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            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "191a23d7f62d4681bd73618558417359": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AlexanderWeismannn/ML-Paper-Replication/blob/main/pytorch_model_deployment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xaa142ik0dYm",
        "outputId": "8f226853-29a1-40a8-f625-0b6ffb66307a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pytorch and Torchvision already imported\n"
          ]
        }
      ],
      "source": [
        "#Deployment Of A Pytorch Model Using Google Colab\n",
        "\n",
        "#Get Data (Pizza/Steak/Sushi 20%)\n",
        "try:\n",
        "  import torch\n",
        "  import torchvision\n",
        "  print(\"Pytorch and Torchvision already imported\")\n",
        "except:\n",
        "  print('[INFO] Pytorch and Torchvision not imported, installing...')\n",
        "  !pip install torch torchvision\n",
        "  import torch\n",
        "  import torchvision\n",
        "  print(\"Pytorch and Torchvision imported\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#clone git repo\n",
        "# Continue with regular imports\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "from torchvision import transforms\n",
        "\n",
        "# Try to get torchinfo, install it if it doesn't work\n",
        "try:\n",
        "    from torchinfo import summary\n",
        "except:\n",
        "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
        "    !pip install -q torchinfo\n",
        "    from torchinfo import summary\n",
        "\n",
        "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
        "try:\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
        "except:\n",
        "    # Get the going_modular scripts\n",
        "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
        "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
        "    !mv pytorch-deep-learning/going_modular .\n",
        "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
        "    !rm -rf pytorch-deep-learning\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves"
      ],
      "metadata": {
        "id": "O9c7fBg51Q-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "963c7532-313f-4a99-9d52-70b5ade4c461"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Couldn't find torchinfo... installing it.\n",
            "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
            "Cloning into 'pytorch-deep-learning'...\n",
            "remote: Enumerating objects: 4056, done.\u001b[K\n",
            "remote: Total 4056 (delta 0), reused 0 (delta 0), pack-reused 4056\u001b[K\n",
            "Receiving objects: 100% (4056/4056), 646.90 MiB | 14.56 MiB/s, done.\n",
            "Resolving deltas: 100% (2372/2372), done.\n",
            "Updating files: 100% (248/248), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#setup device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Q61KL3ny1wYi",
        "outputId": "ae67fa35-3875-4b74-c990-1efe81a960bf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download data for the dataset creation\n",
        "# Download pizza, steak, sushi images from GitHub\n",
        "data_20_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
        "                                     destination=\"pizza_steak_sushi_20_percent\")\n",
        "\n",
        "data_20_percent_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3suR2W-14n2",
        "outputId": "586548e0-2fd8-42f1-b578-2ad73ccb0192"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Did not find data/pizza_steak_sushi_20_percent directory, creating one...\n",
            "[INFO] Downloading pizza_steak_sushi_20_percent.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip...\n",
            "[INFO] Unzipping pizza_steak_sushi_20_percent.zip data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('data/pizza_steak_sushi_20_percent')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = data_20_percent_path/\"train\"\n",
        "test_data = data_20_percent_path/\"test\""
      ],
      "metadata": {
        "id": "wkUiqAO12bTJ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get effnets weight\n",
        "effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
        "\n",
        "#get effnets transforms\n",
        "effnetb2_transforms = effnetb2_weights.transforms()\n",
        "\n",
        "#Setup pretrained model\n",
        "effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)\n",
        "\n",
        "#Freeze the base layers of the model so we can transform the output layer\n",
        "for param in effnetb2.parameters():\n",
        "  param.requires_grad = False\n",
        "\n",
        "effnetb2.classifier"
      ],
      "metadata": {
        "id": "Yx06URWW20-c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b72df2b9-90d4-4d0f-ef86-ce496fe21111"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-c35c1473.pth\n",
            "100%|██████████| 35.2M/35.2M [00:00<00:00, 181MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Dropout(p=0.3, inplace=True)\n",
              "  (1): Linear(in_features=1408, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#update the classifier head\n",
