LeNet_CIFAR10.ipynb

{
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  "metadata": {
    "colab": {
      "name": "PyTorch_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebTDFe6qZOS6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckA245fdAd0w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDoSidZsA2qs",
        "colab_type": "text"
      },
      "source": [
        "The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ph0BIYFAdox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision\n",
        "import torchvision.transforms as transforms\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro5ApVSQAdhq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEljVNsxqRZc",
        "colab_type": "text"
      },
      "source": [
        "<h3> Loading Data </h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bskTS6Qkq2MO",
        "colab_type": "text"
      },
      "source": [
        "<p><b>Note:</b> The CIFAR 10 dataset has been used. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n",
        "\n",
        "The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.\n",
        "</p>\n",
        "\n",
        "https://www.cs.toronto.edu/~kriz/cifar.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orTN31A8Ah4F",
        "colab_type": "code",
        "outputId": "57d4e3cd-dbd3-49b7-92de-d9a3bc412694",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "trainset = torchvision.datasets.CIFAR10('./data', train=True, download = True, transform = transforms.ToTensor())"
      ],
      "execution_count": 4,
      "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 ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "170500096it [00:06, 27487698.77it/s]                               \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UdMtjigAh_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ff3vbTahBxrz",
        "colab_type": "text"
      },
      "source": [
        "More of torch.utils.data.DataLoader at https://pytorch.org/docs/stable/data.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jv2VKzGgAiGe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#shuffle ensures randomness when loading the data\n",
        "#batch size = number of samples used before updating internal model parameters\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvRSoH6cAiOU",
        "colab_type": "code",
        "outputId": "e01972da-27b3-4dd7-838c-47462454ccce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(trainloader)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4jIOZk6AiDu",
        "colab_type": "code",
        "outputId": "fa625233-339c-4f67-f3bb-ef51f9f6b3ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(trainset)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1acqniK2DSHJ",
        "colab_type": "text"
      },
      "source": [
        "len(trainloader) x batch_size = len(trainset)\n",
        "\n",
        "So length of train loader varies according to batch size\n",
        "\n",
        "https://discuss.pytorch.org/t/about-the-relation-between-batch-size-and-length-of-data-loader/10510/3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qopml-EaAh9U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2o_iiNjEdGb",
        "colab_type": "text"
      },
      "source": [
        "NOTE : iter() method is used to convert to convert an iterable to iterator. This presents another way to iterate the container i.e access its elements. iter() uses next() for accessing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPZaD5p-D_e1",
        "colab_type": "code",
        "outputId": "b0274f62-bd17-476b-ddf0-06a4353f1ae5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "dataiter = iter(trainloader) # dataiter contains set of 4 images\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "print(images.shape)\n",
        "\n",
        "print(images[1].shape)\n",
        "print(labels[1].item())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 3, 32, 32])\n",
            "torch.Size([3, 32, 32])\n",
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMy41mz2Esl_",
        "colab_type": "text"
      },
      "source": [
        "Each images is of size 32 x 32, with 3 channels. Its batch has 4 such images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdlB786pqkEa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zqi0pFDKqgeW",
        "colab_type": "text"
      },
      "source": [
        "<h3> Visualising Data </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6ADEvcSD_cF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 10 classes in CIFAR10 dataset\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAVNiBN5D_Zz",
        "colab_type": "code",
        "outputId": "a908af22-bf05-40cc-ac16-62a5877c3ac3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "img = images[1]\n",
        "print(type(img))\n",
        "npimg = img.numpy()\n",
        "print(npimg.shape)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.Tensor'>\n",
            "(3, 32, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPA_PP0tD_XI",
        "colab_type": "code",
        "outputId": "6e11c3ff-85e2-4d27-beb5-06e0d2981d61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Since we want size of image to be (32,32,3) from (3,32,32) we use np.transpose\n",
        "npimg = np.transpose(npimg, (1, 2, 0))\n",
        "print(npimg.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwACwWarD_Tu",
        "colab_type": "code",
        "outputId": "82a4feb7-17eb-4cea-de95-f0be3473e837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "plt.figure(figsize = (1,1))\n",
        "plt.imshow(npimg)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bXzyoxdyfsiqhbrdbZruXlrX+4tiJ42U1fRHPqNXVBKxUK9Sqk3gynM3gY1wHRsxgjZBN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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwjTSCdGAh64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Putting the above code into a function\n",
        "def imshow(img):\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKaAKzv-G2HO",
        "colab_type": "code",
        "outputId": "b7900f03-a07d-4fb7-8a8b-df92044b7127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(' '.join(classes[label] for label in labels ))\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mSDTO35c3eYxP2FREXovdiwJVYWCCizy4KMDAiBQ4zBXrm2Ut5fIbVhV0mBih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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "dog cat frog frog\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-T77DdDG52H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OESVJr3M8Ds",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhDsi71NrhhX",
        "colab_type": "text"
      },
      "source": [
        "<h3> Deep CNN </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xAXBUOTHau1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CNN_v1(nn.Module):\n",
        "  def __init__(self):\n",
        "    #super().__init__() This was giving not implemented error\n",
        "    super(CNN_v1, self).__init__()\n",
        "    self.model = nn.Sequential(\n",
        "        nn.Conv2d(3,8,3), # depth of kernel = 3 , number of kernels = 8 , filter size = 3 x 3\n",
        "        nn.Conv2d(8,16,3) # depth of kernel = 8 = number of kernels in previous layer\n",
        "    )\n",
        "\n",
        "#https://stackoverflow.com/questions/55932767/pytorchs-model-cant-feed-forward-a-dataloader-dataset-notimplementederror\n",
        "#This function has to be named as forward, else it will give 'NotImplementedError'\n",
        "  def forward(self, x):\n",
        "    x = self.model(x)\n",
        "    return x\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0dq7Xp7Jhgc",
        "colab_type": "code",
