03_pytorch_computer_vision.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/patrickabadi/python-pytorch-notebooks/blob/main/03_pytorch_computer_vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pytorch Computer Vision"
      ],
      "metadata": {
        "id": "UOAjg0BIb7bY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Computer Vision libraries in PyTorch\n",
        "\n",
        "* torchvision\n",
        "* torchvision.datasets - get datasets and data loading function\n",
        "* torchvision.models - pre-trained computer vision models\n",
        "* torchvisioin.transforms - functions for manipulating your vision data (images) to b suitable for use with ML model\n",
        "* torch.utils.data.dataset - base dataset class for pytorch\n",
        "* torch.utils.data.DataLoading - python interable over dataset"
      ],
      "metadata": {
        "id": "lnhx-I5YhIoG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dY-6l8xWdrF",
        "outputId": "061d673f-f129-488a-e9cd-fc6bf77d3aa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.13.1+cu116\n",
            "0.14.1+cu116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Getting a dataset\n",
        "\n",
        "We're going to get the fashionMNIST dataset"
      ],
      "metadata": {
        "id": "taDD9vHfXqD8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# setup training data\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root=\"data\", # where to download data to\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor(), #how do we want to transform the data\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(), #how do we want to transform the data\n",
        "    target_transform=None\n",
        ")"
      ],
      "metadata": {
        "id": "M4nhoDThX0rM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data), len(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6QIFHzBHb2O2",
        "outputId": "eb4a720c-8db0-4578-9e38-26e700bf7c2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# see the first training example\n",
        "image, label = train_data[0]\n",
        "image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O0dOP-TcCkm",
        "outputId": "e02a0d94-ce8b-4e81-a72a-ba93cf2a73f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPBT5ZGVcobM",
        "outputId": "d43bfdd5-0cda-4b26-a5bc-76339aba7068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi6Dkrjwc0Wz",
        "outputId": "d82de490-a6d2-4178-970a-be2bc18827d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2 Visualizing our data"
      ],
      "metadata": {
        "id": "iaKyGALVdAE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8C3XGwneMij",
        "outputId": "81be1179-ad97-441b-f357-23aea350c587"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(image.squeeze())\n",
        "plt.title(train_data.classes[label])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "M7o2agppeY9b",
        "outputId": "e654d7f7-a080-4690-f42c-877b3c1664aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "w0aKOT_KfGf3",
        "outputId": "431db295-e128-4d30-fe6a-4fecdbcbdecb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot more images\n",
        "#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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "CjyZHnMGfgtH",
        "outputId": "fa305089-8b86-4c70-e894-64ca365676cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Do you think these items of clothing (images) could be modelled with pure linear lines or need non-linearity"
      ],
      "metadata": {
        "id": "LzIGkDPqhEs4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "from torchvision.datasets.utils import iterable_to_str\n",
        "## 2. Prepare DataLoader\n",
        "\n",
        "Right now, our data is in the form of PyTorch Datasets\n",
        "\n",
        "DataLoader turns our dataset into python iterable_to_str\n",
        "\n",
        "More specifically we want to turn our data into batches (or mini-batches)\n",
        "\n",
        "Why? \n",
        "\n",
        "1. It is more computationally efficient. Break down to 32 images at a time (batch size of 32)\n",
        "2. It gives our neural network more changes to update it's gradients per epoch\n",
        "\n",
        "mini batch gradient descent"
      ],
      "metadata": {
        "id": "pLFDRqv8hXmj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "\n",
        "\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False)\n",
        "\n",
        "train_dataloader, test_dataloader\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKe78lh4hw-c",
        "outputId": "08003789-4ea0-4ae3-e606-a83712710986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7f465a04d820>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7f465a04d7f0>)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets check out what we've create\n",
        "print(f\"DataLoaders: {train_dataloader, test_dataloader}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RQsR0yUj6hW",
        "outputId": "3b5ac956-66f3-4590-aaa6-572ab1ca0816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLoaders: (<torch.utils.data.dataloader.DataLoader object at 0x7f465a04d820>, <torch.utils.data.dataloader.DataLoader object at 0x7f465a04d7f0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Length of train_dataLoader: {len(train_dataloader)} batches of {BATCH_SIZE}\")\n",
        "print(f\"Length of test_dataLoader: {len(test_dataloader)} batches of {BATCH_SIZE}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63ruFiQ0kGV0",
        "outputId": "2424699f-7116-4b55-f3cc-00cf0749dfe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of train_dataLoader: 1875 batches of 32\n",
            "Length of test_dataLoader: 313 batches of 32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape\n",
        "\n",
        "# show a sample\n",
        "torch.manual_seed(42)\n",
        "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
        "\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "\n",
        "plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)\n",
        "\n",
        "print(f\"Image size: {img.shape}\")\n",
        "print(f\"Label: {label}, label size: {label.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "1tJrSZCrkqqN",
        "outputId": "6af6a7f0-92df-4e00-9293-f6abcbedb60e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image size: torch.Size([1, 28, 28])\n",
