cifar10_tutorial.ipynb

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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/insop/ML_crash_course/blob/main/cifar10_tutorial.ipynb)\n"
      ],
      "metadata": {
        "id": "Yi9ireae05r1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This note book is from [pytorch tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)"
      ],
      "metadata": {
        "id": "kAlicAJq07ZE"
      }
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      "cell_type": "markdown",
      "metadata": {
        "id": "llUb7xfLx0NV"
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      "source": [
        "\n",
        "Training a Classifier\n",
        "=====================\n",
        "\n",
        "This is it. You have seen how to define neural networks, compute loss and make\n",
        "updates to the weights of the network.\n",
        "\n",
        "Now you might be thinking,\n",
        "\n",
        "What about data?\n",
        "----------------\n",
        "\n",
        "Generally, when you have to deal with image, text, audio or video data,\n",
        "you can use standard python packages that load data into a numpy array.\n",
        "Then you can convert this array into a ``torch.*Tensor``.\n",
        "\n",
        "-  For images, packages such as Pillow, OpenCV are useful\n",
        "-  For audio, packages such as scipy and librosa\n",
        "-  For text, either raw Python or Cython based loading, or NLTK and\n",
        "   SpaCy are useful\n",
        "\n",
        "Specifically for vision, we have created a package called\n",
        "``torchvision``, that has data loaders for common datasets such as\n",
        "ImageNet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
        "``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
        "\n",
        "This provides a huge convenience and avoids writing boilerplate code.\n",
        "\n",
        "For this tutorial, we will use the CIFAR10 dataset.\n",
        "It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
        "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
        "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
        "\n",
        ".. figure:: /_static/img/cifar10.png\n",
        "   :alt: cifar10\n",
        "\n",
        "   cifar10\n",
        "\n",
        "\n",
        "Training an image classifier\n",
        "----------------------------\n",
        "\n",
        "We will do the following steps in order:\n",
        "\n",
        "1. Load and normalize the CIFAR10 training and test datasets using\n",
        "   ``torchvision``\n",
        "2. Define a Convolutional Neural Network\n",
        "3. Define a loss function\n",
        "4. Train the network on the training data\n",
        "5. Test the network on the test data\n",
        "\n",
        "1. Load and normalize CIFAR10\n",
        "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
        "\n",
        "Using ``torchvision``, it’s extremely easy to load CIFAR10.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZPwlBWOx0NW"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Idg17qHJx0NW"
      },
      "source": [
        "The output of torchvision datasets are PILImage images of range [0, 1].\n",
        "We transform them to Tensors of normalized range [-1, 1].\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31BD9cohx0NW"
      },
      "source": [
        "<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
        "    the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "batch_size = 4\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GJFLflNx0NX"
      },
      "source": [
        "Let us show some of the training images, for fun.\n",
        "\n"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "frog  dog   truck car  \n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# functions to show an image\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMD67oKLx0NY"
      },
      "source": [
        "2. Define a Convolutional Neural Network\n",
        "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
        "Copy the neural network from the Neural Networks section before and modify it to\n",
        "take 3-channel images (instead of 1-channel images as it was defined).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sPK78jjix0NY"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__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 = torch.flatten(x, 1) # flatten all dimensions except batch\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# train on the GPU if availble\n",
        "\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
        "\n",
        "print(device)\n",
        "\n",
        "net = Net()\n",
        "net.to(device)\n",
        "\n",
        "print(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fz1GTiJWzY3m",
        "outputId": "4803f680-e004-4dbe-8752-090444bb7658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Net(\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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-iOKUnMDx0NY"
      },
      "source": [
        "3. Define a Loss function and optimizer\n",
        "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
        "Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q9HDWXE5x0NY"
      },
      "outputs": [],
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5psvU1ax0NY"
      },
      "source": [
        "4. Train the network\n",
        "^^^^^^^^^^^^^^^^^^^^\n",
        "\n",
        "This is when things start to get interesting.\n",
        "We simply have to loop over our data iterator, and feed the inputs to the\n",
        "network and optimize.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbyidmn0x0NZ",
        "outputId": "dbe0c532-8788-49d0-8767-7767ca5f2b35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 1.539\n",
            "[1,  4000] loss: 1.475\n",
            "[1,  6000] loss: 1.455\n"
          ]
        }
      ],
      "source": [
        "for epoch in range(2):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.item()\n",
        "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
        "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKqtl_Wxx0NZ"
      },
      "source": [
        "Let's quickly save our trained model:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2dHKH7EUx0NZ"
      },
      "outputs": [],
      "source": [
        "PATH = './cifar_net.pth'\n",
        "torch.save(net.state_dict(), PATH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAPBHiOIx0NZ"
      },
      "source": [
        "See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\n",
        "for more details on saving PyTorch models.\n",
        "\n",
        "5. Test the network on the test data\n",
        "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
        "\n",
        "We have trained the network for 2 passes over the training dataset.\n",
