05-이미지_처리능력이_탁월한_CNN/cnn.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yellowjs0304/3-min-pytorch/blob/master/05-%EC%9D%B4%EB%AF%B8%EC%A7%80_%EC%B2%98%EB%A6%AC%EB%8A%A5%EB%A0%A5%EC%9D%B4_%ED%83%81%EC%9B%94%ED%95%9C_CNN/cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S18VSfrUy1K-"
      },
      "source": [
        "# CNN으로 패션 아이템 구분하기\n",
        "Convolutional Neural Network (CNN) 을 이용하여 패션아이템 구분 성능을 높여보겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTfeK37Gy1LE"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import transforms, datasets\n",
        "\n",
        "from torchsummary import summary # 모델 구조를 한번에 확인하기 위해 추가한 라이브러리\n",
        "#torchsummary : 파라미터 개수, 레이어 구조를 정리하여 확인할 수 있다."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epg1d2-jy1LF"
      },
      "source": [
        "# GPU 사용여부 확인 코드\n",
        "# DEVICE 안에 cuda(gpu)가 할당되어야 함\n",
        "USE_CUDA = torch.cuda.is_available()\n",
        "DEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XityGkacy1LG"
      },
      "source": [
        "EPOCHS     = 40\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfwZNvhYy1LG"
      },
      "source": [
        "## 데이터셋 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkhDPKeWy1LG",
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          ]
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        "outputId": "092a2357-0d5b-4783-c0d0-9a8649ce87be"
      },
      "source": [
        "# Dataloader : 데이터셋을 batch 단위로 쪼개서 학습할 때 모델의 입력으로 주는 클래스.\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('./.data',\n",
        "                   train=True,#학습\n",
        "                   download=True,#없으면 다운로드\n",
        "                   transform=transforms.Compose([#torchvision.transform = 입력 변환 라이브러리\n",
        "                       transforms.ToTensor(),#이미지를 Tensor로\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))#이미지 정규화\n",
        "                   ])),\n",
        "    batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('./.data',\n",
        "                   train=False, #테스트\n",
        "                   download=True,#없으면 다운로드\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])),\n",
        "    batch_size=BATCH_SIZE, shuffle=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./.data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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          "text": [
            "\n",
            "Extracting ./.data/MNIST/raw/train-images-idx3-ubyte.gz to ./.data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./.data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "text": [
            "\n",
            "Extracting ./.data/MNIST/raw/train-labels-idx1-ubyte.gz to ./.data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./.data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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          "text": [
            "\n",
            "Extracting ./.data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./.data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./.data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "\n",
            "Extracting ./.data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./.data/MNIST/raw\n",
            "\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: 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:143.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccz8xpbyy1LH"
      },
      "source": [
        "## 뉴럴넷으로 Fashion MNIST 학습하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDD6zaOUy1LH"
      },
      "source": [
        "class Net(nn.Module):\n",
        "\n",
        "    # CNN Network Class 정의#\n",
        "    # kernel size 5x5, 2 Convolutional layer#\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "\n",
        "        # 채널 수 : 1(흑백 이미지) / output volume_size : 10(필터의 개수==10개의 특징맵을 생성) / kernel_size(필터의 사이즈) = 5x5\n",