            "Label: 7, label size: torch.Size([])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Model 0: Build baseline model\n",
        "\n",
        "when starting to build ML modeling experiments, it's best practice to start with a baseline model\n",
        "\n",
        "A baseline model is a simple model you will try and improve upon with subsequent models/experiments.\n",
        "\n",
        "In other words start simply and add complexity when necessary\n"
      ],
      "metadata": {
        "id": "iUT7LYRBq9il"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a flatten layer\n",
        "\n",
        "flatten_model = nn.Flatten()\n",
        "\n",
        "# get a single sample\n",
        "x = train_features_batch[0]\n",
        "\n",
        "output = flatten_model(x) #perform forward pass\n",
        "\n",
        "print(f\"Shape before flattening: {x.shape}\")\n",
        "print(f\"Shape after flattening: {output.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtdESzkKrkFE",
        "outputId": "9564e917-e50a-4179-fd03-c05eb6158028"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening: torch.Size([1, 28, 28])\n",
            "Shape after flattening: torch.Size([1, 784])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "class FashionMNISTV0(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(start_dim=1, end_dim=-1),\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",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "model_0 = FashionMNISTV0(\n",
        "    input_shape=784, # this is 28x28\n",
        "    hidden_units=10, # how many units in the hidden layer \n",
        "    output_shape=len(class_names) # one for every class\n",
        "    ).to(\"cpu\")\n",
        "\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSqxbQeTslMu",
        "outputId": "ae96d7ef-e4ba-4b6c-e61a-ce069cd24b7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_x = torch.rand([1,1,28,28])\n",
        "\n",
        "model_0(dummy_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HN9BaBkcuwhG",
        "outputId": "183f5046-f019-4b6b-9f4b-39ce6d415818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0315,  0.3171,  0.0531, -0.2525,  0.5959,  0.2112,  0.3233,  0.2694,\n",
              "         -0.1004,  0.0157]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUq9sFx0wHmd",
        "outputId": "045a7348-5b09-4418-aa0d-53e06bd15f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "             ('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])),\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]])),\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]))])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Setup loss, optimizer and eval metrics\n",
        "\n",
        "* loss function - since we're working with multiclass data loss will be nn.CrossEntropyLoss\n",
        "* Optimizer - will be torch.optim.SGD\n",
        "* Eval metric - since we're working on a classification problem, let's use accuracy as our eval metric"
      ],
      "metadata": {
        "id": "h7vvyeE4u4u4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "#download helper function from learnpytorch repo\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(\"file exists\")\n",
        "else:\n",
        "  request = requests.get('https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py')\n",
        "  with open(\"helper_function.py\", \"wb\") as f:\n",
        "    f.write(request.content)\n",
        "\n"
      ],
      "metadata": {
        "id": "HFzoJ3LUvXlk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_function import accuracy_fn\n",
        "\n",
        "#setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)\n"
      ],
      "metadata": {
        "id": "3P5sjT5vwqF_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Creating a function to time our experiments\n",
        "\n",
        "ML is very experimental\n",
        "\n",
        "Two of the main things you'll often want to track are:\n",
        "1. Model's performance (loss and accuracy values etc)\n",
        "2. How fast it runs"
      ],
      "metadata": {
        "id": "wY4V--7sxWun"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "\n",
        "def print_train_time(start: float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "  \"\"\"Prints difference between start and end time\"\"\"\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "  return total_time\n",
        "  "
      ],
      "metadata": {
        "id": "XxPpRBTZxnBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = timer()\n",
        "\n",
        "#some code\n",
        "\n",
        "end_time = timer()\n",
        "print_train_time(start_time,end_time, device=\"cpu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9bxtqvsyTZU",
        "outputId": "77b12ccd-0c64-442c-b4ab-64fe94005336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train time on cpu: 0.000 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.7696000163123244e-05"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Creating a training loop and training a model on batches of data\n",
        "1. Loop through epochs\n",
        "2. loop through training batches, perform training steps, calculate the train loss *per batch*\n",
        "3. loop through testing batches, perform testing steps, calculate test loss *per batch*\n",
        "4. Print out what's happening\n",
        "5. time it all (for fun)"
      ],
      "metadata": {
        "id": "nM_3Ahrzyk-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "train_time_start_on_cpu = timer()\n",
        "\n",
        "#set number of epochs, keep small for faster training time\n",
        "epochs = 3\n",
        "\n",
        "# create training and test loop\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n------\")\n",
        "\n",
        "  #### Training\n",
        "  train_loss = 0\n",
        "\n",
        "  # Add a loop to loop through training batches\n",
        "  for batch, (X, y) in enumerate(train_dataloader):\n",
        "    model_0.train()\n",
        "\n",
        "    #1. forward pass\n",
        "    y_pred = model_0(X)\n",
        "\n",
        "    #2. calc loss(per batch)\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss #accumulate training loss\n",
        "\n",
        "    #3. optimizer\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    #4. loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    #5 optimizer step\n",