        "outputId": "37c089c4-3408-4055-87bd-48ec53d057c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "net = CNN_v1()\n",
        "out = net(images)\n",
        "out.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 16, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woSYinwCMuqM",
        "colab_type": "text"
      },
      "source": [
        "detach() method constructs a new view on a tensor which is declared not to need gradients, i.e., it is to be excluded from further tracking of operations, and therefore the subgraph involving this view is not recorded. This can be easily visualised using the torchviz package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16OCJYSJK0pm",
        "colab_type": "code",
        "outputId": "0134f13e-991e-4477-bb0b-373bcddea186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(out[0, 0, :, :].detach().numpy())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5a1fad88d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8oGhaSDMHgn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_jPGW09T7PO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSs5BajVMOHh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CNN_v2(nn.Module):\n",
        "    def __init__(self): \n",
        "        super(CNN_v2, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Conv2d(3, 6, 5),          # (N, 3, 32, 32) -> (N, 6, 28, 28)\n",
        "            nn.AvgPool2d(2, stride=2),   # (N, 6, 28, 28) -> (N, 6, 14, 14)\n",
        "            nn.Conv2d(6, 16, 5),         # (N, 6, 14, 14) -> (N, 16, 10, 10)\n",
        "            nn.AvgPool2d(2, stride=2)    # (N, 16, 10, 10) -> (N, 16, 5, 5)\n",
        "        )\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.model(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kh__oBK2NLb9",
        "colab_type": "code",
        "outputId": "327e2120-1e44-4ad9-8db5-055c53bdb3cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "net = CNN_v2()\n",
        "out = net(images)\n",
        "out.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 16, 5, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOv0FFC5Nj-b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7wFcsr5sD5Z",
        "colab_type": "text"
      },
      "source": [
        "<h3> LeNet </h3>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6IrlUXRT8Ca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LeNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(LeNet,self).__init__()\n",
        "    torch.manual_seed(0)\n",
        "    self.cnn_model = nn.Sequential(\n",
        "        # in channels is depth of the filter\n",
        "        # out channels is number of filters used\n",
        "        nn.Conv2d(in_channels = 3 , out_channels = 6, kernel_size = 5),\n",
        "        nn.Tanh(),\n",
        "        nn.AvgPool2d(kernel_size=2, stride = 2),\n",
        "        nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5 ),\n",
        "        nn.Tanh(),\n",
        "        nn.AvgPool2d(kernel_size=2, stride = 2)\n",
        "    )\n",
        "\n",
        "    self.fc_model = nn.Sequential(\n",
        "            nn.Linear(in_features = 400,out_features = 120),         \n",
        "            nn.Tanh(),\n",
        "            nn.Linear(120,84),         \n",
        "            nn.Tanh(),\n",
        "            nn.Linear(84,10)           \n",
        "             )\n",
        "    \n",
        "  def forward(self, x):\n",
        "      print(\"Initial shape is\",x.shape)\n",
        "      x = self.cnn_model(x)\n",
        "      print(\"Shape after passing through all CNN layes is\",x.shape)\n",
        "      x = x.view(x.size(0),-1) # Flattening \n",
        "      print(\"Shape after flattening and before passing to FC layer is \",x.shape)\n",
        "      x = self.fc_model(x)  \n",
        "      return x\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5kqjnK2sLbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7mWUtbQsRqV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oPYSUnKsR9p",
        "colab_type": "text"
      },
      "source": [
        "<h3> Understanding LeNet Input and Output </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPIpi1S1P7OE",
        "colab_type": "code",
        "outputId": "f2d24bc6-5719-49ec-aebb-e076777ae266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "batch_size = 128\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2Tt8nAJP7ew",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluation(dataloader):\n",
        "    total, correct = 0, 0\n",
        "    for data in dataloader:\n",
        "        inputs, labels = data\n",
        "        outputs = net(inputs)\n",
        "        _, pred = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (pred == labels).sum().item()\n",
        "    return 100 * correct / total"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcIFAfZMP7uB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs , labels= iter(trainloader).next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yzxx84KvZVka",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = LeNet()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYkujKf3QaUi",
        "colab_type": "code",
        "outputId": "a5035bc9-2793-4568-aa3c-2ef4b108c337",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Pass input and see sizes after different stages\n",
        "outputs = net(inputs)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial shape is torch.Size([128, 3, 32, 32])\n",
            "Shape after passing through all CNN layes is torch.Size([128, 16, 5, 5])\n",
            "Shape after flattening and before passing to FC layer is  torch.Size([128, 400])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "De5wbpL3Rqtt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#outputs[0].data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1J4mpSt2RX3C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " _,pred = torch.max(outputs.data,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2UGxqHeScjk",
        "colab_type": "code",
        "outputId": "e65f71de-395f-4425-cf5a-146feaa162d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(pred.shape)\n",
        "print(labels.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([128])\n",
            "torch.Size([128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovsUn--VX_cv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z341THM-tS2i",
        "colab_type": "text"
      },
      "source": [
        "<h3> Training LeNet </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK9EniG4Ql8b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LeNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(LeNet,self).__init__()\n",
        "    torch.manual_seed(0)\n",
        "    self.cnn_model = nn.Sequential(\n",
        "        # in channels is depth of the filter\n",
        "        # out channels is number of filters used\n",
        "        nn.Conv2d(in_channels = 3 , out_channels = 6, kernel_size = 5),\n",
        "        nn.Tanh(),\n",
        "        nn.AvgPool2d(kernel_size=2, stride = 2),\n",
        "        nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5 ),\n",
        "        nn.Tanh(),\n",
        "        nn.AvgPool2d(kernel_size=2, stride = 2)\n",
        "    )\n",
        "\n",
        "    self.fc_model = nn.Sequential(\n",
        "            nn.Linear(in_features = 400,out_features = 120),         \n",
        "            nn.Tanh(),\n",
        "            nn.Linear(120,84),         \n",
        "            nn.Tanh(),\n",
        "            nn.Linear(84,10)           \n",
        "             )\n",
        "    \n",
        "  def forward(self, x):\n",
        "      #print(x.shape)\n",
