.62\n",
      "Train Epoch: 51 [6400/50000 (14%)]\tLoss: 0.274037, Accuracy: 91.02\n",
      "Train Epoch: 51 [7680/50000 (17%)]\tLoss: 0.230905, Accuracy: 92.58\n",
      "Train Epoch: 51 [8960/50000 (20%)]\tLoss: 0.322427, Accuracy: 90.62\n",
      "Train Epoch: 51 [10240/50000 (23%)]\tLoss: 0.174788, Accuracy: 93.75\n",
      "Train Epoch: 51 [11520/50000 (26%)]\tLoss: 0.245669, Accuracy: 91.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 51 [12800/50000 (28%)]\tLoss: 0.288771, Accuracy: 91.02\n",
      "Train Epoch: 51 [14080/50000 (31%)]\tLoss: 0.246238, Accuracy: 92.19\n",
      "Train Epoch: 51 [15360/50000 (34%)]\tLoss: 0.220288, Accuracy: 92.19\n",
      "Train Epoch: 51 [16640/50000 (37%)]\tLoss: 0.199961, Accuracy: 94.53\n",
      "Train Epoch: 51 [17920/50000 (40%)]\tLoss: 0.219229, Accuracy: 91.80\n",
      "Train Epoch: 51 [19200/50000 (43%)]\tLoss: 0.234061, Accuracy: 92.19\n",
      "Train Epoch: 51 [20480/50000 (45%)]\tLoss: 0.252298, Accuracy: 90.62\n",
      "Train Epoch: 51 [21760/50000 (48%)]\tLoss: 0.201405, Accuracy: 92.58\n",
      "Train Epoch: 51 [23040/50000 (51%)]\tLoss: 0.267991, Accuracy: 91.41\n",
      "Train Epoch: 51 [24320/50000 (54%)]\tLoss: 0.238909, Accuracy: 90.23\n",
      "Train Epoch: 51 [25600/50000 (57%)]\tLoss: 0.180892, Accuracy: 94.92\n",
      "Train Epoch: 51 [26880/50000 (60%)]\tLoss: 0.200866, Accuracy: 94.14\n",
      "Train Epoch: 51 [28160/50000 (62%)]\tLoss: 0.266211, Accuracy: 91.02\n",
      "Train Epoch: 51 [29440/50000 (65%)]\tLoss: 0.200898, Accuracy: 94.53\n",
      "Train Epoch: 51 [30720/50000 (68%)]\tLoss: 0.271758, Accuracy: 90.62\n",
      "Train Epoch: 51 [32000/50000 (71%)]\tLoss: 0.208422, Accuracy: 92.58\n",
      "Train Epoch: 51 [33280/50000 (74%)]\tLoss: 0.243663, Accuracy: 91.02\n",
      "Train Epoch: 51 [34560/50000 (77%)]\tLoss: 0.319999, Accuracy: 89.45\n",
      "Train Epoch: 51 [35840/50000 (80%)]\tLoss: 0.305197, Accuracy: 89.45\n",
      "Train Epoch: 51 [37120/50000 (82%)]\tLoss: 0.231985, Accuracy: 91.80\n",
      "Train Epoch: 51 [38400/50000 (85%)]\tLoss: 0.272675, Accuracy: 91.41\n",
      "Train Epoch: 51 [39680/50000 (88%)]\tLoss: 0.179894, Accuracy: 94.53\n",
      "Train Epoch: 51 [40960/50000 (91%)]\tLoss: 0.201177, Accuracy: 93.75\n",
      "Train Epoch: 51 [42240/50000 (94%)]\tLoss: 0.211670, Accuracy: 93.75\n",
      "Train Epoch: 51 [43520/50000 (97%)]\tLoss: 0.275415, Accuracy: 91.41\n",
      "Train Epoch: 51 [35000/50000 (99%)]\tLoss: 0.270552, Accuracy: 91.00\n",
      "\n",
      "Validation set: Average loss: 0.2793, Accuracy: 4534/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[40.775988817214966 s]\n",
      "Train Epoch: 52 [0/50000 (0%)]\tLoss: 0.142713, Accuracy: 95.70\n",
      "Train Epoch: 52 [1280/50000 (3%)]\tLoss: 0.227435, Accuracy: 92.58\n",
      "Train Epoch: 52 [2560/50000 (6%)]\tLoss: 0.162517, Accuracy: 94.14\n",
      "Train Epoch: 52 [3840/50000 (9%)]\tLoss: 0.242615, Accuracy: 92.58\n",
      "Train Epoch: 52 [5120/50000 (11%)]\tLoss: 0.171511, Accuracy: 95.31\n",
      "Train Epoch: 52 [6400/50000 (14%)]\tLoss: 0.205924, Accuracy: 94.53\n",
      "Train Epoch: 52 [7680/50000 (17%)]\tLoss: 0.146187, Accuracy: 94.53\n",
      "Train Epoch: 52 [8960/50000 (20%)]\tLoss: 0.160055, Accuracy: 94.92\n",
      "Train Epoch: 52 [10240/50000 (23%)]\tLoss: 0.186699, Accuracy: 92.97\n",
      "Train Epoch: 52 [11520/50000 (26%)]\tLoss: 0.235707, Accuracy: 91.41\n",
      "Train Epoch: 52 [12800/50000 (28%)]\tLoss: 0.213732, Accuracy: 93.75\n",
      "Train Epoch: 52 [14080/50000 (31%)]\tLoss: 0.215551, Accuracy: 93.36\n",
      "Train Epoch: 52 [15360/50000 (34%)]\tLoss: 0.237994, Accuracy: 91.02\n",
      "Train Epoch: 52 [16640/50000 (37%)]\tLoss: 0.222491, Accuracy: 92.58\n",
      "Train Epoch: 52 [17920/50000 (40%)]\tLoss: 0.244185, Accuracy: 92.19\n",
      "Train Epoch: 52 [19200/50000 (43%)]\tLoss: 0.274214, Accuracy: 89.84\n",
      "Train Epoch: 52 [20480/50000 (45%)]\tLoss: 0.202881, Accuracy: 92.58\n",
      "Train Epoch: 52 [21760/50000 (48%)]\tLoss: 0.172635, Accuracy: 92.58\n",
      "Train Epoch: 52 [23040/50000 (51%)]\tLoss: 0.208175, Accuracy: 93.36\n",
      "Train Epoch: 52 [24320/50000 (54%)]\tLoss: 0.190166, Accuracy: 92.97\n",
      "Train Epoch: 52 [25600/50000 (57%)]\tLoss: 0.255050, Accuracy: 92.58\n",
      "Train Epoch: 52 [26880/50000 (60%)]\tLoss: 0.224428, Accuracy: 93.36\n",
      "Train Epoch: 52 [28160/50000 (62%)]\tLoss: 0.171743, Accuracy: 94.14\n",
      "Train Epoch: 52 [29440/50000 (65%)]\tLoss: 0.169966, Accuracy: 94.14\n",
      "Train Epoch: 52 [30720/50000 (68%)]\tLoss: 0.215163, Accuracy: 92.58\n",
      "Train Epoch: 52 [32000/50000 (71%)]\tLoss: 0.219281, Accuracy: 94.92\n",
      "Train Epoch: 52 [33280/50000 (74%)]\tLoss: 0.186382, Accuracy: 93.75\n",
      "Train Epoch: 52 [34560/50000 (77%)]\tLoss: 0.148401, Accuracy: 96.48\n",
      "Train Epoch: 52 [35840/50000 (80%)]\tLoss: 0.219244, Accuracy: 94.92\n",
      "Train Epoch: 52 [37120/50000 (82%)]\tLoss: 0.270463, Accuracy: 89.84\n",
      "Train Epoch: 52 [38400/50000 (85%)]\tLoss: 0.270732, Accuracy: 90.23\n",
      "Train Epoch: 52 [39680/50000 (88%)]\tLoss: 0.237218, Accuracy: 91.41\n",
      "Train Epoch: 52 [40960/50000 (91%)]\tLoss: 0.210834, Accuracy: 93.36\n",
      "Train Epoch: 52 [42240/50000 (94%)]\tLoss: 0.246914, Accuracy: 91.41\n",
      "Train Epoch: 52 [43520/50000 (97%)]\tLoss: 0.199239, Accuracy: 93.75\n",
      "Train Epoch: 52 [35000/50000 (99%)]\tLoss: 0.243597, Accuracy: 92.50\n",
      "\n",
      "Validation set: Average loss: 0.2713, Accuracy: 4544/5000 (90.00%)\n",
      "\n",
      "the time of this epoch:[37.67905592918396 s]\n",
      "\n",
      "Test set: Average loss: 0.2954, Accuracy: 9038/10000 (90.38%)\n",
      "\n",
      "Train Epoch: 53 [0/50000 (0%)]\tLoss: 0.169705, Accuracy: 95.31\n",
      "Train Epoch: 53 [1280/50000 (3%)]\tLoss: 0.164558, Accuracy: 94.14\n",
      "Train Epoch: 53 [2560/50000 (6%)]\tLoss: 0.159410, Accuracy: 95.70\n",
      "Train Epoch: 53 [3840/50000 (9%)]\tLoss: 0.171709, Accuracy: 93.75\n",
      "Train Epoch: 53 [5120/50000 (11%)]\tLoss: 0.179084, Accuracy: 94.53\n",
      "Train Epoch: 53 [6400/50000 (14%)]\tLoss: 0.244268, Accuracy: 90.62\n",
      "Train Epoch: 53 [7680/50000 (17%)]\tLoss: 0.120602, Accuracy: 96.09\n",
      "Train Epoch: 53 [8960/50000 (20%)]\tLoss: 0.242125, Accuracy: 92.19\n",
      "Train Epoch: 53 [10240/50000 (23%)]\tLoss: 0.136200, Accuracy: 95.31\n",
      "Train Epoch: 53 [11520/50000 (26%)]\tLoss: 0.219873, Accuracy: 92.19\n",
      "Train Epoch: 53 [12800/50000 (28%)]\tLoss: 0.169481, Accuracy: 94.92\n",
      "Train Epoch: 53 [14080/50000 (31%)]\tLoss: 0.161555, Accuracy: 94.92\n",
      "Train Epoch: 53 [15360/50000 (34%)]\tLoss: 0.204401, Accuracy: 92.58\n",
      "Train Epoch: 53 [16640/50000 (37%)]\tLoss: 0.098169, Accuracy: 97.27\n",
      "Train Epoch: 53 [17920/50000 (40%)]\tLoss: 0.199614, Accuracy: 93.36\n",
      "Train Epoch: 53 [19200/50000 (43%)]\tLoss: 0.223047, Accuracy: 92.97\n",
      "Train Epoch: 53 [20480/50000 (45%)]\tLoss: 0.135213, Accuracy: 96.48\n",
      "Train Epoch: 53 [21760/50000 (48%)]\tLoss: 0.175012, Accuracy: 93.36\n",
      "Train Epoch: 53 [23040/50000 (51%)]\tLoss: 0.201571, Accuracy: 94.53\n",
      "Train Epoch: 53 [24320/50000 (54%)]\tLoss: 0.237753, Accuracy: 91.80\n",
      "Train Epoch: 53 [25600/50000 (57%)]\tLoss: 0.176771, Accuracy: 94.14\n",
      "Train Epoch: 53 [26880/50000 (60%)]\tLoss: 0.153071, Accuracy: 94.53\n",
      "Train Epoch: 53 [28160/50000 (62%)]\tLoss: 0.224526, Accuracy: 91.41\n",
      "Train Epoch: 53 [29440/50000 (65%)]\tLoss: 0.218045, Accuracy: 92.97\n",
      "Train Epoch: 53 [30720/50000 (68%)]\tLoss: 0.140234, Accuracy: 94.92\n",
      "Train Epoch: 53 [32000/50000 (71%)]\tLoss: 0.174367, Accuracy: 94.14\n",
      "Train Epoch: 53 [33280/50000 (74%)]\tLoss: 0.184617, Accuracy: 94.92\n",
      "Train Epoch: 53 [34560/50000 (77%)]\tLoss: 0.170330, Accuracy: 95.31\n",
      "Train Epoch: 53 [35840/50000 (80%)]\tLoss: 0.181494, Accuracy: 94.14\n",
      "Train Epoch: 53 [37120/50000 (82%)]\tLoss: 0.209505, Accuracy: 93.36\n",
      "Train Epoch: 53 [38400/50000 (85%)]\tLoss: 0.199907, Accuracy: 92.19\n",
      "Train Epoch: 53 [39680/50000 (88%)]\tLoss: 0.177576, Accuracy: 95.31\n",
      "Train Epoch: 53 [40960/50000 (91%)]\tLoss: 0.247580, Accuracy: 92.97\n",
      "Train Epoch: 53 [42240/50000 (94%)]\tLoss: 0.134730, Accuracy: 95.31\n",
      "Train Epoch: 53 [43520/50000 (97%)]\tLoss: 0.219079, Accuracy: 93.36\n",
      "Train Epoch: 53 [35000/50000 (99%)]\tLoss: 0.193854, Accuracy: 93.00\n",
      "\n",
      "Validation set: Average loss: 0.2656, Accuracy: 4554/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.81278848648071 s]\n",
      "Train Epoch: 54 [0/50000 (0%)]\tLoss: 0.132581, Accuracy: 95.70\n",
      "Train Epoch: 54 [1280/50000 (3%)]\tLoss: 0.199315, Accuracy: 93.75\n",
      "Train Epoch: 54 [2560/50000 (6%)]\tLoss: 0.217703, Accuracy: 92.97\n",
      "Train Epoch: 54 [3840/50000 (9%)]\tLoss: 0.187288, Accuracy: 94.92\n",
      "Train Epoch: 54 [5120/50000 (11%)]\tLoss: 0.141165, Accuracy: 95.31\n",
      "Train Epoch: 54 [6400/50000 (14%)]\tLoss: 0.209472, Accuracy: 92.97\n",
      "Train Epoch: 54 [7680/50000 (17%)]\tLoss: 0.191631, Accuracy: 93.36\n",
      "Train Epoch: 54 [8960/50000 (20%)]\tLoss: 0.176302, Accuracy: 93.36\n",
      "Train Epoch: 54 [10240/50000 (23%)]\tLoss: 0.158467, Accuracy: 93.36\n",
      "Train Epoch: 54 [11520/50000 (26%)]\tLoss: 0.185566, Accuracy: 94.92\n",
      "Train Epoch: 54 [12800/50000 (28%)]\tLoss: 0.105306, Accuracy: 96.88\n",
      "Train Epoch: 54 [14080/50000 (31%)]\tLoss: 0.144665, Accuracy: 94.14\n",
      "Train Epoch: 54 [15360/50000 (34%)]\tLoss: 0.105030, Accuracy: 98.05\n",
      "Train Epoch: 54 [16640/50000 (37%)]\tLoss: 0.152803, Accuracy: 94.14\n",
      "Train Epoch: 54 [17920/50000 (40%)]\tLoss: 0.133597, Accuracy: 95.31\n",
      "Train Epoch: 54 [19200/50000 (43%)]\tLoss: 0.121700, Accuracy: 96.09\n",
      "Train Epoch: 54 [20480/50000 (45%)]\tLoss: 0.174838, Accuracy: 92.97\n",
      "Train Epoch: 54 [21760/50000 (48%)]\tLoss: 0.125883, Accuracy: 94.53\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 54 [23040/50000 (51%)]\tLoss: 0.182113, Accuracy: 94.53\n",
      "Train Epoch: 54 [24320/50000 (54%)]\tLoss: 0.159936, Accuracy: 93.75\n",
      "Train Epoch: 54 [25600/50000 (57%)]\tLoss: 0.187109, Accuracy: 96.09\n",
      "Train Epoch: 54 [26880/50000 (60%)]\tLoss: 0.160292, Accuracy: 94.14\n",
      "Train Epoch: 54 [28160/50000 (62%)]\tLoss: 0.135391, Accuracy: 94.92\n",
      "Train Epoch: 54 [29440/50000 (65%)]\tLoss: 0.202805, Accuracy: 93.75\n",
      "Train Epoch: 54 [30720/50000 (68%)]\tLoss: 0.132051, Accuracy: 96.48\n",
      "Train Epoch: 54 [32000/50000 (71%)]\tLoss: 0.135034, Accuracy: 96.48\n",
      "Train Epoch: 54 [33280/50000 (74%)]\tLoss: 0.239380, Accuracy: 91.80\n",
      "Train Epoch: 54 [34560/50000 (77%)]\tLoss: 0.159273, Accuracy: 94.92\n",
      "Train Epoch: 54 [35840/50000 (80%)]\tLoss: 0.131234, Accuracy: 94.92\n",
      "Train Epoch: 54 [37120/50000 (82%)]\tLoss: 0.120807, Accuracy: 95.70\n",
      "Train Epoch: 54 [38400/50000 (85%)]\tLoss: 0.140794, Accuracy: 94.92\n",
      "Train Epoch: 54 [39680/50000 (88%)]\tLoss: 0.163518, Accuracy: 93.75\n",
      "Train Epoch: 54 [40960/50000 (91%)]\tLoss: 0.158744, Accuracy: 95.31\n",
      "Train Epoch: 54 [42240/50000 (94%)]\tLoss: 0.151761, Accuracy: 94.53\n",
      "Train Epoch: 54 [43520/50000 (97%)]\tLoss: 0.225550, Accuracy: 92.58\n",
      "Train Epoch: 54 [35000/50000 (99%)]\tLoss: 0.163480, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2551, Accuracy: 4566/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.71437072753906 s]\n",
      "\n",
      "Test set: Average loss: 0.2931, Accuracy: 9060/10000 (90.60%)\n",
      "\n",
      "Train Epoch: 55 [0/50000 (0%)]\tLoss: 0.146181, Accuracy: 94.92\n",
      "Train Epoch: 55 [1280/50000 (3%)]\tLoss: 0.152329, Accuracy: 94.92\n",
      "Train Epoch: 55 [2560/50000 (6%)]\tLoss: 0.175685, Accuracy: 94.14\n",
      "Train Epoch: 55 [3840/50000 (9%)]\tLoss: 0.189915, Accuracy: 94.53\n",
      "Train Epoch: 55 [5120/50000 (11%)]\tLoss: 0.179540, Accuracy: 93.75\n",
      "Train Epoch: 55 [6400/50000 (14%)]\tLoss: 0.141116, Accuracy: 96.09\n",
      "Train Epoch: 55 [7680/50000 (17%)]\tLoss: 0.169477, Accuracy: 94.53\n",
      "Train Epoch: 55 [8960/50000 (20%)]\tLoss: 0.138805, Accuracy: 95.31\n",
      "Train Epoch: 55 [10240/50000 (23%)]\tLoss: 0.183095, Accuracy: 94.14\n",
      "Train Epoch: 55 [11520/50000 (26%)]\tLoss: 0.181118, Accuracy: 94.53\n",
      "Train Epoch: 55 [12800/50000 (28%)]\tLoss: 0.155611, Accuracy: 94.53\n",
      "Train Epoch: 55 [14080/50000 (31%)]\tLoss: 0.190244, Accuracy: 91.80\n",
      "Train Epoch: 55 [15360/50000 (34%)]\tLoss: 0.187784, Accuracy: 95.31\n",
      "Train Epoch: 55 [16640/50000 (37%)]\tLoss: 0.108948, Accuracy: 96.88\n",
      "Train Epoch: 55 [17920/50000 (40%)]\tLoss: 0.150613, Accuracy: 96.09\n",
      "Train Epoch: 55 [19200/50000 (43%)]\tLoss: 0.154246, Accuracy: 95.70\n",
      "Train Epoch: 55 [20480/50000 (45%)]\tLoss: 0.126255, Accuracy: 96.09\n",
      "Train Epoch: 55 [21760/50000 (48%)]\tLoss: 0.145424, Accuracy: 95.70\n",
      "Train Epoch: 55 [23040/50000 (51%)]\tLoss: 0.186242, Accuracy: 94.53\n",
