LeNet-5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a38c893",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    " \n",
    "# 定义网络模型\n",
    "class LeNet5(nn.Module):\n",
    "    #初始化网络\n",
    "    def __init__(self):\n",
    "        super(LeNet5, self).__init__()\n",
    "        \n",
    "        self.c1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, padding=2)        \n",
    "        self.Sigmoid = nn.Sigmoid()\n",
    "        self.s2 = nn.AvgPool2d(kernel_size=2, stride=2)       \n",
    "        self.c3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5)\n",
    "        self.s4 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        self.c5 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5)\n",
    "        \n",
    "        self.flatten = nn.Flatten()\n",
    "        self.f6 = nn.Linear(in_features=256, out_features=512)\n",
    "        self.output = nn.Linear(in_features=512, out_features=10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.Sigmoid(self.c1(x))\n",
    "        x = self.s2(x)\n",
    "        x = self.Sigmoid(self.c3(x))\n",
    "        x = self.s4(x)\n",
    "        x = self.c5(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.f6(x)\n",
    "        x = self.output(x)\n",
    "        return x\n",
    "x = torch.rand([1, 1, 28, 28])\n",
    "model = LeNet5()\n",
    "y = model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e32bb2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch1\n",
      "-----------------------------------\n",
      "train_loss  2.3011375446955364\n",
      "train_acc  0.11778333333333334\n",
      "test_loss  2.2816249340057375\n",
      "test_acc  0.1423\n",
      "save best model!!!!!!\n",
      "epoch2\n",
      "-----------------------------------\n",
      "train_loss  1.3250462160209815\n",
      "train_acc  0.55395\n",
      "test_loss  0.5080360377192498\n",
      "test_acc  0.8458\n",
      "save best model!!!!!!\n",
      "epoch3\n",
      "-----------------------------------\n",
      "train_loss  0.44101752282083034\n",
      "train_acc  0.8656\n",
      "test_loss  0.3593500443041325\n",
      "test_acc  0.8943\n",
      "save best model!!!!!!\n",
      "epoch4\n",
      "-----------------------------------\n",
      "train_loss  0.3498826849584778\n",
      "train_acc  0.8937333333333334\n",
      "test_loss  0.31659866755604743\n",
      "test_acc  0.9067\n",
      "save best model!!!!!!\n",
      "epoch5\n",
      "-----------------------------------\n",
      "train_loss  0.29336957574958605\n",
      "train_acc  0.9117333333333333\n",
      "test_loss  0.24674546437859535\n",
      "test_acc  0.925\n",
      "save best model!!!!!!\n",
      "epoch6\n",
      "-----------------------------------\n",
      "train_loss  0.24199639877850812\n",
      "train_acc  0.9275833333333333\n",
      "test_loss  0.20428638576492666\n",
      "test_acc  0.9397\n",
      "save best model!!!!!!\n",
      "epoch7\n",
      "-----------------------------------\n",
      "train_loss  0.1990972778674215\n",
      "train_acc  0.9398\n",
      "test_loss  0.16726163480877876\n",
      "test_acc  0.95\n",
      "save best model!!!!!!\n",
      "epoch8\n",
      "-----------------------------------\n",
      "train_loss  0.16750992501421522\n",
      "train_acc  0.94915\n",
      "test_loss  0.1396509276844561\n",
      "test_acc  0.9556\n",
      "save best model!!!!!!\n",
