Computer_Vision_FashionMNIST_Dataset.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7P3oNU7PK97J"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "#torch vision\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "\n",
        "# Import matplotlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "djZbhN63OMX7",
        "outputId": "d6bc8dc3-8450-4c8a-c3eb-0e78c379496b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26421880/26421880 [00:00<00:00, 120138107.18it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 5469904.67it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 4422102/4422102 [00:00<00:00, 61283111.49it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 7758633.49it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "#setup traning data\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root=\"data\",#where to dwonload\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfK57RnwRHbQ",
        "outputId": "f020aed4-9119-4d23-8e34-69ddabeec0ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_data), len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnnOE92PRlgy",
        "outputId": "19c5e9c9-a11e-4040-c078-fccd388288da"
      },
      "outputs": [
        {
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              " 9)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image, label = train_data[0]\n",
        "image, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLp3FxyFRwGn",
        "outputId": "aac01382-3b70-48cc-8035-1acc5ca20e99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmvUueCiSPa1",
        "outputId": "dc3eb588-3dbc-41b4-e11b-5a4ea49775bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'T-shirt/top': 0,\n",
              " 'Trouser': 1,\n",
              " 'Pullover': 2,\n",
              " 'Dress': 3,\n",
              " 'Coat': 4,\n",
              " 'Sandal': 5,\n",
              " 'Shirt': 6,\n",
              " 'Sneaker': 7,\n",
              " 'Bag': 8,\n",
              " 'Ankle boot': 9}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "3n1aOPh5Sl_1",
        "outputId": "1f354745-6455-412f-8978-806cb59f6196"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "image Shape: torch.Size([1, 28, 28])\n",
            "Label Ankle boot\n"
          ]
        }
      ],
      "source": [
        "print(f\"image Shape: {image.shape}\")\n",
        "print(f\"Label {class_names[label]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "kqjiYLt5S3aL",
        "outputId": "36f4c485-4338-4811-e492-37008814c68a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image shape: torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c0cb5254490>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image, label = train_data[0]\n",
        "print(f\"Image shape: {image.shape}\")\n",
        "plt.imshow(image.squeeze())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "LPqL-ndFUvPa",
        "outputId": "1ff34d54-b18d-4113-e15a-2da03cce5a28"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(image.squeeze(), cmap='gray')\n",
        "plt.title(class_names[label]);\n",
        "plt.axis(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "OmgAdgteVbTI",
        "outputId": "25084f2e-9ca9-4b31-8ae3-0282046758fc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize=(9, 9))\n",
        "rows, cols = 4, 4\n",
        "for i in range(1, rows*cols+1):\n",
        "  random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "  img, label = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols, i)\n",
        "  plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "  plt.title(class_names[label])\n",
        "  plt.axis(False);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYZsm4IHWFVB",
        "outputId": "0f608096-75a4-4b8b-d95b-0d991c70ef26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7c0cb511c730>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7c0cb511fe80>)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Prepare Data Loader\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "#Turn datasets into itrable\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False)\n",
        "train_dataloader,test_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "PN8yGGj4U3qh",
        "outputId": "4735dfb3-9db8-4a0b-fecd-412beb6b2314"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DataLoaders: (<torch.utils.data.dataloader.DataLoader object at 0x7c0cb511c730>, <torch.utils.data.dataloader.DataLoader object at 0x7c0cb511fe80>)\n",
            "Lennght of train dataloader: 1875\n",
            "Lenght of test dataloader: 313\n"
          ]
        }
      ],
      "source": [
        "# Cheak what we created\n",
        "print(f\"DataLoaders: {train_dataloader, test_dataloader}\")\n",
        "print(f\"Lennght of train dataloader: { len(train_dataloader)}\")\n",
        "print(f\"Lenght of test dataloader: {len(test_dataloader)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zw8g1bKdYGur"
      },
      "outputs": [],
      "source": [
        "train_features_batch , train_labels_batch = next(iter(train_dataloader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "Zw0e9Cj4VIo_",
        "outputId": "ddf15afb-6c1a-4afe-de76-58e21134e1f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image size: torch.Size([1, 28, 28])\n",
            "label: 8, label size: torch.Size([])\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#torch.manual_seed(42)\n",
        "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)\n",
        "print(f\"Image size: {img.shape}\")\n",
        "print(f\"label: {label}, label size: {label.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlGt67tvXkj-",
        "outputId": "42f3666e-635a-4d3d-e098-20986dd4d613"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape before flatten: torch.Size([1, 28, 28])\n",
            "shape after flatting: torch.Size([1, 784])\n"
          ]
        }
      ],
      "source": [
        "# 3 Model0\n",
        "\n",
        "flatten_model = nn.Flatten()\n",
        "\n",
        "x = train_features_batch[0]\n",
        "\n",
        "\n",
        "\n",
        "output = flatten_model(x)\n",
        "\n",
        "print(f\"Shape before flatten: {x.shape}\")\n",
        "print(f\"shape after flatting: {output.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FW-LtLb9avaX"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "class FashionMNISTV0(nn.Module):\n",
        "  def __init__(self, input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape:int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=input_shape,\n",
        "                  out_features=hidden_units),\n",
        "        nn.Linear(in_features=hidden_units,\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dwNg9xedWuT",
        "outputId": "a716c1b7-bbb7-42ca-a804-da904a9bd9c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "FashionMNISTV0(\n",
              "  (layer_stack): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=784, out_features=10, bias=True)\n",
              "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "model_0 = FashionMNISTV0(\n",
        "    input_shape=28*28,\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names)\n",
        ").to('cuda')\n",
        "model_0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "SE-xTJaSeAuo",
        "outputId": "ebac18a6-7dd9-4113-9641-1bd06192a8ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dummy_x = torch.rand([1, 1, 28, 28])\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXR3aTjhevSZ",
        "outputId": "1018dd16-0c4f-464e-fcb6-2b30b71171ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('layer_stack.1.weight',\n",
              "              tensor([[ 0.0273,  0.0296, -0.0084,  ..., -0.0142,  0.0093,  0.0135],\n",
              "                      [-0.0188, -0.0354,  0.0187,  ..., -0.0106, -0.0001,  0.0115],\n",
              "                      [-0.0008,  0.0017,  0.0045,  ..., -0.0127, -0.0188,  0.0059],\n",
              "                      ...,\n",
              "                      [-0.0116,  0.0273, -0.0344,  ...,  0.0176,  0.0283, -0.0011],\n",
              "                      [-0.0230,  0.0257,  0.0291,  ..., -0.0187, -0.0087,  0.0001],\n",
              "                      [ 0.0176, -0.0147,  0.0053,  ..., -0.0336, -0.0221,  0.0205]],\n",
              "                     device='cuda:0')),\n",
              "             ('layer_stack.1.bias',\n",
              "              tensor([-0.0093,  0.0283, -0.0033,  0.0255,  0.0017,  0.0037, -0.0302, -0.0123,\n",
              "                       0.0018,  0.0163], device='cuda:0')),\n",
              "             ('layer_stack.2.weight',\n",
              "              tensor([[ 0.0614, -0.0687,  0.0021,  0.2718,  0.2109,  0.1079, -0.2279, -0.1063,\n",
              "                        0.2019,  0.2847],\n",
              "                      [-0.1495,  0.1344, -0.0740,  0.2006, -0.0475, -0.2514, -0.3130, -0.0118,\n",
              "                        0.0932, -0.1864],\n",
              "                      [ 0.2488,  0.1500,  0.1907,  0.1457, -0.3050, -0.0580,  0.1643,  0.1565,\n",
              "                       -0.2877, -0.1792],\n",
              "                      [ 0.2305, -0.2618,  0.2397, -0.0610,  0.0232,  0.1542,  0.0851, -0.2027,\n",
              "                        0.1030, -0.2715],\n",
              "                      [-0.1596, -0.0555, -0.0633,  0.2302, -0.1726,  0.2654,  0.1473,  0.1029,\n",
              "                        0.2252, -0.2160],\n",
              "                      [-0.2725,  0.0118,  0.1559,  0.1596,  0.0132,  0.3024,  0.1124,  0.1366,\n",
              "                       -0.1533,  0.0965],\n",
              "                      [-0.1184, -0.2555, -0.2057, -0.1909, -0.0477, -0.1324,  0.2905,  0.1307,\n",
              "                       -0.2629,  0.0133],\n",
              "                      [ 0.2727, -0.0127,  0.0513,  0.0863, -0.1043, -0.2047, -0.1185, -0.0825,\n",
              "                        0.2488, -0.2571],\n",
              "                      [ 0.0425, -0.1209, -0.0336, -0.0281, -0.1227,  0.0730,  0.0747, -0.1816,\n",
              "                        0.1943,  0.2853],\n",
              "                      [-0.1310,  0.0645, -0.1171,  0.2168, -0.0245, -0.2820,  0.0736,  0.2621,\n",
              "                        0.0012, -0.0810]], device='cuda:0')),\n",
              "             ('layer_stack.2.bias',\n",
              "              tensor([-0.0087,  0.1791,  0.2712, -0.0791,  0.1685,  0.1762,  0.2825,  0.2266,\n",
              "                      -0.2612, -0.2613], device='cuda:0'))])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_0.state_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "81eiacMKf78Z",
        "outputId": "fa8310b2-0fcb-4d0e-911b-97921992527f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(f\"helper_function.py already exist, skiping download...\")\n",
        "else:\n",
        "  print(\"Downloading...\")\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vKjDFWrPiHhY"
      },
      "outputs": [],
      "source": [
        "# Import accurac metric\n",
        "from helper_functions import accuracy_fn\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "YWpI1gU3ijgC"
      },
      "outputs": [],
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start:float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} secounds\")\n",
        "  return total_time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "AomWa6GBkWih",
        "outputId": "c55c85de-942e-4d3a-b1f1-294871e25249"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train time on cuda: 0.000 secounds\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "6.538100001307612e-05"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "start_time = timer()\n",
        "end_time = timer()\n",
        "\n",
        "print_train_time(start=start_time, end=end_time, device=\"cuda\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557,
          "referenced_widgets": [
            "cd30a5fc747a4019b322728db4b5f652",
            "8363710fdf774a58a1c281cd80c2f9f1",
            "bbd89470fcd44bcc9319429308127155",
            "8f2f92ca234240d28ffc5479049b6e09",
            "ce50be3859b64deb91d88f8e351a2976",
            "abb065ea1974490aae61a31a97a74d3e",
            "964b5acaf36641bab660639d6c7eb1b4",
            "0b146d6360e04a42b9e7cff58cdd419a",
            "e07a1300b83144d0b6f8c0beef829614",
            "985b9bd0ac614aaf953d7475ef137446",
            "e0006127fd694955838c4d2ecc2a0572"
          ]
        },
        "id": "g7gatUgNknWQ",
        "outputId": "3205f845-273e-4f32-93ac-319caa8365d6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd30a5fc747a4019b322728db4b5f652",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 0\n",
            "----------\n",
            "Looked at 0/60000sapmles.\n",
            "Looked at 12800/60000sapmles.\n",
            "Looked at 25600/60000sapmles.\n",
            "Looked at 38400/60000sapmles.\n",
            "Looked at 51200/60000sapmles.\n",
            "\n",
            "Train_loss: 0.5904 | Test_loss:0.5095, Testacc: 82.04\n",
            "Epochs: 1\n",
            "----------\n",
            "Looked at 0/60000sapmles.\n",
            "Looked at 12800/60000sapmles.\n",
            "Looked at 25600/60000sapmles.\n",
            "Looked at 38400/60000sapmles.\n",
            "Looked at 51200/60000sapmles.\n",
            "\n",
            "Train_loss: 0.4763 | Test_loss:0.4799, Testacc: 83.20\n",
            "Epochs: 2\n",
            "----------\n",
            "Looked at 0/60000sapmles.\n",
            "Looked at 12800/60000sapmles.\n",
            "Looked at 25600/60000sapmles.\n",
            "Looked at 38400/60000sapmles.\n",
            "Looked at 51200/60000sapmles.\n",
            "\n",
            "Train_loss: 0.4550 | Test_loss:0.4766, Testacc: 83.43\n",
            "Train time on cuda:0: 30.755 secounds\n"
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "torch.manual_seed(42)\n",
        "train_time_start_on_cpu = timer()\n",
        "\n",
        "\n",
        "epochs = 3\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epochs: {epoch}\\n----------\")\n",
        "  ###Training\n",
        "  train_loss = 0\n",
        "  # Add a loop to loop throgh thr traning batches\n",
        "  for batch, (X, y) in enumerate(train_dataloader):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    model_0.train()\n",
        "    y_pred = model_0(X)\n",
        "\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "\n",
        "    if batch %400 == 0:\n",
        "      print(f\"Looked at {batch * len(X)}/{len(train_dataloader.dataset)}sapmles.\")\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test, y_test in test_dataloader:\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "      test_pred = model_0(X_test)\n",
        "\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      test_acc += accuracy_fn(y_true=y_test, y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "  print(f\"\\nTrain_loss: {train_loss:.4f} | Test_loss:{test_loss:.4f}, Testacc: {test_acc:.2f}\")\n",
        "\n",
        "train_time_end_on_cpu = timer()\n",
        "total_train_time_model_0 = print_train_time(start=train_time_start_on_cpu,\n",
        "                                            end=train_time_end_on_cpu,\n",
        "                                            device=str(next(model_0.parameters()).device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ydjOipwaprkP",
        "outputId": "e4e5b547-7561-4587-e52f-1cc6b2caf1ea"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda:0'"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "str(next(model_0.parameters()).device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436,
          "referenced_widgets": [
            "f6dca447d9774617be5f4b96474203b0",
            "4688c21473234322b6cbd1d6ebc4c2af",
            "c15072b6cc2749c09ed66308025dcd4f",
            "6e6385de2d8846bc8ac40d764289a4f2",
            "c6da1fbfd53645d1868df9bf7bff0efa",
            "042e86c7bf9d4ca590eaeddb8b091377",
            "2dd7e0bb26d349a28ec9471e50c24885",
            "f69a51dc9a564b7eb67a781b41b69b27",
            "a9afd106d85e4d7ea093b592fc4367b4",
            "411d3834759b4b68972f6a7e278adb30",
            "ea2994723ba2485ba38a8ae0be5ca2d3"
          ]
        },
        "id": "UpERPls7odAV",
        "outputId": "4216cf91-25c5-4105-cfbc-f8ae52294b31"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6dca447d9774617be5f4b96474203b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "RuntimeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-65-322b76d8cefa>\u001b[0m in \u001b[0;36m<cell line: 29>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m# calculate model 0 result on test datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m model_2_result = eval_model = eval_model(model=model_2,\n\u001b[0m\u001b[1;32m     30\u001b[0m                                          \u001b[0mdata_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m                                          \u001b[0mloss_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-65-322b76d8cefa>\u001b[0m in \u001b[0;36meval_model\u001b[0;34m(model, data_loader, loss_fn, accuracy_fn, device)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m       \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m       \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m       \u001b[0;31m#Accumalate loss and acc pre batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-31-6e9b14276784>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     16\u001b[0m     )\n\u001b[1;32m     17\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (32x10 and 784x10)"
          ]
        }
      ],
      "source": [
        "## Make predictions\n",
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn,\n",
        "               device: torch.device= device):\n",
        "  loss, acc= 0, 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X, y in tqdm(data_loader):\n",
        "      X, y = X.to(device), y.to(device)\n",
        "      y_pred = model(X)\n",
        "\n",
        "      #Accumalate loss and acc pre batch\n",
        "      loss += loss_fn(y_pred, y)\n",
        "      acc += accuracy_fn(y_true=y,\n",
        "                        y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "      #Scale the loss and acc find avg loss/acc pre batch\n",
        "      loss /= len(data_loader)\n",
        "      acc /= len(data_loader)\n",
        "\n",
        "  return {\"model_name\": model.__class__.__name__,\n",
        "          \"model_loss\": loss.item(),\n",
        "          \"model_acc\": acc}\n",
        "# calculate model 0 result on test datasets\n",
        "\n",
        "model_2_result = eval_model = eval_model(model=model_2,\n",
        "                                         data_loader=test_dataloader,\n",
        "                                         loss_fn=loss_fn,\n",
        "                                         accuracy_fn=accuracy_fn,\n",
        "                                         device=device)\n",
        "\n",
        "model_2_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AN5rS5wpqaob",
        "outputId": "d4ced832-f328-446e-d842-b4d4067fa862"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fri Jan  5 11:33:55 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P0              27W /  70W |    159MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oflQcMur2IU",
        "outputId": "8273a607-a0a3-436c-ba24-26b03d23a2d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.cuda.is_available()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tikuV_WZsIcj"
      },
      "outputs": [],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Is49hl_sdXS"
      },
      "outputs": [],
      "source": [
        "# Bulding non-linear model\n",
        "class FashionMNISTModelV1(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "       nn.Flatten (),\n",
        "       nn.Linear(in_features=input_shape,\n",
        "                 out_features=hidden_units),\n",
        "       nn.ReLU(),\n",
        "       nn.Linear(in_features=input_shape,\n",
        "                 out_features=output_shape),\n",
        "       nn.ReLU()\n",
        "    )\n",
        "  def forward(self, x:torch.Tensor):\n",
        "    return self.layer_stack(x)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEYaIN4B4t9_",
        "outputId": "56de28ea-db54-46be-f75c-718a328d111a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "next(model_0.parameters()).device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "6J_1fwEY49G2",
        "outputId": "212d4f11-ce35-42b0-aa30-c3ce5e207a8e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda:0'"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "model_1 = FashionMNISTModelV1(input_shape=784,\n",