        "effnetb2.classifier = nn.Sequential(\n",
        "    nn.Dropout(p=0.3,inplace=True),\n",
        "    nn.Linear(in_features= 1408, out_features= 3))"
      ],
      "metadata": {
        "id": "CRnGGiYbYoWV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchinfo import summary\n",
        "summary(effnetb2,\n",
        "        input_size= (1,3,224,224),\n",
        "        col_names= [\"input_size\",\"output_size\",\"num_params\",\"trainable\"],\n",
        "        col_width= 20,\n",
        "        row_settings=[\"var_names\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wldMZaz-ZWNK",
        "outputId": "acb22f63-75d4-4a9f-b1c7-74c2b0ab29d0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 3]               --                   Partial\n",
              "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
              "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
              "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
              "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
              "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
              "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
              "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
              "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
              "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
              "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
              "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
              "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
              "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
              "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
              "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
              "├─Sequential (classifier)                                    [1, 1408]            [1, 3]               --                   True\n",
              "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
              "│    └─Linear (1)                                            [1, 1408]            [1, 3]               4,227                True\n",
              "============================================================================================================================================\n",
              "Total params: 7,705,221\n",
              "Trainable params: 4,227\n",
              "Non-trainable params: 7,700,994\n",
              "Total mult-adds (M): 657.64\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 156.80\n",
              "Params size (MB): 30.82\n",
              "Estimated Total Size (MB): 188.22\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_effnetb2_model(num_classes: int=3,\n",
        "                          seed:int=42):\n",
        "  #1,2,3 create weights transforms and model\n",
        "  #get effnets weight\n",
        "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
        "\n",
        "  #get effnets transforms\n",
        "  transforms = weights.transforms()\n",
        "\n",
        "  #Setup pretrained model\n",
        "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
        "\n",
        "  #4 Freeze all layers in the base model\n",
        "  for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  #5. Change classifier head to our desired num_classes\n",
        "  torch.manual_seed(seed)\n",
        "  model.classifier = nn.Sequential(\n",
        "    nn.Dropout(p=0.3,inplace=True),\n",
        "    nn.Linear(in_features= 1408, out_features= num_classes))\n",
        "\n",
        "  return model,transforms\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YqBOP_XCZl8w"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "effnetb2,effnetb2_transforms = create_effnetb2_model(num_classes=3,seed=42)\n",
        "from torchinfo import summary\n",
        "summary(effnetb2,\n",
        "        input_size= (1,3,224,224),\n",
        "        col_names= [\"input_size\",\"output_size\",\"num_params\",\"trainable\"],\n",
        "        col_width= 20,\n",
        "        row_settings=[\"var_names\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "z_Jwc2KH3_Jg",
        "outputId": "50c10ee8-0256-43de-d3e6-54e5a595e2ad"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 3]               --                   Partial\n",
              "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
              "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
              "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
              "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
              "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
              "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
              "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
              "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
              "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
              "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
              "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
              "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
              "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