        "        # kernel size를 하나만 입력하면 NxN으로 간주. / NxM을 원할경우, (N,M)으로 정의\n",
        "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5) \n",
        "        \n",
        "        # 입력 채널 수 : 10 (conv1의 결과물) / output volume size : 20 (20개의 특징맵을 생성) / kernel_size(필터의 사이즈) = 5x5\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "\n",
        "        #Dropout 적용\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "\n",
        "        # 일반 신경망을 거치면서 이전 출력 크기인 320을 기준으로  50, 10순으로 작아지도록 설정\n",
        "        # 이때 50과 같은 중간 값은 임의로 설정한 값. 10은 분류해야 할 클래스의 개수(FASHION MNIST CLASS 개수)\n",
        "        self.fc1 = nn.Linear(320, 50)\n",
        "        self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "    #CNN Network의 동작(forward)함수, 본격적으로 데이터가 지나갈 길을 닦아준다 생각하면 됨.#\n",
        "    def forward(self, x):\n",
        "        # 각 레이어는 conv - pooling - relu를 하나의 묶음으로 간주\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))# max pooling with (2x2)kernel\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "\n",
        "        # 데이터를 FC레이어에 넣기 위해 2차원에서 1차원 형태로 변경\n",
        "        x = x.view(-1, 320)\n",
        "\n",
        "        #RELU activation function+Dropout\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        \n",
        "        # 출력 클래스 10개인 Output생성\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yRmoGnay1LH"
      },
      "source": [
        "## 하이퍼파라미터 \n",
        "\n",
        "`to()` 함수는 모델의 파라미터들을 지정한 곳으로 보내는 역할을 합니다. 일반적으로 CPU 1개만 사용할 경우 필요는 없지만, GPU를 사용하고자 하는 경우 `to(\"cuda\")`로 지정하여 GPU로 보내야 합니다. 지정하지 않을 경우 계속 CPU에 남아 있게 되며 빠른 훈련의 이점을 누리실 수 없습니다.\n",
        "\n",
        "최적화 알고리즘으로 파이토치에 내장되어 있는 `optim.SGD`를 사용하겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcR-YGoxy1LH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7f0356a-4435-4323-e6be-6f26ebd8fe60"
      },
      "source": [
        "model     = Net().to(DEVICE)\n",
        "summary(model, (1, 28, 28)) #모델 구조를 보기 위하여 삽입한 코드\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n",
        "print(model)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 10, 24, 24]             260\n",
            "            Conv2d-2             [-1, 20, 8, 8]           5,020\n",
            "         Dropout2d-3             [-1, 20, 8, 8]               0\n",
            "            Linear-4                   [-1, 50]          16,050\n",
            "            Linear-5                   [-1, 10]             510\n",
            "================================================================\n",
            "Total params: 21,840\n",
            "Trainable params: 21,840\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.06\n",
            "Params size (MB): 0.08\n",
            "Estimated Total Size (MB): 0.15\n",
            "----------------------------------------------------------------\n",
            "Net(\n",
            "  (conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (conv2): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (conv2_drop): Dropout2d(p=0.5, inplace=False)\n",
            "  (fc1): Linear(in_features=320, out_features=50, bias=True)\n",
            "  (fc2): Linear(in_features=50, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHKjRvYuy1LI"
      },
      "source": [
        "## 학습하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZYBHLKfy1LI"
      },
      "source": [
        "def train(model, train_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(DEVICE), target.to(DEVICE)\n",
        "        optimizer.zero_grad()#for문을 지날때마다 기울기 새로 계산\n",
        "        output = model(data)#모델 예측값\n",
        "        loss = F.cross_entropy(output, target)#예측값, 정답 간의 Cross entropy loss\n",
        "        loss.backward()#역전파 기울기 계산\n",
        "        optimizer.step()#weight고치기\n",
        "\n",