        "                             hidden_units=10,\n",
        "                             output_shape=len(class_names)).to(device)\n",
        "str(next(model_1.parameters()).device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1xQnGlqq5Tw2"
      },
      "outputs": [],
      "source": [
        "from helper_functions import accuracy_fn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model_1.parameters(),\n",
        "                             lr=0.1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UhNGRuV_KhXO"
      },
      "outputs": [],
      "source": [
        "  test_loss, test_acc = 0, 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test, y_test in test_dataloader:\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "      test_pred = model_0(X_test)\n",
        "\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      test_acc += accuracy_fn(y_true=y_test, y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "    test_acc /= len(test_dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R7jVZbIz9sAv"
      },
      "outputs": [],
      "source": [
        "# Functianizing traning and evaluation loop\n",
        "\n",
        "def train_step(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               accuracy_fn,\n",
        "               device: torch.device = device):\n",
        "  train_loss, train_acc = 0, 0\n",
        "  #Put model into traning mode\n",
        "  model.train()\n",
        "\n",
        "  # Add a loop to loop throgh thr traning batches\n",
        "  for batch, (X, y) in enumerate(data_loader):\n",
        "    #Put data to target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    # Forward Pass\n",
        "    y_pred = model(X)\n",
        "\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss\n",
        "    train_acc += accuracy_fn(y_true=y,\n",
        "                             y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "\n",
        "\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "  print(f\"Train Loss: {train_loss: .5f} | Train_acc: {train_acc:.5f}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X21I2P8RK-5O"
      },
      "outputs": [],
      "source": [
        "def test_step(model: torch.nn.Module,\n",
        "              dataloader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device= device):\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X, y in dataloader:\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      test_pred = model(X)\n",
        "\n",
        "      test_loss += loss_fn(test_pred, y)\n",
        "\n",
        "      test_acc += accuracy_fn(y_true=y,\n",
        "                              y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "    test_loss /= len(dataloader)\n",
        "    test_acc /= len(dataloader)\n",
        "    print(f\"Test Loss: {test_loss:.5f} | Test Acc: {test_acc:.5f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Wpork2nMNNqm",
        "outputId": "044be28e-c812-4f24-acda-46819135ee0f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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s3LmT+++/n88//5xhw4YRGxtLeno6y5Yt47///W+J80dK8uKLLzJkyBC6d+/OHXfc4XyMOSIiotj+UL169eK5555j7969LglL7969effdd4mPj6d+/four7n44ot5++23efbZZ2natCmxsbH069fPrdg8Fbe7Lr/8cp5++mluu+02evTowYYNG/j000+LzYNyV8OGDd2K5c033+TSSy+lXbt23HXXXTRu3JjDhw+zYsUK9u3b51wnxt34Bg0aRO3atenZsye1atXizz//5I033mDo0KHOOVY33ngjjz/+OFdffTUPPPAAeXl5vP322zRv3tztSbrnex+LAHr8WqqHuXPnGrfffrvRsmVLIzQ01PD39zeaNm1q3H///cbhw4ddzv3++++N9u3bG4GBgUZ8fLwxffp048MPPzQAY+fOnc7zTj4qezrAGD16tEvdyceXX3zxRWfdyJEjjZCQEGP79u3GoEGDjODgYKNWrVrGpEmTij0iy2mPXxuGYRw+fNgYPXq0ERcXZ/j5+Rm1a9c2+vfvb7z33ntutUlRUZExa9Yso1+/fkZUVJTh6+trREdHG/379zfeeecd4/jx42eM/1Q//vij0bNnTyMoKMgIDw83hg0bZmzatKnYeVlZWYaPj48RFhZmFBUVOes/+eQTAzBuueWWYq85dOiQMXToUCMsLMwAnI9il/QYsmH89bj0qY+6l8aduMv6+PUjjzxi1KlTxwgKCjJ69uxprFixwkhISCjxEfLTlXZPnaq0z719+3bj1ltvNWrXrm34+fkZ9erVMy6//HLjq6++KnN87777rtG7d2+jZs2aRkBAgNGkSRPj0UcfNTIzM13ec8GCBUbbtm0Nf39/o0WLFsYnn3xS6uPXp/8/cdL53sciFsPwwow4EWHUqFF89dVXxZ4eEhER92mOjIiIiFRaSmRERESk0lIiIyIiIpWW5siIiIhIpaUeGREREam0lMiIiIhIpVXlF8RzOBwcOHCAsLCwclnqXERERDzPMAyys7OpW7euc9f2klT5RObAgQPFdiIWERGRymHv3r3FVvw+VZVPZE4up713717Cw8M9dl2bzcaCBQsYNGgQfn5+HrtuVaX2cp/ayn1qK/eprdyntnJfebZVVlYWcXFxzt/jpanyiczJ4aTw8HCPJzLBwcGEh4frRneD2st9aiv3qa3cp7Zyn9rKfReirc42LUSTfUVERKTSUiIjIiIilZYSGREREam0qvwcGXfZ7XZsNpvb59tsNnx9fcnPz8dut5djZFXD2drLz88PHx8fL0QmIiKVWbVPZAzD4NChQ2RkZJT5dbVr12bv3r1an8YN7rRXjRo1qF27ttpTRETcVu0TmZNJTGxsLMHBwW7/EnU4HOTk5BAaGnrGhXrEdKb2MgyDvLw8UlNTAahTp443QhQRkUqoWicydrvdmcTUrFmzTK91OBwUFhYSGBioRMYNZ2uvoKAgAFJTU4mNjdUwk4iIuKVa/wY+OScmODjYy5EI/PV9KMtcJRERqd6qdSJzkuZkVAz6PoiISFkpkREREZFKy6uJzOLFixk2bBh169bFYrHw3XffOY/ZbDYef/xx2rVrR0hICHXr1uXWW2/lwIED3gtYzsvSpUvx8fEp8xNiIiIipfFqIpObm0uHDh148803ix3Ly8tj9erVTJw4kdWrV/PNN9+QkpLCFVdc4YVIK55Ro0ZhsViYNm2aS/13332nIRoREak2vPrU0pAhQxgyZEiJxyIiIli4cKFL3RtvvEHXrl3Zs2cPDRo0uBAhVmiBgYFMnz6de+65h8jISI9cs7CwEH9/f49cS0REqjjDQWzmOjBK/l1+IVSqx68zMzOxWCzUqFGj1HMKCgooKChwlrOysgBzqOr0p2FsNhuGYeBwOHA4HGWKxTAM53/L+lpPMAyD/v37s337dp5//nmmT58O4Izl5H+//vprJk+ezLZt26hTpw5jxozh4Ycfdl6ncePG3H777WzdupX/+7//4+qrryYhIYGHH36Yf/3rXzz66KPs3buXIUOG8NFHH/Hll18yZcoUMjMzufnmm5kxY4bzUemPP/6Y119/nZSUFEJCQujbty+vvPIKsbGxzvY6GVtJbeZwODAMA5vNVq0fvz55n+rprbNTW7lPbeU+tZV7LPt/xzp/PN0PrqFgU2tsba706PXdbf9Kk8jk5+fz+OOP87e//Y3w8PBSz5s6dSpTpkwpVr9gwYJij1n7+vpSu3ZtcnJyKCwsxDAM8m1lS0qOH8ko0/mlCfSzlmlI6GQS9sQTT3DXXXcxatQo6tWrx/HjxwEzgVu7di033ngj48aN4+qrr+a3337jH//4B8HBwYwYMQIwk4eXXnqJxx57jEWLFgGwYsUK8vLymDlzJu+//z7Z2dnceuutXHnllURERPD555+za9cuRo4cyUUXXcQ111wDQHZ2No8//jjNmjUjLS2NJ554gltuuYUvv/zSJfbs7OwS15IpLCzk+PHjLF68mKKionNqx6rk9B5JKZ3ayn1qK/eprUoWWHiU1ge+IO7YcgCKrIFsWr2cPbv9PPo+eXl5bp1nMU79U9mLLBYL3377LVdddVWxYzabjeHDh7Nv3z6Sk5PPmMiU1CMTFxdHenp6sdfl5+ezd+9e4uPjCQwMJK+wiLaTvXPjbpw8kGB/9/PK2267jYyMDL799lt69uxJq1at+OCDD/juu+8YPnw4drudm2++mbS0NObPn+983eOPP86cOXPYsGEDYPbIdOzYkW+++cZ5zuzZs7njjjvYsmULTZo0AeDee+/lk08+4eDBg4SGhgLm0GB8fDxvv/12iTH+/vvvdOvWjczMTEJCQpg7dy7Dhg3jyJEjJfaq5efns2vXLuLi4ggMDHS7Laoam83GwoULGThwIH5+nv3BUNWordyntnKf2qoUtjysv7yJdcXrWGx5GFiwt7uRH43u9L7sOo+3VVZWFtHR0WRmZp7x936F75Gx2Wxcf/317N69m59//vmMHwYgICCAgICAYvV+fn7FGtlut2OxWLBarc4vbynr+1ssFmfs06dPp1+/fjz66KPOa1itVjZv3syVV17pct1LL72UV199FcMwnMM3Xbp0cTnHarUSHBxMs2bNnHW1a9cmPj7epf1r165NWlqa87WrVq1i8uTJrFu3jmPHjjmHj/bt20fLli3P+lmtVrNXqqTvVXWkdnCf2sp9aiv3qa1OMAzY+DUsnARZ+8y6uEuwDJmGEdOWgjlzyqWt3L1ehU5kTiYxW7duJSkpqczbCJRVkJ8Pm55OdOtch8NBdlY2YeFhHkmAgvzOfU5I7969SUxMZPz48YwaNarMrw8JCSlWd/oNdDLBOL3uZLKSm5tLYmIiiYmJfPrpp8TExLBnzx4SExMpLCwsc0wiIlIBHFgDc8fB3l/MckQcDJwCba4BiwUqwDwiryYyOTk5bNu2zVneuXMna9euJSoqijp16nDttdeyevVqfvjhB+x2O4cOHQIgKiqqXJ6ssVgsbg/vOBwOivx9CPb3rRB7LU2bNo2OHTvSokULZ12rVq1YtmyZy3nLli2jefPmHp9Mu3nzZo4cOcK0adOIi4sDzKElERGphLIPw09Pw9pPAQP8guHSh6DH/eAX5O3oXHg1kfn999/p27evs3zyaZqRI0cyefJkvv/+ewA6duzo8rqkpCT69OlzocKsFNq1a8dNN93Ea6+95qx75JFH6NKlC8888ww33HADK1as4I033uCtt97y+Ps3aNAAf39/Xn/9df7+97+zceNGnnnmGY+/j4iIlCNbPvzyFix5GQpzzLp218OAyRBRz6uhlcariUyfPn0401zjCjIPudJ4+umn+c9//uMsd+rUiS+++IKnnnqKZ555hjp16vD000+f0/DT2cTExDB79mwmTJjAa6+9RqdOnXjppZe0gKGISGVgGLD5B5j/BGTsNuvqXQyDp0NcF+/GdhYVeo6MlG727NnF6uLj412e2AIYPnw4w4cPL/U6u3btKlY3atSoYsnO5MmTmTx58hlj+Nvf/sbf/vY3l7qTyajD4eDSSy/FbrdXiKE4ERE54dBGmDcOdi0xy2F1zB6YdtdDJfh5rURGRESkOspNh5+fhdUfgeEA30BzDkzPsRAQ6u3o3KZERkREpDopKoTf3oNFL0BBplnX+ioY+DRENvRqaOdCiYyIiEh1YBiwdQHMnwBHTjwxXLs9DJ4G8T29G9t5UCIjIiJS1aVuNhOY7T+Z5ZAY6P8UdLwJrJV7bzslMiIiIlVV3lFIngYrPwDDDlY/6H4f9PoHBJ55pfzKQomMiIhIVWMvglWzIOk5OH7MrGsxFAY9AzWbeDc2D1MiIyIiUpVs/xnmTYC0P81ybGsYPBUa9/FqWOVFiYyIiEhVcGS7uaDdlrlmOSgK+j0BnUaBT9X9dV91P5mIiEh1kJ9pPkr967vgsIHVF7rcBX0eh6BIb0dX7pTIiIiIVEYOO6z5GH56BvLSzbqmAyHxeYhp7t3YLqCKv/awlGjUqFFYLJZiX6fuJl5WycnJWCwWMjIyPBeoiIh43q6l8G4C/PdBM4mp2Qxu+gpu/qpaJTGgHplKbfDgwcyaNculLiYmxkvRiIhIuTu2CxZMhD+/N8uBEdBnPHS5E3z8vBqat6hHphILCAigdu3aLl+vvvoq7dq1IyQkhLi4OO677z5ycnKcr9m9ezfDhg0jMjKSkJAQ2rRpw5w5c9i1axd9+/YFIDIyEovFUi67ZIuIyDkoyIYfp8AbXc0kxmKFznfA/WvgknurbRID6pFxZRhgy3PvXIfDPLfQxzO7g/oFg8Vy3pexWq289tprNGrUiB07dnDffffx2GOP8dZbbwEwevRoCgsLWbx4MSEhIWzatInQ0FDi4uL4+uuvGT58OCkpKYSHhxMUFHTe8YiIyHlwOGD952YSk3PIrGuUYD5OXauNd2OrIJTInMqWB8/XdetUK1DDk+894QD4h5TpJT/88AOhoX/tUDpkyBC+/PJLZzk+Pp5nn32Wv//9785EZs+ePQwfPpx27doB0LhxY+f5UVFRAMTGxlKjRo1z/SQiIuIJe36FeePgwGqzHNkIEp+DFpd55A/fqkKJTCXWt29f3n77bWc5JCSEH3/8kalTp7J582aysrIoKioiPz+fvLw8goODeeCBB7j33ntZsGABAwYMYPjw4bRv396Ln0JERFxk7oOFk2DjV2bZPwx6/8McQvIN8G5sFZASmVP5BZs9I25wOBxkZWcTHhaG1VNDS2UUEhJC06ZNneVdu3Zx+eWXc++99/Lcc88RFRXF0qVLueOOOygsLCQ4OJg777yTxMRE/ve//7FgwQKmTp3Kyy+/zP3333/+n0FERM5dYR4sfw2WzoSi44AFLrrZ3NwxNNbb0VVYSmROZbG4P7zjcICf3TzfE4mMB6xatQqHw8HLL7/sTK6++OKLYufFxcXx97//nb///e+MHz+e999/n/vvvx9/f38A7Hb7BY1bRKRaMwzY+LXZC5O1z6xr0MOcB1O3o1dDqwyUyFQhTZs2xWaz8frrrzNs2DCWLVvGO++843LO2LFjGTJkCM2bN+fYsWMkJSXRqlUrABo2bIjFYuGHH37gsssuIygoyGUOjoiIeNj+VTBvPOz91SxHNIBBT0PrqzQPxk0VoytBPKJDhw7MmDGD6dOn07ZtWz799FOmTp3qco7dbmf06NG0atWKwYMH07x5c+dE4Hr16jFlyhTGjRtHrVq1GDNmjDc+hohI1Zd9CL69F97vZyYxfsHQ90kY8xu0uVpJTBmoR6aSmj17don1Dz30EA899JBL3S233OL89+uvv37G606cOJGJEyeed3wiIlICWz6seAOWzABbrlnX/kYYMAnC3XtqVlwpkRERESlvhmEuZLfgScjYY9bV7wKDp0H9zt6NrZJTIiMiIlKeDq4358HsXmqWw+rCwCnQ9toK87BIZaZERkREpDzkpMHPz8DqfwEG+AZCjwfg0rFlXgBVSqdERkRExJOKCuG3d2HRC1CQZda1ucbshanRwLuxVUFKZERERDzBMGDLPJj/BBzdbtbV6QCDp0PD7t6NrQpTIiMiInK+Uv8058HsSDLLIbHmirwdb9I8mHKmREZERORc5R2FpOfh9w/BsIOPP1xyH/R6BALDvR1dtaBERkREpKzsNjN5SXoe8jPMupaXw6BnIKqxV0OrbpTIiIiIlMW2H2HeBEhPMcuxbcx9kRoneDeuakoDd1WUxWLhu+++83YYIiJVR/o2+PR6+GS4mcQE14ShM+CexUpivEiJTCWVlpbGvffeS4MGDQgICKB27dokJiaybNkyb4cmIlK1HM8we2De6gZb54PVFy4ZDfevhi53gI8GN7xJrV9JDR8+nMLCQj766CMaN27M4cOH+emnnzhy5Ii3QxMRqRocdvj9Y/j5Wcg78bO12SBIfB6im3k3NnFSj0wllJGRwZIlS5g+fTp9+/alYcOGdO3alfHjx3PFFVeU+JoNGzbQr18/goKCqFmzJnfffTc5OTkAbNy4EavVSlpaGgBHjx7FarVy4403Ol//7LPPcumll5b/hxMRqQCiszfh+89+8MNDZhIT3Rxu+hpu+lJJTAWjHplTGIbB8aLjbp3rcDg4XnQcX5svVg+sERDkG4TFzW3bQ0NDCQ0N5bvvvuOSSy4hICDgjOfn5uaSmJhI9+7dWblyJampqdx5552MGTOG2bNn06ZNG2rWrMmiRYu49tprWbJkibN80qJFi+jTp8/5fEQRkYrv6E585j9Bz23/M8uBEdBnwokhJD/vxiYlUiJziuNFx+n2WTevvPevI34l2C/YrXN9fX2ZPXs2d911F++88w6dOnUiISGBG2+8kfbt2xc7/7PPPiM/P59//etfhISY+3u88cYbDBs2jOnTp1OrVi169+5NcnIy1157LcnJydx222188MEHbN68mSZNmrB8+XIee+wxj35mEZEKoyAblrwMK97Eai/EwILj4tvx6f8kBEd5Ozo5Aw0tVVLDhw/nwIEDfP/99wwePJjk5GQ6derE7Nmzi537559/0qFDB2cSA9CzZ08cDgcpKebjgwkJCSQnJwNm70u/fv2cyc3KlSux2Wz07NnzQnw0EZELx+GANZ/A6xfD0lfAXoijUQJJLZ/DMXi6kphKQD0ypwjyDeLXEb+6da7D4SA7O5uwsDCPDS2VVWBgIAMHDmTgwIFMnDiRO++8k0mTJjFq1KgyX6tPnz6MHTuWrVu3smnTJi699FI2b95McnIyx44do3PnzgQHu9djJCJSKexeAfPGwcG1ZjmqMQx6DnvjAWTPnevV0MR9SmROYbFY3B7ecTgcFPkWEewX7JFExhNat25d4toxrVq1Yvbs2eTm5jp7ZZYtW4bVaqVFixYAtGvXjsjISJ599lk6duxIaGgoffr0Yfr06Rw7dkzzY0Sk6sjYCwufgj++McsB4dD7Ueh2D/gGgM3m3fikTCrGb2ApkyNHjtCvXz8++eQT1q9fz86dO/nyyy954YUXuPLKK4udf9NNNxEYGMjIkSPZuHEjSUlJ3H///dxyyy3UqlULMJO43r178+mnnzqTlvbt21NQUMBPP/1EQoIWexKRSq4w19xS4I3OJ5IYC3QaCfevgp4PmEmMVDrqkamEQkND6datG6+88grbt2/HZrMRFxfHXXfdxYQJE4qdHxwczPz583nwwQfp0qULwcHBDB8+nBkzZricl5CQwHfffedMZKxWK7179+Z///uf5seISOVlGLDhS1g4CbIPmHUNe5rbCtTp4N3Y5LwpkamEAgICmDp1KlOnTi31HMMwXMrt2rXj559/PuN1x44dy9ixY13qtM2BiFRq+1bBvMdh30qzXKMBDHwGWl8Jbi55IRWbEhkREal6sg7CT1Ng3b/Nsl8I9HoYuo8Bv0DvxiYepURGRESqDttxWPEGLHkFbLlmXYcR0P8pCK/j3dikXCiRERGRys8wYNN3sOApyNxj1tXvCoOnQf2LvRqalC+vPrW0ePFihg0bRt26dbFYLMXmYxiGwVNPPUWdOnUICgpiwIABbN261TvBiohIxXRwHcweCl+OMpOY8HpwzQdwxwIlMdWAVxOZ3NxcOnTowJtvvlni8RdeeIHXXnuNd955h19//ZWQkBASExPJz8/3aBynT4wV79D3QUTKJCcVvr8f3k2A3cvANwgSxsGYldD+Ok3mrSa8OrQ0ZMgQhgwZUuIxwzCYOXMmTz75pHNtlH/961/UqlWL7777zmVn5nPl52duAJaXl0dQUNlX1hXPysvLA/76voiIlKioAH59Bxa9CIXZZl3b4TBgCtSI825scsFV2DkyO3fu5NChQwwYMMBZFxERQbdu3VixYoVHEhkfHx9q1KhBamoqYK634u4O1A6Hg8LCQvLz8yvMyr4V2ZnayzAM8vLySE1NpUaNGvj4+HgpShGp0AwDUubA/Cfg2E6zrk5HGDIdGlzi1dDEeypsInPo0CEA58qzJ9WqVct5rCQFBQUUFBQ4y1lZWQDYbDZsJSw7XbNmTex2O4cPHy5TfIZhkJ+fT2BgoNvJT3XmTnuFh4dTs2bNEr9P1cnJz1/d28Edaiv3Vfq2Sv0Tn4VPYN21GAAjJBZ734kY7W8Ai9Wj2wpU+ra6gMqzrdy9ZoVNZM7V1KlTmTJlSrH6BQsWnHHTQ4vFop4AL7Lb7Zojc5qFCxd6O4RKQ23lvsrWVv5F2bQ8+A3x6T9jwcBu8WV77BC21rqcov1BsH9eub13ZWsrbyqPtjo53eBsKmwiU7t2bQAOHz5MnTp/Pft/+PBhOnbsWOrrxo8fz8MPP+wsZ2VlERcXx6BBgwgPD/dYfDabjYULFzJw4EDN6XCD2st9aiv3qa3cV+naym7DuupDrEtewJKfCYCjxeU4+k+mUWQ8jcrxrStdW3lRebbVyRGVs6mwiUyjRo2oXbs2P/30kzNxycrK4tdff+Xee+8t9XUBAQEEBBTf+MvPz69cbsjyum5VpfZyn9rKfWor91WKttq6EOZPgPQtZrlWWxg8DWujXhf0UdtK0VYVRHm0lbvX82oik5OTw7Zt25zlnTt3snbtWqKiomjQoAFjx47l2WefpVmzZjRq1IiJEydSt25drrrqKu8FLSIi5SNti5nAbDsxTBFcE/pNhE63glVD/1IyryYyv//+O3379nWWTw4JjRw5ktmzZ/PYY4+Rm5vL3XffTUZGBpdeeinz5s0jMFD7ZIiIVBnHj8GiF+C398BRBFZf6PZ36P0oBNXwdnRSwXk1kenTp88ZJ3haLBaefvppnn766QsYlYiIXBD2Ilg9G35+Do4fNeuaD4ZBz0F0U6+GJpVHhZ0jIyIiVdiOZJg3HlI3meWYlpD4PDTt79WwpPJRIiMiIhfOke2w8CnY/INZDqwBfZ+AzreBjybWStkpkRERkfKXnwVLXoJf3gZ7IVh8oMsd0Gc8BEd5OzqpxJTIiIhI+XHYYe2n8NMzkGtuB0OTfuYwUmwr78YmVYISGRERKR+7l8Pcx+HQerMc1cRMYJonamdq8RglMiIi4lkZe8x5MH98a5YDwiHhceh6N/j6ezc2qXKUyIiIiGcU5sLSV2D561CUD1jg4pHQ90kIjfF2dFJFKZEREZHz43DAhi/gx8mQfdCsi+8Fg6dC7XZeDU2qPiUyIiJy7vauhHnjYP/vZrlGQxj0LLQapnkwckEokRERkbLLOmD2wKz/j1n2C4Hej8Alo8FP28jIhaNERkRE3Gc7bs6BWfoK2PLMuo43Qf+nIKy2d2OTakmJjIiInJ1hwB/fwMJJkLnXrIvrBoOnQb1O3o1NqjUlMiIicmYH1pj7Iu1ZYZbD68HAp6HtcM2DEa9TIiMiIiXLPgw/Pw1rPgUM8A2CS8dCjwfAP9jb0YkASmREROR0RQXwy1uw+GUozDbr2l0HAyZDRH2vhiZyOiUyIiJiMgxzV+oFT8KxXWZd3Ytg8HRo0M2roYmURomMiIjAoY3mejC7lpjl0NowYBK0vxGsVu/GJnIGSmRERKqz3HRIeg5WzQbDAT4B0GMMXPowBIR6OzqRs1IiIyJSHRUVwsr3IXk6FGSada2vNJ9Gioz3amgiZaFERkSkutmyAOaPhyPbzHKtdjBkGsRf6t24RM6BEhkRkWoiNH8/Pv++AXb8ZFYER0P/iXDRLWD18W5wIudIiYyISFWXdxRr0lT6/vkBVhxg9YNL/g69H4XACG9HJ3JelMiIiFRV9iJYNQuSnsPn+DEAHM0GYx38PNRs4uXgRDxDiYyISFW0PcncViDtTwCMmJasCL+CLtc/htXPz8vBiXiOEhkRkarkyHZzQbuUOWY5KBL6PkFRh5tJm7fAu7GJlAMlMiIiVUF+Jix+EX55Bxw2sPhA17sg4XEIjgKbzdsRipQLJTIiIpWZww5rPoGfn4HcNLOuSX8YPBViWng3NpELQImMiEhltWsZzHscDm0wyzWbQuLz0GwQWCzejU3kAlEiIyJS2RzbDQsnwqb/M8sBEdDncehyF/j6ezc2kQtMiYyISGVRkANLZ8DyN8BeABYrXDwK+j4BIdHejk7EK5TIiIhUdA4HrP8cfpwCOYfMuvheMHga1G7r3dhEvEyJjIhIRbb3N5j7OBxYbZYj42HQs9Dycs2DEUGJjIhIxZS5H36cBBu+NMv+odD7H3DJfeAb4N3YRCoQJTIiIhVJYR4sfw2WzoSi44AFLroJ+j0FYbW8HZ1IhaNERkSkIjAM2Pg1LJwEWfvMugbdzfVg6l7k3dhEKjAlMiIi3rZ/tbkv0t5fzHJEHAycAm2u0TwYkbNQIiMi4i3Zh+Cnp2HtZ4ABfsFw6UPQ437wC/J2dCKVghIZEZELzZYPv7wJS2ZAYY5Z1/4G6D8JIup5NzaRSkaJjIjIhWIY8Od/zd2pM3abdfUuhsHTIa6Ld2MTqaSUyIiIXAiHNpjzYHYtMcthdWDAZGh3PVitXg1NpDJTIiMiUp5y082dqVf/CwwH+Aaac2B6joWAUG9HJ1LpKZERESkPRYXw23uw6AUoyDTrWl8FA5+GyIZeDU2kKlEiIyLiSYYBW+bDgifgyDazrnZ7c1+k+J7ejU2kClIiIyLiKambYf542P6zWQ6Jgf5PQcebwOrj3dhEqiglMiIi5yvvKCRPg5UfgGEHqx90vw96/QMCw70dnUiVpkRGRORc2Yvg9w8h+Xk4fsysazEUBj0DNZt4NzaRakKJjIjIudj2E8yfAGmbzXJsa3NfpMZ9vBqWSHXjViKzfv16ty/Yvn37cw7mdHa7ncmTJ/PJJ59w6NAh6taty6hRo3jyySexaP8REfGG9G3mRN4t88xyUBT0ewI6jQIf/W0ocqG59X9dx44dsVgsGIZx1gTCbrd7JDCA6dOn8/bbb/PRRx/Rpk0bfv/9d2677TYiIiJ44IEHPPY+IiJndTwDFr8Iv74LDhtYfaHr3ZDwGARFejs6kWrLrURm586dzn+vWbOGf/zjHzz66KN0794dgBUrVvDyyy/zwgsveDS45cuXc+WVVzJ06FAA4uPj+fe//81vv/3m0fcRESmVw24uZvfzs5CXbtY1HQiJz0NMc+/GJiLuJTING/61eNN1113Ha6+9xmWXXeasa9++PXFxcUycOJGrrrrKY8H16NGD9957jy1bttC8eXPWrVvH0qVLmTFjRqmvKSgooKCgwFnOysoCwGazYbPZPBbbyWt58ppVmdrLfWor95V3W1l2L8VnwZNYUjcCYNRshn3AMxhNB5wMoFzetzzovnKf2sp95dlW7l7TYhiGUZYLBwUFsXr1alq1auVS/+eff9KpUyeOHz9elsudkcPhYMKECbzwwgv4+Phgt9t57rnnGD9+fKmvmTx5MlOmTClW/9lnnxEcHOyx2ESk6gouSKXN/s+pm/k7AIU+waTUvpqdMf0xLJoHI3Ih5OXlMWLECDIzMwkPL30ZgzInMp06daJt27Z88MEH+Pv7A1BYWMidd97Jxo0bWb169flFforPP/+cRx99lBdffJE2bdqwdu1axo4dy4wZMxg5cmSJrympRyYuLo709PQzNkRZ2Ww2Fi5cyMCBA/Hz8/PYdasqtZf71Fbu83hbFWRjXf4q1l/fwmIvxLBYcXQahaP34xBc8/yv70W6r9yntnJfebZVVlYW0dHRZ01kyvynxTvvvMOwYcOoX7++8wml9evXY7FY+O9//3vuEZfg0UcfZdy4cdx4440AtGvXjt27dzN16tRSE5mAgAACAgKK1fv5+ZXLDVle162q1F7uU1u577zbyuGAdf+Gn6ZAzmGzrlEClsFT8anVhqq0Jq/uK/eprdxXHm3l7vXKnMh07dqVHTt28Omnn7J5s7l+wg033MCIESMICQkp6+XOKC8vD+tp29v7+PjgcDg8+j4iUo3t+QXmjYMDa8xyZCNIfA5aXAZa5kGkwjunwd6QkBDuvvtuT8dSzLBhw3juuedo0KABbdq0Yc2aNcyYMYPbb7+93N9bRKq4jL3w4yTY+LVZ9g+DhEeh29/Bt3ivrohUTOeUyHz88ce8++677NixgxUrVtCwYUNeeeUVGjduzJVXXumx4F5//XUmTpzIfffdR2pqKnXr1uWee+7hqaee8th7iEg1U5gHy141v4qOAxa46GZzc8fQWG9HJyJlZD37Ka7efvttHn74YYYMGcKxY8ecC+BFRkYyc+ZMjwYXFhbGzJkz2b17N8ePH2f79u08++yzzknGIiJuMwxY/yW80RkWTTOTmAY94J5FcOUbSmJEKqkyJzKvv/4677//Pk888QS+vn916HTu3JkNGzZ4NDgREY/Yvwr+OQi+uROy9kNEA7huNtw2B+p08HZ0InIeyjy0tHPnTi666KJi9QEBAeTm5nokKBERj8g6CD89Des+M8t+IdDrIeg+BvyCvBubiHhEmROZRo0asXbtWpfVfgHmzZtXbJE8ERGvsOXDijdgyQywnfgDq/2NMGAShNf1bmwi4lFlTmQefvhhRo8eTX5+PoZh8Ntvv/Hvf/+bqVOn8sEHH5RHjCIi7jEM2PR/sHAiZOwx6+p3gcHToH5n78YmIuWizInMnXfeSVBQEE8++aRz+eC6devy6quvOheuExG54A6uh3njYfdSsxxWFwZOgbbXgrXM0wFFpJIoUyJTVFTEZ599RmJiIjfddBN5eXnk5OQQG6vZ/iLiHf62LHz+9xCs/QQwwDcQej5ofvl7dpFOEal4ypTI+Pr68ve//50///wTgODgYG3EKCLeUVSI9Zc3GbBpOlbHic1q21xj9sLUaODd2ETkgjmnLQrWrFlTbLKviMgFYRiwZR7Mn4DP0R34AI7aHbAOmQ4Nu3s7OhG5wMqcyNx333088sgj7Nu3j4svvrjY/konN5IUEfG41D/NeTA7kgAwQmJZW/MK2t78PFZ/bSsgUh2VOZE5OaH3gQcecNZZLBYMw8BisThX+hUR8Zi8o5D0PPz+IRh28PGH7qMpuuQB9vy0hLYWTeYVqa7OaUE8EZELwm6Dlf+E5KmQn2HWtbwcBj0LUY3AZvNqeCLifWVOZDQ3RkQuiK0/wvwJkJ5ilmu1hcFToVFv78YlIhVKmROZf/3rX2c8fuutt55zMCIipG+F+U/A1vlmObgm9HsSOo0Eq493YxORCqfMicyDDz7oUrbZbOTl5eHv709wcLASGRE5N8czYNEL8Nu74CgCqy90+zv0fhSCang7OhGpoMqcyBw7dqxY3datW7n33nt59NFHPRKUiFQjDjusmg1Jz0HeEbOuWSIkPgfRzbwamohUfGVOZErSrFkzpk2bxs0338zmzZs9cUkRqQ52LDIfp079wyxHt4DBz0PTAd6NS0QqDY8kMmCu+nvgwAFPXU5EqrKjO2HBk7D5B7McWAP6ToDOt4OPn1dDE5HKpcyJzPfff+9SNgyDgwcP8sYbb9CzZ0+PBSYiVVBBNix+CX55C+yFYPExk5e+EyA4ytvRiUglVOZE5qqrrnIpWywWYmJi6NevHy+//LKn4hKRqsThgLWfwk9PQ26qWde4r/k4dWwr78YmIpVamRMZh8NRHnGISFW1ewXMGwcH15rlqMaQ+Dw0HwwWi1dDE5HK77znyNjtdjZs2EDDhg2JjIz0REwiUhVk7IWFT8Ef35jlgHBIeAy63gO+/t6NTUSqjDJvUDJ27Fj++c9/AmYS07t3bzp16kRcXBzJycmejk9EKpvCXPj5OXij84kkxmIuZnf/auhxv5IYEfGoMicyX331FR06dADgv//9L7t27WLz5s089NBDPPHEEx4PUEQqCcOA9V/A651h8QtQlA8NL4V7FsMVr0FojLcjFJEqqMxDS+np6dSuXRuAOXPmcN1119G8eXNuv/12Xn31VY8HKCKVwL5VMO9x2LfSLNdoYG7s2OoKzYMRkXJV5kSmVq1abNq0iTp16jBv3jzefvttAPLy8vDx0T4oItVK1gH4cQqs/9ws+4VA70fgktHgF+jd2ESkWihzInPbbbdx/fXXU6dOHSwWCwMGmCtw/vrrr7Rs2dLjAYpIBWQ7DivegCUzwJZn1nUYAf2fgvA63o1NRKqVMicykydPpm3btuzdu5frrruOgIAAAHx8fBg3bpzHAxSRCsQwYNN3sOApyNxj1sV1M9eDqXexV0MTkerpnB6/vvbaa4vVjRw58ryDEZEK7OA6mDsO9iw3y+H1YODT0Ha45sGIiNecUyKTm5vLokWL2LNnD4WFhS7HHnjgAY8EJiIVRE6quSLvmk8AA3yDoOeD5pd/sLejE5FqrsyJzJo1a7jsssvIy8sjNzeXqKgo0tPTCQ4OJjY2VomMSFVRVAC/vgOLXoTCbLOu7bUwcApE1PdubCIiJ5R5HZmHHnqIYcOGcezYMYKCgvjll1/YvXs3F198MS+99FJ5xCgiF5JhwOb/wZvdzJV5C7Oh7kVw+wK49p9KYkSkQilzj8zatWt59913sVqt+Pj4UFBQQOPGjXnhhRcYOXIk11xzTXnEKSIXwuFN5r5IOxeZ5dBaMGAytL8RrGX+u0dEpNyVOZHx8/PDeuIHWmxsLHv27KFVq1ZERESwd+9ejwcoIhdA7hFIeg5WzQLDAT4B0H009HoYAsK8HZ2ISKnKnMhcdNFFrFy5kmbNmpGQkMBTTz1Feno6H3/8MW3bti2PGEWkvNhtsPIDSJ4K+ZlmXasrYNAzEBnv1dBERNxR5kTm+eefJzvbnPj33HPPceutt3LvvffSrFkzPvzwQ48HKCLlZOtCmD8B0reY5VrtzPVgGvXyblwiImVQ5kSmc+fOzn/HxsYyb948jwYkIuUsbYuZwGxbaJaDo6Hfk9DpVrBqmxERqVzOaR2ZoqIikpOT2b59OyNGjCAsLIwDBw4QHh5OaGiop2MUEU84fgySp8PK98FRBFY/6HYPJDwGgRHejk5E5JyUOZHZvXs3gwcPZs+ePRQUFDBw4EDCwsKYPn06BQUFvPPOO+URp4icK3sRrJ4NPz8Hx4+adc2HQOJzULOJV0MTETlfZU5kHnzwQTp37sy6deuoWbOms/7qq6/mrrvu8mhwInKediTDvPGQusksx7Q058E06efVsEREPKXMicySJUtYvnw5/v7+LvXx8fHs37/fY4GJyHk4sh0WTISU/5nloEjo+wRcfBv4nNOIsohIhVTmn2gOhwO73V6sft++fYSFab0JEa/Kz4LFL5pbC9gLweIDXe6EPuMgOMrb0YmIeFyZl+ocNGgQM2fOdJYtFgs5OTlMmjSJyy67zJOxiYi7HHZY/S94vRMsf81MYpr0h3uXw2UvKIkRkSqrzD0yL7/8MomJibRu3Zr8/HxGjBjB1q1biY6O5t///nd5xCgiZ7J7Ocx9HA6tN8s1m0Li89BsEFgs3o1NRKSclTmRqV+/PuvWrePzzz9n/fr15OTkcMcdd3DTTTcRFBRUHjGKSEmO7TY3ddz0nVkOiDAfpe56N/j6n/GlIiJVxTnN+vP19eXmm2/2dCwi4o6CHFg2E5a9BvYCsFih00hzUbuQaG9HJyJyQbmdyCxevNit83r37n3OwYjIGTgcsOEL+HEyZB806+J7weBpUFv7nIlI9eR2ItOnTx8sJ8bbDcMo8RyLxVLiE03nY//+/Tz++OPMnTuXvLw8mjZtyqxZs1y2ShCp8vauhHnjYP/vZjkyHgY9Cy0v1zwYEanW3E5kIiMjCQsLY9SoUdxyyy1ER5d/F/axY8fo2bMnffv2Ze7cucTExLB161YiIyPL/b1FKoTM/WYPzIYvzLJ/KPR6BC65D/wCvRqaiEhF4HYic/DgQb799ls+/PBDXnjhBS677DLuuOMOBg8e7Oyp8bTp06cTFxfHrFmznHWNGjUql/cSqUisjkKsS16CFa+BLQ+wQMeboP9ECKvt7fBERCoMt9eR8ff354YbbmD+/Pls3ryZ9u3bM2bMGOLi4njiiScoKiryeHDff/89nTt35rrrriM2NpaLLrqI999/3+PvI1JhGAaWTd/Sf9Pj+CyeZiYxcZfA3Ulw1ZtKYkSkQtmfcZylhywcySnwWgzn9NRSgwYNeOqpp7jlllu44447mDZtGo888ghRUZ5ddGvHjh28/fbbPPzww0yYMIGVK1fywAMP4O/vz8iRI0t8TUFBAQUFfzVoVlYWADabDZvN5rHYTl7Lk9esytRebji4Fp+FT+K79xd8AUdYXRwDpmC0usqcB6O2K0b3lfvUVu5TW5WusMjBqj3HWLQlnUVb0tmWlgv40PHPw1zfJcCj7+Vu+1uM0mbulqKgoICvv/6aDz/8kBUrVjB06FBuv/12Bg8efE6Bnom/vz+dO3dm+fLlzroHHniAlStXsmLFihJfM3nyZKZMmVKs/rPPPiM4ONjjMYqcrwBbBq0OfEWDo0uwYFBk9Wdr7OVsrzUEu9WzPxhERMoqowA2ZVj4M8NCSoaFAsdf00ksGMSHQd86DjrULFM6cVZ5eXmMGDGCzMxMwsPDSz3P7R6Z3377jVmzZvH5558THx/PbbfdxhdffOHxXphT1alTh9atW7vUtWrViq+//rrU14wfP56HH37YWc7KyiIuLo5BgwadsSHKymazsXDhQgYOHIifn5/HrltVqb1KUFSA9bd3sS6bgaUwBwBH22ux9RrPll/+UFu5QfeV+9RW7qvubWWzO1izN8PZ65JyOMfleM0Qf3o3jyahWTRdG4azcmlyubTVyRGVs3E7kbnkkkto0KABDzzwABdffDEAS5cuLXbeFVdc4e4lz6pnz56kpKS41G3ZsoWGDRuW+pqAgAACAor/Fevn51cuN2R5XbeqUnsBhgGbf4AFT8KxXWZdvYth8HSscV3wtdmAP9RWZaC2cp/ayn3Vqa0OZ+WzKCWN5C2pLNmSTnbBX/NeLRboGFeDvi1i6dMihrZ1I7BazV6Zk8M/5dFW7l6vTHNk9uzZwzPPPFPqcU+vI/PQQw/Ro0cPnn/+ea6//np+++033nvvPd577z2PvYfIBXVoo7kezK4lZjm0NgyYDO1vAGuZ93AVETknRXYHa/dmkJSSStLmNDYddO39iArxJ6F5DH1axNCrWQxRIRV32xO3ExmHw1GecZSoS5cufPvtt4wfP56nn36aRo0aMXPmTG666aYLHovIeclNh6TnYNVsMBzgEwA97odLH4KAUG9HJyLVQFp2AYu2pJGcksriLWlk5bs+bdyhfgR9TvS6tK9fAx9r5Vhs85yeWrqQLr/8ci6//HJvhyFybooKYeX7kDwdCjLNutZXwcCnIbL0IVIRkfNldxis25dB8uZUklLS2LA/0+V4RJAfvZvH0LdFDL2bxxAdWjkfLqjwiYxIpbVlAcwfD0e2meXa7c19keJ7ejcuEamyjuQUsHhrGskpaSzeksaxPNdHmNvWC3fOdelQvwa+PpV/SFuJjIinpaXA/Amw7UezHBID/Z8yV+a1+ng3NhGpUhwOgw37M0lKSSU5JY11+zI4dVGVsEBfejcz57oktIghNqzqbW2iREbEU/KOwqLp8Nv7YNjB6geX3Au9H4VAzz36LyLV27HcQhZvTWNRShqLtqRxJLfQ5XirOuH0bRFDnxaxdGpQNXpdzkSJjMj5shfBqlnmZN7jx8y6FkNh0DNQs4l3YxORSs/hMNh0MIukzakkb0ljzZ5jOE7pdQkN8OXSptH0bRlDQvNYakdUvV6XMylzIrN3714sFgv169cHzIXyPvvsM1q3bs3dd9/t8QBFKrTtP8O8CZD2p1mObQ2Dp0LjPl4NS0Qqt8zjNpZuTXcOGaWftpdRi1ph9GkZQ5/msVzcMBJ/36rd63ImZU5kRowYwd13380tt9zCoUOHGDhwIG3atOHTTz/l0KFDPPXUU+URp0jFcmS7uaBdyhyzHBQFfSfAxbeBjzo6RaRsDMPgz4PZJKWksigljVV7jmE/pdsl2N+Hnk2jnRN169YI8mK0FUuZf+Ju3LiRrl27AvDFF1/Qtm1bli1bxoIFC/j73/+uREaqtvxMWPwi/PIOOGxg9YUud0GfxyEo0tvRiUglkp1vY9m2dJI2myvqHs5y7XVpGhvqnOvSOT6SAF89LFCSMicyNpvNuQXAjz/+6NySoGXLlhw8eNCz0YlUFA47rPkYfn4WctPMuqYDIfF5iGnu3dhEpFIwDIMth3NODBel8vuuYxSd0usS5OdDjyY16dMylj7NY4iL0kbH7ihzItOmTRveeecdhg4dysKFC51bFhw4cICaNWt6PEARr9u11NxW4NAGs1yzmZnANB/k3bhEpMLLLSgye11S0liUksqBzHyX442jQ5yr6XZtFEWgn3pdyqrMicz06dO5+uqrefHFFxk5ciQdOnQA4Pvvv3cOOYlUCcd2w8KJsOn/zHJgBCSMg653gU/12EhORMrGMAy2p+U4h4t+23kUm/2vXpcAXyvdm9R0znVpWDPEi9FWDWVOZPr06UN6ejpZWVlERv41J+Duu+8mOFjdYFIFFOTA0hmw/A2wF4DFak7i7fsEhKjXUURc5RUWsWL7EecTRvuOHXc53iAqmH4tY0loEUP3xjXV6+Jh5/R4hWEYrFq1iu3btzNixAjCwsLw9/dXIiOVm8MB6z+HH6dAziGzrlFvc1uBWm28G5uIVBiGYbAzPZekFHMDxl93HqWw6K+Nlf19rXRrFOXsdWkUHYLFUjk2YKyMypzI7N69m8GDB7Nnzx4KCgoYOHAgYWFhTJ8+nYKCAt55553yiFOkfO39DeY+DgdWm+XIRjDoWWg5FPQDSKTay7fZWbHjiHMDxj1H81yO148MciYu3ZvUJNhfyzBcKGVu6QcffJDOnTuzbt06l8m9V199NXfddZdHgxMpd5n74MfJsOFLs+wfBr3/YW4t4Fs5d4IVEc/YfTSPpduOkrwljRXbj1BwSq+Ln4+Frqf0ujSJCVWvi5eUOZFZsmQJy5cvx9/f36U+Pj6e/fv3eywwkXJVmAfLX4OlM6HoOGCBi26GfhMhrJa3oxMRL8i32flt51F++vMQc9f4kLpiqcvxuhGBJLSIpW+LGHo0jSY0QL0uFUGZvwsOhwO73V6sft++fYSFhXkkKJFyYxiw8WtYOAmy9pl1DXqY2wrU7ejV0ETkwtt7NI/kE5N0l28/wnHbyd9vFnytFjrHR57odYmleS31ulREZU5kBg0axMyZM3nvvfcAsFgs5OTkMGnSJC677DKPByjiMftXw7zxsPcXsxwRBwOfhjZXax6MSDVRUGTn913HnBswbkvNcTleKzyAhGbRhGbvYcx1A4kK01YAFV2ZE5mXX36ZxMREWrduTX5+PiNGjGDr1q1ER0fz73//uzxiFDk/2Yfgp6dh7adm2S8YLn0YeowBP/2QEqnqDmQcJzkljaSUVJZtSyev8K9RBR+rhYsbRDo3YGxVJ4yioiLmzNlNWKCGjiqDMn+X6tevz7p16/j8889Zv349OTk53HHHHdx0000EBemXglQgtnz45U1YMgMKT/zV1f5GGDAJwut6NzYRKTc2u4Pfdx1zDhmlHM52OR4TFkBC8xj6tojl0mbRRARpgcvK7JzSTV9fX26++WZPxyLiGYYBf/7X3J06Y7dZV68zDJkO9Tt7NzYRKReHMvNZtCWVpM1pLN2WTk5BkfOY1QIXNYh0bsDYuk44VquGk6uKMicy//rXv854/NZbbz3nYETO26EN5jyYXUvMclhdGDAZ2l0HVqtXQxMRzymyO1i9J8O5mu6fB7NcjtcM8SfhROLSu1k0NYL9S7mSVHbntI7MqWw2G3l5ec6VfZXIiFfkpsPPz8Dqf4HhAN9A6PEAXDoW/LWXiUhVkJqdz6KUNJJT0li8NY3s/L96XSwW6FC/hnNdl3b1ItTrUk2UOZE5duxYsbqtW7dy77338uijj3okKBG3FRXCb+/Coheg4MRfZG2ugYFToEYD78YmIufF7jBYu/eYcwPGjftde10ig/3ofWKuS69m0dQM1SKW1ZFHpmQ3a9aMadOmcfPNN7N582ZPXFLkzAwDtsyH+RPg6Hazrk4Hc1+khj28G5uInLP0nAIWb0kjKSWNJVvTyMizuRxvXz+CPid6XTrUr4GPel2qPY89W+br68uBAwc8dTmR0qVuhvnjYfvPZjkkFvo/BR1v0jwYkUrG7jBYvy+DpJQ0FqWksn5/Jobx1/GIID96NYumb4tYejePISZMvS7iqsyJzPfff+9SNgyDgwcP8sYbb9CzZ0+PBSZSTN5RSJ4KK/8Jhh18/OGS+6DXIxAY7u3oRMRNx3ILWbw1jaTNqSzems7R3EKX423qhjvnunSMq4Gvj/5AkdKVOZG56qqrXMoWi4WYmBj69evHyy+/7Km4RP5iL4LfP4Sk5yA/w6xreTkMegaiGns1NBE5O4fDYOOBTOeidGv3Zrj0uoQF+tKrWbQ5ZNQ8htjwQO8FK5XOOe21JHLBbPvJnAeTdmLuVWwbc1+kxgnejUtEzigzz2b2uqSksnhLGuk5rr0uLWuH0belmbh0ahiJn3pd5Bxp/WWpmNK3wYInYMs8sxwUBf2ehE4jwUe3rUhFYxgGfxzIYtEWc8ho9Z5jOE7pdQkN8KVn05r0bRFLQosY6kRoJXjxDLd+Izz88MNuX3DGjBnnHIwIxzNg8Yvw67vgsIHVF7reAwmPQVANb0cnIqfIyrexdGs6SZtTWbQljdTsApfjzWuFOhOXzg2j8PdVr4t4nluJzJo1a9y6mLY3l3PmsJuL2f38LOSlm3XNBkHi8xDdzLuxiQhg9rpsPpTtnOuyavcx7Kd0uwT7+9CjSTR9W5or6taroV4XKX9uJTJJSUnlHYdUZzuXmNsKHN5glqObQ+JUaDbAu3GJCDkFRSzdmu7cgPFQVr7L8SYxISeeMIqlS6NIAnx9vBSpVFeabCDec3QnLJxobvAIEBgBfSZAlzvAR7vRiniDYRhsTc0hOcXcgPH33Uex2f/qdQn0s5q9Lif2MYqLCvZitCLnmMj8/vvvfPHFF+zZs4fCQteZ6N98841HApMqrCAblsyAFW+AvRAsVuh8B/SdAMFR3o5OpNrJLShi+fYjJKWksigljf0Zx12ON4oOIaF5DH1bxtKtURSBfup1kYqjzInM559/zq233kpiYiILFixg0KBBbNmyhcOHD3P11VeXR4xSVRgOLOv+DcnPQs5hs65xH3MYqVZrr4YmUp0YhsH2tFzncNFvO49SaP9raY0AXyuXNK7p7HWJj9bGq1JxlTmRef7553nllVcYPXo0YWFhvPrqqzRq1Ih77rmHOnXqlEeMUgVY9v5K7y1T8F2706yIagyDnoMWQ8xta0WkXB0vtPPHMQsrf/iTRVvT2XvUtdelQVSwM3G5pHFNgvzV6yKVQ5kTme3btzN06FAA/P39yc3NxWKx8NBDD9GvXz+mTJni8SClEsvYCz9Ownfj10QCRkAYlt6PQbd7wFd7poiUp53pZq9LUkoav+w4QmGRD7AXAH8fK90aRzk3YGwcHaInT6VSKnMiExkZSXZ2NgD16tVj48aNtGvXjoyMDPLy8jweoFRShXmw7FXzq+g4BhZ210yg3s1v4RdZz9vRiVRJ+TY7v+w4QnJKGskpqew64vozOdLfYHCHOPq3qk33JjUJCdDzHlL5lfku7t27NwsXLqRdu3Zcd911PPjgg/z8888sXLiQ/v37l0eMUpkYBmz4Cn6cBFn7zbqGPSka8AzrVu+jXmisd+MTqWL2HMkjeUsqSZtTWbHjCPm2v+a6+PlY6BIfRd8WsfRsEsmWlYsZOrQ1fn56KlCqDrcTmY0bN9K2bVveeOMN8vPNdQSeeOIJ/Pz8WL58OcOHD+fJJ58st0ClEti/CuaOg32/meUaDWDgM9D6SigqAvZ5NTyRqqCgyM5vO486F6XbkZbrcrxORCB9Tsx16dk0mtATvS42m42tGjmSKsjtRKZ9+/Z06dKFO++8kxtvvBEAq9XKuHHjyi04qSSyDsJPU2Ddv82yXwj0ehi6jwE/7WIrcr72HctzDhct336EvEK785iv1cLFDSPNDRhbxNCiVpjmuki14nYis2jRImbNmsUjjzzCQw89xPDhw7nzzjvp1atXecYnFZkt31wLZskMsJ34q7DDCOj/FITrCTaRc1VY5OD3XUdJPrEB49bUHJfjsWEB9GkRYw4ZNYsmPFBDRVJ9uZ3I9OrVi169evH666/zxRdfMHv2bBISEmjatCl33HEHI0eOpHbt2uUZq1QUhgGb/s9clTdjj1lXvysMngb1L/ZubCKV1MHM4+Zw0eZUlm1LJ/eUXherBS5uGOl8wqh1nXD1uoicUObJviEhIdx2223cdtttbNu2jVmzZvHmm28yceJEBg8ezPfff18ecUpFcXC9uS/S7qVmObweDJgC7a7VejAiZWCzO1i1+5hzyGjzoWyX49GhASdW042hV9MYIoLV6yJSkvN69q5p06ZMmDCBhg0bMn78eP73v/95Ki6paHLS4OdnzB2qMcA3CHo+CD0fAH+t+inijsNZ+Sw6MUl36dZ0sguKnMcsFrgorgZ9WsTSt0UsbeqGY7XqjwORsznnRGbx4sV8+OGHfP3111itVq6//nruuOMOT8YmFUFRIfz6Dix+EQqyzLq2w81emBpx3o1NpIIrsjtYszfDuQHjpoNZLsejQvxJaB5DnxYx9G4WQ2SIv5ciFam8ypTIHDhwgNmzZzN79my2bdtGjx49eO2117j++usJCdFf5VWKYUDKXFjwBBzdYdbV6QhDpkODS7wamkhFlpZdwKItZq/Lki1pZOW79rq0r1+DPic2YGxXLwIf9bqInBe3E5khQ4bw448/Eh0dza233srtt99OixYtyjO2YqZNm8b48eN58MEHmTlz5gV972rl8CaYPx52JJvl0FrQfxJ0+BtYrV4NTaSisTsM1u3LIHmzuRXAhv2ZLsdrBPvRu5k516V3sxhqhmprDhFPcjuR8fPz46uvvuLyyy/Hx+fCbya2cuVK3n33Xdq3b3/B37vayDsKSc/D7x+CYQcff3MtmF4PQ0CYt6MTqTCO5BSweGsaySlpLN6SxrE8m8vxdvUinIvSdYyroV4XkXLkdiLjzaeRcnJyuOmmm3j//fd59tlnvRZHlWW3wcp/QvJUyM8w61oNM1fljWrk1dBEKgKHw2D9/kznBozr92VgGH8dDwv0pXdzc12X3s2jiQ3TQpAiF0ql2DFs9OjRDB06lAEDBpw1kSkoKKCgoMBZzsoyJ9fZbDZsNltpLyuzk9fy5DW9wbL9J3x+nIglfQsARmwb7IOew2h4qXmChz5fVWmvC0Ft5b7ybKtjeYUs3XaExVvSWbwtnaO5ru/RqnYYCc2jSWgeTcf6Efj6/DXsWhG/d7qv3Ke2cl95tpW717QYxql/V1Q8n3/+Oc899xwrV64kMDCQPn360LFjx1LnyEyePJkpU6YUq//ss88IDg4u52grj9D8g7TZ/xm1s9YBUOAbxp91rmV3zQSwaB6MVD8OA/bnwqYMC5uOWdmdAwZ/DQkF+Bi0jDBoVcP8qqGpLiLlKi8vjxEjRpCZmUl4eHip51XoRGbv3r107tyZhQsXOufGnC2RKalHJi4ujvT09DM2RFnZbDYWLlzIwIEDK9dOsvmZWJe8iPX3D7A4ijCsvji63I3j0n9AoOfa53SVtr28QG3lvvNtq8zjNpZtO8Kireks3ppOek6hy/EWtULp3czsdenUoAZ+PpU3ydd95T61lfvKs62ysrKIjo4+ayJToYeWVq1aRWpqKp06dXLW2e12Fi9ezBtvvEFBQUGxiccBAQEEBBT/U8nPz69cbsjyuq7HOeywajYkPQd5R8y65oOxDHoOn+imXKjp25WmvSoAtZX73G0rwzD482A2SSmpJKeksnpPBnbHX3/Lhfj70LNptHMrgLo1gsozbK/QfeU+tZX7yqOt3L1ehU5k+vfvz4YNG1zqbrvtNlq2bMnjjz/ulaenKqUdi8xtBVL/MMsxLSHxeWja37txiVwAWfk2lm1NN7cC2JLK4awCl+PNYkOdGzB2jo/C37fy9rqIVEcVOpEJCwujbdu2LnUhISHUrFmzWL2U4OgOWDARNv9glgNrQN8noPPt4FOhv/Ui58wwDLYcziEpJZWkzams2n2MolN6XYL8fOjZtCYJLWLp0zyGuCjNnROpzPTbrCoqyIbFL8Evb4G9ECw+0OUO6DMegqO8HZ2Ix+UUFPHbliPODRgPZua7HG8cHeIcLuraKIpAP/XmilQVlS6RSU5O9nYIFZfDAWs/hZ+ehtxUs65JP3MYKbaVd2MT8SDDMNielsOPmw7x9R9W/vFbEjb7X70uAb5WejSp6UxeGtbUFioiVVWlS2SkFLtXwLzH4aD5ODVRTcwEpnmiucGLSCWXV1jE8m1HSN5ibsC4P+P4iSNWwKBhzWD6nkhcLmlcU70uItWEEpnKLmMvLHwK/vjGLAeEQ8Lj0PVu8NVOulJ5GYbBzvRckk4MF/264yiFdofzuL+vlW7xkUQXpXLfVb1pVruG94IVEa9RIlNZFebC0pmw/DUoyjcXset0K/R9EkJjvB2dyDnJt9lZseOIcwPGPUfzXI7Xjwxy9rp0b1ITP4vBnDlziNfQkUi1pUSmsnE4YMOX8ONkyD5g1sX3gsFToXY7r4Ymci52H8klaXMqyVvSWLH9CAVFf/W6+PlY6Noo6kTyEkuTmBAspwyVagl5EVEiU5nsW2XOg9m30izXaAiDnjU3eNQ8GKkk8m12ft15lOSUVJJT0tiZnutyvG5EIH1amo9G92gaTWiAfkyJSOn0E6IyyDoAP06B9Z+bZf9Q6PUwXDIa/LTLrlR8e4/mOROX5duPcNxmdx7ztVroHB9J3xax9G0ZS7PYUJdeFxGRM1EiU5HZjsPyN2DpDLCdmCvQ8Sbo/xSE1fZubCJnUFBk5/ddx5xDRttSc1yO1woPcM516dk0mrBALQMvIudGiUxFZBiw6TtY8BRk7jHr4rrB4GlQr9MZXyriLfszjjt7XZZtSyev8K9eFx+rhYsbRNKnZQx9msfSqk6Yel1ExCOUyFQ0B9fB3HGwZ7lZDq8PA6dA2+GaByMVis3u4Pddx0hOSSUpJZUth117XWLCAujTPIY+LWK5tFk0EUHqdRERz1MiU1HkpJor8q75BDDANwgufQh63A/+2gtGKoZDmfnOXpel29LJKShyHrNa4KIGkfRtYSYvreuEY7Uq+RaR8qVExtuKCuCXt829kQqzzbp218GAyRBR36uhiRTZHazek0HSieTlz4NZLsdrhviTcCJx6d0smhrBWoRRRC4sJTLeYhiw+X+w4Ek4ttOsq9sJhkyHuK7ejU2qtdTsfBalpJGcksbirWlk5//V62KxQIf6NZwTddvVi1Cvi4h4lRIZbzj8B8wbDzsXmeXQ2mYPTPsbwGr1amhS/dgdBmv3HiNpcxrJW1LZuN+11yUy2I/ezWPo2yKWXs2iqRka4KVIRUSKUyJzIeUegaTnYNUsMBzgEwA9xsClD0NAqLejk2okPafA7HXZksbiLWlkHnddIbd9/QjnztEd6tfAR70uIlJBKZG5EOw2+O19WDQN8jPNutZXwsCnITLeq6FJ9WB3GKzfl0FSShqLUlJZvz8Tw/jreESQH72aRdO3RSy9m8cQE6ZeFxGpHJTIlLetC81hpCNbzXLtduZ6MPGXejcuqfKO5hayZGsaSZtTWbQljWN5rr0ubeqGO+e6dIyrga+PhjVFpPJRIlNe0rbA/AmwbaFZDomBfhPhopvB6uPd2KRKcjgMNh7IdM51Wbs3w6XXJSzAl17No80ho+YxxIZrewsRqfyUyHja8WOQPB1Wvg+OIrD6wSV/h96PQmCEt6OTKiYzz8birWkkpaSyeEsa6TmFLsdb1g6jT4tY+raIoVPDSPzU6yIiVYwSGU+xF5mTeJOeh+NHzboWl5m7U9ds4t3YpMowDIM/DmQ5F6VbvecYjlN6XUIDfOnZtCZ9W8SS0CKGOhFB3gtWROQCUCLjCTuSzXkwqZvMckwrGPw8NOnn1bCkasjKt7F0a7pzrktqdoHL8ea1Qp1PGHVuGIW/r3pdRKT6UCJzHkIKDuPz5S2wZa5ZERQJfZ+Ai28DHzWtnBvDMNifC+8u3snibUdYtfsY9lO6XYL8fOjZNJq+LWNIaB5D/UhtYSEi1Zd+254ja/JU+v05E6thB4sPdL0LEh6H4ChvhyaVUHa+jWXbjjg3YDyc5QtsdR5vEhNyYq5LLF0aRRLgqwnjIiKgRObc2QuwGnYcjftjHTIVYlp4OyKpRAzDYGtqDkmbzbkuK3cdpeiUXhc/q0HPpjH0b1WLPi1iiYtSr4uISEmUyJwjx6WP8FtqAJ3/Nh6rn5+3w5FKILegiOXbj5CUksqilDT2Zxx3Od4oOoSE5jH0bhrFkc2/cdWwTvjp3hIROSMlMucqIIzUiA7ejkIqMMMw2J6W63zC6LedRym0O5zH/X2tdG9ck74ndo+Ojw4BwGazMWdraVcVEZFTKZER8aDjhXZW7Eh3Lkq396hrr0tcVBB9T8x1uaRxTYL8NddFROR8KJEROU8703NPTNJN45cdRygsOqXXxcdKt8ZRJDSPoW/LWBpHh2CxaANGERFPUSIjUkb5Nju/7DhCckoaySmp7DqS53K8Xo0g+rSIoW+LWLo3qUlIgP43ExEpL/oJK+KGPUfySN6SStLmVFbsOEK+7a9eFz8fC13io5zJS9PYUPW6iIhcIEpkREpQUGTnt51HnXNddqTluhyvExFInxOTdHs2jSZUvS4iIl6hn74iJ+w7luccLlq27QjHbXbnMR+rhc4NI81F6VrG0KJWmHpdREQqACUyUm0VFjn4fddRkrekkbQ5la2pOS7HY8MCnMNFPZtFEx6oNV1ERCoaJTJSrRzMPE5yipm4LNuWTm7hX70uVgtcfKLXpU+LGFrXCVevi4hIBadERqo0m93Bqt3HnENGmw9luxyPDvUnobk5XNSraQwRwep1ERGpTJTISJVzOCufRSlpJKWksnRrOtkFRc5jFgtcFFfDuQFjm7rhWK3qdRERqayUyEilV2R3sGZvhrko3eY0Nh3McjkeFeJPQvMY+rSIoXezGCJD/L0UqYiIeJoSGamU0rILWLTF7HVZsiWNrHzXXpf29WvQ58Rquu3qReCjXhcRkSpJiYxUCnaHwdq9GSw6sRXAhv2ZLsdrBPvRu1kMfVuavS41QwO8FKmIiFxISmSkwjqSU8DirWkkbU5j8dY0MvJsLsfb1YtwLkrXMa6Gel1ERKohJTJSYTgcBuv3Zzo3YFy/LwPD+Ot4WKAvvZub67r0bh5NbFig94IVEZEKQYmMeNWx3EIWb00jOSWNxVvSOJJb6HK8dZ1wc1G6lrFcFFcDXx+rlyIVEZGKSImMXFAOh8GGfSd7XVJZuzcDx6m9LgG+XNosmr4tYkloEUOtcPW6iIhI6ZTISLnLzLORvPkQn26z8syLi0jPce11aVk7jIQTWwFc3DASP/W6iIiIm5TIiMcZhsGmg1nO1XRX78nA7jAAK1BIiL8PPZtG07dlLAnNY6hbI8jbIYuISCWlREY8IivfxrKt6WbysiWVw1kFLsebxoQQ55vNbYO7cEmTWPx91esiIiLnT4mMnBPDMEg5nO3cgHHV7mMUnTLZJcjPh55Nazo3YKwV6secOXPo3rgmfkpiRETEQ5TIiNtyCopYti3dOWR0MDPf5XjjmBD6nNiAsUt8FIF+Ps5jNpvt9MuJiIictwqdyEydOpVvvvmGzZs3ExQURI8ePZg+fTotWrTwdmjVgmEYbEvNMXtdUlJZuesoNvtfvS4BvlZ6NKlJ35ax9GkeS4OawV6MVkREqqMKncgsWrSI0aNH06VLF4qKipgwYQKDBg1i06ZNhISEeDu8KimvsIjl246QvMXcgHF/xnGX4w1rBtP3xHDRJY1ruvS6iIiIXGgVOpGZN2+eS3n27NnExsayatUqevfu7aWoqhbDMNiZnkvSieGiX3ccpdDucB7397VySeOa9D2xFUCjaCWQIiJScVToROZ0mZnmRoFRUVGlnlNQUEBBwV9PzGRlZQHmHA1PztM4ea3KOPfjeKGdX3cdZfGWdJK3pLP3mGuvS/0agSQ0jyGheTSXNIoiyP/857pU5va60NRW7lNbuU9t5T61lfvKs63cvabFME7dzabicjgcXHHFFWRkZLB06dJSz5s8eTJTpkwpVv/ZZ58RHFx953CkHYc/MyxsyrCwLdOCzfhrg0Ufi0GTcIPWNQxaRxrEBoJF+y+KiIgX5eXlMWLECDIzMwkPDy/1vEqTyNx7773MnTuXpUuXUr9+/VLPK6lHJi4ujvT09DM2RFnZbDYWLlzIwIED8fPz89h1PaXAZue3XcdYtDWdRVvS2XUkz+V43YhAejePpk+zaC5pHEVIQPl2zlX09qpI1FbuU1u5T23lPrWV+8qzrbKysoiOjj5rIlMphpbGjBnDDz/8wOLFi8+YxAAEBAQQEBBQrN7Pz69cbsjyuu652Hs0j+SUVJJT0li+/QjHbXbnMV+rhS7xUfRtac51aRYbisUL3S4Vqb0qOrWV+9RW7lNbuU9t5b7yaCt3r1ehExnDMLj//vv59ttvSU5OplGjRt4OqUIpKLKzcucx5waM29NyXY7XDg+kz4lJuj2b1iQsUP9DiohI1VKhE5nRo0fz2Wef8X//93+EhYVx6NAhACIiIggKqp778+zPOO7sdVm2LZ28wr96XXysFi5uGEmfExswtqwd5pVeFxERkQulQicyb7/9NgB9+vRxqZ81axajRo268AF5QWGRg993H2XRiUXpthzOcTkeExZAn+Yx9G0ZS8+m0UQEqddFRESqjwqdyFSSecgedygz39nrsnRbOjkFRc5jVgt0ahDp3Dm6dZ1wrFb1uoiISPVUoROZ6qLI7mD1ngySUlJJ2pzK5kPZLsejQ/3p3dwcLurVLJoawf5eilRERKRiUSLjJalZ+SRvSWNRShqLt6aRnf9Xr4vFAh3jajg3YGxbN0K9LiIiIiVQInOB2B0Ga/ceI2mzOdfljwNZLsejQvzp3Syavi1j6dUshqgQ9bqIiIicjRKZcpSeU8CilDSSt6SxeEsamcddl1vuUD+CPic2YGxfvwY+6nUREREpEyUyHmR3GKzfl0FSShqLUlJZvz+TU+crRwT5nZjrEkPv5jFEhxZfuE9ERETcp0TmPB3NLWTFzlSSU1JZtCWNY3muvS5t64U757p0qF8DXx+rlyIVERGpepTInKNPf9vL7A0+7P4l2aXXJSzQl97NYujTIoaEFjHEhgV6L0gREZEqTonMOdpyOJtdOeacllZ1wul7YiuATg3U6yIiInKhKJE5R8MvqocjfTejh/clrmaYt8MRERGplpTInKP29SPYV8ugdriGjkRERLxFYyAiIiJSaalHRkREpJozDIN8ez7ZhdlkFWSRbTP/m1WYZdad+O/p/84syORo3lHYCVc2v9IrsSuRERERqQJsDttfyUbBiQTEluX8d2kJSVahmbAUOYrO/ialyCrMOvtJ5USJjIiISAXgMBzk2nJdk42z9IqcTEKyC7M5XnT8vGPwsfgQ5h9GuH84Yf5hLv8O9w8nPCCcML8T9QHhBFmDWLNiDcMaD/NAC5wbJTIiIiIecOrwTLFk4wy9IifLObYcHIbjvOMI9QstloQ4E5GTyYh/GGF+Yc5/nzwW5BuExeL+djk2m419PvsI8Qs577jPlRIZERGRE2wOG8fyj5FuT+ePI3+Q58gr1kNypl4Rm8N29jc5iwCfgDP3ipz8d0DxJCXELwRfa/X61V69Pq2IiFRpJ4dnSurxODmJ1WUOSaHr0I3L8Mz8c4vh5PDMGZOQU5OUANe6AB/tw1cWSmRERKTCMAyDAntBqT0eJSUk5TE8E0AAkcGRJfZ6lNRTEuYfRkRABGH+YQT7BpdpeEbOjxIZERHxqCJHkTPpyC7MJrMws+T5IQVZZNmyyC5wTVo8MTzjb/UvloScrVck3O9Ej4glgAXzFnDZZZfh5+fngRaR8qRERkREXBiG4RyeOX3opdj8kJNP1dj+Gq7JK8o77xisFmupE1LP+lTNeQ7P2Gznn0jJhaNERkSkCiqwF7jMAzmWd4x1hevI2ZJDrr34HBLnJNYTQzaeGJ4J9g0u9oTMmYZmTj0W4hei4RlxixIZEZEKqMhRRE5hjmuycZZ1RE6dxFroKCz5wr+7H4Of1a/YEzJn6hU5tT7UP7TaPT0j3qG7TESkHJQ2PHOmdUROrc+15Z53DFaLlVC/UDOx8AulMLuQRnUaEREYcdZHe8P8wwj01aa4UvEpkRERKYXz6ZkyrK566hM1nhqecfaI+LnOAzlbr0iwXzBWi7k3sM1mY86cOVzWSxNYpWpRIiMiVVZJwzPu9ohkFWSVPjxTBi7DM/7hhAWEOZ+OOdtTNaH+ofhZlXSInIkSGRGpsAzDIK8or9QekWPHj7EhbwO/rviV7KLsYj0lnhiesWApucfjtD1nShqaCfcPJ8AnQJNWRcqREhkRKVenLm5W2vLuZ5pH4tbwzM4zHw7yDSp1HsjpCcnpE1lD/EKcwzMiUvEokRGRM7I77H8lF7bSk5HSJrMW2AvOOwZfq2+Jq6qG+oaSujeVjq06UiOohpmA+IW79JBoeEakalMiI1LFnRyeyS7MJrOglBVWS5gn4unhmVD/0LMuaHbqMu8uT8/4BJY4PGOz2ZiTPofLWmsCq0h1pURGpBIotBcWfzLGzUmr2YXZ2A37eccQ5BtU6jyQUueQnKjT8IyIlBclMiIXgN1hJ8eW89feMqckI6cnIZn5mezJ3sM/f/in8zFejwzPWHxd5oCccZn305KRML8w/HzU4yEiFY8SGRE3GIbB8aLjJS7nfmrvSGnzRHJsOWV/0yzX4qnDM2camnHOJTltEmtpwzMiIpWZEhmpNk4fnnHpFbGdfdEzjw3P+JW8fohzETOfYLZt2EZC9wQigyKd54b6hWp4RkTkNEpkpNJwDs+UsnjZ2VZe9eTwjDs9IiXVuzM8Y7PZmJMyhy61umgCq4jIWSiRkQvGMAzybHkcLzxe+jLvJTzae/Lf5zQ8U4Kz9YicaUJrkG+QhmdERCoQJTJSJja7jcxCNx7hLWEia1ZBFhO/nHjeMZwcnnFniffTFz4L8Q3Bx+rjgZYQEZGKQIlMNXNyeOaMm9+dsund6Y/55tvzzzuGU4dnSusdOdMjvv4+/h5oCRERqQqUyFQypz49c9bVVUtISHJsORgY5x1HsR6REhKSU5OSIGsQK5eu5KrBVxEWGKbhGRER8QglMl5gs9vKvAvvqY/5FhlF5x1DoE9g8f1mTklIztQjEuoXWubhGZvNxhbrFs0xERERj1Iic45ybbkctR9l89HNHHccL71X5PQ5I7ZsjhcdP+/397X4lroD79menNHwjIiIVBVKZM7RC7+/wH+z/wvzzv0aJxOQ0hKS0npFwv3D1bMhIiKCEplzFuYfhh9+RARFEOEfUSwhOXUp+JKSkXMZnhERERFXSmTO0cOdHqb14dZcdpl23RUREfEWrXd+jrRUvIiIiPfpt7GIiIhUWkpkREREpNKqFInMm2++SXx8PIGBgXTr1o3ffvvN2yGJiIhIBVDhE5n//Oc/PPzww0yaNInVq1fToUMHEhMTSU1N9XZoIiIi4mUVPpGZMWMGd911F7fddhutW7fmnXfeITg4mA8//NDboYmIiIiXVehEprCwkFWrVjFgwABnndVqZcCAAaxYscKLkYmIiEhFUKHXkUlPT8dut1OrVi2X+lq1arF58+YSX1NQUEBBQYGznJWVBZh7/dhsNo/FdvJanrxmVab2cp/ayn1qK/eprdyntnJfebaVu9es0InMuZg6dSpTpkwpVr9gwQKCg4M9/n4LFy70+DWrMrWX+9RW7lNbuU9t5T61lfvKo63y8vLcOq9CJzLR0dH4+Phw+PBhl/rDhw9Tu3btEl8zfvx4Hn74YWc5KyuLuLg4Bg0aRHh4uMdis9lsLFy4kIEDB2plXzeovdyntnKf2sp9aiv3qa3cV55tdXJE5WwqdCLj7+/PxRdfzE8//cRVV10FgMPh4KeffmLMmDElviYgIICAgIBi9X5+fuVyQ5bXdasqtZf71FbuU1u5T23lPrWV+8qjrdy9XoVOZAAefvhhRo4cSefOnenatSszZ84kNzeX2267zduhiYiIiJdV+ETmhhtuIC0tjaeeeopDhw7RsWNH5s2bV2wCsIiIiFQ/FT6RARgzZkypQ0kiIiJSfVWKROZ8GIYBuD9pyF02m428vDyysrI0huoGtZf71FbuU1u5T23lPrWV+8qzrU7+3j75e7w0VT6Ryc7OBiAuLs7LkYiIiEhZZWdnExERUepxi3G2VKeSczgcHDhwgLCwMCwWi8eue/Kx7r1793r0se6qSu3lPrWV+9RW7lNbuU9t5b7ybCvDMMjOzqZu3bpYraVvRFDle2SsViv169cvt+uHh4frRi8DtZf71FbuU1u5T23lPrWV+8qrrc7UE3NShd5rSURERORMlMiIiIhIpaVE5hwFBAQwadKkElcRluLUXu5TW7lPbeU+tZX71FbuqwhtVeUn+4qIiEjVpR4ZERERqbSUyIiIiEilpURGREREKi0lMiIiIlJpKZEpxeLFixk2bBh169bFYrHw3XffnfU1ycnJdOrUiYCAAJo2bcrs2bPLPc6KoKxtlZycjMViKfZ16NChCxOwF02dOpUuXboQFhZGbGwsV111FSkpKWd93ZdffknLli0JDAykXbt2zJkz5wJE613n0lazZ88udl8FBgZeoIi95+2336Z9+/bORcm6d+/O3Llzz/ia6nhPQdnbqrreUyWZNm0aFouFsWPHnvG8C31vKZEpRW5uLh06dODNN9906/ydO3cydOhQ+vbty9q1axk7dix33nkn8+fPL+dIva+sbXVSSkoKBw8edH7FxsaWU4QVx6JFixg9ejS//PILCxcuxGazMWjQIHJzc0t9zfLly/nb3/7GHXfcwZo1a7jqqqu46qqr2Lhx4wWM/MI7l7YCc4XRU++r3bt3X6CIvad+/fpMmzaNVatW8fvvv9OvXz+uvPJK/vjjjxLPr673FJS9raB63lOnW7lyJe+++y7t27c/43leubcMOSvA+Pbbb894zmOPPWa0adPGpe6GG24wEhMTyzGyisedtkpKSjIA49ixYxckpoosNTXVAIxFixaVes71119vDB061KWuW7duxj333FPe4VUo7rTVrFmzjIiIiAsXVAUWGRlpfPDBByUe0z3l6kxtpXvKMLKzs41mzZoZCxcuNBISEowHH3yw1HO9cW+pR8ZDVqxYwYABA1zqEhMTWbFihZciqvg6duxInTp1GDhwIMuWLfN2OF6RmZkJQFRUVKnn6N4yudNWADk5OTRs2JC4uLiz/qVdFdntdj7//HNyc3Pp3r17iefonjK501age2r06NEMHTq02D1TEm/cW1V+08gL5dChQ9SqVculrlatWmRlZXH8+HGCgoK8FFnFU6dOHd555x06d+5MQUEBH3zwAX369OHXX3+lU6dO3g7vgnE4HIwdO5aePXvStm3bUs8r7d6qDnOKTnK3rVq0aMGHH35I+/btyczM5KWXXqJHjx788ccf5bp5bEWwYcMGunfvTn5+PqGhoXz77be0bt26xHOr+z1VlraqzvcUwOeff87q1atZuXKlW+d7495SIiMXXIsWLWjRooWz3KNHD7Zv384rr7zCxx9/7MXILqzRo0ezceNGli5d6u1QKjx326p79+4uf1n36NGDVq1a8e677/LMM8+Ud5he1aJFC9auXUtmZiZfffUVI0eOZNGiRaX+gq7OytJW1fme2rt3Lw8++CALFy6s0BOclch4SO3atTl8+LBL3eHDhwkPD1dvjBu6du1arX6hjxkzhh9++IHFixef9a+60u6t2rVrl2eIFUZZ2up0fn5+XHTRRWzbtq2coqs4/P39adq0KQAXX3wxK1eu5NVXX+Xdd98tdm51v6fK0lanq0731KpVq0hNTXXpKbfb7SxevJg33niDgoICfHx8XF7jjXtLc2Q8pHv37vz0008udQsXLjzjuKv8Ze3atdSpU8fbYZQ7wzAYM2YM3377LT///DONGjU662uq6711Lm11OrvdzoYNG6rFvXU6h8NBQUFBiceq6z1VmjO11emq0z3Vv39/NmzYwNq1a51fnTt35qabbmLt2rXFkhjw0r1VbtOIK7ns7GxjzZo1xpo1awzAmDFjhrFmzRpj9+7dhmEYxrhx44xbbrnFef6OHTuM4OBg49FHHzX+/PNP48033zR8fHyMefPmeesjXDBlbatXXnnF+O6774ytW7caGzZsMB588EHDarUaP/74o7c+wgVz7733GhEREUZycrJx8OBB51deXp7znFtuucUYN26cs7xs2TLD19fXeOmll4w///zTmDRpkuHn52ds2LDBGx/hgjmXtpoyZYoxf/58Y/v27caqVauMG2+80QgMDDT++OMPb3yEC2bcuHHGokWLjJ07dxrr1683xo0bZ1gsFmPBggWGYeieOlVZ26q63lOlOf2ppYpwbymRKcXJR4RP/xo5cqRhGIYxcuRIIyEhodhrOnbsaPj7+xuNGzc2Zs2adcHj9oayttX06dONJk2aGIGBgUZUVJTRp08f4+eff/ZO8BdYSe0EuNwrCQkJzrY76YsvvjCaN29u+Pv7G23atDH+97//XdjAveBc2mrs2LFGgwYNDH9/f6NWrVrGZZddZqxevfrCB3+B3X777UbDhg0Nf39/IyYmxujfv7/zF7Nh6J46VVnbqrreU6U5PZGpCPeWxTAMo/z6e0RERETKj+bIiIiISKWlREZEREQqLSUyIiIiUmkpkREREZFKS4mMiIiIVFpKZERERKTSUiIjIiIilZYSGRGpNiwWC9999523wxARD1IiIyIelZaWxr333kuDBg0ICAigdu3aJCYmsmzZMm+HJiJVkHa/FhGPGj58OIWFhXz00Uc0btyYw4cP89NPP3HkyBFvhyYiVZB6ZETEYzIyMliyZAnTp0+nb9++NGzYkK5duzJ+/HiuuOIKAGbMmEG7du0ICQkhLi6O++67j5ycHOc1Zs+eTY0aNfjhhx9o0aIFwcHBXHvtteTl5fHRRx8RHx9PZGQkDzzwAHa73fm6+Ph4nnnmGf72t78REhJCvXr1ePPNN88Y7969e7n++uupUaMGUVFRXHnllezatct5PDk5ma5duxISEkKNGjXo2bMnu3fv9myjich5USIjIh4TGhpKaGgo3333HQUFBSWeY7Vaee211/jjjz/46KOP+Pnnn3nsscdczsnLy+O1117j888/Z968eSQnJ3P11VczZ84c5syZw8cff8y7777LV1995fK6F198kQ4dOrBmzRrGjRvHgw8+yMKFC0uMw2azkZiYSFhYGEuWLGHZsmWEhoYyePBgCgsLKSoq4qqrriIhIYH169ezYsUK7r77biwWi2caS0Q8o1y3pBSRauerr74yIiMjjcDAQKNHjx7G+PHjjXXr1pV6/pdffmnUrFnTWZ41a5YBGNu2bXPW3XPPPUZwcLCRnZ3trEtMTDTuueceZ7lhw4bG4MGDXa59ww03GEOGDHGWAePbb781DMMwPv74Y6NFixaGw+FwHi8oKDCCgoKM+fPnG0eOHDEAIzk5ueyNICIXjHpkRMSjhg8fzoEDB/j+++8ZPHgwycnJdOrUidmzZwPw448/0r9/f+rVq0dYWBi33HILR44cIS8vz3mN4OBgmjRp4izXqlWL+Ph4QkNDXepSU1Nd3rt79+7Fyn/++WeJca5bt45t27YRFhbm7EmKiooiPz+f7du3ExUVxahRo0hMTGTYsGG8+uqrHDx48HybR0Q8TImMiHhcYGAgAwcOZOLEiSxfvpxRo0YxadIkdu3axeWXX0779u35+uuvWbVqlXMeS2FhofP1fn5+LtezWCwl1jkcjnOOMScnh4svvpi1a9e6fG3ZsoURI0YAMGvWLFasWEGPHj34z3/+Q/Pmzfnll1/O+T1FxPOUyIhIuWvdujW5ubmsWrUKh8PByy+/zCWXXELz5s05cOCAx97n9CTjl19+oVWrViWe26lTJ7Zu3UpsbCxNmzZ1+YqIiHCed9FFFzF+/HiWL19O27Zt+eyzzzwWr4icPyUyIuIxR44coV+/fnzyySesX7+enTt38uWXX/LCCy9w5ZVX0rRpU2w2G6+//jo7duzg448/5p133vHY+y9btowXXniBLVu28Oabb/Lll1/y4IMPlnjuTTfdRHR0NFdeeSVLlixh586dJCcn88ADD7Bv3z527tzJ+PHjWbFiBbt372bBggVs3bq11MRIRLxD68iIiMeEhobSrVs3XnnlFbZv347NZiMuLo677rqLCRMmEBQUxIwZM5g+fTrjx4+nd+/eTJ06lVtvvdUj7//II4/w+++/M2XKFMLDw5kxYwaJiYklnhscHMzixYt5/PHHueaaa8jOzqZevXr079+f8PBwjh8/zubNm/noo484cuQIderUYfTo0dxzzz0eiVVEPMNiGIbh7SBERM5XfHw8Y8eOZezYsd4ORUQuIA0tiYiISKWlREZEREQqLQ0tiYiISKWlHhkRERGptJTIiIiISKWlREZEREQqLSUyIiIiUmkpkREREZFKS4mMiIiIVFpKZERERKTSUiIjIiIilZYSGREREam0/h+WHQxHPYdlmgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "x = np.arange(1, 5)\n",
        "plt.plot(x, x*1.5, label='Normal')\n",
        "#[<matplotlib.lines.Line2D object at 0x2ab5f50>]\n",
        "plt.plot(x, x*3.0, label='Fast')\n",
        "#[<matplotlib.lines.Line2D object at 0x2ac5210>]\n",
        "plt.plot(x, x/3.0, label='Slow')\n",
        "##[<matplotlib.lines.Line2D object at 0x2ac5650>]\n",
        "plt.grid(True)\n",
        "plt.title('Sample Growth of a Measure')\n",
        "#<matplotlib.text.Text object at 0x2aa8890>\n",
        "plt.xlabel('Samples')\n",
        "#<matplotlib.text.Text object at 0x2aa6150>\n",
        "plt.ylabel('Values Measured')\n",
        "#<matplotlib.text.Text object at 0x2aa6d10>\n",
        "plt.legend(loc='upper left')\n",
        "#<matplotlib.legend.Legend object at 0x2ac5c50>\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FVqbXUVE2AY"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285,
          "referenced_widgets": [
            "a00406347ea040ee876d4471ad2ba4f4",
            "b7c474f82c7742f6992f6b9a73163483",
            "85625457fdf84307a4084c66256022dc",
            "789423c2348f4a3d8ce2855be1d48ed9",
            "8d67d46492804c29b5e2b98a5fbfa66b",
            "4dbe4501a68f4daf9ace19b6b90d1df2",
            "123006ec2a504c6481fc16a7339053f4",
            "0091007118864ff08061159282c40c2e",
            "0604a26fda424d2d8cbfbffa7605ddf6",
            "ce6cc073f2694ee49e87628b2c97bee9",
            "2c24395d2fe34e3ca2cb15c2df950700"
          ]
        },
        "id": "ea93chEweCw_",
        "outputId": "df7eba54-7146-4dfa-a352-4ab133e90b38"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a00406347ea040ee876d4471ad2ba4f4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 0\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Epoch 1\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Epoch 2\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Train time on cuda: 0.000 secounds\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "from timeit import default_timer as timer\n",
