PyTorch_CNN_FachionMNIST.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/selimunal/Pytorch/blob/main/PyTorch_CNN_FachionMNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AI2pw4bDk05Q"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LvhjzJTorC3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset=torchvision.datasets.FashionMNIST(\n",
        "    root='.',\n",
        "    train=True,\n",
        "    transform=transforms.ToTensor(),\n",
        "    download=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
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        "id": "TQTsAxdblIHa",
        "outputId": "bb6e9765-4c87-400c-b93d-b232a35ab15e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/train-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/29515 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fc87d5d8ef7d485eacb0c4026cc42093"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFzP1_VylYdH",
        "outputId": "99fc5f8a-535f-42e5-d539-d96034f5dbf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbLy22UEll4P",
        "outputId": "ae46c4e8-6d88-4775-97e6-53c037a43627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset=torchvision.datasets.FashionMNIST(\n",
        "    root='.',\n",
        "    train=False,\n",
        "    transform=transforms.ToTensor(),\n",
        "    download=True\n",
        ")"
      ],
      "metadata": {
        "id": "DwAjo0XLlp9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O18aMEbMluMH",
        "outputId": "b42924fc-3fd3-4837-9926-e9b63d32eaa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU_k3LyOlwRu",
        "outputId": "d976e308-c30c-4043-e191-90169f728020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.targets.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_4V350QlzIv",
        "outputId": "9a90dbaa-cfe3-477c-bb63-9ef317728385"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(9)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of classes\n",
        "K=len(set(train_dataset.targets.numpy()))\n",
        "print('number of classes:',K)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sIkyd3IoV93",
        "outputId": "84837011-a48c-4f78-eefa-ed1e673215d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of classes: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the model\n",
        "class CNN(nn.Module):\n",
        "  def __init__(self,K):\n",
        "    super(CNN,self).__init__()\n",
        "    self.conv_layers=nn.Sequential(\n",
        "        nn.Conv2d(in_channels=1,out_channels=32,kernel_size=3,stride=2),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=32,out_channels=64,kernel_size=3,stride=2),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3,stride=2)\n",
        "    )\n",
        "\n",
        "    self.dense_layers=nn.Sequential(\n",
        "        nn.Dropout(0.2),\n",
        "        nn.Linear(128*2*2,512),\n",
        "        nn.ReLU(),\n",
        "        nn.Dropout(0.2),\n",
        "        nn.Linear(512,K)\n",
        "    )\n",
        "\n",
        "  def forward(self,X):\n",
        "    out=self.conv_layers(X)\n",
        "    out=out.view(out.size(0),-1)\n",
        "    out=self.dense_layers(out)\n",
        "    return out\n"
      ],
      "metadata": {
        "id": "wqfZ-znDom1P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=CNN(K)"
      ],
      "metadata": {
        "id": "qdKk9zXr4cVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The same model Using the newly introduced 'flatten' format\n",
        "# model=nn.Sequential(\n",
        "#     nn.Conv2d(in_channels=1,out_channels=32,kernel_size=3,stride=2),\n",
        "#     nn.ReLU(),\n",
        "#     nn.Conv2d(in_channels=32,out_channels=64,kernel_size=3,stride=2),\n",
        "#     nn.ReLU(),\n",
        "#     nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3,stride=2),\n",
        "#     nn.ReLU(),\n",
        "#     nn.Flatten(),\n",
        "#     nn.Dropout(0.2),\n",
        "#     nn.Linear(128*2*2,512),\n",
        "#     nn.ReLU(),\n",
        "#     nn.Dropout(0.2),\n",
        "#     nn.Linear(512,K)\n",
        "# )"
      ],
      "metadata": {
        "id": "oExAbBiv4eko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XurEJ6ya5Y2Z",
        "outputId": "6618946e-6d88-456b-8d87-f5c1713b530a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CNN(\n",
              "  (conv_layers): Sequential(\n",
              "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2))\n",
              "    (3): ReLU()\n",
              "    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2))\n",
              "  )\n",
              "  (dense_layers): Sequential(\n",
              "    (0): Dropout(p=0.2, inplace=False)\n",
              "    (1): Linear(in_features=512, out_features=512, bias=True)\n",
              "    (2): ReLU()\n",
              "    (3): Dropout(p=0.2, inplace=False)\n",
              "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loss and optimizer\n",
        "criterion=nn.CrossEntropyLoss()\n",