      "Train Epoch: 55 [24320/50000 (54%)]\tLoss: 0.166696, Accuracy: 93.75\n",
      "Train Epoch: 55 [25600/50000 (57%)]\tLoss: 0.163152, Accuracy: 94.53\n",
      "Train Epoch: 55 [26880/50000 (60%)]\tLoss: 0.099582, Accuracy: 96.09\n",
      "Train Epoch: 55 [28160/50000 (62%)]\tLoss: 0.185507, Accuracy: 93.75\n",
      "Train Epoch: 55 [29440/50000 (65%)]\tLoss: 0.171012, Accuracy: 95.70\n",
      "Train Epoch: 55 [30720/50000 (68%)]\tLoss: 0.172742, Accuracy: 94.14\n",
      "Train Epoch: 55 [32000/50000 (71%)]\tLoss: 0.115531, Accuracy: 96.09\n",
      "Train Epoch: 55 [33280/50000 (74%)]\tLoss: 0.110321, Accuracy: 97.66\n",
      "Train Epoch: 55 [34560/50000 (77%)]\tLoss: 0.120885, Accuracy: 96.09\n",
      "Train Epoch: 55 [35840/50000 (80%)]\tLoss: 0.108701, Accuracy: 98.05\n",
      "Train Epoch: 55 [37120/50000 (82%)]\tLoss: 0.149599, Accuracy: 94.14\n",
      "Train Epoch: 55 [38400/50000 (85%)]\tLoss: 0.083210, Accuracy: 98.05\n",
      "Train Epoch: 55 [39680/50000 (88%)]\tLoss: 0.095541, Accuracy: 96.48\n",
      "Train Epoch: 55 [40960/50000 (91%)]\tLoss: 0.228994, Accuracy: 91.80\n",
      "Train Epoch: 55 [42240/50000 (94%)]\tLoss: 0.150700, Accuracy: 95.70\n",
      "Train Epoch: 55 [43520/50000 (97%)]\tLoss: 0.180494, Accuracy: 93.36\n",
      "Train Epoch: 55 [35000/50000 (99%)]\tLoss: 0.153720, Accuracy: 94.00\n",
      "\n",
      "Validation set: Average loss: 0.2602, Accuracy: 4576/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.79286789894104 s]\n",
      "Train Epoch: 56 [0/50000 (0%)]\tLoss: 0.092504, Accuracy: 96.48\n",
      "Train Epoch: 56 [1280/50000 (3%)]\tLoss: 0.123819, Accuracy: 96.88\n",
      "Train Epoch: 56 [2560/50000 (6%)]\tLoss: 0.175144, Accuracy: 95.31\n",
      "Train Epoch: 56 [3840/50000 (9%)]\tLoss: 0.223422, Accuracy: 93.36\n",
      "Train Epoch: 56 [5120/50000 (11%)]\tLoss: 0.148086, Accuracy: 95.70\n",
      "Train Epoch: 56 [6400/50000 (14%)]\tLoss: 0.095861, Accuracy: 97.66\n",
      "Train Epoch: 56 [7680/50000 (17%)]\tLoss: 0.177620, Accuracy: 95.31\n",
      "Train Epoch: 56 [8960/50000 (20%)]\tLoss: 0.174537, Accuracy: 92.58\n",
      "Train Epoch: 56 [10240/50000 (23%)]\tLoss: 0.166858, Accuracy: 94.14\n",
      "Train Epoch: 56 [11520/50000 (26%)]\tLoss: 0.179338, Accuracy: 94.92\n",
      "Train Epoch: 56 [12800/50000 (28%)]\tLoss: 0.182632, Accuracy: 93.75\n",
      "Train Epoch: 56 [14080/50000 (31%)]\tLoss: 0.164328, Accuracy: 95.70\n",
      "Train Epoch: 56 [15360/50000 (34%)]\tLoss: 0.114239, Accuracy: 95.31\n",
      "Train Epoch: 56 [16640/50000 (37%)]\tLoss: 0.127075, Accuracy: 94.14\n",
      "Train Epoch: 56 [17920/50000 (40%)]\tLoss: 0.219809, Accuracy: 92.97\n",
      "Train Epoch: 56 [19200/50000 (43%)]\tLoss: 0.142958, Accuracy: 96.09\n",
      "Train Epoch: 56 [20480/50000 (45%)]\tLoss: 0.154873, Accuracy: 94.92\n",
      "Train Epoch: 56 [21760/50000 (48%)]\tLoss: 0.161289, Accuracy: 93.75\n",
      "Train Epoch: 56 [23040/50000 (51%)]\tLoss: 0.075547, Accuracy: 97.27\n",
      "Train Epoch: 56 [24320/50000 (54%)]\tLoss: 0.110397, Accuracy: 95.70\n",
      "Train Epoch: 56 [25600/50000 (57%)]\tLoss: 0.136730, Accuracy: 94.53\n",
      "Train Epoch: 56 [26880/50000 (60%)]\tLoss: 0.116683, Accuracy: 96.48\n",
      "Train Epoch: 56 [28160/50000 (62%)]\tLoss: 0.151794, Accuracy: 96.09\n",
      "Train Epoch: 56 [29440/50000 (65%)]\tLoss: 0.164588, Accuracy: 94.14\n",
      "Train Epoch: 56 [30720/50000 (68%)]\tLoss: 0.164882, Accuracy: 94.14\n",
      "Train Epoch: 56 [32000/50000 (71%)]\tLoss: 0.252067, Accuracy: 92.58\n",
      "Train Epoch: 56 [33280/50000 (74%)]\tLoss: 0.168018, Accuracy: 95.31\n",
      "Train Epoch: 56 [34560/50000 (77%)]\tLoss: 0.147624, Accuracy: 94.92\n",
      "Train Epoch: 56 [35840/50000 (80%)]\tLoss: 0.152398, Accuracy: 94.14\n",
      "Train Epoch: 56 [37120/50000 (82%)]\tLoss: 0.175562, Accuracy: 94.53\n",
      "Train Epoch: 56 [38400/50000 (85%)]\tLoss: 0.217131, Accuracy: 92.19\n",
      "Train Epoch: 56 [39680/50000 (88%)]\tLoss: 0.173773, Accuracy: 94.92\n",
      "Train Epoch: 56 [40960/50000 (91%)]\tLoss: 0.126818, Accuracy: 96.48\n",
      "Train Epoch: 56 [42240/50000 (94%)]\tLoss: 0.188736, Accuracy: 93.75\n",
      "Train Epoch: 56 [43520/50000 (97%)]\tLoss: 0.144867, Accuracy: 94.14\n",
      "Train Epoch: 56 [35000/50000 (99%)]\tLoss: 0.157657, Accuracy: 94.00\n",
      "\n",
      "Validation set: Average loss: 0.2566, Accuracy: 4587/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.64854717254639 s]\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor([[[ 0.0020]],\n",
      "\n",
      "        [[ 0.0041]],\n",
      "\n",
      "        [[ 0.1682]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.0005]],\n",
      "\n",
      "        [[ 0.0008]],\n",
      "\n",
      "        [[ 0.0000]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-03 *\n",
      "       [[[ 2.2073]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-0.1972,  0.2286, -0.0881,  ..., -0.0128,  0.2503, -0.2648]],\n",
      "\n",
      "        [[ 0.6568,  0.7588,  0.8035,  ...,  0.6506,  0.5702,  0.7395]],\n",
      "\n",
      "        [[ 1.0192,  0.8327,  0.6488,  ...,  0.7225,  0.5665,  0.4866]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.1971, -0.2155, -0.1635,  ...,  0.2274, -0.1449, -0.0479]],\n",
      "\n",
      "        [[ 0.0332, -0.0332, -0.0762,  ...,  0.0278,  0.2694,  0.0164]],\n",
      "\n",
      "        [[-0.3120,  0.1277, -0.2518,  ..., -0.0375, -0.2016, -0.1379]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[-0.2338, -0.1861, -0.1638,  ..., -0.1584, -0.1251, -0.0049],\n",
      "         [ 2.3225,  2.7434,  2.9915,  ...,  3.1714,  2.7445,  2.3774],\n",
      "         [ 1.9518,  2.1677,  2.4677,  ...,  2.8351,  2.5857,  2.2597],\n",
      "         ...,\n",
      "         [ 0.2068,  0.1433,  0.1346,  ..., -0.1949, -0.2318, -0.2102],\n",
      "         [ 0.3056,  0.3975,  0.3815,  ...,  0.0946,  0.0564, -0.0009],\n",
      "         [-0.4133, -0.5414, -0.6114,  ..., -0.7017, -0.5635, -0.4589]]], device='cuda:0')\n",
      "\n",
      "Test set: Average loss: 0.2937, Accuracy: 9062/10000 (90.62%)\n",
      "\n",
      "Train Epoch: 57 [0/50000 (0%)]\tLoss: 0.143547, Accuracy: 94.53\n",
      "Train Epoch: 57 [1280/50000 (3%)]\tLoss: 0.106361, Accuracy: 96.09\n",
      "Train Epoch: 57 [2560/50000 (6%)]\tLoss: 0.159246, Accuracy: 93.36\n",
      "Train Epoch: 57 [3840/50000 (9%)]\tLoss: 0.140832, Accuracy: 96.09\n",
      "Train Epoch: 57 [5120/50000 (11%)]\tLoss: 0.085168, Accuracy: 95.70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 57 [6400/50000 (14%)]\tLoss: 0.126439, Accuracy: 96.48\n",
      "Train Epoch: 57 [7680/50000 (17%)]\tLoss: 0.130849, Accuracy: 95.70\n",
      "Train Epoch: 57 [8960/50000 (20%)]\tLoss: 0.089782, Accuracy: 96.88\n",
      "Train Epoch: 57 [10240/50000 (23%)]\tLoss: 0.104704, Accuracy: 96.09\n",
      "Train Epoch: 57 [11520/50000 (26%)]\tLoss: 0.133179, Accuracy: 94.53\n",
      "Train Epoch: 57 [12800/50000 (28%)]\tLoss: 0.140492, Accuracy: 94.92\n",
      "Train Epoch: 57 [14080/50000 (31%)]\tLoss: 0.093499, Accuracy: 97.66\n",
      "Train Epoch: 57 [15360/50000 (34%)]\tLoss: 0.159256, Accuracy: 94.53\n",
      "Train Epoch: 57 [16640/50000 (37%)]\tLoss: 0.175418, Accuracy: 92.97\n",
      "Train Epoch: 57 [17920/50000 (40%)]\tLoss: 0.092608, Accuracy: 97.27\n",
      "Train Epoch: 57 [19200/50000 (43%)]\tLoss: 0.101113, Accuracy: 96.09\n",
      "Train Epoch: 57 [20480/50000 (45%)]\tLoss: 0.121153, Accuracy: 95.31\n",
      "Train Epoch: 57 [21760/50000 (48%)]\tLoss: 0.210125, Accuracy: 93.36\n",
      "Train Epoch: 57 [23040/50000 (51%)]\tLoss: 0.138941, Accuracy: 96.09\n",
      "Train Epoch: 57 [24320/50000 (54%)]\tLoss: 0.105716, Accuracy: 97.27\n",
      "Train Epoch: 57 [25600/50000 (57%)]\tLoss: 0.156103, Accuracy: 93.36\n",
      "Train Epoch: 57 [26880/50000 (60%)]\tLoss: 0.139759, Accuracy: 95.70\n",
      "Train Epoch: 57 [28160/50000 (62%)]\tLoss: 0.084549, Accuracy: 96.88\n",
      "Train Epoch: 57 [29440/50000 (65%)]\tLoss: 0.145090, Accuracy: 94.92\n",
      "Train Epoch: 57 [30720/50000 (68%)]\tLoss: 0.196423, Accuracy: 92.58\n",
      "Train Epoch: 57 [32000/50000 (71%)]\tLoss: 0.113544, Accuracy: 96.09\n",
      "Train Epoch: 57 [33280/50000 (74%)]\tLoss: 0.160331, Accuracy: 95.70\n",
      "Train Epoch: 57 [34560/50000 (77%)]\tLoss: 0.153832, Accuracy: 95.31\n",
      "Train Epoch: 57 [35840/50000 (80%)]\tLoss: 0.111017, Accuracy: 96.88\n",
      "Train Epoch: 57 [37120/50000 (82%)]\tLoss: 0.168254, Accuracy: 93.75\n",
      "Train Epoch: 57 [38400/50000 (85%)]\tLoss: 0.144450, Accuracy: 95.31\n",
      "Train Epoch: 57 [39680/50000 (88%)]\tLoss: 0.139195, Accuracy: 93.75\n",
      "Train Epoch: 57 [40960/50000 (91%)]\tLoss: 0.152940, Accuracy: 95.31\n",
      "Train Epoch: 57 [42240/50000 (94%)]\tLoss: 0.138011, Accuracy: 95.70\n",
      "Train Epoch: 57 [43520/50000 (97%)]\tLoss: 0.144113, Accuracy: 97.27\n",
      "Train Epoch: 57 [35000/50000 (99%)]\tLoss: 0.093135, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2598, Accuracy: 4581/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.81005573272705 s]\n",
      "Train Epoch: 58 [0/50000 (0%)]\tLoss: 0.162614, Accuracy: 94.53\n",
      "Train Epoch: 58 [1280/50000 (3%)]\tLoss: 0.113276, Accuracy: 96.09\n",
      "Train Epoch: 58 [2560/50000 (6%)]\tLoss: 0.100868, Accuracy: 96.48\n",
      "Train Epoch: 58 [3840/50000 (9%)]\tLoss: 0.143851, Accuracy: 94.92\n",
      "Train Epoch: 58 [5120/50000 (11%)]\tLoss: 0.195168, Accuracy: 92.97\n",
      "Train Epoch: 58 [6400/50000 (14%)]\tLoss: 0.113230, Accuracy: 96.48\n",
      "Train Epoch: 58 [7680/50000 (17%)]\tLoss: 0.143151, Accuracy: 96.48\n",
      "Train Epoch: 58 [8960/50000 (20%)]\tLoss: 0.115021, Accuracy: 96.48\n",
      "Train Epoch: 58 [10240/50000 (23%)]\tLoss: 0.117580, Accuracy: 95.31\n",
      "Train Epoch: 58 [11520/50000 (26%)]\tLoss: 0.129656, Accuracy: 95.70\n",
      "Train Epoch: 58 [12800/50000 (28%)]\tLoss: 0.107425, Accuracy: 96.48\n",
      "Train Epoch: 58 [14080/50000 (31%)]\tLoss: 0.123207, Accuracy: 96.48\n",
      "Train Epoch: 58 [15360/50000 (34%)]\tLoss: 0.090427, Accuracy: 98.05\n",
      "Train Epoch: 58 [16640/50000 (37%)]\tLoss: 0.182408, Accuracy: 92.97\n",
      "Train Epoch: 58 [17920/50000 (40%)]\tLoss: 0.101362, Accuracy: 96.09\n",
      "Train Epoch: 58 [19200/50000 (43%)]\tLoss: 0.172697, Accuracy: 93.36\n",
      "Train Epoch: 58 [20480/50000 (45%)]\tLoss: 0.120431, Accuracy: 96.09\n",
      "Train Epoch: 58 [21760/50000 (48%)]\tLoss: 0.096059, Accuracy: 96.09\n",
      "Train Epoch: 58 [23040/50000 (51%)]\tLoss: 0.123036, Accuracy: 94.92\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.0466]],\n",
      "\n",
      "        [[ 0.0611]],\n",
      "\n",
      "        [[ 0.0490]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0074]],\n",
      "\n",
      "        [[ 0.0098]],\n",
      "\n",
      "        [[ 0.0983]],\n",
      "\n",
      "        [[ 0.1396]],\n",
      "\n",
      "        [[ 0.0226]],\n",
      "\n",
      "        [[ 0.1415]],\n",
      "\n",
      "        [[ 0.0105]],\n",
      "\n",
      "        [[ 0.4544]],\n",
      "\n",
      "        [[ 0.0240]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0061]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0404]],\n",
      "\n",
      "        [[ 0.0276]],\n",
      "\n",
      "        [[ 0.0002]],\n",
      "\n",
      "        [[ 0.0747]],\n",
      "\n",
      "        [[ 0.0401]],\n",
      "\n",
      "        [[ 0.0026]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0690]],\n",
      "\n",
      "        [[ 0.0878]],\n",
      "\n",
      "        [[ 0.0248]],\n",
      "\n",
      "        [[ 0.0212]],\n",
      "\n",
      "        [[ 0.0186]],\n",
      "\n",
      "        [[ 0.0054]],\n",
      "\n",
      "        [[ 0.0703]],\n",
      "\n",
      "        [[ 0.0121]],\n",
      "\n",
      "        [[ 0.0423]],\n",
      "\n",
      "        [[ 0.0763]],\n",
      "\n",
      "        [[ 0.0125]],\n",
      "\n",
      "        [[ 0.0106]],\n",
      "\n",
      "        [[ 0.1326]],\n",
      "\n",
      "        [[ 0.1189]],\n",
      "\n",
      "        [[ 0.0243]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0043]],\n",
      "\n",
      "        [[ 0.0354]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0220]],\n",
      "\n",
      "        [[ 0.0414]],\n",
      "\n",
      "        [[ 0.3474]],\n",
      "\n",
      "        [[ 0.0582]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0436]],\n",
      "\n",
      "        [[ 0.0615]],\n",
      "\n",
      "        [[ 0.0036]],\n",
      "\n",
      "        [[ 0.0211]],\n",
      "\n",
      "        [[ 0.0588]],\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0231]],\n",
      "\n",
      "        [[ 0.0101]],\n",
      "\n",
      "        [[ 0.0324]],\n",
      "\n",
      "        [[ 0.0187]],\n",
      "\n",
      "        [[ 0.0270]],\n",
      "\n",
      "        [[ 0.0156]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.1653]],\n",
      "\n",
      "        [[ 0.0559]],\n",
      "\n",
      "        [[ 0.0209]],\n",
      "\n",
      "        [[ 0.0029]],\n",
      "\n",
      "        [[ 0.0007]],\n",
      "\n",
      "        [[ 0.0002]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0009]],\n",