        "train_time_start_on_gpu = timer()\n",
        "\n",
        "epochs = 3\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch {epoch}\\n---------\")\n",
        "  train_step(model=model_1,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "              accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "\n",
        "  test_step(model=model_1,\n",
        "            dataloader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_1 = print_train_time(start=train_time_end_on_gpu,\n",
        "                                            end=train_time_end_on_gpu,\n",
        "                                            device=device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPN-HzUHEWEE",
        "outputId": "71083ae7-ce65-401c-bbff-d1144ce675a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV0',\n",
              " 'model_loss': 0.0010611432371661067,\n",
              " 'model_acc': 0.30028917735831345}"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_0_result\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc-nK4miGzTM",
        "outputId": "f652f3cc-d0b6-4177-b879-6a07c6dd78af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.47664 | Test Acc: 83.42652\n"
          ]
        }
      ],
      "source": [
        "# Get model_1 result dictionary\n",
        "\n",
        "model_1_results = test_step(model=model_1,\n",
        "                             dataloader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn,\n",
        "                             device=device)\n",
        "model_1_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71rdZxx6MOGz",
        "outputId": "a5a8d13e-b779-4e3a-db7d-67ec365a3f94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV0',\n",
              " 'model_loss': 0.0010611432371661067,\n",
              " 'model_acc': 0.30028917735831345}"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_0_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BJsTSqZqN3hK"
      },
      "outputs": [],
      "source": [
        "# Model2 CNN'S\n",
        "class FashionMNISTModelV2(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=input_shape,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.conv_block_2 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=hidden_units*7*7,\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    x = self.conv_block_1(x)\n",
        "    print(x.shape)\n",
        "    x = self.conv_block_2(x)\n",
        "    print(x.shape)\n",
        "    x = self.classifier(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8x9rmZ9qpm6w"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV2(input_shape=1,\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYRm_V8TvpYE",
        "outputId": "d14efbb3-6788-4e87-f9fe-6bfe7c97b674"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('conv_block_1.0.weight',\n",
              "              tensor([[[[ 0.2548,  0.2767, -0.0781],\n",
              "                        [ 0.3062, -0.0730,  0.0673],\n",
              "                        [-0.1623,  0.1958,  0.2938]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2445,  0.2897,  0.0624],\n",
              "                        [ 0.2463,  0.0451,  0.1607],\n",
              "                        [-0.0471,  0.2570,  0.0493]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1556,  0.0850, -0.1536],\n",
              "                        [-0.0391, -0.1354,  0.2211],\n",
              "                        [-0.2631, -0.1537, -0.0941]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2004,  0.0315, -0.3292],\n",
              "                        [ 0.3010, -0.2832,  0.2573],\n",
              "                        [ 0.0555, -0.1082,  0.2060]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0520,  0.2693,  0.0364],\n",
              "                        [-0.1051,  0.0896, -0.0904],\n",
              "                        [ 0.1403,  0.2976,  0.1927]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1457,  0.1924,  0.0596],\n",
              "                        [ 0.1693, -0.2032, -0.3300],\n",
              "                        [-0.1288, -0.2557,  0.2735]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0960,  0.1381,  0.1054],\n",
              "                        [-0.0058,  0.2609, -0.2368],\n",
              "                        [ 0.0210, -0.2275,  0.1028]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1148,  0.1021, -0.0694],\n",
              "                        [ 0.2765, -0.1976, -0.1988],\n",
              "                        [-0.1988,  0.2998,  0.1111]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.3208, -0.2751, -0.3306],\n",
              "                        [-0.2608, -0.2242,  0.1350],\n",
              "                        [ 0.1194,  0.2770, -0.1721]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2272,  0.1769, -0.1347],\n",
              "                        [ 0.2023, -0.0791,  0.1907],\n",
              "                        [-0.2590, -0.1682,  0.1016]]]], device='cuda:0')),\n",
              "             ('conv_block_1.0.bias',\n",
              "              tensor([ 0.0705, -0.0850,  0.1987,  0.2266, -0.2417, -0.1780,  0.3052, -0.1125,\n",
              "                      -0.1182, -0.3225], device='cuda:0')),\n",
              "             ('conv_block_1.2.weight',\n",
              "              tensor([[[[-0.0604,  0.0263, -0.0139],\n",
              "                        [-0.0765,  0.0025, -0.0720],\n",
              "                        [-0.0894, -0.0580, -0.0923]],\n",
              "              \n",
              "                       [[-0.0671,  0.1054,  0.0199],\n",
              "                        [ 0.0325, -0.0983, -0.0692],\n",
              "                        [-0.0351,  0.0165, -0.0928]],\n",
              "              \n",
              "                       [[-0.0454, -0.0631,  0.0003],\n",
              "                        [-0.0392, -0.0073, -0.0714],\n",
              "                        [-0.0724, -0.0615, -0.0361]],\n",
              "              \n",
              "                       [[-0.0832,  0.0884, -0.0209],\n",
              "                        [ 0.0907,  0.0328, -0.0893],\n",
              "                        [ 0.0729, -0.0290, -0.0404]],\n",
              "              \n",
              "                       [[-0.0875, -0.1048,  0.0302],\n",
              "                        [-0.0230,  0.0410, -0.0865],\n",
              "                        [ 0.0783, -0.0774, -0.0182]],\n",
              "              \n",
              "                       [[ 0.0220,  0.0544,  0.0851],\n",
              "                        [ 0.0960, -0.0836,  0.0265],\n",
              "                        [-0.0453, -0.0116, -0.0789]],\n",
              "              \n",
              "                       [[ 0.0960, -0.0774,  0.0563],\n",
              "                        [ 0.0370,  0.0343, -0.0570],\n",
              "                        [ 0.0958,  0.0232,  0.0136]],\n",
              "              \n",
              "                       [[-0.0929,  0.0442, -0.0158],\n",
              "                        [-0.0483,  0.0905,  0.0235],\n",
              "                        [-0.0583, -0.0534, -0.0050]],\n",
              "              \n",
              "                       [[ 0.0589, -0.0269, -0.0601],\n",
              "                        [-0.0361, -0.0787,  0.0376],\n",
              "                        [ 0.0816, -0.0992,  0.0245]],\n",
              "              \n",
              "                       [[ 0.0545,  0.0191, -0.0375],\n",
              "                        [ 0.0550,  0.0554,  0.0394],\n",
              "                        [-0.0185, -0.0279,  0.0113]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0186, -0.0314,  0.0674],\n",
              "                        [ 0.0906, -0.0104, -0.0236],\n",
              "                        [ 0.0015, -0.0063,  0.0253]],\n",
              "              \n",
              "                       [[ 0.0295, -0.0957, -0.0389],\n",
              "                        [ 0.0888,  0.0411, -0.0052],\n",
              "                        [-0.0636, -0.0645, -0.0944]],\n",
              "              \n",
              "                       [[-0.0344,  0.0356,  0.0672],\n",
              "                        [ 0.0487, -0.0932, -0.0634],\n",
              "                        [-0.0166,  0.1020,  0.0152]],\n",
              "              \n",
              "                       [[-0.0273,  0.0436, -0.0401],\n",
              "                        [-0.0682,  0.0769, -0.0479],\n",
              "                        [-0.0211, -0.1049,  0.0705]],\n",
              "              \n",
              "                       [[ 0.0799,  0.0384, -0.0735],\n",
              "                        [-0.1040, -0.0856,  0.0786],\n",
              "                        [ 0.0506,  0.0887,  0.0552]],\n",
              "              \n",
              "                       [[ 0.0267, -0.0010, -0.0802],\n",
              "                        [-0.0903, -0.0986,  0.0432],\n",
              "                        [-0.0518, -0.0212, -0.0607]],\n",
              "              \n",
              "                       [[-0.0192, -0.0742, -0.0689],\n",
              "                        [ 0.0350, -0.0313,  0.0651],\n",
              "                        [-0.0338, -0.0773, -0.0186]],\n",
              "              \n",
              "                       [[-0.0511, -0.0322, -0.1003],\n",
              "                        [ 0.0590, -0.0734,  0.0530],\n",
              "                        [ 0.0478,  0.0753, -0.0809]],\n",
              "              \n",
              "                       [[ 0.0758, -0.0498,  0.0391],\n",
              "                        [ 0.0990, -0.0149, -0.0008],\n",
              "                        [-0.0243, -0.0880,  0.0506]],\n",
              "              \n",
              "                       [[-0.1046,  0.0654,  0.0789],\n",
              "                        [ 0.0997, -0.0249, -0.0866],\n",
              "                        [ 0.0237,  0.0582, -0.1049]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0239, -0.0632, -0.0092],\n",
              "                        [-0.0519, -0.0431, -0.0335],\n",
              "                        [-0.1002,  0.0865,  0.0884]],\n",
              "              \n",
              "                       [[-0.0165, -0.0120, -0.0430],\n",
              "                        [-0.0952, -0.1026,  0.0392],\n",
              "                        [-0.0579, -0.0678, -0.0082]],\n",
              "              \n",
              "                       [[-0.0351, -0.0341,  0.0034],\n",
              "                        [-0.0224, -0.0363, -0.0505],\n",
              "                        [-0.0858,  0.0884, -0.0422]],\n",
              "              \n",
              "                       [[ 0.0279, -0.0366,  0.0086],\n",
              "                        [ 0.0983,  0.0486, -0.0913],\n",
              "                        [ 0.0418,  0.1001,  0.0277]],\n",
              "              \n",
              "                       [[ 0.0707,  0.1039, -0.0162],\n",
              "                        [ 0.0219, -0.0733, -0.0217],\n",
              "                        [ 0.0781,  0.0540, -0.0667]],\n",
              "              \n",
              "                       [[-0.0845, -0.0720, -0.1040],\n",
              "                        [-0.0813, -0.0261,  0.0711],\n",
              "                        [ 0.0176, -0.0802, -0.0846]],\n",
              "              \n",
              "                       [[ 0.0524, -0.0784, -0.0130],\n",
              "                        [ 0.0506, -0.0488, -0.0115],\n",
              "                        [-0.0092, -0.0249, -0.0534]],\n",
              "              \n",
              "                       [[-0.0940, -0.0852, -0.0564],\n",
              "                        [ 0.1018, -0.0509, -0.0708],\n",
              "                        [ 0.0256,  0.0291,  0.0578]],\n",
              "              \n",
              "                       [[ 0.0801,  0.0587, -0.1045],\n",
              "                        [ 0.0093,  0.0639, -0.0097],\n",
              "                        [-0.0621,  0.1005, -0.0394]],\n",
              "              \n",
              "                       [[-0.0600, -0.0950,  0.0047],\n",
              "                        [ 0.0467,  0.0233,  0.0208],\n",
              "                        [-0.0799, -0.0984,  0.0019]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0961,  0.0608, -0.0614],\n",
              "                        [-0.0137, -0.0777, -0.0509],\n",
              "                        [ 0.0191,  0.0574,  0.0873]],\n",
              "              \n",
              "                       [[-0.0968,  0.0705, -0.0743],\n",
              "                        [ 0.0395,  0.0892,  0.0015],\n",
              "                        [ 0.0959, -0.0898, -0.0403]],\n",
              "              \n",
              "                       [[ 0.0615, -0.0230, -0.0216],\n",
              "                        [-0.0439,  0.0727,  0.0517],\n",
              "                        [ 0.0338, -0.0592, -0.0856]],\n",
              "              \n",
              "                       [[ 0.0114,  0.0312, -0.0487],\n",
              "                        [-0.0295,  0.0712,  0.0084],\n",
              "                        [ 0.0048, -0.0259, -0.0955]],\n",
              "              \n",
              "                       [[-0.0991, -0.0504, -0.0536],\n",
              "                        [ 0.0328, -0.0307, -0.0412],\n",
              "                        [ 0.1005,  0.0367,  0.0751]],\n",
              "              \n",
              "                       [[-0.0510, -0.0431,  0.0387],\n",
              "                        [-0.0702, -0.0689, -0.0051],\n",
              "                        [-0.0386, -0.0790,  0.0625]],\n",
              "              \n",
              "                       [[ 0.0848,  0.0171, -0.0184],\n",
              "                        [-0.0976, -0.0384,  0.0268],\n",
              "                        [ 0.0497, -0.0133, -0.0417]],\n",
              "              \n",
              "                       [[ 0.0587, -0.0839,  0.0666],\n",
              "                        [-0.0409,  0.0016, -0.0208],\n",
              "                        [ 0.0128, -0.0319,  0.0766]],\n",
              "              \n",
              "                       [[-0.0027,  0.0823,  0.1013],\n",
              "                        [-0.0514, -0.0769,  0.0846],\n",
              "                        [ 0.0826, -0.0805, -0.0081]],\n",
              "              \n",
              "                       [[-0.1039, -0.0863,  0.0204],\n",
              "                        [ 0.0280,  0.0223, -0.0287],\n",
              "                        [ 0.0972,  0.0151, -0.0622]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0060,  0.0253,  0.0369],\n",
              "                        [-0.0745,  0.0395, -0.0539],\n",
              "                        [-0.0876, -0.0576,  0.1017]],\n",
              "              \n",
              "                       [[ 0.0901,  0.0944,  0.0619],\n",
              "                        [ 0.0796, -0.0141, -0.0580],\n",
              "                        [ 0.0527, -0.0546, -0.0711]],\n",
              "              \n",
              "                       [[-0.0337,  0.0221,  0.0543],\n",
              "                        [-0.0409, -0.0620,  0.0142],\n",
              "                        [-0.0621, -0.0686,  0.0549]],\n",
              "              \n",
              "                       [[-0.0177,  0.0963,  0.1025],\n",
              "                        [ 0.0315,  0.0363,  0.0243],\n",
              "                        [ 0.0017, -0.0077,  0.0014]],\n",
              "              \n",
              "                       [[ 0.0394,  0.0980, -0.0273],\n",
              "                        [-0.0446, -0.0255, -0.0509],\n",
              "                        [ 0.0179,  0.0787,  0.0824]],\n",
              "              \n",
              "                       [[ 0.0484, -0.0776, -0.0566],\n",
              "                        [-0.0232, -0.0194,  0.0087],\n",
              "                        [-0.0968,  0.0328, -0.0804]],\n",
              "              \n",
              "                       [[-0.0667, -0.0876,  0.0918],\n",
              "                        [-0.0998,  0.0795, -0.0035],\n",
              "                        [-0.0123,  0.0659, -0.0097]],\n",
              "              \n",
              "                       [[ 0.0661,  0.0762, -0.0915],\n",
              "                        [ 0.0406,  0.0199,  0.0227],\n",
              "                        [ 0.0154,  0.0288, -0.0507]],\n",
              "              \n",
              "                       [[-0.0135,  0.1002,  0.0708],\n",
              "                        [-0.0040, -0.0991,  0.0046],\n",
              "                        [-0.0718,  0.0857, -0.0640]],\n",
              "              \n",
              "                       [[-0.0076, -0.0234,  0.0188],\n",
              "                        [ 0.0992,  0.0100,  0.0610],\n",
              "                        [ 0.0818,  0.0851, -0.0364]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0236,  0.0508, -0.0288],\n",
              "                        [ 0.0494, -0.0230, -0.0715],\n",
              "                        [ 0.0429,  0.0162,  0.0470]],\n",
              "              \n",
              "                       [[ 0.1047,  0.0720,  0.0999],\n",
              "                        [ 0.0056, -0.0907, -0.0739],\n",
              "                        [-0.0655, -0.0929, -0.0528]],\n",
              "              \n",
              "                       [[-0.0970, -0.0973, -0.0630],\n",
              "                        [-0.1039, -0.0647,  0.0402],\n",
              "                        [ 0.0879, -0.0314, -0.0307]],\n",
              "              \n",
              "                       [[ 0.0563, -0.0520, -0.0498],\n",
              "                        [ 0.0649, -0.0918,  0.0129],\n",
              "                        [ 0.0931,  0.0181,  0.0287]],\n",
              "              \n",
              "                       [[-0.0614, -0.0015,  0.0058],\n",
              "                        [ 0.0259,  0.0410,  0.0916],\n",
              "                        [-0.0805,  0.0032, -0.0527]],\n",
              "              \n",
              "                       [[-0.0834, -0.0084, -0.0928],\n",
              "                        [ 0.0736,  0.0122, -0.0568],\n",
              "                        [ 0.0551, -0.0998, -0.0408]],\n",
              "              \n",
              "                       [[-0.0205, -0.0896, -0.0670],\n",
              "                        [-0.0172,  0.0800,  0.1018],\n",
              "                        [ 0.0671, -0.0629, -0.0690]],\n",
              "              \n",
              "                       [[ 0.0920,  0.0373,  0.0028],\n",
              "                        [ 0.0143, -0.0847, -0.0352],\n",
              "                        [ 0.1015, -0.0260, -0.0053]],\n",
              "              \n",
              "                       [[-0.0875, -0.0590, -0.0022],\n",
              "                        [-0.0655, -0.0131,  0.0429],\n",
              "                        [-0.1031,  0.0313, -0.0697]],\n",
              "              \n",
              "                       [[-0.0514,  0.0405,  0.0838],\n",
              "                        [-0.0288, -0.0433, -0.0953],\n",
              "                        [-0.0544, -0.0923, -0.0241]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0215, -0.0988,  0.0920],\n",
              "                        [ 0.0661, -0.1032, -0.0503],\n",
              "                        [ 0.0344, -0.0217, -0.0115]],\n",
              "              \n",
              "                       [[-0.0476,  0.0847, -0.0589],\n",
              "                        [ 0.0874,  0.0068,  0.0212],\n",
              "                        [ 0.0822, -0.0174, -0.0600]],\n",
              "              \n",
              "                       [[-0.0170,  0.0855, -0.0782],\n",
              "                        [ 0.0239, -0.1036,  0.0553],\n",
              "                        [ 0.0389,  0.0045,  0.0452]],\n",
              "              \n",
