pytorch_TRAINING_A_CLASSIFIER_from_scratch.ipynb

{
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  "metadata": {
    "colab": {
      "name": "pytorch_TRAINING A CLASSIFIER from scratch.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Paritoshyadav/pytorch_TRAINING-A-CLASSIFIER-from-scratch/blob/master/pytorch_TRAINING_A_CLASSIFIER_from_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9zeTqK-7N41",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Load and normalizing the CIFAR10 training and test datasets using torchvision\n",
        "Define a Convolutional Neural Network\n",
        "Define a loss function\n",
        "Train the network on the training data\n",
        "Test the network on the test data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5asZdcs7TiIX",
        "colab_type": "code",
        "outputId": "82ca750f-79ad-4515-f327-922ead5379e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "!pip install --upgrade torch torchvision"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: torch in /usr/local/lib/python3.6/dist-packages (1.3.0+cu100)\n",
            "Requirement already up-to-date: torchvision in /usr/local/lib/python3.6/dist-packages (0.4.1+cu100)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.16.5)\n",
            "Requirement already satisfied, skipping upgrade: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (4.3.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFAh6HlATiM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bt4xh8Dq7acC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transform\n",
        "import torch.utils.data.dataloader as Dataloader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuhaG2D57afT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trans =  transform.Compose([\n",
        "    transform.Resize(32),#always mention resize before to tensor\n",
        "    transform.ToTensor(),\n",
        "    transform.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
        "    ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UrvP4rk7PuM",
        "colab_type": "code",
        "outputId": "3de99c29-af47-4e7f-d81a-3915d323074f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "train=torchvision.datasets.CIFAR10(root='train',train=True,transform=trans,download=True)\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to train/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "170500096it [00:06, 26220790.22it/s]                               \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting train/cifar-10-python.tar.gz to train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Hv4rFE87P8E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data=torch.utils.data.DataLoader(train,batch_size=4,shuffle=True,num_workers=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEKOG-R3-SPZ",
        "colab_type": "code",
        "outputId": "c61ff9e4-df00-4085-f691-6a8f0835934a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "test=torchvision.datasets.CIFAR10(root='test',train=False,download=True,transform=trans)\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to test/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "170500096it [00:06, 28316552.36it/s]                               \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting test/cifar-10-python.tar.gz to test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vko2C5eOCauJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data=torch.utils.data.DataLoader(test,batch_size=4,shuffle=True,num_workers=4) #num_worker = multiproceess if any +ve integers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUFmPFmMChvY",