      "epoch9\n",
      "-----------------------------------\n",
      "train_loss  0.14431546908998863\n",
      "train_acc  0.9559666666666666\n",
      "test_loss  0.13551616888530552\n",
      "test_acc  0.9564\n",
      "save best model!!!!!!\n",
      "epoch10\n",
      "-----------------------------------\n",
      "train_loss  0.12770559827939917\n",
      "train_acc  0.9612666666666667\n",
      "test_loss  0.10259477456249297\n",
      "test_acc  0.9677\n",
      "save best model!!!!!!\n",
      "epoch11\n",
      "-----------------------------------\n",
      "train_loss  0.11496816935685153\n",
      "train_acc  0.9640666666666666\n",
      "test_loss  0.09982995859049261\n",
      "test_acc  0.9703\n",
      "save best model!!!!!!\n",
      "epoch12\n",
      "-----------------------------------\n",
      "train_loss  0.1043684540630163\n",
      "train_acc  0.9679\n",
      "test_loss  0.09059112115427852\n",
      "test_acc  0.9705\n",
      "save best model!!!!!!\n",
      "epoch13\n",
      "-----------------------------------\n",
      "train_loss  0.0961067680461177\n",
      "train_acc  0.9707166666666667\n",
      "test_loss  0.08829035353511572\n",
      "test_acc  0.9726\n",
      "save best model!!!!!!\n",
      "epoch14\n",
      "-----------------------------------\n",
      "train_loss  0.0888933635830724\n",
      "train_acc  0.973\n",
      "test_loss  0.07818956518899649\n",
      "test_acc  0.9739\n",
      "save best model!!!!!!\n",
      "epoch15\n",
      "-----------------------------------\n",
      "train_loss  0.08424071889031136\n",
      "train_acc  0.9743833333333334\n",
      "test_loss  0.06956301875114441\n",
      "test_acc  0.9762\n",
      "save best model!!!!!!\n",
      "epoch16\n",
      "-----------------------------------\n",
      "train_loss  0.07904852733019119\n",
      "train_acc  0.97525\n",
      "test_loss  0.06507488210238516\n",
      "test_acc  0.9781\n",
      "save best model!!!!!!\n",
      "epoch17\n",
      "-----------------------------------\n",
      "train_loss  0.07496396978123424\n",
      "train_acc  0.97675\n",
      "test_loss  0.07090626379991881\n",
      "test_acc  0.9762\n",
      "epoch18\n",
      "-----------------------------------\n",
      "train_loss  0.07036588005773955\n",
      "train_acc  0.9785833333333334\n",
      "test_loss  0.0653458062873222\n",
      "test_acc  0.9781\n",
      "epoch19\n",
      "-----------------------------------\n",
      "train_loss  0.06804987351332481\n",
      "train_acc  0.9794833333333334\n",
      "test_loss  0.059769639151520094\n",
      "test_acc  0.9808\n",
      "save best model!!!!!!\n",
      "epoch20\n",
      "-----------------------------------\n",
      "train_loss  0.06463067782647752\n",
      "train_acc  0.97955\n",
      "test_loss  0.05887622418003157\n",
      "test_acc  0.9812\n",
      "save best model!!!!!!\n",
      "OK!!!!!!!!!!!!!!!!!\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.optim import lr_scheduler\n",
    "from torchvision import datasets, transforms\n",
    "import os\n",
    " \n",
    "#数据转化为tensor格式\n",
    "data_transform = transforms.Compose([\n",
    "    transforms.ToTensor()\n",
    "])\n",
    " \n",
    "#加载训练数据集\n",
    "train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True)\n",
    "train_dataloader = torch.utils.data.DataLoader(dataset = train_dataset, batch_size=16, shuffle=True)\n",