      "\n",
      "        [[ 0.0011]],\n",
      "\n",
      "        [[ 0.0038]],\n",
      "\n",
      "        [[ 0.0024]],\n",
      "\n",
      "        [[ 0.0002]],\n",
      "\n",
      "        [[ 0.0044]],\n",
      "\n",
      "        [[ 0.0004]],\n",
      "\n",
      "        [[ 0.0002]],\n",
      "\n",
      "        [[ 0.0557]],\n",
      "\n",
      "        [[ 0.0021]],\n",
      "\n",
      "        [[ 0.0053]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0004]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0004]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0082]],\n",
      "\n",
      "        [[ 0.0037]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0024]],\n",
      "\n",
      "        [[ 0.0014]],\n",
      "\n",
      "        [[ 0.0095]],\n",
      "\n",
      "        [[ 0.0087]],\n",
      "\n",
      "        [[ 0.0121]],\n",
      "\n",
      "        [[ 0.0013]],\n",
      "\n",
      "        [[ 0.0035]],\n",
      "\n",
      "        [[ 0.0087]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0029]],\n",
      "\n",
      "        [[ 0.0017]],\n",
      "\n",
      "        [[ 0.0074]],\n",
      "\n",
      "        [[ 0.0010]],\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0006]],\n",
      "\n",
      "        [[ 0.0028]],\n",
      "\n",
      "        [[ 0.0084]],\n",
      "\n",
      "        [[ 0.0219]],\n",
      "\n",
      "        [[ 0.0009]],\n",
      "\n",
      "        [[ 0.0015]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0556]],\n",
      "\n",
      "        [[ 0.0011]],\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0082]],\n",
      "\n",
      "        [[ 0.0010]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0008]],\n",
      "\n",
      "        [[ 0.0011]],\n",
      "\n",
      "        [[ 0.0327]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0050]],\n",
      "\n",
      "        [[ 0.0002]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0019]],\n",
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      "        [[ 0.0028]],\n",
      "\n",
      "        [[ 0.0043]],\n",
      "\n",
      "        [[ 0.0065]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 1.9132]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 3.9717e-03,  6.8899e-03,  7.3819e-03,  ...,  6.9312e-03,\n",
      "           2.4777e-03,  2.7792e-03]],\n",
      "\n",
      "        [[ 4.2150e-05,  2.3700e-03,  2.3229e-03,  ...,  1.7246e-03,\n",
      "          -1.7842e-03, -1.5554e-03]],\n",
      "\n",
      "        [[-2.2950e-03, -3.7171e-04, -1.5530e-03,  ..., -2.9170e-03,\n",
      "          -1.2525e-03,  1.3219e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 6.3515e-04, -1.0923e-03, -8.1970e-04,  ...,  2.9402e-03,\n",
      "           2.9568e-03, -2.3531e-03]],\n",
      "\n",
      "        [[-5.2438e-04,  1.6689e-03,  1.6989e-03,  ...,  2.0813e-04,\n",
      "           4.4864e-05,  3.5160e-03]],\n",
      "\n",
      "        [[ 2.2528e-04,  5.8392e-04,  2.8697e-03,  ...,  1.5649e-03,\n",
      "          -1.3965e-03, -5.6604e-04]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
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      "           1.2715e-03,  1.1701e-03],\n",
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     "text": [
      "Train Epoch: 58 [24320/50000 (54%)]\tLoss: 0.113918, Accuracy: 95.70\n",
      "Train Epoch: 58 [25600/50000 (57%)]\tLoss: 0.154254, Accuracy: 95.70\n",
      "Train Epoch: 58 [26880/50000 (60%)]\tLoss: 0.070233, Accuracy: 98.05\n",
      "Train Epoch: 58 [28160/50000 (62%)]\tLoss: 0.087972, Accuracy: 96.88\n",
      "Train Epoch: 58 [29440/50000 (65%)]\tLoss: 0.125038, Accuracy: 94.53\n",
      "Train Epoch: 58 [30720/50000 (68%)]\tLoss: 0.153229, Accuracy: 95.70\n",
      "Train Epoch: 58 [32000/50000 (71%)]\tLoss: 0.125467, Accuracy: 96.88\n",
      "Train Epoch: 58 [33280/50000 (74%)]\tLoss: 0.129481, Accuracy: 96.09\n",
      "Train Epoch: 58 [34560/50000 (77%)]\tLoss: 0.111650, Accuracy: 96.88\n",
      "Train Epoch: 58 [35840/50000 (80%)]\tLoss: 0.140033, Accuracy: 95.31\n",
      "Train Epoch: 58 [37120/50000 (82%)]\tLoss: 0.153795, Accuracy: 94.92\n",
      "Train Epoch: 58 [38400/50000 (85%)]\tLoss: 0.123279, Accuracy: 94.53\n",
      "Train Epoch: 58 [39680/50000 (88%)]\tLoss: 0.147017, Accuracy: 93.36\n",
      "Train Epoch: 58 [40960/50000 (91%)]\tLoss: 0.124560, Accuracy: 96.88\n",
      "Train Epoch: 58 [42240/50000 (94%)]\tLoss: 0.101194, Accuracy: 97.66\n",
      "Train Epoch: 58 [43520/50000 (97%)]\tLoss: 0.127607, Accuracy: 94.53\n",
      "Train Epoch: 58 [35000/50000 (99%)]\tLoss: 0.166844, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2678, Accuracy: 4582/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.69853758811951 s]\n",
      "\n",
      "Test set: Average loss: 0.3040, Accuracy: 9080/10000 (90.80%)\n",
      "\n",
      "Train Epoch: 59 [0/50000 (0%)]\tLoss: 0.166419, Accuracy: 95.31\n",
      "Train Epoch: 59 [1280/50000 (3%)]\tLoss: 0.050252, Accuracy: 99.22\n",
      "Train Epoch: 59 [2560/50000 (6%)]\tLoss: 0.093174, Accuracy: 98.05\n",
      "Train Epoch: 59 [3840/50000 (9%)]\tLoss: 0.137762, Accuracy: 95.70\n",
      "Train Epoch: 59 [5120/50000 (11%)]\tLoss: 0.066172, Accuracy: 97.66\n",
      "Train Epoch: 59 [6400/50000 (14%)]\tLoss: 0.152674, Accuracy: 95.70\n",
      "Train Epoch: 59 [7680/50000 (17%)]\tLoss: 0.150466, Accuracy: 95.70\n",
      "Train Epoch: 59 [8960/50000 (20%)]\tLoss: 0.118923, Accuracy: 95.31\n",
      "Train Epoch: 59 [10240/50000 (23%)]\tLoss: 0.137186, Accuracy: 94.92\n",
      "Train Epoch: 59 [11520/50000 (26%)]\tLoss: 0.092548, Accuracy: 97.66\n",
      "Train Epoch: 59 [12800/50000 (28%)]\tLoss: 0.127371, Accuracy: 96.09\n",
      "Train Epoch: 59 [14080/50000 (31%)]\tLoss: 0.087122, Accuracy: 95.70\n",
      "Train Epoch: 59 [15360/50000 (34%)]\tLoss: 0.129497, Accuracy: 95.70\n",
      "Train Epoch: 59 [16640/50000 (37%)]\tLoss: 0.097391, Accuracy: 97.66\n",
      "Train Epoch: 59 [17920/50000 (40%)]\tLoss: 0.096339, Accuracy: 96.48\n",
      "Train Epoch: 59 [19200/50000 (43%)]\tLoss: 0.097726, Accuracy: 95.70\n",
      "Train Epoch: 59 [20480/50000 (45%)]\tLoss: 0.096894, Accuracy: 96.48\n",
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      "tensor(1.00000e-02 *\n",
      "       [[[ 1.8952]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
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      "\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 59 [21760/50000 (48%)]\tLoss: 0.093969, Accuracy: 96.48\n",
      "Train Epoch: 59 [23040/50000 (51%)]\tLoss: 0.116493, Accuracy: 96.09\n",
      "Train Epoch: 59 [24320/50000 (54%)]\tLoss: 0.102543, Accuracy: 96.48\n",
      "Train Epoch: 59 [25600/50000 (57%)]\tLoss: 0.148017, Accuracy: 94.53\n",
      "Train Epoch: 59 [26880/50000 (60%)]\tLoss: 0.117158, Accuracy: 97.27\n",
      "Train Epoch: 59 [28160/50000 (62%)]\tLoss: 0.143834, Accuracy: 94.92\n",
      "Train Epoch: 59 [29440/50000 (65%)]\tLoss: 0.108776, Accuracy: 96.09\n",
      "Train Epoch: 59 [30720/50000 (68%)]\tLoss: 0.137692, Accuracy: 94.14\n",
      "Train Epoch: 59 [32000/50000 (71%)]\tLoss: 0.112453, Accuracy: 96.09\n",
      "Train Epoch: 59 [33280/50000 (74%)]\tLoss: 0.132241, Accuracy: 95.31\n",
      "Train Epoch: 59 [34560/50000 (77%)]\tLoss: 0.131001, Accuracy: 94.92\n",
      "Train Epoch: 59 [35840/50000 (80%)]\tLoss: 0.111947, Accuracy: 96.48\n",
      "Train Epoch: 59 [37120/50000 (82%)]\tLoss: 0.102416, Accuracy: 95.70\n",
      "Train Epoch: 59 [38400/50000 (85%)]\tLoss: 0.151575, Accuracy: 95.70\n",
      "Train Epoch: 59 [39680/50000 (88%)]\tLoss: 0.079760, Accuracy: 97.66\n",
      "Train Epoch: 59 [40960/50000 (91%)]\tLoss: 0.102532, Accuracy: 96.88\n",
      "Train Epoch: 59 [42240/50000 (94%)]\tLoss: 0.133660, Accuracy: 96.88\n",
      "Train Epoch: 59 [43520/50000 (97%)]\tLoss: 0.142530, Accuracy: 93.75\n",
      "Train Epoch: 59 [35000/50000 (99%)]\tLoss: 0.087894, Accuracy: 96.50\n",
      "\n",
      "Validation set: Average loss: 0.2694, Accuracy: 4574/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.823256492614746 s]\n",
      "Train Epoch: 60 [0/50000 (0%)]\tLoss: 0.102565, Accuracy: 96.88\n",
      "Train Epoch: 60 [1280/50000 (3%)]\tLoss: 0.092093, Accuracy: 97.27\n",
      "Train Epoch: 60 [2560/50000 (6%)]\tLoss: 0.095489, Accuracy: 96.48\n",
      "Train Epoch: 60 [3840/50000 (9%)]\tLoss: 0.109606, Accuracy: 95.70\n",
      "Train Epoch: 60 [5120/50000 (11%)]\tLoss: 0.146675, Accuracy: 95.31\n",
      "Train Epoch: 60 [6400/50000 (14%)]\tLoss: 0.057021, Accuracy: 98.05\n",
      "Train Epoch: 60 [7680/50000 (17%)]\tLoss: 0.072011, Accuracy: 98.05\n",
      "Train Epoch: 60 [8960/50000 (20%)]\tLoss: 0.074115, Accuracy: 98.83\n",
      "Train Epoch: 60 [10240/50000 (23%)]\tLoss: 0.098008, Accuracy: 96.88\n",
      "Train Epoch: 60 [11520/50000 (26%)]\tLoss: 0.152847, Accuracy: 95.31\n",
      "Train Epoch: 60 [12800/50000 (28%)]\tLoss: 0.089958, Accuracy: 96.09\n",
      "Train Epoch: 60 [14080/50000 (31%)]\tLoss: 0.073799, Accuracy: 97.66\n",
      "Train Epoch: 60 [15360/50000 (34%)]\tLoss: 0.109343, Accuracy: 96.09\n",
      "Train Epoch: 60 [16640/50000 (37%)]\tLoss: 0.075278, Accuracy: 97.27\n",
      "Train Epoch: 60 [17920/50000 (40%)]\tLoss: 0.133611, Accuracy: 95.70\n",
      "Train Epoch: 60 [19200/50000 (43%)]\tLoss: 0.164183, Accuracy: 92.97\n",
      "Train Epoch: 60 [20480/50000 (45%)]\tLoss: 0.167246, Accuracy: 95.70\n",
      "Train Epoch: 60 [21760/50000 (48%)]\tLoss: 0.118960, Accuracy: 96.09\n",
      "Train Epoch: 60 [23040/50000 (51%)]\tLoss: 0.119470, Accuracy: 95.31\n",
      "Train Epoch: 60 [24320/50000 (54%)]\tLoss: 0.074442, Accuracy: 97.27\n",
      "Train Epoch: 60 [25600/50000 (57%)]\tLoss: 0.096548, Accuracy: 96.88\n",
      "Train Epoch: 60 [26880/50000 (60%)]\tLoss: 0.063144, Accuracy: 98.05\n",
      "Train Epoch: 60 [28160/50000 (62%)]\tLoss: 0.137955, Accuracy: 94.53\n",
      "Train Epoch: 60 [29440/50000 (65%)]\tLoss: 0.069490, Accuracy: 97.27\n",
      "Train Epoch: 60 [30720/50000 (68%)]\tLoss: 0.141495, Accuracy: 94.53\n",
      "Train Epoch: 60 [32000/50000 (71%)]\tLoss: 0.098777, Accuracy: 96.48\n",
      "Train Epoch: 60 [33280/50000 (74%)]\tLoss: 0.108572, Accuracy: 96.48\n",
      "Train Epoch: 60 [34560/50000 (77%)]\tLoss: 0.075713, Accuracy: 98.05\n",
      "Train Epoch: 60 [35840/50000 (80%)]\tLoss: 0.135332, Accuracy: 96.09\n",
      "Train Epoch: 60 [37120/50000 (82%)]\tLoss: 0.143539, Accuracy: 94.92\n",
      "Train Epoch: 60 [38400/50000 (85%)]\tLoss: 0.124043, Accuracy: 96.09\n",
      "Train Epoch: 60 [39680/50000 (88%)]\tLoss: 0.126719, Accuracy: 96.48\n",
      "Train Epoch: 60 [40960/50000 (91%)]\tLoss: 0.077934, Accuracy: 96.09\n",
      "Train Epoch: 60 [42240/50000 (94%)]\tLoss: 0.090835, Accuracy: 97.27\n",
      "Train Epoch: 60 [43520/50000 (97%)]\tLoss: 0.094365, Accuracy: 97.27\n",
      "Train Epoch: 60 [35000/50000 (99%)]\tLoss: 0.073045, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2657, Accuracy: 4579/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.65437579154968 s]\n",
      "\n",
      "Test set: Average loss: 0.3028, Accuracy: 9077/10000 (90.77%)\n",
      "\n",
      "Train Epoch: 61 [0/50000 (0%)]\tLoss: 0.118273, Accuracy: 95.31\n",
      "Train Epoch: 61 [1280/50000 (3%)]\tLoss: 0.112115, Accuracy: 96.88\n",
      "Train Epoch: 61 [2560/50000 (6%)]\tLoss: 0.121356, Accuracy: 96.09\n",
      "Train Epoch: 61 [3840/50000 (9%)]\tLoss: 0.098316, Accuracy: 98.44\n",
      "Train Epoch: 61 [5120/50000 (11%)]\tLoss: 0.085707, Accuracy: 97.27\n",
      "Train Epoch: 61 [6400/50000 (14%)]\tLoss: 0.091070, Accuracy: 97.27\n",
      "Train Epoch: 61 [7680/50000 (17%)]\tLoss: 0.103085, Accuracy: 96.48\n",
      "Train Epoch: 61 [8960/50000 (20%)]\tLoss: 0.121231, Accuracy: 95.70\n",
      "Train Epoch: 61 [10240/50000 (23%)]\tLoss: 0.120850, Accuracy: 95.70\n",
      "Train Epoch: 61 [11520/50000 (26%)]\tLoss: 0.098168, Accuracy: 97.66\n",
      "Train Epoch: 61 [12800/50000 (28%)]\tLoss: 0.097311, Accuracy: 97.27\n",
      "Train Epoch: 61 [14080/50000 (31%)]\tLoss: 0.110342, Accuracy: 96.09\n",
      "Train Epoch: 61 [15360/50000 (34%)]\tLoss: 0.089226, Accuracy: 97.27\n",
      "Train Epoch: 61 [16640/50000 (37%)]\tLoss: 0.104015, Accuracy: 96.09\n",
      "Train Epoch: 61 [17920/50000 (40%)]\tLoss: 0.123238, Accuracy: 96.88\n",
      "Train Epoch: 61 [19200/50000 (43%)]\tLoss: 0.087296, Accuracy: 95.70\n",
      "Train Epoch: 61 [20480/50000 (45%)]\tLoss: 0.088698, Accuracy: 96.09\n",
      "Train Epoch: 61 [21760/50000 (48%)]\tLoss: 0.139468, Accuracy: 95.70\n",
      "Train Epoch: 61 [23040/50000 (51%)]\tLoss: 0.144094, Accuracy: 95.31\n",
      "Train Epoch: 61 [24320/50000 (54%)]\tLoss: 0.125296, Accuracy: 96.48\n",
      "Train Epoch: 61 [25600/50000 (57%)]\tLoss: 0.063043, Accuracy: 97.66\n",
      "Train Epoch: 61 [26880/50000 (60%)]\tLoss: 0.170418, Accuracy: 94.92\n",
      "Train Epoch: 61 [28160/50000 (62%)]\tLoss: 0.094211, Accuracy: 96.88\n",
      "Train Epoch: 61 [29440/50000 (65%)]\tLoss: 0.110628, Accuracy: 96.48\n",
      "Train Epoch: 61 [30720/50000 (68%)]\tLoss: 0.097744, Accuracy: 96.88\n",
      "Train Epoch: 61 [32000/50000 (71%)]\tLoss: 0.069497, Accuracy: 96.09\n",
      "Train Epoch: 61 [33280/50000 (74%)]\tLoss: 0.119107, Accuracy: 96.09\n",