        "      x = self.cnn_model(x)\n",
        "      #print(x.shape)\n",
        "      x = x.view(x.size(0),-1) # Flattening \n",
        "      #print(x.shape)\n",
        "      x = self.fc_model(x)  \n",
        "      return x\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zddMUsC8bHYj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "net = LeNet()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZv9ANpVRRvQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YquvbkwdSve7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.Adam(net.parameters())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj_mnv_eTu_z",
        "colab_type": "code",
        "outputId": "b5659ddd-9a94-4f14-9069-b7f4755a9f2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "loss_arr = []\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 1\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "\n",
        "        inputs, labels = data\n",
        "\n",
        "        opt.zero_grad() #Make all gradients zero\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        #print(outputs.shape, labels.shape)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "        \n",
        "        loss_arr.append(loss.item())\n",
        "        \n",
        "    loss_epoch_arr.append(loss.item())\n",
        "        \n",
        "    print('Epoch: %d/%d, Test acc: %0.2f, Train acc: %0.2f' % (epoch, max_epochs, evaluation(testloader), evaluation(trainloader)))\n",
        "    \n",
        "    \n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0/1, Test acc: 37.25, Train acc: 37.18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIa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\nMgAkqVH/H//B6CxIBJWzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyFyrnyAUNlK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0p95C26ybUGx",
        "colab_type": "text"
      },
      "source": [
        "Even for running 2 epochs we take a very long time so we go for GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcwD0ymFtj8C",
        "colab_type": "text"
      },
      "source": [
        "<h3> Running LeNet on GPU </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WD3FCPUGb2EI",
        "colab_type": "code",
        "outputId": "6a95bf0e-c2f7-4583-e27f-e39cb2f0c906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(torch.cuda.device_count())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uz08ijT1bZdK",
        "colab_type": "code",
        "outputId": "dd77cb2e-3488-4f3d-8da8-5b69cddcb968",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXfG05JKdcX8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluation(dataloader):\n",
        "  total, correct = 0,0\n",
        "  for inputs, label in dataloader:\n",
        "    inputs, label = inputs.to(device), label.to(device)\n",
        "    outputs = net(inputs)\n",
        "    #print(output.shape)\n",
        "    _,pred = torch.max(outputs.data , -1) # See in more detail\n",
        "    total += labels.size(0)\n",
        "    correct += (pred == label).sum().item()\n",
        "  return 100 * correct / total"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrG8f7hQgymQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = LeNet().to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "# Weight decay helps in L2 Regularization\n",
        "opt = optim.Adam(net.parameters(), weight_decay = 1e-5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YV8R2k6Tg_Va",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFtHAHyxhsJN",
        "colab_type": "code",
        "outputId": "b25a68e3-7e9b-4718-8695-e3eec018e955",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "%%time\n",
        "max_epochs = 4\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "  for i, data in enumerate(trainloader,0):\n",
        "    inputs , labels = data # you cannot do this in for loop itself because of the format in which enumerate returns data\n",
        "    inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "    opt.zero_grad()\n",
        "    outputs = net(inputs)\n",
        "    loss = loss_fn(outputs, labels)\n",
        "    loss.backward()\n",
        "    opt.step()\n",
        "\n",
        "  \n",
        "  print('Epoch: %d/%d' % (epoch, max_epochs))\n",
        "\n",
        "  "
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0/4\n",
            "Epoch: 1/4\n",
            "Epoch: 2/4\n",
            "Epoch: 3/4\n",
            "CPU times: user 33.3 s, sys: 200 ms, total: 33.5 s\n",
            "Wall time: 33.7 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qB0smo4DhwG0",
        "colab_type": "code",
        "outputId": "074cfa3f-1a01-4732-bf23-4ddbab56a58b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('Test acc: %0.2f, Train acc: %0.2f' % (evaluation(testloader), evaluation(trainloader)))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test acc: 76.95, Train acc: 79.49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMzfsGbTjkbZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWRrXyk2sBKO",
        "colab_type": "text"
      },
      "source": [
        "<h3> Visualising Output of First Conv layer of LeNet </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6T8O-ZroyNs",
        "colab_type": "code",
        "outputId": "5bc61999-9fd8-4ee7-85f2-cf8acc9bbf6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "imshow(torchvision.utils.make_grid(images))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mSDTO35c3eYxP2FREXovdiwJVYWCCizy4KMDAiBQ4zBXrm2Ut5fIbVhV0mBih\n/BcHZkjaqC2Ki1J7lSK62kbezBFOsdm7qspm78D6uiTPd6W9dGSqk1q1BO4+h2RnT8bS5dwwmsRc\nXiTSaXxUSMaPPPUUAODdt98EAFy5JC5vhsnl6RlJ05niUm0rdZFoWuzyeZWJpeExybUSi3G5MLXg\nNY4c1Xk14nEngbMkqdwIt3gedBpXTfDuBcaVSFP7zl0+z3dRWu3hCkc+llShgSSP5emHZHx13mPj\n4zQv67MiccbZ7Sw7IuRXbpK0wqKR9EPFLbpu8RjNaWxMztGuEwE5PS0S+CyntTUrqrxehAbTZlfR\nzU3p97tnaF3uOTIetm0xCV1a7e/a2lXkf5FLlF08czZsW16gff8jj0uul/vuOQ4AKLPL52tviqve\n9Bhp06YrUny7TdJ2YVik21iKjuc4X0tE3edXWdvTJRxd9fqGcucdn6R7ND9M93tmSe6vFhen6Cmt\n0EUHj41KoY0Uz+Uop82tbInG895F0hgeOyk5naK9G5UZuT68BO7h4eExoPAPcA8PD48Bxb6bUEaY\nhKjqXItsQsmkVQIoNqFEWR1qqyjDBEfpaZOIi+5yhB4AuGy2LrVlT1UMT3AipZZKa3v1Mqlb2byQ\nMXlWZyNMtNa3xDfWslqmk1O1ulxp3YiPejbPUXrHiKg5WxXVPu580wMVVcqkTTGhK19uR6UiJgkX\ngalNKI4saTTEtOA+N1uu4roimNjEkYxrP3fyp33pxT8K25YWKSJv9iKRONrkssGRrllVVb3DFU6g\nyE6XtnWdSSpNyI6Okmqs/cAlTa7smQ5XR+ny+Vtt6Uedx6LNJp1u/zSoIXmpEhOlUnT9sYL4nq/x\nWmU5wVlX5bJd2qRjbWWSQ3OezyFjyWaZCLUUQ9Cpib/x5Qv024zaTz2+X6KqzuM4k2OZe0mNT0ZV\n8rUl/qyilMtMyKmgSMTYBJVJcsUrRcgur9E65tIyvhInMWvUbzCPyq/apWsub8lef+bHfxwA8MCH\nJJrz4uwcAOAspylOqdgOZyrVJpFIlI6nc5L0ClHaC20m5XWMRCpF3xuNypy++vobdC5VSqvO0cEV\nTgKXVEnuGsu0P1uKxIzx86BZElPLqZkZAECVqyI9//wr4bFknO/pR8Uvvl7t71PfD14C9/Dw8BhQ\n7LsEHotRF3o9lfvDpQJVb7gEE0DOhSjYllrVcpu8jyLskmOVVBTlyDmXO6WnyBD3Zs6r6LFEZHel\nc8OkqIv+tCrFf4f7nVfRZlHud00li++xj+OxqUMAgI1lqVZdbpB0tKUiCbcsSdJjOYmA3ImOcgF0\n6TOrZZFCXX+bqthE00WkshzyQEEAACAASURBVMTeVSSO+2xV5fcqS8YvPf+HYdvbr1MC+3Wu0K01\nnlaV+l1TUle5TJFnFUViOrK6xsfaNfn+5AiRWqm0SEDrLMBGYqoQBke2lWIVPr9IQu02tfW6IplG\ndPTrDkQcUa4kyBS75p08LGTZEUPSWYk1mIoiTp1ra7cr1zk3T/N9aEqi7376Rw9wJ0k6Tyj3WLtG\n8zd9j0io9Qa1DWVEAm9w9PBSjQi/blzmZaJI15y9ImlqG6w5TI4JKelSpXRbFLEZURHJLSa3r27I\n+GKs9R49IvOxE3mVivilH3wfgKRzBYBHn6B6si/+8fd3/dZpXBOTQuAuXqbI5YlRdU3enytr4iKa\nztH4NzZov9bron3McwrYTE606vFJIpUTSsp+7xxFYmY5x87oiMoNw7Ve4wnJxdOo0x57+gkhZE9w\n1Pi3f/e/cD/k3stxQQmj9qSODN8rvATu4eHhMaDYdwm8HqW3ez6iErKz5K3tlHUOOnF2a2e3BYBo\ndLeLlwtEiGi3r5bLEUJvwrTKYOZKjiUy4u6XdDZnZa+NpUny6fVIgowoCTyXZzt9XCTldteNRd6u\nLsvhCJdymlJuYk12rSqkxdbaY/tuJdLfRtZqyLG2K1GlbJxhcQKlkbhsad22s4XLOTrMBQTqkpkE\nu+qpgJg6J+VvNl2QlKxZu5Xk84uU4crZ1VoqdwUXgTBs046qAhqjnJND28BbFVqrqCrykM/yWrH7\n5caWyCaxuKugLmNR8Ve7EOXAHL2fWmww1lLUqeO0bi9fILt1VZVgG8qyjVPla3GBU9dKcmKXJyPC\nGlKrKmOf4twzxWFxO3yL8+ccPSzS8xQHSs1eo3MVHpB7o21JCp2bl37H2NY7WRQp1OX3SMacTV7t\nedYiofIVTU2Qu9yJUwfQD4eOiovc2veoBOHomLjZvf02aQwrK8IjHWGpdWyMpOFcTuYvl6aAIqvc\nR1fXSFvTOXi6hsZQr9E9evmyaLhVzrhZrskcDRdJup5XBRqarE3Pc8ZEp6EDwL33End1RRWsePjh\nhwEAf/Ovfj5sy/Lz5b77TwEAnv2fL4bHZk+/AwCYmpLnTTTSP9dRP9xUAjfGHDLGPGeMedcY844x\n5ue5vWiM+Z4x5jz/Hb7ZuTw8PDw8PjjsxYTSAfD3rLX3A3gKwM8ZY+4H8FUAz1prTwJ4lv/v4eHh\n4XGHsJeSaosAFvlz2RhzGsABAJ8D1coEgG8AeB7AP7jVDmRZDY6q/AYtp2EqPXf+CrkVHTxO6uRw\nXOU4KZOaWFME2uYqkSXrK0rdrxB5lGSXsNExIUOiaVITUylVJCBCn2MxRWxyessISD1qKvc9Zzxw\ntTT5LPS7iLgtOXdH58Y1NiTq8BUmWAOVhyPBppYO9Hm3Q9dvjDCBFqq+AAyrnR1F9AbsJueI3p4y\nFTkyMqJMIs7VslYRktGZFDp8fR0h2+m5SuRy3jSroppwdog60lBFtwpJLP3oOfVeuYE6YtgyMa1T\njrpo2ZgqTtHu9Hd/i7jcLarKfJ2j5MrKTFLZomuurtG6dFR/mvy9oaKYADJcJKCizCRz75GL230n\naA+8dU5cPzMJMjcsq1qJS9dIzXf5OACgy/NbWqTbeeIBMZcsLpGbZw9CwuW5+EY8Kv1tcW6OCBOz\nTeVOG2dXPW0e6wVceKS4O/Wvw8QBMa+cPHUCALC2KdGIlzhFayojphyXv2dqmiI3h4fl2MYa7bFa\nRe6Dq9coOtilbgWA9ibNx9gIzdGIIj0P58lkMatyrFy+QmbLTRWpG2W3SpeOt6mcBN55910AwPSk\nmLZ+9mf+GgAgrYh1l5DpyEGa+y/89KfDQ6UPE4EbV8VfNtfEhLNX3JIN3BgzA+BRAC8DmOCHOwBc\nAzDR5zdfAfCVW+6Zh4eHh8cNsecHuDEmC+A/AfgFa+2Wdq2z1lpjzHVpIWvt1wB8jc+x6zs5TmTf\n04nyOZhgSAW/LMzNAQBee5mkqPvvOxEeG+LcDglFSg6PkqQcC+TNWQ5cpjpyMausyJt87AgHCUBc\nvHI5egsblUkwy1KoI1KChKr8zgRdMitSV4slwSClAlEMZ1RkoiSrcosEHNihJYooOFBJSUw7oaUj\nl+VNS8Nudre7RAbb2qzKheJc7jotnY2Q3S9VDgjDkneWAy501rYU5/LQvc6wW5bOKOeyIdYbrrSb\njN0yuRdElDbBe8UoqdxlPgxrFCgJ3PJYArUXIjcwHlrOCxJLy5dcmoq2ItZX19klskJzlIjL3OZG\naE0LyqXUZaRcWNVV0ilYrNkgqXhhWea7kKG5XNgSF8BkjIuXxFRgDpffizdp75x/VUjB+ybp81hh\nSsbHxS9iMVWV3hHNrCjqIiZZrvi+rlxbo1EaQ9f0J9bHp+Sah2fIZbZy+r2wLc6EaaACppxW7DS0\n0pYEdV1hSbleE6nV5ajRJOYSB+4k4rRnhpWW4AJ0Nkpyn7vfaq3QspYci9O+dlkMASDC/X36wx8P\n2xzpX1NZMDM5Gl+bc/fYpuy/fIa0A5cvCABMtH+gXj/syY3QGBMDPbx/01r7O9y8ZIyZ4uNTAJb7\n/d7Dw8PD44PHXrxQDICvAzhtrf0X6tB3AHyJP38JwO9/8N3z8PDw8OiHvZhQPgLgrwN4yxjzOrf9\nQwD/BMBvG2O+DOASgC/cTgdqXGQhkdSVyPm9onIewJJ6sbpMKmHjpMr3YEktH8oK4TE2zvkhTknx\ngSar9++deYv/vhseW50jUmN1XhSJaJrUnKFRISsyGVJ1i6N0rU5HVKbNZSJM43GVU4EJqKlDklI1\nNkyElYtQ3FwR8sJlxI0mVG4HrlfY6fSPHuwpEvMGLs7b1EQXzWpDF3FVIMHVI1WXNBzdGk2Jn3sq\nT+f7i5/9LADghRdeCI+5XCtRRexscg3AbeqqS+XbdN+Xi8Y5qlC3ObOKIy4BoFig9UhlSN0vK6K1\nxsn7XdECYHuE6U6wWzqGR6XfqQxXVVe5ZGyW0xLzvI0OCw104ij5LEOZbZod6kenK/N8eYnO22vT\n+PIZiYdY4QIDlZbk8njsISqqUEyLSQk1Ij4Psy/87Jb0uzlM51dDwVqFztu0QqwnmUgMxtjvWZms\nulfoWOWqmDOCHkeh1sRJYCdGChIt+uiD9wEAOi0xGUQDGuvR41LDM2CzytwVuh/X1q9JP5r025S6\nN5yZs2PFn7rmcvww8VipyT06xz7hOk9QwM8bowq8OBNKib9nFanr4keuroup6j//0R8DAA6Oi8ns\n0DTd58M5+n5MmYoibJOzHW227H9/98NevFBeBPqWgvnULV/Rw8PDw+MDwb5HYuar9FaKNlVmLyaD\nGipPwBjnIAnKREJUL8mbOT9Dx5pK8t1gwaehvJySXF5teJLe+K13JPPbtbNErtSaQsrU+aNVb84Y\nv4k//pPPAAAmZiSK8tU/pUirhnIjXF8iifOnPv/XwjYX8fXaa39K16lI3o5ald7qJUWGRHiZeq3+\nJGYQ0aQyuyKqUEL3Ube5MmVOGtZSsUQjiiTmJN7MkEg7bc7d4hjT4yeEXH75T18GAMRUdkGX+D6p\nssxFOYtjjEMl4wklQXLEZkS5AE5MkHbVVaSQI1tdfptA6SFJPp+uKG9M//JVjnyaOiRjL45xuTxV\nzD7F5z06Q1LxzKHjcg52P1vaEOk5z6ToiXGRTNdYql1YIOkrlxGX1ZUw94jM33SRNLqjh6X/Bd73\ncaKksHZJpMvZM7SJc8MqEyMT8O/Ny2C6cVrb9DT9dnRS+jG/TvfmyopoNVsROt/KWv/8HbatolaP\nkkvhwvxVOe8V0naH0jKWBmtGTkvZUkUQ8mlHlGt3P6edqvuW8+w4wnl1Xe8/OhZXkb3uWro8oruf\nnIut1SQ6l4dbKUvfNi+SxnDmmtzL43M0vskh6vepGXGrnOEK9Qkje72lopP3Cp8LxcPDw2NA4R/g\nHh4eHgOKfTeh3MM+qQnlLRx1BJOyvPdcIieuaxmcE7Kxu0JqXOeUqh5/lH4cV2lIu0xSZGKksj+i\nqp+fPT8HAND5onqsPmmf2HqFvtBmE87I8Znw2JEafa+9IWplqkPXTKlIzAbXfoyfo/qRBZVlqd1k\nv9a6IlTYhNOz/UmOfFbV/uxxKlNFetY52VWrpZ3D6XiDE+V3lQmqzdGc6KkkWXX2v1bdKK/TWH/v\nt74NAJhQ0a2GzU11RSI5E05BEVxd52vOSb4yyp/fRU9GVL1T3gJoq5Sni0tETrn6l1eV325pg8xS\nOtLUon8kpsvUeuCw2N8yrOavr4svdKZAYz2Vo7/GKlMAJwbrKLI04L1+aFSIyiwT8MtrNKm6SECD\nzQL5jNym+TSZ5BpViSQ0vE+PHqJEUO+uiwmlwamFuxEhnpe36N4ptdR5mZDtsump3pE9vF5a5LGL\n73SGVf+6WCd2waj1iXI63uKwrHs6RedYW5Z7+ewlMrG8eZYKhEAl1SpnaF9sKPKwOEpE4VZV+ruy\nRseHOFpVV34P4yBUUjfnNOFq5gJAgu9JZ2ZsNqQfLtv1hvKLz7CJRUdLl8s0rmtLnBhrScY5M0X9\nPn5QfOVHVOKuvcJL4B4eHh4Din2XwJMu4qmr3ogsBSRUxJ+TTHoswUUUn9db5cIBgaSNLEzQmy0X\nF8ItEqM3XH2Jig9MRETS63AKWFMV6cXxYB1FNDS5FFKDc6zUrogLYGGT3vSRNZGiukyWNEoiNWTX\niPw4xByI0bGKLD33lITQ4ijLdrv/+3ZyQqRFR7zoqMt2hySDhirosLREY2hywvtAhydyHpCOSjJf\n5TSvgaqenWSfuzYXj6hsiJTWYMk7O6wqdXN0prEiFW2ts+bEl3cRdPqzTuNa57Jt2nUyxURoOkVr\nqquDZ5gwrVRkDXSF+p2IMomu59tpXuNjQlpnh2mP1bsspSlJz7lGxuMibXc5BfG6GkucJb2FRdp3\nS4sy38sbXGV+UvbfWokle4gEHgQ0p8Uoubsm27KvT46RC2C3KdKi7dGcToyJy9tWlTTKZJM01saG\nquR+hfqkyw0WeItXlmW9dyJQrp8Rdgc9cfJI2HZ5jrSkSlmRy/xxcYHuq3RKNNc2u4FWVIGGNBOy\nXeXvGktyzh52J9SlGZ1ar90Ic1wyMa6cFZIcfZrlqOrFRbnPexyxXG9IP3pMttqc9CPK7r/tNvWx\ntaXyntRI07i4IG6Yo0OinewVXgL38PDwGFD4B7iHh4fHgGLfTShpVluaUVGj4i7JEnb7PYf+nrr+\nJZ+jsy5q8dq7RIJks2Ja6FjyyW1xsqSt196UY2wCiKooOWdC0ZYF5x3bZPPD1Rel0nSq5FRj5ZPN\niZpWXz8dtmWXWJVnjaqkKoZ32USUUX7djqTSZoedyOVE/eqFyaxUPzj9bbutqmtzFOwWJ/aJNuX7\nzhfbKh9dp4a3GjJHEa6YXmfzQF1FO3bZHz2WVGYEntNYRM4bZxNHl0lG5ysOiB+wHkvgTFoq7Wy7\nzdV8OElWUkXrhSYllR42kRLT2k44znppQfbTUIbMDYenZsK2StOl4aUfxKLKVzisq6nmgz9WVNKw\n0jKNNWVp/zVVPMQqVzM/OKX8xtlM12urtMdcYaoWnAEAFFTF+iqr7c2KEGg5TlMbi8q+u7pJ63fh\nD7mmqFrH0iVal3xaHhf3s+97XlV334mmSiMMXpchVdf18DSRv2sbKkKWTU4Xr7gkX2ovVMjskVTV\ncXJcw3bp4lzYFrDptcEVeYbyMh8tJpf1fnJpjBsqBiTKzg9xNgMFuvYtJ/7a2pT9UWQHiW5Sxtxz\nlcJ4XwTQsQdsiq3Kei+X+0e19oOXwD08PDwGFPsugYcZZpU0FfDbSaccDVg6S7jUrsobrssSUEq5\nzTX4Db45cVGuxW5edRYa1mclEjPNb+ZIRKak50Qxda2ANYUISyj1y0Ju5LmGZltFDaaY8Ku/I/3o\ncC4Px5H1FIEbjzi3Q3lbR1nybsRuEImpNBKRLnZnQOgo1y439/EER8Mq0niIyca6iird4JwmrZZy\nt2JpaKNL0kOnI1Jdks/xyGOPh21tlsrWV0UiXGdVp8LEUiQQ6TLGEl5XuXLGmTjtqbYuS7VOuDXK\n1zHgubRmt3ZwPfS6dOzAtOTA6d1DLmm6cHiPc3kkmaAL1JolUiQ5jh+S+T5/mjoX1e6P6yTxPjhG\nc7CuIg83N+gcEwnJ8ZPrkTTZrQtReeIUEW1/9AZ1rpwUqfX8GUpFe2RMIivve5zWrK6kyiV2gZ37\n76wJKKLwoYN0zeKokJ6FPF1fF2LZiXhSpO0O75mYcrNzdTXPX/hB2FaqUD8KBerj8pLMx+Q4fV+7\nDLo8N+2mjNmlfJ7gNM01leLYaWPDBdHA3L2TTOh9x+6rvE2Mciqo8jVRl7G4TMINldY2ydcojJDL\nYLur3CpDTVG00/47sj+8BO7h4eExoNh3CXyZk+dP9ER6qbAdOqGkShff4N6W28o7sQ1cB+FEOHBl\n85rkTIkYzlzGElOjrZKp8wtWu6a1W/TGjCe1NEefXQmzjpKea67CuDpHlLONtVXF94Bf13Eee0Ll\n5Qj4nRrokmAsVtobVCHQ2oouYebgxmV1AAPb6DJZkmhHhiUIZ5LznWwpe7TjBC4oe2MrSlJtlIOA\nYkqDKU6S5HHspGSbS7NU/sJz/yNsa3BOE5ftsKXmqlolKSerimSE2oYKvIixe2eP947ONuiKmJu2\nksBN/xweiQhda7ooVdWvWJJkCyqtXyZOboSdBmkO9aZItF0mOIqHxM0uuEjHG8qdLMLa1XqV+r+x\nJWN/+MQBPq9Isq+/R789OCny2pNJyoKYZhv1uYU5uSZ3N58Rid0l19zqyrUON0j6THMxlclhkbYf\nfIAk2ZV12QtRXoNKQ8ayG8o+HjgeR/bmgUnq930PSA6ZM//5OQDA6gpp0AWVd+fDTz9N47sgRSHO\nnKNSi1lVCtGV02tzgJgrGAKI5BtVuVBcoZKKytljeLO3m3SOjto7UX5YpFTOnl6NNIWqUtEMa7a1\nCvVtKKtcmvme17d0LKZt5HuDl8A9PDw8BhT+Ae7h4eExoLipCcUYkwTwAkgfigL4trX2HxtjjgL4\nFoARAD8E8NettTfSp66L83VSPYZioiY6q0S7rV36ODqO3zmtlspN4PJkBPJ9582zOTuvxkJqZMBq\nflupf21WZdMRPSUc5aXci1wJu66rianIsiqraknFRrgk7g3Vt7RLjctjaSoCt8sXsCodapuZlCXb\nn8TUBKQj/LQppcIuWHVVbzLL9SmznHB+akxcLosc2VhSFcDXuBhDPi+EWJbTptZLRBQV1LEgSarj\nm2+Ku+YY50pZULlK1ji3hSNVr8zLsSxXUB/KC5EXZ7IpkRAV3fUpze5fsZio1J06u3PpaETsNjM5\nNKp07Nxpiey1EZrTex+Xa3bWeG3Xae4raq5aHFFbX1HukmyyCGJizjh6iOwZNT7/j3zsUekIr9/p\nc7NhU4ELGDx5SMw7S5zBdGWLTBxxVcM1c4DWNJKXfke4b48cmwnbDjPhtvoIrcX4zH3hsegkjaF2\nei5sm+bUzEcPjaMfOqqSuzNjGBWtGuP1e+SRB8O2Fc4vUm649MByfy1xBfpcWs8fmZkaKrq1zJXt\nS1U6R02Zbdye6bWFpK0xGRmSkxASM8b3XrejH22calYR/K4ug1F7srZBC9Nlh4eUcl3MuwhTq50K\nbt2ivRcJvAngk9bahwE8AuAZY8xTAP4pgF+21p4AsAHgy7d8dQ8PDw+P28ZeKvJYAM6XJ8b/LIBP\nAnBVCr4B4P8C8Ku32oF5LlxQyElXTvJbLKmCapyH4Ba/saySfLssPVuVsS5giT1Rlze+KxDh3pap\nuFyzzi/CmrpmJsGkmpIkSkyOOhI1H5c3rulF3cXDthgTmxlVHs5J1y77WUuTniyFRlVuhzIH98zq\nPC07sK3kGJxbmxx3nJ1OfB+wJhBlabW0JVJJu07jW9+Q/CHOfe+ZT/+4nJfn+TfXyI1Q51qZ5Irs\nn/rw02Hbu++8DQC4dmUubFvmbHQxltgnDki+kQcfpZJ4NeWe5fJYLCxJcYDzs0RmZZgk1QUjkqxN\nxJWbWDLRP+9EwLlC4lEhTu+5lyTZ0WkJtiizZN/jivIJ7f7YornqbIiM5ITVrMr6GGNpfKRILnLZ\nvASptFjCO6Yk5WSazpuLyz69tLZGv53mwhJjIj0nmNhcW5R1PP8OSYYF5VZ56ihVjf9Ln6bzlwuH\nwmNrHFzkNFgAKLKmOFoUjWs3VIAO5+LpKYmzxxJpVGlLH//okwAk381LL70eHrs4S+RlQgUqOZfj\nqXHRBB5+gEjzqxxsd25ONKkmu/JtbQq53GXiUdU6wVZpk8/vCEuVTZSl/abak3COFKqeYYsl+06a\nvleKyg2ZYJfcwoiU4ev0+mvY/bDXqvQRroe5DOB7AN4DsGltuBrzAA70+e1XjDE