    " \n",
    "#加载测试数据集\n",
    "test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True)\n",
    "test_dataloader = torch.utils.data.DataLoader(dataset = test_dataset, batch_size=16, shuffle=True)\n",
    " \n",
    "#如果有显卡可以转到GPU\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    " \n",
    "#调用net里定义的网络模型LeNet5 将数据模型转到GPU\n",
    "model = LeNet5().to(device)\n",
    " \n",
    "#定义损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    " \n",
    "#定义优化器\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)\n",
    " \n",
    "#学习率每隔10轮变为原来的0.1\n",
    "lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
    " \n",
    "#定义训练函数\n",
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    loss, current, n = 0.0, 0.0, 0\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        output = model(X)\n",
    "        cur_loss = loss_fn(output,y)\n",
    "        _, pred = torch.max(output, axis=1)\n",
    "        \n",
    "        cur_acc = torch.sum(y == pred)/output.shape[0]\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        cur_loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        loss += cur_loss.item()\n",
    "        current += cur_acc.item()\n",
    "        n = n + 1\n",
    "    print(\"train_loss  \" + str(loss/n))\n",
    "    print(\"train_acc  \" + str(current/n))\n",
    " \n",
    "def val(dataloader, model, loss_fn):\n",
    "    model.eval()\n",
    "    loss, current, n = 0.0, 0.0, 0\n",
    "    with torch.no_grad():\n",
    "        for batch, (X, y) in enumerate(dataloader):\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            output = model(X)\n",
    "            cur_loss = loss_fn(output,y)\n",
    "            _, pred = torch.max(output, axis=1)           \n",
    "            cur_acc = torch.sum(y == pred)/output.shape[0]          \n",
    "            loss += cur_loss.item()\n",
    "            current += cur_acc.item()\n",
    "            n = n + 1\n",
    "        print(\"test_loss  \" + str(loss/n))\n",
    "        print(\"test_acc  \" + str(current/n))   \n",
    "        \n",
    "        return current/n\n",
    "        \n",
    "#开始训练\n",
    "epoch = 20\n",
    "min_acc = 0\n",
    " \n",
    "for t in range(epoch):\n",
    "    print(f'epoch{t+1}\\n-----------------------------------')\n",
    "    train(train_dataloader, model, loss_fn, optimizer)\n",
    "    a = val(test_dataloader, model, loss_fn)\n",
    "    \n",
    "    #保存最优模型\n",
    "    if a > min_acc:\n",
    "        folder = 'save_model'\n",
    "        if not os.path.exists(folder):\n",
    "            os.mkdir('save_model')\n",
    "        min_acc = a\n",
    "        print('save best model!!!!!!')\n",
    "        torch.save(model.state_dict(), 'save_model/best_model.pth')\n",
    "print('OK!!!!!!!!!!!!!!!!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d626273b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "figure 0: predicted: \"7\", actual: \"7\"\n",
      "figure 1: predicted: \"2\", actual: \"2\"\n",
      "figure 2: predicted: \"1\", actual: \"1\"\n",
      "figure 3: predicted: \"0\", actual: \"0\"\n",
      "figure 4: predicted: \"4\", actual: \"4\"\n",
      "figure 5: predicted: \"1\", actual: \"1\"\n",
      "figure 6: predicted: \"4\", actual: \"4\"\n",
      "figure 7: predicted: \"9\", actual: \"9\"\n",
      "figure 8: predicted: \"5\", actual: \"5\"\n",
