4-2.mlp.ipynb

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            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tjrudrnr2/A.I/blob/main/4-2.mlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcyiVUeZr2cJ"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/sjchoi86/upstage-basic-deeplearning/blob/main/notebook/mlp.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/sjchoi86/upstage-basic-deeplearning/blob/main/notebook/mlp.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View Source</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0aqRluNsI38"
      },
      "source": [
        "# Multilayer Perceptron (MLP)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXxbX-Diq9rq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4951a55-0a69-4e3c-d31f-13d1ba452a72"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "print (\"PyTorch version:[%s].\"%(torch.__version__))\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print (\"device:[%s].\"%(device))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch version:[1.10.0+cu111].\n",
            "device:[cuda:0].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HWvC0ZltF8a"
      },
      "source": [
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uaokkwJwsN5I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744,
          "referenced_widgets": [
            "90e70067338f4ff3b40c86445cca73fa",
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            "0763a4e280324933b34345dbcbeb0316",
            "2870f8a9df5048c68ecb4995fc86e0d2",
            "2e2ef7cbbd0643619c0a8c9ded0f7f32",
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            "92c9f31765e34e8b880b350e964de0f2",
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            "c53b9a955fd74da7a88a2510080074cc",
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            "371aa4ecdc934ae0a794f4eb4576f453",
            "cae5fe9fd943486a8fd79a1942bd1fa4",
            "efc415a5d0b84ec5afbc653d1b4a4c97",
            "6fba0d99d7f840129be78c0a2b16e7d9",
            "7179f01633d44cb889c9231a9fea24f0",
            "9b141dcc42a445648aa5c397842db280",
            "925df762cdff41ca91901d14ec4b7dbc",
            "c69fcbea2a504c4499043a046c301bca",
            "ab7cfb3b403143cfacbebb7b07d111a2",
            "edb341adbc13487aac69f1df9cf1916f",
            "e3ad55de6c1d4b36b45622e86f073c7b",
            "75c33d04d8b9407fb6719623ef9aeb16",
            "df3d165e4a844361b3783ea7c7b2f0c1"
          ]
        },
        "outputId": "0637d9e8-f0cd-4b2b-bb71-a298462a465b"
      },
      "source": [
        "from torchvision import datasets,transforms\n",
        "mnist_train = datasets.MNIST(root='./data/',train=True,transform=transforms.ToTensor(),download=True)\n",
        "mnist_test = datasets.MNIST(root='./data/',train=False,transform=transforms.ToTensor(),download=True)\n",
        "print (\"mnist_train:\\n\",mnist_train,\"\\n\")\n",
        "print (\"mnist_test:\\n\",mnist_test,\"\\n\")\n",
        "print (\"Done.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90e70067338f4ff3b40c86445cca73fa",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "85423cf9357b4bd787ba63391d02823c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b53ac4a22674fad9bc51baaf2336392",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efc415a5d0b84ec5afbc653d1b4a4c97",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "mnist_train:\n",
            " Dataset MNIST\n",
            "    Number of datapoints: 60000\n",
            "    Root location: ./data/\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: ToTensor() \n",
            "\n",
            "mnist_test:\n",
            " Dataset MNIST\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./data/\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: ToTensor() \n",
            "\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4lP2QQoBXGw"
      },
      "source": [
        "### Data Iterator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0TKrHSCBWzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d779564-5713-41e8-ec45-ccb8d4dd4636"
      },
      "source": [
        "BATCH_SIZE = 256\n",
        "train_iter = torch.utils.data.DataLoader(mnist_train,batch_size=BATCH_SIZE,shuffle=True,num_workers=1)\n",
        "test_iter = torch.utils.data.DataLoader(mnist_test,batch_size=BATCH_SIZE,shuffle=True,num_workers=1)\n",
        "print (\"Done.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "081T7_3lvk-N"
      },
      "source": [
        "### Define the MLP model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4mWpPXouPCR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88768a3b-e84a-4404-d780-acb1427f572f"
      },
      "source": [
        "class MultiLayerPerceptronClass(nn.Module):\n",
        "    \"\"\"\n",
        "        Multilayer Perceptron (MLP) Class\n",
        "    \"\"\"\n",