/MMb84HrHPTw8\nPDxuD3t6gFtru9baRwAcBPAEgHv3egFr7destY9bax+/+bc9PDw8PPaKW7KaW2s3jTHPAXgaQMEY\nE2Up/CCAhRv/+vrosZ5/Vfn+jnB036R6vbTYr9f58qbjcrDHPuQJ9f0gTA0pakmK/Z5dBF9cmRii\nbE7QlctdUoxAxUhF2HQTY5NFR5GNxpGMKi2ldb9V+pkj6yz3LaG+73zadb6MOa58v36d3DAOOhJz\niH1ntc+3I/eKxeGwzZklKkz6lMti/nA+7ZmMqKvTBw4DAI4fU36771KOlzaTPJp8nea6f2fYbAIA\n/+273wUALKn0nBkmIJ/57GcAAH/585+XY2wS6aootq0tMqHkVTTd6iqpy0ucKnj2kkTZVqs0vmxW\nxpLJ9M+FUuC8J08/KqafZJZjAkal39ky9Xt82KUvlT3c5fw2ubz2nebapir/RXFkhH9L89dWfsQj\nBTr/kYNCWNZ5j5VrQsgePEB1SE9wdGRPrUGEydHCsCL5rpAJZbWsiLwzNL504Rj1+4Bcc5wryR8c\nlxS9rkr70IgUJNgFK+MMIxpj6pHTpT2ro2zT7M/90Y/8OQBAR1Vt/6/P/gkA4MKcPGqiAZk2Li9K\nZO+RI0QM33vyJI1N1e5ouRTR09KYYjPW4oqYxy7OUuR0g8ceUymO4+ylUFcmFBdn0VOOBq6tUaG1\nKq1IfVTX7/yQ+NsnErdelf6mErgxZswYU+DPKQA/BuA0gOcA/BX+2pcA/P4tX93Dw8PD47axFwl8\nCsA3DCWXCAD8trX2D4wx7wL4ljHm/wXwGoCv304HApbAyyoqcoFzFxglUie5OrphSTOiJPAh/hhV\n5GGV3ZVUIXKk+VoBfy+iGIckS8oxJW07ItSoaMs0S95Rlqitlor57dsNZCwuCjCms5m5JPR8LufW\nBQCrTJiuqe/PM9XQjfd/Q1dUFXsDV1JN+hFjQvZ6EZuuLZcV6XxqnNz9pqckH8irr70KAPjmN78l\n32O3wL/4mZ8EAGQzQvw9/8LzAIDf+kOJuqywNHLqgQfCts9/8QsAgCeefgoAUBiSfjglolEXzci5\nbo4URXoZ49JaM0dIctQJ+M+fJ4Lz8mUhs+ZmhQDdiSKXSvvQvSoCN8rFFbIHw7YWk67tJkfc1VRu\nDJamVDqQcF2gIoadZFdz0YKK0E7ysZgiv6J8T2jNL9S0eP8ZTZhzVGJrVDSY+iTtlZ5yM3VSqNsL\nEVWK0PkLWAix6bI/ZlJqgDvQ0dHHfN/GlKbYbDfciVV/XS4UOu8nP/UR+T7nqNn4znNh2+wcRVqv\nlYSUfG+e1nlllYjbICqunIa1+6RamBgXdhkaFULxEK+Vk7Jryk1xfY0k6Y4ayzDvxayKHI3wdbdY\n8rYd5azAz5mkcvnU7p97xV68UN4E8Oh12i+C7OEeHh4eHvsAH4np4eHhMaDY92RWMfYFTSjzR5zT\nsi60RG05EiN1LM5dvqwImAkuOtBTfqfrTLLYQPw3U+zjnUlyJKRS3TKsJ7Zi8k5rcSVqTTKmuIah\nq88XV+qZ05A3W9I39ymv/NZdSckykzemI2ab8+w7uqwKOoDNH0lFSu7EhXOSrnaEiTH97dIWETQr\nK2JaOHCAzAFPPkm+t9mMqJXObBRVCfsrTHK+8YaQksucsvMTH/sYgO3Rd2dPU4EBnWb1kz/2aQDA\nX/6CEJX3P/QhuiarpHVlLnGpcWMx5W/Pc5nPD+/6nqt16ZJgAcADD1Ck35HDQsxZll2++/Vfx044\nM1BkShHPAUWCGlWMtZuic1j2/+8p4jm0VBmVMI2vqc16zmLnamhuK1zhCpsYTYCbXdfavtKA+nr4\nPWWZQa/Iphb1u5hL0sZ7TJU9Dc2cGpbNNdc7FvbjOqaRjo7taO8uUBJwxHKXo1bzWUm3+pm/8FEA\nwKFxiRN48aXXAAAvvSJeykucqvgSxwakC2JqG5okk2BHReI2+Tmja2dOTRNhH1wnXe7cLBWLWbgq\nUd5F9kMfGRMzjDNlDXFMwvrKWngsyX7lEWWGsX9WfuAeHh4eHncfjL2BVPeBX8yYO3cxDw8Pj/91\n8MPrxdJ4CdzDw8NjQOEf4B4eHh4DCv8A9/Dw8BhQ+Ae4h4eHx4DiTrsRrgKo8t9BxigGewyD3n9g\n8Mcw6P0HBn8Mg9T/I9drvKNeKABgjPnBoGcmHPQxDHr/gcEfw6D3Hxj8MQx6/wFvQvHw8PAYWPgH\nuIeHh8eAYj8e4F/bh2t+0Bj0MQx6/4HBH8Og9x8Y/DEMev/vvA3cw8PDw+ODgTeheHh4eAwo7ugD\n3BjzjDHmrDHmgjHmq3fy2rcDY8whY8xzxph3jTHvGGN+ntuLxpjvGWPO89/hm51rP8FFqV8zxvwB\n//+oMeZlXoffMsbEb3aO/YQxpmCM+bYx5owx5rQx5ukBXIO/y3vobWPMN40xybt5HYwxv2aMWTbG\nvK3arjvnhvCveRxvGmMe27+eC/qM4f/jffSmMeZ3XbUxPvaLPIazxphP70+vbw137AHOFX1+BcBP\nALgfwM8YY+6/U9e/ihZU6wAAA9ZJREFUTXQA/D1r7f0AngLwc9znrwJ41lp7EsCz/P+7GT8PKoPn\n8E8B/LK19gSADQBf3pde7R3/CsB/tdbeC+Bh0FgGZg2MMQcA/J8AHrfWfgiUePiLuLvX4TcAPLOj\nrd+c/wSAk/zvKwB+9Q718Wb4Dewew/cAfMha+xCAcwB+EQD4vv4igAf4N//GGNM/V+5dgjspgT8B\n4IK19qK1tgXgWwA+dwevf8uw1i5aa1/lz2XQg+MAqN/f4K99A8BP708Pbw5jzEEAPwng3/H/DYBP\nAvg2f+Vu7/8QgI+BS/ZZa1vW2k0M0BowogBSxpgogDSARdzF62CtfQHA+o7mfnP+OQD/3hL+BFTw\n/AbVju8MrjcGa+1/50LsAPAnoILsAI3hW9baprV2FsAFDEDFsTv5AD8A4Ir6/zy3DQSMMTOg0nIv\nA5iw1rrKCNcATPT52d2Afwng7wNhkc0RAJtqE9/t63AUwAqAX2cz0L8zxmQwQGtgrV0A8M8AXAY9\nuEsAfojBWgeg/5wP6r39twD8F/48kGPwJOYeYIzJAvhPAH7BWrulj1ly47krXXmMMZ8FsGyt/eF+\n9+V9IArgMQC/aq19FJSKYZu55G5eAwBgW/HnQC+jaQAZ7FbtBwp3+5zfDMaYXwKZSH9zv/vyfnAn\nH+ALgCprTarLwh28/m3BGBMDPbx/01r7O9y85FRE/ru8X/27CT4C4KeMMXMgk9UnQfbkAqvywN2/\nDvMA5q21L/P/vw16oA/KGgDAXwAwa61dsda2AfwOaG0GaR2A/nM+UPe2MeZvAvgsgJ+14kc9UGNw\nuJMP8FcAnGTmPQ4iDL5zB69/y2B78dcBnLbW/gt16DsAvsSfvwTg9+903/YCa+0vWmsPWmtnQPP9\nP6y1PwvgOQB/hb921/YfAKy11wBcMcac4qZPAXgXA7IGjMsAnjLGpHlPuTEMzDow+s35dwD8DfZG\neQpASZla7ioYY54BmRR/ylpbU4e+A+CLxpiEMeYoiJD90/3o4y3BWnvH/gH4DIj5fQ/AL93Ja99m\nfz8KUhPfBPA6//sMyI78LIDzAP4QQHG/+7qHsXwCwB/w52OgzXkBwH8EkNjv/t2k748A+AGvw+8B\nGB60NQDwfwM4A+BtAP8BQOJuXgcA3wTZ69sgLejL/eYcVJr5V/i+fgvkbXO3juECyNbt7ud/q77/\nSzyGswB+Yr/7v5d/PhLTw8PDY0DhSUwPDw+PAYV/gHt4eHgMKPwD3MPDw2NA4R/gHh4eHgMK/wD3\n8PDwGFD4B7iHh4fHgMI/wD08PDwGFP4B7uHh4TGg+P8BTYGS5mjA2hMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XE97EiWTrdIw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Since all images on CPU bring the CNN network from GPU to CPU\n",
        "net = net.to('cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtPwk976tDjV",
        "colab_type": "code",
        "outputId": "c298680f-6752-4829-9f1a-c1ca9c7ee275",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "net.cnn_model"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (1): Tanh()\n",
              "  (2): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "  (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (4): Tanh()\n",
              "  (5): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ1DFN8jtJDr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "69f6061a-df8e-4865-9882-28ef3d49f066"
      },
      "source": [
        "out = net.cnn_model[0](images)\n",
        "out.shape"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 6, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ie5JZKXKuFek",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "98e31f31-7269-4a0b-e028-8b64ae84c00a"
      },
      "source": [
        "image_id = 3\n",
        "plt.figure(figsize = (6,6))\n",
        "plt.subplot(321)\n",
        "for i in range(6):\n",
        "    ax1 = plt.subplot(3, 2, i+1)\n",
        "    plt.imshow(out[image_id, i, :, :].detach().numpy(), cmap=\"binary\")\n",
        "plt.show()\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8DnvRIH5+bxpCsmTJkhn7r3gGZ4xpLR7gjDGtxQOcMaa1NK7BdTUu1bBYm9L0\nIl5a1lQP1gxYI+BEXSDX6nQ5nZeqVbPic+sqJADABx9c3EBIKz7we1Ot5b333quOOaRANRJ+n5qS\ns3Llxawi7Zd+zqb/sF2rdsbhDBr+xHaimjR/j/wdaviTanIMa1qa3sewLsZ2rNdXuyulofG9+ff0\nueRnVj87ftY1nESfsTo8gzPGtBYPcMaY1tKoizoyMlJN0zUSmaecGkrB6DSZI6h5Gn7kyJHsPA4h\nUVeBp9fq0rFrqNHaDLuvpaKEWlqH21577bXqWKumcD/UTecob11O37JlS22fTf/o2lBdeAcwPXSJ\nnwF1Udk1ZDeuVDNNo/0ZlX343nwvDSfhMCYNoeL3qvdml7L0mfD11a5fffXV2utrqFQdnsEZY1qL\nBzhjTGvxAGeMaS2NanCTk5OVvlbSwdTXZ/9bK5qyr8861euvv56dxzpASf8rbUjDuphWVmAtQfdM\n4FAQ1cj4fqzRrFq1KjvvxRdfrI41lIWvqcvwJd3Q9IeJiYnqO9bvl+26pM3qHiJadaOL2i4/R5yO\nqPfTe/MzVaf3AbkupiEkfE3V+Pj3+H3q88tVgjWUhW1Xq/CwXl3CMzhjTGvxAGeMaS2NuqgXLlyo\nNmDRJeFSMUlGXUgO/+Dp+80335ydx8vKen12ITViml/zeepScP+1IgP3Ud83T8v5mO8FlPdu5Wuq\nC6Db2Jn+ExGV7akryLKDfodsW9rGds72quex3anN8PNQyrDg0BDtP28moyFaHNqi9+ZwKH4vav+c\n2VPaM1jDRDQjqA7P4IwxrcUDnDGmtTTqop49exavvDK1OZEWZixtnssrRTrV5mk+T2k10pl3ANdp\nOLdp0T12Cfj6uu8CT6/VBeaCf6V9Stm9VFeZE6JLe6vqCq7uZWn6z6JFi3DHHXcAmO6qsV3oinbd\nSqO+ZrsoFYPVVU7OelAXj+2J3VW1LbZX3b2e9wzWyAcuSMmurbqh/CzqKjBHLWh2Uyk7gvEMzhjT\nWjzAGWNaiwc4Y0xraVSDGx8fr3QIrdrBvj5rYkDuz6sGwdodRzvrkjZfQ3U2Pld1AO5nKaOCN/wo\nFe7TZfJDhw5Vx6yFaFQ3axVa8YF1E9XuetUqzOUzOjpaZbKUtGS1Lf5u9HvijAi2T80A4gKYqm+x\nXWhhzLpQLH02SqEs/Mzq88xhWqw9qk5YCo3ijB39fDQUqw7P4IwxrWXWAS4iHo2IMxHxMv1scUQ8\nExFjnf/r/2wZM6TYtttPLy7qYwD+GcD/o5/tALAzpfRwROzovP7ubBcaGRmppsalfUvVReXpr/4e\nT9HZxdOk51LENE9/9ffY7W16mzkAACAASURBVOV+6f6OHJ6h4QA8LdepPP8eu7mlgpec2K/X1yT9\nUi1+0z/b7qISCtunuqh1bqheh89T+yzti1Aq5MqvOcRDQ6g4W0Gvz8+b7lO6bNmy6pifm1LCvoah\nsJyjBUF7ZdYZXErpFwDekR/fD+DxzvHjAL52WXc3ZoDYttvP5WpwS1NK3anHGwCW1p0YEQ9FxO6I\n2K0CpjFDSE+2zXatsxIzPFzxIkOamkemQvsjKaVtKaVtlzvNNGYQlGyb7bq0dZ8ZLJcbJnI6Ipal\nlE5FxDIAPZWsGB0drZaPT5w4kbWxBrF27draNi1kyX46V/jQVKpSNQ7W5FTjYF2A9RQtLMkVQ1TH\nYI1Dl7s5/YuXxfWh4dAQvheQayHaL4eJXDKXbNsTExOVtqQhPBz6oN8Fh2pwOh+QhySxXqb2yaEa\nGmbBNqT3ZpvkiYemOHK6l4ZGsUemkxd+3vj51VRFrlai2js/26pRalWhOi53BvcUgO2d4+0AnrzM\n6xgzbNi2W0QvYSI/BPBrABsj4kREPAjgYQBfiYgxAPd2XhvzscK23X5mneellB6oafrypd5sZGRk\n2nJ1lzVr1tT+Hkf7a9UCDpngZXidMrMbqi4kT+V1KsyR6RxqokvafE0O99A2DRXga/J5uufDgQMH\nqmN1RXhJ/uTJk1nb8uXLYWamX7Y9Pj5eFRZV++G9PbUIK0sL+j3xwgWfxzKJvtYsFpY89N78e+y+\natUOdolVGuE2tWt2udltV2mKxwOVV9iF1wwOlaDqcCaDMaa1eIAzxrQWD3DGmNbSeDWRbgrS5z73\nuayN9af9+/dnbex/a+oRpzCxNqX+fCllhvUOXU7n0BP+PdVauB+aqsVpLKoNchtXSNXKqlwBWfWI\nF154YcY+AtNDbkz/WbhwYfU5j42N1Z6ntss6lX6n/DxwOEYpqFhTEDlUQ7U77guHXqmOx5sWaagV\nh3VoxWBOQWQtUPf0Zf1bQ2W+8IUv1P4eh5v853/+J+rwDM4Y01o8wBljWkujLurExES11KxhFhz+\noUvaPI09fPhw1sauIU/DdUrLS+26FM5otDYvjfPvcWgGkLu2WgmEl7vVjfibv/mb6pin6HqN++67\nrzrubtwz0zU1A6LX5XRz+YyOjlZhPa+99lrWxrasbiK7fBrhz/bE7qRW9ODvW91EzkJQ6aVO9tEw\nLHaPNZecnwd1sfn53rt3b+012BXXEDJ+trVQ7E033YRe8AzOGNNaPMAZY1rLwPZk0Kn2xo0bq2Nd\nbeKpsK60cNJtyU3k1VCtR8/TZl0dZZeV+8WFMGe6JsPR23fffXfWxq4uuyx/8Rd/kZ3H71tXiO+8\n887qWFdw+fW//uu/1vbRXD4LFiyo9p/lVUcAOHr0aHWs0gi/1hVKdhV5pV3dOH6ONFmdbVmT9Hm1\nlN1ElnKAPJNHbYvR7BrOPuKoCP18+H6aAcQRB+pia1GAOjyDM8a0Fg9wxpjW4gHOGNNaGtXgLly4\nUPnxWj3h4MGD1bEuAbPOoPocLx+XshVY7+Dlc23TzTPqKoHovoyswemSP19DdRIOe7n33nurY85q\n0PtppRHW5/Sz08/B9J/58+dXuqiGM5T2xGVUk+bQn9LGQfx7GobC2nJpQxrW+zTUpLQfL1+DtUYg\n19pKRTlZ49NKI7yxE2vowHStvA7P4IwxrcUDnDGmtTTqoi5cuLAqbKnL3TyN1ak2T+3VxeMpNE/l\n1YXkqbFOw0uuA/eFQ0/UbeBld3UHeDqtke489eZilVpzfuXKldWxuticmK9FCbngopkbUkqV/art\nsqupYSKc5aB73WrGSxfNGOBrqhzBdlfak4FDNVRe0eetri/sagK5e8muMoc0AXkRCT4G6ovBAs5k\nMMYYD3DGmPbiAc4Y01oa1eC46oJqAqzJqSbGPrzqGJzOwbqFXp/P04oGrJlpG+t1rFVo6gun2qhe\nwO9H9T9OBXvuueeqY02L4f0ju2lBXVatWlUdqz5XCjEw/WFycrLSfFU7Za1LtVm2SbUntnPWVTVc\ngrVf1c/YXjWsgu/NtqvPTSnMhd+raoasp7E+p9o723Jp4x3VNnvFMzhjTGvxAGeMaS2NuqjAxemw\n1pbnAnk6DS9NoXmaXLc/g6JL3+w6aFUHXqrm6bXWrmd3QENU2P3Q3+N6+JxFoeEdXLhS27gKg+7B\noFkPpv9ERBUyoYVQeb9flhKAsvt65MiR6pjtVat9sOSh8oRKJQy7s3wNdXP5tRbD5GdDXVR2Szl8\nRcNcbr311tpr8GsNgek1Q8czOGNMa5l1gIuIlRHxbETsi4hXIuJbnZ8vjohnImKs8//1s13LmGHC\ntt1+epnBjQP4TkppE4C7AHwzIjYB2AFgZ0ppA4CdndfGfJywbbecWTW4lNIpAKc6xx9GxH4AywHc\nD+BLndMeB/BzAN8tXWvBggWVXsRhDwDw0ksvVcdaLZd1N9UqWH/iEA/10XX5nlFdjGGdgfuhegSn\npuiSPC+vq/bIGgdrDlqxlPuh1+AUMq3qoClA5iL9su2RkZEq7GLDhg1ZG1fg0BAn1ns1fIg1V7Zd\n1bA4fEIr4bC+qyEebDOsEatux7bGNg7U69NAHp7Ez42mdLHN657B/Izp+9bNleq4JA0uIlYD2Apg\nF4ClHQMBgDcAzPgkRcRDEbE7Inar+G7MsHCpts12XSrlbQZLzwNcRFwN4McAvp1SyqYQaerPw4wZ\n6ymlR1JK21JK27SelDHDwOXYNtu1rl6a4aGnMJGIGMWUAfwgpfRE58enI2JZSulURCwDcKb+Cp2b\nzZ9fVQHQ6fq6deuqY55a67laLYOX0NnV1OV0nu6qi1fam5Hb2KXQJe2SG8HuR8kd5n6py8JTfm1j\nl1hdc/28TE4/bJszGTRzhO1Jvze2XY3UZ3stZcLUFWQFcltTu2NbZhe1VEiyFEKlzwO7mxyypWEo\nbJ8aAlYq+llXbUXpZRU1AHwfwP6U0veo6SkA2zvH2wE82dMdjRkSbNvtp5cZ3N0AvgHgpYjY0/nZ\n3wN4GMC/RcSDAI4B+Nu56aIxc4Ztu+X0sor6HICoaf7ypdxsZGSkmlpqUjtPT3WPUS72yHssAsCb\nb74547101aiUbM/TX552A7lLwFN5na5z/0srtqVCAlzHXlfEePVYE7NLhQrUpTEX6ZdtT0xMVPJC\nyUVVN5Fdt7GxsayNbYFtTe2Ov1+VI3hRTyUh7hfbrrqJfH21rV4lG9be9fli11zb+PPSFVZ9XYcz\nGYwxrcUDnDGmtXiAM8a0lkariVx11VW47bbbAOT7gQK5XlCK9tdoat4TlJfJORIcyP15zULQ7AiG\ntQReki9VVtDr8bmqoXC/+PpaDYWX7zXuivUUXYbX+5n+Mzk5WWlcahccrqS2y8VOVX9iPY31LbUt\n1vz0u2abUd1Z+99F9WN+rW2sBWu/+P2UtGvW9Up9LO01XMIzOGNMa/EAZ4xpLY3vydDNZPjiF7+Y\ntfGUfNeuXVkbu5sbN27M2m688cbqmN06/jmQF9TkRGMgd4/VxdDXXTRUg6faOn2uc3O1jaf5pYwH\nbWMXppTBYeaGUpgIf1fqhnL4hNorh0qxq6mFKPj71lRI7ouGVbDLx30shT+VQpA0HKkuvKQU5qJ2\nXdoXtZRxwXgGZ4xpLR7gjDGtxQOcMaa1NKrBTU5OVkvBnH4FAHfffXd1rP48p3NwYUwAuPnmm6vj\nrr4HTC88yJt/lDZ00aoOrAtwv1jTA/IQgFKlA31vdRvqqJbGbVp/jHU8LQTYa0qLuXw4TOS1117L\n2kopiGwLqpVyIVdO29LvnrVr1eB48ya1A35uOLxKNUTWulQjK+3JyrpbqWBtKU2Mr6madym0i/EM\nzhjTWjzAGWNaS+P7onbRKS27lOrGcWbDoUOHsjYODTl16lR1rNUTtm3bVh1v3rw5a2N3maPLgdw9\n4Km8Fsbk+2nhQZ5Oq+vJxTHrQkaAPJRFPzt2bzizY6b7mblFq9uw5KF7pnJGChd8BfLvkffnUGmE\nQ57UPea+aBFWtld2/zQUhO1QMxm4rVSFp1QJh21X3WMODdGwEH0G6vAMzhjTWjzAGWNaiwc4Y0xr\naVSD44q+6lOzb75ly5asreSLP/vss9XxgQMHquODBw9m53EoyPr167O25cuXV8daSZc1rFLlA06h\n0aV8fq1VQnifSNYgShVPdLmeNT+9vncym3tSSpVtqHbKYRyqb3E4EYd0ALlmtmLFiuqYbVXvp5s1\n8b01/In16tK+qNyPS6mUwuFP/L71+eXnSPVitl1XEzHGGMEDnDGmtYROm+f0ZhFvYmqXohsADMN2\n4H9s/ViVUrpx9tPMpWC7LtJEX2rtutEBrrppxO6U0rbZz3Q/zMeHYfkeh6UfwOD7YhfVGNNaPMAZ\nY1rLoAa4RwZ0X8X9MP1kWL7HYekHMOC+DESDM8aYJrCLaoxpLR7gjDGtpdEBLiLui4iDEXE4InY0\nfO9HI+JMRLxMP1scEc9ExFjn/+tL1+hDH1ZGxLMRsS8iXomIbw2iH6b/DMq2h8GuO/ccSttubICL\niHkA/gXAXwLYBOCBiNjU1P0BPAbgPvnZDgA7U0obAOzsvJ5LxgF8J6W0CcBdAL7Z+Qya7ofpIwO2\n7ccweLsGhtS2m5zBfQ7A4ZTS0ZTSeQA/AnB/UzdPKf0CwDvy4/sBPN45fhzA1+a4D6dSSi90jj8E\nsB/A8qb7YfrOwGx7GOy604+htO0mB7jlAI7T6xOdnw2SpSmlblmFNwAsberGEbEawFYAuwbZD9MX\nhs22B2pPw2TbXmTokKbiZRqJmYmIqwH8GMC3U0ofcFuT/TDtp2l7GjbbbnKAOwmA9wpc0fnZIDkd\nEcsAoPP/mVnOv2IiYhRTBvCDlNITg+qH6SvDZtsDsadhtO0mB7jnAWyIiDURsQDA1wE81eD9Z+Ip\nANs7x9sBPDmXN4up6oTfB7A/pfS9QfXD9J1hs+3G7WlobbtbjbSJfwC+CuAQgCMA/qHhe/8QwCkA\nFzClkTwIYAmmVnbGAPwUwOI57sM9mJqi7wWwp/Pvq033w//m5LsdiG0Pg113+jGUtu1ULWNMa7ki\nF3WQgbvGzCW27XZw2TO4TnDjIQBfwdTU+HkAD6SU9vWve8Y0j227PVzJrlpVcCMAREQ3uLHWCK66\n6qrU3TlKd8Wp2wkbyHfULu02xTvv6PV5INdBvbTzdl2bnse7CE1MTNS28fFMr+vgz0B3H9Idxxne\ngeujjz56K7lkeS9ckm3fcMMNafXq1QCm2x1//qXd2dXmS7bGlOyOn6nS88D2w7toAfmO9Xw9vYb2\nkV9zH/Ua/JloG78uPZd/+MMfau36Sga4mYIbP1/6hU9+8pP4q7/6q6mTT5zI2niLMx3Ebrjhhup4\n2bJlWRu/cb7m6dOns/N4ezI1BP4SdZs9/vK5jbf4A4APPrgY8vP73/8+a+Nt/XS7Qf6CS4PwTTfd\nVB3r1nFLl16MndQB87XXXquOf/WrXx2D6YVLsu3Vq1dj9+7dAPLtIwHg8OHD1fGhQ4eytuPHL96C\nbRDI7YsHHR0IP/roo+pY7Y77os8DDx433nhxbLjttttq+/H222/XXkMHxmuuuaY6ZpvXz4c/E91u\nk++3aNGirI3/yO/evbvWruc8TCQiHoqI3RGxW/fzNObjCtv1m2++OejumBquZAbXU3BjSukRdKp6\nfupTn0o6SnfhvxTqcvForVNt/ovGf1F0BqSzQoY34OW/PNrGLoUO1rzxLc84AeD999+vjo8dq59E\n8V8pvi+Qz1zvuOOOrO3Tn/50dax/IfWvrumJWW2b7Xrjxo3pZz/7GYB8g3Egn6Wp7fPMXr9v3gia\nvRR1c9lz0M2djx49OuO9gHzDcbZd9az4fjqQa1+Yd965mB7LMzjtB3tu1113XdbGr/X51evUcSUz\nuGELbjSmX9i2W8Jlz+BSSuMR8XcAfgJgHoBHU0qv9K1nxgwI23Z7uBIXFSmlpwE83ae+GDM02Lbb\nwRUNcFeCrlayzsBaF5CveqoGx6s3rOPpkjP/nupnrG+tWbOm9vqsp6i2xZrJ+vXrszbWEEsraYyu\n0rIGwfqJ3ps1GWD652z6z7vvvot///d/BzB9tZJ1MV1B5+9UNaYzZy7mpEtIRHbee++9N+MxkOux\nukp77bXXVsesZ2n/2c51lbYUxsH3Lq22Lly4sDrWCAC2ax0T+PMp4XJJxpjW4gHOGNNaGnVRR0dH\nq6BCDbZlt1HdS56+a8AfT3n5PF12Z9dNMwFWrrwYEaAhGOwS8JK2hmPwFJqDcgFgyZIl1TFPyYE8\nyJLDSdRl4dcabsBhNbrUroGbpv989NFH+M1vfgNguivF36m6qOqu1bWxLasbx9c/depU1lZyDfl5\n4GuqxMG2xW4tkLul+lxyqBS7kxqGxXILvxcgD/UqZUCU8AzOGNNaPMAZY1qLBzhjTGtpXIPrJoqr\nr//6669Xx+rPs9+u+pm+7qKaA+sHrHsBuQanIRisDbK+oulkrLvpNVhDuf3227M21jE4pEP1Dg5z\nGRsby9r4s9yyZUvWppqc6T8XLlyo7Fe1Xw7dYA0XKFfx4BAJDvHQcA+2SU7ZA/LvntOx9DXbj2rj\nHELCRR2AXGdTO+P3w+lrqp3dcsst1bGGTGlIDMOhXRrawngGZ4xpLR7gjDGtpVEXNaVULffqVJjd\nUHX/OERC62HxEje7turm8hT65ptvztp4qq0uAE+12Q1VV0RdSoZDQzQDgl1gdm31ffJrntYD+efj\nPTaaZ2RkpMo80Yh+/u7VDWU718yVuio5+tzwa3UTuW4gu7x6TZY/VPIpucAcgqR2x58DPxtcv07v\nrZRCr7gve/bsqb2GZ3DGmNbiAc4Y01o8wBljWkujGtzExES1bK4+O+sHdaEfwPQUJl7uZh1PU1r4\nPNUBeClfNRQ+l1OuSpqDXoOX2rViKldJZZ1k1apV2Xn8vrWNdRj9fOoqKJv+MTIyUmm+qpFx6E+p\nUrVqxnXfqVaH4eo3qpFpZWmGbZ61QdXxuB/r1q3L2lgX05AOTrtiXVtDtF599dXae/PzzM/eTK/r\n8AzOGNNaPMAZY1pLoy7qvHnzKldR3UR263TJnKfyOu1mV5SvWcpI0Ok0R3JrtY+6fqxYsaL2PL0G\nvx8tGsguat1GIEDufmhICl+TtwkEplc9Mf0npTStiGqXUqUXdg21yga3sVuq57GrpqEUvPGLusd1\nmymV9txV2Yffs4ZXsV1zKIi62Px7Gv7E7003mdHinnV4BmeMaS0e4IwxraVRF3X+/PnVtFOnzHUF\n/rRNVwk5eZen1zqF5VUqdXNLxfM4ory0Pyu/1sKD7FKqe8nJ96V9Kfj3dMWNXQdd3dXrmP4zOTlZ\na0P83WiyOksSapPcxq6nFs3k6+uKOT8D+tywXbDNaAQAZ/2Uim2qLMOZEiwd6bPNEQG6wsruLLvb\nwHSppw7P4IwxrcUDnDGmtXiAM8a0loGFiai/XVc9Acj9b80EYO2CtSmtxlHaw5EzCLQoIcPhHqrB\n8e9x8U4g1y62bdtW28YF/3TZnUNZVFdjPVM1DjP3sF0rrNtqBkqd7ZZQ7Zq1KNXnOCxFbYZfc7aC\n9oN1MLVrfq3PA9t1KfyJX2toFz+zJc2+hGdwxpjWMusAFxGPRsSZiHiZfrY4Ip6JiLHO/9eXrmHM\nMGLbbj+9uKiPAfhnAP+PfrYDwM6U0sMRsaPz+ruzXSgiqqkl11QHcrdx3759WduRI0eqY10e5iXu\ntWvXVsfqAnOhSXWB64paAvm0n0M1NCKb3Vd1L/lc7Re7lDwN1yX/UjI/u+1an94FMIs8hj7Y9vz5\n86vvVV08TobXQqtsh2rXdZH6mmnAtqsZOuw2lgqocoiHuponT56sjjX8ifc7VZvkrATOwlE3ml1l\nlVc4DEWlIw1ZqWPWGVxK6RcA3pEf3w/g8c7x4wC+1tPdjBkibNvt53I1uKUppe5w/gaApXUnRsRD\nEbE7Inb3GpxnzADpybbZrktlt81gueJFhjTlA9X6QSmlR1JK21JK29StM2aYKdk227VmCZjh4XLD\nRE5HxLKU0qmIWAbgzKy/gal0ke7SeKnwHe+jCORVC7TQHS8ts6+vWgjrW6olsD+vRQNZd2OdTQdr\n1fUY1id0P1jWHVgL0aoR3H9NC+LPSzU4rcxiZuWSbXt0dLTSblVn27hx48ULy3fD2qnaD3/fHCal\nKVFsr1qthO1ENTK2a9b1VMerq3YD5KlnOsizNsjPooZ7lEJIuIKIVh/S6iJ1XO4M7ikA2zvH2wE8\neZnXMWbYsG23iF7CRH4I4NcANkbEiYh4EMDDAL4SEWMA7u28NuZjhW27/czqoqaUHqhp+vKl3owj\nvnnqCwCHDh2qjtUF46kwV98A8uk1T1u1ACELwTq95dcaIc0uB7vHOpVXF4BhN1Gn6Ay/F3XhS/1n\n90BDVOoi7E3/bHt0dLQKe9q0aVPWxjZz4MCBrI2