HandwrittenDigitRecognition.ipynb

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  "nbformat_minor": 0,                                                                                                                                                                                                                                                                                                          
  "metadata": {                                                                                                                                                                                                                                                                                                                  
    "colab": {                                                                                                                                                                                                                                                                                                                  
      "provenance": []                                                                                                                                                                                                                                                                   
    },                                                                                                                                                                                                                                                                                   
    "kernelspec": {                                                                                                                                                                                                                                                  
      "name": "python3",                                                                                                                                                                                                                                              
      "display_name": "Python 3"                                                                                                                                                                                                                                                             
    },                                                                                                                                                                                                                                                                                      
    "language_info": {                                                                                                                                                                                                                                         
      "name": "python"                                                                                                                              
    }                                                                                                                                                                      
  },                                                                                                                                                     
  "cells": [    
    {                                                                                                                                                 
      "cell_type": "code",                                                                                                                                                   
      "source": [                                                                                                                                                                                                    
        "!pip3 install torch torchvision matplotlib"                                                                                                                                                     
      ],                                                                                                                                                                                       
      "metadata": {                                                                                                                                                                                              
        "colab": {                                                                                                                                                                                                                                                
          "base_uri": "https://localhost:8080/"                                                                                                                                                                            
        },                                                                                                                                  
        "id": "jN-015ckWzdL",                                                                                                                                                                                                     
        "outputId": "4dfd5c1b-9d36-4d7c-b058-fe2766c1a469"                                                                                                                                                 
      },                                                                                                                                                                            
      "execution_count": null,                                                                                          
      "outputs": [                                       
        {                                                                                
          "output_type": "stream",                                                    
          "name": "stdout",                                                                                   
          "text": [                                                                                                                                                                                                                            
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",                                         
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",                                    
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",                                    
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor"
      ],
      "metadata": {
        "id": "Msyi5p-WXOtW"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = datasets.MNIST(\n",
        "    root = 'data',\n",
        "    train = True,\n",
        "    transform = ToTensor(),\n",
        "    download = True\n",
        ")\n",
        "test_data = datasets.MNIST(\n",
        "    root = 'data',\n",
        "    train = False,\n",
        "    transform = ToTensor(),\n",
        "    download = True\n",
        ")"
      ],
      "metadata": {
        "id": "ESw0s8bkYW6X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73db34fe-7ede-45b4-8fcd-17b64a2e1ca0"
      },
      "execution_count": 2,
      "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": "stream",
          "name": "stderr",   
          "text": [   
            "100%|██████████| 9912422/9912422 [00:00<00:00, 110705899.45it/s]\n"
          ]                             
        },  
        {                                 
          "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 59177183.11it/s]\n"
          ]
        },
        {
          "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 28904976.52it/s]\n"
          ]
        },
        {
          "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 9597243.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8Y_p56eZ_67",
        "outputId": "963626e7-1d01-40bf-cd56-3766b606babc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MNIST\n",
              "    Number of datapoints: 60000\n",
              "    Root location: data\n",
              "    Split: Train\n",
              "    StandardTransform\n",
              "Transform: ToTensor()"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LmmOnfGaDhK",
        "outputId": "4cead419-33ea-4cc4-e214-1f0914100310"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MNIST\n",
              "    Number of datapoints: 10000\n",
              "    Root location: data\n",
              "    Split: Test\n",
              "    StandardTransform\n",
              "Transform: ToTensor()"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTkr_a-BaM1o",
        "outputId": "bc02e0b0-1ad1-42e2-d590-a37a71b96452"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2oGOPCIajgR",
        "outputId": "06a1930f-6deb-40e6-e224-7ecf5157460b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets.size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dv-u_L8nazfC",
        "outputId": "711acfd8-d060-4cc3-9359-c046cc785628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UhQWZdxbAOJ",
