10_全连接层初步分类.ipynb

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      "source": [
        "## 10_全连接层初步分类\n",
        "\n",
        "交叉熵损失\n",
        "\n",
        "底层操作实现全连接层mnist多分类实战\n",
        "\n",
        "高层API实现全连接层mnist\n",
        "\n",
        "GPU全连接层mnist\n",
        "\n",
        "测试集的Loss与Accuracy\n",
        "\n",
        "Visdom可视化"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZ4wvEPfGrau",
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      },
      "source": [
        "### 交叉熵损失"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFWhHuyHSTyW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "x=torch.randn(1,784)\n",
        "w=torch.randn(10,784)\n",
        "logits=x@w.t()\n",
        "logits"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ -4.4273,   3.6157, -29.5624,   5.4576,   3.0141, -27.0873,  16.7052,\n",
              "          10.6995,   7.4828,  77.7712]])"
            ]
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            "tags": []
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          "execution_count": 4
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "a=torch.tensor([3])\n",
        "a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3])"
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          "execution_count": 9
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        "id": "-fkFR1xwFSQl",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "loss=F.cross_entropy(logits,torch.tensor([3])) #交叉熵，已经包含了softmax+log+nll_loss操作\n",
        "loss"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
            "text/plain": [
              "tensor(72.3136)"
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          "execution_count": 5
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      "cell_type": "markdown",
      "metadata": {
        "id": "rQuBxflzF4ts",
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      },
      "source": [
        "等价于"
      ]
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        "id": "RQOEaFNKFoW-",
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      },
      "source": [
        "pred=F.softmax(logits,dim=1) #softmax\n",
        "pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.0028e-36, 6.2324e-33, 0.0000e+00, 3.9319e-32, 3.4151e-33, 0.0000e+00,\n",
              "         3.0155e-27, 7.4322e-30, 2.9795e-31, 1.0000e+00]])"
            ]
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          "execution_count": 6
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "pred_log=torch.log(pred) #取log\n",
        "pred_log"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "tensor([[-82.1985, -74.1555,     -inf, -72.3136, -74.7571,     -inf, -61.0660,\n",
              "         -67.0717, -70.2884,   0.0000]])"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "F.nll_loss(pred_log,torch.tensor([3])) #单独算的交叉熵"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "tensor(72.3136)"
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      "cell_type": "markdown",
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      "source": [
        "### 底层操作实现全连接层mnist多分类实战\n",
        "\n",
        "底层操作的实现全连接网络。\n",
        "\n",
        "底层（有初始化）：Test set: Average loss: 0.0008, Accuracy: 9566/10000 (95%)\n",
        "\n",
        "1、加载数据："
      ]
    },
    {
      "cell_type": "code",
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        "id": "aMdtmX-GezET",
        "colab_type": "code",
        "outputId": "c17f76a7-66c6-4925-b906-f912a6337308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "import  torch\n",
        "import  torch.nn as nn\n",
        "import  torch.nn.functional as F\n",
        "import  torch.optim as optim\n",
        "from    torchvision import datasets, transforms\n",
        "\n",
        "batch_size=200\n",
        "learning_rate=0.01\n",
        "epochs=10\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=False, transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081,))\n",
        "    ])),\n",
        "    batch_size=batch_size, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
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            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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            "Processing...\n",
            "Done!\n"
          ],
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbf53vmgNWui",
        "colab_type": "text"
      },
      "source": [
        "2、网络结构定义\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymyOUc0bNDbB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#注意参数顺序，输出是200，输入是784\n",
        "w1,b1=torch.randn(200,784,requires_grad=True),torch.randn(200,requires_grad=True)\n",
        "w2,b2=torch.randn(200,200,requires_grad=True),torch.randn(200,requires_grad=True)\n",
        "w3,b3=torch.randn(10,200,requires_grad=True),torch.randn(10,requires_grad=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN8VkXmIetVd",
        "colab_type": "code",
        "outputId": "94c5689f-f8e8-46b9-e35b-39c1fd479b2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "#初始化很重要，不加可能会不收敛\n",