        "        if batch_idx % 200 == 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()))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dGRaUa0y1LI"
      },
      "source": [
        "## 테스트하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFeTptB_y1LI"
      },
      "source": [
        "def evaluate(model, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(DEVICE), target.to(DEVICE)\n",
        "            output = model(data)\n",
        "\n",
        "            # 배치 오차를 합산\n",
        "            test_loss += F.cross_entropy(output, target,\n",
        "                                         reduction='sum').item()\n",
        "\n",
        "            # 가장 높은 값을 가진 인덱스가 바로 예측값\n",
        "            pred = output.max(1, keepdim=True)[1]\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    test_accuracy = 100. * correct / len(test_loader.dataset)\n",
        "    return test_loss, test_accuracy"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ww4k0FX0y1LJ"
      },
      "source": [
        "## 코드 돌려보기\n",
        "\n",
        "자, 이제 모든 준비가 끝났습니다. 코드를 돌려서 실제로 학습이 되는지 확인해봅시다!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TySv-hNby1LJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ebdb8ba-70b5-41ed-a61d-d25f88da1fc8"
      },
      "source": [
        "for epoch in range(1, EPOCHS + 1):\n",
        "    train(model, train_loader, optimizer, epoch)\n",
        "    test_loss, test_accuracy = evaluate(model, test_loader)\n",
        "    \n",
        "    print('[{}] Test Loss: {:.4f}, Accuracy: {:.2f}%'.format(\n",
        "          epoch, test_loss, test_accuracy))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.299135\n",
            "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.411538\n",
            "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.648250\n",
            "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.514119\n",
            "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.344039\n",
            "[1] Test Loss: 0.1586, Accuracy: 95.31%\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.469092\n",
            "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.499743\n",
            "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.363924\n",
            "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.136671\n",
            "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.224638\n",
            "[2] Test Loss: 0.1028, Accuracy: 96.81%\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.172936\n",
            "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.268849\n",
            "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.381308\n",
            "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.205941\n",
            "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.358028\n",
            "[3] Test Loss: 0.0813, Accuracy: 97.46%\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.144789\n",
            "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.305467\n",
            "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.106295\n",
            "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.233646\n",
            "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.175044\n",
            "[4] Test Loss: 0.0711, Accuracy: 97.62%\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.239971\n",
            "Train Epoch: 5 [12800/60000 (21%)]\tLoss: 0.144701\n",
            "Train Epoch: 5 [25600/60000 (43%)]\tLoss: 0.156784\n",
            "Train Epoch: 5 [38400/60000 (64%)]\tLoss: 0.224433\n",
            "Train Epoch: 5 [51200/60000 (85%)]\tLoss: 0.157158\n",
            "[5] Test Loss: 0.0645, Accuracy: 97.87%\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.223471\n",
            "Train Epoch: 6 [12800/60000 (21%)]\tLoss: 0.094700\n",
            "Train Epoch: 6 [25600/60000 (43%)]\tLoss: 0.076336\n",