        "outputId": "15a77bad-6019-4b5b-bb28-4c55d0410a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5, 0, 4,  ..., 5, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "loaders = {\n",
        "    'train': DataLoader(train_data,\n",
        "                        batch_size=100,\n",
        "                        shuffle=True,\n",
        "                        num_workers=1),\n",
        "    'test': DataLoader( test_data,\n",
        "                        batch_size=100,\n",
        "                        shuffle=True,\n",
        "                        num_workers=1)\n",
        "}"
      ],
      "metadata": {
        "id": "ty43mRuObMSp"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYxnamoudQDD",
        "outputId": "4363181f-deed-4a89-b16f-49715a501394"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': <torch.utils.data.dataloader.DataLoader at 0x7f221e3e7970>,\n",
              " 'test': <torch.utils.data.dataloader.DataLoader at 0x7f221e3e7460>}"
            ]
          },
          "metadata": {}, 
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "class CNN(nn.Module):\n",
        "\n",
        "  def __init__(self):\n",
        "    super(CNN, self).__init__()\n",
        "\n",
        "    self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
        "    self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "    self.conv2_drop = nn.Dropout2d()\n",
        "    self.fc1 = nn.Linear(320, 50)\n",
        "    self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "    x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "    x = x.view(-1, 320)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.dropout(x, training=self.training)\n",
        "    x = self.fc2(x)\n",
        "\n",
        "    return F.softmax(x, dim=1)"
      ],
      "metadata": {
        "id": "eThfmbP7dTkv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Assurez-vous que l'objet 'loaders' est correctement défini (il semble manquer dans le version du code que vous avez partagé).\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model = CNN().to(device)\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "def train(epoch):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(loaders['train']):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = loss_fn(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % 20 == 0:\n",
        "            print(f'Train Epoch: {epoch} [{batch_idx * len(data)}/{len(loaders[\"train\"].dataset)} ({100. * batch_idx / len(loaders[\"train\"]):.0f}%)]\\t{loss.item():.6f}')\n",
        "\n",
        "def test():\n",
        "    model.eval()\n",
        "\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for data, target in loaders['test']:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += loss_fn(output, target).item()\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(loaders['test'].dataset)  # Correction : 'len(loaders[\"test\"].dataset)' au lieu de 'len(loaders[\"test\".dataset])'\n",
        "    print(f'\\nTest set: Average loss: {test_loss:.4f}, Accuracy {correct}/{len(loaders[\"test\"].dataset)} ({100. * correct / len(loaders[\"test\"].dataset):.0f}%)\\n')\n"
      ],
      "metadata": {
        "id": "AkMn9hbH64zz"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(1,11):\n",
        "  train(epoch)\n",
        "  test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2rA17nv93e7",
        "outputId": "18cc8813-8be2-43f1-88c9-80a5cfefbdfb"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",  
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\t2.301795\n",
            "Train Epoch: 1 [2000/60000 (3%)]\t2.287879\n",
            "Train Epoch: 1 [4000/60000 (7%)]\t2.196730\n",
            "Train Epoch: 1 [6000/60000 (10%)]\t2.047088\n",
            "Train Epoch: 1 [8000/60000 (13%)]\t1.850245\n",
            "Train Epoch: 1 [10000/60000 (17%)]\t1.808692\n",
            "Train Epoch: 1 [12000/60000 (20%)]\t1.786425\n",
            "Train Epoch: 1 [14000/60000 (23%)]\t1.831316\n",
            "Train Epoch: 1 [16000/60000 (27%)]\t1.730146\n",
            "Train Epoch: 1 [18000/60000 (30%)]\t1.640847\n",
            "Train Epoch: 1 [20000/60000 (33%)]\t1.643742\n",
            "Train Epoch: 1 [22000/60000 (37%)]\t1.663372\n",
            "Train Epoch: 1 [24000/60000 (40%)]\t1.684792\n",
            "Train Epoch: 1 [26000/60000 (43%)]\t1.635756\n",
            "Train Epoch: 1 [28000/60000 (47%)]\t1.636644\n",
            "Train Epoch: 1 [30000/60000 (50%)]\t1.626857\n",
            "Train Epoch: 1 [32000/60000 (53%)]\t1.673117\n",
            "Train Epoch: 1 [34000/60000 (57%)]\t1.661624\n",
            "Train Epoch: 1 [36000/60000 (60%)]\t1.593314\n",
            "Train Epoch: 1 [38000/60000 (63%)]\t1.561206\n",