        "colab_type": "code",
        "outputId": "b1d6f3b4-1f63-4d23-ab69-ae976de2dc86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(train_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<torch.utils.data.dataloader.DataLoader object at 0x7ff464a58ef0>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlBBEebbCwk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uRfETecC6-y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfZ25529C7Bw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def imshow(img):\n",
        "  img=img /2 + 0.5\n",
        "  \n",
        "  \n",
        "  np_img=img.numpy()\n",
        "  \n",
        "  np_img=np.transpose(np_img, (1 ,2 ,0))#  in PyTorch, the order of dimension is channel*width*height but in matplotlib it’s width*height*channel\n",
        "  \n",
        "  plt.imshow(np_img)\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iRNX9dCC7En",
        "colab_type": "code",
        "outputId": "a13f9778-6873-429f-f2d9-550212a0c759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "data_iter=iter(train_data)\n",
        "\n",
        "data_img,labels=data_iter.next()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(data_img))\n",
        "print(labels)\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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B6tv+3XR8dVlJ0WyF7tVorDl2KlfoWtmc5ugQcumF50hTKJf1+IMsWZ46rfXIW1xcYXJY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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "tensor([3, 9, 5, 4])\n",
            "  cat truck   dog  deer\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xq4Pz0oJum0j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qn5i3QN7Rq5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device=('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3wAJayc7sOk",
        "colab_type": "code",
        "outputId": "fed5c451-3f91-4ddf-8775-ee65edc97f42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(device)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LR8ihlWQNoN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)#Faltten or x.view(x.size(0), -1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "net = Net()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eHmqQ8HBj-W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBIiv4RG7nxh",
        "colab_type": "code",
        "outputId": "c4fed82d-c0f8-4352-d4c1-269f1b23d821",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "net.to(device)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Net(\n",
              "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
              "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
              "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri9yAGnzQBjv",
        "colab_type": "code",
        "outputId": "6c25077d-37b1-4bd3-b535-881c734dc453",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "t=list(net.parameters())\n",
        "print(len(t))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeGcg6BFuGee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxxgIq31wiLO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "C=nn.CrossEntropyLoss()\n",
        "optimizer=optim.SGD(net.parameters(),lr=0.0001,momentum=0.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CRJmnpZ2Ys9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for epoch in range(50):\n",
        "  running_loss=0\n",
        "\n",
        "  for i,data in enumerate(train_data,0):\n",
        "    inputs,labels=data[0].to(device), data[1].to(device)\n",
        "  \n",