      "Train Epoch: 61 [34560/50000 (77%)]\tLoss: 0.057531, Accuracy: 98.05\n",
      "Train Epoch: 61 [35840/50000 (80%)]\tLoss: 0.165734, Accuracy: 94.14\n",
      "Train Epoch: 61 [37120/50000 (82%)]\tLoss: 0.090482, Accuracy: 96.48\n",
      "Train Epoch: 61 [38400/50000 (85%)]\tLoss: 0.047994, Accuracy: 98.83\n",
      "Train Epoch: 61 [39680/50000 (88%)]\tLoss: 0.065930, Accuracy: 97.66\n",
      "Train Epoch: 61 [40960/50000 (91%)]\tLoss: 0.187281, Accuracy: 93.36\n",
      "Train Epoch: 61 [42240/50000 (94%)]\tLoss: 0.091883, Accuracy: 96.88\n",
      "Train Epoch: 61 [43520/50000 (97%)]\tLoss: 0.111053, Accuracy: 96.09\n",
      "Train Epoch: 61 [35000/50000 (99%)]\tLoss: 0.144649, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2708, Accuracy: 4582/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.78556799888611 s]\n",
      "Train Epoch: 62 [0/50000 (0%)]\tLoss: 0.081100, Accuracy: 98.05\n",
      "Train Epoch: 62 [1280/50000 (3%)]\tLoss: 0.099712, Accuracy: 96.48\n",
      "Train Epoch: 62 [2560/50000 (6%)]\tLoss: 0.112747, Accuracy: 95.31\n",
      "Train Epoch: 62 [3840/50000 (9%)]\tLoss: 0.072475, Accuracy: 96.88\n",
      "Train Epoch: 62 [5120/50000 (11%)]\tLoss: 0.155240, Accuracy: 95.70\n",
      "Train Epoch: 62 [6400/50000 (14%)]\tLoss: 0.133857, Accuracy: 94.53\n",
      "Train Epoch: 62 [7680/50000 (17%)]\tLoss: 0.140670, Accuracy: 94.53\n",
      "Train Epoch: 62 [8960/50000 (20%)]\tLoss: 0.099265, Accuracy: 97.27\n",
      "Train Epoch: 62 [10240/50000 (23%)]\tLoss: 0.094789, Accuracy: 96.88\n",
      "Train Epoch: 62 [11520/50000 (26%)]\tLoss: 0.118886, Accuracy: 94.92\n",
      "Train Epoch: 62 [12800/50000 (28%)]\tLoss: 0.122625, Accuracy: 96.09\n",
      "Train Epoch: 62 [14080/50000 (31%)]\tLoss: 0.117068, Accuracy: 96.09\n",
      "Train Epoch: 62 [15360/50000 (34%)]\tLoss: 0.089585, Accuracy: 96.88\n",
      "Train Epoch: 62 [16640/50000 (37%)]\tLoss: 0.187102, Accuracy: 93.36\n",
      "Train Epoch: 62 [17920/50000 (40%)]\tLoss: 0.138221, Accuracy: 95.31\n",
      "Train Epoch: 62 [19200/50000 (43%)]\tLoss: 0.089244, Accuracy: 96.88\n",
      "Train Epoch: 62 [20480/50000 (45%)]\tLoss: 0.093263, Accuracy: 97.27\n",
      "Train Epoch: 62 [21760/50000 (48%)]\tLoss: 0.191383, Accuracy: 94.53\n",
      "Train Epoch: 62 [23040/50000 (51%)]\tLoss: 0.126061, Accuracy: 95.70\n",
      "Train Epoch: 62 [24320/50000 (54%)]\tLoss: 0.102356, Accuracy: 96.48\n",
      "Train Epoch: 62 [25600/50000 (57%)]\tLoss: 0.091510, Accuracy: 96.88\n",
      "Train Epoch: 62 [26880/50000 (60%)]\tLoss: 0.137479, Accuracy: 94.92\n",
      "Train Epoch: 62 [28160/50000 (62%)]\tLoss: 0.115889, Accuracy: 95.70\n",
      "Train Epoch: 62 [29440/50000 (65%)]\tLoss: 0.094727, Accuracy: 96.09\n",
      "Train Epoch: 62 [30720/50000 (68%)]\tLoss: 0.102496, Accuracy: 96.48\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 62 [32000/50000 (71%)]\tLoss: 0.079459, Accuracy: 97.27\n",
      "Train Epoch: 62 [33280/50000 (74%)]\tLoss: 0.091486, Accuracy: 98.05\n",
      "Train Epoch: 62 [34560/50000 (77%)]\tLoss: 0.059580, Accuracy: 97.66\n",
      "Train Epoch: 62 [35840/50000 (80%)]\tLoss: 0.096972, Accuracy: 96.88\n",
      "Train Epoch: 62 [37120/50000 (82%)]\tLoss: 0.082366, Accuracy: 97.66\n",
      "Train Epoch: 62 [38400/50000 (85%)]\tLoss: 0.128664, Accuracy: 94.53\n",
      "Train Epoch: 62 [39680/50000 (88%)]\tLoss: 0.112904, Accuracy: 96.48\n",
      "Train Epoch: 62 [40960/50000 (91%)]\tLoss: 0.108268, Accuracy: 95.70\n",
      "Train Epoch: 62 [42240/50000 (94%)]\tLoss: 0.111764, Accuracy: 97.27\n",
      "Train Epoch: 62 [43520/50000 (97%)]\tLoss: 0.072846, Accuracy: 98.05\n",
      "Train Epoch: 62 [35000/50000 (99%)]\tLoss: 0.105960, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2698, Accuracy: 4595/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.710078954696655 s]\n",
      "\n",
      "Test set: Average loss: 0.3085, Accuracy: 9074/10000 (90.74%)\n",
      "\n",
      "Train Epoch: 63 [0/50000 (0%)]\tLoss: 0.055899, Accuracy: 98.44\n",
      "Train Epoch: 63 [1280/50000 (3%)]\tLoss: 0.118436, Accuracy: 96.09\n",
      "Train Epoch: 63 [2560/50000 (6%)]\tLoss: 0.079247, Accuracy: 98.44\n",
      "Train Epoch: 63 [3840/50000 (9%)]\tLoss: 0.137111, Accuracy: 94.92\n",
      "Train Epoch: 63 [5120/50000 (11%)]\tLoss: 0.127872, Accuracy: 95.31\n",
      "Train Epoch: 63 [6400/50000 (14%)]\tLoss: 0.092126, Accuracy: 96.88\n",
      "Train Epoch: 63 [7680/50000 (17%)]\tLoss: 0.055728, Accuracy: 98.44\n",
      "Train Epoch: 63 [8960/50000 (20%)]\tLoss: 0.102553, Accuracy: 96.88\n",
      "Train Epoch: 63 [10240/50000 (23%)]\tLoss: 0.092170, Accuracy: 96.48\n",
      "Train Epoch: 63 [11520/50000 (26%)]\tLoss: 0.129560, Accuracy: 94.92\n",
      "Train Epoch: 63 [12800/50000 (28%)]\tLoss: 0.085251, Accuracy: 96.88\n",
      "Train Epoch: 63 [14080/50000 (31%)]\tLoss: 0.132650, Accuracy: 94.53\n",
      "Train Epoch: 63 [15360/50000 (34%)]\tLoss: 0.095848, Accuracy: 96.09\n",
      "Train Epoch: 63 [16640/50000 (37%)]\tLoss: 0.075391, Accuracy: 97.66\n",
      "Train Epoch: 63 [17920/50000 (40%)]\tLoss: 0.097978, Accuracy: 96.88\n",
      "Train Epoch: 63 [19200/50000 (43%)]\tLoss: 0.077906, Accuracy: 97.27\n",
      "Train Epoch: 63 [20480/50000 (45%)]\tLoss: 0.056626, Accuracy: 98.05\n",
      "Train Epoch: 63 [21760/50000 (48%)]\tLoss: 0.110233, Accuracy: 97.27\n",
      "Train Epoch: 63 [23040/50000 (51%)]\tLoss: 0.105155, Accuracy: 96.88\n",
      "Train Epoch: 63 [24320/50000 (54%)]\tLoss: 0.111040, Accuracy: 96.09\n",
      "Train Epoch: 63 [25600/50000 (57%)]\tLoss: 0.090595, Accuracy: 96.48\n",
      "Train Epoch: 63 [26880/50000 (60%)]\tLoss: 0.141184, Accuracy: 94.53\n",
      "Train Epoch: 63 [28160/50000 (62%)]\tLoss: 0.079155, Accuracy: 97.27\n",
      "Train Epoch: 63 [29440/50000 (65%)]\tLoss: 0.122681, Accuracy: 94.92\n",
      "Train Epoch: 63 [30720/50000 (68%)]\tLoss: 0.095114, Accuracy: 94.92\n",
      "Train Epoch: 63 [32000/50000 (71%)]\tLoss: 0.100577, Accuracy: 97.66\n",
      "Train Epoch: 63 [33280/50000 (74%)]\tLoss: 0.086353, Accuracy: 96.48\n",
      "Train Epoch: 63 [34560/50000 (77%)]\tLoss: 0.174416, Accuracy: 93.36\n",
      "Train Epoch: 63 [35840/50000 (80%)]\tLoss: 0.171759, Accuracy: 92.97\n",
      "Train Epoch: 63 [37120/50000 (82%)]\tLoss: 0.119782, Accuracy: 95.70\n",
      "Train Epoch: 63 [38400/50000 (85%)]\tLoss: 0.055082, Accuracy: 98.05\n",
      "Train Epoch: 63 [39680/50000 (88%)]\tLoss: 0.129031, Accuracy: 96.09\n",
      "Train Epoch: 63 [40960/50000 (91%)]\tLoss: 0.110812, Accuracy: 96.88\n",
      "Train Epoch: 63 [42240/50000 (94%)]\tLoss: 0.075768, Accuracy: 98.05\n",
      "Train Epoch: 63 [43520/50000 (97%)]\tLoss: 0.066375, Accuracy: 98.83\n",
      "Train Epoch: 63 [35000/50000 (99%)]\tLoss: 0.078329, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2829, Accuracy: 4586/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.852166175842285 s]\n",
      "Train Epoch: 64 [0/50000 (0%)]\tLoss: 0.060711, Accuracy: 98.44\n",
      "Train Epoch: 64 [1280/50000 (3%)]\tLoss: 0.064199, Accuracy: 98.44\n",
      "Train Epoch: 64 [2560/50000 (6%)]\tLoss: 0.128606, Accuracy: 95.70\n",
      "Train Epoch: 64 [3840/50000 (9%)]\tLoss: 0.077106, Accuracy: 97.27\n",
      "Train Epoch: 64 [5120/50000 (11%)]\tLoss: 0.064534, Accuracy: 97.27\n",
      "Train Epoch: 64 [6400/50000 (14%)]\tLoss: 0.121075, Accuracy: 95.70\n",
      "Train Epoch: 64 [7680/50000 (17%)]\tLoss: 0.122925, Accuracy: 96.09\n",
      "Train Epoch: 64 [8960/50000 (20%)]\tLoss: 0.107182, Accuracy: 96.88\n",
      "Train Epoch: 64 [10240/50000 (23%)]\tLoss: 0.105196, Accuracy: 96.09\n",
      "Train Epoch: 64 [11520/50000 (26%)]\tLoss: 0.046749, Accuracy: 98.83\n",
      "Train Epoch: 64 [12800/50000 (28%)]\tLoss: 0.083516, Accuracy: 96.48\n",
      "Train Epoch: 64 [14080/50000 (31%)]\tLoss: 0.093168, Accuracy: 97.66\n",
      "Train Epoch: 64 [15360/50000 (34%)]\tLoss: 0.084922, Accuracy: 97.27\n",
      "Train Epoch: 64 [16640/50000 (37%)]\tLoss: 0.118387, Accuracy: 96.09\n",
      "Train Epoch: 64 [17920/50000 (40%)]\tLoss: 0.095142, Accuracy: 96.88\n",
      "Train Epoch: 64 [19200/50000 (43%)]\tLoss: 0.097305, Accuracy: 97.66\n",
      "Train Epoch: 64 [20480/50000 (45%)]\tLoss: 0.174929, Accuracy: 93.75\n",
      "Train Epoch: 64 [21760/50000 (48%)]\tLoss: 0.138495, Accuracy: 96.09\n",
      "Train Epoch: 64 [23040/50000 (51%)]\tLoss: 0.101957, Accuracy: 97.27\n",
      "Train Epoch: 64 [24320/50000 (54%)]\tLoss: 0.088577, Accuracy: 96.88\n",
      "Train Epoch: 64 [25600/50000 (57%)]\tLoss: 0.092511, Accuracy: 97.27\n",
      "Train Epoch: 64 [26880/50000 (60%)]\tLoss: 0.139486, Accuracy: 95.70\n",
      "Train Epoch: 64 [28160/50000 (62%)]\tLoss: 0.110685, Accuracy: 95.70\n",
      "Train Epoch: 64 [29440/50000 (65%)]\tLoss: 0.089436, Accuracy: 96.48\n",
      "Train Epoch: 64 [30720/50000 (68%)]\tLoss: 0.079304, Accuracy: 96.88\n",
      "Train Epoch: 64 [32000/50000 (71%)]\tLoss: 0.089276, Accuracy: 98.05\n",
      "Train Epoch: 64 [33280/50000 (74%)]\tLoss: 0.111930, Accuracy: 96.09\n",
      "Train Epoch: 64 [34560/50000 (77%)]\tLoss: 0.092494, Accuracy: 96.09\n",
      "Train Epoch: 64 [35840/50000 (80%)]\tLoss: 0.061253, Accuracy: 97.66\n",
      "Train Epoch: 64 [37120/50000 (82%)]\tLoss: 0.091109, Accuracy: 96.88\n",
      "Train Epoch: 64 [38400/50000 (85%)]\tLoss: 0.136603, Accuracy: 94.53\n",
      "Train Epoch: 64 [39680/50000 (88%)]\tLoss: 0.028877, Accuracy: 99.22\n",
      "Train Epoch: 64 [40960/50000 (91%)]\tLoss: 0.076977, Accuracy: 97.27\n",
      "Train Epoch: 64 [42240/50000 (94%)]\tLoss: 0.132232, Accuracy: 96.09\n",
      "Train Epoch: 64 [43520/50000 (97%)]\tLoss: 0.097215, Accuracy: 98.44\n",
      "Train Epoch: 64 [35000/50000 (99%)]\tLoss: 0.113424, Accuracy: 95.00\n",
      "\n",
      "Validation set: Average loss: 0.2801, Accuracy: 4579/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.750014543533325 s]\n",
      "\n",
      "Test set: Average loss: 0.3195, Accuracy: 9066/10000 (90.66%)\n",
      "\n",
      "Train Epoch: 65 [0/50000 (0%)]\tLoss: 0.067865, Accuracy: 97.27\n",
      "Train Epoch: 65 [1280/50000 (3%)]\tLoss: 0.072492, Accuracy: 98.05\n",
      "Train Epoch: 65 [2560/50000 (6%)]\tLoss: 0.165301, Accuracy: 96.88\n",
      "Train Epoch: 65 [3840/50000 (9%)]\tLoss: 0.097274, Accuracy: 97.27\n",
      "Train Epoch: 65 [5120/50000 (11%)]\tLoss: 0.069136, Accuracy: 96.88\n",
      "Train Epoch: 65 [6400/50000 (14%)]\tLoss: 0.134595, Accuracy: 96.48\n",
      "Train Epoch: 65 [7680/50000 (17%)]\tLoss: 0.033028, Accuracy: 99.22\n",
      "Train Epoch: 65 [8960/50000 (20%)]\tLoss: 0.057578, Accuracy: 98.44\n",
      "Train Epoch: 65 [10240/50000 (23%)]\tLoss: 0.105527, Accuracy: 96.48\n",
      "Train Epoch: 65 [11520/50000 (26%)]\tLoss: 0.082858, Accuracy: 97.27\n",
      "Train Epoch: 65 [12800/50000 (28%)]\tLoss: 0.098707, Accuracy: 97.27\n",
      "torch.Size([512, 1, 1])\n",
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      "        [[ 0.0298]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 1.6775]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-1.7196e-03, -1.8416e-03, -1.5885e-03,  ..., -4.7700e-03,\n",
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      "\n",
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      "           6.1842e-03,  2.6482e-03]],\n",
      "\n",
      "        ...,\n",
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      "        [[ 3.0339e-03,  4.7947e-03,  4.5103e-03,  ...,  3.9800e-03,\n",
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      "\n",
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      "\n",
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      "torch.Size([1, 448, 512])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[-0.9832, -1.1402, -1.2917,  ..., -0.9095, -0.7665, -0.6274],\n",
      "         [ 0.6481,  0.7349,  0.8293,  ...,  0.6650,  0.5467,  0.4332],\n",
      "         [ 0.5282,  0.5912,  0.6747,  ...,  0.4468,  0.4294,  0.3404],\n",
      "         ...,\n",
      "         [ 0.3278,  0.4131,  0.4625,  ...,  0.5789,  0.5226,  0.5183],\n",
      "         [-0.0503, -0.1044, -0.0990,  ..., -0.1149, -0.0571, -0.0746],\n",
      "         [-0.5140, -0.5397, -0.5407,  ..., -0.4255, -0.3119, -0.2335]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 65 [14080/50000 (31%)]\tLoss: 0.084718, Accuracy: 97.66\n",
      "Train Epoch: 65 [15360/50000 (34%)]\tLoss: 0.119498, Accuracy: 96.48\n",
      "Train Epoch: 65 [16640/50000 (37%)]\tLoss: 0.082104, Accuracy: 97.27\n",
      "Train Epoch: 65 [17920/50000 (40%)]\tLoss: 0.079552, Accuracy: 96.88\n",
      "Train Epoch: 65 [19200/50000 (43%)]\tLoss: 0.066711, Accuracy: 97.27\n",
      "Train Epoch: 65 [20480/50000 (45%)]\tLoss: 0.096923, Accuracy: 96.09\n",
      "Train Epoch: 65 [21760/50000 (48%)]\tLoss: 0.129154, Accuracy: 95.31\n",
      "Train Epoch: 65 [23040/50000 (51%)]\tLoss: 0.066255, Accuracy: 98.05\n",
      "Train Epoch: 65 [24320/50000 (54%)]\tLoss: 0.093871, Accuracy: 95.70\n",
      "Train Epoch: 65 [25600/50000 (57%)]\tLoss: 0.145346, Accuracy: 96.09\n",
      "Train Epoch: 65 [26880/50000 (60%)]\tLoss: 0.083097, Accuracy: 97.66\n",
      "Train Epoch: 65 [28160/50000 (62%)]\tLoss: 0.100034, Accuracy: 96.48\n",
      "Train Epoch: 65 [29440/50000 (65%)]\tLoss: 0.116556, Accuracy: 95.70\n",