              "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
              "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
              "├─Sequential (classifier)                                    [1, 1408]            [1, 3]               --                   True\n",
              "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
              "│    └─Linear (1)                                            [1, 1408]            [1, 3]               4,227                True\n",
              "============================================================================================================================================\n",
              "Total params: 7,705,221\n",
              "Trainable params: 4,227\n",
              "Non-trainable params: 7,700,994\n",
              "Total mult-adds (M): 657.64\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 156.80\n",
              "Params size (MB): 30.82\n",
              "Estimated Total Size (MB): 188.22\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into dataloaders\n",
        "from going_modular.going_modular import data_setup\n",
        "train_dataloader,test_dataloader,class_names = data_setup.create_dataloaders(train_dir=train_data,\n",
        "                                                                             test_dir=test_data,\n",
        "                                                                             transform=effnetb2_transforms,\n",
        "                                                                             batch_size=32,)\n",
        "len(train_dataloader),len(test_dataloader),class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XSGcCSY5YCk",
        "outputId": "e97fbb51-f8c5-40a0-e9f2-121b6d88df19"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 5, ['pizza', 'steak', 'sushi'])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train the EffnetB2 Feature Extractor\n",
        "from going_modular.going_modular import engine\n",
        "\n",
        "#setup optimizer\n",
        "optimizer = torch.optim.Adam(params=effnetb2.parameters(),\n",
        "                             lr=1e-3)\n",
        "\n",
        "#setup loss function\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "#Set seeds for reproduction\n",
        "set_seeds()\n",
        "effnetb2_results = engine.train(model=effnetb2,\n",
        "                                train_dataloader=train_dataloader,\n",
        "                                test_dataloader=test_dataloader,\n",
        "                                epochs=10,\n",
        "                                optimizer=optimizer,\n",
        "                                loss_fn=loss_fn,\n",
        "                                device=device)"
      ],
      "metadata": {
        "id": "aFyG5kKH7Kzx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231,
          "referenced_widgets": [
            "01c2e035d48843eaa5480cd5ef0c44f4",
            "d7b47b117bcb44e48eae2e5e87b32563",
            "b89118468ba648149fae7196bd96f7a5",
            "7a7d7ae3d16b47b9a7aa27b397e0e04c",
            "bd8b1c10ace04a7c920a142e0800978a",
            "2777ba7b522c416d845336e7820d0ab0",
            "1a7497778e444aed8ca1e5aea014189b",
            "7e9a8064ebcb4a7891ccde0e5136ca97",
            "166b870b8363483aa83f3fe8c8f2ae1f",
            "646f7c98c52445e0b73a7fc04162169a",
            "747847a5d8604462ad6a46c018b5b3e0"
          ]
        },
        "outputId": "1efac158-86c0-4732-cfdc-2e8b76d03fc2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01c2e035d48843eaa5480cd5ef0c44f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.9839 | train_acc: 0.5667 | test_loss: 0.7393 | test_acc: 0.9409\n",
            "Epoch: 2 | train_loss: 0.7135 | train_acc: 0.8396 | test_loss: 0.5862 | test_acc: 0.9409\n",
            "Epoch: 3 | train_loss: 0.5874 | train_acc: 0.8958 | test_loss: 0.4891 | test_acc: 0.9563\n",
            "Epoch: 4 | train_loss: 0.4488 | train_acc: 0.9146 | test_loss: 0.4338 | test_acc: 0.9409\n",
            "Epoch: 5 | train_loss: 0.4277 | train_acc: 0.9125 | test_loss: 0.3907 | test_acc: 0.9443\n",
            "Epoch: 6 | train_loss: 0.4392 | train_acc: 0.8896 | test_loss: 0.3525 | test_acc: 0.9688\n",
            "Epoch: 7 | train_loss: 0.4246 | train_acc: 0.8771 | test_loss: 0.3263 | test_acc: 0.9563\n",
            "Epoch: 8 | train_loss: 0.3885 | train_acc: 0.8979 | test_loss: 0.3465 | test_acc: 0.9443\n",
            "Epoch: 9 | train_loss: 0.3795 | train_acc: 0.8812 | test_loss: 0.3127 | test_acc: 0.9193\n",
            "Epoch: 10 | train_loss: 0.3752 | train_acc: 0.8688 | test_loss: 0.2811 | test_acc: 0.9625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import plot_loss_curves\n",
        "plot_loss_curves(effnetb2_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "VwXfH54lxhYH",
        "outputId": "fa5b2e04-652f-4738-8637-5eb90838297d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import utils\n",
        "utils.save_model(model=effnetb2,\n",
        "                 target_dir=\"models\",\n",
        "                 model_name=\"effnetb2_20_percent.pth\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoCJI95S0xLf",