        "    optimizer.zero_grad()\n",
        "    outputs=net(inputs)\n",
        "    #print(i)\n",
        "    loss=C(outputs,labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    running_loss+=loss.item()\n",
        "  \n",
        "    if i % 2000 == 1999:\n",
        "      print('[%d, %5d] loss: %.3f' %(epoch + 1, i + 1, running_loss / 2000))\n",
        "      running_loss = 0.0\n",
        "print('finished training')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejNEZoSg4F3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(net.state_dict(),'model.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WuN7qtGthQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHH6v03SIHL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path='model/model_state.pth'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6c8vPNttjWR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(net(),path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqpFkhceuV8S",
        "colab_type": "code",
        "outputId": "b4cb68b0-5e39-480e-d795-e0d71a68fbf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "dataiter = iter(test_data)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OXNoOKQ1kT5FI3x+8KNLexXEx0khJ6HB5Kmg+7AWO6StFLG1+40++GLdl2I3zfR9+GAAw\nMCJ5XnJ8LK92WZol/7S6cOA0BT6vqQTeF4+OAQC++FUphvX/vvrHAID5cSlykmbtqMv1WEmOrQR9\nHt19S9z2zgc/RB9mhfxzKC1OAwB6hiSPSCbjIjSF9G3WXRpc2gy2LdJ7gyXBKJDBPPCeHwUA/Own\nPyH9ZrWkVSVt7ZZbD8bHEuxKazLiujc3Q66XR16Ufpc4N0vK5acJdI4b6ttSRaJkS3UuEFIWqX0g\npGeuZxcRpgN7dsfH8AppnCnlkuoiVoOUSqm7ApEiid3nZFK5Ea8zzbPH+rEeCf33ATy0ou0zAJ60\n1u4H8CT/38PDw8NjA3FJCd1a+7QxZveK5ocBvIs/Pwrg2wD+7ZV0oFYmm+dSU5UT4wCMksqr0uCg\nHVd8oEsVnI7YrbCgJJk4W1xGBZ/USdJN5lP8PWUPZdtoZETiiHh6dMCNYakpySW4qpDSctnGGQBA\nr8oEmbjxfgDA02qm7x+5lfrDUnhW5Tp5f5HycLQ7IvnvG2Ubak3OOzmOFbjUb/NqqT1kydJljNQZ\n/JzdMVA29A5LoBPHxYb+v//LGADg1HGS4n7hH/9ifOzOWziPiNKcXPBQGMqclrl4xLk50nCe+q4U\nPPjiY18AALx+5G+k3y2SNpNK+jVJ+uwKlbSV+2nfXrILv/+npETa/jsoj8jYt1ZL6I0KuWVm88Jj\nZAq0BtmstIVcJjDkPRkqCT3HGuTb3vtjcduHf/6nAQDbhsQm/toL5F5ZmiZ+YmbmjfjY/CLtrYO3\n3h63tRv0nNQ56AgA6qxddtLO3VfllOEHIZ+XZ8MFJdVL4n46NTHGbaR1LFVk/yUzFJCXUs+Xc9E1\nF3FbTCRWH3szpPHrUaK/Xvp0pTb0EWutC7s7D2DkYid7eHh4eLz1uGpS1NJP05o/T8aYR4wxR4wx\nR2q12lqneXh4eHhcJa7UbXHKGLPdWjtpjNkOYHqtE621nwPwOQAYHR1d9eJvN84BAMK6mBNaC5zY\nP3s2bhso0m9PgaMPc4pcyeTomK4x2WLVvhGKityskKrbqZLppaVMASGnzzUp7ebovqsLHbBbFaeX\nbVm5/mKTrjtelevOG7rnKSMRcrdnSd2vdsjE0JoRErWvSm6OvUXJx1GdpR/CQmZtF7HlsCv+itko\npdTxVIHu0d1HkYjavOJIrP5+IWfnZohEq5WE/3ZRfi98h1KlPpESt7TUBz8MADi071DclkiS+n58\nUvKqPPH0twAAT3Huj9d+KNXlW0zmpZPiYucCTxPKHS0CjcvlwukalhTGDzz4DwAANx2SqNBqS0V8\nroDtlHmcQtQHXCglmZZ1yaZp7RsRmV5MICbC224jovv+t90Zt23bTkUszp8+EbdNvEHRt2GV7mlm\nZA8vcvGL5pw8XvUqmVcqM9K3FpusWs5xwCrTCOfRSau6rktT9N3XnxXTVofH8MZxsuWdf+NcfCzH\nOZACo0JcXb3QzvVhavAgXKmE/jiAT/HnTwH42pvTHQ8PDw+PK8V63Ba/ACJAB40xZwH8ewD/AcCX\njDGfBnAawMevuAOGCZeWuFXd2Edk0PsfEGLm0E5y6+rv4RJmOSHEUgEHkRghm1ohuc/VVCBIpcZF\nDeoknZ0LxS1ymosmzJfElasektTUUaXIWpErtEF9bEBlhOwmKfzFeWm7ydC4Hrpf3NGGEiRx1Zsk\nBTVV4YqoRVI7AilZt9Cma2TU+C4O7q/KzZJk7aWrWyS1wVGiPgYGyI8ypfLjOOm32NUVt+3eNcqX\nFWk5yW53IWfdS4ayjke+S5L3mWOvxG3DAxQ4Y1UJuMQsSaw9dSIGd2QlR8t8g9YgUn0rMMHXacn4\nShwkE2bJNXXf3ffFx3buvRkAsFSS6xqzdrGQYpZztFRFEwkyNA+FfpkPw1qiyzezfZu4bN6wl/ZC\nS+3r6hyRkHNnxuK2+ixpZ+0ajTNQbrOJOs3p+XmRxueW3BhkT6ZYc2tZJvZVbpSIyed2TdbsxReP\n0rVUOb3eERrXD79HpeLmp0WDsknSThpNeZYMS+hRuLams1Xh9MLrUTdZj5fLJ9Y49OCb3BcPDw8P\nj6uAjxT18PDw2CLY8FwuzpqRTSzGbQeGyZwxqnJkBEv021Nz0Xl5UZlzfUyY5sU3N5mja2R6dR1E\nIviSKbpuTRVIWGzS57mSUuNZ5S01pB8zTVL3S0t0/bGaXH/KEBH3xpxEGA7l6bqDyYm4rbFIpF+C\nCdtkIISpZVKx1pKxtLjAQJeqN7kaOt8M9Tej0tYODhLxOTwsZoE8+yMX+bzeXjEnOH/rQPkSJwOX\n2lTuleI0uLUyE7zKxDU5SyalF44KyZnK0HztGJKcJWnehj92L5lJ2veJuWRsgojxtsrl0qrR2r/8\niuTAWeDCFt2DNJdDO2QNlso0l/Pz4ncdsEnpQv62hRz1p1ITE01piUx+qYKY6ZxxxHCOoHvffjg+\nNrCN5nZJmUvOvU5pbhcnhOyPqnSPpWk6r9gja+Cy1Rorc9ru1Lgfsu8SHLWZYvOKUZHN4PWrNeV5\nmVqke96Yl2vcuIf2brNNZrJSSYj6JPvjF3JyfpFzDXXlVS3gDUBM/6shG7s6ylm+QM/SMr9xF2ei\nL7LSsKIO2fj6ep7dYiXUee67axtnzDL/9as34ngJ3cPDw2OLYMMldGOJDNo3LJGimQZJMHPnRIqr\nd5MEkeHsjOlApK1zZ0n6aLZFuu7qI3JzaFAiOV1umK5eIiULqoxXrs2kl6okjzZJs1lFLmYTJKXU\nszx1bZFaLLswVvpUpjouABFmxfUsbHOWuYDc2DSx6opYNJXLvslR3zLdIl2v9BR1mfYAwHLxgYKK\nDhwYJEmqq0v6W+DSdoEr79cQrQCc2yNU2fEi1hRqKqrX5RFx0k0xJxLmzPQcnyNrkGTJf2pGyGfD\nmTYHe2l8t9wkuW3efy+VRhvsFzfEsXHSds5NzMdtUzVytxvZwXOUEDJycvKHND7lqugKM4x0yxw5\nFDlLZC0v8s4sE6StivQ7W6Q5zQ+R5rfnztviY+k2aSwLZ8/EbWePUVRoKiVr1Y5o7mtlmtNMQYpZ\nNHkd0wlxNc2zXjDdVpGc7OKaqFNbWpf3Yw3xxhv2xW13c5T1nff+aNxW6KK92N3NxHso2kk3k6K7\nRgZl7Dmat3CDSdELEpRx4/JcRYAqyBFcvSxrQ1nHkEniIKk1ZVcz70K6wmrXYi+he3h4eHjE8C90\nDw8Pjy2CDTe55CJSTXfmRcVLcY3G6qJ07zxHFvb0kqo3OCJq6MI8qcHtlhQO2GZJdS2VpdjEG8fJ\n3znHvuz33H0gPjY7R+aGp/5Ozm/WKJXojp3i/32eq8kPcmrV/LCoskGC7CTJUEwjqSKZjQqj0rfO\nPJlh8j10rFqTFL+dDqlnCyradIEtHOfOy3krkS+opWTCL6+IM5csKqsKZzgep8G1LqdUwqcGRx/W\nlcml1iCVvtVSBGWTjrt0qoWimFyWSpzWVUXfdvOWq1bFZFark9nhjZNknliYFnPS4n7aH7feKsPr\nH6G13b5D1uX4HK1tJkf9btSFeKzVOIlWW0jlwKnD3RLJ6dDFJgyrVOB2h+bI1sU3PeSo2127yESU\n75Oo2iTHPKSUBa80d57asqqR/fjT7GcfqcjL/ADto+5uMcP07aR91Dstpp/SApmXpk+T2SmpSLiR\nPRTPcM/bhWge5ujfmQUxhX3hjygl8cwkmbG6c1KDNM1mitkpZTbkSOJ2JGYHSU587RFEy1hR/sD7\nVJHEswu0J0+ekCRo5RKbF1W8iSOW02l6hooqcruri95BAyqKeqDfzZeq52p536t6uKqTdGhZLdKr\nl6+9hO7h4eGxRbDhEnpXhySC+pSQR9sGSVpJtEWCyDRJCpkb54jAUH4dJ1lC6bTPx21Ndkns6xOJ\n8dUTJIE6mWKgV1yuBodJ2isvSQ4LVxxj6rwQUOfO0b0W56htdySkWlcfF8RIiQQ7v0QE1OSiSJOV\nRfq1X5qhsc+eF+1k+hz18cykSLAnT5O0ObskRN9Hf/pt0OgbUOmEI5qjRFL6scRuhc2WzGmnzdGx\nUyR11pbkWItz1jSV26KTPQIlcSRWVKUoL9M2SHpPqTS3nTqXclOSaGToGo6APXZKcgMvVUnrefmE\naE4DQ6Th1DoyH129rNGwltRqiQTbadL8tpVLZVyWDqsl9CQXx8ir6NRtPQUepkhUS6yxDPRTfxKB\nPE4hX7+jpPxGg+a301aubeyOmXDun6oQRe9O0hC33S7qSaGL9mz3ibG47eQxct+sMJPeUPskYu2o\noaTPOisq07PiKnz61GnqN2szmaRod87Fs61cH52rZC673vxCbw3iQixWk5FxLUMAwPg5iXp9/P99\nEwDw/WdeiNtc1uOEymXkSM42a3W6tOLAADlX7L5BiPqfepjiLPfvUy7IHEFsXaEPrURcoODM5ZbW\nuxC8hO7h4eGxReBf6B4eHh5bBBtucglZDZ0ui/rXXnBGEVETLbgqEZNutZI4ajujR6kuJpfZc5zm\n1g7Ebc5ckkqTuWbstJh59t5MZpD+PiGgJtiHuLQgqlgmoPMy/FuYqIpP9jDXQg2LYkKpVMlc8vUn\nJWKwXCHTwhxHoi6WhEiplkn/W6oIgTc5m+A5WDsqr1gUldBxf1qBq1Wpb+eVeadcpfl1xZGCUH7f\nI/a3N91yz5EbmdhVAatNjnRcnKbIwrAlY0mymm9VfECVydZQR7bybQ2bd8p1MY3Uz0zwMZVo7CRt\n2/5tYnbbeQOR1D28fh3VSVeZqRMJwbucjFqOJFe0j1TyryIToFnIXpiZp7EHTMLpAM3F8mozDzrU\nJxuuNlklOc1tqyH7usH+/pEisptZWpeSqo4UMvnX7Z6NafHPP/sakX9pZUIp7yBTweycELw5NjdM\ns7kmCpWZjO/VVGtrmzRmXR3pSmHUWlys8o+Vk1adn1CpfS2TuMdePwkA+PyX/jg+dnyMnunR7WIu\n6e0hE0qgFrBa4ehidpY4flxI1KlpNsWek7VaWqC5+ehHpELVPT9C1cfEN12/z7jClqprnLjInlwv\nvITu4eHhsUWw4RL6uXmSEtrnVW1O/rFNF9QvFqdb3cHRmKERCaUQ0Oe92ySSrZMkaaypUn5uHyLi\nMB2QtFIrC3lUr9Ov7p137IjbMimSqvt65HcvxXVO+9mVbLBfSNFikd30+kWimq5SP+anpC1IsRTJ\nBTRUFlPUHHllleSTJ8krCtf+Be/vF7eqOhNbHUU8Ov4wbIok4yI0A5YWEioiNnJDVkRRvpciYMO0\n5DNpcb6T7iKRQZVTr0qnGqR12aQiQCN3PS1LsDTocmSofBh1J8lnRXMaHSBXvFpVtLrUEs3zgVuo\nb0kl1dY4r0+9LhJV2FGTvgJJlmabTTkn4MjCjsqr0nThvCGpOMu8EbnOaKeuUiPzerSUBhKGtFZt\nJrKLXeIumOR5qS3KPg2SNC6dl6bDWo9p0jVaC0JMlznd7nfGRXvN99Jc1lXEb4Ul82TEDgmKZGw1\nnbajpHbWNtpKU7hcxLGSUbTqmLmAtCrpUla7ATr3WQD4wfOUO+iPvkRlGk6Oj8XH9hzcDQBIF4XM\nrUVld5G4bWmJnv1yhda4VhNtZnaapPbBXnkOTnJBkEe/8ETcNjlLrqK33nwTACCXUw4URfqcy6j6\nr+HFcjWtD15C9/Dw8NgiWE+Bi10A/gCUmM4C+Jy19jeNMf0AvghgN4AxAB+31i6sdZ214JLm1xry\nS7XEP1SdmgS6OJewKv9izuRE8unLkiRV7YjE0cPZBcsNaStwQFF/nu7Vp/KOzI6TJLNnt0i6+/aR\nRNrbK/bvQpok0VRAv871hkgSIWgsxYLYMPdlqJ/VvXLd+Qr1d3qWZJRjp0SiOj5GkletJG0R29pT\nKZEIVkIXNeiwxFOr6yx9NKm6iIXL4ujseQllm0w6CUy5wI2/8DwAID+0O27LsFuoS8TXSYr05GSm\nUElbseSlk92tOGaVYTHVS7bOwTseitve+w6yU77411+K28aOPQUAGEgQZ7Jvl7j6tbj4RqMpEliT\nXfCaldXSoQuqKtVk/lxwVDNS0nVEe6tWofVeUEUhqlwirrYoWkSL/QXbNbnnIrvcprpoXQYGZE82\nq7T/F09LkFSiRRM3MynZO1scELY4TlJieUn2TqtBq1BW2kmF91ZCZTO1LB1adnXtKNfUiI+FunAG\nfzaJ9cmEztatpeuAFz5Ue6F4aQRUAAAgAElEQVTG7wP3XugoW36b3Vr1OtbZrfWHL4lm+LU/+zoA\n4NVjZPc+cMv++FiS3ZnrVeWCydqXiVThloA0oUyGju3dK9fo6aY1baj3TYrnYXxCnpf/+9g3AABD\nw/weGRI+b2gHWQK29wtHVUzLuK4U61mNDoB/ba29FcADAP65MeZWAJ8B8KS1dj+AJ/n/Hh4eHh4b\nhEu+0K21k9ba5/hzGcBRADsAPAzgUT7tUQAfeas66eHh4eFxaVwWKWqM2Q3gbgDPABix1jod8zwu\nXCvgkggCl+JSVLFkigiDVE5Ub5d/YmmO2s5URRUb54isE7Ni6hhkfjRbUFXuA1YrOWVvW9V7XHiO\nVNgTJ0UV28E1Ig+ItoWuUepnzyC5yQ30S2RYnVUwWxFT0UBAY0kMiqmj2k065g1d1J87dokppfaj\nlN/l+VeFxPriV57l6+v0uctRVeaVMrtSRkqXDbiIRValDW1xfztNF9Gm6nzy57RyOYy4yENdkZH1\nKSbRmLEyNVWrk4m1jrpu8iLFBywTwpEih4pMJKXzEgn77e/9NQBg/qyo2QFI9U4zGbmjIAQ5Aupj\nqMi3Bkd5vlyRIhkOc7NsLlHmOueC2VBqeWTZ/ZRJt9MvvRgfC3mOamVxE7VM3CkrAnLdRIImWLXX\n9wyZkB5Q9qkZztdSVymJa2yGfO0NivbMKJ7ScsRvW7vMsakttczsxaSvc61U5rcOjzla5nbH37sA\noXkhONNMRe2P02dpjx87cTJum5gmq+38Ao2pqkx+1Ra1RSptbYdNStMq/880p22ulfl8ZckoL9F3\ntfkojMeqTI4Jfm4zTCCrCOjpBYpCLi2OST86LqJUmRfTtLaZbnpu+m6QfE7di7S220fFFPbjmSsn\nmB3WTYoaY4oA/gTAr1hrS/qYpdW/oAOpMeYRY8wRY8yRWq12oVM8PDw8PN4ErEtCN8akQC/zz1tr\nv8LNU8aY7dbaSWPMdqysuMCw1n4OwOcAYHR0dNVL//C7PwoAmDku0tbJ18ntqFgUyfWTn/g4AOBv\nvv19AMCzzylpiF2tKuoHrnKefnPaoSKxuHxXYOjXdLBftIIRdvsbUS6H87P06z8l6V0wNExKyfad\nXDhgj7hLDW+nX/OuPiE/YjmgJT9mqRaXM+vQlCWVlDPURRJ6/2E5v5ihPDBfe0I0kJXIF8WtL0rQ\nsmqXso6LNkrIEmTTnKODpQpNhLngDKtc1Qz/ZgfK46/JxKHLTpdRwUku6Z25YIL/1XCFCIzKm1Gb\nG6O2lyQ4pMVCXqMs+Vp2ci6eHBeIqFRF5kjY1XLLsoCfFZjl7J0tRc5aS3Pa7Mj3DB+vzdA6zihJ\ns5/7k1XZJ1MF2mNBQdzXckWS4iY5m6ju6vbdJNHtvlmygj79l6SdGOXCmmQtcIGzBuZVQQyXVbKp\nXQ55njOqsEqG94zpuBJtioy8SOGFiwUCaZxhYvfr3/x23PbUkdcBAIsV0WIcCXqei5csKHfLFmvi\n2qMxYsI7tSxPCvebye3nv/uSHGRtra2k/BYX6QhViccCu4+G7Go6p7T/iJ8lXSMjzuSpnuVCD633\nwG6+57zSqgIaV09TyO1dXFxnTNI+XTYuKaEbcj34XQBHrbX/VR16HMCn+POnAHztyrvh4eHh4XG1\nWI+E/nYAPwfgJWOMS1H27wD8BwBfMsZ8GsBpAB9/a7ro4eHh4bEeXPKFbq39W6yd1/HBq+3Aobve\nAQAo3nFH3PbaK2RWOTMmZhiXG6OX/csjo3xouXtBUkVdsSrYVPk4Wi06L2K9dqkh6vMZrrrepcL9\n+rn+5uiARO8N99M9uk9RoYvtJ5TP+QHyLT10i5g/BrqIHAtCcdFv1Ym0calmF2qKtYmIzGsq8q3R\nov6mUtKPldCRdQETUGFSltep0MuC0di32nD0q0t3CwCRS0uqlt7lmoguEMXnliM0miTjfB/Lz1zH\nGFRujzb7ek9KLo3IkFksn9cxA1TPM8uFGSamxuR850et1OxancnHzGqiueb2hfKZdn78bTVHbk4b\nNbrWwoxEb+a4uEixX/Tn4Z07+e9o3FZlXmmhTXN156FD8bEd+6h4ijUygwvst55RRUOSbCpzs9ZQ\nvvIB5zjRyx7yuiSUSSnpUv+6MAGrny/39/JqXv7wxdfjz8eO0fPy8ivSdvQoFSVpq/qotTI5FNRK\nNKfGyhpnOTLTqKohPZwCu16XZ6jBTgF5Nhsu33H8LKlno8ixEzYpBosM1+ON2ITSc4OM3RH0zbI8\n++MvfQ8A0FpSEbwVjpRuUhrkHhUFXD1HuY9mJ8TB4IuW+j36DiFPLxc+UtTDw8Nji2DDc7kYzlkS\nGckGd/OhuwAAQVLkClfowOXoyKlfO+JsV0iYXCYtULlI4s+h+6Mz/tFUzKsIshK7RM2pjIoDU6Qp\nFDixf/a4uCi+9DqRaaffEKns9pvoZoW8ECJtJp5aEUmHS6FIIe02uUHOz6h+VGl8S8t8i5ZDE48J\nHktCyyZ8z1RWEXI8l/kCzfOCyr7XZK1BZ4NzQpsu2pCIiU92OVS3dMcUD7vs+NrQ2fSY4LVC4LVZ\natqmSoAdvJlLAbKUUyqLpFR3UaEqr4+LNhweXS2hhxfQIiKW0I0iAR1J3GLyraZcCce5nF6hInvH\n9JD2FZRkbee4yMnSEuffMbI++X4iyGvqGk12fcynlfbAY4mLPah+u7XqLCO3HQm+mhh02l2kCmI4\nyfxCEvrFlvMPH/vT+HOQoj3esvKcV2aIKJ06J9rXDs7HlMnSnp9ryJwO7iLtpWdYNJyhbeQtXVUc\nt/Omcy6bEdR7IXAFReTVl+KMkYmUOEQk0ySFW54j5+YKABnOrTT3hlgQwhf/EgAwLP4QGOyl/Tm9\nQHuhNX9DfKxg6fqz0/KOG7c01k/gyuEldA8PD48tAv9C9/Dw8Ngi2HCTy+x58r8dUcSjS+fa1S0q\ndT5J5Mfx40SkaH/gew/fTR+Umjg7O7vsLwBUucjD0hKZSRpKnQtc+J5RkaVc8Xt4m6h4RdZ0JyfI\nOb12XiLIxriAxsQZZRt57z0AgL5BUW9DTgEcBaSCTVXF59x2+P4d6UeuQGMvDqzth+7MLHQR9g1X\nxRhcgQMdLemsBwPDpOYODEnUq0vgVCurFKtsUmi1RU10EXLON1dH8V05lFmDCUHnaw0A/ezb/eCP\nCid/+34iRedmSL1tq3FWuaaoVpvrTIrKiAU9bMrRfvyW90da+ep32IwVsk97ZUlFhfLkBmW5Z8h2\ngdkJCdmYYR/2uUn6Oz4uQQ/7D1P61VxREjjlmfTNqIIfIwM0il07iHSdV3veJWAzyrziTGCRsqe5\nfREwQbjMNBYvxwXI8IsYXf7im38rfdx1CwAgnZd93WrQc3jwJklZff99hwEAzQbtp28/82x8rDxP\nvtu5XrFrTJwjP+4gJSY5t1bOTIaWmFwMx6VY5UTeMMudJQDA8gS0mJRvqMIjLY65mFVOG3muuPvA\nvffGbYND9N4osZnse8/JWHLdlFK3rgjv5NXn5vISuoeHh8dWwYZL6Nt6WQJTzFmTyciUkjqL7CpU\n5mT/oYr0s/xLPDgkkaU9PeQ62N8vpNc8F9NYWuQq9yrSsMR5SprqusUMSbUf/uD747Z8hqTDxx6j\n1K2VpsoPwuk952siLZyrUj8aKpF9xHkicl2UGtal9QWAvu4cj08VRuBIztsHtsdttZqkagWAYlGI\n2MAVaAhFMnE5QipaG+C/CXaBSyd1BXeWUFqKTOM8HEYlIwnYtc8al4p35RWuBKsruGeVO+k730Gu\nrh9490/EbYUc9d206bs6Z0iJU8e2lTtfo7W2JnHgIBGsLVUAolkn7USXZqtWOC0vazGlRdHWXJX4\nICNSu2GJvysvmmeLc7lMcSrbhUUhc6sVul5fl6ztQC/t55lzUj7xwE0HAQDve5A0luef+UF8bIal\n9Y5yQ2zxHm+1ZX90+LMrNpFSLsAuVW6oJPqEubQsWFZrYBdYS5qUOSoW6Jm+YbeQhW5ZOnyv+27f\nFx/76yOUq+Z8R2meTOyndago99e2+a8qZhJHLysJPXKEvtIuQ3awCENXfk/thQaNoTV9Om47yCUQ\nCz2S0spwTqqRLurjbQfk+X31FK1zT0HWNo2r1269hO7h4eGxReBf6B4eHh5bBBtucklxkaPAiOrR\nzyRdS3gw/N23/woA8MoLlLhroFvOP3+W1M/ZGaksZDlCrqVMKK4OY1cX+cT2b9sWH8uy6ltTaTKz\n7As7PjEWt40Mktq0Zy9Fc50eFx/affuo7cCBvXFb9wCZgXJ58XEtsjkoYp9fnSdqYZ5InpSKWutY\nUtEbkVxj5S+xq+hE46O56VJqpfN9r6hESItVmvtyjaLV2sq8kubalUZVIHLRt0YnemIzglPVL2xm\nUSlF+fNyj+bl6UuXJV/iFLUppVIf2LcHALBNmdhSnJCqwyaXmvI5r7DJpaT8vxtNHTu5HAcPUTKs\njjJ7Ndy+UGzh/DSZ/xZnaB4X5yXqr8SkcltFzk6fp9S3iZysS98gEXwPvPNtdH5bkdZMyCUgCb7m\n5siEMn5aTC6j24lUTPEe6O+RZ8Oy+VKbGJz5RZtc6kwYu7SyOp9ag9dYZ0t19S9ttNo3PR6bipJd\n4riA+qIQwhyMiYFeSXXcxQnMukbo2eyoKNIjL5BDRKIpzgEJ0Pm2I/s/4Hlwkc02pX3qeXwqEZer\n1JUwcp6r59px66dI9gyb/9rKl32QCeluVYFo3x7apzVOFDdXkvnrnuY0z9tlbYcH5btXCi+he3h4\neGwRbLiE3pii3A5hSvKflLhowvyM/JqXWfrp4SgtLZFmkyRdawmzFZH00VGEiCv4MDFBhGLbqtS3\n20hS2r5LCJpGnWs1liSCcu9ecjcqdue5H/Krfvc9FOF66Oab47aJM+SGVl2QiNLoLF0vYuG33ZHf\n1a4ekoxv3CspU7MpIk+jupCWnapoBgAwWxKpJZ8jqSWXE1fQArv6FbpEIsiUaQ7PT5M7WK0u7pZz\nNdJ2VPAt2jynutE4qcYNYVmEoYtcXC0habhrOAJUu9NlszTP994l7mA3H6D5Tah6lgETu92suQ0P\n7YyPVZtcLEHlzClV1g67LfTSvFUqIsXVuaBKWkUT9u2gPdM9SHPauygSlu24sch459glcfa8aJLb\nd+8GAIxwnpdUVq6fYS0znxWC8oabaH+eOfpK3PbaC5RK2vL+7ig3vSQT+0mlafVwxXktW7e44n3s\niqlJVJbQm8rts8LS+sXSEBezsj4TExQVatUeW2DN6exZ0TZuu+12AEC5VFp1zPL+sx1xN44VplDG\nZ5zkfIEU0BET4zqvj3O97SiNxVUhiTinklWEZYbnMqlyykzP0ruqT9WEvZlz8UxM0POlo9Yf/kki\nsO+6+664bc+NRKz+2Ze/gCuFl9A9PDw8tgg2XEI3c1QCbLok0tPEAkkCS6qtlzMf9nD5toaSDJIp\nOrZMYgtpaA0l7eW44r2NSEJpteUXs7ZANs+zZZGku7qdS5RISGUOTorYTQ/KRhq54gCq9Nu5cZJM\nyhWxn1XZvuvyUBy85Zb42O6bqN7d0OjuuK3S4mUqKT5AhBQAwNSSuGAWQ5q3XEfln+ACG7mslHJL\n50iaGBpiSVpJEK4MW10FXyUyLHWq+XAFLlp1F8ykszPyemijuAuIUW5vToh1ZdCKygb8wH1vBwD8\nxHs+ELf195DtXNub222WwjlvC5QG191F5w8OiHRYa65dPavJEmlLuX2mXSm8lBA7hiXGvn7SLlMZ\ndc8iaUm6DOAMF0noKAP1zDnSFmemSMJLqwySt95CwVLbRsQV7sff9U4a55Ss99EfUgGHbCbLf2V9\nXIBTpCTugMvdJdOi8WWx/BmqKfdWcMZDJ8kCwBBft3URCf2RT0lG7W/8FRXm+M7T347bSovE56TU\nfnIcQXc37U3N+czOUIbCEKIxu8Azbf/GimygyzJH2tUl9py0rl1SRX3hwCx1/USc70beLYEhHqCo\nSiWeemMMAJDPk8Z3cI9o/z//C1RKoq9HJHr39P3Zl3HF8BK6h4eHxxaBf6F7eHh4bBFc0uRijMkC\neBpAhs//srX23xtj9gB4DMAAgGcB/Jy19rLLVhtLqmYAVf+SCZdGTVTeCkjVTXHaUJtQKmSaIxh1\nVCN/bKkuJTjfQoq/m1Iqp3O7W1wUVbbBkXof/ImH4rZUjtTDWo1MM1EoZqHnnqPCHItzU3Fbms0Y\nyYz0rSfrTDk83paolWGDzAIz41KNvs65IExSCLOVWCqJy1ybTQUubSwApJKOHFN1JFlFd2SNTq2b\n58IBqCl3N5e3JSOmmYgJKFdK1DaUO2DbuSEq9ZYJqkxG7tXbQ7lI9u4hsvPuu+6Pj+3fQyaoYZXX\nx7poUFWn1Y25zG3lqsxpvc77w0i/telpJZJc/EAXDRliF0mr3BabVdofCc7t0dOrov54ca0yQW3n\n+qFGmZvKS3QNp/bPzIhZ6Ad/9z0+JnOa58jIXftuittm5sg8luJ1bCtyz7nwtXTkcUR7tqe/S7U5\ndz6OClUkYMAuobp8aCZL+ymbkXS4K/HAvVK05uwZIvGP/J1ct8rzV6+LWW9ujm5Sq9H6nRkXUrTE\nxH8ikD3sIpUDZW5NsIumM40kjDYD0l+dCtiZa6xycBBTlTPRKBKV3zNtZYJa4gjfjsr/U1oi01