        "    optimizer.step()\n",
        "\n",
        "    if batch% 400 == 0:\n",
        "      print(f\"Looked at {batch*len(X)}/{len(train_dataloader.dataset)} samples\")\n",
        "\n",
        "  # div total train loss by length of train dataloader\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  ### testing\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",
        "      test_pred = model_0(X_test)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      #calc accuracy\n",
        "      test_acc += accuracy_fn(y_true=y_test, y_pred=test_pred.argmax(dim=1))\n",
        "    \n",
        "    # calculate the test loss average per batch\n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "    #calculate the test acc average per batch\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "  print(f\"\\nTrain loss: {train_loss:.4f} | Test loss: {test_loss:.4f}, Test acc: {test_acc:.2f}%\")\n",
        "\n",
        "# calculate training time\n",
        "train_time_end_on_cpu = timer()\n",
        "\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))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535,
          "referenced_widgets": [
            "da34cc20436243b2829723392960a40c",
            "0b15999aa7b34a8e952c7f62b98a4163",
            "6ec2623f7b824425933ecc4711a3171f",
            "799a6110e2c649c8938db5ce8961ec9d",
            "8d1fe8fd83ed4a39af0fc43c806f4999",
            "696097e3591f4714b5d4154384f2fed9",
            "f27928ad02194f13b186a4f46a31c993",
            "ad376809e66b4630b868ee6f4f898017",
            "1912dfba7d1f416681a19fe72207e3dd",
            "37a2f00b87504874828bf00103d3062c",
            "1823df61dda84f0fac5ea89974b2736f"
          ]
        },
        "id": "wfVGyLCuzB5U",
        "outputId": "8922660a-6ca1-4fb8-ee79-ec6a7e836172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da34cc20436243b2829723392960a40c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.5904 | Test loss: 0.5095, Test acc: 82.04%\n",
            "Epoch: 1\n",
            "------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.4763 | Test loss: 0.4799, Test acc: 83.20%\n",
            "Epoch: 2\n",
            "------\n",
            "Looked at 0/60000 samples\n",
            "Looked at 12800/60000 samples\n",
            "Looked at 25600/60000 samples\n",
            "Looked at 38400/60000 samples\n",
            "Looked at 51200/60000 samples\n",
            "\n",
            "Train loss: 0.4550 | Test loss: 0.4766, Test acc: 83.43%\n",
            "Train time on cpu: 28.082 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Make predictions and get Model 0 results"
      ],
      "metadata": {
        "id": "hHg5KPhc4Q1u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## 5. Setup device agnostic-code (for gpu)\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Nu1ZAQl_8hug",
        "outputId": "ff09ffb2-ef28-4a64-e831-cb3fc47516a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn=accuracy_fn,\n",
        "               device=device):\n",
        "  \"\"\" Returns a dictionary containing the results of mode predicting on data_loader \"\"\"\n",
        "  loss, acc = 0,0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X,y in tqdm(data_loader):\n",
        "\n",
        "      X,y = X.to(device), y.to(device)\n",
        "\n",
        "      # make predictions\n",
        "      y_pred = model(X)\n",
        "\n",
        "      # accumulate the loss and acc values per 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",
        "    loss/=len(data_loader)\n",
        "    acc/=len(data_loader)\n",
        "\n",
        "  return {\n",
        "          \"model_name\": model.__class__.__name__,\n",
        "          \"model_loss\": loss.item(),\n",
        "          \"model_acc\": acc\n",
        "          }\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "K_PjHI2s6rfU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_0_results = eval_model(model=model_0,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn = loss_fn,\n",
        "                             accuracy_fn=accuracy_fn,\n",
        "                             device=\"cpu\")\n",
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "d2023a137d794cde90f2a044c2be43ca",
            "5881020cc6b34879bc8a77c9a79c2911",
            "943e1ada8dda4abd979f70da89d6afad",
            "6e8abe455c8e4cf2aa3b2ba47a4a3741",
            "1ca408d5af674208b48dbd9f0e24810f",
            "2eefc59a785c455cb3b7081aeae04fd5",
            "77811463fc264ad3af36fd335d64b802",
            "bdf3897250fa45d99ef3578167934eea",
            "a5583bd595bb47f1a2777419210aaffc",
            "60dc71ab87134a05b0677727a583dfff",
            "fc30095e5c40473b875c59d350d61240"
          ]
        },
        "id": "ToONyJgJml4R",
        "outputId": "b1c6955d-b721-4232-ee97-b6b9ec60e5d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2023a137d794cde90f2a044c2be43ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 83.42651757188499}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Model 1: Building a better model with non-linearity\n",
        "\n",
        "We learned about the power of non-linearity in section 2"
      ],
      "metadata": {
        "id": "I2NMwmTt9Yaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.modules.activation import ReLU\n",
        "# create a model with non-linear and linear layers\n",
        "class FashionMNISTV1(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(), # flatten the inputs into a single vector\n",
        "        nn.Linear(in_features= input_shape,\n",
        "                  out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units,\n",
        "                  out_features=output_shape),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)\n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "model_1 = FashionMNISTV1(input_shape=784, #output of 28x28\n",
        "                         hidden_units=10,\n",
        "                         output_shape=len(class_names)).to(device)\n",
        "model_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_ufg_hR-Djz",
        "outputId": "5a6f7fb0-1f23-429b-e0a4-2cebd74d9185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTV1(\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): ReLU()\n",
              "    (3): Linear(in_features=10, out_features=10, bias=True)\n",