            "Train Epoch: 6 [38400/60000 (64%)]\tLoss: 0.201240\n",
            "Train Epoch: 6 [51200/60000 (85%)]\tLoss: 0.214663\n",
            "[6] Test Loss: 0.0608, Accuracy: 98.09%\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.228136\n",
            "Train Epoch: 7 [12800/60000 (21%)]\tLoss: 0.267289\n",
            "Train Epoch: 7 [25600/60000 (43%)]\tLoss: 0.217788\n",
            "Train Epoch: 7 [38400/60000 (64%)]\tLoss: 0.180484\n",
            "Train Epoch: 7 [51200/60000 (85%)]\tLoss: 0.082613\n",
            "[7] Test Loss: 0.0536, Accuracy: 98.31%\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.089656\n",
            "Train Epoch: 8 [12800/60000 (21%)]\tLoss: 0.254504\n",
            "Train Epoch: 8 [25600/60000 (43%)]\tLoss: 0.298762\n",
            "Train Epoch: 8 [38400/60000 (64%)]\tLoss: 0.086490\n",
            "Train Epoch: 8 [51200/60000 (85%)]\tLoss: 0.079179\n",
            "[8] Test Loss: 0.0519, Accuracy: 98.24%\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.187032\n",
            "Train Epoch: 9 [12800/60000 (21%)]\tLoss: 0.119931\n",
            "Train Epoch: 9 [25600/60000 (43%)]\tLoss: 0.081376\n",
            "Train Epoch: 9 [38400/60000 (64%)]\tLoss: 0.116026\n",
            "Train Epoch: 9 [51200/60000 (85%)]\tLoss: 0.180554\n",
            "[9] Test Loss: 0.0479, Accuracy: 98.40%\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.146411\n",
            "Train Epoch: 10 [12800/60000 (21%)]\tLoss: 0.147941\n",
            "Train Epoch: 10 [25600/60000 (43%)]\tLoss: 0.083656\n",
            "Train Epoch: 10 [38400/60000 (64%)]\tLoss: 0.202950\n",
            "Train Epoch: 10 [51200/60000 (85%)]\tLoss: 0.130179\n",
            "[10] Test Loss: 0.0461, Accuracy: 98.49%\n",
            "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.097125\n",
            "Train Epoch: 11 [12800/60000 (21%)]\tLoss: 0.099541\n",
            "Train Epoch: 11 [25600/60000 (43%)]\tLoss: 0.017871\n",
            "Train Epoch: 11 [38400/60000 (64%)]\tLoss: 0.184559\n",
            "Train Epoch: 11 [51200/60000 (85%)]\tLoss: 0.084621\n",
            "[11] Test Loss: 0.0447, Accuracy: 98.50%\n",
            "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.180068\n",
            "Train Epoch: 12 [12800/60000 (21%)]\tLoss: 0.238580\n",
            "Train Epoch: 12 [25600/60000 (43%)]\tLoss: 0.091356\n",
            "Train Epoch: 12 [38400/60000 (64%)]\tLoss: 0.159096\n",
            "Train Epoch: 12 [51200/60000 (85%)]\tLoss: 0.202558\n",
            "[12] Test Loss: 0.0406, Accuracy: 98.71%\n",
            "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.272695\n",
            "Train Epoch: 13 [12800/60000 (21%)]\tLoss: 0.210366\n",
            "Train Epoch: 13 [25600/60000 (43%)]\tLoss: 0.492805\n",
            "Train Epoch: 13 [38400/60000 (64%)]\tLoss: 0.128399\n",
            "Train Epoch: 13 [51200/60000 (85%)]\tLoss: 0.077777\n",
            "[13] Test Loss: 0.0418, Accuracy: 98.58%\n",
            "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.174338\n",
            "Train Epoch: 14 [12800/60000 (21%)]\tLoss: 0.234835\n",
            "Train Epoch: 14 [25600/60000 (43%)]\tLoss: 0.062571\n",
            "Train Epoch: 14 [38400/60000 (64%)]\tLoss: 0.230459\n",
            "Train Epoch: 14 [51200/60000 (85%)]\tLoss: 0.043814\n",
            "[14] Test Loss: 0.0392, Accuracy: 98.70%\n",
            "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.067752\n",
            "Train Epoch: 15 [12800/60000 (21%)]\tLoss: 0.153611\n",
            "Train Epoch: 15 [25600/60000 (43%)]\tLoss: 0.100428\n",
            "Train Epoch: 15 [38400/60000 (64%)]\tLoss: 0.265182\n",
            "Train Epoch: 15 [51200/60000 (85%)]\tLoss: 0.064867\n",
            "[15] Test Loss: 0.0385, Accuracy: 98.76%\n",
            "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.194453\n",
            "Train Epoch: 16 [12800/60000 (21%)]\tLoss: 0.267916\n",
