pytorch_lenet5.ipynb

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  "cells": [
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      "cell_type": "code",
      "metadata": {
        "id": "z6DFfSZj1ejo"
      },
      "source": [
        "# 导入必要的包\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.optim as optim\n",
        "#import visdom"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqxKMvTh76nA",
        "outputId": "b68e7893-8ca2-4081-e28a-4ddc8d109e83"
      },
      "source": [
        "# 预置一些必要的（超）参数\n",
        "BATCHSIZE = 128 # 批量大小\n",
        "LR = 0.02       # 学习率\n",
        "EPOCH = 10      # 迭代整个训练集的次数\n",
        "INTERVAL = 100  # 输出训练过程中间信息（损失值）的间隔\n",
        "\n",
        "# 选择使用GPU设备，如果有的话\n",
        "device = torch.device(\n",
        "    \"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "\n",
        "print(device)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KJAvW-jqUJi",
        "outputId": "8f33d40a-9adc-4780-fa7b-5e72bdee5488"
      },
      "source": [
        "# 定义LeNet5 网络模型\n",
        "class LeNet5(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LeNet5, self).__init__()\n",
        "        self.features = nn.Sequential(\n",
        "            nn.Conv2d(1, 6, 5), # 32x32x1 -> 28x28x6\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2, 2), # 28x28x6 -> 14x14x6\n",
        "            nn.Conv2d(6, 16, 5), # 14x14x6 -> 10x10x16\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2, 2), # 10x10x16 -> 5x5x16\n",
        "            #nn.Conv2d(16, 120, 5) # 5x5x16 -> 1x1x120\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(5*5*16, 120),\n",
        "            nn.ReLU(True),\n",
        "            nn.Linear(120, 84),\n",
        "            nn.ReLU(True),\n",
        "            nn.Linear(84, 10)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = x.view(-1, 5 * 5 * 16)\n",
        "        x = self.classifier(x)\n",
        "        return x\n",
        "\n",
        "# 需要训练的模型\n",
        "net = LeNet5()\n",
        "net.to(device) # 将模型移动到对应的设备上(CPU or GPU)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeNet5(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=400, out_features=120, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Linear(in_features=120, out_features=84, bias=True)\n",
              "    (3): ReLU(inplace=True)\n",
              "    (4): Linear(in_features=84, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
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        "id": "G6q390QI7LQn",
        "outputId": "f51870e4-75f1-42fc-cc05-d4d0276141ca"
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      "source": [
        "# 加载并标准化MNIST数据集\n",
        "# 如果本地不存在MNIST数据集，那么程序将自动从网络上下载\n",
        "# transforms\n",
        "transform = transforms.Compose(\n",
        "    [transforms.Resize((32, 32)),\n",
        "     transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "# datasets, download MNIST if download is True\n",
        "trainset = torchvision.datasets.MNIST('./data',\n",
        "    download=True,\n",
        "    train=True,\n",
        "    transform=transform)\n",
        "testset = torchvision.datasets.MNIST('./data',\n",
        "    download=True,\n",
        "    train=False,\n",
        "    transform=transform)\n",
        "\n",
        "# dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCHSIZE,\n",
        "                                        shuffle=True, num_workers=2)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCHSIZE,\n",
        "                                        shuffle=False, num_workers=2)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n"
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        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhnOfzBv8IOf"
      },
      "source": [
        "# 定义损失函数和优化器。一个损失函数接受一对(output, target)作为输入，计算一个值来估计网络的输出和目标值相差多少。\n",
        "# 这里使用交叉熵损失函数，SGD作为优化器\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=LR)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoiOn8N-9pQO"
      },
      "source": [
        "\"\"\"\n",
        "完成一个Epoch的训练，即迭代训练一遍训练集\n",