      "figure 9: predicted: \"9\", actual: \"9\"\n",
      "figure 10: predicted: \"0\", actual: \"0\"\n",
      "figure 11: predicted: \"6\", actual: \"6\"\n",
      "figure 12: predicted: \"9\", actual: \"9\"\n",
      "figure 13: predicted: \"0\", actual: \"0\"\n",
      "figure 14: predicted: \"1\", actual: \"1\"\n",
      "figure 15: predicted: \"5\", actual: \"5\"\n",
      "figure 16: predicted: \"9\", actual: \"9\"\n",
      "figure 17: predicted: \"7\", actual: \"7\"\n",
      "figure 18: predicted: \"3\", actual: \"3\"\n",
      "figure 19: predicted: \"4\", actual: \"4\"\n",
      "figure 20: predicted: \"9\", actual: \"9\"\n",
      "figure 21: predicted: \"6\", actual: \"6\"\n",
      "figure 22: predicted: \"6\", actual: \"6\"\n",
      "figure 23: predicted: \"5\", actual: \"5\"\n",
      "figure 24: predicted: \"4\", actual: \"4\"\n",
      "figure 25: predicted: \"0\", actual: \"0\"\n",
      "figure 26: predicted: \"7\", actual: \"7\"\n",
      "figure 27: predicted: \"4\", actual: \"4\"\n",
      "figure 28: predicted: \"0\", actual: \"0\"\n",
      "figure 29: predicted: \"1\", actual: \"1\"\n",
      "figure 30: predicted: \"3\", actual: \"3\"\n",
      "figure 31: predicted: \"1\", actual: \"1\"\n",
      "figure 32: predicted: \"3\", actual: \"3\"\n",
      "figure 33: predicted: \"4\", actual: \"4\"\n",
      "figure 34: predicted: \"7\", actual: \"7\"\n",
      "figure 35: predicted: \"2\", actual: \"2\"\n",
      "figure 36: predicted: \"7\", actual: \"7\"\n",
      "figure 37: predicted: \"1\", actual: \"1\"\n",
      "figure 38: predicted: \"2\", actual: \"2\"\n",
      "figure 39: predicted: \"1\", actual: \"1\"\n",
      "figure 40: predicted: \"1\", actual: \"1\"\n",
      "figure 41: predicted: \"7\", actual: \"7\"\n",
      "figure 42: predicted: \"4\", actual: \"4\"\n",
      "figure 43: predicted: \"2\", actual: \"2\"\n",
      "figure 44: predicted: \"3\", actual: \"3\"\n",
      "figure 45: predicted: \"5\", actual: \"5\"\n",
      "figure 46: predicted: \"1\", actual: \"1\"\n",
      "figure 47: predicted: \"2\", actual: \"2\"\n",
      "figure 48: predicted: \"4\", actual: \"4\"\n",
      "figure 49: predicted: \"4\", actual: \"4\"\n",
      "figure 50: predicted: \"6\", actual: \"6\"\n",
      "figure 51: predicted: \"3\", actual: \"3\"\n",
      "figure 52: predicted: \"5\", actual: \"5\"\n",
      "figure 53: predicted: \"5\", actual: \"5\"\n",
      "figure 54: predicted: \"6\", actual: \"6\"\n",
      "figure 55: predicted: \"0\", actual: \"0\"\n",
      "figure 56: predicted: \"4\", actual: \"4\"\n",
      "figure 57: predicted: \"1\", actual: \"1\"\n",
      "figure 58: predicted: \"9\", actual: \"9\"\n",
      "figure 59: predicted: \"5\", actual: \"5\"\n",
      "figure 60: predicted: \"7\", actual: \"7\"\n",
      "figure 61: predicted: \"8\", actual: \"8\"\n",
      "figure 62: predicted: \"9\", actual: \"9\"\n",
      "figure 63: predicted: \"3\", actual: \"3\"\n",
      "figure 64: predicted: \"7\", actual: \"7\"\n",
      "figure 65: predicted: \"4\", actual: \"4\"\n",
      "figure 66: predicted: \"6\", actual: \"6\"\n",
      "figure 67: predicted: \"4\", actual: \"4\"\n",
      "figure 68: predicted: \"3\", actual: \"3\"\n",
      "figure 69: predicted: \"0\", actual: \"0\"\n",
      "figure 70: predicted: \"7\", actual: \"7\"\n",
      "figure 71: predicted: \"0\", actual: \"0\"\n",
      "figure 72: predicted: \"2\", actual: \"2\"\n",