            "Train Epoch: 16 [25600/60000 (43%)]\tLoss: 0.039848\n",
            "Train Epoch: 16 [38400/60000 (64%)]\tLoss: 0.189191\n",
            "Train Epoch: 16 [51200/60000 (85%)]\tLoss: 0.222558\n",
            "[16] Test Loss: 0.0387, Accuracy: 98.79%\n",
            "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.093061\n",
            "Train Epoch: 17 [12800/60000 (21%)]\tLoss: 0.098777\n",
            "Train Epoch: 17 [25600/60000 (43%)]\tLoss: 0.142854\n",
            "Train Epoch: 17 [38400/60000 (64%)]\tLoss: 0.096347\n",
            "Train Epoch: 17 [51200/60000 (85%)]\tLoss: 0.067367\n",
            "[17] Test Loss: 0.0375, Accuracy: 98.79%\n",
            "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.065023\n",
            "Train Epoch: 18 [12800/60000 (21%)]\tLoss: 0.110272\n",
            "Train Epoch: 18 [25600/60000 (43%)]\tLoss: 0.221166\n",
            "Train Epoch: 18 [38400/60000 (64%)]\tLoss: 0.059074\n",
            "Train Epoch: 18 [51200/60000 (85%)]\tLoss: 0.177639\n",
            "[18] Test Loss: 0.0372, Accuracy: 98.77%\n",
            "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.062215\n",
            "Train Epoch: 19 [12800/60000 (21%)]\tLoss: 0.085581\n",
            "Train Epoch: 19 [25600/60000 (43%)]\tLoss: 0.089208\n",
            "Train Epoch: 19 [38400/60000 (64%)]\tLoss: 0.098122\n",
            "Train Epoch: 19 [51200/60000 (85%)]\tLoss: 0.133589\n",
            "[19] Test Loss: 0.0352, Accuracy: 98.81%\n",
            "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.261426\n",
            "Train Epoch: 20 [12800/60000 (21%)]\tLoss: 0.229431\n",
            "Train Epoch: 20 [25600/60000 (43%)]\tLoss: 0.056747\n",
            "Train Epoch: 20 [38400/60000 (64%)]\tLoss: 0.105823\n",
            "Train Epoch: 20 [51200/60000 (85%)]\tLoss: 0.061023\n",
            "[20] Test Loss: 0.0354, Accuracy: 98.83%\n",
            "Train Epoch: 21 [0/60000 (0%)]\tLoss: 0.064591\n",
            "Train Epoch: 21 [12800/60000 (21%)]\tLoss: 0.117648\n",
            "Train Epoch: 21 [25600/60000 (43%)]\tLoss: 0.150654\n",
            "Train Epoch: 21 [38400/60000 (64%)]\tLoss: 0.238576\n",
            "Train Epoch: 21 [51200/60000 (85%)]\tLoss: 0.188030\n",
            "[21] Test Loss: 0.0346, Accuracy: 98.92%\n",
            "Train Epoch: 22 [0/60000 (0%)]\tLoss: 0.262279\n",
            "Train Epoch: 22 [12800/60000 (21%)]\tLoss: 0.082272\n",
            "Train Epoch: 22 [25600/60000 (43%)]\tLoss: 0.112726\n",
            "Train Epoch: 22 [38400/60000 (64%)]\tLoss: 0.076479\n",
            "Train Epoch: 22 [51200/60000 (85%)]\tLoss: 0.060693\n",
            "[22] Test Loss: 0.0376, Accuracy: 98.76%\n",
            "Train Epoch: 23 [0/60000 (0%)]\tLoss: 0.188320\n",
            "Train Epoch: 23 [12800/60000 (21%)]\tLoss: 0.160465\n",
            "Train Epoch: 23 [25600/60000 (43%)]\tLoss: 0.168887\n",
            "Train Epoch: 23 [38400/60000 (64%)]\tLoss: 0.093986\n",
            "Train Epoch: 23 [51200/60000 (85%)]\tLoss: 0.134701\n",
            "[23] Test Loss: 0.0348, Accuracy: 98.87%\n",
            "Train Epoch: 24 [0/60000 (0%)]\tLoss: 0.171358\n",
            "Train Epoch: 24 [12800/60000 (21%)]\tLoss: 0.297921\n",
            "Train Epoch: 24 [25600/60000 (43%)]\tLoss: 0.101640\n",
            "Train Epoch: 24 [38400/60000 (64%)]\tLoss: 0.033706\n",
            "Train Epoch: 24 [51200/60000 (85%)]\tLoss: 0.127467\n",
            "[24] Test Loss: 0.0325, Accuracy: 98.95%\n",
            "Train Epoch: 25 [0/60000 (0%)]\tLoss: 0.148559\n",
            "Train Epoch: 25 [12800/60000 (21%)]\tLoss: 0.058518\n",
            "Train Epoch: 25 [25600/60000 (43%)]\tLoss: 0.091853\n",
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            "Train Epoch: 25 [51200/60000 (85%)]\tLoss: 0.228347\n",
            "[25] Test Loss: 0.0343, Accuracy: 98.89%\n",