        "optimizer=torch.optim.Adam(model.parameters())"
      ],
      "metadata": {
        "id": "uQlOZN8p5k0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Loader\n",
        "#Useful because it automatically generates batches in the training loop\n",
        "#and takes care of shuffling\n",
        "batch_size=99\n",
        "train_loader=torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                         batch_size=batch_size,\n",
        "                                         shuffle=True)\n",
        "test_loader=torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                        batch_size=batch_size,\n",
        "                                        shuffle=False)"
      ],
      "metadata": {
        "id": "JxLBURlk5wPo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4UiRNCUx9J3c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#A function to encapsulate the training loop\n",
        "def batch_gd(model,criterion,optimizer,train_loader,test_loader,epochs):\n",
        "  train_losses=np.zeros(epochs)\n",
        "  test_losses=np.zeros(epochs)\n",
        "\n",
        "  for it in range(epochs):\n",
        "    model.train()\n",
        "    t0=datetime.now()\n",
        "    train_loss=[]\n",
        "    for inputs,targets in train_loader:\n",
        "      #move data to GPU\n",
        "      inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "      #zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      #forward pass\n",
        "      outputs=model(inputs)\n",
        "      loss=criterion(outputs,targets)\n",
        "\n",
        "      #backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss.append(loss.item())\n",
        "\n",
        "    #get train loss and test loss\n",
        "    train_loss=np.mean(train_loss) #little misleading\n",
        "\n",
        "    model.eval()\n",
        "    test_loss=[]\n",
        "    for inputs,targets in test_loader:\n",
        "      inputs,targets=inputs.to(device),targets.to(device)\n",
        "      outputs=model(inputs)\n",
        "      loss=criterion(outputs,targets)\n",
        "      test_loss.append(loss.item())\n",
        "    test_loss=np.mean(test_loss)\n",
        "\n",
        "    #save losses\n",
        "    train_losses[it]=train_loss\n",
        "    test_losses[it]=test_loss\n",
        "\n",
        "    dt=datetime.now()-t0\n",
        "\n",
        "    print(f'Epoch {it+1}/{epochs},Train Loss: {train_loss:.4f},Test Loss: {test_loss:.4f} ,\\\n",
        "    Duration: {dt}')\n",
        "\n",
        "  return train_losses,test_losses"
      ],
      "metadata": {
        "id": "C-4n13m29TIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_losses, test_losses=batch_gd(model,criterion,optimizer,train_loader,test_loader, epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "FuKj3ox8C0FL",
        "outputId": "6aeb394a-ea1a-4028-ff6a-c5d24ad4547d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15,Train Loss: 0.5937,Test Loss: 0.4575 ,    Duration: 0:00:27.889608\n",
            "Epoch 2/15,Train Loss: 0.4035,Test Loss: 0.3752 ,    Duration: 0:00:26.064111\n",
            "Epoch 3/15,Train Loss: 0.3497,Test Loss: 0.3470 ,    Duration: 0:00:27.034675\n",
            "Epoch 4/15,Train Loss: 0.3160,Test Loss: 0.3330 ,    Duration: 0:00:26.207314\n",
            "Epoch 5/15,Train Loss: 0.2862,Test Loss: 0.3197 ,    Duration: 0:00:25.982047\n",
            "Epoch 6/15,Train Loss: 0.2655,Test Loss: 0.3255 ,    Duration: 0:00:26.216457\n",
            "Epoch 7/15,Train Loss: 0.2486,Test Loss: 0.2997 ,    Duration: 0:00:26.390667\n",
            "Epoch 8/15,Train Loss: 0.2336,Test Loss: 0.2973 ,    Duration: 0:00:27.131867\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-99739204a408>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_losses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_gd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-17-d4c74174b5fa>\u001b[0m in \u001b[0;36mbatch_gd\u001b[0;34m(model, criterion, optimizer, train_loader, test_loader, epochs)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m       \u001b[0;31m#forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m       \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m       \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-712e95ac63be>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_layers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense_layers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    452\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    453\u001b[0m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0;32m--> 454\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs,targets=next(iter(train_loader))\n",
        "inputs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFRSTzK9n_HP",
        "outputId": "da6c50a2-83c1-4231-be97-4c0e07593fb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([99, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXZBO7zrvjH0",