/R3VRV65cmV2/i0ovLHFoG4dZ6DNVt1eE7p9a\n0s3ZXkuFYjlTorRPBGfyAPnndezYsayt1yo5zmQwxrQWD3DGmNbiAc4Y01oarSZy7ty5alMUTctg\n7Uj99Ntuu6061oq4rOWxpqHhEbzMXNqDUlNA6sI4VKvj62s4AOsFupTP74evoRoD6ynaR15q1305\ne11ON5fP+Ph4lQqoKVasLWv6HWtrGl7C2inbgtoF25raBadFaRgH28Xy5curY9WIuU2r6rK9lioB\n83OjzyX3eWxsLGvj96apZtqXOjyDM8a0Fg9wxpjW0qiLClycknIlAiCfGrNLCuQhE7qUzOEadXuk\nAvkUXae7HIaimRK8vM5TZs144OV1zVbgEA92WYC8Agq7Hy+88EJ2Hk/J1d3YsGFDdbxu3bqs7Sc/\n+QnM3DIxMVFVu/jNb36TtfF3v379+qyNK9eo68a2wIUltToM24W6qCz7aBUSljLYXdVrsHup7ivL\nK9ovzsRZvXp1daxhJ5yfrhlMn/3sZ6tjlXb27t1bHf/yl79EHZ7BGWNaiwc4Y0xradxF7bqHugrC\nLp9mIbz44ou1bTz15mmxrnKyC6BtvFKkbewq8PRdMzG4/+omckS2Jvoz3KauDk/DNdr8G9/4RnWs\nBTtd8HLuSSlVmQhaOmnFihXVsRZ5ZVvTTAOWWNjuNIOG3UZ9NnglVos3sJ1wFgWv1gP5+1EXlaMd\ntJAlu8cqCTH8e5rMz8+2Rk+wLFPCMzhjTGvxAGeMaS0e4IwxraVRDW5kZKTKDNBQEN5U4ujRo1kb\nh4ao/sQaB290ocvKvCyuISSsB2qYCGtYrGmUCvxpNDv/npZt371794y/pxoc63i6v+Z//Md/VMdc\nYBEoF+I0/YE3nVENl8MsNMuEwyI0PIOfB7Y11bo400ZDTTgkQ6vKcF9KG7/wc1Sq0KNVbLiNC3uq\nhsg2r/ola3eqeTuTwRjzR48HOGNMa2nURZ03b1613KuZDLw8rZkAPDVW15BdSp6Sq6vJU2Od5vP9\ndKrNISScUaEuMLsUupzOyf1aGJCX73ft2lUdHzx4MDuP76fX4Om7uqT6fkz/ueqqq3DrrbcCmO7G\nsb3qd8NhQWrzbEMcjlEK91D3kkNPtF+cvcD30lCW0n6/7CZqiAe73Jx1oAn1pWvws6dhNOrq1uEZ\nnDGmtXiAM8a0Fg9wxpjW0qgGd+HChaoiBy8dd9u6aPE8Tn3iyh9ArgtwCIbqBaxh6ZK5Vlpg6vZF\n1SV/fq39Z41DU6l4mZw1FN1chzUH1VrqztN7m7lhZGSk0n81LYm1WQ11YC1YtTVO/WMbVP2Y9VjV\n+Ni2VMPiZ4D1au1HKdWP+6VVQrgCCj/rGkLFn4/qhHyu2nFpkxvGMzhjTGvxAGeMaS2N+i9cGFCn\n0xwmotkKXDCPMxeAfFrL02ndd6HO1QRy91hdzzoXQCPP+bVO6zm6XfdIYHdz8+bN1bHui8rut2ZD\n8DS/1/0iTf84f/58lW2jWTIcFqHfPbuJug8J21Npf1O2V3Xx+BqaYcGSTUnyYFtWKYf7r64z94Wf\nNw3fKu0ZzLas702fgTo8gzPGtJZZB7iIWBkRz0bEvoh4JSK+1fn54oh4JiLGOv9fP9u1jBkmbNvt\np5cZ3DiA76SUNgG4C8A3I2ITgB0AdqaUNgDY2XltzMcJ23bLmVWDSymdAnCqc/xhROwHsBzA/QC+\n1DntcQA/B/Dd0rXGx8erpV9NdeI0DdUjVq5cWR2XUrBYL1Cdjdu0OijrGqqfsT6n+grDeoHqi/xa\nwwg4nIVDA0rL9ao/cPiBVgzWZXlzkX7Z9vnz53Hs2DEA07VZti0NXWLNSfW57j6r+nulsB/V2fhZ\n0dAltq9SOh/rc1rtg3VuvX5dCqWGeZU0RH62S7pziUvS4CJiNYCtAHYBWNoxEAB4A8DSmt95KCJ2\nR8TuXvPHjGmaS7Vttmt9MM3w0PMAFxFXA/gxgG+nlLLM4DQ1vZgxIjCl9EhKaVtKaVtptcaYQXE5\nts123WvQqWmensJEImIUUwbwg5TSE50fn46IZSmlUxGxDMCZ+itMceHCBZw8eRLA9Gkxh3+wSwrk\n01pdJmeXj91EDZfg6Xtpj8jS5hn8l1rdUO6XhppwH3Uqz64091ldWX6t7iu/1hAbnsr/13/9F0xO\nP2x7YmKiqgZS2uxIM2jWrFlTe02uQsKuodou30+lF3Yh1a7Vle6idsd2ri4qo9IO35vft16fXW59\nZtlt181w3nrrrdq+ML2sogaA7wPYn1L6HjU9BWB753g7gCd7uqMxQ4Jtu/30MoO7G8A3ALwUEXs6\nP/t7AA8D+LeIeBDAMQB/OzddNGbOsG23nF5WUZ8DULex4Zcv5Wbnzp3D4cOHAUzPNOBVVV0h4f0b\ndIrLq4a8yqnCL0+F1b1kt7G0hwFrLXp9nkLrShe7uZqUzPfma6qbW8qiYNekFG1ucvpl2ymlyn3T\nz5tdSHXx+PvWfTb4X/9yvQAABZZJREFUe+SikKV9EUr7hKg0wnCb2ha7nioPlZ4Vtnk+T/VK7rOO\nCSy36AKlRlPU4UwGY0xr8QBnjGktHuCMMa2l0Woik5OTlS+tWkKdzw6gCi0Byr436xa65FyqPsDn\nqvZVV1BT9QjWKnTJnHUHzbDg2EC9N8Ma3+UW2zRzw+TkZBWGwRkIQJ6Vo98bbzRT+j0OA1Kdim2t\npM+pHbCWzdcohYmUApo17Ksuy6Fku9pH1hB1QxrO+inhGZwxprV4gDPGtJaB7cmgoSDsXuo0/H//\n939nPE9f33zzzdWx7u+o/WA4IV3vXZehoBHrPA1XN5GvqVP5ugwFddP5PA4bAHrfI9LMHd3wCo2w\nZ0lCbZKLmqp7xvsYsHumEg3/noZ4sNurbWyjfA213ZJbyiEeml3DlIpZ1PVJf09DYDTcqvaaPZ1l\njDEfQzzAGWNaiwc4Y0xraVSDGx8frzSKAwcOZG2sR6jOwK/37duXtXGVBNYSWI8Dcp2qVPFBi1rW\nhXiUtEANIWFtQVNVWJNgnUE3nWEdT3UM1uu0/6XQE9MfRkZGKptSDZftU+2ObUHDmF599dXqmAua\n3nnnndl5pb1JOW1MC2qeOXOxQArba2nTotKGTPq++VlkzVv171Kh2NLvldLEGM/gjDGtxQOcMaa1\nNJ7J0J0O836RQD6l1f0a7rrrruqY90gF8qk2L63rdJrdS80mYHdWI6Z5Cl3nTmqbupDsAqibwu+b\nXQpdMuff02hzfq+lQpxmbuBMBrUfthPOyAHy8CQNH+Lvm8M99LlZvnz5jL8D5NH+WgiVnwEOIVHb\nYlvWopkc6qVtbJOlPR/Y9Szth6J2rPerwzM4Y0xr8QBnjGktHuCMMa2l8VITXd9ctaJupV8A+OlP\nf5q1sQ9/zz33ZG2sQXDFDQ2X4Mq/qgmwBqHpNNzPUkUS1jR0X1fWC1RL4OV11ms0DIX1CQ0HYM1S\nwwGswc09XCVH7Zq/Gw3B4DQo1cg4/IO1KH5OgFzXU7tmbVkrBvMmT2xPqoPxs6F2x2EiuikMa3l8\nDdUJuZpOKY1RNelSyhfjGZwxprV4gDPGtJbodarXl5tFvImpXYpuANDbxoZzyx9bP1allG6c/TRz\nKdiuizTRl1q7bnSAq24asTultK3xG7sfZg4Zlu9xWPoBDL4vdlGNMa3FA5wxprUMaoB7ZED3VdwP\n00+G5Xscln4AA+7LQDQ4Y4xpAruoxpjW4gHOGNNaGh3gIuK+iDgYEYcjYkfD9340Is5ExMv0s8UR\n8UxEjHX+v750jT70YWVEPBsR+yLilYj41iD6YfrPoGx7GOy6c8+htO3GBriImAfgXwD8JYBNAB6I\niE1N3R/AYwDuk5/tALAzpbQBwM7O67lkHMB3UkqbANwF4Judz6Dpfpg+MmDbfgyDt2tgSG27yRnc\n5wAcTikdTSmdB/AjAPc3dfOU0i8AvCM/vh/A453jxwF8bY77cCql9ELn+EMA+wEsb7ofpu8MzLaH\nwa47/RhK225ygFsO4Di9PtH52SBZmlLqlhl5A8DSpm4cEasBbAWwa5D9MH1h2Gx7oPY0TLbtRYYO\naSpeppGYmYi4GsCPAXw7pfTBoPph2k/T9jRstt3kAHcSwEp6vaLzs0FyOiKWAUDn/zOznH/FRMQo\npgzgBymlJwbVD9NXhs22B2JPw2jbTQ5wzwPYEBFrImIBgK8DeKrB+8/EUwC2d463A3hyLm8WUxX8\nvg9gf0rpe4Pqh+k7w2bbjdvT0Np2SqmxfwC+CuAQgCMA/qHhe/8QwCkAFzClkTwIYAmmVnbGAPwU\nwOI57sM9mJqi7wWwp/Pvq033w//m5LsdiG0Pg113+jGUtu1ULWNMa/EigzGmtXiAM8a0Fg9wxpjW\n4gHOGNNaPMAZY1qLBzhjTGvxAGeMaS3/H9JDZJYOgTIcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eFcJHuWuMIR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wCDKoj3uQCw",
        "colab_type": "text"
      },
      "source": [
        "<h3> Visualising ouput of second Convolution layer </h3>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG8AR_dhuLuI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0N6Y5nToykN",
        "colab_type": "code",
        "outputId": "6261f753-bf74-47d6-ac72-0f1194b931d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "out3 = net.cnn_model[3](net.cnn_model[2](net.cnn_model[1](net.cnn_model[0](images))))\n",
        "out3.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 16, 10, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm-jzDLSsZYE",
        "colab_type": "code",
        "outputId": "3572aa29-c2b4-4771-f772-09f6a9092a43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        }
      },
      "source": [
        "image_id = 1\n",
        "plt.figure(figsize = (9,9))\n",
        "plt.subplot(821)\n",
        "for i in range(16):\n",
        "    ax1 = plt.subplot(8, 2, i+1)\n",
        "    plt.imshow(out3[image_id, i, :, :].detach().numpy(), cmap=\"binary\")\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4zWht0e3bt1e6t5Tusvqv//qv4XEAKms5Rzo7O9XT05PUItHvfF9fOvntW9/6\nVlL77Gc/G14z2lk5WqP48OHD4fnl9YP//u//PjyuSnPEVkknb/t8aroGAFWRIxnztrOGme2VtFeS\nVq1aNV+XBbCI3J4jUXPAQlQlhAcl3f5v+W3TtQJ336fpGSebNm3yycnJwn/v7u4OLx4F9j333JPU\n1q1bl9TWrFkTXvPChQthvco1o3rUngygJS3nyMaNG/25554r/PejR4+GF4+2Kvvoo4+S2sGDB5Pa\nV7/61fCaUX/V5s2bk1rUFCJJt27dKnzOfalU+ao5KGm3me0ysy5JL0r6SYXzAGAGOZIx55uwu0+Y\n2cuSfimpU9L33P292p8MwIJBjuRVahN29/2S9tf8LAAWMHIktjhavgHgjxQhDAANmrchardbtmyZ\n7r///uKNlsS3Kg9oltKZJlI8kuGxxx4LrxkNno5mx+UGfpd7RXOzdADUqzyioDziYMbSpUuT2uBg\nMvhCJ06cSGqXLl0Kr1meKCJJU1NTSS03Sqs8y7c8YmwGb8IA0CBCGAAaRAgDQIMIYQBoUC0dcytW\nrNBTTz1VqEUro0nS+fPnk9qRI0eSWtRZ9/DDD4fXvHnzZlKLlpvLLYVZ7oiL7g2gXsPDw/rd735X\nqOU6ybdt25bUomnHUQd9NL1Zkq5evZrUBgYGKl1Tko4fP174nJvezJswADSIEAaABhHCANAgQhgA\nGlTbjLkHHnigUMvtw3Tu3LmkFs1qidYdjhrjpXgro3IjuRTvWyd9vKfc7aItTQDUa3R0NPm93bBh\nQ3hs9DsfdcxFM+5++9vfhteM9p6L1gTO7Z/53nvFReJyGcibMAA0iBAGgAYRwgDQIEIYABpUqWPO\nzAYkDUmalDTh7n2zHT8xMZF0ekUz46S0Eywn6sCLNgSV4mUzo2Xtopl1kuTuhc/Dw8NVHhHALFrN\nkampKV2/fr1Q+9znPhce29vbm9SiDrNoKcotW7bM9hgFUefa2bNnw2PLnYDRvaXWRkc85+7p4r8A\nUB05UkJzBAA0qGoIu6RXzexNM9tb5wMBWLDIkUDV5ohn3X3QzHolvWZmH7j7gdsPmP6h7pVaa2MB\nsGi0lCOLRaU3YXcfnP7zvKRXJO0Jjtnn7n3u3hftBwdgcWs1R9r9fE2Z803YzFZK6nD3oem/vyDp\nO7Odc/PmzWQqYG5N3mhqYTRNOBpdEY14kKQbN25UOjZ3fnl0RG66IYBq7iRHOjs7tXr16kItN6Ip\nyowoB8rXk/LTjru6upJaNMIhN+qhPLIj2oBYqtYcsVnSK9OLKS+R9H13/0WF8wBgBjmSMWcIu/sx\nSU+24VkALFDkSB5D1ACgQWGpV14AABd6SURBVIQwADSolvWEx8bGks3zclOMo2nLJ0+eTGqXLl1K\narnpglFHWjT1OFpbVIob9AG0l5lp6dKlhVquEyz6nd+4cWNSiwYI5EZzResJd3Z2JrXckNzy8gn7\n9+8Pj+NNGAAaRAgDQIMIYQBoECEMAA2qpWNufHxcFy5cKNRyDerj4+NJLdroM+rAi9YQleJZMVGD\nenlmHIA/HkuXLk02843WCpekrVu3JrXNmzcntWXLliW1++67L7zmmTNnklqUV7ls6+7uLnyenqiS\n4E0YABpECANAgwhhAGgQIQwADSKEAaBBtYyOmJyc1JUrVwq13Jq85R5EKZ4uGK0XWl6vczZDQ0NJ\nLbc2afmZWE8YaL9ly5Zp586dhVpu5/NopFO0LMGjjz5a6VwpHn21cuXK8NiqzxThTRgAGkQIA0CD\nCGEAaBAhDAANsjqm7prZBUnHJfVIujjvN0jVfZ8d7r6pxusDKFksOVJLCH9ycbP+dmxd3a77AGi/\nhZ4jNEcAQIMIYQBoUN0hvK/m67f7PgDab0HnSK1twgCA2dEcAQANqi2EzexrZnbYzI