        "outputId": "8b65719c-6233-40ff-92fc-8746927a56bc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/effnetb2_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "#get the models size\n",
        "pretrained_effnetb2_model_size = Path(\"models/effnetb2_20_percent.pth\").stat().st_size // (1024*1024)\n",
        "print(f\"Pretrained EffnetB2 Feature Extractor Model Size: {pretrained_effnetb2_model_size} MB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bao8cwn0xsJZ",
        "outputId": "c8032fe4-5ff7-4a89-ec54-261ae0f7f913"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained EffnetB2 Feature Extractor Model Size: 29 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# colelct effnet feature extractor stats\n",
        "effnet_b2_total_params = sum(torch.numel(param) for param in effnetb2.parameters())\n",
        "effnet_b2_total_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kuf4_wJmTmr5",
        "outputId": "04714b1c-4fad-4cce-9929-883f8b129244"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7705221"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "effnet_b2_stats = {\"test_loss\":effnetb2_results[\"test_loss\"][-1],\n",
        "                   \"test_acc\":effnetb2_results[\"test_acc\"][-1],\n",
        "                   \"num_params\":effnet_b2_total_params,\n",
        "                   \"model_size\":pretrained_effnetb2_model_size}\n",
        "effnet_b2_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNHoP0tGUHJP",
        "outputId": "0c62caab-685f-4400-9fee-f8f0a72c3c44"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.28108686208724976,\n",
              " 'test_acc': 0.9625,\n",
              " 'num_params': 7705221,\n",
              " 'model_size': 29}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now creating the same feature extractor using a Vit model\n",
        "vit = torchvision.models.vit_b_16()\n",
        "vit.heads"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0Z_xXTHXE6n",
        "outputId": "d39a9fd3-82ea-4a83-cfdb-058ca2343c23"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def create_vit_model(num_classes:int=3,seed:int=42):\n",
        "  #import values\n",
        "  weights= torchvision.models.ViT_B_16_Weights.DEFAULT\n",
        "  transforms= weights.transforms()\n",
        "  model=torchvision.models.vit_b_16(weights=weights)\n",
        "\n",
        "  #freeze all layers\n",
        "  for param in model.parameters():\n",
        "    param.requries_grad= False\n",
        "\n",
        "  #change the classifier head to the num_classes size and unfreeze\n",
        "  torch.manual_seed(seed)\n",
        "  model.heads= nn.Sequential(\n",
        "      nn.Linear(in_features= 768,out_features= num_classes)\n",
        "  )\n",
        "  return model,transforms"
      ],
      "metadata": {
        "id": "s9bO74eAUpdj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vit, vit_transforms = create_vit_model(num_classes=3,seed=42)\n",
        "#summarize the model spec\n",
        "from torchinfo import summary\n",
        "summary(vit,\n",
        "        input_size=[1,3,224,224],\n",
        "        col_names=['input_size','output_size','num_params','trainable'],\n",
        "        col_width=20,\n",
        "        row_settings=['var_names'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UnjHA-ZWGMe",
        "outputId": "30a38881-28ea-4c63-ba07-10d9d1085eeb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /root/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n",
            "100%|██████████| 330M/330M [00:02<00:00, 168MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "VisionTransformer (VisionTransformer)                        [1, 3, 224, 224]     [1, 3]               768                  True\n",
              "├─Conv2d (conv_proj)                                         [1, 3, 224, 224]     [1, 768, 14, 14]     590,592              True\n",
              "├─Encoder (encoder)                                          [1, 197, 768]        [1, 197, 768]        151,296              True\n",
              "│    └─Dropout (dropout)                                     [1, 197, 768]        [1, 197, 768]        --                   --\n",
              "│    └─Sequential (layers)                                   [1, 197, 768]        [1, 197, 768]        --                   True\n",
              "│    │    └─EncoderBlock (encoder_layer_0)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_1)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_2)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_3)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_4)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_5)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_6)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_7)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_8)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_9)                   [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_10)                  [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    │    └─EncoderBlock (encoder_layer_11)                  [1, 197, 768]        [1, 197, 768]        7,087,872            True\n",
