CIFAR10_pytorch.ipynb

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    {
      "cell_type": "code",
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "#An observation I made while doing this was that the Adam Optimiser requires more hyperparameter tuning than the SGD optimiser."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4115xjlCknZ"
      },
      "source": [
        "import torch\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import torch.optim as optim"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize( (0.5, 0.5, 0.5), (0.5, 0.5, 0.5) )\n",
        "                               ])"
      ],
      "execution_count": 3,
      "outputs": []
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      "source": [
        "# Training and test datasets\n",
        "trainingData = datasets.CIFAR10('data', train=True,\n",
        "                              download=True, transform=transform)\n",
        "testData = datasets.CIFAR10('data', train=False,\n",
        "                             download=True, transform=transform)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "Extracting data/cifar-10-python.tar.gz to data\n",
            "Files already downloaded and verified\n"
          ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uc_3eKzUCknl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24358a22-3df4-47b6-df94-7a38f9dd3ea4"
      },
      "source": [
        "dataIndices = np.arange(len(trainingData))\n",
        "np.random.shuffle(dataIndices)\n",
        "print(f\"Training Images:{len(trainingData)}\")\n",
        "print(f\"Test images: {len(testData)}\")\n",
        "print(dataIndices)\n",
        "#Taking a random 20% of training images for the purpose of Validation\n",
        "validationIndices, trainingIndices = dataIndices[:10000], dataIndices[10000:]\n",
        "print(f\"Images for validation {len(validationIndices)}\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Images:50000\n",
            "Test images: 10000\n",
            "[36771  7369 35524 ... 48317 36180 24438]\n",
            "Images for validation 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWRcTntsCkno",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "577498dc-6ab3-404c-b8f7-539f8299b4c3"
      },
      "source": [
        "indexToClass = {n:l for l, n in trainingData.class_to_idx.items()}\n",
        "#A hash map that has a mapping between the labels and indices\n",
        "\n",
        "for index, label in indexToClass.items():\n",
        "    print(label)\n",
        "#The different classes in alphabetical order\n",
        "\n",
        "def classDistribution(dataset):\n",
        "    freqDict = {l:0 for l,n in dataset.class_to_idx.items()}\n",
        "    #empty frequency dictionary\n",
        "    \n",
        "    for element in dataset:\n",
        "        label = element[1]\n",
        "        label = indexToClass[label]\n",
        "        freqDict[label] += 1\n",
        "\n",
        "    return freqDict\n",
        "\n",
        "print(f\"Frequency of image classes {classDistribution(trainingData)}\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "airplane\n",
            "automobile\n",
            "bird\n",
            "cat\n",
            "deer\n",
            "dog\n",
            "frog\n",
            "horse\n",
            "ship\n",
            "truck\n",
            "Frequency of image classes {'airplane': 5000, 'automobile': 5000, 'bird': 5000, 'cat': 5000, 'deer': 5000, 'dog': 5000, 'frog': 5000, 'horse': 5000, 'ship': 5000, 'truck': 5000}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ny7nBquCknr"
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      "source": [
        "#Creating samplers for training and validation\n",
        "validationSampler = SubsetRandomSampler(validationIndices)\n",
        "trainingSampler = SubsetRandomSampler(trainingIndices)\n",
        "\n",
        "dataLoader_train = torch.utils.data.DataLoader(trainingData, batch_size = 4, sampler = trainingSampler)\n",
        "dataLoader_valid = torch.utils.data.DataLoader(trainingData, batch_size = 4, sampler = validationSampler)  \n",
        "dataLoader_test  = torch.utils.data.DataLoader(testData    , batch_size = 4)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDqK-3w-Cknu"
      },
      "source": [
        "#A utility funtion to un-normalise and convert back from a tensor\n",
        "def imshow(image):\n",
        "    image = (image/2) + 0.5 #as currently it has values in the range [-1,1]\n",
        "    plt.imshow(np.transpose(image, (1, 2, 0)))\n",
        "    \n",
        "#Utility function for correct number of labels\n",
        "def correctLabels(preds, labels):\n",
        "    return preds.argmax(dim = 1).eq(labels).sum().item()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kGsFcLBCkny"
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      "source": [
        "#Defining layers as class attributes after extending the nn.Module base class\n",
        "#We have to call the super class constructor to take advantage of the pytorch nn.Functional module\n",
        "#Using the layers that come in the pytorch library in the class\n",
        "#A CNN with 2 convolutional layers\n",
        "#CNN layers get bigger and the FC Layers get smaller\n",
        "class Network(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Network, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "    \n",
        "    # def __init__(self): #Dubund init\n",
        "    #     super(Network, self).__init__()\n",
        "    #     self.conv1 = nn.Conv2d(in_channels = 3, out_channels = 16, kernel_size = 3, padding = 1)\n",
        "    #     self.conv2 = nn.Conv2d(in_channels = 16, out_channels = 32, kernel_size = 3, padding = 1)\n",
        "    #     self.conv3 = nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1)        \n",
        "        \n",
        "    #     self.fc1 = nn.Linear(in_features = 64 * 4 * 4, out_features = 512)\n",
        "    #     self.fc2 = nn.Linear(in_features = 512, out_features = 64)\n",
        "    #     self.out = nn.Linear(in_features = 64, out_features = 10)\n",
        "    #     self.pool = nn.MaxPool2d(kernel_size = 2, stride = 2) #2x2 kernel for max Pooling\n",
        "    #     # self.dropout = nn.Dropout(0.5) #Hyper parameter\n",
        "        \n",
        "    # def forward(self, t):\n",
        "    #     t = self.pool(F.relu(self.conv1(t)))\n",
        "    #     t = self.pool(F.relu(self.conv2(t)))\n",
        "    #     t = self.pool(F.relu(self.conv3(t)))\n",
        "        \n",
        "    #     #print(t.shape)\n",
        "    #     t = t.view(-1, 64 * 4 * 4)\n",
        "    #     #t = t.view(32 * 4 * 4, -1)\n",
        "    #     #We want 32 * 4 * 4 input features, letting pytorch handle the number of batches\n",
        "        \n",
        "    #     t = self.dropout(t)\n",
        "    #     t = F.relu(self.fc1(t))\n",
        "    #     t = self.dropout(t)\n",
        "    #     t = F.relu(self.fc2(t))\n",
        "    #     t = self.dropout(t)\n",
        "    #     t = self.out(t)\n",
        "    #     #Usually use a softmax layer at the end but the loss that we will be using is \n",
        "    #     #the cross entropy loss which implicity performs the softmax operation\n",
        "        \n",
        "    #     return t\n",
        "    \n",
        "    \n",
        "\n",
        "#    def __repr__(self): #Overriding a python function\n",
        "#        return \"A simple python class\""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
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        "outputId": "2a0bbd89-58fc-43a3-d25e-eb6590b7db66"
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      "source": [
        "Net = Network()\n",
        "print(Net)\n",
        "#We get a nice text description as we have extended the nn.Module class"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Network(\n",
            "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
            "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
            "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
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      "source": [
        "Net.conv1.weight.shape\n",
        "#A tensor dimension has all the information that we need about a layer\n",
        "#It is like packaging all our filters into a tensor\n",
        "#We can see the first axis is representative of our output channels\n",
        "#The second axis is the number of input channels (depth to each of the filters)\n",
        "#The third and four axis represent the height and width of the kernel\n",
        "#On printing we see that this is a rank 4 tensor"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "torch.Size([6, 3, 5, 5])"
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      },
      "source": [
        "Net.fc1.weight.shape\n",
        "#We see that linear layers can be parametrised by flattened, rank 1 tensors.\n",
        "#The input is transformed to the output by a rank 2 tensor of weights (weight tensor)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([120, 400])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7RiDZtaCkn-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "892fb580-f41d-4e8d-8306-408e14e3ae84"
      },
      "source": [
        "for Name, Param in Net.named_parameters():\n",
        "    print(Name, '\\t\\t', Param.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conv1.weight \t\t torch.Size([6, 3, 5, 5])\n",
            "conv1.bias \t\t torch.Size([6])\n",
            "conv2.weight \t\t torch.Size([16, 6, 5, 5])\n",
            "conv2.bias \t\t torch.Size([16])\n",
            "fc1.weight \t\t torch.Size([120, 400])\n",
            "fc1.bias \t\t torch.Size([120])\n",
            "fc2.weight \t\t torch.Size([84, 120])\n",
            "fc2.bias \t\t torch.Size([84])\n",
            "fc3.weight \t\t torch.Size([10, 84])\n",
            "fc3.bias \t\t torch.Size([10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkWtEew4CkoB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fde6314d-0bd3-40e1-deea-4f1ff0d90abc"
      },
      "source": [
        "print(Net.conv1.weight.grad)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzRZ1lBNCkoF"
      },
      "source": [
        "def weights_init(model):\n",
        "    for m in model.modules():\n",
        "        if isinstance(m, nn.Linear):\n",
        "            # initialize the weight tensor, here we use a normal distribution\n",
        "            m.weight.data = torch.randn(m.weight.data.shape)\n",
        "            #print(m.weight.data.shape)\n",