              "                       [[ 0.0001,  0.0583, -0.0834],\n",
              "                        [-0.0155,  0.0468,  0.1050],\n",
              "                        [ 0.0537, -0.0767,  0.0811]],\n",
              "              \n",
              "                       [[-0.0235, -0.0225, -0.0958],\n",
              "                        [-0.0166,  0.0746,  0.0147],\n",
              "                        [-0.0614,  0.0324, -0.0338]],\n",
              "              \n",
              "                       [[ 0.0962, -0.0915, -0.0333],\n",
              "                        [-0.1018, -0.0415,  0.0332],\n",
              "                        [ 0.1015,  0.0177,  0.1033]],\n",
              "              \n",
              "                       [[ 0.0206,  0.0609,  0.0845],\n",
              "                        [ 0.0881, -0.0590,  0.0969],\n",
              "                        [ 0.0639, -0.0493, -0.0503]],\n",
              "              \n",
              "                       [[-0.0884,  0.0265, -0.0854],\n",
              "                        [ 0.0445,  0.0333, -0.0916],\n",
              "                        [ 0.0287, -0.0086,  0.0482]],\n",
              "              \n",
              "                       [[ 0.0605, -0.1048,  0.0967],\n",
              "                        [ 0.0884,  0.0419, -0.0963],\n",
              "                        [-0.0377, -0.0305, -0.0271]],\n",
              "              \n",
              "                       [[ 0.0594,  0.0383,  0.0835],\n",
              "                        [-0.0395,  0.0355,  0.0375],\n",
              "                        [-0.0878, -0.1022, -0.0547]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0722, -0.0992, -0.0918],\n",
              "                        [ 0.0591,  0.0569,  0.0867],\n",
              "                        [-0.0796, -0.0771,  0.0541]],\n",
              "              \n",
              "                       [[ 0.0917,  0.0631,  0.0165],\n",
              "                        [ 0.0347,  0.1000, -0.0680],\n",
              "                        [-0.0479,  0.0737, -0.0721]],\n",
              "              \n",
              "                       [[-0.0581,  0.0769,  0.0333],\n",
              "                        [ 0.0341, -0.0447, -0.0015],\n",
              "                        [ 0.0965, -0.0633,  0.0008]],\n",
              "              \n",
              "                       [[ 0.0501, -0.0728,  0.1024],\n",
              "                        [-0.0527, -0.0253, -0.0285],\n",
              "                        [-0.0687, -0.1034,  0.0594]],\n",
              "              \n",
              "                       [[ 0.0280, -0.0987, -0.0678],\n",
              "                        [ 0.1042,  0.0403,  0.0423],\n",
              "                        [-0.0631, -0.0462, -0.0159]],\n",
              "              \n",
              "                       [[-0.0193, -0.0722,  0.0087],\n",
              "                        [ 0.0105, -0.0133,  0.0146],\n",
              "                        [-0.0418,  0.0274,  0.0398]],\n",
              "              \n",
              "                       [[-0.0555, -0.1045,  0.0552],\n",
              "                        [ 0.0251, -0.0536,  0.1016],\n",
              "                        [-0.0477,  0.0712,  0.0535]],\n",
              "              \n",
              "                       [[-0.0884,  0.0680, -0.0969],\n",
              "                        [-0.0584, -0.0176, -0.0711],\n",
              "                        [ 0.1030, -0.0211,  0.0419]],\n",
              "              \n",
              "                       [[-0.0941,  0.0607, -0.0328],\n",
              "                        [-0.0802,  0.0154,  0.0511],\n",
              "                        [ 0.0912, -0.0644, -0.0519]],\n",
              "              \n",
              "                       [[ 0.0203,  0.0286,  0.0405],\n",
              "                        [ 0.0579, -0.0239,  0.0586],\n",
              "                        [ 0.0777, -0.0275,  0.0750]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0515,  0.0930, -0.0599],\n",
              "                        [-0.0521, -0.0305,  0.0053],\n",
              "                        [ 0.0633, -0.0602,  0.0528]],\n",
              "              \n",
              "                       [[-0.0378,  0.0637, -0.0050],\n",
              "                        [-0.0923, -0.0580, -0.0763],\n",
              "                        [ 0.0523, -0.0707, -0.0088]],\n",
              "              \n",
              "                       [[ 0.0227, -0.0578,  0.0304],\n",
              "                        [-0.1029, -0.0754, -0.0955],\n",
              "                        [-0.0319, -0.0384,  0.0151]],\n",
              "              \n",
              "                       [[-0.0195,  0.0496,  0.0966],\n",
              "                        [ 0.0378, -0.0415, -0.0987],\n",
              "                        [ 0.0382, -0.0522,  0.0536]],\n",
              "              \n",
              "                       [[ 0.0705,  0.0407,  0.0989],\n",
              "                        [ 0.1001,  0.0223, -0.0768],\n",
              "                        [ 0.0942, -0.0500, -0.0498]],\n",
              "              \n",
              "                       [[ 0.0882,  0.0817,  0.0318],\n",
              "                        [ 0.0066, -0.0887, -0.0109],\n",
              "                        [ 0.1011,  0.0268,  0.0090]],\n",
              "              \n",
              "                       [[-0.0219, -0.0368,  0.0628],\n",
              "                        [ 0.0065,  0.0686, -0.0187],\n",
              "                        [ 0.0461,  0.0435,  0.0168]],\n",
              "              \n",
              "                       [[ 0.0662,  0.0661,  0.0977],\n",
              "                        [ 0.0810, -0.0270, -0.0892],\n",
              "                        [ 0.0193, -0.0009, -0.0275]],\n",
              "              \n",
              "                       [[-0.0177,  0.0050,  0.0769],\n",
              "                        [ 0.0329, -0.0374, -0.0433],\n",
              "                        [-0.0261, -0.0407,  0.0948]],\n",
              "              \n",
              "                       [[ 0.0558,  0.0952,  0.0003],\n",
              "                        [ 0.0213,  0.0366, -0.0998],\n",
              "                        [ 0.0094, -0.0071, -0.0591]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0818,  0.0933,  0.0857],\n",
              "                        [ 0.0489,  0.1006, -0.0428],\n",
              "                        [-0.0182,  0.0399, -0.0174]],\n",
              "              \n",
              "                       [[-0.0207, -0.0871,  0.0283],\n",
              "                        [-0.0637,  0.0038,  0.1028],\n",
              "                        [-0.0324, -0.0332,  0.0636]],\n",
              "              \n",
              "                       [[-0.0388, -0.0091,  0.0984],\n",
              "                        [-0.0432, -0.0754, -0.0590],\n",
              "                        [-0.0292, -0.0500, -0.0547]],\n",
              "              \n",
              "                       [[ 0.0426,  0.0179, -0.0337],\n",
              "                        [-0.0819, -0.0332, -0.0445],\n",
              "                        [-0.0343, -0.0951,  0.0227]],\n",
              "              \n",
              "                       [[-0.0774, -0.0821, -0.0861],\n",
              "                        [ 0.0440, -0.0635, -0.0435],\n",
              "                        [ 0.0826,  0.0560,  0.0604]],\n",
              "              \n",
              "                       [[-0.1001, -0.0756, -0.0398],\n",
              "                        [ 0.0871,  0.0108, -0.0788],\n",
              "                        [ 0.0007, -0.0819, -0.0231]],\n",
              "              \n",
              "                       [[-0.0290,  0.0912,  0.0326],\n",
              "                        [-0.0184,  0.0178, -0.0304],\n",
              "                        [ 0.0414,  0.0417,  0.0283]],\n",
              "              \n",
              "                       [[-0.0411,  0.0899, -0.0152],\n",
              "                        [-0.0410,  0.0660,  0.0859],\n",
              "                        [ 0.1049,  0.0312, -0.0359]],\n",
              "              \n",
              "                       [[ 0.0535,  0.0904, -0.1034],\n",
              "                        [-0.0131, -0.0719,  0.0196],\n",
              "                        [ 0.0436, -0.0218, -0.0088]],\n",
              "              \n",
              "                       [[ 0.0474, -0.0177, -0.0885],\n",
              "                        [ 0.0843, -0.0531, -0.0116],\n",
              "                        [ 0.0099, -0.0063, -0.0992]]]], device='cuda:0')),\n",
              "             ('conv_block_1.2.bias',\n",
              "              tensor([ 0.0484, -0.0479, -0.0547,  0.0252, -0.0550, -0.0487, -0.0355, -0.0396,\n",
              "                      -0.0440, -0.0284], device='cuda:0')),\n",
              "             ('conv_block_2.0.weight',\n",
              "              tensor([[[[ 2.7393e-02, -8.5299e-02, -6.3802e-02],\n",
              "                        [ 1.5381e-03,  1.4659e-02,  5.8217e-02],\n",
              "                        [-7.4044e-02,  3.3646e-02,  5.9914e-02]],\n",
              "              \n",
              "                       [[ 5.8530e-02, -9.8180e-02, -4.0225e-02],\n",
              "                        [-9.0606e-02, -6.6704e-02,  5.8711e-02],\n",
              "                        [-1.5740e-02,  4.4769e-02, -6.1876e-02]],\n",
              "              \n",
              "                       [[ 1.6018e-02, -6.3758e-02,  5.2693e-02],\n",
              "                        [-4.6104e-02, -2.6432e-02, -9.1456e-02],\n",
              "                        [ 3.4822e-04,  1.0008e-01,  5.1163e-02]],\n",
              "              \n",
              "                       [[-5.6240e-02,  1.4176e-03, -1.1558e-02],\n",
              "                        [-8.4862e-02,  8.2650e-02,  1.6993e-03],\n",
              "                        [ 2.2199e-02, -4.2567e-02, -4.9323e-02]],\n",
              "              \n",
              "                       [[ 1.7381e-02,  3.8971e-02,  2.3643e-02],\n",
              "                        [-5.0801e-02,  1.0234e-01, -1.5517e-02],\n",
              "                        [-6.4554e-02, -4.9301e-02,  1.0377e-01]],\n",
              "              \n",
              "                       [[ 5.0766e-06, -1.4309e-02, -4.3867e-02],\n",
              "                        [-2.7633e-02, -8.8779e-02, -8.3767e-02],\n",
              "                        [ 6.1695e-02,  9.0172e-02,  1.0059e-01]],\n",
              "              \n",
              "                       [[-7.6099e-02,  5.7012e-02, -6.5245e-02],\n",
              "                        [ 6.2883e-02,  7.6058e-02,  8.1573e-02],\n",
              "                        [ 7.5900e-02,  6.5941e-02,  2.0516e-03]],\n",
              "              \n",
              "                       [[ 4.8434e-02, -3.7712e-02,  4.5899e-02],\n",
              "                        [-3.3879e-02, -1.7700e-03, -9.1746e-02],\n",
              "                        [-2.7562e-02, -5.5432e-02, -3.5557e-02]],\n",
              "              \n",
              "                       [[-6.7313e-02, -9.4810e-02,  6.8639e-03],\n",
              "                        [ 6.8408e-02,  9.6001e-02,  6.1512e-02],\n",
              "                        [-5.4638e-02, -1.0425e-01,  3.9983e-02]],\n",
              "              \n",
              "                       [[ 5.9062e-02, -9.0495e-02,  3.7798e-02],\n",
              "                        [ 8.9121e-02,  6.3853e-03, -6.3505e-02],\n",
              "                        [ 8.6423e-02,  4.5011e-02,  6.9802e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-7.1287e-02,  6.1342e-02, -7.2002e-02],\n",
              "                        [ 1.0430e-01, -4.4662e-02,  6.3516e-02],\n",
              "                        [ 2.1107e-02,  2.7935e-02, -1.6165e-02]],\n",
              "              \n",
              "                       [[ 4.3295e-02, -4.3932e-02, -9.9357e-02],\n",
              "                        [-4.0499e-02,  8.2592e-02, -2.7751e-02],\n",
              "                        [ 3.3132e-02, -3.8973e-02,  7.9073e-02]],\n",
              "              \n",
              "                       [[ 6.3086e-02,  3.7211e-02, -5.3881e-02],\n",
              "                        [-8.6133e-02,  3.9686e-03, -6.1839e-02],\n",
              "                        [ 8.6667e-02, -1.0130e-01,  4.7104e-02]],\n",
              "              \n",
              "                       [[ 1.0508e-01,  5.2792e-02,  3.5942e-02],\n",
              "                        [-1.0142e-01,  1.0139e-01, -1.8030e-02],\n",
              "                        [-9.8495e-02,  1.0406e-01, -4.2894e-02]],\n",
              "              \n",
              "                       [[-7.4575e-03,  9.6479e-02, -7.3070e-02],\n",
              "                        [-7.4576e-02,  1.7141e-02, -1.4109e-02],\n",
              "                        [ 2.4280e-02, -8.8407e-02,  3.1524e-03]],\n",
              "              \n",
              "                       [[-4.6882e-02, -5.1820e-02, -9.6517e-02],\n",
              "                        [ 5.5890e-02,  2.0306e-02, -8.9118e-02],\n",
              "                        [ 8.3648e-02,  3.1794e-02,  1.9560e-02]],\n",
              "              \n",
              "                       [[-6.1890e-02,  1.5896e-02,  1.0157e-01],\n",
              "                        [ 7.2299e-02, -8.2100e-02,  9.6220e-02],\n",
              "                        [ 8.1702e-03,  5.0698e-02,  8.1869e-02]],\n",
              "              \n",
              "                       [[ 8.9862e-02, -8.2170e-02,  9.2303e-02],\n",
              "                        [-7.1591e-02,  7.9021e-03, -7.3656e-02],\n",
              "                        [-2.3109e-02, -4.7901e-03, -1.2611e-02]],\n",
              "              \n",
              "                       [[-1.6652e-02,  8.3137e-03,  1.0398e-01],\n",
              "                        [ 6.1244e-02,  5.8973e-02,  4.2190e-02],\n",
              "                        [ 8.1606e-02, -4.8645e-03,  8.3813e-03]],\n",
              "              \n",
              "                       [[ 2.1693e-02, -9.1931e-02, -8.4913e-02],\n",
              "                        [ 1.2923e-02, -4.1241e-02, -1.9342e-03],\n",
              "                        [-2.4187e-02,  1.6408e-02,  6.8581e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.4958e-02,  8.4418e-02,  8.3227e-02],\n",
              "                        [-8.0901e-02, -8.1400e-02, -8.5284e-02],\n",
              "                        [-5.7766e-02, -4.1033e-02, -7.9341e-03]],\n",
              "              \n",
              "                       [[-2.5635e-02, -5.3258e-02, -3.3488e-02],\n",
              "                        [-3.8131e-02,  1.0341e-01, -3.9068e-02],\n",
              "                        [-7.5473e-02,  4.3818e-02, -6.0886e-03]],\n",
              "              \n",
              "                       [[ 8.0698e-02,  6.5863e-02,  9.6843e-02],\n",
              "                        [-7.7197e-02,  6.7764e-02,  8.8464e-02],\n",
              "                        [-5.2054e-02,  9.6890e-02,  7.9019e-02]],\n",
              "              \n",
              "                       [[ 1.1544e-03,  5.0823e-02, -3.6853e-02],\n",
              "                        [-9.1936e-02,  2.6645e-02,  3.1425e-02],\n",
              "                        [-6.8891e-02,  5.1123e-02, -9.0043e-02]],\n",
              "              \n",
              "                       [[ 9.0718e-02,  1.0208e-01,  2.8699e-02],\n",
              "                        [-6.6137e-02,  5.1300e-02,  1.7963e-02],\n",
              "                        [ 2.8663e-02,  3.4643e-02,  8.0254e-02]],\n",
              "              \n",
              "                       [[-4.5309e-02, -2.3711e-02,  2.8746e-02],\n",
              "                        [ 1.1486e-02,  8.5000e-02, -5.5365e-02],\n",
              "                        [-3.8387e-03,  1.9696e-02, -2.7996e-02]],\n",
              "              \n",
              "                       [[ 7.1859e-02,  1.1530e-02, -9.7422e-02],\n",
              "                        [-1.1420e-02, -4.7809e-02,  1.0243e-02],\n",
              "                        [-1.2250e-02, -1.0456e-01, -1.9208e-02]],\n",
              "              \n",
              "                       [[-1.0096e-02, -3.1083e-02,  9.6848e-02],\n",
              "                        [-2.3000e-02,  6.7717e-02,  2.6112e-02],\n",
              "                        [-8.8979e-02,  2.4770e-02,  8.7356e-02]],\n",
              "              \n",
              "                       [[-6.8948e-02, -6.8134e-02,  1.0318e-01],\n",
              "                        [ 8.4697e-02, -5.8807e-02,  6.3429e-02],\n",
              "                        [-1.3485e-02, -1.0393e-01,  7.9198e-03]],\n",
              "              \n",
              "                       [[ 3.4057e-02, -3.1619e-02,  3.6670e-02],\n",
              "                        [-9.0136e-02,  7.3050e-02,  8.9865e-02],\n",
              "                        [ 5.8130e-02,  1.7866e-02,  3.4716e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-7.6269e-02, -2.6339e-02, -1.0063e-02],\n",
              "                        [-5.8659e-02, -7.7857e-02,  7.0900e-02],\n",
              "                        [ 7.1535e-02, -9.5731e-02,  3.3542e-02]],\n",
              "              \n",
              "                       [[ 4.2881e-02,  1.0014e-01,  6.0985e-02],\n",
              "                        [ 9.6907e-02, -3.4510e-02,  7.3827e-02],\n",
              "                        [ 8.5740e-02, -9.9541e-02, -8.4613e-02]],\n",
              "              \n",
              "                       [[ 2.1335e-02,  5.7557e-02, -5.2369e-02],\n",
              "                        [ 1.1609e-02, -1.5303e-04,  2.6680e-02],\n",
              "                        [-5.6642e-02,  5.9455e-02,  7.0098e-02]],\n",
              "              \n",
              "                       [[-7.3139e-02,  1.0211e-03,  2.9247e-04],\n",
              "                        [ 3.3849e-02,  9.8198e-02,  3.0913e-02],\n",
              "                        [-2.3951e-02,  9.4672e-02, -4.0112e-02]],\n",
              "              \n",
              "                       [[-3.0608e-02,  7.1969e-03, -8.0270e-02],\n",
              "                        [ 1.1470e-02, -7.1518e-02,  1.0838e-02],\n",
              "                        [ 1.0099e-02,  1.4591e-02, -8.8891e-02]],\n",
              "              \n",
              "                       [[-1.0012e-01,  4.8501e-02,  9.0399e-02],\n",
              "                        [-9.3537e-02,  3.9043e-02, -7.7594e-02],\n",
              "                        [ 6.6082e-03,  9.8068e-02,  7.9965e-02]],\n",
              "              \n",
              "                       [[-7.7069e-02,  6.5203e-02,  5.5057e-02],\n",
              "                        [-1.6168e-04,  1.0211e-01, -4.1866e-02],\n",
              "                        [-2.4530e-02, -5.3275e-02,  1.5168e-02]],\n",
              "              \n",
              "                       [[ 2.7911e-02,  8.3990e-03, -5.9307e-02],\n",
              "                        [-4.7452e-02,  3.5855e-02, -9.2426e-02],\n",
              "                        [-1.6416e-02, -2.3350e-03, -4.2708e-02]],\n",
              "              \n",
              "                       [[ 3.8360e-02,  6.7940e-03,  7.4004e-02],\n",
              "                        [-9.3616e-03, -6.6528e-02,  7.4477e-02],\n",
              "                        [ 1.4720e-02, -3.0189e-02, -6.9476e-02]],\n",
              "              \n",
              "                       [[ 2.4707e-02, -1.0053e-01,  2.7762e-02],\n",
              "                        [ 5.2119e-02, -9.2465e-02, -6.9009e-02],\n",
              "                        [-7.5781e-02,  8.8597e-02,  8.9611e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.5987e-03,  9.8959e-02, -3.5239e-02],\n",
              "                        [-1.0233e-01,  3.6819e-02,  3.7343e-02],\n",
              "                        [ 1.0334e-01, -3.0510e-05,  8.0785e-02]],\n",
              "              \n",