        "torch.nn.init.kaiming_normal_(w1) #凯明的初始化方法\n",
        "torch.nn.init.kaiming_normal_(w2)\n",
        "torch.nn.init.kaiming_normal_(w3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0035,  0.1014, -0.0175,  ...,  0.0059,  0.0378,  0.1596],\n",
              "        [-0.0755, -0.0902, -0.0388,  ...,  0.0690,  0.2342, -0.1109],\n",
              "        [-0.0518,  0.1989,  0.1145,  ...,  0.1274,  0.0069, -0.0583],\n",
              "        ...,\n",
              "        [-0.1391, -0.0236,  0.0157,  ...,  0.1356, -0.0018,  0.0320],\n",
              "        [ 0.0128, -0.0160,  0.1446,  ..., -0.0239, -0.0216, -0.0415],\n",
              "        [ 0.0124, -0.2328, -0.1789,  ..., -0.0717, -0.0126,  0.1702]],\n",
              "       requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx70bFmON73t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def forward(x):\n",
        "    x=x@w1.t()+b1 #b1是200，要自动扩展\n",
        "    x=F.relu(x)\n",
        "    x=x@w2.t()+b2\n",
        "    x=F.relu(x)\n",
        "    x=x@w3.t()+b3\n",
        "    x=F.relu(x) #没经过softmax和sigmoid的就是logits，relu可有可没有 \n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fmkFxMlaiPo",
        "colab_type": "text"
      },
      "source": [
        "3、训练与评估：\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LP6y8NY4akI4",
        "colab_type": "code",
        "outputId": "3aa58127-1bef-4aa5-f456-f470ba34a785",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "optimizer=optim.SGD([w1,b1,w2,b2,w3,b3],lr=learning_rate) #优化器\n",
        "criteon=nn.CrossEntropyLoss() #交叉熵损失，不用再多加softmax了，和F.cross_entropy一样\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    for batch_idx,(data,target) in enumerate(train_loader):\n",
        "        data=data.view(-1,28*28)\n",
        "\n",
        "        logits=forward(data)\n",
        "        loss=criteon(logits,target) #损失\n",
        "        # loss=F.cross_entropy(logits,target)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step() #更新\n",
        "       \n",
        "        if batch_idx % 100 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                       100. * batch_idx / len(train_loader), loss.item()))\n",
        "\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    for data, target in test_loader:\n",
        "        data = data.view(-1, 28 * 28)\n",
        "        logits = forward(data)\n",
        "        test_loss += criteon(logits, target).item()\n",
        "\n",
        "        pred = logits.data.max(1)[1]\n",
        "        correct += pred.eq(target.data).sum()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/60000 (0%)]\tLoss: 3.538675\n",
            "Train Epoch: 0 [20000/60000 (33%)]\tLoss: 0.834864\n",
            "Train Epoch: 0 [40000/60000 (67%)]\tLoss: 0.546448\n",
            "\n",
            "Test set: Average loss: 0.0019, Accuracy: 8893/10000 (88%)\n",
            "\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.328865\n",
            "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 0.458407\n",
            "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 0.307354\n",
            "\n",
            "Test set: Average loss: 0.0014, Accuracy: 9169/10000 (91%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.388060\n",
            "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 0.334696\n",
            "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 0.217570\n",
            "\n",
            "Test set: Average loss: 0.0012, Accuracy: 9305/10000 (93%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.250966\n",
            "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 0.300960\n",
            "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 0.303266\n",
            "\n",
            "Test set: Average loss: 0.0011, Accuracy: 9374/10000 (93%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.222222\n",
            "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 0.224267\n",
            "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 0.223342\n",
            "\n",
            "Test set: Average loss: 0.0010, Accuracy: 9412/10000 (94%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.183069\n",
            "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 0.268348\n",
            "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 0.195760\n",
            "\n",
            "Test set: Average loss: 0.0009, Accuracy: 9463/10000 (94%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.149278\n",
            "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 0.155900\n",
            "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 0.146113\n",
            "\n",
            "Test set: Average loss: 0.0009, Accuracy: 9490/10000 (94%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.153186\n",
            "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 0.171221\n",
            "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 0.131967\n",
            "\n",
            "Test set: Average loss: 0.0008, Accuracy: 9515/10000 (95%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.158746\n",