      "Train Epoch: 65 [30720/50000 (68%)]\tLoss: 0.087993, Accuracy: 97.27\n",
      "Train Epoch: 65 [32000/50000 (71%)]\tLoss: 0.109938, Accuracy: 95.70\n",
      "Train Epoch: 65 [33280/50000 (74%)]\tLoss: 0.066124, Accuracy: 97.66\n",
      "Train Epoch: 65 [34560/50000 (77%)]\tLoss: 0.083356, Accuracy: 98.05\n",
      "Train Epoch: 65 [35840/50000 (80%)]\tLoss: 0.067671, Accuracy: 97.27\n",
      "Train Epoch: 65 [37120/50000 (82%)]\tLoss: 0.155352, Accuracy: 94.92\n",
      "Train Epoch: 65 [38400/50000 (85%)]\tLoss: 0.073930, Accuracy: 97.66\n",
      "Train Epoch: 65 [39680/50000 (88%)]\tLoss: 0.057175, Accuracy: 97.66\n",
      "Train Epoch: 65 [40960/50000 (91%)]\tLoss: 0.102539, Accuracy: 96.88\n",
      "Train Epoch: 65 [42240/50000 (94%)]\tLoss: 0.073182, Accuracy: 97.66\n",
      "Train Epoch: 65 [43520/50000 (97%)]\tLoss: 0.116912, Accuracy: 96.09\n",
      "Train Epoch: 65 [35000/50000 (99%)]\tLoss: 0.106790, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2736, Accuracy: 4579/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[41.09983158111572 s]\n",
      "Train Epoch: 66 [0/50000 (0%)]\tLoss: 0.063965, Accuracy: 97.66\n",
      "Train Epoch: 66 [1280/50000 (3%)]\tLoss: 0.051036, Accuracy: 98.44\n",
      "Train Epoch: 66 [2560/50000 (6%)]\tLoss: 0.071941, Accuracy: 97.66\n",
      "Train Epoch: 66 [3840/50000 (9%)]\tLoss: 0.062424, Accuracy: 97.66\n",
      "Train Epoch: 66 [5120/50000 (11%)]\tLoss: 0.069619, Accuracy: 98.05\n",
      "Train Epoch: 66 [6400/50000 (14%)]\tLoss: 0.113783, Accuracy: 96.88\n",
      "Train Epoch: 66 [7680/50000 (17%)]\tLoss: 0.069596, Accuracy: 98.05\n",
      "Train Epoch: 66 [8960/50000 (20%)]\tLoss: 0.077105, Accuracy: 98.05\n",
      "Train Epoch: 66 [10240/50000 (23%)]\tLoss: 0.077923, Accuracy: 96.88\n",
      "Train Epoch: 66 [11520/50000 (26%)]\tLoss: 0.165704, Accuracy: 93.75\n",
      "Train Epoch: 66 [12800/50000 (28%)]\tLoss: 0.108750, Accuracy: 96.48\n",
      "Train Epoch: 66 [14080/50000 (31%)]\tLoss: 0.090290, Accuracy: 96.88\n",
      "Train Epoch: 66 [15360/50000 (34%)]\tLoss: 0.081226, Accuracy: 97.27\n",
      "Train Epoch: 66 [16640/50000 (37%)]\tLoss: 0.087697, Accuracy: 97.27\n",
      "Train Epoch: 66 [17920/50000 (40%)]\tLoss: 0.089407, Accuracy: 97.27\n",
      "Train Epoch: 66 [19200/50000 (43%)]\tLoss: 0.069246, Accuracy: 97.27\n",
      "Train Epoch: 66 [20480/50000 (45%)]\tLoss: 0.108466, Accuracy: 94.92\n",
      "Train Epoch: 66 [21760/50000 (48%)]\tLoss: 0.069549, Accuracy: 97.66\n",
      "Train Epoch: 66 [23040/50000 (51%)]\tLoss: 0.066370, Accuracy: 98.05\n",
      "Train Epoch: 66 [24320/50000 (54%)]\tLoss: 0.100468, Accuracy: 96.09\n",
      "Train Epoch: 66 [25600/50000 (57%)]\tLoss: 0.049946, Accuracy: 98.83\n",
      "Train Epoch: 66 [26880/50000 (60%)]\tLoss: 0.069878, Accuracy: 98.05\n",
      "Train Epoch: 66 [28160/50000 (62%)]\tLoss: 0.101128, Accuracy: 97.66\n",
      "Train Epoch: 66 [29440/50000 (65%)]\tLoss: 0.052286, Accuracy: 98.44\n",
      "Train Epoch: 66 [30720/50000 (68%)]\tLoss: 0.073641, Accuracy: 97.27\n",
      "Train Epoch: 66 [32000/50000 (71%)]\tLoss: 0.064923, Accuracy: 98.05\n",
      "Train Epoch: 66 [33280/50000 (74%)]\tLoss: 0.116723, Accuracy: 96.48\n",
      "Train Epoch: 66 [34560/50000 (77%)]\tLoss: 0.094623, Accuracy: 96.88\n",
      "Train Epoch: 66 [35840/50000 (80%)]\tLoss: 0.117580, Accuracy: 96.48\n",
      "Train Epoch: 66 [37120/50000 (82%)]\tLoss: 0.107220, Accuracy: 97.27\n",
      "Train Epoch: 66 [38400/50000 (85%)]\tLoss: 0.146544, Accuracy: 95.31\n",
      "Train Epoch: 66 [39680/50000 (88%)]\tLoss: 0.124480, Accuracy: 94.92\n",
      "Train Epoch: 66 [40960/50000 (91%)]\tLoss: 0.076393, Accuracy: 97.27\n",
      "Train Epoch: 66 [42240/50000 (94%)]\tLoss: 0.061879, Accuracy: 98.05\n",
      "Train Epoch: 66 [43520/50000 (97%)]\tLoss: 0.111780, Accuracy: 94.92\n",
      "Train Epoch: 66 [35000/50000 (99%)]\tLoss: 0.077661, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.2854, Accuracy: 4583/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.65669250488281 s]\n",
      "\n",
      "Test set: Average loss: 0.3383, Accuracy: 9040/10000 (90.40%)\n",
      "\n",
      "Train Epoch: 67 [0/50000 (0%)]\tLoss: 0.090522, Accuracy: 96.88\n",
      "Train Epoch: 67 [1280/50000 (3%)]\tLoss: 0.103952, Accuracy: 98.05\n",
      "Train Epoch: 67 [2560/50000 (6%)]\tLoss: 0.068270, Accuracy: 98.05\n",
      "Train Epoch: 67 [3840/50000 (9%)]\tLoss: 0.060215, Accuracy: 97.66\n",
      "Train Epoch: 67 [5120/50000 (11%)]\tLoss: 0.073431, Accuracy: 97.27\n",
      "Train Epoch: 67 [6400/50000 (14%)]\tLoss: 0.083298, Accuracy: 97.66\n",
      "Train Epoch: 67 [7680/50000 (17%)]\tLoss: 0.082652, Accuracy: 97.27\n",
      "Train Epoch: 67 [8960/50000 (20%)]\tLoss: 0.126090, Accuracy: 95.70\n",
      "Train Epoch: 67 [10240/50000 (23%)]\tLoss: 0.062128, Accuracy: 97.66\n",
      "Train Epoch: 67 [11520/50000 (26%)]\tLoss: 0.076601, Accuracy: 96.88\n",
      "Train Epoch: 67 [12800/50000 (28%)]\tLoss: 0.055658, Accuracy: 98.44\n",
      "Train Epoch: 67 [14080/50000 (31%)]\tLoss: 0.103236, Accuracy: 96.09\n",
      "Train Epoch: 67 [15360/50000 (34%)]\tLoss: 0.094564, Accuracy: 96.09\n",
      "Train Epoch: 67 [16640/50000 (37%)]\tLoss: 0.094193, Accuracy: 96.48\n",
      "Train Epoch: 67 [17920/50000 (40%)]\tLoss: 0.140557, Accuracy: 95.70\n",
      "Train Epoch: 67 [19200/50000 (43%)]\tLoss: 0.076209, Accuracy: 97.66\n",
      "Train Epoch: 67 [20480/50000 (45%)]\tLoss: 0.092420, Accuracy: 96.48\n",
      "Train Epoch: 67 [21760/50000 (48%)]\tLoss: 0.088540, Accuracy: 96.88\n",
      "Train Epoch: 67 [23040/50000 (51%)]\tLoss: 0.067047, Accuracy: 97.66\n",
      "Train Epoch: 67 [24320/50000 (54%)]\tLoss: 0.081653, Accuracy: 98.05\n",
      "Train Epoch: 67 [25600/50000 (57%)]\tLoss: 0.120213, Accuracy: 96.48\n",
      "Train Epoch: 67 [26880/50000 (60%)]\tLoss: 0.075745, Accuracy: 97.66\n",
      "Train Epoch: 67 [28160/50000 (62%)]\tLoss: 0.084254, Accuracy: 98.05\n",
      "Train Epoch: 67 [29440/50000 (65%)]\tLoss: 0.050924, Accuracy: 98.83\n",
      "Train Epoch: 67 [30720/50000 (68%)]\tLoss: 0.084588, Accuracy: 96.48\n",
      "Train Epoch: 67 [32000/50000 (71%)]\tLoss: 0.101187, Accuracy: 95.70\n",
      "Train Epoch: 67 [33280/50000 (74%)]\tLoss: 0.094148, Accuracy: 96.48\n",
      "Train Epoch: 67 [34560/50000 (77%)]\tLoss: 0.111008, Accuracy: 97.27\n",
      "Train Epoch: 67 [35840/50000 (80%)]\tLoss: 0.151532, Accuracy: 95.70\n",
      "Train Epoch: 67 [37120/50000 (82%)]\tLoss: 0.084472, Accuracy: 97.27\n",
      "Train Epoch: 67 [38400/50000 (85%)]\tLoss: 0.134469, Accuracy: 95.31\n",
      "Train Epoch: 67 [39680/50000 (88%)]\tLoss: 0.085093, Accuracy: 97.27\n",
      "Train Epoch: 67 [40960/50000 (91%)]\tLoss: 0.094142, Accuracy: 96.88\n",
      "Train Epoch: 67 [42240/50000 (94%)]\tLoss: 0.065513, Accuracy: 97.27\n",
      "Train Epoch: 67 [43520/50000 (97%)]\tLoss: 0.084994, Accuracy: 98.05\n",
      "Train Epoch: 67 [35000/50000 (99%)]\tLoss: 0.141390, Accuracy: 94.50\n",
      "\n",
      "Validation set: Average loss: 0.2841, Accuracy: 4594/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.74527621269226 s]\n",
      "Train Epoch: 68 [0/50000 (0%)]\tLoss: 0.065333, Accuracy: 98.44\n",
      "Train Epoch: 68 [1280/50000 (3%)]\tLoss: 0.087053, Accuracy: 97.27\n",
      "Train Epoch: 68 [2560/50000 (6%)]\tLoss: 0.046622, Accuracy: 98.05\n",
      "Train Epoch: 68 [3840/50000 (9%)]\tLoss: 0.093729, Accuracy: 96.88\n",
      "Train Epoch: 68 [5120/50000 (11%)]\tLoss: 0.071569, Accuracy: 97.27\n",
      "Train Epoch: 68 [6400/50000 (14%)]\tLoss: 0.124217, Accuracy: 96.48\n",
      "Train Epoch: 68 [7680/50000 (17%)]\tLoss: 0.062118, Accuracy: 98.44\n",
      "Train Epoch: 68 [8960/50000 (20%)]\tLoss: 0.065441, Accuracy: 97.66\n",
      "Train Epoch: 68 [10240/50000 (23%)]\tLoss: 0.082929, Accuracy: 97.27\n",
      "Train Epoch: 68 [11520/50000 (26%)]\tLoss: 0.090162, Accuracy: 96.48\n",
      "Train Epoch: 68 [12800/50000 (28%)]\tLoss: 0.090747, Accuracy: 97.27\n",
      "Train Epoch: 68 [14080/50000 (31%)]\tLoss: 0.060176, Accuracy: 98.44\n",
      "Train Epoch: 68 [15360/50000 (34%)]\tLoss: 0.080318, Accuracy: 97.66\n",
      "Train Epoch: 68 [16640/50000 (37%)]\tLoss: 0.054830, Accuracy: 97.66\n",
      "Train Epoch: 68 [17920/50000 (40%)]\tLoss: 0.087440, Accuracy: 97.27\n",
      "Train Epoch: 68 [19200/50000 (43%)]\tLoss: 0.064126, Accuracy: 98.05\n",
      "Train Epoch: 68 [20480/50000 (45%)]\tLoss: 0.065052, Accuracy: 97.27\n",
      "Train Epoch: 68 [21760/50000 (48%)]\tLoss: 0.069082, Accuracy: 97.66\n",
      "Train Epoch: 68 [23040/50000 (51%)]\tLoss: 0.124868, Accuracy: 94.14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 68 [24320/50000 (54%)]\tLoss: 0.122718, Accuracy: 96.48\n",
      "Train Epoch: 68 [25600/50000 (57%)]\tLoss: 0.080876, Accuracy: 96.88\n",
      "Train Epoch: 68 [26880/50000 (60%)]\tLoss: 0.080535, Accuracy: 96.88\n",
      "Train Epoch: 68 [28160/50000 (62%)]\tLoss: 0.101510, Accuracy: 96.09\n",
      "Train Epoch: 68 [29440/50000 (65%)]\tLoss: 0.068934, Accuracy: 97.27\n",
      "Train Epoch: 68 [30720/50000 (68%)]\tLoss: 0.085571, Accuracy: 96.88\n",
      "Train Epoch: 68 [32000/50000 (71%)]\tLoss: 0.102875, Accuracy: 96.88\n",
      "Train Epoch: 68 [33280/50000 (74%)]\tLoss: 0.065415, Accuracy: 98.44\n",
      "Train Epoch: 68 [34560/50000 (77%)]\tLoss: 0.054027, Accuracy: 98.83\n",
      "Train Epoch: 68 [35840/50000 (80%)]\tLoss: 0.039773, Accuracy: 98.44\n",
      "Train Epoch: 68 [37120/50000 (82%)]\tLoss: 0.074097, Accuracy: 97.66\n",
      "Train Epoch: 68 [38400/50000 (85%)]\tLoss: 0.097184, Accuracy: 95.70\n",
      "Train Epoch: 68 [39680/50000 (88%)]\tLoss: 0.145705, Accuracy: 95.70\n",
      "Train Epoch: 68 [40960/50000 (91%)]\tLoss: 0.066808, Accuracy: 98.05\n",
      "Train Epoch: 68 [42240/50000 (94%)]\tLoss: 0.062476, Accuracy: 98.44\n",
      "Train Epoch: 68 [43520/50000 (97%)]\tLoss: 0.099333, Accuracy: 96.48\n",
      "Train Epoch: 68 [35000/50000 (99%)]\tLoss: 0.140638, Accuracy: 92.50\n",
      "\n",
      "Validation set: Average loss: 0.2951, Accuracy: 4561/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.623172760009766 s]\n",
      "\n",
      "Test set: Average loss: 0.3426, Accuracy: 9017/10000 (90.17%)\n",
      "\n",
      "Train Epoch: 69 [0/50000 (0%)]\tLoss: 0.089707, Accuracy: 96.88\n",
      "Train Epoch: 69 [1280/50000 (3%)]\tLoss: 0.074943, Accuracy: 98.05\n",
      "Train Epoch: 69 [2560/50000 (6%)]\tLoss: 0.138798, Accuracy: 96.09\n",
      "Train Epoch: 69 [3840/50000 (9%)]\tLoss: 0.059587, Accuracy: 98.05\n",
      "Train Epoch: 69 [5120/50000 (11%)]\tLoss: 0.071337, Accuracy: 96.88\n",
      "Train Epoch: 69 [6400/50000 (14%)]\tLoss: 0.078125, Accuracy: 97.66\n",
      "Train Epoch: 69 [7680/50000 (17%)]\tLoss: 0.060386, Accuracy: 98.05\n",
      "Train Epoch: 69 [8960/50000 (20%)]\tLoss: 0.064597, Accuracy: 98.05\n",
      "Train Epoch: 69 [10240/50000 (23%)]\tLoss: 0.049882, Accuracy: 98.44\n",
      "Train Epoch: 69 [11520/50000 (26%)]\tLoss: 0.068654, Accuracy: 97.66\n",
      "Train Epoch: 69 [12800/50000 (28%)]\tLoss: 0.089697, Accuracy: 96.88\n",
      "Train Epoch: 69 [14080/50000 (31%)]\tLoss: 0.091060, Accuracy: 96.09\n",
      "Train Epoch: 69 [15360/50000 (34%)]\tLoss: 0.056902, Accuracy: 97.27\n",
      "Train Epoch: 69 [16640/50000 (37%)]\tLoss: 0.079067, Accuracy: 98.05\n",
      "Train Epoch: 69 [17920/50000 (40%)]\tLoss: 0.098803, Accuracy: 96.48\n",
      "Train Epoch: 69 [19200/50000 (43%)]\tLoss: 0.054565, Accuracy: 97.66\n",
      "Train Epoch: 69 [20480/50000 (45%)]\tLoss: 0.092919, Accuracy: 95.70\n",
      "Train Epoch: 69 [21760/50000 (48%)]\tLoss: 0.088609, Accuracy: 96.88\n",
      "Train Epoch: 69 [23040/50000 (51%)]\tLoss: 0.094955, Accuracy: 97.27\n",
      "Train Epoch: 69 [24320/50000 (54%)]\tLoss: 0.056747, Accuracy: 97.66\n",
      "Train Epoch: 69 [25600/50000 (57%)]\tLoss: 0.085119, Accuracy: 98.44\n",
      "Train Epoch: 69 [26880/50000 (60%)]\tLoss: 0.064753, Accuracy: 98.05\n",
      "Train Epoch: 69 [28160/50000 (62%)]\tLoss: 0.099661, Accuracy: 97.27\n",
      "Train Epoch: 69 [29440/50000 (65%)]\tLoss: 0.095227, Accuracy: 96.88\n",
      "Train Epoch: 69 [30720/50000 (68%)]\tLoss: 0.079781, Accuracy: 96.88\n",
      "Train Epoch: 69 [32000/50000 (71%)]\tLoss: 0.073442, Accuracy: 98.83\n",
      "Train Epoch: 69 [33280/50000 (74%)]\tLoss: 0.116934, Accuracy: 95.31\n",
      "Train Epoch: 69 [34560/50000 (77%)]\tLoss: 0.110547, Accuracy: 96.09\n",
      "Train Epoch: 69 [35840/50000 (80%)]\tLoss: 0.057888, Accuracy: 98.83\n",
      "Train Epoch: 69 [37120/50000 (82%)]\tLoss: 0.114804, Accuracy: 96.09\n",
      "Train Epoch: 69 [38400/50000 (85%)]\tLoss: 0.083814, Accuracy: 96.09\n",
      "Train Epoch: 69 [39680/50000 (88%)]\tLoss: 0.025119, Accuracy: 100.00\n",
      "Train Epoch: 69 [40960/50000 (91%)]\tLoss: 0.083459, Accuracy: 96.88\n",
      "Train Epoch: 69 [42240/50000 (94%)]\tLoss: 0.098107, Accuracy: 96.48\n",
      "Train Epoch: 69 [43520/50000 (97%)]\tLoss: 0.089580, Accuracy: 97.66\n",
      "Train Epoch: 69 [35000/50000 (99%)]\tLoss: 0.100610, Accuracy: 97.00\n",
      "\n",
      "Validation set: Average loss: 0.2959, Accuracy: 4570/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[40.76450443267822 s]\n",
      "Train Epoch: 70 [0/50000 (0%)]\tLoss: 0.087355, Accuracy: 98.05\n",