      "figure 73: predicted: \"9\", actual: \"9\"\n",
      "figure 74: predicted: \"1\", actual: \"1\"\n",
      "figure 75: predicted: \"7\", actual: \"7\"\n",
      "figure 76: predicted: \"3\", actual: \"3\"\n",
      "figure 77: predicted: \"2\", actual: \"2\"\n",
      "figure 78: predicted: \"4\", actual: \"9\"\n",
      "figure 79: predicted: \"7\", actual: \"7\"\n",
      "figure 80: predicted: \"7\", actual: \"7\"\n",
      "figure 81: predicted: \"6\", actual: \"6\"\n",
      "figure 82: predicted: \"2\", actual: \"2\"\n",
      "figure 83: predicted: \"7\", actual: \"7\"\n",
      "figure 84: predicted: \"8\", actual: \"8\"\n",
      "figure 85: predicted: \"4\", actual: \"4\"\n",
      "figure 86: predicted: \"7\", actual: \"7\"\n",
      "figure 87: predicted: \"3\", actual: \"3\"\n",
      "figure 88: predicted: \"6\", actual: \"6\"\n",
      "figure 89: predicted: \"1\", actual: \"1\"\n",
      "figure 90: predicted: \"3\", actual: \"3\"\n",
      "figure 91: predicted: \"6\", actual: \"6\"\n",
      "figure 92: predicted: \"4\", actual: \"9\"\n",
      "figure 93: predicted: \"3\", actual: \"3\"\n",
      "figure 94: predicted: \"1\", actual: \"1\"\n",
      "figure 95: predicted: \"4\", actual: \"4\"\n",
      "figure 96: predicted: \"1\", actual: \"1\"\n",
      "figure 97: predicted: \"7\", actual: \"7\"\n",
      "figure 98: predicted: \"6\", actual: \"6\"\n",
      "figure 99: predicted: \"9\", actual: \"9\"\n",
      "figure 100: predicted: \"6\", actual: \"6\"\n",
      "figure 101: predicted: \"0\", actual: \"0\"\n",
      "figure 102: predicted: \"5\", actual: \"5\"\n",
      "figure 103: predicted: \"4\", actual: \"4\"\n",
      "figure 104: predicted: \"9\", actual: \"9\"\n",
      "figure 105: predicted: \"9\", actual: \"9\"\n",
      "figure 106: predicted: \"2\", actual: \"2\"\n",
      "figure 107: predicted: \"1\", actual: \"1\"\n",
      "figure 108: predicted: \"9\", actual: \"9\"\n",
      "figure 109: predicted: \"4\", actual: \"4\"\n",
      "figure 110: predicted: \"8\", actual: \"8\"\n",
      "figure 111: predicted: \"7\", actual: \"7\"\n",
      "figure 112: predicted: \"3\", actual: \"3\"\n",
      "figure 113: predicted: \"9\", actual: \"9\"\n",
      "figure 114: predicted: \"7\", actual: \"7\"\n",
      "figure 115: predicted: \"4\", actual: \"4\"\n",
      "figure 116: predicted: \"4\", actual: \"4\"\n",
      "figure 117: predicted: \"4\", actual: \"4\"\n",
      "figure 118: predicted: \"9\", actual: \"9\"\n",
      "figure 119: predicted: \"2\", actual: \"2\"\n",
      "figure 120: predicted: \"5\", actual: \"5\"\n",
      "figure 121: predicted: \"4\", actual: \"4\"\n",
      "figure 122: predicted: \"7\", actual: \"7\"\n",
      "figure 123: predicted: \"6\", actual: \"6\"\n",
      "figure 124: predicted: \"7\", actual: \"7\"\n",
      "figure 125: predicted: \"9\", actual: \"9\"\n",
      "figure 126: predicted: \"0\", actual: \"0\"\n",
      "figure 127: predicted: \"5\", actual: \"5\"\n",
      "figure 128: predicted: \"8\", actual: \"8\"\n",
      "figure 129: predicted: \"5\", actual: \"5\"\n",
      "figure 130: predicted: \"6\", actual: \"6\"\n",
      "figure 131: predicted: \"6\", actual: \"6\"\n",
      "figure 132: predicted: \"5\", actual: \"5\"\n",
      "figure 133: predicted: \"7\", actual: \"7\"\n",
      "figure 134: predicted: \"8\", actual: \"8\"\n",
      "figure 135: predicted: \"1\", actual: \"1\"\n",