        "But we need to check if the network has learnt anything at all.\n",
        "\n",
        "We will check this by predicting the class label that the neural network\n",
        "outputs, and checking it against the ground-truth. If the prediction is\n",
        "correct, we add the sample to the list of correct predictions.\n",
        "\n",
        "Okay, first step. Let us display an image from the test set to get familiar.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "kG_RLhDZx0NZ",
        "outputId": "10dbf69a-1458-4f72-9766-0c5ed016e0d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GroundTruth:  cat   ship  ship  plane\n"
          ]
        }
      ],
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zM3Bd9_Cx0NZ"
      },
      "source": [
        "Next, let's load back in our saved model (note: saving and re-loading the model\n",
        "wasn't necessary here, we only did it to illustrate how to do so):\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNMCeDnex0NZ",
        "outputId": "7eda1e75-7b3f-4cdf-fa14-ccd99362fbb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "net = Net()\n",
        "net.load_state_dict(torch.load(PATH))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzjjUU1hx0NZ"
      },
      "source": [
        "Okay, now let us see what the neural network thinks these examples above are:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i7jWJWsSx0NZ"
      },
      "outputs": [],
      "source": [
        "outputs = net(images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEfUQREDx0Na"
      },
      "source": [
        "The outputs are energies for the 10 classes.\n",
        "The higher the energy for a class, the more the network\n",
        "thinks that the image is of the particular class.\n",
        "So, let's get the index of the highest energy:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ej0isI-kx0Na",
        "outputId": "de864187-fa4a-4748-a725-5b3765044241"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted:  cat   ship  plane plane\n"
          ]
        }
      ],
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'\n",
        "                              for j in range(4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3bSdQyBx0Na"
      },
      "source": [
        "The results seem pretty good.\n",
        "\n",
        "Let us look at how the network performs on the whole dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QEU0pkax0Na",
        "outputId": "76fb6331-8233-4e72-cb38-fca65f2daeab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 54 %\n"
          ]
        }
      ],
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = net(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VyflFM2x0Na"
      },
      "source": [
        "That looks way better than chance, which is 10% accuracy (randomly picking\n",
        "a class out of 10 classes).\n",
        "Seems like the network learnt something.\n",
        "\n",
        "Hmmm, what are the classes that performed well, and the classes that did\n",
        "not perform well:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L9q0j7dux0Na"
      },
      "outputs": [],
      "source": [
        "# prepare to count predictions for each class\n",
        "correct_pred = {classname: 0 for classname in classes}\n",
        "total_pred = {classname: 0 for classname in classes}\n",
        "\n",
        "# again no gradients needed\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predictions = torch.max(outputs, 1)\n",
        "        # collect the correct predictions for each class\n",
        "        for label, prediction in zip(labels, predictions):\n",
        "            if label == prediction:\n",
        "                correct_pred[classes[label]] += 1\n",
        "            total_pred[classes[label]] += 1\n",
        "\n",
        "\n",
        "# print accuracy for each class\n",
        "for classname, correct_count in correct_pred.items():\n",
        "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7-6Kr_6x0Na"
      },
      "source": [
        "Okay, so what next?\n",
        "\n",
        "How do we run these neural networks on the GPU?\n",
        "\n",
        "Training on GPU\n",
        "----------------\n",
        "Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
        "net onto the GPU.\n",
        "\n",
        "Let's first define our device as the first visible cuda device if we have\n",
        "CUDA available:\n",
        "\n"
      ]
    },
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        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
        "\n",
        "print(device)"
      ]
    },
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      "source": [
        "The rest of this section assumes that ``device`` is a CUDA device.\n",
        "\n",
        "Then these methods will recursively go over all modules and convert their\n",
        "parameters and buffers to CUDA tensors:\n",
        "\n",
        ".. code:: python\n",
        "\n",
        "    net.to(device)\n",
        "\n",
        "\n",
        "Remember that you will have to send the inputs and targets at every step\n",
        "to the GPU too:\n",
        "\n",
        ".. code:: python\n",
        "\n",
        "        inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "Why don't I notice MASSIVE speedup compared to CPU? Because your network\n",
        "is really small.\n",
        "\n",
        "**Exercise:** Try increasing the width of your network (argument 2 of\n",
        "the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\n",
        "they need to be the same number), see what kind of speedup you get.\n",
        "\n",
        "**Goals achieved**:\n",
        "\n",
        "- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
        "- Train a small neural network to classify images\n",
        "\n",
        "Training on multiple GPUs\n",
        "-------------------------\n",
        "If you want to see even more MASSIVE speedup using all of your GPUs,\n",
        "please check out :doc:`data_parallel_tutorial`.\n",
        "\n",
        "Where do I go next?\n",
        "-------------------\n",
        "\n",
        "-  :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n",
        "-  `Train a state-of-the-art ResNet network on imagenet`_\n",
        "-  `Train a face generator using Generative Adversarial Networks`_\n",
        "-  `Train a word-level language model using Recurrent LSTM networks`_\n",
        "-  `More examples`_\n",
        "-  `More tutorials`_\n",
        "-  `Discuss PyTorch on the Forums`_\n",
        "-  `Chat with other users on Slack`_\n",
        "\n",
        "\n"
      ]
    },
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      "execution_count": null,
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        "id": "GFppvQsXx0Na"
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      "source": [
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