      "Train Epoch: 70 [1280/50000 (3%)]\tLoss: 0.102352, Accuracy: 96.48\n",
      "Train Epoch: 70 [2560/50000 (6%)]\tLoss: 0.091214, Accuracy: 95.70\n",
      "Train Epoch: 70 [3840/50000 (9%)]\tLoss: 0.051524, Accuracy: 98.44\n",
      "Train Epoch: 70 [5120/50000 (11%)]\tLoss: 0.105883, Accuracy: 96.09\n",
      "Train Epoch: 70 [6400/50000 (14%)]\tLoss: 0.096847, Accuracy: 96.09\n",
      "Train Epoch: 70 [7680/50000 (17%)]\tLoss: 0.097278, Accuracy: 97.66\n",
      "Train Epoch: 70 [8960/50000 (20%)]\tLoss: 0.116444, Accuracy: 94.53\n",
      "Train Epoch: 70 [10240/50000 (23%)]\tLoss: 0.088900, Accuracy: 97.66\n",
      "Train Epoch: 70 [11520/50000 (26%)]\tLoss: 0.048234, Accuracy: 98.83\n",
      "Train Epoch: 70 [12800/50000 (28%)]\tLoss: 0.042604, Accuracy: 98.44\n",
      "Train Epoch: 70 [14080/50000 (31%)]\tLoss: 0.051882, Accuracy: 98.44\n",
      "Train Epoch: 70 [15360/50000 (34%)]\tLoss: 0.065688, Accuracy: 97.27\n",
      "Train Epoch: 70 [16640/50000 (37%)]\tLoss: 0.047623, Accuracy: 98.05\n",
      "Train Epoch: 70 [17920/50000 (40%)]\tLoss: 0.121194, Accuracy: 94.53\n",
      "Train Epoch: 70 [19200/50000 (43%)]\tLoss: 0.076917, Accuracy: 97.27\n",
      "Train Epoch: 70 [20480/50000 (45%)]\tLoss: 0.096564, Accuracy: 96.88\n",
      "Train Epoch: 70 [21760/50000 (48%)]\tLoss: 0.086080, Accuracy: 97.27\n",
      "Train Epoch: 70 [23040/50000 (51%)]\tLoss: 0.066659, Accuracy: 97.66\n",
      "Train Epoch: 70 [24320/50000 (54%)]\tLoss: 0.055540, Accuracy: 98.83\n",
      "Train Epoch: 70 [25600/50000 (57%)]\tLoss: 0.049974, Accuracy: 98.44\n",
      "Train Epoch: 70 [26880/50000 (60%)]\tLoss: 0.069517, Accuracy: 97.66\n",
      "Train Epoch: 70 [28160/50000 (62%)]\tLoss: 0.094655, Accuracy: 95.70\n",
      "Train Epoch: 70 [29440/50000 (65%)]\tLoss: 0.083458, Accuracy: 97.27\n",
      "Train Epoch: 70 [30720/50000 (68%)]\tLoss: 0.090864, Accuracy: 96.09\n",
      "Train Epoch: 70 [32000/50000 (71%)]\tLoss: 0.056999, Accuracy: 98.44\n",
      "Train Epoch: 70 [33280/50000 (74%)]\tLoss: 0.060728, Accuracy: 97.66\n",
      "torch.Size([1024, 1, 1])\n",
      "tensor([[[ 0.0009]],\n",
      "\n",
      "        [[ 0.0261]],\n",
      "\n",
      "        [[ 0.0361]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.0003]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0000]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-03 *\n",
      "       [[[ 3.5066]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-1.7470e-03,  2.0246e-03, -7.8079e-04,  ..., -1.1325e-04,\n",
      "           2.2171e-03, -2.3460e-03]],\n",
      "\n",
      "        [[ 6.8094e-03,  7.6597e-03,  8.0307e-03,  ...,  6.6976e-03,\n",
      "           5.9855e-03,  7.4852e-03]],\n",
      "\n",
      "        [[ 9.9205e-03,  8.2281e-03,  6.5797e-03,  ...,  7.2458e-03,\n",
      "           5.8645e-03,  5.1570e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.7457e-03, -1.9087e-03, -1.4487e-03,  ...,  2.0145e-03,\n",
      "          -1.2839e-03, -4.2458e-04]],\n",
      "\n",
      "        [[ 2.9434e-04, -2.9422e-04, -6.7528e-04,  ...,  2.4616e-04,\n",
      "           2.3864e-03,  1.4489e-04]],\n",
      "\n",
      "        [[-2.8131e-03,  1.0813e-03, -2.2806e-03,  ..., -3.8334e-04,\n",
      "          -1.8373e-03, -1.2731e-03]]], device='cuda:0')\n",
      "torch.Size([1, 896, 1024])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[-0.2079, -0.1655, -0.1459,  ..., -0.1409, -0.1112, -0.0044],\n",
      "         [ 2.3662,  2.7834,  3.0471,  ...,  3.2072,  2.7847,  2.4153],\n",
      "         [ 2.0082,  2.2390,  2.5449,  ...,  2.8721,  2.6116,  2.2828],\n",
      "         ...,\n",
      "         [ 0.1839,  0.1277,  0.1198,  ..., -0.1733, -0.2062, -0.1870],\n",
      "         [ 0.2720,  0.3535,  0.3393,  ...,  0.0842,  0.0502, -0.0008],\n",
      "         [-0.3842, -0.5005, -0.5651,  ..., -0.6451, -0.5198, -0.4244]]], device='cuda:0')\n",
      "Train Epoch: 70 [34560/50000 (77%)]\tLoss: 0.073513, Accuracy: 97.27\n",
      "Train Epoch: 70 [35840/50000 (80%)]\tLoss: 0.083643, Accuracy: 96.88\n",
      "Train Epoch: 70 [37120/50000 (82%)]\tLoss: 0.063051, Accuracy: 98.05\n",
      "Train Epoch: 70 [38400/50000 (85%)]\tLoss: 0.164497, Accuracy: 94.92\n",
      "Train Epoch: 70 [39680/50000 (88%)]\tLoss: 0.081545, Accuracy: 97.66\n",
      "Train Epoch: 70 [40960/50000 (91%)]\tLoss: 0.101142, Accuracy: 96.48\n",
      "Train Epoch: 70 [42240/50000 (94%)]\tLoss: 0.102454, Accuracy: 96.88\n",
      "Train Epoch: 70 [43520/50000 (97%)]\tLoss: 0.062244, Accuracy: 96.48\n",
      "Train Epoch: 70 [35000/50000 (99%)]\tLoss: 0.114964, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2943, Accuracy: 4575/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.6570885181427 s]\n",
      "\n",
      "Test set: Average loss: 0.3473, Accuracy: 9054/10000 (90.54%)\n",
      "\n",
      "Train Epoch: 71 [0/50000 (0%)]\tLoss: 0.082365, Accuracy: 96.48\n",
      "Train Epoch: 71 [1280/50000 (3%)]\tLoss: 0.064131, Accuracy: 98.05\n",
      "Train Epoch: 71 [2560/50000 (6%)]\tLoss: 0.057525, Accuracy: 98.44\n",
      "Train Epoch: 71 [3840/50000 (9%)]\tLoss: 0.048417, Accuracy: 98.44\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 71 [5120/50000 (11%)]\tLoss: 0.055259, Accuracy: 97.66\n",
      "Train Epoch: 71 [6400/50000 (14%)]\tLoss: 0.049084, Accuracy: 98.44\n",
      "Train Epoch: 71 [7680/50000 (17%)]\tLoss: 0.051555, Accuracy: 99.22\n",
      "Train Epoch: 71 [8960/50000 (20%)]\tLoss: 0.097704, Accuracy: 96.88\n",
      "Train Epoch: 71 [10240/50000 (23%)]\tLoss: 0.026434, Accuracy: 98.83\n",
      "Train Epoch: 71 [11520/50000 (26%)]\tLoss: 0.058796, Accuracy: 98.05\n",
      "Train Epoch: 71 [12800/50000 (28%)]\tLoss: 0.069214, Accuracy: 97.66\n",
      "Train Epoch: 71 [14080/50000 (31%)]\tLoss: 0.054145, Accuracy: 99.22\n",
      "Train Epoch: 71 [15360/50000 (34%)]\tLoss: 0.064576, Accuracy: 98.05\n",
      "Train Epoch: 71 [16640/50000 (37%)]\tLoss: 0.074546, Accuracy: 98.05\n",
      "Train Epoch: 71 [17920/50000 (40%)]\tLoss: 0.070738, Accuracy: 97.66\n",
      "Train Epoch: 71 [19200/50000 (43%)]\tLoss: 0.049052, Accuracy: 98.05\n",
      "Train Epoch: 71 [20480/50000 (45%)]\tLoss: 0.047183, Accuracy: 98.83\n",
      "Train Epoch: 71 [21760/50000 (48%)]\tLoss: 0.045071, Accuracy: 99.61\n",
      "Train Epoch: 71 [23040/50000 (51%)]\tLoss: 0.053411, Accuracy: 98.44\n",
      "Train Epoch: 71 [24320/50000 (54%)]\tLoss: 0.040419, Accuracy: 99.22\n",
      "Train Epoch: 71 [25600/50000 (57%)]\tLoss: 0.084955, Accuracy: 96.88\n",
      "Train Epoch: 71 [26880/50000 (60%)]\tLoss: 0.051316, Accuracy: 98.05\n",
      "Train Epoch: 71 [28160/50000 (62%)]\tLoss: 0.052187, Accuracy: 98.83\n",
      "Train Epoch: 71 [29440/50000 (65%)]\tLoss: 0.086861, Accuracy: 96.88\n",
      "Train Epoch: 71 [30720/50000 (68%)]\tLoss: 0.069711, Accuracy: 97.27\n",
      "Train Epoch: 71 [32000/50000 (71%)]\tLoss: 0.052717, Accuracy: 98.44\n",
      "Train Epoch: 71 [33280/50000 (74%)]\tLoss: 0.052183, Accuracy: 98.05\n",
      "Train Epoch: 71 [34560/50000 (77%)]\tLoss: 0.033687, Accuracy: 98.83\n",
      "Train Epoch: 71 [35840/50000 (80%)]\tLoss: 0.064125, Accuracy: 97.66\n",
      "Train Epoch: 71 [37120/50000 (82%)]\tLoss: 0.057431, Accuracy: 96.88\n",
      "Train Epoch: 71 [38400/50000 (85%)]\tLoss: 0.051314, Accuracy: 98.05\n",
      "Train Epoch: 71 [39680/50000 (88%)]\tLoss: 0.051821, Accuracy: 98.44\n",
      "Train Epoch: 71 [40960/50000 (91%)]\tLoss: 0.058283, Accuracy: 98.44\n",
      "Train Epoch: 71 [42240/50000 (94%)]\tLoss: 0.081199, Accuracy: 96.09\n",
      "Train Epoch: 71 [43520/50000 (97%)]\tLoss: 0.046605, Accuracy: 98.44\n",
      "Train Epoch: 71 [35000/50000 (99%)]\tLoss: 0.044057, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2705, Accuracy: 4606/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[40.73953819274902 s]\n",
      "Train Epoch: 72 [0/50000 (0%)]\tLoss: 0.038383, Accuracy: 98.44\n",
      "Train Epoch: 72 [1280/50000 (3%)]\tLoss: 0.078979, Accuracy: 98.05\n",
      "Train Epoch: 72 [2560/50000 (6%)]\tLoss: 0.036928, Accuracy: 98.44\n",
      "Train Epoch: 72 [3840/50000 (9%)]\tLoss: 0.077326, Accuracy: 96.88\n",
      "Train Epoch: 72 [5120/50000 (11%)]\tLoss: 0.070892, Accuracy: 97.27\n",
      "Train Epoch: 72 [6400/50000 (14%)]\tLoss: 0.084694, Accuracy: 97.27\n",
      "Train Epoch: 72 [7680/50000 (17%)]\tLoss: 0.093550, Accuracy: 98.05\n",
      "Train Epoch: 72 [8960/50000 (20%)]\tLoss: 0.046239, Accuracy: 97.66\n",
      "Train Epoch: 72 [10240/50000 (23%)]\tLoss: 0.036469, Accuracy: 99.22\n",
      "Train Epoch: 72 [11520/50000 (26%)]\tLoss: 0.118925, Accuracy: 95.31\n",
      "Train Epoch: 72 [12800/50000 (28%)]\tLoss: 0.045156, Accuracy: 98.83\n",
      "Train Epoch: 72 [14080/50000 (31%)]\tLoss: 0.033100, Accuracy: 99.22\n",
      "Train Epoch: 72 [15360/50000 (34%)]\tLoss: 0.056376, Accuracy: 98.83\n",
      "Train Epoch: 72 [16640/50000 (37%)]\tLoss: 0.040233, Accuracy: 98.44\n",
      "Train Epoch: 72 [17920/50000 (40%)]\tLoss: 0.066729, Accuracy: 98.44\n",
      "Train Epoch: 72 [19200/50000 (43%)]\tLoss: 0.058253, Accuracy: 98.05\n",
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      "Train Epoch: 72 [21760/50000 (48%)]\tLoss: 0.055372, Accuracy: 97.66\n",
      "Train Epoch: 72 [23040/50000 (51%)]\tLoss: 0.073259, Accuracy: 97.27\n",
      "Train Epoch: 72 [24320/50000 (54%)]\tLoss: 0.057682, Accuracy: 97.66\n",
      "Train Epoch: 72 [25600/50000 (57%)]\tLoss: 0.042167, Accuracy: 99.22\n",
      "Train Epoch: 72 [26880/50000 (60%)]\tLoss: 0.051960, Accuracy: 98.05\n",
      "Train Epoch: 72 [28160/50000 (62%)]\tLoss: 0.047871, Accuracy: 98.83\n",
      "Train Epoch: 72 [29440/50000 (65%)]\tLoss: 0.047378, Accuracy: 98.83\n",
      "Train Epoch: 72 [30720/50000 (68%)]\tLoss: 0.076331, Accuracy: 96.88\n",
      "Train Epoch: 72 [32000/50000 (71%)]\tLoss: 0.025652, Accuracy: 99.61\n",
      "Train Epoch: 72 [33280/50000 (74%)]\tLoss: 0.062570, Accuracy: 97.66\n",
      "Train Epoch: 72 [34560/50000 (77%)]\tLoss: 0.047232, Accuracy: 98.05\n",
      "Train Epoch: 72 [35840/50000 (80%)]\tLoss: 0.057553, Accuracy: 98.83\n",
      "Train Epoch: 72 [37120/50000 (82%)]\tLoss: 0.036398, Accuracy: 98.83\n",
      "Train Epoch: 72 [38400/50000 (85%)]\tLoss: 0.064930, Accuracy: 98.05\n",
      "Train Epoch: 72 [39680/50000 (88%)]\tLoss: 0.034249, Accuracy: 98.83\n",
      "Train Epoch: 72 [40960/50000 (91%)]\tLoss: 0.038774, Accuracy: 98.83\n",
      "Train Epoch: 72 [42240/50000 (94%)]\tLoss: 0.078604, Accuracy: 96.48\n",
      "Train Epoch: 72 [43520/50000 (97%)]\tLoss: 0.030167, Accuracy: 98.83\n",
      "Train Epoch: 72 [35000/50000 (99%)]\tLoss: 0.036575, Accuracy: 99.00\n",
      "\n",
      "Validation set: Average loss: 0.2714, Accuracy: 4618/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[37.66299748420715 s]\n",
      "\n",
      "Test set: Average loss: 0.3219, Accuracy: 9119/10000 (91.19%)\n",
      "\n",
      "Train Epoch: 73 [0/50000 (0%)]\tLoss: 0.027082, Accuracy: 99.22\n",
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      "Train Epoch: 73 [32000/50000 (71%)]\tLoss: 0.038696, Accuracy: 98.44\n",
      "Train Epoch: 73 [33280/50000 (74%)]\tLoss: 0.071903, Accuracy: 98.44\n",
      "Train Epoch: 73 [34560/50000 (77%)]\tLoss: 0.038593, Accuracy: 98.44\n",
      "Train Epoch: 73 [35840/50000 (80%)]\tLoss: 0.070155, Accuracy: 96.88\n",
      "Train Epoch: 73 [37120/50000 (82%)]\tLoss: 0.071941, Accuracy: 98.05\n",
      "Train Epoch: 73 [38400/50000 (85%)]\tLoss: 0.038408, Accuracy: 98.83\n",
      "Train Epoch: 73 [39680/50000 (88%)]\tLoss: 0.034608, Accuracy: 99.22\n",
      "Train Epoch: 73 [40960/50000 (91%)]\tLoss: 0.044822, Accuracy: 98.05\n",
      "Train Epoch: 73 [42240/50000 (94%)]\tLoss: 0.053856, Accuracy: 98.05\n",
      "Train Epoch: 73 [43520/50000 (97%)]\tLoss: 0.055188, Accuracy: 98.44\n",
      "Train Epoch: 73 [35000/50000 (99%)]\tLoss: 0.073569, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2754, Accuracy: 4616/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[40.86830925941467 s]\n",
      "Train Epoch: 74 [0/50000 (0%)]\tLoss: 0.037486, Accuracy: 98.83\n",
      "Train Epoch: 74 [1280/50000 (3%)]\tLoss: 0.035212, Accuracy: 99.22\n",
      "Train Epoch: 74 [2560/50000 (6%)]\tLoss: 0.078602, Accuracy: 95.70\n",
      "Train Epoch: 74 [3840/50000 (9%)]\tLoss: 0.038596, Accuracy: 99.22\n",
      "Train Epoch: 74 [5120/50000 (11%)]\tLoss: 0.029265, Accuracy: 99.61\n",
      "Train Epoch: 74 [6400/50000 (14%)]\tLoss: 0.096576, Accuracy: 96.88\n",
      "Train Epoch: 74 [7680/50000 (17%)]\tLoss: 0.041106, Accuracy: 98.83\n",
      "Train Epoch: 74 [8960/50000 (20%)]\tLoss: 0.031765, Accuracy: 98.83\n",
      "Train Epoch: 74 [10240/50000 (23%)]\tLoss: 0.045271, Accuracy: 99.22\n",
      "Train Epoch: 74 [11520/50000 (26%)]\tLoss: 0.028729, Accuracy: 98.83\n",
      "Train Epoch: 74 [12800/50000 (28%)]\tLoss: 0.056061, Accuracy: 98.44\n",
      "Train Epoch: 74 [14080/50000 (31%)]\tLoss: 0.057209, Accuracy: 98.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 74 [15360/50000 (34%)]\tLoss: 0.094457, Accuracy: 96.09\n",
      "Train Epoch: 74 [16640/50000 (37%)]\tLoss: 0.045758, Accuracy: 98.05\n",
      "Train Epoch: 74 [17920/50000 (40%)]\tLoss: 0.080220, Accuracy: 97.27\n",
      "Train Epoch: 74 [19200/50000 (43%)]\tLoss: 0.056549, Accuracy: 98.83\n",