      "figure 136: predicted: \"0\", actual: \"0\"\n",
      "figure 137: predicted: \"1\", actual: \"1\"\n",
      "figure 138: predicted: \"6\", actual: \"6\"\n",
      "figure 139: predicted: \"4\", actual: \"4\"\n",
      "figure 140: predicted: \"6\", actual: \"6\"\n",
      "figure 141: predicted: \"7\", actual: \"7\"\n",
      "figure 142: predicted: \"3\", actual: \"3\"\n",
      "figure 143: predicted: \"1\", actual: \"1\"\n",
      "figure 144: predicted: \"7\", actual: \"7\"\n",
      "figure 145: predicted: \"1\", actual: \"1\"\n",
      "figure 146: predicted: \"8\", actual: \"8\"\n",
      "figure 147: predicted: \"2\", actual: \"2\"\n",
      "figure 148: predicted: \"0\", actual: \"0\"\n",
      "figure 149: predicted: \"4\", actual: \"2\"\n",
      "figure 150: predicted: \"9\", actual: \"9\"\n",
      "figure 151: predicted: \"9\", actual: \"9\"\n",
      "figure 152: predicted: \"5\", actual: \"5\"\n",
      "figure 153: predicted: \"5\", actual: \"5\"\n",
      "figure 154: predicted: \"1\", actual: \"1\"\n",
      "figure 155: predicted: \"5\", actual: \"5\"\n",
      "figure 156: predicted: \"6\", actual: \"6\"\n",
      "figure 157: predicted: \"0\", actual: \"0\"\n",
      "figure 158: predicted: \"3\", actual: \"3\"\n",
      "figure 159: predicted: \"4\", actual: \"4\"\n",
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      "figure 162: predicted: \"5\", actual: \"5\"\n",
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      "figure 171: predicted: \"7\", actual: \"7\"\n",
      "figure 172: predicted: \"2\", actual: \"2\"\n",
      "figure 173: predicted: \"3\", actual: \"3\"\n",
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      "figure 176: predicted: \"1\", actual: \"1\"\n",
      "figure 177: predicted: \"8\", actual: \"8\"\n",
      "figure 178: predicted: \"1\", actual: \"1\"\n",
      "figure 179: predicted: \"8\", actual: \"8\"\n",
      "figure 180: predicted: \"1\", actual: \"1\"\n",
      "figure 181: predicted: \"8\", actual: \"8\"\n",
      "figure 182: predicted: \"5\", actual: \"5\"\n",
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      "figure 184: predicted: \"8\", actual: \"8\"\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "figure 244: predicted: \"2\", actual: \"2\"\n",
      "figure 245: predicted: \"3\", actual: \"3\"\n",
      "figure 246: predicted: \"0\", actual: \"0\"\n",
      "figure 247: predicted: \"2\", actual: \"4\"\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "figure 467: predicted: \"2\", actual: \"2\"\n",
      "figure 468: predicted: \"7\", actual: \"7\"\n",
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      "figure 716: predicted: \"1\", actual: \"1\"\n"
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      "figure 717: predicted: \"0\", actual: \"0\"\n",
      "figure 718: predicted: \"8\", actual: \"8\"\n",
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      "figure 720: predicted: \"8\", actual: \"5\"\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "figure 971: predicted: \"4\", actual: \"4\"\n",
      "figure 972: predicted: \"0\", actual: \"0\"\n",
      "figure 973: predicted: \"6\", actual: \"6\"\n",
      "figure 974: predicted: \"2\", actual: \"2\"\n",
      "figure 975: predicted: \"2\", actual: \"2\"\n",
      "figure 976: predicted: \"3\", actual: \"3\"\n",
      "figure 977: predicted: \"1\", actual: \"1\"\n",