            "Train Epoch: 1 [40000/60000 (67%)]\t1.687903\n",
            "Train Epoch: 1 [42000/60000 (70%)]\t1.562903\n",
            "Train Epoch: 1 [44000/60000 (73%)]\t1.668362\n",
            "Train Epoch: 1 [46000/60000 (77%)]\t1.552014\n",
            "Train Epoch: 1 [48000/60000 (80%)]\t1.574968\n",
            "Train Epoch: 1 [50000/60000 (83%)]\t1.604706\n",
            "Train Epoch: 1 [52000/60000 (87%)]\t1.580365\n",
            "Train Epoch: 1 [54000/60000 (90%)]\t1.567169\n",
            "Train Epoch: 1 [56000/60000 (93%)]\t1.611043\n",
            "Train Epoch: 1 [58000/60000 (97%)]\t1.589530\n",
            "\n",
            "Test set: Average loss: 0.0152, Accuracy 9373/10000 (94%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\t1.583878\n",
            "Train Epoch: 2 [2000/60000 (3%)]\t1.569530\n",
            "Train Epoch: 2 [4000/60000 (7%)]\t1.596777\n",
            "Train Epoch: 2 [6000/60000 (10%)]\t1.586998\n",
            "Train Epoch: 2 [8000/60000 (13%)]\t1.572052\n",
            "Train Epoch: 2 [10000/60000 (17%)]\t1.588683\n",
            "Train Epoch: 2 [12000/60000 (20%)]\t1.597826\n",
            "Train Epoch: 2 [14000/60000 (23%)]\t1.571407\n",
            "Train Epoch: 2 [16000/60000 (27%)]\t1.588692\n",
            "Train Epoch: 2 [18000/60000 (30%)]\t1.607841\n",
            "Train Epoch: 2 [20000/60000 (33%)]\t1.561282\n",
            "Train Epoch: 2 [22000/60000 (37%)]\t1.535817\n",
            "Train Epoch: 2 [24000/60000 (40%)]\t1.610334\n",
            "Train Epoch: 2 [26000/60000 (43%)]\t1.577982\n",
            "Train Epoch: 2 [28000/60000 (47%)]\t1.613692\n",
            "Train Epoch: 2 [30000/60000 (50%)]\t1.574011\n",
            "Train Epoch: 2 [32000/60000 (53%)]\t1.632367\n",
            "Train Epoch: 2 [34000/60000 (57%)]\t1.588362\n",
            "Train Epoch: 2 [36000/60000 (60%)]\t1.569684\n",
            "Train Epoch: 2 [38000/60000 (63%)]\t1.643043\n",
            "Train Epoch: 2 [40000/60000 (67%)]\t1.636954\n",
            "Train Epoch: 2 [42000/60000 (70%)]\t1.605855\n",
            "Train Epoch: 2 [44000/60000 (73%)]\t1.542016\n",
            "Train Epoch: 2 [46000/60000 (77%)]\t1.589547\n",
            "Train Epoch: 2 [48000/60000 (80%)]\t1.514476\n",
            "Train Epoch: 2 [50000/60000 (83%)]\t1.563236\n",
            "Train Epoch: 2 [52000/60000 (87%)]\t1.544674\n",
            "Train Epoch: 2 [54000/60000 (90%)]\t1.580215\n",
            "Train Epoch: 2 [56000/60000 (93%)]\t1.588085\n",
            "Train Epoch: 2 [58000/60000 (97%)]\t1.529103\n",
            "\n",
            "Test set: Average loss: 0.0151, Accuracy 9552/10000 (96%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\t1.559467\n",
            "Train Epoch: 3 [2000/60000 (3%)]\t1.643209\n",
            "Train Epoch: 3 [4000/60000 (7%)]\t1.530934\n",
            "Train Epoch: 3 [6000/60000 (10%)]\t1.576558\n",
            "Train Epoch: 3 [8000/60000 (13%)]\t1.519405\n",
            "Train Epoch: 3 [10000/60000 (17%)]\t1.555540\n",
            "Train Epoch: 3 [12000/60000 (20%)]\t1.531749\n",
            "Train Epoch: 3 [14000/60000 (23%)]\t1.517982\n",
            "Train Epoch: 3 [16000/60000 (27%)]\t1.571740\n",
            "Train Epoch: 3 [18000/60000 (30%)]\t1.572412\n",
            "Train Epoch: 3 [20000/60000 (33%)]\t1.607146\n",
            "Train Epoch: 3 [22000/60000 (37%)]\t1.550686\n",
            "Train Epoch: 3 [24000/60000 (40%)]\t1.521101\n",
            "Train Epoch: 3 [26000/60000 (43%)]\t1.535179\n",
            "Train Epoch: 3 [28000/60000 (47%)]\t1.533134\n",
            "Train Epoch: 3 [30000/60000 (50%)]\t1.525784\n",
            "Train Epoch: 3 [32000/60000 (53%)]\t1.543644\n",
            "Train Epoch: 3 [34000/60000 (57%)]\t1.543049\n",
            "Train Epoch: 3 [36000/60000 (60%)]\t1.552179\n",
            "Train Epoch: 3 [38000/60000 (63%)]\t1.552878\n",
            "Train Epoch: 3 [40000/60000 (67%)]\t1.528816\n",
            "Train Epoch: 3 [42000/60000 (70%)]\t1.583639\n",
            "Train Epoch: 3 [44000/60000 (73%)]\t1.557847\n",
            "Train Epoch: 3 [46000/60000 (77%)]\t1.537323\n",
            "Train Epoch: 3 [48000/60000 (80%)]\t1.506031\n",
            "Train Epoch: 3 [50000/60000 (83%)]\t1.550284\n",
            "Train Epoch: 3 [52000/60000 (87%)]\t1.567991\n",
            "Train Epoch: 3 [54000/60000 (90%)]\t1.565398\n",
            "Train Epoch: 3 [56000/60000 (93%)]\t1.544778\n",
            "Train Epoch: 3 [58000/60000 (97%)]\t1.557019\n",
            "\n",
            "Test set: Average loss: 0.0150, Accuracy 9612/10000 (96%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\t1.502341\n",
            "Train Epoch: 4 [2000/60000 (3%)]\t1.550756\n",