              "                       [[ 6.4612e-02,  7.6292e-02, -1.0460e-01],\n",
              "                        [ 8.6800e-02, -8.9856e-02,  9.4501e-02],\n",
              "                        [-4.3682e-03, -9.3415e-02,  2.9314e-02]],\n",
              "              \n",
              "                       [[-2.1456e-02, -9.4678e-02, -3.8215e-02],\n",
              "                        [ 1.0868e-02,  8.2098e-02, -3.2406e-02],\n",
              "                        [ 6.2610e-02,  1.3200e-02,  3.5531e-03]],\n",
              "              \n",
              "                       [[ 2.0170e-02, -6.9177e-02, -8.7616e-02],\n",
              "                        [-3.3121e-02, -9.8226e-02, -4.9158e-02],\n",
              "                        [ 4.8494e-03, -6.9424e-02, -4.3723e-02]],\n",
              "              \n",
              "                       [[-1.8941e-02, -1.2144e-02, -5.8187e-02],\n",
              "                        [ 5.0650e-03, -1.4795e-02,  3.0147e-02],\n",
              "                        [ 4.7611e-03, -5.2638e-02, -3.6291e-02]],\n",
              "              \n",
              "                       [[-1.2149e-03, -6.5774e-02,  8.2520e-03],\n",
              "                        [-7.4425e-03,  4.0897e-02,  2.4947e-02],\n",
              "                        [ 7.8887e-02, -3.4749e-03, -7.7887e-02]],\n",
              "              \n",
              "                       [[ 4.7119e-02, -7.1240e-02, -1.4489e-02],\n",
              "                        [-3.4132e-02, -3.9997e-02, -3.9000e-02],\n",
              "                        [ 9.6863e-02,  6.0342e-02,  2.9213e-02]],\n",
              "              \n",
              "                       [[ 9.8975e-02, -9.5524e-02,  1.7010e-02],\n",
              "                        [ 6.7481e-02,  7.0022e-02, -8.3890e-02],\n",
              "                        [ 3.7514e-02, -6.0050e-02, -4.1187e-03]],\n",
              "              \n",
              "                       [[-2.1996e-02, -8.8013e-02, -1.0055e-01],\n",
              "                        [-6.9349e-02,  4.7832e-02,  4.8218e-02],\n",
              "                        [-9.1681e-02, -3.9586e-02,  1.7218e-03]],\n",
              "              \n",
              "                       [[-9.1135e-02,  5.9393e-02,  9.5473e-02],\n",
              "                        [ 1.8643e-02, -7.8321e-02,  2.4580e-02],\n",
              "                        [ 3.8265e-02,  8.3468e-02, -5.6085e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-9.4437e-02,  4.6312e-02,  6.5624e-03],\n",
              "                        [-3.4345e-02, -4.4169e-02, -5.4351e-02],\n",
              "                        [ 8.5328e-02, -1.8187e-02,  7.6022e-02]],\n",
              "              \n",
              "                       [[ 9.4094e-02,  1.3353e-02,  2.2454e-02],\n",
              "                        [-7.1789e-03,  7.2397e-02, -9.4983e-02],\n",
              "                        [ 4.1919e-02, -1.7174e-02,  4.8132e-02]],\n",
              "              \n",
              "                       [[-4.6949e-04, -3.9029e-02, -1.1379e-02],\n",
              "                        [ 5.6920e-02, -7.3210e-02, -6.6629e-02],\n",
              "                        [-2.3611e-02, -3.8235e-02,  4.1409e-02]],\n",
              "              \n",
              "                       [[ 7.0937e-02, -1.1289e-02,  9.9672e-02],\n",
              "                        [-4.4042e-02, -5.9151e-02, -4.7191e-02],\n",
              "                        [-7.2624e-02, -7.3885e-02, -9.3921e-02]],\n",
              "              \n",
              "                       [[-9.3422e-02,  2.7512e-02,  6.4284e-02],\n",
              "                        [ 9.8963e-02,  8.9787e-02, -6.0709e-03],\n",
              "                        [ 2.0454e-02, -6.3068e-02,  4.0743e-02]],\n",
              "              \n",
              "                       [[-1.0107e-01,  4.9719e-02,  1.9334e-02],\n",
              "                        [ 3.2393e-02,  3.8595e-02, -4.8394e-02],\n",
              "                        [ 9.0452e-02,  5.0307e-02,  6.9243e-02]],\n",
              "              \n",
              "                       [[ 1.3922e-02,  6.6196e-02,  7.0941e-02],\n",
              "                        [ 4.7775e-02,  8.0297e-02, -1.9119e-02],\n",
              "                        [ 6.9310e-02,  2.4286e-02,  6.3424e-02]],\n",
              "              \n",
              "                       [[ 1.0267e-01,  2.3869e-02, -3.9124e-02],\n",
              "                        [-1.0488e-02,  2.9676e-02,  1.7773e-02],\n",
              "                        [-2.8795e-02,  8.2590e-02,  6.3331e-02]],\n",
              "              \n",
              "                       [[-6.5475e-02, -8.5889e-03, -1.0119e-02],\n",
              "                        [-6.6063e-02,  1.5374e-02, -3.2360e-02],\n",
              "                        [-5.4419e-02, -3.3894e-02, -3.7584e-02]],\n",
              "              \n",
              "                       [[ 1.0084e-01,  4.0432e-02,  1.0373e-01],\n",
              "                        [ 2.8903e-02,  2.3868e-02,  4.3333e-02],\n",
              "                        [ 1.8092e-02, -8.2722e-02, -6.2334e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.5538e-02,  1.5846e-03,  3.9709e-02],\n",
              "                        [ 4.0588e-02,  8.3623e-02,  2.1458e-02],\n",
              "                        [-3.5975e-02, -7.9271e-02, -7.7203e-02]],\n",
              "              \n",
              "                       [[-6.2965e-02,  3.1792e-02,  5.6950e-02],\n",
              "                        [ 9.2224e-02, -3.3342e-02, -8.3150e-03],\n",
              "                        [-3.1303e-02, -3.8517e-04,  3.3837e-02]],\n",
              "              \n",
              "                       [[-2.3160e-03,  4.8799e-03,  1.3354e-02],\n",
              "                        [ 3.9256e-02, -3.1981e-02, -6.2855e-02],\n",
              "                        [ 2.4869e-02, -1.2481e-02, -4.7753e-02]],\n",
              "              \n",
              "                       [[ 4.4268e-02,  9.5597e-04, -1.5333e-02],\n",
              "                        [-5.1027e-02, -1.3868e-02, -8.9632e-02],\n",
              "                        [ 2.3980e-02,  1.5818e-03,  6.3966e-02]],\n",
              "              \n",
              "                       [[ 6.8063e-03,  8.4277e-03,  2.8715e-02],\n",
              "                        [ 8.0210e-02, -4.9812e-02,  6.2930e-02],\n",
              "                        [ 2.5779e-02, -7.0320e-02,  3.6702e-02]],\n",
              "              \n",
              "                       [[-6.3217e-02, -3.3181e-02, -5.0245e-02],\n",
              "                        [-7.1711e-02,  8.3017e-02, -9.4217e-02],\n",
              "                        [ 5.2706e-02, -9.4870e-02, -1.2829e-02]],\n",
              "              \n",
              "                       [[ 6.2868e-03,  7.4937e-02, -3.8147e-02],\n",
              "                        [ 3.0340e-02,  1.6329e-02,  6.2021e-02],\n",
              "                        [ 6.2667e-03,  3.9470e-02, -6.3677e-02]],\n",
              "              \n",
              "                       [[-7.3250e-02,  9.3928e-02, -7.6808e-02],\n",
              "                        [-1.7945e-02, -1.2742e-02,  1.0308e-01],\n",
              "                        [-2.2780e-02, -8.0249e-02, -2.6721e-02]],\n",
              "              \n",
              "                       [[ 5.4372e-02,  4.1773e-02,  8.7204e-02],\n",
              "                        [-2.1579e-02,  4.9653e-02, -9.9194e-02],\n",
              "                        [ 4.0787e-02,  4.8432e-02,  6.7998e-02]],\n",
              "              \n",
              "                       [[-6.0446e-02, -2.8142e-02,  2.5502e-02],\n",
              "                        [-7.4905e-02, -8.3851e-02, -1.0141e-01],\n",
              "                        [ 5.8842e-03,  6.5458e-02,  2.7075e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.4263e-03,  3.6727e-02, -6.6240e-02],\n",
              "                        [ 1.1113e-02, -2.6186e-02, -5.2193e-02],\n",
              "                        [ 9.0902e-02, -8.1550e-02,  1.5448e-02]],\n",
              "              \n",
              "                       [[-9.2624e-02, -3.5762e-03, -4.6840e-02],\n",
              "                        [ 3.4695e-02, -5.9191e-02,  6.7466e-02],\n",
              "                        [-8.5536e-02,  6.3313e-02, -7.9181e-02]],\n",
              "              \n",
              "                       [[ 5.6456e-02, -4.4384e-02, -2.4556e-04],\n",
              "                        [-1.9238e-02,  6.8414e-02,  3.4546e-02],\n",
              "                        [-9.2887e-02,  9.6914e-03, -7.2718e-02]],\n",
              "              \n",
              "                       [[ 7.8800e-02,  1.7319e-02, -2.7109e-02],\n",
              "                        [-5.3777e-02,  3.6485e-02, -6.3129e-02],\n",
              "                        [ 4.9992e-02,  5.7519e-02,  6.4701e-02]],\n",
              "              \n",
              "                       [[ 2.7537e-02, -9.2272e-02,  7.5823e-02],\n",
              "                        [-3.2700e-02, -3.1163e-02, -1.1325e-02],\n",
              "                        [ 7.7068e-02,  8.1052e-02,  1.6276e-02]],\n",
              "              \n",
              "                       [[ 5.0296e-02, -9.8241e-02,  2.4900e-04],\n",
              "                        [-9.3254e-02,  3.5876e-02, -7.5099e-02],\n",
              "                        [-3.7568e-02,  7.3684e-02,  1.0074e-01]],\n",
              "              \n",
              "                       [[-6.3286e-02, -5.8503e-02,  1.3055e-02],\n",
              "                        [ 4.1437e-02, -1.7168e-02, -3.2918e-02],\n",
              "                        [-6.9237e-02,  4.4997e-02,  1.0328e-01]],\n",
              "              \n",
              "                       [[-5.1026e-02,  4.9718e-02,  5.1481e-02],\n",
              "                        [ 8.4728e-02, -1.2001e-02,  3.3202e-03],\n",
              "                        [ 7.7444e-02,  6.6631e-02,  1.0411e-01]],\n",
              "              \n",
              "                       [[-3.0207e-02,  4.1709e-02,  7.3605e-02],\n",
              "                        [-7.1553e-02,  2.0940e-02, -2.3586e-02],\n",
              "                        [ 6.7760e-02, -4.7342e-02,  7.3933e-03]],\n",
              "              \n",
              "                       [[ 6.3067e-02, -9.6567e-02, -8.9004e-02],\n",
              "                        [-5.3989e-02,  6.7611e-02,  7.0680e-02],\n",
              "                        [-7.1991e-02,  2.0100e-02, -5.5854e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-4.8926e-02,  9.0907e-02,  5.0914e-02],\n",
              "                        [-2.8828e-02,  1.5516e-02,  2.0424e-02],\n",
              "                        [ 2.4691e-02, -3.6079e-02, -6.2074e-02]],\n",
              "              \n",
              "                       [[ 6.9788e-02,  1.4164e-02,  4.4119e-02],\n",
              "                        [-3.9922e-02,  5.1057e-02,  7.6713e-02],\n",
              "                        [ 6.4107e-02,  2.8660e-02,  1.0371e-01]],\n",
              "              \n",
              "                       [[-2.3053e-04,  2.2441e-02,  1.0015e-01],\n",
              "                        [ 1.0245e-01, -4.4506e-02,  9.4953e-02],\n",
              "                        [ 3.8902e-02, -1.1799e-02,  9.2038e-02]],\n",
              "              \n",
              "                       [[-5.4605e-02,  6.8490e-02,  1.0445e-01],\n",
              "                        [-7.2701e-02, -6.2201e-02, -1.0445e-01],\n",
              "                        [-1.8970e-02, -9.5733e-02, -3.5304e-02]],\n",
              "              \n",
              "                       [[ 3.2002e-02,  7.4511e-02,  5.8717e-02],\n",
              "                        [ 5.8511e-02,  4.3730e-02, -6.5378e-02],\n",
              "                        [-8.3694e-02,  4.3696e-03,  1.0009e-01]],\n",
              "              \n",
              "                       [[ 5.9351e-03, -9.0662e-03, -7.1545e-02],\n",
              "                        [-5.2266e-02, -8.1256e-02,  8.4398e-02],\n",
              "                        [-1.7174e-02, -9.3119e-02,  1.1308e-02]],\n",
              "              \n",
              "                       [[ 7.6494e-03, -1.3023e-02,  3.7733e-02],\n",
              "                        [ 5.6687e-02, -9.9128e-02, -8.0753e-02],\n",
              "                        [-5.0639e-03, -9.7729e-02, -9.5750e-02]],\n",
              "              \n",
              "                       [[ 9.3067e-02, -8.0174e-03, -5.2113e-02],\n",
              "                        [-3.6157e-02, -8.2295e-02,  8.2258e-02],\n",
              "                        [-2.2857e-02, -5.9265e-02, -7.9944e-02]],\n",
              "              \n",
              "                       [[ 6.1611e-02, -1.4571e-02, -1.1074e-02],\n",
              "                        [-2.7473e-02, -5.0883e-02,  1.8751e-02],\n",
              "                        [ 8.1099e-02, -6.1093e-02,  5.0504e-03]],\n",
              "              \n",
              "                       [[-8.0165e-02, -4.9426e-02,  9.2525e-02],\n",
              "                        [ 1.1052e-03,  1.0154e-01, -1.8468e-02],\n",
              "                        [-5.7453e-02, -6.2981e-02,  9.3426e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-8.1058e-02,  5.5318e-02,  2.6203e-02],\n",
              "                        [ 3.1107e-02,  5.9476e-02, -2.7577e-02],\n",
              "                        [ 6.5223e-02, -8.3982e-02, -3.7087e-02]],\n",
              "              \n",
              "                       [[ 7.7164e-02,  3.1283e-02, -1.4038e-02],\n",
              "                        [-2.4616e-02, -6.4364e-02,  6.4098e-02],\n",
              "                        [-3.3520e-03, -3.5664e-03,  2.4929e-02]],\n",
              "              \n",
              "                       [[ 7.7787e-02, -5.3778e-02, -3.6303e-02],\n",
              "                        [ 7.1429e-02,  5.9532e-02, -5.1855e-02],\n",
              "                        [-1.0428e-01,  1.9555e-02,  5.5434e-02]],\n",
              "              \n",
              "                       [[ 2.5178e-02,  7.4768e-02, -8.3640e-02],\n",
              "                        [ 5.3156e-02, -6.5531e-02,  5.9325e-02],\n",
              "                        [ 7.8394e-02,  3.3385e-02,  8.5284e-02]],\n",
              "              \n",
              "                       [[-6.9481e-02, -9.4275e-02, -1.0135e-01],\n",
              "                        [ 6.6179e-02,  3.6926e-02, -7.7188e-02],\n",
              "                        [ 5.1048e-02,  9.6177e-02, -1.0394e-01]],\n",
              "              \n",
              "                       [[ 7.6466e-02,  1.6167e-02,  9.8053e-03],\n",
              "                        [ 9.4847e-02,  9.5458e-02,  4.4414e-02],\n",
              "                        [ 8.3288e-02,  4.3853e-02,  1.7176e-02]],\n",
              "              \n",
              "                       [[-9.2656e-02,  1.9689e-02, -7.4993e-02],\n",
              "                        [ 3.2452e-02,  1.8598e-02,  2.3681e-03],\n",
              "                        [-7.2071e-02, -6.3899e-02,  7.7912e-02]],\n",
              "              \n",
              "                       [[ 5.1336e-02,  5.5576e-02, -3.1410e-02],\n",
              "                        [-1.8151e-02, -2.7014e-02,  7.2489e-02],\n",
              "                        [-4.5504e-02,  6.6394e-02,  7.2679e-02]],\n",
              "              \n",
              "                       [[-9.6403e-02,  6.4369e-04, -2.0076e-02],\n",
              "                        [-5.8273e-02,  4.5507e-02, -1.2807e-02],\n",
              "                        [ 9.2287e-02, -6.5976e-02,  4.8976e-02]],\n",
              "              \n",
              "                       [[-8.9998e-02, -5.2833e-02,  7.1903e-03],\n",
              "                        [ 8.3283e-02,  5.5521e-02, -8.6550e-02],\n",
              "                        [ 1.1676e-02, -6.2138e-02,  4.5674e-03]]]], device='cuda:0')),\n",
              "             ('conv_block_2.0.bias',\n",
              "              tensor([-0.0878, -0.0309,  0.0723, -0.0967, -0.1005,  0.0192,  0.0144, -0.0193,\n",
              "                       0.0920, -0.0635], device='cuda:0')),\n",
              "             ('conv_block_2.2.weight',\n",
              "              tensor([[[[-6.3992e-02, -7.8791e-02, -1.9619e-02],\n",
              "                        [-2.6901e-02,  6.5222e-02, -5.9186e-03],\n",
              "                        [ 3.3663e-02, -4.3804e-02,  8.5507e-02]],\n",
              "              \n",
              "                       [[ 8.8862e-02, -9.4401e-02, -2.7090e-02],\n",
              "                        [-8.9439e-02,  4.4781e-02, -9.2094e-02],\n",
              "                        [-4.9839e-02,  1.0532e-01, -1.0066e-01]],\n",
              "              \n",
              "                       [[ 7.7771e-02,  8.9049e-03,  8.4289e-02],\n",
              "                        [-5.3494e-02,  6.9236e-02,  1.2718e-02],\n",
              "                        [ 8.1073e-03,  7.1945e-02, -1.0019e-01]],\n",
              "              \n",
              "                       [[-8.4902e-02,  1.0180e-01, -6.3298e-02],\n",
              "                        [-7.5980e-02, -5.1539e-03, -3.3742e-02],\n",
              "                        [-1.4421e-02, -7.0623e-02,  3.8034e-02]],\n",
              "              \n",
              "                       [[-9.0703e-02,  8.5374e-03,  6.1510e-02],\n",
              "                        [ 2.0253e-02,  1.4006e-02,  1.5418e-02],\n",
              "                        [-3.0880e-02, -2.0080e-02, -4.4450e-02]],\n",
              "              \n",
              "                       [[-7.1207e-02, -5.5810e-02,  1.0420e-01],\n",
              "                        [-1.7641e-02,  3.6924e-02,  7.2896e-02],\n",
              "                        [-8.2343e-03, -5.6707e-02, -7.1419e-02]],\n",
              "              \n",
              "                       [[-3.8833e-02,  3.7624e-02, -8.8771e-02],\n",
              "                        [-1.2870e-02,  4.0096e-02,  8.5999e-02],\n",
              "                        [ 3.1721e-02,  2.0846e-02,  7.2162e-02]],\n",
              "              \n",
              "                       [[ 4.8708e-02,  3.5661e-02, -3.2682e-02],\n",
              "                        [-8.4528e-02, -2.2769e-02, -1.9117e-02],\n",
              "                        [ 7.7410e-03, -1.1593e-02,  4.2616e-02]],\n",
              "              \n",
              "                       [[ 7.0050e-02, -4.2735e-02, -1.0002e-01],\n",
              "                        [-5.4081e-02, -5.0436e-02,  5.9750e-02],\n",
              "                        [-6.7994e-02, -9.9145e-03, -2.2340e-02]],\n",
              "              \n",
              "                       [[-6.3976e-02,  4.7780e-02, -4.3909e-02],\n",
              "                        [-5.4531e-03, -7.4112e-02, -1.0632e-02],\n",
              "                        [ 1.4977e-02, -4.2894e-03, -3.9386e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 3.1315e-02, -2.7311e-02, -5.8439e-02],\n",
              "                        [-7.7732e-02, -2.2329e-02, -9.9578e-02],\n",
              "                        [ 8.7492e-02, -5.0357e-02, -4.3684e-02]],\n",
              "              \n",
              "                       [[ 9.7439e-03,  2.7326e-02, -9.9393e-03],\n",
              "                        [ 7.2313e-02, -6.1448e-02,  3.7777e-02],\n",
              "                        [-2.3773e-04, -8.5747e-02, -4.0824e-02]],\n",
              "              \n",
              "                       [[ 2.6825e-02,  2.0138e-02,  7.6647e-02],\n",
              "                        [ 7.0518e-02, -5.7493e-02, -4.5013e-02],\n",
              "                        [-2.2351e-02, -7.5517e-02, -2.8459e-02]],\n",
              "              \n",
              "                       [[-8.6258e-02,  4.0092e-02,  7.4583e-02],\n",
              "                        [ 8.3459e-03, -7.5460e-02, -7.9827e-02],\n",
              "                        [-4.1036e-02,  3.0659e-02,  2.5711e-03]],\n",
              "              \n",
              "                       [[ 1.9166e-02,  9.9346e-02,  4.8956e-02],\n",