            "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 0.155135\n",
            "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 0.264796\n",
            "\n",
            "Test set: Average loss: 0.0008, Accuracy: 9537/10000 (95%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.153050\n",
            "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 0.165005\n",
            "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 0.146903\n",
            "\n",
            "Test set: Average loss: 0.0008, Accuracy: 9566/10000 (95%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UEkYehgPJGcH",
        "colab_type": "text"
      },
      "source": [
        "### 高层API实现全连接层mnist\n",
        "\n",
        "高层实现，nn.Linear()\n",
        "\n",
        "ReLU：Test set: Average loss: 0.0011, Accuracy: 9373/10000 (93%)，不稳定，也有65%、76%、84%、93%\n",
        "\n",
        "LeakyReLU：Test set: Average loss: 0.0011, Accuracy: 9372/10000 (93%)，稳定，训练刚一开始的准确率就比ReLU高很多"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zw_4wZX1_OcE",
        "colab_type": "code",
        "outputId": "fe56e027-1d30-4e42-be91-39a49f8287b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import  torch\n",
        "import  torch.nn as nn\n",
        "import  torch.nn.functional as F\n",
        "import  torch.optim as optim\n",
        "from    torchvision import datasets, transforms\n",
        "\n",
        "batch_size=200\n",
        "learning_rate=0.01\n",
        "epochs=10\n",
        "\n",
        "# 加载数据\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=False, transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081,))\n",
        "    ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "\n",
        "# 网络结构\n",
        "class MLP(nn.Module): #继承自nn.Module类\n",
        "\n",
        "    def __init__(self): #初始化，传自己的参数，这里不需要\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential( #nn.Sequential可以添加任何继承自nn.Module的类\n",
        "            nn.Linear(784, 200),\n",
        "            nn.ReLU(inplace=True), #inplace可以节省多出来的内存\n",
        "            nn.Linear(200, 200),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(200, 10),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "    def forward(self, x): #实现前向传播，不用实现反向传播\n",
        "        x = self.model(x) #使用了model.forward\n",
        "        return x\n",
        "\n",
        "# 训练\n",
        "net = MLP() #实例化\n",
        "#不需要认为w b了，nn.Module会自动把这些tensor加载到parameters里去一次返回\n",
        "#w b也不用初始化了，Linear里包含了\n",
        "optimizer = optim.SGD(net.parameters(), lr=learning_rate) \n",
        "criteon = nn.CrossEntropyLoss()\n",
        "\n",
        "for epoch in range(epochs):\n",
        "\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data = data.view(-1, 28*28)\n",
        "\n",
        "        logits = net(data) #给网络送入数据\n",
        "        loss = criteon(logits, target)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        # print(w1.grad.norm(), w2.grad.norm())\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch_idx % 100 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                       100. * batch_idx / len(train_loader), loss.item()))\n",
        "\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    for data, target in test_loader:\n",
        "        data = data.view(-1, 28 * 28)\n",
        "        logits = net(data)\n",
        "        test_loss += criteon(logits, target).item()\n",
        "\n",
        "        pred = logits.data.max(1)[1]\n",
        "        correct += pred.eq(target.data).sum()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.306402\n",
            "Train Epoch: 0 [20000/60000 (33%)]\tLoss: 2.022543\n",
            "Train Epoch: 0 [40000/60000 (67%)]\tLoss: 1.504543\n",
            "\n",
            "Test set: Average loss: 0.0056, Accuracy: 6823/10000 (68%)\n",
            "\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 1.220166\n",
            "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 0.936042\n",
            "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 1.052351\n",
            "\n",
            "Test set: Average loss: 0.0043, Accuracy: 7241/10000 (72%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 1.027007\n",
            "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 0.944400\n",
            "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 0.820583\n",
            "\n",
            "Test set: Average loss: 0.0038, Accuracy: 7517/10000 (75%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.933284\n",
            "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 0.730454\n",
            "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 0.646702\n",
            "\n",
            "Test set: Average loss: 0.0037, Accuracy: 7745/10000 (77%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.664488\n",
            "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 0.860299\n",
            "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 0.712083\n",
            "\n",
            "Test set: Average loss: 0.0036, Accuracy: 7881/10000 (78%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.692236\n",