            "Train Epoch: 4 [4000/60000 (7%)]\t1.565877\n",
            "Train Epoch: 4 [6000/60000 (10%)]\t1.588983\n",
            "Train Epoch: 4 [8000/60000 (13%)]\t1.540684\n",
            "Train Epoch: 4 [10000/60000 (17%)]\t1.531673\n",
            "Train Epoch: 4 [12000/60000 (20%)]\t1.567540\n",
            "Train Epoch: 4 [14000/60000 (23%)]\t1.526711\n",
            "Train Epoch: 4 [16000/60000 (27%)]\t1.531462\n",
            "Train Epoch: 4 [18000/60000 (30%)]\t1.531970\n",
            "Train Epoch: 4 [20000/60000 (33%)]\t1.591350\n",
            "Train Epoch: 4 [22000/60000 (37%)]\t1.520685\n",
            "Train Epoch: 4 [24000/60000 (40%)]\t1.558188\n",
            "Train Epoch: 4 [26000/60000 (43%)]\t1.556943\n",
            "Train Epoch: 4 [28000/60000 (47%)]\t1.579213\n",
            "Train Epoch: 4 [30000/60000 (50%)]\t1.569247\n",
            "Train Epoch: 4 [32000/60000 (53%)]\t1.547799\n",
            "Train Epoch: 4 [34000/60000 (57%)]\t1.528184\n",
            "Train Epoch: 4 [36000/60000 (60%)]\t1.576830\n",
            "Train Epoch: 4 [38000/60000 (63%)]\t1.551826\n",
            "Train Epoch: 4 [40000/60000 (67%)]\t1.605491\n",
            "Train Epoch: 4 [42000/60000 (70%)]\t1.543114\n",
            "Train Epoch: 4 [44000/60000 (73%)]\t1.564250\n",
            "Train Epoch: 4 [46000/60000 (77%)]\t1.575574\n",
            "Train Epoch: 4 [48000/60000 (80%)]\t1.577818\n",
            "Train Epoch: 4 [50000/60000 (83%)]\t1.534437\n",
            "Train Epoch: 4 [52000/60000 (87%)]\t1.505562\n",
            "Train Epoch: 4 [54000/60000 (90%)]\t1.527130\n",
            "Train Epoch: 4 [56000/60000 (93%)]\t1.506820\n",
            "Train Epoch: 4 [58000/60000 (97%)]\t1.577940\n",
            "\n",
            "Test set: Average loss: 0.0150, Accuracy 9627/10000 (96%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\t1.536916\n",
            "Train Epoch: 5 [2000/60000 (3%)]\t1.514418\n",
            "Train Epoch: 5 [4000/60000 (7%)]\t1.588781\n",
            "Train Epoch: 5 [6000/60000 (10%)]\t1.551156\n",
            "Train Epoch: 5 [8000/60000 (13%)]\t1.548551\n",
            "Train Epoch: 5 [10000/60000 (17%)]\t1.515674\n",
            "Train Epoch: 5 [12000/60000 (20%)]\t1.583218\n",
            "Train Epoch: 5 [14000/60000 (23%)]\t1.538925\n",
            "Train Epoch: 5 [16000/60000 (27%)]\t1.539717\n",
            "Train Epoch: 5 [18000/60000 (30%)]\t1.546428\n",
            "Train Epoch: 5 [20000/60000 (33%)]\t1.576483\n",
            "Train Epoch: 5 [22000/60000 (37%)]\t1.534681\n",
            "Train Epoch: 5 [24000/60000 (40%)]\t1.539389\n",
            "Train Epoch: 5 [26000/60000 (43%)]\t1.552941\n",
            "Train Epoch: 5 [28000/60000 (47%)]\t1.585585\n",
            "Train Epoch: 5 [30000/60000 (50%)]\t1.548194\n",
            "Train Epoch: 5 [32000/60000 (53%)]\t1.581761\n",
            "Train Epoch: 5 [34000/60000 (57%)]\t1.556450\n",
            "Train Epoch: 5 [36000/60000 (60%)]\t1.532245\n",
            "Train Epoch: 5 [38000/60000 (63%)]\t1.551249\n",
            "Train Epoch: 5 [40000/60000 (67%)]\t1.547589\n",
            "Train Epoch: 5 [42000/60000 (70%)]\t1.524435\n",
            "Train Epoch: 5 [44000/60000 (73%)]\t1.544171\n",
            "Train Epoch: 5 [46000/60000 (77%)]\t1.552341\n",
            "Train Epoch: 5 [48000/60000 (80%)]\t1.519524\n",
            "Train Epoch: 5 [50000/60000 (83%)]\t1.547182\n",
            "Train Epoch: 5 [52000/60000 (87%)]\t1.533524\n",
            "Train Epoch: 5 [54000/60000 (90%)]\t1.598005\n",
            "Train Epoch: 5 [56000/60000 (93%)]\t1.530396\n",
            "Train Epoch: 5 [58000/60000 (97%)]\t1.514993\n",
            "\n",
            "Test set: Average loss: 0.0150, Accuracy 9648/10000 (96%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\t1.534416\n",
            "Train Epoch: 6 [2000/60000 (3%)]\t1.491164\n",
            "Train Epoch: 6 [4000/60000 (7%)]\t1.545429\n",
            "Train Epoch: 6 [6000/60000 (10%)]\t1.524222\n",
            "Train Epoch: 6 [8000/60000 (13%)]\t1.532327\n",
            "Train Epoch: 6 [10000/60000 (17%)]\t1.549772\n",
            "Train Epoch: 6 [12000/60000 (20%)]\t1.576069\n",
            "Train Epoch: 6 [14000/60000 (23%)]\t1.521736\n",
            "Train Epoch: 6 [16000/60000 (27%)]\t1.528024\n",
            "Train Epoch: 6 [18000/60000 (30%)]\t1.542240\n",
            "Train Epoch: 6 [20000/60000 (33%)]\t1.533082\n",
            "Train Epoch: 6 [22000/60000 (37%)]\t1.545512\n",
            "Train Epoch: 6 [24000/60000 (40%)]\t1.515995\n",
            "Train Epoch: 6 [26000/60000 (43%)]\t1.567107\n",
            "Train Epoch: 6 [28000/60000 (47%)]\t1.538908\n",
            "Train Epoch: 6 [30000/60000 (50%)]\t1.525344\n",