              "                        [ 2.2665e-02, -2.1327e-02,  4.9864e-02],\n",
              "                        [ 3.8563e-02, -9.4879e-02, -6.2266e-02]],\n",
              "              \n",
              "                       [[ 3.5381e-03,  3.9997e-02,  5.1282e-02],\n",
              "                        [-6.2748e-02, -1.0458e-01, -5.4909e-03],\n",
              "                        [-1.2050e-02,  3.0588e-02, -2.8988e-02]],\n",
              "              \n",
              "                       [[ 8.0588e-02,  7.0333e-03,  7.6975e-02],\n",
              "                        [-7.3398e-02,  4.2167e-02,  1.2560e-02],\n",
              "                        [-5.2720e-02,  5.2256e-02, -1.0372e-01]],\n",
              "              \n",
              "                       [[ 8.5220e-02,  8.4947e-03,  1.0178e-02],\n",
              "                        [ 4.8746e-02,  8.7503e-03,  4.5184e-02],\n",
              "                        [ 6.7063e-02, -8.2268e-02,  6.9735e-02]],\n",
              "              \n",
              "                       [[-1.5784e-02, -2.4513e-02,  2.1217e-02],\n",
              "                        [ 8.2446e-02, -5.7302e-02, -7.1039e-02],\n",
              "                        [ 6.5418e-02, -4.9507e-02,  3.3937e-02]],\n",
              "              \n",
              "                       [[-1.5530e-02,  2.9014e-02,  8.0439e-02],\n",
              "                        [-5.3421e-02, -5.1151e-02,  5.1716e-02],\n",
              "                        [ 5.7714e-03, -1.1601e-02, -9.2590e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 8.9309e-02, -3.9919e-03, -1.9415e-02],\n",
              "                        [-4.3269e-02, -2.0801e-02,  5.1233e-02],\n",
              "                        [-2.4227e-03,  9.0147e-02, -6.0858e-03]],\n",
              "              \n",
              "                       [[-1.5122e-02,  5.9498e-02, -2.7275e-03],\n",
              "                        [-2.1039e-02,  3.5231e-02,  8.3129e-02],\n",
              "                        [ 2.6305e-02,  7.3398e-02,  6.8309e-02]],\n",
              "              \n",
              "                       [[ 2.9810e-02,  3.6650e-02,  3.4014e-02],\n",
              "                        [ 1.0934e-02,  8.9675e-02,  9.7308e-02],\n",
              "                        [ 3.7524e-02, -5.2640e-03,  9.4509e-02]],\n",
              "              \n",
              "                       [[-8.2042e-02,  7.7453e-02,  5.5849e-02],\n",
              "                        [ 6.7687e-02, -8.0992e-03, -7.8646e-02],\n",
              "                        [ 7.5193e-02, -4.6091e-02,  2.7734e-02]],\n",
              "              \n",
              "                       [[ 5.9719e-02, -9.8508e-02,  6.9954e-03],\n",
              "                        [-3.7444e-02,  7.4815e-02, -6.7114e-02],\n",
              "                        [ 6.4001e-02,  6.5730e-02,  5.8156e-02]],\n",
              "              \n",
              "                       [[ 1.0119e-01,  1.5964e-02, -9.5541e-02],\n",
              "                        [ 7.5248e-02,  9.6499e-03,  2.0918e-03],\n",
              "                        [-1.0041e-01, -2.3691e-02, -5.1162e-02]],\n",
              "              \n",
              "                       [[ 1.0324e-01,  7.5054e-02,  7.8634e-02],\n",
              "                        [ 7.2188e-02, -6.5340e-02, -4.5270e-02],\n",
              "                        [-4.1252e-02, -4.2257e-02,  8.2054e-02]],\n",
              "              \n",
              "                       [[ 3.5815e-02,  8.4470e-02, -4.9309e-03],\n",
              "                        [-9.3965e-02, -3.0582e-02,  7.4081e-02],\n",
              "                        [ 6.4174e-02,  3.2632e-02, -3.0919e-02]],\n",
              "              \n",
              "                       [[-9.8386e-02, -5.6639e-02,  5.4958e-02],\n",
              "                        [-4.2518e-02,  5.0421e-02,  2.8781e-02],\n",
              "                        [-4.0486e-02,  6.4202e-02, -3.3871e-02]],\n",
              "              \n",
              "                       [[-3.5020e-03, -4.0152e-02, -9.9988e-02],\n",
              "                        [ 1.6996e-02,  3.0460e-02, -5.3072e-02],\n",
              "                        [ 6.4663e-02, -9.4558e-02, -1.0161e-01]]],\n",
              "              \n",
              "              \n",
              "                      [[[-6.5106e-02, -3.6430e-02, -1.1707e-02],\n",
              "                        [-2.0370e-02,  4.8108e-02, -9.2510e-02],\n",
              "                        [ 1.5521e-02,  1.8254e-03,  2.7842e-02]],\n",
              "              \n",
              "                       [[ 1.0479e-01,  6.4874e-02, -5.8366e-02],\n",
              "                        [-8.6378e-02, -2.5520e-02, -5.2876e-02],\n",
              "                        [ 3.6820e-02,  9.6628e-04,  8.4783e-02]],\n",
              "              \n",
              "                       [[ 4.1405e-02, -1.9382e-02,  3.6229e-03],\n",
              "                        [ 2.5244e-02, -1.3080e-02,  8.5058e-02],\n",
              "                        [-8.2420e-02,  5.1377e-02, -6.7192e-02]],\n",
              "              \n",
              "                       [[-9.2347e-02, -2.1640e-02,  5.1366e-02],\n",
              "                        [ 7.4478e-02,  2.6452e-02, -9.1104e-03],\n",
              "                        [-5.9092e-03, -4.2731e-02, -9.4592e-03]],\n",
              "              \n",
              "                       [[-7.2831e-03,  8.9699e-02,  6.1690e-02],\n",
              "                        [-8.4351e-02,  4.3604e-04, -6.4834e-02],\n",
              "                        [-1.6733e-02, -8.3776e-02,  2.7402e-02]],\n",
              "              \n",
              "                       [[-7.6008e-02,  1.0406e-01,  7.9605e-02],\n",
              "                        [-7.2559e-02, -9.9239e-02,  4.1128e-03],\n",
              "                        [-2.9425e-02,  3.0945e-02, -7.1353e-02]],\n",
              "              \n",
              "                       [[ 4.3148e-02, -9.1047e-02, -5.5632e-02],\n",
              "                        [-5.5414e-02,  5.1007e-02, -2.7597e-03],\n",
              "                        [-1.0130e-01, -6.0201e-02, -4.8781e-02]],\n",
              "              \n",
              "                       [[-9.7802e-02,  1.3497e-02,  3.7561e-02],\n",
              "                        [-1.9340e-02, -4.1947e-02, -6.3926e-04],\n",
              "                        [-8.3725e-02, -6.4184e-02, -2.4040e-03]],\n",
              "              \n",
              "                       [[ 9.3643e-02, -3.2414e-02,  5.2247e-02],\n",
              "                        [-4.1484e-02, -2.8060e-02, -1.0034e-01],\n",
              "                        [ 8.7330e-02,  1.0264e-01, -2.2139e-03]],\n",
              "              \n",
              "                       [[ 6.6974e-02,  8.6219e-02,  5.2359e-02],\n",
              "                        [ 5.4288e-02, -1.0035e-01, -9.9050e-02],\n",
              "                        [-8.0906e-02,  3.2970e-02, -9.1177e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-8.0464e-02, -5.1092e-02, -9.7154e-02],\n",
              "                        [ 1.4203e-04,  1.5207e-02, -6.1686e-02],\n",
              "                        [ 6.9018e-02, -4.0018e-02, -2.9676e-02]],\n",
              "              \n",
              "                       [[ 8.0309e-02,  9.0499e-02, -1.2093e-02],\n",
              "                        [-7.5671e-02, -5.2881e-02,  1.3423e-02],\n",
              "                        [ 6.1790e-02,  5.2477e-02, -4.6547e-02]],\n",
              "              \n",
              "                       [[-9.9650e-02, -9.2249e-02, -3.3537e-02],\n",
              "                        [ 1.3223e-03, -4.7347e-02, -8.3348e-02],\n",
              "                        [ 1.1109e-02, -8.3668e-02, -8.0946e-02]],\n",
              "              \n",
              "                       [[-8.5692e-02, -2.8563e-02,  9.3104e-02],\n",
              "                        [ 4.1207e-02, -1.2498e-02,  2.1694e-02],\n",
              "                        [ 4.1975e-02,  6.1414e-04, -8.5020e-02]],\n",
              "              \n",
              "                       [[-6.4944e-02, -7.1610e-02, -2.6766e-03],\n",
              "                        [-9.6492e-02, -1.9166e-02, -3.8545e-02],\n",
              "                        [ 1.0345e-01,  8.5679e-02,  6.1227e-02]],\n",
              "              \n",
              "                       [[ 5.9116e-03, -3.4129e-02,  2.6887e-02],\n",
              "                        [-7.2830e-02, -4.4957e-02, -2.1175e-02],\n",
              "                        [-2.4766e-02, -9.9854e-02,  4.1903e-02]],\n",
              "              \n",
              "                       [[ 8.6803e-02, -5.8141e-02,  2.8415e-02],\n",
              "                        [-1.2225e-02, -3.8445e-03,  6.1443e-03],\n",
              "                        [ 9.1346e-02,  1.4124e-02, -6.6690e-02]],\n",
              "              \n",
              "                       [[-3.7917e-02,  5.1495e-02,  3.2893e-02],\n",
              "                        [ 2.0487e-03, -1.3912e-02, -4.1012e-02],\n",
              "                        [-3.7413e-02, -5.5602e-02,  1.7273e-02]],\n",
              "              \n",
              "                       [[ 2.9603e-02,  8.0717e-02, -2.3813e-02],\n",
              "                        [ 7.5461e-03,  6.8125e-02,  4.5852e-02],\n",
              "                        [ 1.3544e-02,  3.2390e-02,  5.4714e-03]],\n",
              "              \n",
              "                       [[-9.0419e-02,  4.0636e-03, -2.3040e-02],\n",
              "                        [ 9.5123e-02,  9.5145e-02,  2.0912e-02],\n",
              "                        [ 9.4215e-02, -5.4288e-02,  9.1619e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 9.0756e-02, -4.0288e-03, -8.4592e-02],\n",
              "                        [-3.4015e-02, -2.8189e-02,  1.7411e-03],\n",
              "                        [-9.5569e-02,  1.9535e-02, -4.3839e-02]],\n",
              "              \n",
              "                       [[-2.6989e-02, -5.4443e-02, -2.2255e-02],\n",
              "                        [-9.7896e-02, -5.5885e-02,  9.7108e-03],\n",
              "                        [ 6.9072e-02,  9.5790e-02, -7.9737e-02]],\n",
              "              \n",
              "                       [[ 4.4264e-02, -5.9419e-02, -8.1498e-02],\n",
              "                        [-4.6417e-03, -6.0468e-02, -9.0783e-02],\n",
              "                        [-9.8509e-02, -7.0556e-02,  8.6619e-02]],\n",
              "              \n",
              "                       [[ 5.8788e-02, -4.1726e-02, -7.0553e-02],\n",
              "                        [-8.1085e-02, -6.2246e-02, -4.3376e-02],\n",
              "                        [ 6.3308e-02,  3.4496e-02, -4.0622e-02]],\n",
              "              \n",
              "                       [[ 7.2567e-02, -6.5484e-02, -8.5876e-02],\n",
              "                        [ 2.3006e-02, -5.8123e-02,  2.9987e-02],\n",
              "                        [ 8.9306e-02, -4.9849e-02, -7.3556e-02]],\n",
              "              \n",
              "                       [[ 3.9676e-02, -9.5200e-02,  9.4044e-02],\n",
              "                        [-4.9780e-02,  5.0961e-02, -8.3818e-02],\n",
              "                        [-7.1348e-02,  1.1611e-02,  3.7463e-02]],\n",
              "              \n",
              "                       [[ 8.1734e-02,  8.8158e-02, -6.0623e-03],\n",
              "                        [-1.3552e-02,  1.7424e-02, -2.4486e-02],\n",
              "                        [ 3.5882e-03, -9.9828e-02, -8.6531e-02]],\n",
              "              \n",
              "                       [[ 7.2233e-02, -6.1597e-02,  8.3008e-02],\n",
              "                        [ 1.1568e-02,  2.5676e-02,  9.5804e-02],\n",
              "                        [-5.8628e-02, -1.6640e-02,  1.8675e-02]],\n",
              "              \n",
              "                       [[ 3.6012e-02, -1.0259e-01,  3.7464e-02],\n",
              "                        [-6.2163e-02,  1.3846e-02,  7.1315e-02],\n",
              "                        [-1.0500e-02, -3.3346e-03, -7.8757e-03]],\n",
              "              \n",
              "                       [[ 8.7962e-02,  5.9907e-02,  1.7727e-02],\n",
              "                        [-6.3437e-02, -5.7241e-02,  8.3964e-02],\n",
              "                        [ 7.5834e-02,  6.1033e-02, -8.2189e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 8.2092e-02, -1.0076e-02,  7.7661e-02],\n",
              "                        [ 9.1553e-02,  1.1554e-02, -4.3863e-02],\n",
              "                        [ 9.9153e-02, -5.4931e-02,  6.8876e-02]],\n",
              "              \n",
              "                       [[-1.0108e-01, -3.3153e-02, -9.1902e-02],\n",
              "                        [-4.7284e-02,  4.4759e-02, -7.5529e-02],\n",
              "                        [-9.1158e-02,  7.5371e-02,  5.6270e-02]],\n",
              "              \n",
              "                       [[-1.1527e-03, -7.4309e-02, -2.7927e-02],\n",
              "                        [-3.4129e-02,  6.5100e-02, -3.4478e-02],\n",
              "                        [-3.0360e-02, -7.4720e-02, -4.9646e-02]],\n",
              "              \n",
              "                       [[ 5.7074e-02,  6.7914e-02,  1.5315e-02],\n",
              "                        [-3.9549e-02,  1.0124e-01,  2.0806e-02],\n",
              "                        [-4.0688e-02, -3.6535e-02, -1.4752e-02]],\n",
              "              \n",
              "                       [[ 4.9974e-02,  3.8555e-02,  7.6418e-02],\n",
              "                        [-4.7494e-03,  8.7183e-02, -4.2816e-02],\n",
              "                        [-4.8547e-02, -3.8927e-02, -9.8896e-02]],\n",
              "              \n",
              "                       [[-6.9195e-02, -9.5382e-02, -6.2294e-03],\n",
              "                        [ 9.9374e-04, -2.7358e-02, -7.2035e-02],\n",
              "                        [ 9.5637e-02, -3.4926e-02,  5.0233e-02]],\n",
              "              \n",
              "                       [[ 7.3408e-02, -6.9291e-02, -1.3179e-02],\n",
              "                        [ 6.0923e-02,  1.0218e-01, -1.3299e-02],\n",
              "                        [ 7.6382e-02, -8.2732e-02, -6.8489e-02]],\n",
              "              \n",
              "                       [[ 8.6682e-02, -9.9801e-03,  1.0414e-01],\n",
              "                        [ 7.6651e-03, -4.3714e-02,  1.0011e-01],\n",
              "                        [ 9.2179e-02,  9.7826e-03, -6.3900e-02]],\n",
              "              \n",
              "                       [[-4.5639e-03, -5.0693e-02,  7.6810e-02],\n",
              "                        [ 4.8829e-03,  2.2191e-02,  6.3927e-02],\n",
              "                        [ 3.4916e-02, -6.5803e-02,  8.7566e-02]],\n",
              "              \n",
              "                       [[ 6.4758e-02, -6.5073e-02,  7.9700e-02],\n",
              "                        [ 2.9905e-02, -2.0750e-02, -7.5385e-02],\n",
              "                        [-1.7490e-02, -1.0335e-01,  6.0163e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 7.6343e-02, -3.0347e-02,  9.7720e-02],\n",
              "                        [-3.9032e-02,  1.8051e-02, -7.3459e-02],\n",
              "                        [-4.4565e-03,  4.2610e-02,  4.5403e-02]],\n",
              "              \n",
              "                       [[-3.5346e-03, -5.3154e-02,  7.3680e-02],\n",
              "                        [ 6.9788e-02,  1.6916e-02, -4.8475e-02],\n",
              "                        [ 2.2349e-02,  2.8186e-04,  9.6302e-02]],\n",
              "              \n",
              "                       [[ 1.5621e-02,  8.1301e-03,  7.2057e-03],\n",
              "                        [ 5.6079e-02, -1.3024e-03,  9.0351e-02],\n",
              "                        [ 5.4917e-02, -7.9650e-02, -1.2063e-06]],\n",
              "              \n",
              "                       [[-8.9472e-02, -8.0934e-02,  2.0480e-02],\n",
              "                        [ 2.3687e-02, -9.2246e-03,  1.0019e-01],\n",
              "                        [-5.6627e-02, -4.4176e-02, -1.6881e-02]],\n",
              "              \n",
              "                       [[ 6.3911e-04, -8.9284e-03,  9.4909e-02],\n",
              "                        [-4.4519e-02, -5.5137e-02,  9.0599e-03],\n",
              "                        [ 7.9171e-02,  2.5019e-02,  5.6787e-02]],\n",
              "              \n",
              "                       [[ 2.0406e-02,  8.9839e-02,  6.3311e-02],\n",
              "                        [ 7.5428e-02, -1.4198e-02, -8.7268e-02],\n",
              "                        [-5.0002e-02,  3.5910e-02,  7.3950e-02]],\n",
              "              \n",
              "                       [[-4.1184e-02,  8.7218e-02,  1.5150e-02],\n",
              "                        [ 4.1869e-04,  4.1093e-03, -1.8623e-02],\n",
              "                        [ 9.8683e-02,  4.5784e-03,  6.4564e-02]],\n",
              "              \n",
              "                       [[-8.8967e-02, -5.4309e-02,  1.1852e-02],\n",
              "                        [ 8.4169e-02,  5.0184e-02,  2.0076e-02],\n",
              "                        [-1.0414e-01,  1.9816e-03, -6.9581e-02]],\n",
              "              \n",
              "                       [[-9.0006e-02,  1.4414e-02, -6.6693e-02],\n",
              "                        [ 9.5674e-02, -5.7294e-02,  3.3970e-02],\n",
              "                        [ 6.1871e-02, -8.1928e-02,  5.3946e-02]],\n",
              "              \n",
              "                       [[-1.4114e-02,  5.4619e-02,  1.0201e-01],\n",
              "                        [-4.4922e-02, -4.5653e-02,  8.3753e-02],\n",
              "                        [ 1.1722e-02, -1.0513e-02,  7.9971e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-5.0928e-02, -5.2047e-03,  7.2403e-02],\n",
              "                        [ 4.1195e-02, -6.8180e-02,  2.7398e-02],\n",
              "                        [-8.0368e-02, -5.7245e-02,  6.7779e-02]],\n",
              "              \n",
              "                       [[-2.8093e-02, -5.3691e-02,  7.4717e-03],\n",
              "                        [ 2.5759e-02, -6.5524e-02, -7.1084e-02],\n",
              "                        [-1.0209e-01,  2.7236e-02, -6.8013e-02]],\n",
              "              \n",
              "                       [[ 8.0331e-03, -2.3576e-02, -6.8923e-02],\n",
              "                        [-3.3636e-02, -8.1027e-02, -5.5797e-02],\n",
              "                        [-3.2857e-03, -9.0116e-02, -9.2447e-02]],\n",
              "              \n",
              "                       [[ 7.8958e-02,  9.9188e-03, -4.6618e-02],\n",
              "                        [-3.5047e-03,  7.8168e-02, -8.7939e-02],\n",
              "                        [-5.5886e-02, -7.6226e-02, -7.6634e-03]],\n",
              "              \n",
              "                       [[-3.6274e-03, -8.2146e-02,  7.3163e-02],\n",
              "                        [-8.0946e-02,  9.8414e-02, -7.2560e-02],\n",
              "                        [-1.4446e-02,  1.9710e-02, -4.6852e-02]],\n",
              "              \n",
              "                       [[ 9.6939e-02, -7.2673e-02, -5.8427e-03],\n",
              "                        [-7.7398e-02,  2.9261e-02,  8.9871e-02],\n",
              "                        [ 9.7776e-02,  1.2514e-02, -5.2773e-02]],\n",
              "              \n",
              "                       [[ 1.0244e-01,  7.8667e-03,  7.1317e-02],\n",
              "                        [-5.4751e-02, -4.8920e-02, -8.7504e-02],\n",
              "                        [ 9.6990e-02,  1.7486e-02, -7.5704e-02]],\n",
              "              \n",
              "                       [[ 9.0535e-03, -4.5211e-02,  5.2659e-03],\n",
              "                        [ 3.4988e-02, -5.2308e-02,  1.8394e-02],\n",
              "                        [-6.6553e-02,  2.0312e-02, -1.0178e-01]],\n",
              "              \n",
              "                       [[ 1.6797e-02,  1.0473e-01,  9.7094e-02],\n",