        "outputId": "97c65c22-6ee7-477d-9921-a24169abfc35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([99])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "      outputs=model(inputs)\n",
        "      loss=criterion(outputs,targets)\n"
      ],
      "metadata": {
        "id": "owxJB7_M9O6r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs.shape"
      ],
      "metadata": {
        "id": "zAuxF_gS9Scd",
        "outputId": "9db4cb92-4023-4b43-bbcb-7486640468d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([99, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the train and test loss per iteration\n",
        "plt.plot(train_losses,label='train loss')\n",
        "plt.plot(test_losses,label='test loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "61Sivjn_DGxD",
        "outputId": "c2537bf4-9445-4d92-b135-58eff5439cbe"
      },
      "execution_count": null,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy\n",
        "model.eval()\n",
        "n_correct=0.\n",
        "n_total=0.\n",
        "for inputs,targets in train_loader:\n",
        "  #move the data to GPU\n",
        "  inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "  #forward pass\n",
        "  outputs=model(inputs)\n",
        "\n",
        "  #get prediction\n",
        "  #torch.max returns bath max and argmax\n",
        "  _,predictions=torch.max(outputs,1)\n",
        "\n",
        "  #update counts\n",
        "  n_correct += (predictions==targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "train_acc=n_correct/n_total\n",
        "\n",
        "\n",
        "n_correct=0.\n",
        "n_total=0.\n",
        "for inputs,targets in test_loader:\n",
        "  #move the data to GPU\n",
        "  inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "  #forward pass\n",
        "  outputs=model(inputs)\n",
        "\n",
        "  #get prediction\n",
        "  #torch.max returns bath max and argmax\n",
        "  _,predictions=torch.max(outputs,1)\n",
        "\n",
        "  #update counts\n",
        "  n_correct += (predictions==targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "test_acc=n_correct/n_total\n",
        "\n",
        "print(f'Train acc: {train_acc:.4f},Test acc:{test_acc:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uhFlUflD0wn",
        "outputId": "16d5d959-0172-42ef-e5da-c3e50c3143ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train acc: 0.9626,Test acc:0.9013\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "  \"\"\"\n",
        "  This function prints and plots the confusion matrix.\n",
        "  Normalization can be applied by setting `normalize=True`.\n",
        "  \"\"\"\n",
        "  if normalize:\n",
        "      cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "      print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "      plt.text(j, i, format(cm[i, j], fmt),\n",
        "               horizontalalignment=\"center\",\n",
        "               color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()\n",
        "\n",
        "# Do these results make sense?\n",
        "# It's easy to confuse 9 <--> 4, 9 <--> 7, 2 <--> 7, etc. "
      ],
      "metadata": {
        "id": "CS03kfW8H4B7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get all predictions in an array and plot confusion matrix\n",
        "\n",
        "x_test = test_dataset.data.numpy()\n",
        "y_test = test_dataset.targets.numpy()\n",
        "p_test = np.array([])\n",
        "for inputs, targets in test_loader:\n",
        "  # move data to GPU\n",
        "  inputs,targets = inputs.784(device),targets.to(device)\n",
        "\n",
        "  # reshape the input\n",
        "  #inputs = inputs.view(-1, 784)\n",
        "\n",
        "  # Forward pass\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  # Get prediction\n",
        "  _, predictions = torch.max(outputs, 1)\n",
        "  \n",
        "  # update p_test\n",
        "  p_test = np.concatenate((p_test, predictions.cpu().numpy()))\n",
        "\n",
        "cm = confusion_matrix(y_test, p_test)\n",
        "plot_confusion_matrix(cm, list(range(10)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "m4kk5omeIuho",
        "outputId": "915086e3-efc7-417d-f8d4-add641f81d4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[860   0  27  11   1   1  96   0   4   0]\n",
            " [  6 971   2  13   3   0   4   0   1   0]\n",
            " [ 14   0 892   8  24   0  61   0   1   0]\n",
            " [ 11   4  18 907  27   0  31   0   1   1]\n",
            " [  3   0 100  29 764   0 103   0   1   0]\n",
            " [  0   0   0   1   0 975   0  17   1   6]\n",
            " [ 98   0  61  17  58   0 760   0   6   0]\n",
            " [  0   0   0   0   0  19   0 949   0  32]\n",
            " [  2   0   6   1   2   1  10   3 973   2]\n",