              "    (4): ReLU()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6.1 Setup loss and optimizer"
      ],
      "metadata": {
        "id": "Wjbd6qknA5LT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_1.parameters(),\n",
        "                            lr=0.1)\n"
      ],
      "metadata": {
        "id": "VLuKLtDpAVCG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.2 Functionizing training and evaluation loops\n",
        "\n",
        "* training_loop - strain_step()\n",
        "* test_loop - test_step()"
      ],
      "metadata": {
        "id": "CzHmYqlHBPwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "  \"\"\" Performs a training with model trying to learn on data_loader \"\"\"\n",
        "\n",
        "  #### Training\n",
        "  train_loss, train_acc = 0,0\n",
        "\n",
        "  model.train()\n",
        "\n",
        "  # Add a loop to loop through training batches\n",
        "  for batch, (X, y) in enumerate(data_loader):\n",
        "    # put data on target device\n",
        "    X, y = X.to(device), y.to(device)    \n",
        "\n",
        "    #1. forward pass\n",
        "    y_pred = model(X)\n",
        "\n",
        "    #2. calc loss(per batch)\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss #accumulate training loss\n",
        "    train_acc += accuracy_fn(y_true=y,\n",
        "                             y_pred=y_pred.argmax(dim=1)) #go from logits -> prediction labels\n",
        "\n",
        "    #3. optimizer\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    #4. loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    #5 optimizer step\n",
        "    optimizer.step()\n",
        "\n",
        "  # div total train loss by length of train dataloader\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "\n",
        "  print(f\"Train loss: {train_loss:.5f} | Train acc: {train_acc:.2f}%\")"
      ],
      "metadata": {
        "id": "jz8_urlnBiQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_step(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",
        "  \"\"\" performs a testing loop step on model going over data_loader \"\"\"\n",
        "  test_loss, test_acc=0,0\n",
        "\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X,y in data_loader:\n",
        "      #send data to target device\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",
        "      test_acc += accuracy_fn(y_true= y,\n",
        "                              y_pred = test_pred.argmax(dim=1))\n",
        "      \n",
        "    #adjust metrics and print out\n",
        "    test_loss /= len(data_loader)\n",
        "    test_acc /= len(data_loader)\n",
        "    print(f\"Test loss: {test_loss:.5f} | Test acc: {test_acc:.2f}%\")\n",
        "\n"
      ],
      "metadata": {
        "id": "iVWFMEOJDdBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# measure time\n",
        "from timeit import default_timer as timer\n",
        "train_time_start_on_gpu = timer()\n",
        "\n",
        "epochs = 3\n",
        "\n",
        "# create a optimization and eval loop using strain steps and test step\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch {epoch}...\")\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",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn, \n",
        "            accuracy_fn=accuracy_fn, \n",
        "            device=device)\n",
        "\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_1 = print_train_time(train_time_start_on_gpu,\n",
        "                                            train_time_end_on_gpu,\n",
        "                                            device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "ec3718adb52a4c089e703506a82255ba",
            "bf15aee9ebe74765a92a78907c058dc1",
            "e0aca349ed364f5cac2143a982d8a777",
            "396ec7830a70412c913dc32f8673f993",
            "bef766edb4d8444c97cf25dcfb28765d",
            "5b5a41b6fbf94bd1a7f715980fcaed08",
            "23fc779514474f7fbe803602299d4e26",
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            "9d13145d2ea24cd7a5c2c84bfb618b82",
            "99db53b7367d42ec9d9f4a1673ef9571"
          ]
        },
        "id": "u7XCASbUFJYf",
        "outputId": "875f0a20-20bf-4e1d-c126-54a5f2a45929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec3718adb52a4c089e703506a82255ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0...\n",
            "Train loss: 1.09199 | Train acc: 61.34%\n",
            "Test loss: 0.95636 | Test acc: 65.00%\n",
            "Epoch 1...\n",
            "Train loss: 0.78101 | Train acc: 71.93%\n",
            "Test loss: 0.72227 | Test acc: 73.91%\n",
            "Epoch 2...\n",
            "Train loss: 0.67027 | Train acc: 75.94%\n",
            "Test loss: 0.68500 | Test acc: 75.02%\n",
            "Train time on cuda: 24.269 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "** Note:** Sometimes, depending on your data/hardware you might find that your model trains faster on CPU than get_autocast_gpu_dtype\n",
        " Why is this?\n",
        "\n",
        " 1. It could be that the overhead for copying data/model to and from the GPU outweighs the compute benefits offered by the GPU\n",
        " 2. The hardware you're using has a better CPU in terms of compute capability than the GPU"
      ],
      "metadata": {
        "id": "5f5waPNAkd1H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model 1 results dictionary\n",
        "model_1_results = 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_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "49b882b3fc5249b28d38efe8bc16b589",
            "3f8780bc4dd9494187c42e7ad8366421",
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            "1af08a8073224b4aa3a02666ebb989eb",
            "2ef4eeeb68094e38a44afb5f72a2b7a7",
            "6ff108a50add4dedb31887be024a0235",
            "7a93228d2690444ca058b68e983c84ac",
            "ca6deba632a0400f8820156f712512af",
            "0889fc194858410abbff4b821b73528a",
            "d66021466969466ea837a4b345bf7d98"
          ]
        },
        "id": "9nppf5ldlJK4",
        "outputId": "e95a48b9-6a6a-4fe3-964f-2e29c307fc60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49b882b3fc5249b28d38efe8bc16b589"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV1',\n",
              " 'model_loss': 0.6850008964538574,\n",