              "                        [ 3.8451e-02,  7.7563e-02,  1.0248e-01],\n",
              "                        [ 2.9870e-02,  3.5156e-02,  1.3707e-02]],\n",
              "              \n",
              "                       [[ 9.3322e-02,  9.0551e-02, -4.9570e-02],\n",
              "                        [-4.3333e-03, -5.3110e-02,  3.7824e-02],\n",
              "                        [-1.0214e-01,  3.7301e-02, -2.8929e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 3.8227e-02,  3.2899e-02, -5.2454e-02],\n",
              "                        [ 5.4687e-02,  4.4762e-02, -8.9602e-02],\n",
              "                        [ 1.0517e-01,  9.0731e-02,  6.5584e-02]],\n",
              "              \n",
              "                       [[-1.0699e-02,  3.7345e-02, -5.7028e-02],\n",
              "                        [-3.5818e-02,  4.9749e-02,  4.6925e-02],\n",
              "                        [ 4.1741e-02, -1.0053e-01,  8.7350e-02]],\n",
              "              \n",
              "                       [[-4.4028e-02,  9.1223e-02,  8.6852e-02],\n",
              "                        [ 3.9070e-02,  1.0502e-01,  6.0528e-02],\n",
              "                        [ 6.1821e-02, -3.5794e-02,  9.7766e-02]],\n",
              "              \n",
              "                       [[ 2.7627e-02,  6.2280e-02, -2.3834e-02],\n",
              "                        [ 7.6340e-02,  9.3509e-02, -8.0770e-02],\n",
              "                        [ 8.6415e-02, -6.9664e-02, -7.2571e-02]],\n",
              "              \n",
              "                       [[-8.8089e-02,  3.0459e-02, -7.9144e-02],\n",
              "                        [-3.9680e-02, -5.2988e-02,  2.8172e-02],\n",
              "                        [-1.0349e-01, -4.8324e-02,  7.7112e-04]],\n",
              "              \n",
              "                       [[ 9.4660e-03, -4.7605e-02,  3.7764e-02],\n",
              "                        [-6.9544e-02, -8.9270e-02, -1.4986e-02],\n",
              "                        [-5.6989e-02,  6.6443e-02, -7.2049e-02]],\n",
              "              \n",
              "                       [[-8.8494e-03,  4.3782e-02, -9.2311e-02],\n",
              "                        [ 8.1599e-02, -4.7895e-02, -2.8684e-02],\n",
              "                        [-6.4480e-02, -3.9279e-02, -4.0645e-02]],\n",
              "              \n",
              "                       [[-9.3801e-02,  3.6019e-02, -3.3768e-04],\n",
              "                        [ 1.0311e-01,  7.1117e-02,  9.1699e-02],\n",
              "                        [ 3.1014e-02,  5.5388e-02,  9.8704e-02]],\n",
              "              \n",
              "                       [[ 8.6545e-02, -8.0996e-02, -2.3636e-02],\n",
              "                        [-1.0166e-01,  3.9877e-03, -3.7229e-02],\n",
              "                        [ 9.1486e-02,  1.6666e-02,  1.1601e-03]],\n",
              "              \n",
              "                       [[-7.6248e-02, -8.2718e-02,  1.6594e-02],\n",
              "                        [-5.2376e-02, -4.8409e-02,  7.3938e-02],\n",
              "                        [-5.4952e-02, -4.6918e-02,  8.0934e-02]]]], device='cuda:0')),\n",
              "             ('conv_block_2.2.bias',\n",
              "              tensor([ 0.0412, -0.0599,  0.0319,  0.0531, -0.0936,  0.0197,  0.0241, -0.0041,\n",
              "                       0.1011, -0.0697], device='cuda:0')),\n",
              "             ('classifier.1.weight',\n",
              "              tensor([[ 0.0245, -0.0240, -0.0387,  ...,  0.0094, -0.0015, -0.0225],\n",
              "                      [ 0.0228,  0.0067, -0.0439,  ..., -0.0302,  0.0368,  0.0293],\n",
              "                      [ 0.0303,  0.0347, -0.0211,  ...,  0.0207, -0.0423, -0.0240],\n",
              "                      ...,\n",
              "                      [-0.0359, -0.0343,  0.0166,  ...,  0.0324,  0.0113, -0.0143],\n",
              "                      [-0.0294, -0.0316,  0.0251,  ..., -0.0056,  0.0300, -0.0396],\n",
              "                      [-0.0246, -0.0035, -0.0046,  ..., -0.0146, -0.0358,  0.0175]],\n",
              "                     device='cuda:0')),\n",
              "             ('classifier.1.bias',\n",
              "              tensor([ 0.0320, -0.0445,  0.0246, -0.0357, -0.0442,  0.0156, -0.0010, -0.0277,\n",
              "                       0.0404,  0.0037], device='cuda:0'))])"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_2.state_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiuIy8dhw_cg",
        "outputId": "0cc4c5b1-1245-409a-c4f8-ab5e4a4bfb95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image batch shape: torch.Size([32, 3, 64, 64])\n",
            "Single image Shape: torch.Size([3, 64, 64])\n",
            "Test image:\n",
            " tensor([[[ 1.9269,  1.4873,  0.9007,  ...,  1.8446, -1.1845,  1.3835],\n",
            "         [ 1.4451,  0.8564,  2.2181,  ...,  0.3399,  0.7200,  0.4114],\n",
            "         [ 1.9312,  1.0119, -1.4364,  ..., -0.5558,  0.7043,  0.7099],\n",
            "         ...,\n",
            "         [-0.5610, -0.4830,  0.4770,  ..., -0.2713, -0.9537, -0.6737],\n",
            "         [ 0.3076, -0.1277,  0.0366,  ..., -2.0060,  0.2824, -0.8111],\n",
            "         [-1.5486,  0.0485, -0.7712,  ..., -0.1403,  0.9416, -0.0118]],\n",
            "\n",
            "        [[-0.5197,  1.8524,  1.8365,  ...,  0.8935, -1.5114, -0.8515],\n",
            "         [ 2.0818,  1.0677, -1.4277,  ...,  1.6612, -2.6223, -0.4319],\n",
            "         [-0.1010, -0.4388, -1.9775,  ...,  0.2106,  0.2536, -0.7318],\n",
            "         ...,\n",
            "         [ 0.2779,  0.7342, -0.3736,  ..., -0.4601,  0.1815,  0.1850],\n",
            "         [ 0.7205, -0.2833,  0.0937,  ..., -0.1002, -2.3609,  2.2465],\n",
            "         [-1.3242, -0.1973,  0.2920,  ...,  0.5409,  0.6940,  1.8563]],\n",
            "\n",
            "        [[-0.7978,  1.0261,  1.1465,  ...,  1.2134,  0.9354, -0.0780],\n",
            "         [-1.4647, -1.9571,  0.1017,  ..., -1.9986, -0.7409,  0.7011],\n",
            "         [-1.3938,  0.8466, -1.7191,  ..., -1.1867,  0.1320,  0.3407],\n",
            "         ...,\n",
            "         [ 0.8206, -0.3745,  1.2499,  ..., -0.0676,  0.0385,  0.6335],\n",
            "         [-0.5589, -0.3393,  0.2347,  ...,  2.1181,  2.4569,  1.3083],\n",
            "         [-0.4092,  1.5199,  0.2401,  ..., -0.2558,  0.7870,  0.9924]]])\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "images = torch.randn(size=(32, 3, 64, 64))\n",
        "test_image = images[0]\n",
        "\n",
        "print(f\"Image batch shape: {images.shape}\")\n",
        "print(f\"Single image Shape: {test_image.shape}\")\n",
        "print(f\"Test image:\\n {test_image}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhkiVvtMfR_5",
        "outputId": "b312eba3-974e-46fe-e9dd-26beea9fb7dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([10, 64, 64])"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Single conv2d layer\n",
        "torch.manual_seed(42)\n",
        "cov_layer = nn.Conv2d(in_channels=3,\n",
        "                     out_channels=10,\n",
        "                     kernel_size=(3, 3),\n",
        "                     stride=1,\n",
        "                     padding=1)\n",
        "\n",
        "conv_output = cov_layer(test_image)\n",
        "conv_output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbLue4LUkLdd",
        "outputId": "3e48ea5f-6356-4a0f-bb5b-a590f927e2a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test iamge original shape: torch.Size([3, 64, 64])\n",
            "Test image with unsqueezed dimansions: torch.Size([1, 3, 64, 64])\n",
            "Shape after going conv_layer(): torch.Size([1, 10, 64, 64])\n",
            "Shape after going throgh conv_layer() and max_pool_layer(): torch.Size([1, 10, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "print(f\"Test iamge original shape: {test_image.shape}\")\n",
        "print(f\"Test image with unsqueezed dimansions: {test_image.unsqueeze(0).shape}\")\n",
        "\n",
        "#Create smaple nn.maxpool2d layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "test_image_throgh_conv = cov_layer(test_image.unsqueeze(dim=0))\n",
        "print(f\"Shape after going conv_layer(): {test_image_throgh_conv.shape}\")\n",
        "\n",
        "\n",
        "# Pass data throgh the max pool layer\n",
        "test_image_throgh_conv_and_max_pool = max_pool_layer(test_image_throgh_conv)\n",
        "print(f\"Shape after going throgh conv_layer() and max_pool_layer(): {test_image_throgh_conv_and_max_pool.shape}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suLOX16zohag",
        "outputId": "6d63a12b-5b15-4639-d899-c218f42393d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Max Pool tensor\n",
            " tensor([[[[0.3367, 0.1288],\n",
            "          [0.2345, 0.2303]]]])\n",
            "Max pool tensor shape: torch.Size([1, 1, 2, 2])\n",
            "\n",
            "Max Pool tensor\n",
            " tensor([[[[0.3367]]]])\n",
            "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "#Create a random tensor with a similar number of dims to our images\n",
        "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
        "print(f\"\\nMax Pool tensor\\n {random_tensor}\")\n",
        "print(f\"Max pool tensor shape: {random_tensor.shape}\")\n",
        "\n",
        "# Create max pool layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "#Pass The random tensor\n",
        "max_pool_tensor = max_pool_layer(random_tensor)\n",
        "print(f\"\\nMax Pool tensor\\n {max_pool_tensor}\")\n",
        "print(f\"Max pool tensor shape: {max_pool_tensor.shape}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXsGmBz0wmtg",
        "outputId": "ee19c9ce-e7e9-494e-d201-0ff238ca99c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "BZ33PQ7grCax",
        "outputId": "cd53bf0f-2e64-4147-bd27-0f90ae6b3f32"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcOHghF-rP4c",
        "outputId": "e3d44121-e716-4b3c-a927-ab1f0843f23e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rand_image_tensor = torch.randn(size=(1, 28, 28))\n",
        "rand_image_tensor.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8Yb6fzOrmba",
        "outputId": "c759c076-20ba-4ebe-c17f-d2e0fa332acf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 10, 14, 14])\n",
            "torch.Size([1, 10, 7, 7])\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([[ 0.0424, -0.0839,  0.0839, -0.0463,  0.0053,  0.0437,  0.0025,  0.0108,\n",
              "         -0.0158, -0.0177]], device='cuda:0', grad_fn=<AddmmBackward0>)"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_2(rand_image_tensor.unsqueeze(0).to(device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "JkZF_FPurr-p",
        "outputId": "d06592b4-5cf5-44cf-c75d-90cd3465ab2b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(rand_image_tensor.squeeze(), cmap=\"gray\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "65lbgPDMoSlx",
        "outputId": "c2975b0d-f3ef-4c16-b7ba-db25012b2b37"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda:0'"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV1(input_shape=784,\n",
        "                             hidden_units=10,\n",
        "                             output_shape=len(class_names)).to(device)\n",
        "str(next(model_2.parameters()).device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZWHchHrsEGL"
      },
      "outputs": [],
      "source": [
        "# Setup loss Functiom\n",
        "from helper_functions import accuracy_fn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model_2.parameters(),\n",
        "                             lr=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 321,
          "referenced_widgets": [
            "43bfaae284f2401088ade31cabafd0c5"
          ]
        },
        "id": "3GeWaoxahWBK",
        "outputId": "6479aac2-a7fe-4b18-ea5c-fcc4b7d473ae"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43bfaae284f2401088ade31cabafd0c5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Train time on cuda: 9.085 secounds\n",
            "Epoch: 1\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Train time on cuda: 18.901 secounds\n",
            "Epoch: 2\n",
            "---------\n",
            "Train Loss:  0.43026 | Train_acc: 84.94167\n",
            "Test Loss: 0.47664 | Test Acc: 83.42652\n",
            "Train time on cuda: 28.713 secounds\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "\n",
        "# Measure Time\n",
        "from timeit import default_timer as timer\n",
        "train_time_start_model_2 = timer()\n",
        "\n",
        "epochs = 3\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n---------\")\n",
        "  train_step(model=model_2,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "  test_step(model=model_2,\n",
        "            dataloader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "  train_time_end_model_2 = timer()\n",
        "  total_train_time_model_2 = print_train_time(start=train_time_start_model_2,\n",
        "                                              end=train_time_end_model_2,\n",
        "                                              device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QdpQhlgk1qW",
        "outputId": "ca584809-0f97-41af-f390-6dbf0f673fa9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<generator object Module.parameters at 0x7c0ca850ce40>"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_2.parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "RlJ-oNVYt3rU",
        "outputId": "ef65f7a7-f9ff-49e1-e5d3-859361379e70"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "'dict' object is not callable",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-8c877eec51bd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model_1_result = eval_model(model=model_1,\n\u001b[0m\u001b[1;32m      2\u001b[0m                             \u001b[0mdata_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                             \u001b[0mloss_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                             \u001b[0maccuracy_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccuracy_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                             device=device)\n",
            "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not callable"
          ]
        }
      ],
      "source": [
        "model_1_result = eval_model(model=model_1,\n",
        "                            data_loader=test_dataloader,\n",
        "                            loss_fn=loss_fn,\n",
        "                            accuracy_fn=accuracy_fn,\n",
        "                            device=device)\n",
        "model_1_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "A6wScFBhnAad",
        "outputId": "921ffc28-3a3d-44f5-bd54-cc0eb31cac04"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "'dict' object is not callable",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-89-b4c49338936c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model_2_result = eval_model = eval_model(model=model_2,\n\u001b[0m\u001b[1;32m      2\u001b[0m                             \u001b[0mdata_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                             \u001b[0mloss_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                             \u001b[0maccuracy_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccuracy_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                             device=device)\n",
            "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not callable"
          ]
        }
      ],
      "source": [
        "model_2_result = eval_model = eval_model(model=model_2,\n",
        "                            data_loader=test_dataloader,\n",
        "                            loss_fn=loss_fn,\n",
        "                            accuracy_fn=accuracy_fn,\n",
        "                            device=device)\n",
        "model_2_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "g83rBgwkqzl1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "compare_result = pd.DataFrame([model_0_result,\n",
        "                               model_2_result])\n",
        "compare_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "04Io0vdltysH"
      },
      "outputs": [],
      "source": [
        "# Visualize\n",
        "compare_result.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n",
        "plt.xlabel(\"accuracy (%)\")\n",
        "plt.ylabel(\"model\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "MXt60y7Aw5As"
      },
      "outputs": [],
      "source": [
        "def make_predictions(model: torch.nn.Module,\n",
        "                     data: list,\n",
        "                     device: torch.device = device):\n",
        "  pred_probs = []\n",
        "  model.to(device)\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data:\n",
        "      sample = torch.unsqueeze(sample, dim=0).to(device)\n",
        "\n",
        "\n",
        "      pred_logit = model(sample)\n",
        "\n",
        "      pred_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n",
        "\n",
        "      # Get pred prob off gpu for matplotlib\n",
        "      pred_probs.append(pred_prob.cpu())\n",
        "\n",
        "  return torch.stack(pred_probs)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "txQ91gPNzF8h"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "random.seed(42)\n",
        "test_samples = []\n",
        "test_labels = []\n",
        "for sample, label in random.sample(list(test_data), k=9):\n",
        "  test_samples.append(sample)\n",
        "  test_labels.append(label)\n",
        "\n",
        "\n",
        "test_samples[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vrKiwtyzz4M7"
      },
      "outputs": [],
      "source": [
        "plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zC0MqTqn0Bah"
      },
      "outputs": [],
      "source": [
        "pred_probs = make_predictions(model=model_2,\n",
        "                              data=test_samples)\n",
        "pred_probs[:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "j1AR_kyv0e8F"
      },
      "outputs": [],
      "source": [
        "#convert prediction s to labels\n",
        "pred_classes = pred_probs.argmax(dim=1)\n",
        "pred_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6f8LJ03h1KHT"
      },
      "outputs": [],
      "source": [
        "test_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "rpA4LmCe1OwA"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(9, 9))\n",
        "rows = 3\n",
        "ncols = 3\n",
        "for i, samples in enumerate(test_samples):\n",
        "  plt.subplot(rows, ncols, i+1)\n",
        "\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "\n",
        "  pred_label = class_names[pred_classes[i]]\n",
        "\n",
        "  #Get true albel\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
        "\n",
        "  if pred_label == truth_label:\n",
        "    plt.title(title_text, fontsize=100, c=\"g\")\n",
        "  else:\n",
        "    plt.title(title_text, fontsize=10, c=\"r\")\n",
        "\n",
        "  plt.axis(False);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6M5TCgDUdHHX"
      },
      "outputs": [],
      "source": [
        "# Making confusin matrix\n",
        "from tqdm.auto import tqdm\n",
        "y_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X, y in tqdm(test_dataloader, desc=\"making Predictions...\"):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    y_logit = model_2(X)\n",
        "    y_pred = torch.softmax(y_logit.squeeze(), dim=0).argmax(dim=1)\n",
        "    # Put predictions on CPU For matplotlib\n",
        "    y_preds.append(y_pred.cpu())\n",
        "\n",
        "#print(y_preds)\n",
        "y_pred_tensor = torch.cat(y_preds)\n",
        "y_pred_tensor[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "O2vvMIRXiocg"
      },
      "outputs": [],
      "source": [
        "mlxtend.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "gFT9f5mai_fx"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend: {mlxtend.__version__}\")\n",
        "  assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend version should be 0.019.0 or higher\"\n",
        "except:\n",
        "  !pip install torchmetrics -U mlxtend\n",
        "  import torchmetrics , mlxtend\n",
        "  print(f\"mlxtend version: {mlxtend.__version __}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "NkzlIX7zjGKZ"
      },
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      "source": []
    }
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