        "    def __init__(self,name='mlp',xdim=784,hdim=256,ydim=10):\n",
        "        super(MultiLayerPerceptronClass,self).__init__()\n",
        "        self.name = name\n",
        "        self.xdim = xdim\n",
        "        self.hdim = hdim\n",
        "        self.ydim = ydim\n",
        "        self.lin_1 = nn.Linear(xdim, hdim) # xdim에서 hdim으로 \n",
        "        self.lin_2 = nn.Linear(hdim, ydim) # hdim에서 ydim으로\n",
        "        self.init_param() # initialize parameters\n",
        "        \n",
        "    def init_param(self):\n",
        "        nn.init.kaiming_normal_(self.lin_1.weight)\n",
        "        nn.init.zeros_(self.lin_1.bias)\n",
        "        nn.init.kaiming_normal_(self.lin_2.weight)\n",
        "        nn.init.zeros_(self.lin_2.bias)\n",
        "\n",
        "    def forward(self,x):\n",
        "        net = x\n",
        "        net = self.lin_1(net)\n",
        "        net = F.relu(net)\n",
        "        net = self.lin_2(net)\n",
        "        return net\n",
        "\n",
        "M = MultiLayerPerceptronClass(name='mlp',xdim=784,hdim=256,ydim=10).to(device)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "optm = optim.Adam(M.parameters(),lr=1e-3)\n",
        "print (\"Done.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrPPFQi56NDk"
      },
      "source": [
        "### Simple Forward Path of the MLP Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rOz8a1Gw1Xi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a3e873d-6ca2-4908-bf5d-000d1d78301b"
      },
      "source": [
        "x_numpy = np.random.rand(2,784)\n",
        "x_torch = torch.from_numpy(x_numpy).float().to(device)\n",
        "y_torch = M.forward(x_torch) # forward path\n",
        "# y_torch = M(x_torch)  ---> forward 안하고 이렇게 해도 돌아감.\n",
        "y_numpy = y_torch.detach().cpu().numpy() # torch tensor to numpy array\n",
        "print (\"x_numpy:\\n\",x_numpy)\n",
        "print (\"x_torch:\\n\",x_torch)\n",
        "print (\"y_torch:\\n\",y_torch)\n",
        "print (\"y_numpy:\\n\",y_numpy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_numpy:\n",
            " [[0.08961081 0.18147166 0.82284339 ... 0.84840581 0.90093389 0.86800257]\n",
            " [0.79697043 0.9744219  0.98733082 ... 0.88764824 0.73242556 0.59012406]]\n",
            "x_torch:\n",
            " tensor([[0.0896, 0.1815, 0.8228,  ..., 0.8484, 0.9009, 0.8680],\n",
            "        [0.7970, 0.9744, 0.9873,  ..., 0.8876, 0.7324, 0.5901]],\n",
            "       device='cuda:0')\n",
            "y_torch:\n",
            " tensor([[-0.6431,  1.3221, -1.3152, -0.1687, -0.2975, -0.2779, -1.0333, -0.9139,\n",
            "         -1.6322,  0.6329],\n",
            "        [-1.1973,  0.4971, -1.7976,  0.7908,  0.0111,  0.1825, -0.1725, -0.7619,\n",
            "         -1.5564,  1.4570]], device='cuda:0', grad_fn=<AddmmBackward0>)\n",
            "y_numpy:\n",
            " [[-0.64311135  1.3221313  -1.3152044  -0.16869058 -0.29749155 -0.27788705\n",
            "  -1.0333161  -0.9139252  -1.6321993   0.63292193]\n",
            " [-1.1973425   0.4970888  -1.7976186   0.7908228   0.01110302  0.1825141\n",
            "  -0.17250922 -0.7619058  -1.5563928   1.4570248 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzd12JKl7NpX"
      },
      "source": [
        "### Check Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Rmd2r_kw1s0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "715ec3b5-e1a7-46b7-b1b7-ce7a46da9d8e"
      },
      "source": [
        "np.set_printoptions(precision=3)\n",
        "n_param = 0\n",
        "for p_idx,(param_name,param) in enumerate(M.named_parameters()):\n",
        "    param_numpy = param.detach().cpu().numpy()\n",
        "    n_param += len(param_numpy.reshape(-1))\n",
        "    print (\"[%d] name:[%s] shape:[%s].\"%(p_idx,param_name,param_numpy.shape))\n",
        "    print (\"    val:%s\"%(param_numpy.reshape(-1)[:5]))\n",
        "print (\"Total number of parameters:[%s].\"%(format(n_param,',d')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0] name:[lin_1.weight] shape:[(256, 784)].\n",
            "    val:[-0.04  -0.05   0.021 -0.026 -0.009]\n",
            "[1] name:[lin_1.bias] shape:[(256,)].\n",
            "    val:[0. 0. 0. 0. 0.]\n",
            "[2] name:[lin_2.weight] shape:[(10, 256)].\n",
            "    val:[ 0.093  0.097  0.021  0.134 -0.007]\n",
            "[3] name:[lin_2.bias] shape:[(10,)].\n",
            "    val:[0. 0. 0. 0. 0.]\n",
            "Total number of parameters:[203,530].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVaqrcXUA5EB"
      },
      "source": [
        "### Evaluation Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxXyYXH75Veq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e6fc3ef-5ce2-4c31-b6dd-a671be48187b"
      },
      "source": [
        "def func_eval(model,data_iter,device):\n",