              "│    └─LayerNorm (ln)                                        [1, 197, 768]        [1, 197, 768]        1,536                True\n",
              "├─Sequential (heads)                                         [1, 768]             [1, 3]               --                   True\n",
              "│    └─Linear (0)                                            [1, 768]             [1, 3]               2,307                True\n",
              "============================================================================================================================================\n",
              "Total params: 85,800,963\n",
              "Trainable params: 85,800,963\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (M): 172.47\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 104.09\n",
              "Params size (MB): 229.20\n",
              "Estimated Total Size (MB): 333.89\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create dataloaders for the vit model\n",
        "from going_modular.going_modular import data_setup\n",
        "train_dataloader,test_dataloader,class_names = data_setup.create_dataloaders(train_dir=train_data,\n",
        "                                                                             test_dir=test_data,\n",
        "                                                                             transform=vit_transforms,\n",
        "                                                                             batch_size=32,)\n",
        "len(train_dataloader),len(test_dataloader),class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zm-hSonvXmba",
        "outputId": "1bb6bb92-e883-4b23-9a5b-b629fc6796ae"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 5, ['pizza', 'steak', 'sushi'])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train the EffnetB2 Feature Extractor\n",
        "from going_modular.going_modular import engine\n",
        "\n",
        "#setup optimizer\n",
        "optimizer = torch.optim.Adam(params=vit.parameters(),\n",
        "                             lr=1e-3)\n",
        "\n",
        "#setup loss function\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "#Set seeds for reproduction\n",
        "set_seeds()\n",
        "vit_results = engine.train(model=vit,\n",
        "                                train_dataloader=train_dataloader,\n",
        "                                test_dataloader=test_dataloader,\n",
        "                                epochs=10,\n",
        "                                optimizer=optimizer,\n",
        "                                loss_fn=loss_fn,\n",
        "                                device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231,
          "referenced_widgets": [
            "c381902c65e64d56a98c185ec45a48e9",
            "c2d6cdf12f9944799c076153caaf805e",
            "1af02fa2f0b54548bf503882c61dde6d",
            "98e07f81ec2a461da7f554eefe66d6a6",
            "6cc36d9009c740c4af632534c7931858",
            "4d2baa4aa7c744b59683c6c738606874",
            "9fd268359999448f8f8a4698bd131f53",
            "aa49d209706f4450835b31cc145077a0",
            "cd05057af7b54a768254bbb1e5baa6f0",
            "87d36107dd5a4864a57a74cfc15df355",
            "191a23d7f62d4681bd73618558417359"
          ]
        },
        "id": "JAmBRJ5AZCgk",
        "outputId": "b2e68273-87fe-45e4-ac55-68c0bedb3a25"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c381902c65e64d56a98c185ec45a48e9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 1.6258 | train_acc: 0.3083 | test_loss: 1.1380 | test_acc: 0.3625\n",
            "Epoch: 2 | train_loss: 1.1046 | train_acc: 0.3875 | test_loss: 1.0463 | test_acc: 0.3443\n",
            "Epoch: 3 | train_loss: 1.0470 | train_acc: 0.4792 | test_loss: 1.0855 | test_acc: 0.3659\n",
            "Epoch: 4 | train_loss: 1.0013 | train_acc: 0.5146 | test_loss: 0.9693 | test_acc: 0.5369\n",
            "Epoch: 5 | train_loss: 0.9730 | train_acc: 0.5042 | test_loss: 0.9863 | test_acc: 0.5233\n",
            "Epoch: 6 | train_loss: 0.9716 | train_acc: 0.5083 | test_loss: 1.0732 | test_acc: 0.3818\n",
            "Epoch: 7 | train_loss: 0.9753 | train_acc: 0.5188 | test_loss: 1.0449 | test_acc: 0.4653\n",
            "Epoch: 8 | train_loss: 0.9433 | train_acc: 0.5771 | test_loss: 0.9448 | test_acc: 0.5062\n",
            "Epoch: 9 | train_loss: 0.9305 | train_acc: 0.5563 | test_loss: 1.0018 | test_acc: 0.4903\n",
            "Epoch: 10 | train_loss: 0.9990 | train_acc: 0.5125 | test_loss: 0.9174 | test_acc: 0.6244\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss_curves(vit_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "yXhd2rnHZcsJ",
        "outputId": "41b935df-e229-44c5-e228-52631c9474e2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import utils\n",
        "utils.save_model(model=vit,\n",
        "                 target_dir=\"models\",\n",
        "                 model_name=\"vit_20_percent.pth\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jd2uuVyObGoP",
        "outputId": "c58010aa-6592-420c-be79-59cdd005c704"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/vit_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "#get the models size\n",
        "pretrained_vit_model_size = Path(\"models/vit_20_percent.pth\").stat().st_size // (1024*1024)\n",