      "figure 978: predicted: \"5\", actual: \"5\"\n",
      "figure 979: predicted: \"1\", actual: \"1\"\n",
      "figure 980: predicted: \"2\", actual: \"2\"\n",
      "figure 981: predicted: \"0\", actual: \"0\"\n",
      "figure 982: predicted: \"3\", actual: \"3\"\n",
      "figure 983: predicted: \"8\", actual: \"8\"\n",
      "figure 984: predicted: \"1\", actual: \"1\"\n",
      "figure 985: predicted: \"2\", actual: \"2\"\n",
      "figure 986: predicted: \"6\", actual: \"6\"\n",
      "figure 987: predicted: \"7\", actual: \"7\"\n",
      "figure 988: predicted: \"1\", actual: \"1\"\n",
      "figure 989: predicted: \"6\", actual: \"6\"\n",
      "figure 990: predicted: \"2\", actual: \"2\"\n",
      "figure 991: predicted: \"3\", actual: \"3\"\n",
      "figure 992: predicted: \"9\", actual: \"9\"\n",
      "figure 993: predicted: \"0\", actual: \"0\"\n",
      "figure 994: predicted: \"1\", actual: \"1\"\n",
      "figure 995: predicted: \"2\", actual: \"2\"\n",
      "figure 996: predicted: \"2\", actual: \"2\"\n",
      "figure 997: predicted: \"0\", actual: \"0\"\n",
      "figure 998: predicted: \"8\", actual: \"8\"\n",
      "figure 999: predicted: \"9\", actual: \"9\"\n",
      "figurenums 1000: acc: \"0.983\"\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision.transforms import ToPILImage\n",
    " \n",
    "#数据转化为tensor格式\n",
    "data_transform = transforms.Compose([\n",
    "    transforms.ToTensor()\n",
    "])\n",
    " \n",
    "#加载训练数据集\n",
    "train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True)\n",
    "train_dataloader = torch.utils.data.DataLoader(dataset = train_dataset, batch_size=16, shuffle=True)\n",
    " \n",
    "#加载测试数据集\n",
    "test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True)\n",
    "test_dataloader = torch.utils.data.DataLoader(dataset = test_dataset, batch_size=16, shuffle=True)\n",
    " \n",
    "#如果有显卡可以转到GPU\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    " \n",
    "#调用net里定义的网络模型LeNet5 将数据模型转到GPU\n",
    "model = LeNet5().to(device)\n",
    " \n",
    "model.load_state_dict(torch.load(r\"D:\\Study Software\\Jupyter\\计算机视觉与应用实践作业\\作业二\\save_model\\best_model.pth\"))\n",
    " \n",
    "#获取结果\n",
    "classes = [\n",
    "    \"0\",\n",
    "    \"1\",\n",
    "    \"2\",\n",
    "    \"3\",\n",
    "    \"4\",\n",
    "    \"5\",\n",
    "    \"6\",\n",
    "    \"7\",\n",
    "    \"8\",\n",
    "    \"9\",\n",
    "]\n",
    " \n",
    "#把tensor转化为图片，方便可视化\n",
    "show = ToPILImage()\n",
    " \n",
    "#验证\n",
    "print(len(test_dataset))\n",
    "acc = 0\n",
    "fal = 0\n",
    "for i in range(len(test_dataset)//10):\n",
    "    X, y = test_dataset[i][0], test_dataset[i][1]\n",
    "#    show(X).show()\n",
    "    X = Variable(torch.unsqueeze(X, dim=0).float(), requires_grad=False).to(device)  \n",
    "    with torch.no_grad():\n",
    "        pred = model(X)\n",
    "        predicted, actual = classes[torch.argmax(pred[0])], classes[y]\n",
    "        if predicted == actual:\n",
    "            acc += 1\n",
    "        else:\n",
    "            fal += 1\n",
    "        print(f'figure {str(i)}: predicted: \"{predicted}\", actual: \"{actual}\"')\n",
    "print(f'figurenums {acc+fal}: acc: \"{acc/(acc+fal)}\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bcbd033",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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