        "    with torch.no_grad():  # gradient를 계산하지 않겠다.\n",
        "        model.eval() # evaluate (affects DropOut and BN)\n",
        "        n_total,n_correct = 0,0\n",
        "        for batch_in,batch_out in data_iter:\n",
        "            y_trgt = batch_out.to(device)\n",
        "            model_pred = model(batch_in.view(-1,28*28).to(device))\n",
        "            _,y_pred = torch.max(model_pred.data,1)\n",
        "            n_correct += (y_pred==y_trgt).sum().item()\n",
        "            n_total += batch_in.size(0)\n",
        "        val_accr = (n_correct/n_total)\n",
        "        model.train() # back to train mode \n",
        "    return val_accr\n",
        "print (\"Done\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmmJjAFKKOrB"
      },
      "source": [
        "### Initial Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNlGD1TlA4T8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b641bf9-aa73-4c78-aa61-bcc9ba627f5f"
      },
      "source": [
        "M.init_param() # initialize parameters\n",
        "train_accr = func_eval(M,train_iter,device)\n",
        "test_accr = func_eval(M,test_iter,device)\n",
        "print (\"train_accr:[%.3f] test_accr:[%.3f].\"%(train_accr,test_accr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_accr:[0.072] test_accr:[0.071].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yT_r2wMZLjTm"
      },
      "source": [
        "### Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AS5BdrMw1E9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d39c376c-3603-40da-c3ae-cfdd3b3db899"
      },
      "source": [
        "print (\"Start training.\")\n",
        "M.init_param() # initialize parameters\n",
        "M.train()\n",
        "EPOCHS,print_every = 10,1\n",
        "for epoch in range(EPOCHS):\n",
        "    loss_val_sum = 0\n",
        "    for batch_in,batch_out in train_iter:\n",
        "        # Forward path\n",
        "        y_pred = M.forward(batch_in.view(-1, 28*28).to(device))\n",
        "        loss_out = loss(y_pred,batch_out.to(device))\n",
        "        # Update\n",
        "        optm.zero_grad()      # reset gradient \n",
        "        loss_out.backward()      # backpropagate\n",
        "        optm.step()      # optimizer update\n",
        "        loss_val_sum += loss_out\n",
        "    loss_val_avg = loss_val_sum/len(train_iter)\n",
        "    # Print\n",
        "    if ((epoch%print_every)==0) or (epoch==(EPOCHS-1)):\n",
        "        train_accr = func_eval(M,train_iter,device)\n",
        "        test_accr = func_eval(M,test_iter,device)\n",
        "        print (\"epoch:[%d] loss:[%.3f] train_accr:[%.3f] test_accr:[%.3f].\"%\n",
        "               (epoch,loss_val_avg,train_accr,test_accr))\n",
        "print (\"Done\")        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start training.\n",
            "epoch:[0] loss:[0.378] train_accr:[0.945] test_accr:[0.944].\n",
            "epoch:[1] loss:[0.165] train_accr:[0.961] test_accr:[0.959].\n",
            "epoch:[2] loss:[0.118] train_accr:[0.974] test_accr:[0.968].\n",
            "epoch:[3] loss:[0.090] train_accr:[0.980] test_accr:[0.972].\n",
            "epoch:[4] loss:[0.072] train_accr:[0.983] test_accr:[0.973].\n",
            "epoch:[5] loss:[0.058] train_accr:[0.987] test_accr:[0.976].\n",
            "epoch:[6] loss:[0.048] train_accr:[0.990] test_accr:[0.977].\n",
            "epoch:[7] loss:[0.041] train_accr:[0.993] test_accr:[0.978].\n",
            "epoch:[8] loss:[0.034] train_accr:[0.992] test_accr:[0.977].\n",
            "epoch:[9] loss:[0.029] train_accr:[0.995] test_accr:[0.977].\n",
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHQIhg-aNok5"
      },
      "source": [
        "### Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52zoFQxdMWRU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "da6100f3-4927-439d-86c1-e1e0ff9fee06"
      },
      "source": [
        "n_sample = 25\n",
        "sample_indices = np.random.choice(len(mnist_test.targets), n_sample, replace=False)\n",
        "test_x = mnist_test.data[sample_indices]\n",
        "test_y = mnist_test.targets[sample_indices]\n",
        "with torch.no_grad():\n",
        "    y_pred = M.forward(test_x.view(-1, 28*28).type(torch.float).to(device)/255.)\n",
        "y_pred = y_pred.argmax(axis=1)\n",
        "plt.figure(figsize=(10,10))\n",
        "for idx in range(n_sample):\n",
        "    plt.subplot(5, 5, idx+1)\n",
        "    plt.imshow(test_x[idx], cmap='gray')\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Pred:%d, Label:%d\"%(y_pred[idx],test_y[idx]))\n",
        "plt.show()    \n",
        "print (\"Done\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 569,
              "height": 573
            },
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ikf5C7uV_ExD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}