      "Train Epoch: 74 [20480/50000 (45%)]\tLoss: 0.049295, Accuracy: 99.22\n",
      "Train Epoch: 74 [21760/50000 (48%)]\tLoss: 0.075746, Accuracy: 97.27\n",
      "Train Epoch: 74 [23040/50000 (51%)]\tLoss: 0.048200, Accuracy: 98.05\n",
      "Train Epoch: 74 [24320/50000 (54%)]\tLoss: 0.037172, Accuracy: 98.83\n",
      "Train Epoch: 74 [25600/50000 (57%)]\tLoss: 0.055875, Accuracy: 98.05\n",
      "Train Epoch: 74 [26880/50000 (60%)]\tLoss: 0.025476, Accuracy: 99.22\n",
      "Train Epoch: 74 [28160/50000 (62%)]\tLoss: 0.063368, Accuracy: 96.88\n",
      "Train Epoch: 74 [29440/50000 (65%)]\tLoss: 0.059211, Accuracy: 98.05\n",
      "Train Epoch: 74 [30720/50000 (68%)]\tLoss: 0.076062, Accuracy: 97.27\n",
      "Train Epoch: 74 [32000/50000 (71%)]\tLoss: 0.096218, Accuracy: 97.27\n",
      "Train Epoch: 74 [33280/50000 (74%)]\tLoss: 0.038096, Accuracy: 98.83\n",
      "Train Epoch: 74 [34560/50000 (77%)]\tLoss: 0.039050, Accuracy: 98.83\n",
      "Train Epoch: 74 [35840/50000 (80%)]\tLoss: 0.042394, Accuracy: 99.22\n",
      "Train Epoch: 74 [37120/50000 (82%)]\tLoss: 0.040528, Accuracy: 99.22\n",
      "Train Epoch: 74 [38400/50000 (85%)]\tLoss: 0.023868, Accuracy: 99.61\n",
      "Train Epoch: 74 [39680/50000 (88%)]\tLoss: 0.045745, Accuracy: 98.83\n",
      "Train Epoch: 74 [40960/50000 (91%)]\tLoss: 0.054685, Accuracy: 97.66\n",
      "Train Epoch: 74 [42240/50000 (94%)]\tLoss: 0.071295, Accuracy: 97.66\n",
      "Train Epoch: 74 [43520/50000 (97%)]\tLoss: 0.117780, Accuracy: 96.88\n",
      "Train Epoch: 74 [35000/50000 (99%)]\tLoss: 0.093963, Accuracy: 96.00\n",
      "\n",
      "Validation set: Average loss: 0.2789, Accuracy: 4597/5000 (91.00%)\n",
      "\n",
      "the time of this epoch:[37.69062089920044 s]\n",
      "\n",
      "Test set: Average loss: 0.3257, Accuracy: 9126/10000 (91.26%)\n",
      "\n",
      "Train Epoch: 75 [0/50000 (0%)]\tLoss: 0.073603, Accuracy: 97.66\n",
      "Train Epoch: 75 [1280/50000 (3%)]\tLoss: 0.075161, Accuracy: 98.44\n",
      "Train Epoch: 75 [2560/50000 (6%)]\tLoss: 0.092583, Accuracy: 97.66\n",
      "Train Epoch: 75 [3840/50000 (9%)]\tLoss: 0.099125, Accuracy: 97.27\n",
      "Train Epoch: 75 [5120/50000 (11%)]\tLoss: 0.037841, Accuracy: 99.22\n",
      "Train Epoch: 75 [6400/50000 (14%)]\tLoss: 0.045381, Accuracy: 99.22\n",
      "Train Epoch: 75 [7680/50000 (17%)]\tLoss: 0.044837, Accuracy: 99.22\n",
      "Train Epoch: 75 [8960/50000 (20%)]\tLoss: 0.046958, Accuracy: 98.05\n",
      "Train Epoch: 75 [10240/50000 (23%)]\tLoss: 0.046480, Accuracy: 99.22\n",
      "Train Epoch: 75 [11520/50000 (26%)]\tLoss: 0.027717, Accuracy: 98.83\n",
      "Train Epoch: 75 [12800/50000 (28%)]\tLoss: 0.058845, Accuracy: 97.66\n",
      "Train Epoch: 75 [14080/50000 (31%)]\tLoss: 0.024004, Accuracy: 99.22\n",
      "Train Epoch: 75 [15360/50000 (34%)]\tLoss: 0.047734, Accuracy: 97.66\n",
      "Train Epoch: 75 [16640/50000 (37%)]\tLoss: 0.026054, Accuracy: 99.22\n",
      "Train Epoch: 75 [17920/50000 (40%)]\tLoss: 0.075759, Accuracy: 98.44\n",
      "Train Epoch: 75 [19200/50000 (43%)]\tLoss: 0.050118, Accuracy: 98.05\n",
      "Train Epoch: 75 [20480/50000 (45%)]\tLoss: 0.042095, Accuracy: 98.83\n",
      "Train Epoch: 75 [21760/50000 (48%)]\tLoss: 0.056279, Accuracy: 98.05\n",
      "Train Epoch: 75 [23040/50000 (51%)]\tLoss: 0.042415, Accuracy: 98.83\n",
      "Train Epoch: 75 [24320/50000 (54%)]\tLoss: 0.040905, Accuracy: 99.22\n",
      "Train Epoch: 75 [25600/50000 (57%)]\tLoss: 0.024531, Accuracy: 99.61\n",
      "Train Epoch: 75 [26880/50000 (60%)]\tLoss: 0.065250, Accuracy: 96.88\n",
      "Train Epoch: 75 [28160/50000 (62%)]\tLoss: 0.049338, Accuracy: 98.83\n",
      "Train Epoch: 75 [29440/50000 (65%)]\tLoss: 0.020493, Accuracy: 100.00\n",
      "Train Epoch: 75 [30720/50000 (68%)]\tLoss: 0.023031, Accuracy: 99.22\n",
      "Train Epoch: 75 [32000/50000 (71%)]\tLoss: 0.033882, Accuracy: 99.61\n",
      "Train Epoch: 75 [33280/50000 (74%)]\tLoss: 0.056898, Accuracy: 98.83\n",
      "Train Epoch: 75 [34560/50000 (77%)]\tLoss: 0.053035, Accuracy: 98.05\n",
      "Train Epoch: 75 [35840/50000 (80%)]\tLoss: 0.045787, Accuracy: 98.83\n",
      "Train Epoch: 75 [37120/50000 (82%)]\tLoss: 0.034087, Accuracy: 99.22\n",
      "Train Epoch: 75 [38400/50000 (85%)]\tLoss: 0.050380, Accuracy: 98.83\n",
      "Train Epoch: 75 [39680/50000 (88%)]\tLoss: 0.038635, Accuracy: 98.44\n",
      "Train Epoch: 75 [40960/50000 (91%)]\tLoss: 0.066167, Accuracy: 97.66\n",
      "Train Epoch: 75 [42240/50000 (94%)]\tLoss: 0.061130, Accuracy: 98.05\n",
      "Train Epoch: 75 [43520/50000 (97%)]\tLoss: 0.024896, Accuracy: 99.61\n",
      "Train Epoch: 75 [35000/50000 (99%)]\tLoss: 0.032423, Accuracy: 99.50\n",
      "\n",
      "Validation set: Average loss: 0.2778, Accuracy: 4617/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[40.88454818725586 s]\n",
      "Train Epoch: 76 [0/50000 (0%)]\tLoss: 0.028461, Accuracy: 99.22\n",
      "Train Epoch: 76 [1280/50000 (3%)]\tLoss: 0.029730, Accuracy: 99.61\n",
      "Train Epoch: 76 [2560/50000 (6%)]\tLoss: 0.023257, Accuracy: 99.61\n",
      "Train Epoch: 76 [3840/50000 (9%)]\tLoss: 0.063205, Accuracy: 97.27\n",
      "Train Epoch: 76 [5120/50000 (11%)]\tLoss: 0.033244, Accuracy: 99.22\n",
      "Train Epoch: 76 [6400/50000 (14%)]\tLoss: 0.051441, Accuracy: 98.83\n",
      "Train Epoch: 76 [7680/50000 (17%)]\tLoss: 0.105414, Accuracy: 96.09\n",
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      "Train Epoch: 76 [12800/50000 (28%)]\tLoss: 0.030874, Accuracy: 98.83\n",
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      "Train Epoch: 76 [15360/50000 (34%)]\tLoss: 0.048116, Accuracy: 99.22\n",
      "Train Epoch: 76 [16640/50000 (37%)]\tLoss: 0.039254, Accuracy: 98.44\n",
      "Train Epoch: 76 [17920/50000 (40%)]\tLoss: 0.045036, Accuracy: 98.83\n",
      "Train Epoch: 76 [19200/50000 (43%)]\tLoss: 0.084896, Accuracy: 97.66\n",
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      "Train Epoch: 76 [21760/50000 (48%)]\tLoss: 0.046796, Accuracy: 98.83\n",
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      "Train Epoch: 76 [24320/50000 (54%)]\tLoss: 0.043249, Accuracy: 98.05\n",
      "Train Epoch: 76 [25600/50000 (57%)]\tLoss: 0.023524, Accuracy: 99.22\n",
      "Train Epoch: 76 [26880/50000 (60%)]\tLoss: 0.038574, Accuracy: 98.05\n",
      "Train Epoch: 76 [28160/50000 (62%)]\tLoss: 0.053189, Accuracy: 98.83\n",
      "Train Epoch: 76 [29440/50000 (65%)]\tLoss: 0.066523, Accuracy: 98.05\n",
      "Train Epoch: 76 [30720/50000 (68%)]\tLoss: 0.030993, Accuracy: 98.83\n",
      "Train Epoch: 76 [32000/50000 (71%)]\tLoss: 0.019402, Accuracy: 99.61\n",
      "Train Epoch: 76 [33280/50000 (74%)]\tLoss: 0.032842, Accuracy: 99.22\n",
      "Train Epoch: 76 [34560/50000 (77%)]\tLoss: 0.035653, Accuracy: 98.83\n",
      "Train Epoch: 76 [35840/50000 (80%)]\tLoss: 0.033658, Accuracy: 98.83\n",
      "Train Epoch: 76 [37120/50000 (82%)]\tLoss: 0.073775, Accuracy: 98.44\n",
      "Train Epoch: 76 [38400/50000 (85%)]\tLoss: 0.047888, Accuracy: 98.44\n",
      "Train Epoch: 76 [39680/50000 (88%)]\tLoss: 0.053955, Accuracy: 98.05\n",
      "Train Epoch: 76 [40960/50000 (91%)]\tLoss: 0.053311, Accuracy: 98.05\n",
      "Train Epoch: 76 [42240/50000 (94%)]\tLoss: 0.039691, Accuracy: 99.22\n",
      "Train Epoch: 76 [43520/50000 (97%)]\tLoss: 0.042970, Accuracy: 98.44\n",
      "Train Epoch: 76 [35000/50000 (99%)]\tLoss: 0.067824, Accuracy: 97.50\n",
      "\n",
      "Validation set: Average loss: 0.2774, Accuracy: 4612/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[37.74062395095825 s]\n",
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      "        [[ 0.0020]],\n",
      "\n",
      "        [[ 0.0025]],\n",
      "\n",
      "        [[ 0.0050]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 1.7697]]], device='cuda:0')\n",
      "torch.Size([896, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 4.0002e-03,  6.5743e-03,  6.9916e-03,  ...,  6.5162e-03,\n",
      "           2.5431e-03,  2.8115e-03]],\n",
      "\n",
      "        [[ 3.7567e-05,  2.1140e-03,  2.0719e-03,  ...,  1.5383e-03,\n",
      "          -1.5916e-03, -1.3875e-03]],\n",
      "\n",
      "        [[-2.0682e-03, -3.5542e-04, -1.4106e-03,  ..., -2.6292e-03,\n",
      "          -1.1446e-03,  1.1519e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 5.6636e-04, -9.7449e-04, -7.3133e-04,  ...,  2.6225e-03,\n",
      "           2.6372e-03, -2.0991e-03]],\n",
      "\n",
      "        [[-4.6901e-04,  1.4871e-03,  1.5138e-03,  ...,  1.8363e-04,\n",
      "           3.7987e-05,  3.1342e-03]],\n",
      "\n",
      "        [[ 2.0092e-04,  5.2082e-04,  2.5598e-03,  ...,  1.3959e-03,\n",
      "          -1.2457e-03, -5.0495e-04]]], device='cuda:0')\n",
      "torch.Size([1, 896, 512])\n",
      "tensor([[[ 8.3763e-04,  9.8934e-04,  1.0623e-03,  ...,  1.2933e-03,\n",
      "           1.1527e-03,  1.0582e-03],\n",
      "         [ 3.8801e-05,  1.6946e-05,  7.7538e-06,  ...,  1.2173e-04,\n",
      "           1.0633e-04,  1.0870e-04],\n",
      "         [-2.8645e-04, -3.5279e-04, -3.4444e-04,  ..., -3.4381e-04,\n",
      "          -3.6748e-04, -3.3277e-04],\n",
      "         ...,\n",
      "         [ 2.4127e-04,  2.5546e-04,  2.2460e-04,  ...,  7.6205e-05,\n",
      "          -2.1336e-06, -3.5222e-05],\n",
      "         [ 3.4146e-04,  3.8325e-04,  4.0334e-04,  ...,  3.0968e-04,\n",
      "           2.3924e-04,  1.7144e-04],\n",
      "         [-1.8272e-05, -8.4557e-06, -3.4487e-05,  ...,  1.9094e-05,\n",
      "          -2.2957e-05, -1.5042e-05]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.3249, Accuracy: 9126/10000 (91.26%)\n",
      "\n",
      "Train Epoch: 77 [0/50000 (0%)]\tLoss: 0.033818, Accuracy: 98.83\n",
      "Train Epoch: 77 [1280/50000 (3%)]\tLoss: 0.040264, Accuracy: 98.83\n",
      "Train Epoch: 77 [2560/50000 (6%)]\tLoss: 0.026574, Accuracy: 99.61\n",
      "Train Epoch: 77 [3840/50000 (9%)]\tLoss: 0.027768, Accuracy: 99.22\n",
      "Train Epoch: 77 [5120/50000 (11%)]\tLoss: 0.026647, Accuracy: 99.22\n",
      "Train Epoch: 77 [6400/50000 (14%)]\tLoss: 0.068554, Accuracy: 97.66\n",
      "Train Epoch: 77 [7680/50000 (17%)]\tLoss: 0.078147, Accuracy: 97.66\n",
      "Train Epoch: 77 [8960/50000 (20%)]\tLoss: 0.045335, Accuracy: 98.44\n",
      "Train Epoch: 77 [10240/50000 (23%)]\tLoss: 0.021985, Accuracy: 99.22\n",
      "Train Epoch: 77 [11520/50000 (26%)]\tLoss: 0.076191, Accuracy: 98.05\n",
      "torch.Size([512, 1, 1])\n",
      "tensor([[[ 0.1214]],\n",
      "\n",
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      "\n",
      "        [[ 0.0016]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[-6.2714]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[ 2.7257e-04,  2.8471e-03,  2.4323e-03,  ...,  2.3276e-03,\n",
      "           9.8422e-04,  4.1346e-03]],\n",
      "\n",
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      "\n",
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      "\n",
      "        ...,\n",
      "\n",
      "        [[-2.8544e-03, -2.2378e-03, -1.7145e-03,  ...,  5.3885e-04,\n",
      "          -2.9251e-03,  1.0709e-03]],\n",
      "\n",
      "        [[-1.3394e-03, -1.0153e-03, -3.2843e-03,  ..., -3.4555e-03,\n",
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      "\n",
      "        [[ 4.4118e-05, -1.4696e-03, -3.0972e-03,  ..., -5.0431e-03,\n",
      "          -4.7752e-03, -5.4374e-03]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor([[[ 4.3114e-04,  4.5569e-04,  4.8668e-04,  ...,  3.5066e-04,\n",
      "           2.5883e-04,  1.7679e-04],\n",
      "         [-3.5487e-05, -2.9872e-05, -7.8908e-05,  ..., -2.2002e-04,\n",
      "          -2.0843e-04, -1.7129e-04],\n",
      "         [-1.7224e-04, -2.1456e-04, -2.2886e-04,  ..., -2.8861e-04,\n",
      "          -2.4089e-04, -2.0418e-04],\n",
      "         ...,\n",
      "         [-4.6765e-05, -2.1401e-05, -8.9697e-05,  ..., -2.4689e-04,\n",
      "          -2.5368e-04, -2.5441e-04],\n",
      "         [-3.5970e-04, -4.4043e-04, -5.1230e-04,  ..., -4.1357e-04,\n",
      "          -3.4720e-04, -2.9479e-04],\n",
      "         [-5.9739e-04, -6.9653e-04, -8.0975e-04,  ..., -5.8393e-04,\n",
      "          -4.6827e-04, -4.0211e-04]]], device='cuda:0')\n",
      "Train Epoch: 77 [12800/50000 (28%)]\tLoss: 0.051971, Accuracy: 98.05\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 77 [14080/50000 (31%)]\tLoss: 0.055759, Accuracy: 98.44\n",
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      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.1441]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0193]],\n",
      "\n",
      "        [[ 0.0132]],\n",
      "\n",
      "        [[ 0.0169]],\n",
      "\n",
      "        [[ 0.0452]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0178]],\n",
      "\n",
      "        [[ 0.0105]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0765]],\n",
      "\n",
      "        [[ 0.0001]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0006]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0226]],\n",
      "\n",
      "        [[ 0.0295]],\n",
      "\n",
      "        [[ 0.0388]],\n",
      "\n",
      "        [[ 0.0066]],\n",
      "\n",
      "        [[ 0.0938]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.1259]],\n",