            "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 0.742436\n",
            "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 0.721325\n",
            "\n",
            "Test set: Average loss: 0.0035, Accuracy: 7981/10000 (79%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.570521\n",
            "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 0.692517\n",
            "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 0.687250\n",
            "\n",
            "Test set: Average loss: 0.0034, Accuracy: 8019/10000 (80%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.662483\n",
            "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 0.543044\n",
            "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 0.673437\n",
            "\n",
            "Test set: Average loss: 0.0034, Accuracy: 8086/10000 (80%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.722589\n",
            "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 0.706904\n",
            "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 0.679069\n",
            "\n",
            "Test set: Average loss: 0.0033, Accuracy: 8135/10000 (81%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.696604\n",
            "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 0.598332\n",
            "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 0.652221\n",
            "\n",
            "Test set: Average loss: 0.0033, Accuracy: 8155/10000 (81%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qK6KZoxR9_k",
        "colab_type": "text"
      },
      "source": [
        "### GPU全连接层mnist\n",
        "\n",
        "LeakyReLU：Test set: Average loss: 0.0011, Accuracy: 9345/10000 (93%)，几次GPU训练稳定在这个值\n",
        "\n",
        "ReLU：Test set: Average loss: 0.0021, Accuracy: 8530/10000 (85%)，几次GPU训练不稳定，63%、73%、85%、93%都有过"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Cs9I9kbR88s",
        "colab_type": "code",
        "outputId": "c44fec82-92d7-4522-9539-86cd6aab44d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import  torch\n",
        "import  torch.nn as nn\n",
        "import  torch.nn.functional as F\n",
        "import  torch.optim as optim\n",
        "from  torchvision import datasets, transforms\n",
        "\n",
        "batch_size=200\n",
        "learning_rate=0.01\n",
        "epochs=10\n",
        "\n",
        "# 加载数据\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=False, transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081,))\n",
        "    ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "\n",
        "# 网络结构\n",
        "class MLP(nn.Module): #继承自nn.Module类\n",
        "\n",
        "    def __init__(self): #初始化，传自己的参数，这里不需要\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential( #nn.Sequential可以添加任何继承自nn.Module的类\n",
        "            nn.Linear(784, 200),\n",
        "            nn.LeakyReLU(inplace=True), #inplace可以节省多出来的内存\n",
        "            nn.Linear(200, 200),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "            nn.Linear(200, 10),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "    def forward(self, x): #实现前向传播，不用实现反向传播\n",
        "        x = self.model(x) #使用了model.forward\n",
        "        return x\n",
        "\n",
        "# 训练\n",
        "device = torch.device('cuda:0,1') #GPU\n",
        "net = MLP().to(device) #GPU\n",
        "#不需要认为w b了，nn.Module会自动把这些tensor加载到parameters里去一次返回\n",
        "#w b也不用初始化了，Linear里包含了\n",
        "optimizer = optim.SGD(net.parameters(), lr=learning_rate) \n",
        "criteon = nn.CrossEntropyLoss().to(device) #GPU\n",
        "\n",
        "for epoch in range(epochs):\n",
        "\n",
        "    for batch_idx, (data, target) in enumerate(train_loader): #train_loader设置了每次取batch_size=200来同时计算\n",
        "        data = data.view(-1, 28*28)\n",
        "        data, target = data.to(device), target.cuda() #GPU\n",
        "\n",
        "        logits = net(data) #给网络送入数据\n",
        "        loss = criteon(logits, target) #训练的loss\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        # print(w1.grad.norm(), w2.grad.norm())\n",
        "        optimizer.step() #更新\n",
        "\n",
        "        if batch_idx % 100 == 0: #每200*100打印一次\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                       100. * batch_idx / len(train_loader), loss.item())) #item转为数值\n",
        "\n",
        "    # 测试，每epochs做一次\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    for data, target in test_loader: #取整个数据集和真实标签\n",
        "        data = data.view(-1, 28 * 28)\n",
        "        data, target = data.to(device), target.cuda() #GPU\n",
        "        logits = net(data) #数据送到网络\n",
        "        test_loss += criteon(logits, target).item() #测试的loss的总和\n",
        "\n",
        "        # pred = logits.data.max(1)[1]\n",
        "        pred = logits.argmax(dim=1) #计算预测标签\n",
        "        correct += pred.eq(target).float().sum().item() #预测正确的图像数量\n",
        "\n",
        "    test_loss /= len(test_loader.dataset) #测试loss的平均值\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset))) #ACC"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.297979\n",