        "print(f\"Pretrained Vit Feature Extractor Model Size: {pretrained_vit_model_size} MB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWQJLdcqZvpu",
        "outputId": "43971bd1-70cc-4200-a26e-d93d85432473"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained Vit Feature Extractor Model Size: 327 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# colelct effnet feature extractor stats\n",
        "vit_params = sum(torch.numel(param) for param in vit.parameters())\n",
        "vit_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgxiPp63avEg",
        "outputId": "1d16ab04-fdff-4a55-e88d-b6ee4d159d53"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85800963"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vit_stats = {\"test_loss\":vit_results[\"test_loss\"][-1],\n",
        "                   \"test_acc\":vit_results[\"test_acc\"][-1],\n",
        "                   \"num_params\":vit_params,\n",
        "                   \"model_size\":pretrained_vit_model_size}\n",
        "vit_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aufbmZp9a5JS",
        "outputId": "33a00a15-a22e-4c08-fe84-3dbf96fd2dc6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.9174204230308532,\n",
              " 'test_acc': 0.6244318181818181,\n",
              " 'num_params': 85800963,\n",
              " 'model_size': 327}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Worse accuracy and size makes the effnet the obvious choice\n",
        "#Just in case ill test the prediction time\n",
        "import pandas as pd\n",
        "df = pd.DataFrame([effnet_b2_stats,vit_stats])\n",
        "df['model'] = [\"EffnetB2\",\"ViT\"]\n",
        "df['tets_acc'] = round(df['test_acc'] * 100,2)\n",
        "df"
      ],
      "metadata": {
        "id": "qn5b4R-5bjac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "41aa6cc2-7760-45fc-cb50-1f0a36f9d39c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   test_loss  test_acc  num_params  model_size     model  tets_acc\n",
              "0   0.281087  0.962500     7705221          29  EffnetB2     96.25\n",
              "1   0.917420  0.624432    85800963         327       ViT     62.44"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c8d1e36d-21eb-4df2-b0ec-e8f2b3431561\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>num_params</th>\n",
              "      <th>model_size</th>\n",
              "      <th>model</th>\n",
              "      <th>tets_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.281087</td>\n",
              "      <td>0.962500</td>\n",
              "      <td>7705221</td>\n",
              "      <td>29</td>\n",
              "      <td>EffnetB2</td>\n",
              "      <td>96.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.917420</td>\n",
              "      <td>0.624432</td>\n",
              "      <td>85800963</td>\n",
              "      <td>327</td>\n",
              "      <td>ViT</td>\n",
              "      <td>62.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8d1e36d-21eb-4df2-b0ec-e8f2b3431561')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c8d1e36d-21eb-4df2-b0ec-e8f2b3431561 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c8d1e36d-21eb-4df2-b0ec-e8f2b3431561');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c2eac4bb-ae96-4c9b-94a9-f719483cd4c7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2eac4bb-ae96-4c9b-94a9-f719483cd4c7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2eac4bb-ae96-4c9b-94a9-f719483cd4c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_713ccc34-e74a-47d6-b9b0-1680e542be11\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_713ccc34-e74a-47d6-b9b0-1680e542be11 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"test_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44995577603980524,\n        \"min\": 0.28108686208724976,\n        \"max\": 0.9174204230308532,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9174204230308532,\n          0.28108686208724976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2390503038670431,\n        \"min\": 0.6244318181818181,\n        \"max\": 0.9625,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6244318181818181,\n          0.9625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_params\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55222028,\n        \"min\": 7705221,\n        \"max\": 85800963,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          85800963,\n          7705221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 210,\n        \"min\": 29,\n        \"max\": 327,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          327,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ViT\",\n          \"EffnetB2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tets_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.907280271917173,\n        \"min\": 62.44,\n        \"max\": 96.25,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          62.44,\n          96.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compare model speeds"
      ],
      "metadata": {
        "id": "p3TXdP7PkV_B"
      },
      "execution_count": 29,
      "outputs": []
    }
  ]
}