6a2bdrvM+A\nmR0ys7fNrL+u+wBov8WQI3WNE+6UdETS85JOSToo6SV3f7+Gew1I6nP3dowjBNAmiyVHKr0J38G3\n0R5JR939mLuPSfqhpG/ezYMC+HQjR2JzhvD0t9H/k/R1SY9KesnM0vXgirZKOnnb51PTtTq4pFfN\n7E0z21vTPQDcBXIkb87mCDN7RtL/cff/Nf35f0uSu//f3Dlr1qzx8lqc0RrBktTRkX4PRGsHR+sB\nR7Xc+ZOTk0kt2pVZSndmvn79uoaHh+OtUgHM6U5zZNOm4izfVatWhcdGu6lHO7RHa4jn1iWPdlZe\nvnx5UstlaHl35ZGREY2Pjyc5UmVR9+jb6E9mO6G3t1ff/e53C7Wnn346PDb6HzU4OJjUXn/99aR2\n4MCB8JrHjh1LalFgf+UrXwnPX7NmTeHzv//7v4fHAais5RzZtGmT/uEf/qFQe/bZZ8Njo3Dev39/\nUuvvT/vcfv7zn4fXjLa8f/jhh5NaLoSXLl06572leRwdYWZ7zazfzPpb2fECAGYsxhypEsKDkrbf\n9nnbdK3A3fe5e5+795XfJAEseuRIRpXmiIOSdpvZLn38Q3tR0l/PdkJXV5e2bdtWqG3fvj08Nmqj\nuXz5clL7/e9/n9Ryr/enT59OatE/Ge65557w/PK+VhMTE+FxACprOUc6OzuTZoYLFy5kjy2LmiDL\nTQSStG7duvCahw4dSmrnz59Palu3xn2FUT9UZM4QdvcJM3tZ0i8ldUr6nru/V+nqACByZDaVdlt2\n9/2S0lZuAKiIHImxdgQANIgQBoAGEcIA0KBKbcKt6uzs1Nq1awu13Oy2q1evJrVoFl00u+2pp54K\nr1kemSHFIy42btwYnr9ly5bC5yVLavkxAZhDedTD0aNHw+Nu3bqV1KJRTVNTU0ntwQcfDK/54Ycf\nJrWzZ88mte7u7vD88kiM6N4Sb8IA0ChCGAAaRAgDQIMIYQBoUC09Tu6eNEJfuXIlPDZaMS23XF3Z\n448/HtajjrkTJ04ktdzqR7t37y58jlZ6A1CvqampZFmD4eHh8Njod7k8OECSHnnkkaSW+/3u6+tL\natHKbNFqa1LawZ/LG96EAaBBhDAANIgQBoAGEcIA0KBaOuY6Ojq0bNmyQi3X2RatuRnNZPvsZz+b\n1HIz2Z555pmk9tFHHyW1aN1hKV37mI45oP2mpqaSmba5HImyIJpFF60nXO5AmxFtyRZtqZbrLCzP\n8o1mAku8CQNAowhhAGgQIQwADSKEAaBBlTrmzGxA0pCkSUkT7p5OJbnN1NRU0lhd7qibEc1qGRsb\nS2rRRn7j4+PhNaO6mSW13Aae5a22q27YByCv1Rzp6OhIOuJyneRRvkS/39Fs2txs3uiau3btSmrv\nv/9+pfNzHXOtjI54zt0vtnA8AJSRIyU0RwBAg6qGsEt61czeNLO90QFmttfM+s2sP9rFAsCi11KO\nlJsFF6qqzRHPuvugmfVKes3MPnD3wqhld98naZ8kPf744/FyQQAWs5Zy5MEHH1wUOVLpTdjdB6f/\nPC/pFUl76nwoAAsPORKb803YzFZK6nD3oem/vyDpO7OdMzU1pdHR0UItt+bmtWvXklo0tXDlypVJ\n7de//nV4zXXr1iW1S5cuJbVoIz8pnRqZm5YIoJo7yZElS5aop6enUItGN0jxZpsPPfRQUovOP378\neHjNY8eOVTr/vffeC88v58jdjI7YLOmV6SFeSyR9391/UeE8AJhBjmTMGcLufkzSk214FgALFDmS\nxxA1AGgQIQwADaplPeHOzk6tWbOmUDt16lR47Llz55Lapk2bKt0najiXpM2bNye1qHPtyJEj4fnl\nBvWRkZFKzwNg/phZMvU3t4RANJ25t7c3qUUDAaJ1h6V4gMB9992X1MqdhzPKAwRy65/zJgwADSKE\nAaBBhDAANIgQBoAG1dIx5+5JZ1Zu7d9okY5yp54kDQwMJLV33nknvObWrVuTWjRb5erVq+H55XVI\nWU8YaD93TzrUb9y4ER4bbQ4cddBHs9tyM9mi86NOuCivJKmrq6vwOVrTXOJNGAAaRQgDQIMIYQBo\nECEMAA0ihAGgQbWMjpDSnsDcbsvRVL6TJ08mtYsX070Bt2zZEl4z6kGNdmvOTUcub88U7f4MoF7u\nnoxMyu2QHq0hHv3eRlOU77333vCa0ZIK0QiHaGSGJK1fv77wOcogiTdhAGgUIQwADSKEAaBBhDAA\nNMjc539XaTO7IOm4pB5JaY/a/Kv7PjvcvdoixwDmxWLJkVpC+JOLm/W7e19tN2jzfQC030LPEZoj\nAKBBhDAANKjuEN5X8/XbfR8A7begc6TWNmEAwOxojgCABtUWwmb2NTM7bGZHzezbNd5nwMwOmdnb\nZtZf130AtN9iyJG6xgl3Sjoi6XlJpyQdlPSSu79fw70GJPW5ezvGEQJok8WSI5XehO/g22iPpKPu\nfszdxyT9UNI37+ZBAXy6kSOxOd+E7+TbaMOGDb59+/ZC7cqVK+Gx165dS2o3b95MatFmm7ml4bq7\nu5Pa8uXLk9rU1FR4/ooVKwqfL1++rJs3b8a79AGY053kiJl5eRPO6HdbipeyjGpRjgwNDYXXjHIo\nWh4zl0Pl+q1btzQ6OprkSJX1hD/5NpIkM5v5Nsr+8LZv3679+/cXaj/+8Y/DY3/+858ntTfeeCOp\nRTsjr169OrzmE088kdQ+85nPJLXR0dHw/M9+9rOFz//4j/8YHgegspZzpKOjIwnd8u/mjG984xtJ\n7S//8i+TWnmtcEn69a9/HV7zN7/5TVKL1jrP5VD5S+D1118Pj6vSHLFV0u13PjVdA4CqyJGMeRsd\nYWZ7zazfzPovXbo0X5cFsIjcniOLZQ5DleaIQUm3N/Bum64VuPs+Tc842bVrl//qV78q/Pd33nkn\nvHjUxvLUU08ltej/kB07doTXfOSRR5Lafffdl9Sif5pI0gMPPFD4HLUnA2hJyznS0dHh4+Pjhf+e\na7+Ntj+Ltk575plnktqGDRvCa27bti2pHTlyJKnl+rvKW7r9z//8T3hclTfhg5J2m9kuM+uS9KKk\nn1Q4DwBmkCMZc74Ju/uEmb0s6ZeSOiV9z93fq/3JACwY5Ehepd2W3X2/pP1zHggAGeRIjLUjAKBB\nhDAANKhSc0SrxsfHde7cuUItN9wk6pl8/PHHk1o02aLc+zhj06Z0O7i1a9cmtWj2jCRt3ry58Lk8\ngw5A/bq6ulSeebtx48bw2Ggm3fXr15NaNEs2yhZJiobalkdrSPkZczdu3AjrZbwJA0CDCGEAaBAh\nDAANIoQBoEG1dMxNTU0ly8CVG9hnHD9+PKlFjexbtmxJan/4wx/Ca0ZL0O3cuTOp3XvvveH55U68\nXMM7gPqsWLFCn//855NaJOp4P3bsWKX77Nq1K6zfunUrqUVTlHPTlj/66KPC59yqjbwJA0CDCGEA\naBAhDAANIoQBoEG1dMyZmbq6ugq13t7e8Nho26IHH3wwqUV70eW2TIrWHP3TP/3TpPbQQw+F55e3\nUBkZGQmPA1Cf7u7uZPZsbiuhct5I0pkzZ5JaNONt1apV4TXN0m0lo3WLoy2PJKm/v7/wORowIPEm\nDACNIoQBoEGEMAA0iBAGgAZV6pgzswFJQ5ImJU24e99sx09NTSXLyOVmp0UbcG7dmu6EHXXMvfHG\nG+E1o8b3aMZc1JgvSStXrix8zs10AVBdqznS0dGRzJDLdaJNTEwktbNnzya1aHbbPffck3vepBbN\nns11uF24cGHOZ5RaGx3xnLunXYMAUB05UkJzBAA0qGoIu6RXzexNM9tb5wMBWLDIkUDV5ohn3X3Q\nzHolvWZmH7j7gdsPmP6h7pWkNWvWzPNjAlgAWsqR9evXN/GMbVfpTdjdB6f/PC/pFUl7gmP2uXuf\nu/exJxuAslZzpNxBvlDN+SZsZisldbj70PTfX5D0ndnOmZqaSqb6RusGS/FmfFEtWjs4N7oh2gww\nWo94x44d4fnl0RlLly4NjwNQzZ3kyOjoqI4ePVqo9fT0hMd2dKTvk5cvX05q0UiGRx55JLxmlBnR\nusW59carjqqq0hyxWdIr08M1lkj6vrv/otLVAeBj5EjGnCHs7sckPdmGZwGwQJEjeQxRA4AGEcIA\n0KBa1hPu6upKpiPn1gF9//33k1o0Hfmxxx5LarlG+mhq4/3335/Unn766fD88sZ/0cajAOo1PDyc\n5EO0/IAUd6JFHffR8gfDw8PhNaPzoxy723zgTRgAGkQIA0CDCGEAaBAhDAANqq1jrjzrLNegHq3v\nGXniiSeSWnlDztmuGa1nHK1bLKUN8tG6ogDqNTo6qg8//DCpRSYnJ5NaNO05mlmX+/2O1v+Nzs9N\nry6vfRHNBJZ4EwaARhHCANAgQhgAGkQIA0CDCGEAaFAtoyOktMcxtyNpJFrfM1obtLyb6Yzdu3cn\ntWXLliW13DrB5WmIUY8ogHqNj48nOybnciQa4bBp06akFo14yE1bPnXqVFKL1kXPnb98+fLC5xs3\nboTHkS4A0CBCGAAaRAgDQIMIYQBokLn7/F/U7IKk45J6JF2c9xuk6r7PDndPW/kB1Gax5EgtIfzJ\nxc363b2vthu0+T4A2m+h5wjNEQDQIEIYABpUdwjvq/n67b4PgPZb0DlSa5swAGB2NEcAQINqC2Ez\n+5qZHTazo2b27RrvM2Bmh8zsbTPrr+s+ANpvMeRIXeOEOyUdkfS8pFOSDkp6yd3fr+FeA5L63L0d\n4wgBtMliyZFKb8J38G20R9JRdz/m7mOSfijpm3fzoAA+3ciR2JwhPP1t9P8kfV3So5JeMrNH5zht\nq6STt30+NV2rg0t61czeNLO9Nd0DwF0gR/LmbI4ws2ck/R93/1/Tn/+3JLn7/82ds2bNGu/t7S3U\nxsbGwmOXLEmXNI6OjXZT7ezsDK8ZnX/r1q2klttltbz28I0bNzQyMsKWy8AdupMcWb58ua9atapQ\nGx8fz10/qUXrhZfXCpfitcaleB3x6Pwow6T0WU+dOqVLly4lD1plUffo2+hPZjuht7dX3/3ud5MH\niGzcuDGpDQwMJLVou+h169aF14zOf/vtt5Na7of3wAMPFD7/7Gc/C48DUFnLObJq1Sr9xV/8RaE2\nODgYHhsF6T333JPUHn/88aT24IMPhteMAvfRR9OX9/IL54xz584VPr/wwgvhcfM2OsLM9ppZv5n1\nR4EJAHO5PUdGRkaafpy2qBLCg5K23/Z523StwN33uXufu/etWbNmvp4PwMLQco6UtwdaqKo0RxyU\ntNvMdunjH9qLkv56thPGxsaS5ofNmzeHx0ZNCkNDQ0ntnXfeSWq5vd+if7JETRTl/atmlNuPc3tI\nAajsjnKkvKdbbl/IqElhw4YNSS3qA/vNb34TXvPixXS0WpQ5Tz31VHh+eU+5K1euhMfNGcLuPmFm\nL0v6paROSd9z9/fmOg8AZpAjeZV2W3b3/ZL21/wsABYwciTG2hEA0CBCGAAaRAgDQIMqtQm3anR0\nVEeOHCnUop5KSYqGs0Uz3g4ePJjUcgO377333qQW9armRleUR0dMTU2FxwGoz8TEhC5fvlyobd0a\nz1qOZsxFORJNGosmcknxiKqTJ08mtc997nPh+T09PYXPufkTvAkDQIMIYQBoECEMAA0ihAGgQbV0\nzE1MTCRT/nKN0r/73e+SWnn1ISnu2Dt27Fh4zbVr1ya19evXJ7XcFMjysVFjPIB6RTlSXtpyxpkz\nZ5JaV1dXUos688+fPx9eM1o2M1pU6OjRo+H55WfNdfDzJgwADSKEAaBBhDAANIgQBoAG1dIx19HR\nodWrVxdq0ewTKW48v3btWlLbuXNnUjt06FB4zajx/gtf+EJSyy0aXd5y6fDhw+FxAOozNTWlmzdv\nFmqnT58Oj40686OOtWiAQG4275YtWyrVRkdHw/N37NhR+PzWW2+Fx/EmDAANIoQBoEGEMAA0qFKb\nsJkNSBqSNClpwt376nwoAAsPORJrpWPuOXdPd74LdHV1afv27YVabtnJ8rKRkrRy5cqktnv37qQW\nLVkpxZuHRsdmN95bUvyxdHZ2hscBaFnlHDEzLVu2rFC7evVq5RtFm3pGmwh/5jOfCc+PMifqmMvN\nBr7//vsLn8v/W2bQHAEADaoawi7pVTN708z21vlAABYsciRQtTniWXcfNLNeSa+Z2QfufuD2A6Z/\nqHuleAEdAIteSzmS2/lmoan0v9LdB6f/PC/pFUl7gmP2uXufu/dFbboAFrdWc4QQnmZmK81s9czf\nJb0g6d26HwzAwkGO5FVpjtgs6ZXpjfSWSPq+u/9ithO6u7v12GOPFWrR9GQp7sEsTxuWlEyDlqQD\nBw4kNSmethzdP1obVEqnIU5MTITHAais5RxZtmyZHnjggULtwoUL4bHDw8NJrTzKSYpHOkV5IykZ\n4SVJ27Ztq3RvKc2s6HmkCiHs7sckPTnXcQCQQ47kLY5GFwD4I0UIA0CDCGEAaFAt6wkvW7YsmfLX\n3d0dHhutHRxNDYyGvUWddZKSzQEl6caNG5XuLUmXLl0qfB4bGwuPA1CfFStW6IknnijUcssfRJ3n\nk5OTSS2aYhzljRQvdbBixYqkFq1bLKUbe0aDECTehAGgUYQwADSIEAaABhHCANCgWjrmli5dmjRq\n5xqlz58/n9SiNT+jWS3R5p+SdPbs2aQWzWqJNgeMzs81vAOoz8qVK/XMM88UaidOnAiPLXeCSXEH\nfVRbunRpeM1o/d9olm2UYVK6AFEuR3gTBoAGEcIA0CBCGAAaRAgDQIMIYQBoUC2jI6ampnTz5s1C\nrfx5xvLly5Na1Iv4+OOPJ7Unn4xXxoumKEe7Om/atCk8v9zTmlvDFEB9li1bpl27dhVqGzZsCI+N\nfkej6cgnT55MauVlCmZEI7qm10MuyO0AUl7ugGnLAPBHiBAGgAYRwgDQIEIYABpkucbiu7qo2QVJ\nxyX1SErnCc6/uu+zw93jXjwAtVgsOVJLCH9ycbN+d++r7QZtvg+A9lvoOUJzBAA0iBAGgAbVHcL7\nar5+u+8DoP0WdI7U2iYMAJgdzREA0KDaQtjMvmZmh83sqJl9u8b7DJjZITN728z667oPgPZbDDlS\n1zjhTklHJD0v6ZSkg5Jecvf3a7jXgKQ+d2/HOEIAbbJYcqSuN+E9ko66+zF3H5P0Q0nfrOleABam\nRZEjdYXwVkm3rxl3arpWB5f0qpm9aWZ7a7oHgPZbFDlSy3rCbfasuw+aWa+k18zsA3c/0PRDAfhU\naSxH6noTHpS0/bbP26Zr887dB6f/PC/pFX38TxgAn36LIkfqCuGDknab2S4z65L0oqSfzPdNzGyl\nma2e+bukFyS9O9/3AdCIRZEjtTRHuPuEmb0s6ZeSOiV9z93fq+FWmyW9Mr3lyBJJ33f3X9RwHwBt\ntlhyhBlzANAgZswBQIMIYQBoECEMAA0ihAGgQYQwADSIEAaABhHCANAgQhgAGvT/AX1WhJR8pJZm\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtB9IWiesrVo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}