            "Train Epoch: 6 [32000/60000 (53%)]\t1.563347\n",
            "Train Epoch: 6 [34000/60000 (57%)]\t1.519482\n",
            "Train Epoch: 6 [36000/60000 (60%)]\t1.499259\n",
            "Train Epoch: 6 [38000/60000 (63%)]\t1.511819\n",
            "Train Epoch: 6 [40000/60000 (67%)]\t1.508511\n",
            "Train Epoch: 6 [42000/60000 (70%)]\t1.554459\n",
            "Train Epoch: 6 [44000/60000 (73%)]\t1.500054\n",
            "Train Epoch: 6 [46000/60000 (77%)]\t1.567810\n",
            "Train Epoch: 6 [48000/60000 (80%)]\t1.498956\n",
            "Train Epoch: 6 [50000/60000 (83%)]\t1.533422\n",
            "Train Epoch: 6 [52000/60000 (87%)]\t1.589669\n",
            "Train Epoch: 6 [54000/60000 (90%)]\t1.569462\n",
            "Train Epoch: 6 [56000/60000 (93%)]\t1.586440\n",
            "Train Epoch: 6 [58000/60000 (97%)]\t1.546920\n",
            "\n",
            "Test set: Average loss: 0.0149, Accuracy 9686/10000 (97%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\t1.483148\n",
            "Train Epoch: 7 [2000/60000 (3%)]\t1.560448\n",
            "Train Epoch: 7 [4000/60000 (7%)]\t1.521146\n",
            "Train Epoch: 7 [6000/60000 (10%)]\t1.540460\n",
            "Train Epoch: 7 [8000/60000 (13%)]\t1.548208\n",
            "Train Epoch: 7 [10000/60000 (17%)]\t1.553817\n",
            "Train Epoch: 7 [12000/60000 (20%)]\t1.607162\n",
            "Train Epoch: 7 [14000/60000 (23%)]\t1.507438\n",
            "Train Epoch: 7 [16000/60000 (27%)]\t1.500260\n",
            "Train Epoch: 7 [18000/60000 (30%)]\t1.556025\n",
            "Train Epoch: 7 [20000/60000 (33%)]\t1.524369\n",
            "Train Epoch: 7 [22000/60000 (37%)]\t1.528410\n",
            "Train Epoch: 7 [24000/60000 (40%)]\t1.509499\n",
            "Train Epoch: 7 [26000/60000 (43%)]\t1.619963\n",
            "Train Epoch: 7 [28000/60000 (47%)]\t1.504058\n",
            "Train Epoch: 7 [30000/60000 (50%)]\t1.513677\n",
            "Train Epoch: 7 [32000/60000 (53%)]\t1.516986\n",
            "Train Epoch: 7 [34000/60000 (57%)]\t1.504562\n",
            "Train Epoch: 7 [36000/60000 (60%)]\t1.541300\n",
            "Train Epoch: 7 [38000/60000 (63%)]\t1.573031\n",
            "Train Epoch: 7 [40000/60000 (67%)]\t1.540828\n",
            "Train Epoch: 7 [42000/60000 (70%)]\t1.510604\n",
            "Train Epoch: 7 [44000/60000 (73%)]\t1.557747\n",
            "Train Epoch: 7 [46000/60000 (77%)]\t1.483129\n",
            "Train Epoch: 7 [48000/60000 (80%)]\t1.523876\n",
            "Train Epoch: 7 [50000/60000 (83%)]\t1.535472\n",
            "Train Epoch: 7 [52000/60000 (87%)]\t1.564390\n",
            "Train Epoch: 7 [54000/60000 (90%)]\t1.512333\n",
            "Train Epoch: 7 [56000/60000 (93%)]\t1.505989\n",
            "Train Epoch: 7 [58000/60000 (97%)]\t1.557061\n",
            "\n",
            "Test set: Average loss: 0.0149, Accuracy 9702/10000 (97%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\t1.573346\n",
            "Train Epoch: 8 [2000/60000 (3%)]\t1.522138\n",
            "Train Epoch: 8 [4000/60000 (7%)]\t1.554316\n",
            "Train Epoch: 8 [6000/60000 (10%)]\t1.533461\n",
            "Train Epoch: 8 [8000/60000 (13%)]\t1.495715\n",
            "Train Epoch: 8 [10000/60000 (17%)]\t1.477595\n",
            "Train Epoch: 8 [12000/60000 (20%)]\t1.536265\n",
            "Train Epoch: 8 [14000/60000 (23%)]\t1.549956\n",
            "Train Epoch: 8 [16000/60000 (27%)]\t1.583966\n",
            "Train Epoch: 8 [18000/60000 (30%)]\t1.548075\n",
            "Train Epoch: 8 [20000/60000 (33%)]\t1.531774\n",
            "Train Epoch: 8 [22000/60000 (37%)]\t1.539565\n",
            "Train Epoch: 8 [24000/60000 (40%)]\t1.518155\n",
            "Train Epoch: 8 [26000/60000 (43%)]\t1.508949\n",
            "Train Epoch: 8 [28000/60000 (47%)]\t1.532576\n",
            "Train Epoch: 8 [30000/60000 (50%)]\t1.529199\n",
            "Train Epoch: 8 [32000/60000 (53%)]\t1.567085\n",
            "Train Epoch: 8 [34000/60000 (57%)]\t1.593139\n",
            "Train Epoch: 8 [36000/60000 (60%)]\t1.555089\n",
            "Train Epoch: 8 [38000/60000 (63%)]\t1.526368\n",
            "Train Epoch: 8 [40000/60000 (67%)]\t1.482664\n",
            "Train Epoch: 8 [42000/60000 (70%)]\t1.490220\n",
            "Train Epoch: 8 [44000/60000 (73%)]\t1.518204\n",
            "Train Epoch: 8 [46000/60000 (77%)]\t1.544638\n",
            "Train Epoch: 8 [48000/60000 (80%)]\t1.544274\n",
            "Train Epoch: 8 [50000/60000 (83%)]\t1.552091\n",
            "Train Epoch: 8 [52000/60000 (87%)]\t1.515438\n",
            "Train Epoch: 8 [54000/60000 (90%)]\t1.522246\n",
            "Train Epoch: 8 [56000/60000 (93%)]\t1.521731\n",
            "Train Epoch: 8 [58000/60000 (97%)]\t1.562285\n",