            "Train Epoch: 0 [20000/60000 (33%)]\tLoss: 2.021637\n",
            "Train Epoch: 0 [40000/60000 (67%)]\tLoss: 1.445032\n",
            "\n",
            "Test set: Average loss: 0.0056, Accuracy: 7203/10000 (72%)\n",
            "\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 1.075741\n",
            "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 0.943719\n",
            "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 0.738639\n",
            "\n",
            "Test set: Average loss: 0.0033, Accuracy: 8211/10000 (82%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.629387\n",
            "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 0.674149\n",
            "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 0.717169\n",
            "\n",
            "Test set: Average loss: 0.0028, Accuracy: 8337/10000 (83%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.664478\n",
            "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 0.521124\n",
            "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 0.343371\n",
            "\n",
            "Test set: Average loss: 0.0017, Accuracy: 8978/10000 (89%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.324872\n",
            "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 0.310015\n",
            "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 0.256808\n",
            "\n",
            "Test set: Average loss: 0.0015, Accuracy: 9128/10000 (91%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.309582\n",
            "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 0.320750\n",
            "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 0.231473\n",
            "\n",
            "Test set: Average loss: 0.0014, Accuracy: 9210/10000 (92%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.244921\n",
            "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 0.311157\n",
            "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 0.275998\n",
            "\n",
            "Test set: Average loss: 0.0013, Accuracy: 9255/10000 (92%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.334565\n",
            "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 0.209743\n",
            "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 0.211830\n",
            "\n",
            "Test set: Average loss: 0.0013, Accuracy: 9287/10000 (92%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.242724\n",
            "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 0.240965\n",
            "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 0.221280\n",
            "\n",
            "Test set: Average loss: 0.0012, Accuracy: 9326/10000 (93%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.249260\n",
            "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 0.259100\n",
            "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 0.188031\n",
            "\n",
            "Test set: Average loss: 0.0011, Accuracy: 9347/10000 (93%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEKMtYEIdx8u",
        "colab_type": "text"
      },
      "source": [
        "### 测试集的Loss与Accuracy\n",
        "argmax与ACC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaFT7RStd0Sv",
        "colab_type": "code",
        "outputId": "d1a231ed-1d8f-4364-aa12-77209eef079a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "logits=torch.rand(4,10)\n",
        "pred=F.softmax(logits,dim=1) #softmax转为概率值，在1维上做\n",
        "pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0742, 0.1039, 0.0904, 0.1096, 0.0616, 0.1237, 0.1294, 0.1240, 0.1133,\n",
              "         0.0698],\n",
              "        [0.1211, 0.0891, 0.0808, 0.0938, 0.0743, 0.0642, 0.1078, 0.1417, 0.1062,\n",
              "         0.1208],\n",
              "        [0.1095, 0.0679, 0.0702, 0.1710, 0.0825, 0.1283, 0.0751, 0.0991, 0.0679,\n",
              "         0.1285],\n",
              "        [0.1052, 0.1343, 0.0619, 0.1205, 0.0726, 0.1204, 0.0995, 0.0851, 0.0860,\n",
              "         0.1144]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0k50JduLyuPp",
        "colab_type": "code",
        "outputId": "ce9567e1-f0f5-449d-b827-d5fa71b5ab5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "pred_label=pred.argmax(dim=1) #argmax找到最大值的索引，在1维上做\n",
        "pred_label"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([6, 7, 3, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ye6zVD54E1Q",
        "colab_type": "code",
        "outputId": "6ca5fbff-c99a-4daf-a599-de28c7772822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "logits.argmax(dim=1) #也可以直接不经过softmax，结果一样"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([6, 7, 3, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYJgleMd4R3u",
        "colab_type": "code",
        "outputId": "42099a46-3e74-40b8-cab2-8fc8a0667494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "label=torch.tensor([6,6,2,1]) #真实标签\n",
        "correct=torch.eq(pred_label,label) #eq判断预测标签和真实标签的相同性，逐位检查，相等为1\n",
        "correct"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 0, 0, 1], dtype=torch.uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2_1we9f5PAm",