        "            #exit()\n",
        "        if isinstance(m, nn.Conv2d):\n",
        "            m.weight.data = torch.randn(m.weight.data.shape)\n",
        "\n",
        "weights_init(Net)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5CM2y_GCkoI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "eee0da0c-560e-443a-bcbc-13575d4c0e47"
      },
      "source": [
        "optimizer = optim.SGD(Net.parameters(), lr = 0.1)\n",
        "\n",
        "#1 epoch is training the network over all the batches\n",
        "maxEpoch = 500\n",
        "\n",
        "for epoch in range(1, 1 + maxEpoch):\n",
        "    trainingLoss = 0\n",
        "    validationLoss = 0\n",
        "    correctClassifications = 0\n",
        "\n",
        "    for batch in dataLoader_train:        \n",
        "        images, labels = batch\n",
        "\n",
        "        predictions = Net(images)\n",
        "        loss = F.cross_entropy(predictions, labels)\n",
        "\n",
        "        optimizer.zero_grad() #As pytorch adds to the gradients, we need to initalise it to zero\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        trainingLoss += loss.item()\n",
        "        correctClassifications += correctLabels(predictions, labels)\n",
        "\n",
        "    # for tester in dataLoader_valid:\n",
        "    #     validationImage, validationLabel = tester\n",
        "    #     predictions = Net(validationImage)\n",
        "    #     loss2 = F.cross_entropy(predictions, labels)\n",
        "    #     validationLoss += loss2.item()\n",
        " \n",
        "\n",
        "    print(f\"epoch {epoch}, total correct: {correctClassifications}, loss: {trainingLoss}, accuracy: ~{np.floor(100 * (correctClassifications / len(trainingData)))}\")\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, total correct: 4021, loss: 23079.83822798729, accuracy: ~8.0\n",
            "epoch 2, total correct: 3895, loss: 23086.77760028839, accuracy: ~7.0\n",
            "epoch 3, total correct: 3972, loss: 23081.49997997284, accuracy: ~7.0\n",
            "epoch 4, total correct: 4122, loss: 23082.018139362335, accuracy: ~8.0\n",
            "epoch 5, total correct: 3998, loss: 23083.186648607254, accuracy: ~7.0\n",
            "epoch 6, total correct: 3994, loss: 23082.97275543213, accuracy: ~7.0\n",
            "epoch 7, total correct: 3997, loss: 23078.023597955704, accuracy: ~7.0\n",
            "epoch 8, total correct: 3994, loss: 23086.685501098633, accuracy: ~7.0\n",
            "epoch 9, total correct: 4018, loss: 23083.770142793655, accuracy: ~8.0\n",
            "epoch 10, total correct: 4027, loss: 23079.61675429344, accuracy: ~8.0\n",
            "epoch 11, total correct: 4029, loss: 23082.91792654991, accuracy: ~8.0\n",
            "epoch 12, total correct: 3948, loss: 23083.479597330093, accuracy: ~7.0\n",
            "epoch 13, total correct: 4059, loss: 23086.380138397217, accuracy: ~8.0\n",
            "epoch 14, total correct: 4014, loss: 23085.42527461052, accuracy: ~8.0\n",
            "epoch 15, total correct: 4032, loss: 23083.91813826561, accuracy: ~8.0\n",
            "epoch 16, total correct: 3955, loss: 23084.394935131073, accuracy: ~7.0\n",
            "epoch 17, total correct: 4072, loss: 23078.26538324356, accuracy: ~8.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-1a8d2ddb2a71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mtrainingLoss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mcorrectClassifications\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcorrectLabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;31m# for tester in dataLoader_valid:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-22ab51428128>\u001b[0m in \u001b[0;36mcorrectLabels\u001b[0;34m(preds, labels)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#Utility function for correct number of labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcorrectLabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5vRV2mtCkoL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a58473fa-16ed-410c-d433-b74c2268eee0"
      },
      "source": [
        "correctClassifications / len(trainingData)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07472"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPJynF0hCkoO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58b8350d-c1aa-4827-d338-560973705be2"
      },
      "source": [
        "print(len(trainingData))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQL_IzVjCkoQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "133a5760-a751-4868-94ee-3259a04a3894"
      },
      "source": [
        "#Testing accuracy\n",
        "totalLoss = 0.0\n",
        "correctClassification = list(0. for i in range(10))\n",
        "totalClassification = list(0. for i in range(10))\n",
        "\n",
        "for batch in dataLoader_test:\n",
        "    data, labels = batch\n",
        "    predictions = Net(data)\n",
        "    loss = F.cross_entropy(predictions, labels)\n",
        "    print(loss.item())\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2838497161865234\n",
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            "2.2838497161865234\n",
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpQVJDucCkoT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}