        "\"\"\"\n",
        "def train(epoch):\n",
        "    net.train() # 将模型设置为train 模式\n",
        "    running_loss = 0.0\n",
        "    for i, (images, labels) in enumerate(trainloader):\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        # 模型梯度值清零\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        output = net(images)\n",
        "        loss = criterion(output, labels)\n",
        "\n",
        "        # 统计并输出训练loss\n",
        "        running_loss += loss.detach().cpu().item()\n",
        "        if (i+1) % INTERVAL == 0:\n",
        "            print('Train - [Epoch %d /Iteration %d] Loss: %f' % (epoch, i+1, running_loss/INTERVAL))\n",
        "            running_loss = 0.0\n",
        "\n",
        "        # forward and update model parameters\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "\"\"\"\n",
        "在测试集上对模型进行测试，输出 test loss 和准确率\n",
        "\"\"\"\n",
        "def test(epoch):\n",
        "    # 将模型设置为测试模式\n",
        "    net.eval()\n",
        "    total_correct = 0\n",
        "    avg_loss = 0.0\n",
        "    val_iteration = 0\n",
        "    for i, (images, labels) in enumerate(testloader):\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        output = net(images)\n",
        "        avg_loss += criterion(output, labels).sum()\n",
        "        # 统计预测正确的样本个数\n",
        "        pred = output.detach().max(1)[1]\n",
        "        total_correct += pred.eq(labels.view_as(pred)).sum()\n",
        "        val_iteration += 1\n",
        "\n",
        "    avg_loss /= val_iteration\n",
        "    print('Test - [Epoch %d] Avg Loss: %f, Accuracy: %f %%' % (epoch, avg_loss.detach().cpu().item(), 100.0*float(total_correct) / len(testset)))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ihuLxBg-sH2",
        "outputId": "a7b730fe-2872-4484-b07f-f652bd531886"
      },
      "source": [
        "# loop over the dataset multiple times\n",
        "for epoch in range(EPOCH):\n",
        "  train(epoch+1)\n",
        "  test(epoch+1)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train - [Epoch 1 /Iteration 100] Loss: 2.295071\n",
            "Train - [Epoch 1 /Iteration 200] Loss: 2.261444\n",
            "Train - [Epoch 1 /Iteration 300] Loss: 2.027999\n",
            "Train - [Epoch 1 /Iteration 400] Loss: 1.155399\n",
            "Test - [Epoch 1] Avg Loss: 0.520879, Accuracy: 84.280000 %\n",
            "Train - [Epoch 2 /Iteration 100] Loss: 0.452537\n",
            "Train - [Epoch 2 /Iteration 200] Loss: 0.304626\n",
            "Train - [Epoch 2 /Iteration 300] Loss: 0.241123\n",
            "Train - [Epoch 2 /Iteration 400] Loss: 0.213320\n",
            "Test - [Epoch 2] Avg Loss: 0.156939, Accuracy: 95.120000 %\n",
            "Train - [Epoch 3 /Iteration 100] Loss: 0.170911\n",
            "Train - [Epoch 3 /Iteration 200] Loss: 0.152231\n",
            "Train - [Epoch 3 /Iteration 300] Loss: 0.149747\n",
            "Train - [Epoch 3 /Iteration 400] Loss: 0.128738\n",
            "Test - [Epoch 3] Avg Loss: 0.105213, Accuracy: 96.690000 %\n",
            "Train - [Epoch 4 /Iteration 100] Loss: 0.118659\n",
            "Train - [Epoch 4 /Iteration 200] Loss: 0.116046\n",
            "Train - [Epoch 4 /Iteration 300] Loss: 0.108657\n",
            "Train - [Epoch 4 /Iteration 400] Loss: 0.104477\n",
            "Test - [Epoch 4] Avg Loss: 0.087813, Accuracy: 97.190000 %\n",
            "Train - [Epoch 5 /Iteration 100] Loss: 0.099965\n",
            "Train - [Epoch 5 /Iteration 200] Loss: 0.086683\n",
            "Train - [Epoch 5 /Iteration 300] Loss: 0.092032\n",
            "Train - [Epoch 5 /Iteration 400] Loss: 0.089453\n",
            "Test - [Epoch 5] Avg Loss: 0.068883, Accuracy: 97.710000 %\n",
            "Train - [Epoch 6 /Iteration 100] Loss: 0.087565\n",
            "Train - [Epoch 6 /Iteration 200] Loss: 0.080555\n",
            "Train - [Epoch 6 /Iteration 300] Loss: 0.081706\n",
            "Train - [Epoch 6 /Iteration 400] Loss: 0.073794\n",
            "Test - [Epoch 6] Avg Loss: 0.068499, Accuracy: 97.690000 %\n",
            "Train - [Epoch 7 /Iteration 100] Loss: 0.071711\n",