            "Train Epoch: 26 [0/60000 (0%)]\tLoss: 0.206059\n",
            "Train Epoch: 26 [12800/60000 (21%)]\tLoss: 0.130978\n",
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            "[26] Test Loss: 0.0323, Accuracy: 98.94%\n",
            "Train Epoch: 27 [0/60000 (0%)]\tLoss: 0.135292\n",
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            "[27] Test Loss: 0.0323, Accuracy: 98.94%\n",
            "Train Epoch: 28 [0/60000 (0%)]\tLoss: 0.043982\n",
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            "Train Epoch: 28 [38400/60000 (64%)]\tLoss: 0.045085\n",
            "Train Epoch: 28 [51200/60000 (85%)]\tLoss: 0.086107\n",
            "[28] Test Loss: 0.0331, Accuracy: 98.93%\n",
            "Train Epoch: 29 [0/60000 (0%)]\tLoss: 0.154580\n",
            "Train Epoch: 29 [12800/60000 (21%)]\tLoss: 0.127193\n",
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            "Train Epoch: 29 [51200/60000 (85%)]\tLoss: 0.168236\n",
            "[29] Test Loss: 0.0321, Accuracy: 99.02%\n",
            "Train Epoch: 30 [0/60000 (0%)]\tLoss: 0.090241\n",
            "Train Epoch: 30 [12800/60000 (21%)]\tLoss: 0.328705\n",
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            "Train Epoch: 30 [38400/60000 (64%)]\tLoss: 0.147059\n",
            "Train Epoch: 30 [51200/60000 (85%)]\tLoss: 0.055730\n",
            "[30] Test Loss: 0.0326, Accuracy: 98.92%\n",
            "Train Epoch: 31 [0/60000 (0%)]\tLoss: 0.117487\n",
            "Train Epoch: 31 [12800/60000 (21%)]\tLoss: 0.096325\n",
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            "Train Epoch: 31 [51200/60000 (85%)]\tLoss: 0.106295\n",
            "[31] Test Loss: 0.0308, Accuracy: 98.98%\n",
            "Train Epoch: 32 [0/60000 (0%)]\tLoss: 0.060272\n",
            "Train Epoch: 32 [12800/60000 (21%)]\tLoss: 0.027359\n",
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            "Train Epoch: 32 [51200/60000 (85%)]\tLoss: 0.057197\n",
            "[32] Test Loss: 0.0297, Accuracy: 99.02%\n",
            "Train Epoch: 33 [0/60000 (0%)]\tLoss: 0.102750\n",
            "Train Epoch: 33 [12800/60000 (21%)]\tLoss: 0.144509\n",
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            "Train Epoch: 33 [51200/60000 (85%)]\tLoss: 0.086947\n",
            "[33] Test Loss: 0.0314, Accuracy: 98.97%\n",
            "Train Epoch: 34 [0/60000 (0%)]\tLoss: 0.153573\n",
            "Train Epoch: 34 [12800/60000 (21%)]\tLoss: 0.020875\n",
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            "[34] Test Loss: 0.0311, Accuracy: 99.01%\n",
            "Train Epoch: 35 [0/60000 (0%)]\tLoss: 0.061603\n",
            "Train Epoch: 35 [12800/60000 (21%)]\tLoss: 0.064925\n",
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            "[35] Test Loss: 0.0304, Accuracy: 98.99%\n",
            "Train Epoch: 36 [0/60000 (0%)]\tLoss: 0.123589\n",
            "Train Epoch: 36 [12800/60000 (21%)]\tLoss: 0.061880\n",
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            "[36] Test Loss: 0.0295, Accuracy: 98.95%\n",
            "Train Epoch: 37 [0/60000 (0%)]\tLoss: 0.017727\n",
            "Train Epoch: 37 [12800/60000 (21%)]\tLoss: 0.258544\n",
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            "[37] Test Loss: 0.0303, Accuracy: 98.94%\n",
            "Train Epoch: 38 [0/60000 (0%)]\tLoss: 0.024901\n",
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            "[38] Test Loss: 0.0310, Accuracy: 98.94%\n",
            "Train Epoch: 39 [0/60000 (0%)]\tLoss: 0.130940\n",
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            "[39] Test Loss: 0.0304, Accuracy: 98.97%\n",
            "Train Epoch: 40 [0/60000 (0%)]\tLoss: 0.075963\n",
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            "[40] Test Loss: 0.0295, Accuracy: 98.99%\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}