              " 'model_acc': 75.01996805111821}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiycKWQ_oLT5",
        "outputId": "b4c4589f-5d2e-4e62-9550-e194bfd22027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV0',\n",
              " 'model_loss': 0.47663894295692444,\n",
              " 'model_acc': 83.42651757188499}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Building a convolutional neural network (CNN)\n",
        "\n",
        "CNN's are known as ConvNet\n",
        "\n",
        "CNN's are known for their capabilities to find pattern in visual data\n",
        "\n",
        "To find out what's happening see this website https://poloclub.github.io/cnn-explainer/"
      ],
      "metadata": {
        "id": "bKDTgjtXoSH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a convolutional neural network\n",
        "class FashionMNISTV2(nn.Module):\n",
        "  \"\"\"\n",
        "  Model architecture that replicate the TinyVGG\n",
        "  model from CNN explainer website\n",
        "  \"\"\"\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), # value we can set ourselves are hyperparameters\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, #there's a trick to calculating this\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv_block_1(x)\n",
        "    #print(f\"Output shape of conv_block_1: {x.shape}\")\n",
        "    x = self.conv_block_2(x)\n",
        "    #print(f\"Output shape of conv_block_2: {x.shape}\")\n",
        "    x = self.classifier(x)\n",
        "    #print(f\"Output shape of classifier: {x.shape}\")\n",
        "    return x\n",
        "\n"
      ],
      "metadata": {
        "id": "_V3PV1H4pR9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "model_2 = FashionMNISTV2(\n",
        "    input_shape=1,\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names)).to(device)"
      ],
      "metadata": {
        "id": "kdXXJ541uxPx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2rNuZKUvloMt",
        "outputId": "d02e33a7-02a7-486a-ad4f-e098a9da6813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4651349a90>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pass image through model\n",
        "rand_image_tensor = torch.randn(size=(1,28,28))\n",
        "rand_image_tensor.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ywuk-OkQl40o",
        "outputId": "ec13e8c6-6caa-4289-949a-cd921d078247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2(rand_image_tensor.unsqueeze(0).to(device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMRCb2dTmSTW",
        "outputId": "0b3c6c9f-3ba6-4d40-eb2e-197223ce6b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0366, -0.0940,  0.0686, -0.0485,  0.0068,  0.0290,  0.0132,  0.0084,\n",
              "         -0.0030, -0.0185]], device='cuda:0', grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.1 Stepping through 'nn.Conv2d'"
      ],
      "metadata": {
        "id": "B6CBed9kcB7v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# create a batch of images\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}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLlUFhi_cHi9",
        "outputId": "4d6fec78-b6b6-4b84-c62f-e9a5c8e90cdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a single conv2d layer\n",
        "torch.manual_seed(42)\n",
        "conv_layer = nn.Conv2d(in_channels=3,\n",
        "                       out_channels=10,\n",
        "                       kernel_size=(3,3),\n",
        "                       stride=1,\n",
        "                       padding=0)\n",
        "\n",
        "# pass the data through the convolutional layer\n",
        "conv_ouput = conv_layer(test_image)\n",
        "conv_ouput.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xw6sCtTcdUnS",
        "outputId": "4d43f489-4229-4655-ef70-d4fc1f6edab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 62, 62])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 stepping through nn.MaxPool2d()\n",
        "\n",
        "https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html"
      ],
      "metadata": {
        "id": "Zi7qSPbIft6h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Test image original shape: {test_image.shape}\")\n",
        "\n",
        "#create a sample nn.MaxPool2d layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "# Pass through just the conv layer\n",
        "\n",
        "test_image_through_conv = conv_layer(test_image.unsqueeze(dim=0))\n",
        "print(f\"Shape after going throught the conv_layer: {test_image_through_conv.shape}\")\n",
        "\n",
        "test_image_through_conv_and_max_pool = max_pool_layer(test_image_through_conv)\n",
        "print(f\"Shape after max pool layer: {test_image_through_conv_and_max_pool.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDSQePYWf859",
        "outputId": "c8949184-e797-4765-ac8f-901898bc7d82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test image original shape: torch.Size([3, 64, 64])\n",
            "Shape after going throught the conv_layer: torch.Size([1, 10, 62, 62])\n",
            "Shape after max pool layer: torch.Size([1, 10, 31, 31])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "random_tensor = torch.randn(size=(1,1,2,2))\n",
        "print(f\"Random pool tensor: {random_tensor}\")\n",
        "print(f\"Random pool tensor shape: {random_tensor.shape}\")\n",
        "\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "max_pool_tensor = max_pool_layer(random_tensor)\n",
        "print(f\"Max pool tensor: {max_pool_tensor}\")\n",
        "print(f\"Max pool tensor shape: {max_pool_tensor.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhSDtiF6ifNL",
        "outputId": "6b9c93a0-fb60-467d-934c-dc0fe866b0d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random pool tensor: tensor([[[[0.3367, 0.1288],\n",
            "          [0.2345, 0.2303]]]])\n",
            "Random pool tensor shape: torch.Size([1, 1, 2, 2])\n",
            "Max pool tensor: tensor([[[[0.3367]]]])\n",
            "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.3 Setup a loss function and optimizer for model_2"
      ],
      "metadata": {
        "id": "drLrk5iXkbMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_function import accuracy_fn\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_2.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "RqNmxMIzo01H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.4 Training and testing 'model_2' using our training and testing functions\n"