            " [  1   0   0   0   0   9   0  27   1 962]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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1VzP46/DhbNnsHdafpSvWPHLu5dGvERG1n12R+2jV5hk+Gv9+tuoAcHNzY+2GTezeu5+IPdGsX7eWiPDwbNuFh9Ksy1asYd+BwyyaPy/b7fI4KZKyemK032H9+rNsZfYTNz9OTrS3xQjHypRuew+s5ObNm2zf/gf9B2jZtF1dXXF3d7f4ft/yJYk8eJb7DxJISkpmW9RxOobWeqRM5xb+LFwbBcC9B/HsjD7Fg/jEbPnt6emJf8BDWVxf36qPyOJmhUYhjSn8mByuuSDavXt3dVmmIoQgf35N4yghIYHEhATdlr8oSdm0MUp+2K6kcEH1LI3kzOnTFCtWnKGDB1K/XgDDhw7m7l3LxckOnbxIsH9FihTKS57cuWjVqDreJQunXg8OqMjl67c5ee6qEe4DcPbMGaKj91Ev8F8qG7ow9t0x+FQsw4J5v/D2e9nvWYLWIwmqU5sypUoQ2rwFgUH6+K4kZXMWe/NbCGHxYWuMVHf8XghxRQhxUE+7iUmJRO/by+ChwwiP3Eu+fPmYYsW4y9HTl5n6wwZWzBzB8i+eZ//RGJKSH8pCdGtZh0WmXqUR3Llzh57dOjN56qeP9AL1ZOz74zl68m+69+zF119+oYtNZ2dnIqKiOXEmhj2Ruzl0UNev1RD+U5KyDogmwaOCJcAPQCu9jXp5eePl7U2gqVf2bKcuREfvs8rGnGXhBPeeTIvBnxF3+z7Hz2q9SGdnJzqE1mLx+r16uw1oj7A9u3Wme8/edHy2U+Y3ZJPuPXqzbMlvutp0d3fnqSZNWb9en/E0JSmbs9iV30IgnCw/bI1hwVJK+QdwXW+7Hh4eeHuX5tjRowBs3rSRqlWtG7wvXlgbfyvtUZgOTWuxYM0eAEKDfDh25jLnr8Tp6zSaSNWw5wbh41uVl0aN1t1+CieOH099vXLFMqr4+Gbb5tWrV4mL09rk/v37bPx9Az462AUlKZvT2JvfjtSztPk6S3Mp3NJlylh0z9Rp0xnQrw8J8fGUK1+Br7/73qo6500ZTJFCeUlITObljxdy846mMd716TqpEzvmHFk5lgL5cuOay4V2TWrQ9vmZHDl9yao6d+7YwS8//4SfXw2C6tQGYNyHE2jVuo1VdszpbyaHW6VCaca8M5Z1a9dw/NhRnJycKFOmLJ998WWW7adw6eJFnhvYj6SkJJJlMp27dKPNM22zbRfsTJrVCoz22yj5YXtrb3sIgpZimBQugBCiHLBSSmnRYrGAOnXljvBIQ3wpEvSiIXbBsRNpONvB401WcEQ5WUfGCClc5yLlZf6Wlk9A3prf94mVwlUoFIr0EabDQVDBUqFQ2ASBfYxFWoqRS4fmAbsAHyFEjBBC31XBCoXC4dFzgkcIMUoIcUgIcVAIMU8IkVsIUV4IESGEOCGEWCCEcDWVdTO9P2G6Xi4z+0bOhveUUnpKKXNJKb2llLOMqkuhUDgmegVLIYQX8CJQ1zRH4gz0AD4GpkkpKwE3gJRO2yDghun8NFO5DHG4HTwKheLJQeelQy5AHiGEC5AXuAiEAotN1+cAHU2vO5jeY7reTGRSiQqWCoXCNggrDygmhNhjdgxJMSWlPA9MAf5GC5I3gSggTkqZktghBkhZge8FnDPdm2gqXzQjd9UEj0KhsAkCYe2e/dj0lg4JIQqj9RbLA3HAInTeQah6lgqFwmbo+BjeHDgtpbwqpUwAfgOCAXfTYzmAN5CSNeQ8UNrkgwtQCLiWUQUqWCoUCtth3WN4RvwN1BdC5DWNPTYDDgObgS6mMv2AlHx0y03vMV3fJDPZ6WB3j+FGbVYxcpdN4TZTDLMNcG3lK4bZTkhMzrxQFsnlYtzf4jsPspdfNCMK5MllmG2FGUK/3VJSygghxGJgL5AI7AO+AVYB84UQH5rOpazKmQX8JIQ4gZbDokdmddhdsFQoFP8d9FyULqV8D3hc8+QUEJhG2QeAVToxKlgqFAqb4Ug7eFSwVCgUNsHRtjuqYKlQKGyH48RKx5gNT0vyNYXp06aS382J2NhYXerSQyb0hU51iPqmP3u+6c+cN5/BLZczT9Uuzc4ZYez5pj/fvtY6NTVaj9Cq7P6qH5Ff92PztJ7UqFA8S3V+/tk06tb2o65/DfqF9eLBgwdZsgMQc+4cz7RsRj1/PwIDajDzi+mP1vXpJxTM48w1O2pzc27GxTEwrDsN6/gRXLcGkRHhLF+ymJDAWpQs5Eb0Xn1kQ5QUbjYRjpX81yGCZVqSr6D9Um/8fYPFSYMzQw+Z0FJF8/N8xwCCR86l7pAfcHZyontoVb57rTV9J6yk7pAf+PvyLfo8rSVcPXPpJk+/Op96Q+fw0S/hzHj5aav9vnD+PF/O+JxtuyLZs+9PkpOSWLRwvtV2UnBxcWH8xMlE7jvIxq07+fbrmRz5S2uHmHPn2LhxPaVL20+bP86Y10cT2rwlO6MOsnlnFFV8fPGtVp3ZPy+kQXCI3fptzn9CChcVLHUnLclXgNdfG82HH32sW0PqJRPq4izI4+aCs5P2/70HCcQnJHPi/A0ANu09Q8dGVQAIP3yBuDv/ALD7rwt4FcufJd8TkxK5f/8+iYmJ3Lt3D0/PUlmyA+Dh6Ult/4eyvT6+vqmyvW/+bzQfjLe/Nk/h1s2bhO/cTu++AwBNKrmQuztVfKpSqbKPLj6DksLVC6XBkwOsXL6MUqVKUaNmrcwLW4geMqEXrt3h00V7ODZ3CKfnD+fWvX9YvPUoLs5OBFQuCcCzIVXwLl7gX/f2b1WDdZGnrfa7lJcXL738Cr6VylKxbCkKFipE8xbW91DT4uzZMxyIjqZuvSBWrViGZykvu2tzc86ePU3RosV4cfhgQhvVY9TIoVZJJVuKvUnKWoq9+a16loAQorQQYrMQ4rApx9xLetm+d+8eUyZ9pJsmtp6453ejbcNKVO37LRV6fkW+3Lno0awqfSesYNKwpmyb3pvb9+P/JRXRuFZp+rWqwdvf/WF1nTdu3GDlyuUcOnqKE2fOc+/uXeb9Mjfbn+XOnTuE9ezKxMmf4OLiwpRJExnz7rhs2zWSpMQkDuzfR/9BQ9m0PZK8efPx+SeTbO2WIg2sCZRPdLBEW0X/ipSyGlAfGCGEqKaH4VOnTnLmzGka1KtNtSrlOR8TQ6P6dbh8yToRscfRQyY01L8sZy7dJPbmfRKTklm6/Tj1q3kR8ddFmr8yn5AXf2b7nzGciLmReo9f+WJ8OaolXd9byvXb1k/MbN70O+XKlaN48eLkypWL9h2fJWLXTqvtmJOQkECfnl3o1r0X7Tt24vSpk5w9e5rgQH/8fCpw/nwMIQ3q2kWbm+Pp5UUpL2/q1NPWIbfr2IkD+6Oz5WNa2JWkrBXYm98qWAJSyotSyr2m17eBv3iYHilb+PnV4EzMZQ4fO83hY6fx8vZme3gUJT08smVXD5nQc1dvEejrSR43bVVWU/+yHP37GsXd8wLgmsuZV7oF8u0q7Re4dPECzH+3A4MmrU4d07SW0qXLEBkRwb1795BSsmXzJnx8rZMHNkdKyYhhg/HxqcrIl0YBUN2vBqf+vsTBo6c4ePQUXl7ebNu1xy7a3JySJT0o5eXNieOaVPIfWzZRJRttkR72JilrKfbmtyMFyxxZZym0lO3+QEQa1zKVwk1L8rXfAP1VKvSQCY08cokl246xa2YYiUmS/ScuM2v1Acb2b0TroAo4CcG3K6PZGq39dX+zTwOKFMzDpy80ByAxKZlGI617hK4XGETHTp0JDqqDs4sLtWr7M3DwkMxvTIfwnTuY/8tcqvvVIDhIm+h5d9yHtGyVddne9DBCmnXC5GkMH9yP+Ph4ypYrz/SZ37FqxVLeem0U12Kv0qtrB/xq1GLh0lV25bc5/xUpXEdaZ2moFC6AECI/sBUYL6X8LaOyAXXqym27jJHCNVLy1ZETaRgps2tkIo3b9xMMs60SafwbI6Rw3UpWll69P7O4/Olpzzy5UrhCiFzAr8DPmQVKhULxH0PHrEM5gWHB0pRTbhbwl5TyE6PqUSgUjokAHChWGjobHgyEAaFCiGjTof+gl0KhcFAETk6WH7bGsJ6llHI7DjV8q1Aochr1GK5QKBSZIRzrMVwFS4VCYRME2MXjtaWoYKlQKGyG6lkqFAqFBagxS4VCocgMNWapUCgUmaOts3ScaGl3wdKopjNyW+fVFaMNsw1QtNVHhtm+vPoNw2wbiauBWykVOYV9JMiwFLsLlgqF4r+DA8VKFSwVCoWNEGrpkEKhUGSKo41ZOsTAz7AhAynrXZK6ZlK4v/26iLq1/cif25m9UXt0rS8pKYn69QLo1LFdtuwMHzKI8qU9CAyomXruwP5omjZuSMPAABo3DGRP5G6rbI7oVI89s54j6vvnGNm5HgCFC+Rm5aSe/PnjMFZO6ol7/twAjOoeRPg3gwj/ZhB7Zj3HnQ1vULhA7kzriDl3jrYtmxHo70dQQA2+NEnh9u/Tg0ZBATQKCqCGTwUamXJdZhc9pVkfPHhAaEh9goMCqF+nJhM+GAvAN1/OwN/PB/e8LnYr4WuOkVK4Rtq2FiEsP2yNQwTLPmlI4Var5scvC36lUUhj3eub8fln+OqQXbt3WD+WLF/9yLl33nqdN8e8w87dexnz7ljeecvyCZZq5Yoz4JnahDw/m8DB39G6fiUqlCrMqz0bsGXfGWr0/Yot+87was8GAExbEEH9IbOoP2QW7363mW0H/uaGBbIVLi4ufDhxMrv3HeR3MyncH+bOZ3vEXrZH7KV9x0606/CsdQ2SBnpLs7q5ubF8ze/siNjLtvAoNm5YR+TucIIaNGTpqnWULlM22z4b4ffjGCWFa7Rta3GkTOkOESwbhTSmyGNSuL5Vq1LFRz9p0xRiYmJYu2a1Llmp05LwFUJw+9YtQJNt9fT0tNieb9miRP51nvv/JJKULNm2/286hvjQNrgKc9cdAGDuugO0M8nsmtMttDoLN1n2y5yRFC5oKwuW/LqILt16WOx7eugtzSqEIH9+TU44ISGBhIREBIJatf0pW7Zctv1NwVGlcI22bS2qZ+nA/O+VUXz40cc4ORnTNBOnTOPtN1/Ht2JZxrz5P8Z+MMHiew+dvkpwjdIUKZiHPG4utAqqiHeJgpQonI9L1zW510vX71KicL5H7svj5kKLehVY+scRq/01l8JNYeeObRQvWZKKlSpbbe9xjJBmTUpKolFQHSqX9aRps2bUDQzK/CYrsTdJWYdEqJ4lAEKI3EKI3UKI/SYpXPvWUAVWr1pJ8RLFCQioY1gds775iomTp3Lk5FkmTprKiGHPWXzv0b+vMXV+OCsm9WD5xz3Yf/IKScnJ/yr3+JrSZxpUZtehGIsewc1JkcL9aPInFCxYMPX84oXz6dI1+71Ko3B2dmZ7RBSHjp8lak8khw8dtLVLijRISf6repbwDxAqpawF1AZaCSHqG1hftgnfuYNVK1fgW7k8ffv0ZOvmTQzsF6ZrHb/M/ZH2HTsB8GznrkTtsW6CZ86a/QQPm02Ll+cSd/s+x89d58qNu3gU0XqTHkXycTXu3iP3dA2txqKNh6yqJyEhgTAzKdwUEhMTWbFsCZ26dLPKXnoYKc3q7u5OSOMmbNywThd75tibpKxjonTDAZAad0xvc5kOY9XRssn74z/ixOlzHDl+mh/nzuOppqF8P+cnXevw8CzF9j+2ArB18yarH2VTJHVLlyhIhxBfFmw8xKqdx+nTUptx79OyJit3HEstXzCfG41qlmHFzuMW1yGlZORjUrgpbNn0O1Wq+OLl7W2V3+mhtzRr7NWrxMXFAXD//n22bPqdylX0H9u2N0lZR0X1LE0IIZyFENHAFWCDlPJfUriW0C+sF02fasjxY0epXKE0c2bPYvmyJVSuUJqI8F106tiW9s+00td5HRgQ1otmTYI5fuwoPhXLMGf2LD6f+TVvvfEaDer5M+69t5k+4yurbM4b25m93w9h8fiuvPzZOm7e/Ycp83YRWqc8f/44jKYB5Zgyb1dq+faNqrBxz2nuPbBcDTFFCvePrZtTlwqtX6vN6v+6aAGdu3W3yueMMJdmrV2jKp27dsuWNOulSxdp16o5DQP9CQ2pT5PQ5rRq05avZn5OtUpluXA+huBAf14YnnWpYCP8fpy+fXrSJKQBxyS/b9kAACAASURBVI4epWI5b374fpZD2LYK06J0R5GVMFwKF0AI4Q4sAV6QUh587Jq5bnidI8fPGOSDIWYBY+VkAYq31ncNnzlG7g03cv/2PwlJhtl2y+VsmG1HxQgp3AKlfWXtl7+zuPz2V0NsKoWbI7PhUso4YDPwr+6flPIbKWVdKWXdYsWK54Q7CoXCTlBjloAQoripR4kQIg/QArB+7YpCoXhicaQxSyP3hnsCc4QQzmhBeaGUcqWB9SkUCgfDHnqMlmKkFO4BwN8o+wqFwsHRucdoepL9DvBDW3kzEDgKLADKAWeAblLKG0KL0p8BbYB7QH8p5d6M7KsdPAqFwiYI/ddZfgaslVL6ArWAv4A3gI1SysrARtN7gNZAZdMxBPgyM+MqWCoUCpuh15ilEKIQ0BiYBSCljDdNLHcA5piKzQE6ml53AH40rQcPB9yFEBkmalDBUqFQ2AwnISw+MqE8cBWYLYTYJ4T4TgiRDygppbxoKnMJKGl67QWcM7s/xnQufV+t/nQKhUKhE1b2LIsJIfaYHeY7C1yAAOBLKaU/cJeHj9yAtquQbOwiVJnSFQqFTRACnK3bmRObwaL0GCDGbJfgYrRgeVkI4SmlvGh6zL5iun4eKG12v7fpXLqonqVCobAZek3wSCkvAeeEECmJAJoBh4HlQD/TuX5AStLR5UBfoVEfuGn2uJ4mdtWzFDiWgFEKN+7GG2rfyC2JVUfpl7D2cU5+nv1M6ulx836iYbZLqO2OOYbOyyxfAH4WQrgCp4ABmNZ4CyEGAWeBlHRZq9GWDZ1AWzo0IDPj6QZLIcTnZPB8L6V80cIPoFAoFP9CoC0f0gspZTSQ1mN6szTKSmCENfYz6lnqqwKmUCgUj+FID5LpBksp5Rzz90KIvFLKe+mVVygUCquwkwQZlpLpBI8QooEQ4jCmJBhCiFpCiJmGe6ZQKJ54HCmRhiWz4Z8CLYFrAFLK/Wgr5W3CgwcPaNQgkMCAWgTUqs4H497T1X52taBffWEI/j6laR78UFM77sZ1enVqQ+N61enVqQ1xcTcALSP5u2+MJqRuNZ4Oqcuf+/dZXE962t5/HthP86eCaVC3Ft07t+eWSUkyMyqWzM/6t5qmHkc+acvg0IoADGhSga3vNWfTO80Y8+yjCW5LFc7DsWntGNq8ksW+P44ebR7gU5oWj7V5705teKpedXp3asNNU5uvX72CliF1af1UIG1DGxIZvsNmfmeEkdreRvptDQJdF6UbjkVLh6SU5x47ZVzm1Uxwc3Nj7YZN7N67n4g90axft5aI8HBdbOuhBd21Zxg/Llz+yLkZn00huHFT/og8RHDjpsz8dAoAm39fx5lTJ/gj8hATP5nBmFctnzNLT9v7heFDGPvhBHbt2U/b9h2ZPm2KRfZOXr7D0xM28/SEzbT6aDP345NYE32BhlWK0bKWJy3GbyL0g4189fuj8hRju9Rg86HLFvv9OHq1+ZzH2nymqc23PtbmwY2bsvaPSNZs3c3kz7/m9ZeG28zvjDBK29tov63lSetZnhNCNASkECKXEOJVtA3qNuFxXejEhATdxj300IIOahiCe+HCj5zbsHoFXXr0AaBLjz6sX639Yq9fs4LO3XsjhCCgXhC3bsZx+VKGS71SSU/b++SJYwQ30jr+TUNbsHzpb1b5D9DItwRnY+9y/vp9+jYuz4x1x4hP1FQkr91+uEyqZS1P/r52j6MXLeu9poWRbd7Z1Oadzdo8X/78qT8v9+7dzfJvoaPqhhvtt7U8acl/h6FNsXsBF9CUGq2actebpKQkgurUpkypEoQ2b0FgkD660EZpQcdevUJJD22PfomSHsRe1TYRXLp4AU+vh8JfHqW8uHTxgtX2zbW9fatWZ5Xph3/pb4s5H/P4Q0HmdKjrzdLIGAAqlMhPYKWirPjfUyweFUKtsu4A5HVzZsTTVfhkVfb+buZ0mwOsXbmM0KCaDOjxLJM//zpL9h1VN9ye/E7ZwWPpYWsyDZZSylgpZW8pZUkpZXEpZR8p5TVLKzCJlu0TQuiW+NfZ2ZmIqGhOnIlhT+RuDh10HF1oofMzxePa3jO+/o7vvvmSxg3rcefObXK5ulplL5ez4OmaHqzcq/0COTs74Z7XlXaTtvLhbwf5anAgAK88U5VvN57g3j82G5GxmMfbvFXbDmyKOMC3Py1k6gS7l7N/ohFWHLYm0x08QogKaHni6qMtUt8FjJJSnrKwjpfQHtsLZtXJ9HB3d+epJk1Zv34t1f2yPxBulBZ0seIluHzpIiU9PLl86SIpWkMenqW4eD4mtdylC+fx8Cxlsd20tL2r+PiydKWmk33i+DHWrVltla9Nq3vw599xxN7+B4CLN+6zJlrr7UafvUGylBTJ74p/+cI8E1CKMZ2qUzBPLpIl/JOQzA9bLf2x0MjpNjcnqGEIf589zfVrsRQpWswu/DYae/PbHh6vLcWSx/BfgIVoMhGlgEXAPEuMCyG8gWfQshfrwtXHdKE3/r4BHx9fXWwbpQXdonVbFs+fC8Di+XNp0aaddr5VW35d8DNSSvZGRlCgYKHUR8fMSE/b++oV7XEzOTmZyRPHM/A56yRfO9bzZumehwF83f4LNKyiBZoKJfLj6uzE9TvxdJq6jfpvr6f+2+v5btNJPl971OpACca1efPWbfnV1Oa/mrX5mVMnSVE0/XP/PuL/iadwkaJ247fR2JPf2my45YetsWRveF4p5U9m7+cKIV6z0P6nwP+AAukVeEwKN1ODly5e5LmB/UhKSiJZJtO5SzfaPNPWQncyxlwLOikpiX79B1qtBT3yuTB27djGjWuxBPpVZPQbb/P8S68yfGBvFvz8A17eZfjy+58BCG3Ris0b1hJStxp58uRlyuffWFxPirZ3db8aNArSJnreHfchJ0+c4NuvtWWw7To8S5++mW55TSWPqzONfUvw+s8PlzDN33mWqWEBbHynGQmJybz8Y5TF9ixBjzZ/wazNg/wqMsrU5s+btflMU5uvWbGEXxf8TK5cuXDLnYcZs37KUu9GD78zom+fnmzbuoXY2FgqlvPmnXfH0X/goGzbNdpvq7CTiRtLSVc3XAiRMhX3OnADmI/2GN4dKCylfDNDw0K0BdpIKZ8XQjQBXpVSZhjV6tSpK3dEON4uy6u3/jHUfqG8uQyz7aiJNK4Y2OYlCroZZttRMUI3vGiF6rLNB79YXH5un9o21Q3PqGcZhRYcUxpoqNk1CWQYLIFgoL0Qog2QGygohJgrpeyTVWcVCsWThSP1LDPaG14+O4ZNPc83Acx6lipQKhQK4OGYpaNgUT5LIYQfUA2thwiAlPJHo5xSKBT/DZ6InmUKQoj3gCZowXI1moTkdsDiYCml3AJsyYqDCoXiyUQIcHagYGnJ0qEuaMkzL0kpB6Dp8RYy1CuFQvGfwJH2hlvyGH5fSpkshEgUQhREE/wpndlNCoVCkRlP1GM4sEcI4Q58izZDfgdtF49CoVBkCweKlZkHSynl86aXXwkh1gIFpZQHjHVLoVA86QjsI0+lpWQkWBaQ0TUp5V5jXFIoFP8J7GQs0lIy6llOzeCaBEJ19sVhKe7AOz6M3GVTuN5Iw2zfiPzCMNuJScmG2QZwcbYo53aWSG9HXrbtGmL1CRmzlFI2zUlHFArFfw/j/mzoj0WL0hUKhUJvBE9Iz1KhUCiM5onb7qhQKBR6kyIr4ShYohsuhBB9hBDvmt6XEUIEGu9a+hgp5emotu1dOnVEzybsWfQWUYvHMLJXEwB+mjiA8PlvED7/DY6sGkf4/DcAKONZhOu7Pkm9Nn1MD5v6nsLwIYMoX9qDwICaqef69elBw8AAGgYGUL1KBRoGpruIxCqM/D7j4uLo1b0rtf2q4l+jGhHhtls2/aQl/50JJKPNfr8P3AZ+BeoZ6Fe6pEh5rlqzAS9vbxrVr0fbtu2pWq3af9Y2aNKpw54fyeCBfXWxl4Iefler6MmATg0JCZtMfEISy2c8z+ptBwl7Y3ZqmYmjn+Xmnfup70/FxFK/R/aCm95t3jusH0OHj2DIoP6p5+bMnZ/6+s3XX6VQQX12Ahv1fQK8NvplWrRsyS8LFhEfH8+9e/d0r8NSHGjI0qLJqCAp5QjgAYCU8gZgnQqWjhgp5emotsG+pVN9y3sQefAM9x8kkJSUzLaoE3QMrf1Imc4tAli4Vt8s7Hq3eaOQxhQunHYbSylZsngRXbpnvRf8eF1GfJ83b95k+/Y/6D9Ay7ru6uqKu7u77vVYgpaiTVh82BpLgmWCEMIZ01IrIURxtJ6mTTBSytNRbRuJHn4fOnmBYP9KFCmUjzy5c9GqUXW8PR7qfAcHVOTy9duc/Ptq6rlyXkXZNe911n/3EsH+FW3mu6Xs2L6NEiVLUqlSZUPs68WZ06cpVqw4QwcPpH69AIYPHczdu3dt5o+TFYetscSH6cASoIQQYjxaerYJlhgXQpwRQvwphIgWQjieXoRCF46evszUHzawYuYIls8Ywf6jMSSZLfzu1qoui9Y+/PG4FHuLKq3fpUHPj3l96m/8MKE/BfLlTsu03bB44Xy6dNOnV2kkiUmJRO/by+ChwwiP3Eu+fPmYovP4uTU8UVmHpJQ/CyGi0NK0CaCjlPIvK+poKqWMzaqDj2OklKej2jYSvfyes3QXc5ZqEwnjRrbj/GVNodPZ2YkOobUI7jUptWx8QiLXbyYCsO+vc5yKiaVy2RLsPfy3TXzPjMTERJYvW8K2nZG629YbLy9vvLy9CQwMAuDZTl2YMvljm/gi7OTx2lIsmQ0vA9wDVgDLgbumczbBSClPR7VtJHr5XbxwfgBKexSmQ2gtFqzRepKhQT4cO3OZ81fiUssWK5wfJ9P0ZzmvolQqU5zTMdb/vc2pNt+86XeqVPHFy9tbd9t64+Hhgbd3aY4dPQrA5k0bqVq1qs38eaJ6lsAqHgqX5QbKA0cBS/QzJbBeCCGBr6WU/9J6tVYK10gpT0e1DfYvnTpvymCKuOcjITGJlycuTJ357tqyzr8mdhoFVOKd4c+QkJhEcrLkhfHzuXHL+hlbvdt8QFgvtm3byrXYWHwqluGtt9+j34BBLF64gK7du2fZbloY9X0CTJ02nQH9+pAQH0+58hX4+rvvdbGbFexhSZClpCuFm+4NWjai56WUgy0o6yWlPC+EKAFsAF6QUv6RXnlHlcJVpI1KpJE2jphII7h+PfbqLIXrVaWGHDZzicXl321R2aZSuFZ/a6bUbEEWlj1v+v8K2iSRTRezKxQKO8KKBen20AO1RLBstNlbJyAAuGDBffkAJynlbdPrp9EWtSsUCgWgJQB2FCwZsyxg9joRbQzzVwvuKwksMWUVcQF+kVKutdpDhULxRPJE6YabFqMXkFK+aq1hKeUpNCVIhUKhSBNHCpbpjlkKIVyklElAcA76o1Ao/kMIISw+LLTnLITYJ4RYaXpfXggRIYQ4IYRYIIRwNZ13M70/YbpeLjPbGU3w7Db9Hy2EWC6ECBNCdEo5LPJcoVAo0iHlMVznCZ6XAPNNMx8D06SUlYAbQMr6q0HADdP5aaZyGWLJbHhu4Bpa1qG2QDvT/wqFQpF1rFiQbknHUgjhDTwDfGd6L9Di1mJTkTlAR9PrDqb3mK43E5l0XzMasyxhmgk/yMNF6SkYpV+kUCj+Q1i53bHYYzkmvnlso8unwP94OCldFIiTUiaa3scAKftdvYBzAFLKRCHETVP5dLeKZRQsnYH8kObcvgqWCoUiW2RhNjw2vUXpQoi2wBUpZZQQokn2vfs3GQXLi1LKHF0XmZAsib39jyG2ixUwTq729v0Ew2wD5HF1Nsz2gwTjdqsYucum4ouW7/ywlpPTjZMHBuN22YBxAmBGWXXWz99goL0Qog3a0GFB4DPA3TRZnQh4Ayk5+s4DpYEYIYQLUAhtuDFdMhqzdKBJfYVC4Who6o76jFlKKd+UUnpLKcsBPYBNUsrewGagi6lYPyAl+/Ny03tM1zfJTP6KZRQsm2XsnkKhUGSDnNnu+DowWghxAm1Mcpbp/CygqOn8aOCNzAyl+xgupbyeZfcUCoXCAozIZyml3AJsMb0+RRo5KaSUD4Cu1thVUrgKhcImpDyGOwr2IG2RKbO++oJmDQNo1sCf7778HIBDf+6nfYvGtGwcSJvQhuyL0idLtd5ytTfj4hgY1p2GdfwIrluDyIhwli9ZTEhgLUoWciN6b9ZEuoyWZQ2oXonGQbVp0rAOzRtrSab+PBBNq6bBqef27tmdiRXLyG6bVyyRn/VvNk09jkxty+Cmmm7PgCYV2Ppucza93Ywxzz7MZTmyZRW2j23BH+8156mqJWzid0YYKVdrpN/W4kiCZXbfszxy+BC//Pg9K3/fTi5XV8K6tqNZyzaMf+8tRv1vDE1btGTThrVMGPsWi1ZsyFZdRsjVjnl9NKHNW/L9TwuIj4/n/r17FHIvxOyfF/LqSyOybDcnZFmXrPqdosWKpb5//503efXNd2j+dCs2rFvDuHfeZNmajdmqQ482P3nlDk9/tBnQxraiJrRmzf4LNKxSjJY1PWkxYRPxickUza+Jklb2KECHOt6EfriRkoVyM//FYELGbiDZiklqo6WNjZKrNdpva7GDGGgxdt+zPHHsCP516pEnb15cXFwIahjC2pVLEUJw+/YtAG7duklJD89s16W3dOqtmzcJ37md3n0HAJrsaCF3d6r4VKVSZZ9s+ZqTsqypmLX57Vs38fAslW2TusvV+pbgbOxdzl+/T9+Q8sxYd4z4RG151LU78QC0rOXJsqgY4hOTOXftHmeu3sW/nHWys0ZKGxspV2u0JLM1CJ48dUeb4lO1OrvDd3Dj+jXu37vH5g3ruHA+hrETpjD+vTcJ9KvIh+++yRvvfpDtuvSWTj179jRFixbjxeGDCW1Uj1Ejh+aI7KgesqxCCLp2bE2zkEB+/P5bAMZPnMq4t9+glm953hvzOm+P/TDbvurd5h3qeLN0TwwAFUrkJ7BSUVa89hSLR4VQq6wWcDwK5ebCjfup91yMu4+Hu3XqkUbK7BopV2tXksxC/0QaRmJosBRCuAshFgshjggh/hJCNLDWRmUfX55/8RV6d25Ln67tqFajJs5Ozvw0+xveGz+Z3QdP8t6Hk3jtxWFGfIRskZSYxIH9++g/aCibtkeSN28+Pv9kUuY3ZhM9ZFlXrt/Cpu2RzP9tJd9/+yU7t29j9qyv+WDiFPYfOc0HE6fw8oghOnmsD7mcBU/X9GDlXu2X39nZCfd8rrSbvJUPfzvIV4McI1G/vcnVGomw4rA1RvcsPwPWSil90XJbWiOhm0qPsAGs3ryLX1dtpJC7O+UrVWbxvLm0bqftiW/bsTPRUdnX7tFbOtXTy4tSXt7Uqaf9krbr2IkD+6Oz7WdGpMiydu7SLVt2PEtpn7t48RK0adeRfVGRLPjlJ9q213a3dHi2C3t1mFTTs82bVvfgz3NxqbvALt64z5poLal/9NkbJEtJkfyuXLr5gFKF86Te5+meh0txD2zm9+OkJVcbHb1PF9v2JMksAGchLD5sjWHBUghRCGiMaRGolDJeShmX8V1pE3v1CgDnY/5m7cpldOzSnZIenoTv0LTPdvyxmfIVK2XbZ72lU0uW9KCUlzcnjmuyo39s2UQVX2NlR/WQZb179y53bt9Ofb1l4wZ8q1XHw6MUO7drbb5t62Yq2Fmbd6zrzdLImNT36w5coGGV4oD2SO7q4sT1O/GsP3CRDnW8cXVxonTRvJQvkZ99Z6xbVmykzK6RcrX2Jsn8pEnhZpXywFVgthCiFhAFvCSlfGTwxVwK18tsLMWcIf16EHf9Oi65cvHhpE8pVMidjz+bydg3XyUxMRE3t9xMnDYj2w4bIVc7YfI0hg/uR3x8PGXLlWf6zO9YtWIpb702imuxV+nVtQN+NWqxcOkqq+waKct69cpl+vfSdoglJibRqVsPmrVoSb58+Rjz+miSEhNxy52bT6Z/ma16QL82z+PqTGPfErz+y8Me2PydZ5kaFsDGt5uRkJjMy3O0ZVrHLt5mxd4YNr/TjKRkyZj5+62aCdfT7/QwSq7WaL+twz7GIi3Failciw0LURcIB4KllBFCiM+AW1LKd9K7p6Z/Hbl6005D/FGJNNLGyEQa+XMb97dYJdJIG6OCT3BQXaJ0lsKtWK2WnPDzaovL9wjwdiwpXCuIAWKklBGm94vRlCEVCoUCULPhAEgpLwHnhBApCwqbAYeNqk+hUDgejjQbbvQOnheAn00iQaeAAQbXp1AoHAVh3LCBERgaLKWU0YDNxhgUCoX9krKDx1Gw+73hCoXiyUX1LBUKhcICspHUN8dRwVKhUNgE7THccaKlCpYKhcJmONBTuAqWCoXCVgiE6llmjVxOwtCdNkZRIE8uW7uQZfI7GzcfmZhk3O4gI3fZFA5+zTDbADd2TDbM9j8JSYbYtXY7qKWonqVCoVBkghqzVCgUCkuwk2xClqKCpUKhsBkqWCoUCoUFONIEjyPtNkrFSClPZdt422nJ+P55YD+hTwUTVKcWXTu159atW9muB/TxfUT3Ruz55RWi5r3CyB6NHrn2Uq/G3I+YTNFCeQFwL5CHBR/3Y/fc0Wz7/gWqVShpM79TePDgAaEh9QkOCqB+nZpM+GAsAM8NCKNurWo0qFuLEUMHk5BgbKrBxxFoi9ItPWyNwwXLFCnPZSvWsO/AYRbNn8dfh/VJZqRs54zt3mH9WLL80TyGI4cP4f0PJhARtZ927Tvy2SdTslUH6ON7tQolGdAhiJAB0wnsM43WwdWo4F0UAO8ShWgWVIW/L95ILf+//qHsP3aBwD6fMGjcfKaM7mATv81xc3Nj+Zrf2RGxl23hUWzcsI7I3eF07d6TyOhD7IyM5sGD+/w4e1aW68gqjqQb7nDB0kgpT2U7Z2ynJeN74vgxgkMaAxDarAXLlv6WrTpAH999y5Uk8tDf3P8ngaSkZLbtO0XHJjUAmDSqPWO+WPVIMl/f8iXZGnUCgGNnr1LWswgliuTPcb/NEUKQP7/mQ0JCAgkJiQgET7dqk5orMqBuPS6cj8nEkv4IK/7ZGocLlkZKeSrbOWvbHN9q1VMDwpLfFnPeTFQrq+jh+6FTlwiuXZ4iBfOSxy0XrRr64l2yEG0bV+fC1Zv8efziI+X/PH6BDk38AKhbrTRlPNzxKlEox/1+nKSkJBoF1aFyWU+aNmtGXZMYGmgBdMEvP9Ps6ZbZqsNa1GO4CSGEjxAi2uy4JYR42aj6FI7NzK+/47uvvySkQT3u3L5