      "\n",
      "        [[ 0.0000]],\n",
      "\n",
      "        [[ 0.0300]]], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor(1.00000e-02 *\n",
      "       [[[ 1.6132]]], device='cuda:0')\n",
      "torch.Size([448, 1, 13])\n",
      "Parameter containing:\n",
      "tensor([[[-2.0377e-03, -2.1225e-03, -1.8820e-03,  ..., -4.8455e-03,\n",
      "          -5.9607e-03, -1.1417e-02]],\n",
      "\n",
      "        [[ 1.6779e-03,  3.3706e-03,  3.5730e-03,  ...,  2.5742e-03,\n",
      "           7.4315e-03,  1.7918e-03]],\n",
      "\n",
      "        [[ 3.3216e-03,  2.1858e-03,  3.5565e-03,  ...,  3.8089e-03,\n",
      "           5.7968e-03,  2.4026e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 2.8470e-03,  4.5118e-03,  4.2419e-03,  ...,  3.7013e-03,\n",
      "           3.3459e-03,  1.9030e-04]],\n",
      "\n",
      "        [[-1.1251e-03, -5.0063e-04, -4.1935e-04,  ...,  1.9307e-03,\n",
      "          -1.4583e-03,  2.7504e-04]],\n",
      "\n",
      "        [[-3.4025e-04, -4.6923e-03, -1.7035e-04,  ..., -5.0082e-03,\n",
      "          -2.5701e-03, -1.4494e-03]]], device='cuda:0')\n",
      "torch.Size([1, 448, 512])\n",
      "tensor(1.00000e-03 *\n",
      "       [[[-0.9548, -1.1073, -1.2554,  ..., -0.9099, -0.7710, -0.6350],\n",
      "         [ 0.5696,  0.6450,  0.7285,  ...,  0.5810,  0.4765,  0.3759],\n",
      "         [ 0.4848,  0.5426,  0.6206,  ...,  0.4265,  0.4108,  0.3284],\n",
      "         ...,\n",
      "         [ 0.2914,  0.3685,  0.4125,  ...,  0.5235,  0.4732,  0.4704],\n",
      "         [-0.0465, -0.0964, -0.0913,  ..., -0.1060, -0.0529, -0.0688],\n",
      "         [-0.4847, -0.5099, -0.5123,  ..., -0.4057, -0.2993, -0.2254]]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 77 [15360/50000 (34%)]\tLoss: 0.043480, Accuracy: 98.44\n",
      "Train Epoch: 77 [16640/50000 (37%)]\tLoss: 0.034259, Accuracy: 98.83\n",
      "Train Epoch: 77 [17920/50000 (40%)]\tLoss: 0.039795, Accuracy: 98.83\n",
      "Train Epoch: 77 [19200/50000 (43%)]\tLoss: 0.038420, Accuracy: 97.66\n",
      "Train Epoch: 77 [20480/50000 (45%)]\tLoss: 0.062292, Accuracy: 97.66\n",
      "Train Epoch: 77 [21760/50000 (48%)]\tLoss: 0.044614, Accuracy: 98.83\n",
      "Train Epoch: 77 [23040/50000 (51%)]\tLoss: 0.055634, Accuracy: 98.44\n",
      "Train Epoch: 77 [24320/50000 (54%)]\tLoss: 0.048275, Accuracy: 98.83\n",
      "Train Epoch: 77 [25600/50000 (57%)]\tLoss: 0.029857, Accuracy: 99.22\n",
      "Train Epoch: 77 [26880/50000 (60%)]\tLoss: 0.042579, Accuracy: 98.05\n",
      "Train Epoch: 77 [28160/50000 (62%)]\tLoss: 0.056971, Accuracy: 98.83\n",
      "Train Epoch: 77 [29440/50000 (65%)]\tLoss: 0.039083, Accuracy: 98.44\n",
      "Train Epoch: 77 [30720/50000 (68%)]\tLoss: 0.069941, Accuracy: 97.66\n",
      "Train Epoch: 77 [32000/50000 (71%)]\tLoss: 0.042606, Accuracy: 98.83\n",
      "Train Epoch: 77 [33280/50000 (74%)]\tLoss: 0.029177, Accuracy: 99.22\n",
      "Train Epoch: 77 [34560/50000 (77%)]\tLoss: 0.042053, Accuracy: 98.44\n",
      "Train Epoch: 77 [35840/50000 (80%)]\tLoss: 0.059209, Accuracy: 98.05\n",
      "Train Epoch: 77 [37120/50000 (82%)]\tLoss: 0.019173, Accuracy: 99.22\n",
      "Train Epoch: 77 [38400/50000 (85%)]\tLoss: 0.042528, Accuracy: 98.44\n",
      "Train Epoch: 77 [39680/50000 (88%)]\tLoss: 0.036507, Accuracy: 99.22\n",
      "Train Epoch: 77 [40960/50000 (91%)]\tLoss: 0.072450, Accuracy: 97.66\n",
      "Train Epoch: 77 [42240/50000 (94%)]\tLoss: 0.059730, Accuracy: 98.44\n",
      "Train Epoch: 77 [43520/50000 (97%)]\tLoss: 0.079279, Accuracy: 97.27\n",
      "Train Epoch: 77 [35000/50000 (99%)]\tLoss: 0.046479, Accuracy: 98.50\n",
      "\n",
      "Validation set: Average loss: 0.2798, Accuracy: 4616/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[40.96441912651062 s]\n",
      "Train Epoch: 78 [0/50000 (0%)]\tLoss: 0.029535, Accuracy: 98.05\n",
      "Train Epoch: 78 [1280/50000 (3%)]\tLoss: 0.056596, Accuracy: 98.44\n",
      "Train Epoch: 78 [2560/50000 (6%)]\tLoss: 0.052832, Accuracy: 97.66\n",
      "Train Epoch: 78 [3840/50000 (9%)]\tLoss: 0.055554, Accuracy: 97.66\n",
      "Train Epoch: 78 [5120/50000 (11%)]\tLoss: 0.030931, Accuracy: 99.61\n",
      "Train Epoch: 78 [6400/50000 (14%)]\tLoss: 0.023816, Accuracy: 99.61\n",
      "Train Epoch: 78 [7680/50000 (17%)]\tLoss: 0.018429, Accuracy: 100.00\n",
      "Train Epoch: 78 [8960/50000 (20%)]\tLoss: 0.019994, Accuracy: 99.22\n",
      "Train Epoch: 78 [10240/50000 (23%)]\tLoss: 0.042360, Accuracy: 98.44\n",
      "Train Epoch: 78 [11520/50000 (26%)]\tLoss: 0.064842, Accuracy: 98.05\n",
      "Train Epoch: 78 [12800/50000 (28%)]\tLoss: 0.030127, Accuracy: 99.22\n",
      "Train Epoch: 78 [14080/50000 (31%)]\tLoss: 0.035038, Accuracy: 98.83\n",
      "Train Epoch: 78 [15360/50000 (34%)]\tLoss: 0.045356, Accuracy: 99.22\n",
      "Train Epoch: 78 [16640/50000 (37%)]\tLoss: 0.024740, Accuracy: 99.22\n",
      "Train Epoch: 78 [17920/50000 (40%)]\tLoss: 0.049675, Accuracy: 99.61\n",
      "Train Epoch: 78 [19200/50000 (43%)]\tLoss: 0.043865, Accuracy: 98.83\n",
      "Train Epoch: 78 [20480/50000 (45%)]\tLoss: 0.036193, Accuracy: 98.83\n",
      "Train Epoch: 78 [21760/50000 (48%)]\tLoss: 0.072845, Accuracy: 97.66\n",
      "Train Epoch: 78 [23040/50000 (51%)]\tLoss: 0.031067, Accuracy: 99.61\n",
      "Train Epoch: 78 [24320/50000 (54%)]\tLoss: 0.043172, Accuracy: 98.83\n",
      "Train Epoch: 78 [25600/50000 (57%)]\tLoss: 0.064676, Accuracy: 98.05\n",
      "Train Epoch: 78 [26880/50000 (60%)]\tLoss: 0.072651, Accuracy: 97.66\n",
      "Train Epoch: 78 [28160/50000 (62%)]\tLoss: 0.041146, Accuracy: 98.83\n",
      "Train Epoch: 78 [29440/50000 (65%)]\tLoss: 0.040037, Accuracy: 98.83\n",
      "Train Epoch: 78 [30720/50000 (68%)]\tLoss: 0.024909, Accuracy: 99.61\n",
      "Train Epoch: 78 [32000/50000 (71%)]\tLoss: 0.043580, Accuracy: 98.83\n",
      "Train Epoch: 78 [33280/50000 (74%)]\tLoss: 0.049395, Accuracy: 98.44\n",
      "Train Epoch: 78 [34560/50000 (77%)]\tLoss: 0.039195, Accuracy: 99.22\n",
      "Train Epoch: 78 [35840/50000 (80%)]\tLoss: 0.105199, Accuracy: 96.09\n",
      "Train Epoch: 78 [37120/50000 (82%)]\tLoss: 0.050661, Accuracy: 98.44\n",
      "Train Epoch: 78 [38400/50000 (85%)]\tLoss: 0.035954, Accuracy: 98.44\n",
      "Train Epoch: 78 [39680/50000 (88%)]\tLoss: 0.033505, Accuracy: 98.83\n",
      "Train Epoch: 78 [40960/50000 (91%)]\tLoss: 0.047910, Accuracy: 98.44\n",
      "Train Epoch: 78 [42240/50000 (94%)]\tLoss: 0.062335, Accuracy: 97.66\n",
      "Train Epoch: 78 [43520/50000 (97%)]\tLoss: 0.069658, Accuracy: 98.05\n",
      "Train Epoch: 78 [35000/50000 (99%)]\tLoss: 0.062628, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2800, Accuracy: 4613/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[37.82146120071411 s]\n",
      "\n",
      "Test set: Average loss: 0.3272, Accuracy: 9128/10000 (91.28%)\n",
      "\n",
      "Train Epoch: 79 [0/50000 (0%)]\tLoss: 0.021528, Accuracy: 98.83\n",
      "Train Epoch: 79 [1280/50000 (3%)]\tLoss: 0.051128, Accuracy: 98.83\n",
      "Train Epoch: 79 [2560/50000 (6%)]\tLoss: 0.038254, Accuracy: 99.22\n",
      "Train Epoch: 79 [3840/50000 (9%)]\tLoss: 0.072074, Accuracy: 97.27\n",
      "Train Epoch: 79 [5120/50000 (11%)]\tLoss: 0.032581, Accuracy: 99.22\n",
      "Train Epoch: 79 [6400/50000 (14%)]\tLoss: 0.038297, Accuracy: 99.22\n",
      "Train Epoch: 79 [7680/50000 (17%)]\tLoss: 0.042365, Accuracy: 98.83\n",
      "Train Epoch: 79 [8960/50000 (20%)]\tLoss: 0.037754, Accuracy: 98.44\n",
      "Train Epoch: 79 [10240/50000 (23%)]\tLoss: 0.038366, Accuracy: 98.83\n",
      "Train Epoch: 79 [11520/50000 (26%)]\tLoss: 0.032662, Accuracy: 98.44\n",
      "Train Epoch: 79 [12800/50000 (28%)]\tLoss: 0.071039, Accuracy: 98.05\n",
      "Train Epoch: 79 [14080/50000 (31%)]\tLoss: 0.034159, Accuracy: 98.44\n",
      "Train Epoch: 79 [15360/50000 (34%)]\tLoss: 0.027512, Accuracy: 99.61\n",
      "Train Epoch: 79 [16640/50000 (37%)]\tLoss: 0.055103, Accuracy: 98.44\n",
      "Train Epoch: 79 [17920/50000 (40%)]\tLoss: 0.046543, Accuracy: 98.05\n",
      "Train Epoch: 79 [19200/50000 (43%)]\tLoss: 0.027187, Accuracy: 99.22\n",
      "Train Epoch: 79 [20480/50000 (45%)]\tLoss: 0.016699, Accuracy: 99.61\n",
      "Train Epoch: 79 [21760/50000 (48%)]\tLoss: 0.051673, Accuracy: 98.44\n",
      "Train Epoch: 79 [23040/50000 (51%)]\tLoss: 0.031414, Accuracy: 99.61\n",
      "Train Epoch: 79 [24320/50000 (54%)]\tLoss: 0.048828, Accuracy: 98.44\n",
      "Train Epoch: 79 [25600/50000 (57%)]\tLoss: 0.045459, Accuracy: 98.83\n",
      "Train Epoch: 79 [26880/50000 (60%)]\tLoss: 0.043520, Accuracy: 98.83\n",
      "Train Epoch: 79 [28160/50000 (62%)]\tLoss: 0.024331, Accuracy: 99.22\n",
      "Train Epoch: 79 [29440/50000 (65%)]\tLoss: 0.036537, Accuracy: 98.83\n",
      "Train Epoch: 79 [30720/50000 (68%)]\tLoss: 0.046251, Accuracy: 98.44\n",
      "Train Epoch: 79 [32000/50000 (71%)]\tLoss: 0.038405, Accuracy: 99.61\n",
      "Train Epoch: 79 [33280/50000 (74%)]\tLoss: 0.048535, Accuracy: 98.44\n",
      "Train Epoch: 79 [34560/50000 (77%)]\tLoss: 0.032752, Accuracy: 99.22\n",
      "Train Epoch: 79 [35840/50000 (80%)]\tLoss: 0.052498, Accuracy: 98.05\n",
      "Train Epoch: 79 [37120/50000 (82%)]\tLoss: 0.060857, Accuracy: 98.44\n",
      "Train Epoch: 79 [38400/50000 (85%)]\tLoss: 0.053316, Accuracy: 98.05\n",
      "Train Epoch: 79 [39680/50000 (88%)]\tLoss: 0.077525, Accuracy: 97.27\n",
      "Train Epoch: 79 [40960/50000 (91%)]\tLoss: 0.042802, Accuracy: 98.44\n",
      "Train Epoch: 79 [42240/50000 (94%)]\tLoss: 0.041647, Accuracy: 98.83\n",
      "Train Epoch: 79 [43520/50000 (97%)]\tLoss: 0.044213, Accuracy: 98.05\n",
      "Train Epoch: 79 [35000/50000 (99%)]\tLoss: 0.059229, Accuracy: 98.00\n",
      "\n",
      "Validation set: Average loss: 0.2785, Accuracy: 4620/5000 (92.00%)\n",
      "\n",
      "the time of this epoch:[40.91894483566284 s]\n",
      "\n",
      "Test set: Average loss: 0.3284, Accuracy: 9124/10000 (91.24%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fantastic logger for tensorboard and pytorch, \n",
    "# run tensorboard by opening a new terminal and run \"tensorboard --logdir runs\"\n",
    "# open tensorboard at http://localhost:6006/\n",
    "from tensorboardX import SummaryWriter\n",
    "best_loss = None\n",
    "best_acc = None\n",
    "\n",
    "import time \n",
    "SINCE=time.time()\n",
    "\n",
    "for epoch in range(max_epoch):\n",
    "    train(epoch)\n",
    "    \n",
    "    loss, accuracy = validate(epoch)\n",
    "    best_loss, best_acc = save_best(loss, accuracy, best_loss, best_acc)\n",
    "    \n",
    "    NOW=time.time() \n",
    "    DURINGS=NOW-SINCE\n",
    "    SINCE=NOW\n",
    "    print(\"the time of this epoch:[{} s]\".format(DURINGS))\n",
    "    \n",
    "    if epoch>=10 and (epoch-10)%2==0:\n",
    "        test(epoch)\n",
    "    \n",
    "# writer = SummaryWriter() \n",
    "# writer.export_scalars_to_json(\"./all_scalars.json\")\n",
    "\n",
    "# writer.close()\n",
    "\n",
    "#---------------------------- Test ------------------------------\n",
    "test(epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.6902, Accuracy: 8877/10000 (88.77%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test(epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一次 scale 位于[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](http://op4a94iq8.bkt.clouddn.com/18-7-14/70206949.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X31/fe9LvZ2vJWUnW0hr5uH35k3EJth8Y1/tcVGLf/6o7fBVAgd5VXN0TfcmyvzdZ82xC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\n/x9BnCF17x72RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看训练过程的信息\n",
    "import  matplotlib.pyplot as plt\n",
    "\n",
    "def parse(in_file,flag):\n",
    "    num=-1\n",
    "    ys=list()\n",
    "    xs=list()\n",
    "    losses=list()\n",
    "    with open(in_file,\"r\") as reader:\n",
    "        for aLine in reader:\n",
    "            #print(aLine)\n",
    "\n",
    "            res=[e for e in aLine.strip('\\n').split(\" \")]\n",
    "            if res[0]==\"Train\" and flag==\"Train\":\n",
    "                num=num+1\n",
    "                ys.append(float(res[-1]))\n",
    "                xs.append(int(num))\n",
    "                losses.append(float(res[-3].split(',')[0]))\n",
    "            if res[0]==\"Validation\" and flag==\"Validation\":\n",
    "                num=num+1\n",
    "                xs.append(int(num))\n",
    "                tmp=[float(e) for e in res[-2].split('/')]\n",
    "                ys.append(100*float(tmp[0]/tmp[1]))\n",
    "                losses.append(float(res[-4].split(',')[0]))\n",
    "\n",
    "    plt.figure(1)\n",
    "    plt.plot(xs,ys,'ro')\n",
    "\n",
    "\n",
    "    plt.figure(2)\n",
    "    plt.plot(xs, losses, 'ro')\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    in_file=\"D://INFO.txt\"\n",
    "    # 显示训练阶段的正确率和Loss信息\n",
    "    parse(in_file,\"Train\") # \"Validation\"\n",
    "    # 显示验证阶段的正确率和Loss信息\n",
    "    #parse(in_file,\"Validation\") # \"Validation\"\n",
    "\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B79YtXUHXO6N9gp5FvFmdL+sbhwt9tG0D0thdvmmRRgx9yy2lkO+wA3DJJfb2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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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