        "colab_type": "code",
        "outputId": "cb1bf132-d6ce-4e46-9ac1-741916674bf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "correct.sum().float().item()/4 #取correct中的和，即为相等的个数，再转为float，再取tensor(2.)数据类型的值2.0，除以总个数"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1orDrhJ3AS5Z",
        "colab_type": "text"
      },
      "source": [
        "测试的loss与ACC计算见上节"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zs_A1F5f56da",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZIv-wKbDarE",
        "colab_type": "text"
      },
      "source": [
        "### Visdom可视化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J219AODkMr7A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !pip install visdom"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2cyPxnjesPG",
        "colab_type": "text"
      },
      "source": [
        "云notebook使用visdom，方法1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4t-ImSzUsxB",
        "colab_type": "code",
        "outputId": "2345bff6-aac3-4a42-e892-b38712fe19c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# !wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "# !unzip ngrok-stable-linux-amd64.zip\n",
        "get_ipython().system_raw('python3 -m pip install visdom')\n",
        "get_ipython().system_raw('python3 -m visdom.server -port 6006 >> visdomlog.txt 2>&1 &')\n",
        "get_ipython().system_raw('ssh -o ServerAliveInterval=60 -o StrictHostKeyChecking=no -R YOURDOMAINHERE:80:localhost:6006 serveo.net &')\n",
        "print('Visdom view: {}'.format('https://YOURDOMAINHERE.serveo.net/'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Visdom view: https://YOURDOMAINHERE.serveo.net/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wUhHtYie3BD",
        "colab_type": "text"
      },
      "source": [
        "云notebook使用visdom，方法2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQZ_OunnceTh",
        "colab_type": "code",
        "outputId": "634b11de-b87b-4141-9a42-9dfd0ab612ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "# ! npm install -g localtunnel\n",
        "# 8097可修改为自己要用的端口号\n",
        "get_ipython().system_raw('python3 -m pip install visdom')\n",
        "get_ipython().system_raw('python3 -m visdom.server -port 8097 >> visdomlog.txt 2>&1 &')\n",
        "get_ipython().system_raw('lt --port 8097 >> url.txt 2>&1 &')\n",
        "! cat url.txt\n",
        "# import visdom\n",
        "# vis = visdom.Visdom(port='8097')\n",
        "# vis.text('testing')\n",
        "# ! cat visdomlog.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "your url is: https://polite-panther-16.localtunnel.me\n",
            "your url is: https://nasty-bullfrog-62.localtunnel.me\n",
            "your url is: https://wise-dodo-57.localtunnel.me\n",
            "your url is: https://kind-puma-82.localtunnel.me\n",
            "your url is: https://sour-insect-34.localtunnel.me\n",
            "your url is: https://mighty-cat-58.localtunnel.me\n",
            "your url is: https://white-frog-100.localtunnel.me\n",
            "your url is: https://splendid-cougar-89.localtunnel.me\n",
            "your url is: https://shaggy-duck-56.localtunnel.me\n",
            "your url is: https://strong-bat-79.localtunnel.me\n",
            "your url is: https://fluffy-fox-69.localtunnel.me\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HLc3-OMW4KR",
        "colab_type": "code",
        "outputId": "05877869-629e-4952-cc4b-183b363eb938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import  torch\n",
        "import  torch.nn as nn\n",
        "import  torch.nn.functional as F\n",
        "import  torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from visdom import Visdom\n",
        "\n",
        "batch_size=200\n",
        "learning_rate=0.01\n",
        "epochs=10\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       # transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('mnist_data', train=False, transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        # transforms.Normalize((0.1307,), (0.3081,))\n",
        "    ])),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "\n",
        "\n",
        "class MLP(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(784, 200),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "            nn.Linear(200, 200),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "            nn.Linear(200, 10),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.model(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "device = torch.device('cuda:0')\n",
        "net = MLP().to(device)\n",
        "optimizer = optim.SGD(net.parameters(), lr=learning_rate)\n",
        "criteon = nn.CrossEntropyLoss().to(device)\n",
        "\n",
        "viz = Visdom(port='8097') #Visdom实例化，加上端口号\n",
        "#先创建一条曲线，后面再把最新数据添加到上面去。参数：初始点坐标y x，win是小窗口ID，窗口名称\n",
        "viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))\n",
        "#一个窗口同时显示两条曲线，参数：初始点坐标y1 y2 x，ID，两个窗口名称，两个图例说明\n",
        "viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',legend=['loss', 'acc.'])) \n",
        "global_step = 0 #x轴\n",
        "\n",