CB\nXTHHT0tk6Zkxqj/bfevBuM0krl6+Xs8VmgDeY629E8BdAB4yxjwA4D8C+G/W2n0AFgB8+qp74+Hh\n4eFxxVhPCToLwIk7Kf5nAbwHwM9y+6MAfh3Ab19uBzIBSTUpLUkzWZnoaEKTclhE7pdSFb/IF1jy\nyYm0HLqk/1o67Lg0c/Q71tGkGktB+bRIjjXO2fDs95+L2+57xwMAgFMnxgAAVlVfX2Ji9WRbXAqL\nXOG9qvJadHURiTbBbmxHvvtMfOzgTSR57dsrbneOa0soriet3CtpMDIfNa6E0VAFF5zUmVRZ6SKm\nYVzQkSa9Gly0QUt7TmINVHGFfI4k3VyaM0g2lesXJ+YIVCm3gLWAHYr0veMQuSTuvoHI4aEBGXuB\nrwuVXbO8xMR0pHKR8P4ptzijZkU0lsUF0gIXlsQNts7uc709Elzm4LS1rqJIsEOciXJuRgi5Cs9N\ni/dasUsVKmHCTGd9rHBpu5yWat3+5P23e5dkHixxINzXn3gibiuwy2FSSXOuunxfL+2rZlOeA5en\nREuOIQfrZBR56rItOpKzf1A0IrCEHirS0BWTSWTWfoVod8ub9+8GAHz8p38qbhs7c5rvKf3t61P3\nBXD//aKtve997wWwfJ6zTttNy5ymeY8HvOe19O72sCZFHWGsi3o4d0VHfGq3RTdHpSUJQpyfJ+2h\nS7kFD3LQWDcH+u3btz8+NjxA77NAaWtGPSdXinXJ+MaYgAtETwP4JoCTABathFudBbBjje8+Yow5\nYow5oiPNPDw8PDzeXKzrhW6tDa21dwHYCeA+ADdf4iv6u5+z1h621h7O59e2W3p4eHh4XB0uyw/d\nWrtojHkKwNsA9Bpjkiyl7wQwcSUdyHLa03RV1OdUktSbnNJMC13U1QrX/Oy0xZzQYtNJPq2i7PI9\n3Gdde5QUiiT7TNfrOlqQiceEqImJBKl2r7x8NG579bVTAIAGF0jIJEXFSoLzwVREPVuYJ4K0raL3\nZibJHBATc0otb3AK1qJSxdIRjblV1QUulptcMjkZu/jHynVDrrXZbsq8WY6uY+0S7baKqu24AqLK\nZMUqaSjWI0Q1V4uSVNmUym8RpLnCek7mtH+ETBcjO3bFbYVuUj+dj3dCqb4un0pT+Y03GmQBbLUV\n8cnFEma5CMLUrGzHcp3mTav2LhfJ9m0SkeswOEg+xfOKIJ+bI/NHVpGpvZyTx0VSupwxAJBgglIT\neE3OH1NQvuaJgOY8kaX5SyrCuYvnQxc+cDllmsqUmHJFKdgcOTCoUkYv0NitKt7Q1UXHQ2ViC9js\nkGMCTzsMuK2goyvLZepHRRV+UKmXAAC7brxx1eePfPSjcVudUzprX3ZnLkpy+l5twnDRrslA2R5j\nwvPKkzVfPphEVf7trl5trSZpdiM2A/YwoZ9KrSaQl5t+rr5nl5TQjTFDxlDmLGNMDsD7ABwF8BSA\nj/FpnwLwtavvjoeHh4fHlWI9Evp2AI8a8vlKAPiStfYJY8yrAB4zxvwGgOcB/O6VdKDF1dxDiNiX\nY/euKKl/b+jX2YDOT6rscQhdeTUZjnMNCxTxk+WCGQkma9J5lV2wxtnVlBTifj2HhyRSb45zssxO\nk6uYq/gOAE0ncXR0EQkXQancqhy5yERLEOiCEXS9KBTCpdWhe7abIjGupE+6lVufk9AjlVHOub5F\nyv0Kce6PDp+jJHr+vCwXjnNtW5avhbMs8nyrugjIsYpVUORiX56k356clBHLJrnCOmsD9ZpI3uUO\nzUelpshI/jw1Iy5tk+dJc5pfIMm8oYpTuLl3JBkABGZtiW7iHJHVukRhNkt7MpcRjc+5fbaZIOwf\nkDEl2W1xYVHGMthP+8iqCMMka5Uug2VHkW+JJPUxp9wt0yzlBXr/83LU63SvUBHI7rxMQTQLJ/2q\nIFaErEHm+by2cpUssgurVZpkm9W6oop0vBhc8YiE6nex6OaycIFvrIZ7HrXLofOl1JJufNw1LSun\ndzFJ/kLlWVaLzW7raLfWLJP92exKPUUk+Y4q3ShB1IoUvUjP1ov1eLm8CODuC7SfAtnTPTw8PDyu\nA/hIUQ8PD48tgg1PzlXllJzJrHRldBep5Q3l0lxnsiTBhEjeimpjDamQKZXO0nCoqFGpXiOXdIlN\nI/msKDlpTjUb6XA4/tg7KCphvotV2DT7oi5KMi/HarTaSrVP0flGqXqu1mHK0DUyGfld7enjSLmm\nVH/vNFz9QWXPWIFMWog2R1DqxGHONKLTubqq6I4c66hkVM7/XPsvxz65ao5iUwtHomZzotp3FUhV\n7+mWQhTdefL7zmbkPJcMa3aJky/pWqgckbuwING3k5Nkajl95kTcVqqQCcyZfHKKJE5xdGxC+dTr\neViJgA1agZH5LjDJrgJFEWTpullD+yOtUt82OUpXRxNGTIA2FXGWy7i0v3T9akNMNB2uZ5lR+3po\nkOZSE7wh205S7Itdr8uxYpHNJR0ZryNsOy0xKYVsWmvwc9ZUJpcCr7uuseqiKzV5etGKmBfy/77A\nGphVibXsqmMrz+Ez5TxnvLjQaRe1a6zvCxe6/4XG4vruztfmprcKXkL38PDw2CIw+hfwrcbo6Kh9\n5JFHrtn9PDw8PLYCPvvZzz5rrT18qfO8hO7h4eGxReBf6B4eHh5bBP6F7uHh4bFF4F/oHh4eHlsE\n15QUNcbMAKgCmL3Uudc5BrG5x7DZ+w9s/jFs9v4Dm38Mm6n/N1prhy510jV9oQOAMebIetja6xmb\nfQybvf/A5h/DZu8/sPnHsNn7fyF4k4uHh4fHFoF/oXt4eHhsEWzEC/1zG3DPNxubfQybvf/A5h/D\nZu8/sPnHsNn7vwrX3Ibu4eHh4fHWwJtcPDw8PLYIrukL3RjzkDHmNWPMCWPMZ67lva8Exphdxpin\njDGvGmNeMcb8Mrf3G2O+aYw5zn/7LnWtjQQX+X7eGPME/3+PMeYZXocvGmPSl7rGRsIY02uM+bIx\n5pgx5qgx5m2bcA3+Fe+hl40xXzDGZK/ndTDG/J4xZtoY87Jqu+CcG8L/4HG8aIy5Z+N6LlhjDP+J\n99GLxpivumpsfOxXeQyvGWN+YmN6fXW4Zi90rnj0WwA+AOBWAJ8wxtx6re5/hegA+NfW2lsBPADg\nn3OfPwPgSWvtfgBP8v+vZ/wyqGygw38E8N+stfsALAD49Ib0av34TQBft9beDOBO0Fg2zRoYY3YA\n+JcADltrbwMVnPoZXN/r8PsAHlrRttacfwDAfv73CIDfvkZ9vBR+H6vH8E0At1lr7wDwOoBfBQB+\nrn8GwCH+zv/kd9amwrWU0O8DcMJae8pa2wLwGICHr+H9LxvW2klr7XP8uQx6kewA9ftRPu1RAB/Z\nmB5eGsaYnQD+AYDf4f8bAO8B8GU+5Xrvfw+AHweXOLTWtqy1i9hEa8BIAsgZY5IA8gAmcR2vg7X2\naQDzK5rXmvOHAfyBJXwPVEB++7Xp6dq40BistX/Jhe0B4HugAvcAjeExa23TWvsGgBPYhBXZruUL\nfQeAcfX/s9y2KWCM2Q0qxfcMgBFr7SQfOg9gZI2vXQ/47wD+DQCXgX8AwKLa1Nf7OuwBMAPg/7DZ\n6HeMMQVsojWw1k4A+M8AzoBe5EsAnsXmWgdg7TnfrM/2PwbwF/x5s45hGTwpug4YY4oA/gTAr1hr\nS/qYJTeh69JVyBjzIQDT1tpnN7ovV4EkgHsA/La19m5Q6ohl5pXreQ0AgG3ND4N+nEZBVZFXmgI2\nFa73Ob8UjDG/BjKpfn6j+/Jm4lq+0CcA7FL/38lt1zWMMSnQy/zz1tqvcPOUUyn57/RG9e8SeDuA\nnzTGjIFMXO8B2aN7WfUHrv91OAvgrLX2Gf7/l0Ev+M2yBgDwXgBvWGtnrLVtAF8Brc1mWgdg7Tnf\nVM+2MeYXAHwIwCet+G1vqjGshWv5Qv8BgP3M7KdBBMTj1/D+lw22N/8ugKPW2v+qDj0O4FP8+VMA\nvnat+7YeWGt/1Vq701q7GzTf37LWfhLAUwA+xqddt/0HAGvteQDjxpiD3PQggFexSdaAcQbAA8aY\nPO8pN4ZNsw6Mteb8cQA/z94uDwBYUqaZ6wrGmIdAJsiftNbW1KHHAfyMMSZjjNkDIni/vxF9vCpY\na6/ZPwAfBDHLJwH82rW89xX29x0gtfJFAC/wvw+C7NBPAjgO4K8A9G90X9cxlncBeII/7wVt1hMA\n/hhAZqP7d4m+3wXgCK/DnwLo22xrAOCzAI4BeBnAHwLIXM/rAOALIHt/G6QlfXqtOQdVUv4tfq5f\nAnnzXK9jOAGylbvn+Qi2nwgAAABiSURBVH+p83+Nx/AagA9sdP+v5J+PFPXw8PDYIvCkqIeHh8cW\ngX+he3h4eGwR+Be6h4eHxxaBf6F7eHh4bBH4F7qHh4fHFoF/oXt4eHhsEfgXuoeHh8cWgX+he3h4\neGwR/H/0LSsMF4QUIQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "GroundTruth:    car   cat   dog   car\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTKcA07CP2lq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net=Net()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYCVWmqhxFFT",
        "colab_type": "code",
        "outputId": "cdf4ef78-d0e5-4261-c1d1-2f8538714d3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "net.load_state_dict(torch.load('model.pth'))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UydK-QdK-Xj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outputs = net(images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mm-SU07iLRND",
        "colab_type": "code",
        "outputId": "497d8b2c-2c77-4d3d-d07d-4b20417af9c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "_,pred=torch.max(outputs,1)\n",
        "print('pred:',''.join('%s' % classes[pred[j]] for j in range(4)))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pred: trucktrucktrucktruck\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XZwQfJ9LVJx",
        "colab_type": "code",
        "outputId": "d1505bc0-239a-4936-ba85-dd5142f8388d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted:  truck truck truck truck\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrW_riVNRLOU",
        "colab_type": "code",
        "outputId": "12587f88-0825-4147-b87b-17b0a9b7ba1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in test_data:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
        "    100 * correct / total))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 9 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0lMMpPuRwjj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_correct = list(0. for i in range(10))\n",
        "class_total = list(0. for i in range(10))\n",
        "with torch.no_grad():\n",
        "    for data in test_data:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        c = (predicted == labels).squeeze()\n",
        "        for i in range(4):\n",
        "            label = labels[i]\n",
        "            class_correct[label] += c[i].item()\n",
        "            class_total[label] += 1\n",
        "\n",
        "\n",
        "for i in range(10):\n",
        "    print('Accuracy of %5s : %2d %%' % (\n",
        "        classes[i], 100 * class_correct[i] / class_total[i]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ED6WjG6XSCel",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tawVB4cFzaZS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W8EACnDzaec",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjqgXYfvzadS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}