            "\n",
            "Test set: Average loss: 0.0149, Accuracy 9710/10000 (97%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\t1.512169\n",
            "Train Epoch: 9 [2000/60000 (3%)]\t1.536149\n",
            "Train Epoch: 9 [4000/60000 (7%)]\t1.556521\n",
            "Train Epoch: 9 [6000/60000 (10%)]\t1.502977\n",
            "Train Epoch: 9 [8000/60000 (13%)]\t1.536921\n",
            "Train Epoch: 9 [10000/60000 (17%)]\t1.512384\n",
            "Train Epoch: 9 [12000/60000 (20%)]\t1.587031\n",
            "Train Epoch: 9 [14000/60000 (23%)]\t1.540020\n",
            "Train Epoch: 9 [16000/60000 (27%)]\t1.510476\n",
            "Train Epoch: 9 [18000/60000 (30%)]\t1.564492\n",
            "Train Epoch: 9 [20000/60000 (33%)]\t1.528746\n",
            "Train Epoch: 9 [22000/60000 (37%)]\t1.536359\n",
            "Train Epoch: 9 [24000/60000 (40%)]\t1.555352\n",
            "Train Epoch: 9 [26000/60000 (43%)]\t1.496057\n",
            "Train Epoch: 9 [28000/60000 (47%)]\t1.526523\n",
            "Train Epoch: 9 [30000/60000 (50%)]\t1.563344\n",
            "Train Epoch: 9 [32000/60000 (53%)]\t1.507863\n",
            "Train Epoch: 9 [34000/60000 (57%)]\t1.514525\n",
            "Train Epoch: 9 [36000/60000 (60%)]\t1.506622\n",
            "Train Epoch: 9 [38000/60000 (63%)]\t1.572133\n",
            "Train Epoch: 9 [40000/60000 (67%)]\t1.556800\n",
            "Train Epoch: 9 [42000/60000 (70%)]\t1.514132\n",
            "Train Epoch: 9 [44000/60000 (73%)]\t1.522429\n",
            "Train Epoch: 9 [46000/60000 (77%)]\t1.507436\n",
            "Train Epoch: 9 [48000/60000 (80%)]\t1.518905\n",
            "Train Epoch: 9 [50000/60000 (83%)]\t1.513446\n",
            "Train Epoch: 9 [52000/60000 (87%)]\t1.530105\n",
            "Train Epoch: 9 [54000/60000 (90%)]\t1.507753\n",
            "Train Epoch: 9 [56000/60000 (93%)]\t1.518379\n",
            "Train Epoch: 9 [58000/60000 (97%)]\t1.529527\n",
            "\n",
            "Test set: Average loss: 0.0149, Accuracy 9737/10000 (97%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\t1.523190\n",
            "Train Epoch: 10 [2000/60000 (3%)]\t1.539697\n",
            "Train Epoch: 10 [4000/60000 (7%)]\t1.506429\n",
            "Train Epoch: 10 [6000/60000 (10%)]\t1.537649\n",
            "Train Epoch: 10 [8000/60000 (13%)]\t1.506200\n",
            "Train Epoch: 10 [10000/60000 (17%)]\t1.545191\n",
            "Train Epoch: 10 [12000/60000 (20%)]\t1.502184\n",
            "Train Epoch: 10 [14000/60000 (23%)]\t1.498842\n",
            "Train Epoch: 10 [16000/60000 (27%)]\t1.544344\n",
            "Train Epoch: 10 [18000/60000 (30%)]\t1.546297\n",
            "Train Epoch: 10 [20000/60000 (33%)]\t1.486521\n",
            "Train Epoch: 10 [22000/60000 (37%)]\t1.518243\n",
            "Train Epoch: 10 [24000/60000 (40%)]\t1.574983\n",
            "Train Epoch: 10 [26000/60000 (43%)]\t1.560604\n",
            "Train Epoch: 10 [28000/60000 (47%)]\t1.512569\n",
            "Train Epoch: 10 [30000/60000 (50%)]\t1.553447\n",
            "Train Epoch: 10 [32000/60000 (53%)]\t1.534149\n",
            "Train Epoch: 10 [34000/60000 (57%)]\t1.522801\n",
            "Train Epoch: 10 [36000/60000 (60%)]\t1.517091\n",
            "Train Epoch: 10 [38000/60000 (63%)]\t1.572478\n",
            "Train Epoch: 10 [40000/60000 (67%)]\t1.510689\n",
            "Train Epoch: 10 [42000/60000 (70%)]\t1.504148\n",
            "Train Epoch: 10 [44000/60000 (73%)]\t1.527642\n",
            "Train Epoch: 10 [46000/60000 (77%)]\t1.543612\n",
            "Train Epoch: 10 [48000/60000 (80%)]\t1.511860\n",
            "Train Epoch: 10 [50000/60000 (83%)]\t1.550111\n",
            "Train Epoch: 10 [52000/60000 (87%)]\t1.519430\n",
            "Train Epoch: 10 [54000/60000 (90%)]\t1.499400\n",
            "Train Epoch: 10 [56000/60000 (93%)]\t1.522655\n",
            "Train Epoch: 10 [58000/60000 (97%)]\t1.540925\n",
            "\n",
            "Test set: Average loss: 0.0149, Accuracy 9731/10000 (97%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zyKiN2mMBSZa",
        "outputId": "de2f180e-9bf9-4846-cdc4-42efbe3c5c6b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "model.eval()\n",
        "data, target = test_data[0]\n",
        "data = data.unsqueeze(0).to(device)\n",
        "output = model(data)\n",
        "prediction = output.argmax(dim=1, keepdim=True).item()\n",
        "print(f'Prediction: {prediction}')\n",
        "image = data.squeeze(0).squeeze(0).cpu().numpy()\n",
        "plt.imshow(image, cmap='gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ENY55GWqCTPB",
        "outputId": "472e0ce2-c0e5-443c-93f3-13b9b3ef0981"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,28):\n",