      ],
      "metadata": {
        "id": "GIaeP9FApPn3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "#measure time\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "train_time_start_model_2 = timer()\n",
        "\n",
        "epochs=3\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\")\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",
        "  \n",
        "  test_step(model=model_2,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn = loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "  \n",
        "  train_time_end_model_2 = timer()\n",
        "\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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
            "11c39451d59440ccb93594fdfe9245aa",
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            "e6fefd7586664f8a9f225a1602c1ce68",
            "12f9c009d97741448860b45a7fb10605",
            "6cc31c5b013c4fc880d97e2a32b92bd5"
          ]
        },
        "id": "gwo0uuyPpb32",
        "outputId": "ca8c666f-d975-4be9-ea59-7e7f75e1271b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11c39451d59440ccb93594fdfe9245aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "Train loss: 0.59392 | Train acc: 78.55%\n",
            "Test loss: 0.38435 | Test acc: 86.39%\n",
            "Train time on cuda: 16.517 seconds\n",
            "Epoch: 1\n",
            "Train loss: 0.35511 | Train acc: 87.22%\n",
            "Test loss: 0.35971 | Test acc: 86.71%\n",
            "Train time on cuda: 28.601 seconds\n",
            "Epoch: 2\n",
            "Train loss: 0.32065 | Train acc: 88.38%\n",
            "Test loss: 0.31414 | Test acc: 88.81%\n",
            "Train time on cuda: 39.702 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get model 2 results\n",
        "model_2_results = eval_model(\n",
        "    model=model_2,\n",
        "    data_loader=test_dataloader,\n",
        "    loss_fn=loss_fn,\n",
        "    accuracy_fn=accuracy_fn,\n",
        "    device = device\n",
        ")\n",
        "\n",
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "b70ea878a72a41afb23c66f394f4582d",
            "9f9da6acca154fdcb055dc703b777615",
            "2b5211f4b55149e791defebe423a0eb1",
            "365b3157950a4b2dbf2be7142fdad956",
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            "c0a2fbb5642d4d8fb59474a2856e1b2c",
            "7c70c12e48fa47cda12fe7baabd83eef"
          ]
        },
        "id": "1kZR45nkr4BX",
        "outputId": "e2c0a83c-c476-4c1c-ab06-82d4eaf9b8df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b70ea878a72a41afb23c66f394f4582d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV2',\n",
              " 'model_loss': 0.3141421377658844,\n",
              " 'model_acc': 88.80790734824281}"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Compare model results and training time\n"
      ],
      "metadata": {
        "id": "_SAJeA2CsOQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([model_0_results,\n",
        "                                model_1_results,\n",
        "                                model_2_results])\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Eif4a3jssSud",
        "outputId": "e14fca12-7394-4031-a081-d30c324b2d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       model_name  model_loss  model_acc\n",
              "0  FashionMNISTV0    0.476639  83.426518\n",
              "1  FashionMNISTV1    0.685001  75.019968\n",
              "2  FashionMNISTV2    0.314142  88.807907"
            ],
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              "\n",
              "  <div id=\"df-2346d85c-2dcf-43b3-9798-913449f2e17b\">\n",
              "    <div 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>model_name</th>\n",
              "      <th>model_loss</th>\n",
              "      <th>model_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTV0</td>\n",
              "      <td>0.476639</td>\n",
              "      <td>83.426518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTV1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTV2</td>\n",
              "      <td>0.314142</td>\n",
              "      <td>88.807907</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2346d85c-2dcf-43b3-9798-913449f2e17b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\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-2346d85c-2dcf-43b3-9798-913449f2e17b 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-2346d85c-2dcf-43b3-9798-913449f2e17b');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add training time to results comparison\n",
        "compare_results[\"training_time\"]=[total_train_time_model_0, total_train_time_model_1, total_train_time_model_1]\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Xs-92b58swyg",
        "outputId": "2c8c89d5-a6ae-43f5-c3df-ecaabc49692f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       model_name  model_loss  model_acc  training_time\n",
              "0  FashionMNISTV0    0.476639  83.426518      28.082380\n",
              "1  FashionMNISTV1    0.685001  75.019968      24.269418\n",
              "2  FashionMNISTV2    0.314142  88.807907      24.269418"
            ],
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              "\n",
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              "      <div>\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTV0</td>\n",
              "      <td>0.476639</td>\n",
              "      <td>83.426518</td>\n",
              "      <td>28.082380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTV1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "      <td>24.269418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTV2</td>\n",
              "      <td>0.314142</td>\n",
              "      <td>88.807907</td>\n",
              "      <td>24.269418</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\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",
              "    [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-312d214d-2a3b-4bf2-a7d7-01c9a98fbb38 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-312d214d-2a3b-4bf2-a7d7-01c9a98fbb38');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_results.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n",
        "plt.xlabel(\"accuracy (%)\")\n",
        "plt.ylabel(\"model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JWCa_WX5X0Fw",