            "Train - [Epoch 7 /Iteration 200] Loss: 0.070889\n",
            "Train - [Epoch 7 /Iteration 300] Loss: 0.065875\n",
            "Train - [Epoch 7 /Iteration 400] Loss: 0.078004\n",
            "Test - [Epoch 7] Avg Loss: 0.066197, Accuracy: 97.760000 %\n",
            "Train - [Epoch 8 /Iteration 100] Loss: 0.063024\n",
            "Train - [Epoch 8 /Iteration 200] Loss: 0.066813\n",
            "Train - [Epoch 8 /Iteration 300] Loss: 0.062429\n",
            "Train - [Epoch 8 /Iteration 400] Loss: 0.062364\n",
            "Test - [Epoch 8] Avg Loss: 0.050736, Accuracy: 98.250000 %\n",
            "Train - [Epoch 9 /Iteration 100] Loss: 0.059291\n",
            "Train - [Epoch 9 /Iteration 200] Loss: 0.057749\n",
            "Train - [Epoch 9 /Iteration 300] Loss: 0.064181\n",
            "Train - [Epoch 9 /Iteration 400] Loss: 0.051622\n",
            "Test - [Epoch 9] Avg Loss: 0.062792, Accuracy: 97.820000 %\n",
            "Train - [Epoch 10 /Iteration 100] Loss: 0.056045\n",
            "Train - [Epoch 10 /Iteration 200] Loss: 0.055128\n",
            "Train - [Epoch 10 /Iteration 300] Loss: 0.053635\n",
            "Train - [Epoch 10 /Iteration 400] Loss: 0.054391\n",
            "Test - [Epoch 10] Avg Loss: 0.049618, Accuracy: 98.290000 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgplIGQ8uQks",
        "outputId": "ee40db61-e1fd-4781-a56b-20ab9f918351"
      },
      "source": [
        "# 使用新的（超参数）训练一个新的LeNet5模型\n",
        "BATCHSIZE = 64 # 批量大小\n",
        "LR = 0.05       # 学习率\n",
        "EPOCH = 5      # 迭代整个训练集的次数\n",
        "INTERVAL = 200  # 输出训练过程中间信息（损失值）的间隔\n",
        "\n",
        "# 新的dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCHSIZE,\n",
        "                                        shuffle=True, num_workers=2)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCHSIZE,\n",
        "                                        shuffle=False, num_workers=2)\n",
        "\n",
        "# 新的训练模型\n",
        "net = LeNet5()\n",
        "net.to(device) # 将模型移动到对应的设备上(CPU or GPU)\n",
        "\n",
        "# 定义新的损失函数和优化器\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=LR)\n",
        "\n",
        "# 训练和测试\n",
        "# loop over the dataset multiple times\n",
        "for epoch in range(EPOCH):\n",
        "  train(epoch+1)\n",
        "  test(epoch+1)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train - [Epoch 1 /Iteration 200] Loss: 1.847450\n",
            "Train - [Epoch 1 /Iteration 400] Loss: 0.400124\n",
            "Train - [Epoch 1 /Iteration 600] Loss: 0.198089\n",
            "Train - [Epoch 1 /Iteration 800] Loss: 0.140987\n",
            "Test - [Epoch 1] Avg Loss: 0.105098, Accuracy: 96.620000 %\n",
            "Train - [Epoch 2 /Iteration 200] Loss: 0.104183\n",
            "Train - [Epoch 2 /Iteration 400] Loss: 0.085888\n",
            "Train - [Epoch 2 /Iteration 600] Loss: 0.082229\n",
            "Train - [Epoch 2 /Iteration 800] Loss: 0.079476\n",
            "Test - [Epoch 2] Avg Loss: 0.059780, Accuracy: 97.900000 %\n",
            "Train - [Epoch 3 /Iteration 200] Loss: 0.066094\n",
            "Train - [Epoch 3 /Iteration 400] Loss: 0.062402\n",
            "Train - [Epoch 3 /Iteration 600] Loss: 0.059802\n",
            "Train - [Epoch 3 /Iteration 800] Loss: 0.053924\n",
            "Test - [Epoch 3] Avg Loss: 0.048369, Accuracy: 98.420000 %\n",
            "Train - [Epoch 4 /Iteration 200] Loss: 0.052968\n",
            "Train - [Epoch 4 /Iteration 400] Loss: 0.045956\n",
            "Train - [Epoch 4 /Iteration 600] Loss: 0.048635\n",
            "Train - [Epoch 4 /Iteration 800] Loss: 0.046136\n",
            "Test - [Epoch 4] Avg Loss: 0.040025, Accuracy: 98.700000 %\n",
            "Train - [Epoch 5 /Iteration 200] Loss: 0.041963\n",
            "Train - [Epoch 5 /Iteration 400] Loss: 0.039666\n",
            "Train - [Epoch 5 /Iteration 600] Loss: 0.034698\n",
            "Train - [Epoch 5 /Iteration 800] Loss: 0.038775\n",
            "Test - [Epoch 5] Avg Loss: 0.031983, Accuracy: 98.980000 %\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}