        "  model.eval()\n",
        "  data, target = test_data[i]\n",
        "  data = data.unsqueeze(0).to(device)\n",
        "  output = model(data)\n",
        "  prediction = output.argmax(dim=1, keepdim=True).item()\n",
        "  print(f'Prediction: {prediction}')\n",
        "  image = data.squeeze(0).squeeze(0).cpu().numpy()\n",
        "  plt.imshow(image, cmap='gray')\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u6rdqyovCV4p",
        "outputId": "15f838e9-b705-43a7-8fd0-8f87d8508f76"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FRfCx+vp6NTc3KycnJzITAwBGhLCugAoLC7V//34dPnxYCQkJwfd1vF6vxo0bJ6/Xq2effVZFRUVKSkpSYmKinnvuOeXk5PAJOABAiLACtGfPHknSokWLQh7ft2+fNmzYIEn6+c9/rvj4eK1evVrd3d3Kz8/Xr371q4gMCwAYOeKcc856iE8KBALyer3WYyDGlZWVDWq/5cuXh73PyZMnw95nMD8R6OnpCXsfwJLf71diYuKAz3MvOACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgY1G9EBYbS6NGjw95n+vTpUZikf11dXWHvw52tAa6AAABGCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAAT3IwUw15fX1/Y+9TW1g7qtWbPnh32Pg0NDYN6LeBexxUQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCm5Fi2Ovt7Q17n5deemlQr+WcC3ufurq6Qb0WcK/jCggAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMBHnBnP3xSgKBALyer3WYwAA7pLf71diYuKAz3MFBAAwQYAAACbCClBJSYnmz5+vhIQEpaamasWKFaqvrw/ZZtGiRYqLiwtZmzdvjujQAIDYF1aAqqqqVFhYqJqaGh09elQ9PT1asmSJOjs7Q7bbuHGjLl68GFy7du2K6NAAgNgX1m9ELS8vD/m6tLRUqampqqurU25ubvDx8ePHy+fzRWZCAMCIdFfvAfn9fklSUlJSyONvvfWWUlJSNHv2bBUXF+vatWsDfo/u7m4FAoGQBQC4B7hB6u3tdV/72tfcwoULQx5/4403XHl5uTtz5oz73e9+5x588EG3cuXKAb/Pzp07nSQWi8VijbDl9/tv25FBB2jz5s1u6tSprqWl5bbbVVRUOEmuoaGh3+e7urqc3+8PrpaWFvODxmKxWKy7X3cKUFjvAX1s69atOnLkiI4fP65Jkybddtvs7GxJUkNDg6ZPn37L8x6PRx6PZzBjAABiWFgBcs7pueee06FDh1RZWanMzMw77nP69GlJUnp6+qAGBACMTGEFqLCwUPv379fhw4eVkJCg1tZWSZLX69W4cePU2Nio/fv366tf/aqSk5N15swZbd++Xbm5uZo7d25U/gcAAGJUOO/7aICf8+3bt88551xzc7PLzc11SUlJzuPxuBkzZrgXXnjhjj8H/CS/32/+c0sWi8Vi3f2609/93IwUABAV3IwUADAsESAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMDLsAOeesRwAARMCd/j4fdgHq6OiwHgEAEAF3+vs8zg2zS46+vj5duHBBCQkJiouLC3kuEAho8uTJamlpUWJiotGE9jgON3EcbuI43MRxuGk4HAfnnDo6OpSRkaH4+IGvc+4bwpk+k/j4eE2aNOm22yQmJt7TJ9jHOA43cRxu4jjcxHG4yfo4eL3eO24z7H4EBwC4NxAgAICJmAqQx+PRzp075fF4rEcxxXG4ieNwE8fhJo7DTbF0HIbdhxAAAPeGmLoCAgCMHAQIAGCCAAEATBAgAICJmAnQ7t279dBDD2ns2LHKzs7WRx99ZD3SkHvllVcUFxcXsmbNmmU9VtQdP35cy5YtU0ZGhuLi4lRWVhbyvHNOO3bsUHp6usaNG6e8vDydO3fOZtgoutNx2LBhwy3nx9KlS22GjZKSkhLNnz9fCQkJSk1N1YoVK1RfXx+yTVdXlwoLC5WcnKwHHnhAq1evVltbm9HE0fFZjsOiRYtuOR82b95sNHH/YiJA77zzjoqKirRz506dPHlSWVlZys/P16VLl6xHG3KPPvqoLl68GFx//etfrUeKus7OTmVlZWn37t39Pr9r1y69/vrr2rt3r06cOKH7779f+fn56urqGuJJo+tOx0GSli5dGnJ+HDhwYAgnjL6qqioVFhaqpqZGR48eVU9Pj5YsWaLOzs7gNtu3b9d7772ngwcPqqqqShcuXNCqVasMp468z3IcJGnjxo0h58OuXbuMJh6AiwELFixwhYWFwa97e3tdRkaGKykpMZxq6O3cudNlZWVZj2FKkjt06FDw676+Pufz+dxPf/rT4GPt7e3O4/G4AwcOGEw4ND59HJxzbv369W758uUm81i5dOmSk+Sqqqqcczf/vx89erQ7ePBgcJt//OMfTpKrrq62GjPqPn0cnHPuy1/+svv2t79tN9RnMOyvgG7cuKG6ujrl5eUFH4uPj1deXp6qq6sNJ7Nx7tw5ZWRkaNq0aXrmmWfU3NxsPZKppqYmtba2hpwfXq9X2dnZ9+T5UVlZqdTUVM2cOVNbtmzRlStXrEeKKr/fL0lKSkqSJNXV1amnpyfkfJg1a5amTJkyos+HTx+Hj7311ltKSUnR7NmzVVxcrGvXrlmMN6BhdzPST7t8+bJ6e3uVlpYW8nhaWpr++c9/Gk1lIzs7W6WlpZo5c6YuXryoV199VU8++aTOnj2rhIQE6/FMtLa2SlK/58fHz90rli5dqlWrVikzM1ONjY363ve+p4KCAlVXV2vUqFHW40VcX1+ftm3bpoULF2r27NmSbp4PY8aM0YQJE0K2HcnnQ3/HQZLWrVunqVOnKiMjQ2fOnNF3v/td1dfX6/e//73htKGGfYDwfwUFBcE/z507V9nZ2Zo6dareffddPfvss4aTYThYu3Zt8M9z5szR3LlzNX36dFVWVmrx4sWGk0VHYWGhzp49e0+8D3o7Ax2HTZs2Bf88Z84cpaena/HixWpsbNT06dOHesx+DfsfwaWkpGjUqFG3fIqlra1NPp/PaKrhYcKECXrkkUfU0NBgPYqZj88Bzo9bTZs2TSkpKSPy/Ni6dauOHDmiDz74IOTXt/h8Pt24cUPt7e0h24/U82Gg49Cf7OxsSRpW58OwD9CYMWM0b948VVRUBB/r6+tTRUWFcnJyDCezd/XqVTU2Nio9Pd16FDOZmZny+Xwh50cgENCJEyfu+fPj/PnzunLlyog6P5xz2rp1qw4dOqRjx44pMzMz5Pl58+Zp9OjRIedDfX29mpubR9T5cKfj0J/Tp09L0vA6H6w/BfFZvP32287j8bjS0lL397//3W3atMlNmDDBtba2Wo82pL7zne+4yspK19TU5D788EOXl5fnUlJS3KVLl6xHi6qOjg536tQpd+rUKSfJ/exnP3OnTp1y//73v51zzv34xz92EyZMcIcPH3Znzpxxy5cvd5mZme769evGk0fW7Y5DR0eHe/755111dbVrampy77//vvviF7/oHn74YdfV1WU9esRs2bLFeb1eV1lZ6S5evBhc165dC26zefNmN2XKFHfs2DFXW1vrcnJyXE5OjuHUkXen49DQ0OB+8IMfuNraWtfU1OQOHz7spk2b5nJzc40nDxUTAXLOuV/+8pduypQpbsyYMW7BggWupqbGeqQht2bNGpeenu7GjBnjHnzwQbdmzRrX0NBgPVbUffDBB07SLWv9+vXOuZsfxX755ZddWlqa83g8bvHixa6+vt526Ci43XG4du2aW7JkiZs4caIbPXq0mzp1qtu4ceOI+4+0/v73S3L79u0LbnP9+nX3rW99y33uc59z48ePdytXrnQXL160GzoK7nQcmpubXW5urktKSnIej8fNmDHDvfDCC87v99sO/in8OgYAgIlh/x4QAGBkIkAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBM/A+ZiUOBZyjn+wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout", 
          "text": [
            "Prediction: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": { 
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 7\n"
          ] 
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 4\n" 
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}