        "outputId": "2a879fbc-4538-4c08-c0ca-06686a264244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'model')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSA9QwSqYMy3",
        "outputId": "0342de0b-f18f-4a4b-8409-fe368b74699b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jan 30 22:02:46 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\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   59C    P0    28W /  70W |   1134MiB / 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",
            "|    0   N/A  N/A     32910      C                                    1131MiB |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Make and evaluate random predictions with best model"
      ],
      "metadata": {
        "id": "WFUXZJj5YrQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predictions(model: torch.nn.Module,\n",
        "                     data: list,\n",
        "                     device: torch.device = device):\n",
        "  pred_probs = []\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data:\n",
        "      # prepare the sample (add a batch dimension and pass to the target device)\n",
        "      sample = torch.unsqueeze(sample, dim=0).to(device)\n",
        "\n",
        "      # forward pass (model outputs raw logits)\n",
        "      pred_logit = model(sample)\n",
        "\n",
        "      # get prediction probability (logit -> prediction probability)\n",
        "      pred_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n",
        "\n",
        "      #get pred_prob off the GPU for further calculations\n",
        "      pred_probs.append(pred_prob.cpu())\n",
        "\n",
        "  return torch.stack(pred_probs)"
      ],
      "metadata": {
        "id": "KOaVnEh0YwFa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(42)\n",
        "\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",
        "test_samples[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7MdzhAiZqK0",
        "outputId": "9983af37-69fa-4de0-d972-8bbb5750ede3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[test_labels[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "k3KFmc9laOX8",
        "outputId": "520cdd6a-2eba-4fd6-99f0-00bc10ff67a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sandal')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make some predictions\n",
        "pred_probs = make_predictions(model=model_2,\n",
        "                              data=test_samples)\n",
        "\n",
        "# view first two prediction probabilities\n",
        "pred_probs[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ky8T9mRqao9T",
        "outputId": "c4b8f3d0-a2cb-4ac1-a658-ad05c4ceebae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[4.9030e-08, 1.4638e-08, 3.4176e-08, 7.0050e-09, 3.0784e-09, 9.9992e-01,\n",
              "         2.8847e-08, 2.2857e-06, 2.0875e-05, 5.8772e-05],\n",
              "        [4.6282e-02, 8.7006e-01, 6.3767e-03, 5.0169e-02, 2.2469e-02, 8.2236e-05,\n",
              "         4.0646e-03, 1.1727e-04, 5.3785e-05, 3.2009e-04]])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert prediction probabilities to labels\n",
        "pred_classes = pred_probs.argmax(dim=1)\n",
        "pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spx20RtRbEgG",
        "outputId": "a7e3dcde-5352-46a7-d923-31c4e1a76de6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5, 1, 7, 4, 3, 0, 4, 7, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "plt.figure(figsize=(9,9))\n",
        "nrows = 3\n",
        "ncols=3\n",
        "for i, sample in enumerate(test_samples):\n",
        "  plt.subplot(nrows, ncols, i+1)\n",
        "\n",
        "  # plot the target image\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "\n",
        "  # find the prediction (in text form, eg. sandal)\n",
        "  pred_label = class_names[pred_classes[i]]\n",
        "\n",
        "  # truth label (in text form)\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
        "\n",
        "  # check for the equality between pred and truth and change color of title text\n",
        "  if pred_label == truth_label:\n",
        "    plt.title(title_text, fontsize = 10, c=\"g\") # green if predication correct\n",
        "  else:\n",
        "    plt.title(title_text,fontsize=10, c=\"r\")\n",
        "\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "HWzXf341bYqC",
        "outputId": "f4c568de-1f1e-437b-dd52-421ce13d3df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10. Making a confusion matrix for further prediction evaluation\n",
        "\n",
        "A confusion matrix is a fantastic way of evaluating your classification models\n",
        "\n",
        "1. Make predictions with our trained model on the test dataset\n",
        "2. Make a confusion matrix 'torchmetrics.ConfusionMatrix'\n",
        "3. Plot the confusion matrix using mlxtend.plotting.plot_confusion_matrix()"
      ],
      "metadata": {
        "id": "Mdz8FVH1nZyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 1. Make predictions with trained model\n",
        "y_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X,y in tqdm(test_dataloader, desc=\"Making predictions...\"):\n",
        "    # send the data and targets to target device\n",
        "    X,y = X.to(device), y.to(device)\n",
        "\n",
        "    # do the forward pass\n",
        "    y_logit = model_2(X)\n",
        "\n",
        "    # turn logit to prediction probabilities\n",
        "    y_pred = torch.softmax(y_logit.squeeze(), dim=0).argmax(dim=1)\n",
        "\n",
        "    # put prediction on CPU for evaluation\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]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "fe470dd69168443e8113851f93823f92",
            "9a0c8589ddcc40fbb8aaf991c1dbfad6",
            "0b37ddaf9db74512b08ed449c3fa89d4",
            "754512fc2a674d66a9c7311faedc0e69",
            "a563544970ff462c99e89e2dce4498c1",
            "e12e45ffd7a34a57a4de9bf0a702d890",
            "70658b1b49424e3d88241dce02cb85e7",
            "77c0e4f56e494d339a0ff98874bae190",
            "e2c3b4dda34d469ba9ba6fded9845bf0",
            "d3f5c36835494b9cb32264edb4f5b311",
            "ed1dd8a15b764b1ca3a0a138d7afb359"
          ]
        },
        "id": "6mHKCTRupHZD",