        "for epoch in range(epochs):\n",
        "\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data = data.view(-1, 28*28)\n",
        "        data, target = data.to(device), target.cuda()\n",
        "\n",
        "        logits = net(data)\n",
        "        loss = criteon(logits, target) #一个batch的loss？？\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        # print(w1.grad.norm(), w2.grad.norm())\n",
        "        optimizer.step()\n",
        "\n",
        "        #添加新数据到曲线上：loss是numpy类型，x轴时间戳，ID，更新方式为往后添加，否则就会覆盖掉\n",
        "        global_step += 1 #x加1，这里有3000个,3000*200=60000*10\n",
        "        viz.line([loss.item()], [global_step], win='train_loss', update='append') \n",
        "\n",
        "        if batch_idx % 100 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                       100. * batch_idx / len(train_loader), loss.item()))\n",
        "\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    for data, target in test_loader:\n",
        "        data = data.view(-1, 28 * 28)\n",
        "        data, target = data.to(device), target.cuda()\n",
        "        logits = net(data)\n",
        "        test_loss += criteon(logits, target).item() #一个batch的loss？？\n",
        "\n",
        "        pred = logits.argmax(dim=1)\n",
        "        correct += pred.eq(target).float().sum().item()\n",
        "\n",
        "    #添加新数据到曲线上，y1 y2 x，最好y1和y2的范围接近\n",
        "    viz.line([[test_loss, correct/len(test_loader.dataset)]], [global_step], win='test', update='append')\n",
        "\n",
        "    #图片可视化，这里显示test_loader最近一批的200张\n",
        "    viz.images(data.view(-1, 1, 28, 28), win='x') #变形为图片，图像可以直接传tensor类型不用转numpy\n",
        "    viz.text(str(pred.detach().cpu().numpy()), win='pred', opts=dict(title='pred')) #预测的标签，要转numpy\n",
        "\n",
        "    test_loss /= len(test_loader.dataset) #这可能不该除以总数据量，应除以(总数据量/batch)？？\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Setting up a new session...\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.302187\n",
            "Train Epoch: 0 [20000/60000 (33%)]\tLoss: 2.280664\n",
            "Train Epoch: 0 [40000/60000 (67%)]\tLoss: 2.246795\n",
            "\n",
            "Test set: Average loss: 0.0109, Accuracy: 5305.0/10000 (53%)\n",
            "\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.157960\n",
            "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 2.082629\n",
            "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 1.872170\n",
            "\n",
            "Test set: Average loss: 0.0085, Accuracy: 5979.0/10000 (60%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 1.693831\n",
            "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 1.540581\n",
            "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 1.393675\n",
            "\n",
            "Test set: Average loss: 0.0056, Accuracy: 7400.0/10000 (74%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 1.137246\n",
            "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 0.969303\n",
            "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 0.800058\n",
            "\n",
            "Test set: Average loss: 0.0040, Accuracy: 7790.0/10000 (78%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.806434\n",
            "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 0.688107\n",
            "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 0.614671\n",
            "\n",
            "Test set: Average loss: 0.0027, Accuracy: 8585.0/10000 (86%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.488596\n",
            "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 0.548170\n",
            "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 0.507954\n",
            "\n",
            "Test set: Average loss: 0.0022, Accuracy: 8795.0/10000 (88%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.524438\n",
            "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 0.377986\n",
            "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 0.386030\n",
            "\n",
            "Test set: Average loss: 0.0020, Accuracy: 8892.0/10000 (89%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.398271\n",
            "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 0.453912\n",
            "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 0.395887\n",
            "\n",
            "Test set: Average loss: 0.0019, Accuracy: 8966.0/10000 (90%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.354747\n",
            "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 0.283831\n",
            "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 0.333745\n",
            "\n",
            "Test set: Average loss: 0.0018, Accuracy: 9005.0/10000 (90%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.403392\n",
            "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 0.369903\n",
            "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 0.293750\n",
            "\n",
            "Test set: Average loss: 0.0017, Accuracy: 9045.0/10000 (90%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}