NLldXW7sEwNEzV5j642ZWfP4cyz8bzP5jF3B1deF//UJ5/+v1/yo/5cfNFCqQh/CfRjG8WzD7j10gKck4GQlLcXZ2ZntEFIeOnyVqTySHDx1MvfbKSyNp2CiEhsEhOeyVNf1K20dLw2bDpZRHgdoAQghn4DyQbeEUI6U8le2ctW2Oj48vy1atA+D48WOsW2u5Nkt66OX7nBWRzFmhyf6OG96KK9fv0K6xH7vnjgLAq0Qhdv34MiEDPufy9dsM/WBh6r1HlrzJ6QvXbOJ3Wri7uxPSuAkbN6yjWnU/Jo5/n9jYq8z9Ivvic1bjYOssc+oxvBlwUkp5NruGjJTyVLZz1rY5V69ocsfJyclM/mg8AwcPybZNvXwvXjgfAKVLutOhSQ3mrtpD2dbj8H32I3yf/YjzV27SoO+nXL5+m0L5c5PLRROYG9AhkO3Rp7l99x+b+J1C7NWrxMVpKtT3799ny6bfqVzFhx9nz2LT7+uZNednnJxsMyKnZCX+TQ9gXloXzKVwS5cpk6khI6U8le2csZ2WjO/du3f55quZALTv+Cxh/bKvQKKX7/Mm9qVIoXwkJCbx8uQl3LzzIN2yvuVK8u173ZFS8tepywwbv8hmfqdw6dJFhj83kKTkJGRyMh07daFVm7YULeBG6TJladFEWw7VrkNHXn8rXfFV3dHGLO0hDFqGYVK4qRVo+jsXgOpSyssZla1Tp67cEbHHUH8UOYeRiTRcDEwAohJp/JsmwUHs26uvFG7VGv5y9pLNFpdvULmwTaVwc6Jn2RrYm1mgVCgU/0Ecp2OZI8GyJ+k8gisUiv82jvQYbuiorhAiH9ACyP4KY4VC8cShJnhMSCnvAkWNrEOhUDgw9hAFLURlHVIoFDZB6zE6TrR0uO2OCoXiCcG0KN3SI0NTQpQWQmwWQhwWQhwSQrxkOl9ECLFBCHHc9H9h03khhJguhDghhDgghAjIzF0VLBUKhc3QccwyEXhFSlkNqA+MEEJUA94ANkopKwMbTe9BW6VT2XQMATLdwqSCpUKhsB06RUsp5UUp5V7T69vAX4AX0AGYYyo2B+hoet0B+FFqhAPuQgjPjOpQwVKhUNgIYxJpCCHKAf5ABFBSSpmSGuoSkJKN2QswT20VYzqXLmqCR6FQ2Awrl1kWE0KYb/H7Rkr5zaP2RH7gV+BlKeUtYVaBlFIKIbK8ZVEFSx3IgS2jhto3CiO3JCYbJWSNsdsRAQrXG2mY7RuRXxhi14h8kllYPxmb0XZHIUQutED5s5QyZW33ZSGEp5Tyoukx+4rp/HmgtNnt3qZz6aIewxUKhc1IydRuyZGJHQHMAv6SUn5idmk50M/0uh+wzOx8X9OseH3gptnjepqonqVCobAZOj40BQNhwJ9CiGjTubeAicBCIcQg4CzQzXRtNdAGOAHcAzJNc6WCpUKhsBl6xUop5fYMzDVLo7wERlhTh8M9hp87d46WzZviX7MaAbWq88X0z3S1b6SkbFxcHL26d6W2X1X8a1QjInyXbraHDh5ImVIlqFPbTzebYHx76+33sCEDKetdkrr+NVLPXb9+nbatn6ZmtSq0bf00N27cyMCCZTx48IBGDQIJDKhFQK3qfDDuvSzZGdGzCXsWvUXU4jGM7NUEgJ8mDiB8/huEz3+DI6vGET5fWxpYt3rZ1PMRC96gfdOaGVhOG6O/T6uwDiN1QQAAFGBJREFUZtmQHQzbO1ywdHFxYeKkqew7cJit28P5+qsZdi37as5ro1+mRcuWRB/8i4ioaHx8q+pmO6xff5atXKubvRSMbG/Q3+8+Yf1ZumLNI+emTp5Ik9BQDhw+RpPQUKZOzv4fQTc3N9Zu2MTuvfuJ2BPN+nVriQgPt8pGtYqeDOjUkJCwyQR2/4jWjf2oULoYYW/Mpn6PidTvMZGlG6NZtkl7qjx08gLBvSdRv8dEOoyYyedv98TZykk0o79Pa3EkDR6HC5aenp74B2g7kwoUKICvb1UuXNBHbdBISdmbN2+yffsf9B8wCABXV1fc3d11sQ2avGyRIkUyL2glRrY36O93o5DGFHlMZnfViuX07qON8ffu04+Vy7P/nT4uL5uYkGD1qgXf8h5EHjzD/Qcmmd2oE3QMrf1Imc4tAli4NgogtRyAm2uuLK3CMPr7tAaBftsdcwKHC5bmnD1zhujofdQzk/XMDkbKvp45fZpixYozdPBA6tcLYPjQwdy9e1cX2zmF3u2dU1y5chlPT21zhoeHB1eu6JOHOikpiaA6tSlTqgShzVsQGGRduxw6eYFg/0oUKZSPPLlz0apRdbw9CqdeDw6oyOXrtzn599XUc/X8yhK1eAx7Fr3Fi+PnpwbPrGAP36cDPYUbns9ylGlT+0EhxDwhRG69bN+5c4ee3TozeeqnFCxYUC+zhpGYlEj0vr0MHjqM8Mi95MuXjyk6j4kaiaO1d3pYsgzFUpydnYmIiubEmRj2RO7m0MGDmd9kxtHTl5n6wwZWzBzB8hkj2H805pHg161VXRatfVRmJfLgWep0GU+jPpN4beDTuLlmbY7Wbr5PB4qWRuqGewEvAnWllH6AM5pwWbZJSEigZ7fOdO/Zm47PdtLDJGCsBKmXlzde3t4Emv6KP9upC9HR+3SxbTRGtXdOUaJESS5e1JbQXbx4keLFS+hq393dnaeaNGX9euvHXucs3UVw70m0GPQpcbfucfystmba2dmJDqG1WLxub5r3HT19mTv3/qF6pVJW12lP36cas3yIC5BHCOEC5EUTLssWUkqGPTcIH9+qvDRqdLYdNMdI2VcPDw+8vUtz7OhRADZv2kjVqvpN8BiFke2dU7Rp246f52q5FH6eO4dn2mX/O736mLzsxt834OPja7Wd4oW1cc/SHoXpEFqLBWu0nmRokA/Hzlzm/JW41LJlSxVNndAp41kYn/IenLVSk9zevk8nYflhawxbZymlPC+EmAL8DdwH1ksp1z9ezlop3J07dvDLzz/h51eDoDraYPi4DyfQqnWbbPtspKQswNRp0xnQrw8J8fGUK1+Br7/7Xjfbffv0ZNvWLcTGxlKxnDfvvDuO/gMHZduuke0N+vvdL6wX2/7YwrXYWCpXKM3b74zlldfeIKxXd36c/T2ly5Tlp18WZNvvSxcv8tzAfiQlJZEsk+ncpRttnmlrtZ15UwZTxN0ksztxITfv3Aega8s6qRM7KTT0r8CrA54mITGJ5GTJSxMWcC3OunFvo79Pq7GDIGgphknhmpJs/gp0B+KARcBiKeXc9O5xVClctTc85zFyb7iTwd0YR9wbHhxUl6gofaVwa9QKkL+t32Fx+SoeeW0qhWvkY3hz4LSU8qqUMgFNtKyhgfUpFApHQsdM6TmBkcHyb6C+ECKvaZN7M7SEnAqFQgE41GS4oWOWEUKIxcBetJTv+4Bv/t/emUdZUV17+PtBgwIqgyAYEQfmFmQQAQUaZ9CgOCCIBkVFwIhGoivElTgkwTgQZ3zG8aGiQMQBSZ5IoksFjIZBMDSKEQcQR1BUhgiN+/1xTjdtC3LpW2X3bfa31l1dt27V7+xbXXfXOadq7/3DezmOs1NRGbxghqRdCvdqoHxBs47jVHEqxyNBmeJZhxzHqTAqw1xkprizdBynQqgsc5GZ4s7ScZyKI4e8pTtLx3EqjGo5NA53Z+k4ToWRO67SnaXjOBVFJXnYPFPcWSaAhyNunVwNSdxYVP4ckZmQVkgiQP3ul6ai+82bK7a/UbnInd+OO0vHcSqE4kzpuYI7S8dxKowc8pXuLB3HqThyqWeZczV40ir5WkyapXBde+vcecdtdOnUni4d2zH+9lsT1U7S9g9WrKBfn6Pp2qkd3Tq3567xtwMw9Gdn0LNbZ3p260z71gfSs1vnrO1O4jy/6IwC5k0Zw/wpYxg1uHfJ+gsH9WLh1CuYP2UM115yIgBHdWvFnIcvY+7kXzHn4cvo3aVl1t8hE3IpU3rO9SyHnDOUkT8fxbDzzk5cu7gU7t+e+Tv7NG1Kz+6H0q/fSbTNz3ftFLQBCgsX878P3MdLc16lZs2a9O93PMef0I/mLVpkrZ207Xl5eYy9fhwdO3Xm66+/pvfhh3Lk0ccwYeLkkm1+M+Zy9qhbN2vbsz3P85s34dxTDqPX2TezsWgzT98+gv+bVUjTxvXoV9COroNvZOOmzSWZ2levWceA0ffy0aqvyG/ehOl3jKT5Cddk/T22S8X7wIzJuZ5lWiVfId1SuK69dZa++QaHdu1K7dq1ycvLo1dBAdOeeiIR7aRtb7L33nTstKWMbOs2bb5TRtbMePLxxxgwMPtSU9me5232b8zcxe+z4ZtYZnfBMk4+6mCGD+jBnx58jo2bNgPw2RdrAVi0dCUfrfoKgCXLPmbXXWpQs0b1rL/H9silFG055yzTJM1SuK69dfLz2/Hy7NmsXr2a9evX8+yMZ1j5QTKPqaRp+/vvv8frCxfS5dAtZWRfnjOLRo0b07zFjzOE/SEKl31Mj44H0qBubWrtUoO+PfJp2rgeLZrtRY+OB/LShNHMvHsUh+Tv+719Tzm6Awvf/KDEoaaFFCJ4Mn1VNKkOwyX9AriAcGG418ySnZBycp42bdvyy8t/xUk/7UOdOnU4+OAOVKuefo8mG9auXcuQwadz3bibv1NGdupfJjPg9EQKmGbN0vc+4aaHnmP6+AtZv2Eji95ayebNRl5eNRrUrU3B0FvoclAzJl43lLb9/1CyX9sDmzD24hPpd9FdP46hFe8DMybNUrjtCI6yK9AB6Ccp+4moFEmzFK5rb5tzzj2fOa/MY+ZzL1Kvfn1atmyViG4atm/atIkhgwcwcNCZnHTyljKyRUVFTJ/2JKcOGJiVfpI8OO1Vegy5iWOH3xHK7C7/lJWfrOGp518HYF7hcr41o2G9OgDss1ddpow7j2FXP8K7K3esamR58WF4oC3wqpmtN7Mi4EWgUhedTrMUrmtvm08/DbWyVyxfztNPPcnAM85MRDdp282MUSOH0bp1W0b9YvR3Pnvh+X/QqlUb9mnaNFuzE6OkzG7jevQ/6mCmzFjA9Bf/XXKnu0WzRtTMq86qNeuou1stnrh1OFeO/yv/XPTuj2ZjLtXgSXMYvhi4VtKehFK4JwBZl25Mq+QrpFsK17W3zVlnDODz1avJq1GDm28bT7169RLRTdr2V16ew+RHJ3JQu/Yljwdd9buxHNf3BB5/bAqnDRyUiN2QzHk+6cZzaVA3ltm9YSpfrt3Ag9Ne5e6rBjNvyhg2bipi2DWPAjByUE+a79uQK4b14YphfQA4cdRdJTeA0qFyPBKUKamVwgWQdD7wc2AdUAh8Y2aXltmmdN3wQ95a9n5q9jg/Lh4bvnVq5qU3oEstNvyNSXy77pNED3qnzl3s+dmvZrx9gzp5VbYULmZ2v5kdYmYFwBfAW1vZ5h4z62JmXRo1bJSmOY7jOOUm7bvhe5nZp5KaEeYru6fZnuM4uUVlmIvMlLQjeB6Pc5abgIvMbE3K7TmOk0Pk0pxl2qVwe6Wp7zhO7hIeSq9oKzIn52LDHcepQrizdBzH2T4+DHccx8mAXLrB44k0HMepMJIMd5TUV9JSSW9L+nXStrqzdByn4kjIW0qqDtwJHA/kA4MlJZN0NeLO0nGcCiPBTOldgbfN7B0z2whMBvonaWulmrNcsGD+qlo1lGm8Y0NgVUqmpKmdtr5rVx3ttPV3RHu/pBt/bcH8Z2vXVMMd2GVXSaXzS9xjZvfE5X2A0olQPwC6kSCVylmaWcbxjpLmpRUnmqZ22vquXXW009ZP2/btYWZ9K6rt8uDDcMdxqgIrgdJp35vGdYnhztJxnKrAXKClpAMk1QTOAJ5OsoFKNQzfQe7Z/iaVUjttfdeuOtpp66dt+4+GmRVJGgU8C1QHHjCzwiTbSDWfpeM4TlXBh+GO4zgZ4M7ScRwnA9xZOhkh5VIUb0BSnRS1m+TiMXHKT045S0mtJR0mqUYMb0paP5WC1ZJaSOoiaZcUtA+S1DsmWU5au6ekIQBmZkk7B0knxtryiSOpP3CDpL1S0O4DPMl3H1VJSru7pCHxb82EtVvG87BaWud6VSZnnKWkU4FpwFjgfuAiSXv88F4Za7cCMLPNSZ9EkvoBTwDjgAnFbSWkfTwwCRgNPCSpSUK61STtBtwNXCFpJJQ4zETOGUnHAX8AliShV0a7N3ADMM3MPk1Y+7iovTdwWcLaJxHuUB8DXE6CUTOSTgamAlcANwMj0ux5V0VywllKqgEMAs43s6MJTnNfYEy2DjM6s4WSHoVkHaakwwlO8hwzO5JQtC2RbCiSjgBuA4aZ2cnARqBdEtpm9q2ZrQUeJFyYDpc0uvizbPXjcXkYGG5mf5dUV9J+kmpnqx05BLgvav9E0rGSukmqm42opGOA/wHOAloCbSUVJGAvcWRwEXCmmZ0DfAV0lLSXpF0T0B4BDDaz04DXgXOBX0raPUvTdxpywllG9iCcoBCGQH8FagBnlnd4GK+so4BLgY2SJkLiPcwbzOy1uHw10CCh4fgnwAgz+1fsUXYDRkm6W9KAhIbMRYSL0oNAV0k3S7pOgWzOndWEukx7xx/yU8BdhJ53ErYXlVqeCpxH+D/fKal+FrrVgbPj83t1gKXAQZDInG4RUAtoEzsARwBnA7cCv82yF1gE7AY0ATCzB4D3CLHh/bLQ3bkws5x4AccSnsjvFd9XB84EJhKfFy2n7k8IJ1JDwg9rYoI2Vwf2KLXcFHgNaBTX7ZlQO78BfhuXhxIyrjRKQLc58Ou4fBmwHrgzIZs7AO8QEh5cQLhwn0eYVmiQpXZ7giObDJwb1x0I/Bnok4Dt1eLfvsDHQPuEjskAYD7wCnBlXHcUMAHokKX2yPhbGQJcG5dHAPcnYfvO8MqlnuUsYCYwRFKBmW02s0cJzq5DeUXN7EMzW2tmqwgnT63iHqakzpLaZKG92cy+im8FrAE+N7PPJJ0FjJVUq7z6pdq51szGxuUJhF54EjcfNgCtJV1A+LFdDzSTNCJbYTNbROjVXG9m91oY+j8A1AeaZan9b8KcXzfggLjuHcIFK+vi9BanIsxsBmGOsV8CvW3MbCphvnIW4aKKmT0P7E7285eTgGeAI4FaZvYzM7sbaJzU3H9VJ2fCHc3sv5IeAYxw06EN8A3QGPgooTZWR0cwTtKbhB/XkQlpFwFrJa2QdB1wHDDUzDZkoytJFrsO8f1phGPyYVYGEy4kklYAVxJKGU+XdCTwdrbaUX8JpW7wRNsbkcz/8xnCtMc1Uknav04Eh58kiwg32G40s83ZipnZF5KeBwZK2gjsSnD4r2ep+yXwiKRJxc5e0tlAAyBru3cKKrpru6MvoCbBgU0mDE86pdDGaBIcXkVNRduXAcuBlgnbvAtwPlAItEtQd1/gkFLvq6VwvEUYgi8BDkpYuzPwR+CmJP+fZdr4C7B/gnr1gEuAFwmxzlkNwbfRRvHxTuWYVMVXzsaGxxswZgncnS2jW59w8l9mZlldzbehPxSYa0kH+YcnBo4FlpnZ0iS1o/53erBJawO9gY/N7M002kiDNI9J1N+dMB//1XY33nHt/YAaZpbIKGFnIGedZZpI2tXM/puSdqo/MMdx0sGdpeM4Tgbk0t1wx3GcCsOdpeM4Tga4s3Qcx8kAd5aO4zgZ4M6yiiBps6SFkhZLeiybpBSSJkgaEJfvk5T/A9seERNj7Ggb70nfrxm9rfVltlm7g21dI+nyHbXRcUrjzrLqsMHMOppZO0IGopGlP5RUrmgtMxtmIdJmWxwB7LCzdJxcw51l1WQW0CL2+mZJehpYIqm6pHGS5kp6vTjGO8Y1j5e0VNI/gJKEuZJekNQlLveVtEDSIknPSdqf4JRHx15tL0mNJD0e25grqUfcd09JMyUVSrqPELXzg0h6StL8uM/wMp/dEtc/J6lRXNdc0oy4z6xs4vodpyw5ExvuZEbsQR4PzIirOhPCH9+NDudLMztUIU3cHEkzCTHTrYF8Qlz5EuCBMrqNgHuBgqjVwMw+l/RnYK2Z/Slu9yhwi5nNltSMEK7XlhCnPdvMfi/pp4TQzO1xXmyjFjBX0uNmtpqQHm2emY2WdFXUHkVIajHSzP4jqRsh9+RR5TiMjvM93FlWHWpJWhiXZxGT9gL/MrN34/rjgIOL5yOBuoQcoQXAJAuJID6MiRzK0h14qVjLzD7fhh3HAPnakt5xD4Ws6wXAqXHfv0n6IoPvdImkU+LyvtHW1cC3wJS4fiLwRGzjcOCxUm0nXsbD2XlxZ1l12GBmHUuviE5jXelVwMVm9myZ7U5I0I5qQPey4aLawdy4CpngjwEOM7P1kl4gZODZGhbbXVP2GDhOUvic5c7Fs8CFMekGklopZOB+CRgU5zT3Zutp6V4BCiQdEPdtENd/Tci3WMxM4OLiN5KKnddLhGTNxbWDtpexvC7wRXSUbQg922KqERLlEjVnx2QT70o6PbYhSeXOc+o4ZXFnuXNxH2E+coGkxYSCZHmEMh3/iZ89BPyz7I5m9hkwnDDkXcSWYfB04JTiGzyE1GJd4g2kJWy5K/87grMtJAzHl2/H1hlAnqQ3CDkoXyn12TpCmYvFhDnJ38f1ZwHnR/sKgf4ZHBPHyQhPpOE4jpMB3rN0HMfJAHeWjuM4GeDO0nEcJwPcWTqO42SAO0vHcZwMcGfpOI6TAe4sHcdxMuD/AXgFQ/8grHXuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#label mapping\n",
        "labels='''T shisort\n",
        "trouser\n",
        "pullover\n",
        "dress\n",
        "coat\n",
        "sandal\n",
        "shirt\n",
        "sneaker\n",
        "bag\n",
        "anckle boot'''.split('\\n')"
      ],
      "metadata": {
        "id": "XADvJJFiIyGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulVpRTNMNZiZ",
        "outputId": "c3bc50d5-130f-4981-d34b-fa656e99c2d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T shisort',\n",
              " 'trouser',\n",
              " 'pullover',\n",
              " 'dress',\n",
              " 'coat',\n",
              " 'sandal',\n",
              " 'shirt',\n",
              " 'sneaker',\n",
              " 'bag',\n",
              " 'anckle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show some misclassified examples\n",
        "p_test=p_test.astype(np.uint8)\n",
        "misclassified_idx = np.where(p_test != y_test)[0]\n",
        "i = np.random.choice(misclassified_idx)\n",
        "plt.imshow(x_test[i].reshape(28,28), cmap='gray')\n",
        "plt.title(\"True label: %s Predicted: %s\" % (labels[y_test[i]], labels[p_test[i]]));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "xFnaTR6JNahI",
        "outputId": "c97e2d94-5c3f-4fc4-c98a-6365b766101f"
      },
      "execution_count": null,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z2MIRcJCNjlo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}