        "outputId": "85e378a8-65a8-4a91-fbdf-033cd357923b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Making predictions...:   0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe470dd69168443e8113851f93823f92"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mlxtend\n",
        "\n",
        "mlxtend.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "_ajHKIiqo8Ci",
        "outputId": "124a882c-fe27-40b1-b69c-7d3631e1afaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.21.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend version {mlxtend.__version__}\")\n",
        "  assert int(mlxtend.__version__.split(\".\")[1]) >= 19\n",
        "except:\n",
        "  !pip install -q torchmetrics -U mlxtend\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlsxtedn version: {mlxtend.__version__}\")\n",
        "\n",
        "# might have to restart colab after running this just to make sure 0.19.0 is installed properly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04bK5Kj-qk0u",
        "outputId": "27dd4aff-66db-4639-fcfc-e53f6ba800c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mlxtend version 0.21.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "# 2. Setup confusion matrix instance and compare predictions to targets\n",
        "confmat = ConfusionMatrix(task=\"multiclass\", num_classes=len(class_names))\n",
        "\n",
        "confmat_tensor = confmat(preds = y_pred_tensor,\n",
        "                         target = test_data.targets)\n",
        "\n",
        "confmat_tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPZlIrE8tC-E",
        "outputId": "663731e7-eff9-4565-8f6c-1b09c5855bce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[641,   5,  46,  63,  10,   1, 227,   0,   7,   0],\n",
              "        [  8, 893,   7,  72,  14,   0,   3,   0,   3,   0],\n",
              "        [  9,   4, 832,  11,  79,   0,  63,   0,   2,   0],\n",
              "        [ 22,   7,  23, 853,  44,   0,  45,   0,   6,   0],\n",
              "        [  7,   5, 129,  36, 723,   0,  95,   0,   5,   0],\n",
              "        [  2,   2,   3,   9,   0, 811,   2,  60,  87,  24],\n",
              "        [ 76,   6, 121,  51,  98,   0, 638,   0,  10,   0],\n",
              "        [  0,   2,   1,   0,   0,  27,   0, 914,  11,  45],\n",
              "        [ 11,   2,  25,  15,  15,   5,  21,   6, 899,   1],\n",
              "        [  0,   0,   1,   3,   1,  14,   5,  99,  11, 866]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Plot confusion matrix\n",
        "fig, ax = plot_confusion_matrix(\n",
        "    conf_mat=confmat_tensor.numpy(), # matplot likes working with numpy\n",
        "    class_names=class_names,\n",
        "    figsize=(10,7)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "9_7oOx_ZyI61",
        "outputId": "a0daac56-76af-4431-892e-b847978c2c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Save and load best performing model"
      ],
      "metadata": {
        "id": "fOo290FizGWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# create model directory path\n",
        "MODEL_PATH = Path(\"models\")\n",
        "\n",
        "MODEL_PATH.mkdir(parents=True,\n",
        "                 exist_ok=True)\n",
        "\n",
        "MODEL_NAME = \"03_pytorch_computer_vision_model_2.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "# save the model state dict\n",
        "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
        "torch.save(obj=model_2.state_dict(),\n",
        "           f=MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dpu3Hk9UzXMS",
        "outputId": "06485f5e-5886-49d8-c019-a5ea2762a619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to: models/03_pytorch_computer_vision_model_2.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2 = FashionMNISTV2(input_shape=1,\n",
        "                                hidden_units=10,\n",
        "                                output_shape=len(class_names))\n",
        "\n",
        "loaded_model_2.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n",
        "\n",
        "#send model to target device\n",
        "loaded_model_2.to(device)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGymUTqv2oPW",
        "outputId": "3e36ba5d-eab8-46fa-eb0a-86fe072d457b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTV2(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate loaded model\n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2_results = eval_model(\n",
        "    model=loaded_model_2,\n",
        "    data_loader=test_dataloader,\n",
        "    loss_fn = loss_fn,\n",
        "    accuracy_fn=accuracy_fn\n",
        ")\n",
        "\n",
        "loaded_model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "f93a142ac5d64a8598c9602993d9dd8c",
            "bbcfd3a609e34edebc1fe8cdbe70f8ba",
            "5edc4c09f49b4fae97403178e291a37c",
            "5a005a4a4b1a402b851c27f3a697fe58",
            "5d2dd2b3c9fb4dbdb101853fd395cc48",
            "401e5c6497fb4b15baf722138edd3cfb",
            "9979148069194c799b1341bfa72d905b",
            "8814bb1c36684671a9cd926320a77c9c",
            "b92f25d2c842445dbf4ea74ab4e922a9",
            "1bb2cd93b3314d159950aaa1b8171417",
            "283d53ba6c3b49cf904faf56ac306cb3"
          ]
        },
        "id": "syG_VlAM3ZLF",
        "outputId": "87e77557-4ea6-4ddb-e3ca-1029a813c51b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f93a142ac5d64a8598c9602993d9dd8c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV2',\n",
              " 'model_loss': 0.3141421377658844,\n",
              " 'model_acc': 88.80790734824281}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mky3faKS31oE",
        "outputId": "b0ba1b50-74f6-4acc-8174-9abfe700307b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTV2',\n",
              " 'model_loss': 0.3141421377658844,\n",
              " 'model_acc': 88.80790734824281}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check if model results are close to eachother\n",
        "torch.isclose(torch.tensor(model_2_results[\"model_loss\"]),\n",
        "              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n",
        "              atol=1e-02)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pR6VGVv83_wK",
        "outputId": "0b7e6313-09a0-4d97-f7a1-e0e876ff9228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(True)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MuT05h-n_GX1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}