003_pytorch_computer_vision_exercises.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/mekhiya/pytorch-playground/blob/main/003_pytorch_computer_vision_exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 03. PyTorch Computer Vision Exercises\n",
        "\n",
        "The following is a collection of exercises based on computer vision fundamentals in PyTorch.\n",
        "\n",
        "They're a bunch of fun.\n",
        "\n",
        "You're going to get to write plenty of code!\n",
        "\n",
        "## Resources\n",
        "\n",
        "1. These exercises are based on [notebook 03 of the Learn PyTorch for Deep Learning course](https://www.learnpytorch.io/03_pytorch_computer_vision/).\n",
        "2. See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/_PibmqpEyhA).\n",
        "  * **Note:** Going through these exercises took me just over 3 hours of solid coding, so you should expect around the same.\n",
        "3. See [other solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions)."
      ],
      "metadata": {
        "id": "Vex99np2wFVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for GPU\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaeYzOTLwWh2",
        "outputId": "d7ab6d66-fdb3-45d6-e96d-8d8df4440417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Dec  4 14:14:18 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\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   72C    P0    32W /  70W |   1189MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import torch\n",
        "import torch\n",
        "\n",
        "# Exercises require PyTorch > 1.10.0\n",
        "print(torch.__version__)\n",
        "# TODO: Setup device agnostic code\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNwZLMbCzJLk",
        "outputId": "044176e6-a241-4388-cff1-a38761e4b663"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.1.0+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. What are 3 areas in industry where computer vision is currently being used?"
      ],
      "metadata": {
        "id": "FSFX7tc1w-en"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Auto, Agriculture, Manufacturing , Medicine"
      ],
      "metadata": {
        "id": "VyWRkvWGbCXj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Search \"what is overfitting in machine learning\" and write down a sentence about what you find."
      ],
      "metadata": {
        "id": "oBK-WI6YxDYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss curve kind of goes down, almost try to reach zero. If there is lot of area above loss curve, it is under fitting, if area below loss curve, it is\n",
        "# over fitting"
      ],
      "metadata": {
        "id": "d1rxD6GObCqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Search \"ways to prevent overfitting in machine learning\", write down 3 of the things you find and a sentence about each.\n",
        "> **Note:** there are lots of these, so don't worry too much about all of them, just pick 3 and start with those."
      ],
      "metadata": {
        "id": "XeYFEqw8xK26"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "increase quality fo data, remove noise\n",
        "reduce training epoch\n",
        "reduce features\n",
        "use linear if possible\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "ocvOdWKcbEKr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "702d60cc-df9e-4ffe-dd29-3f1215ca990d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nincrease quality fo data, remove noise\\nreduce training epoch\\nreduce features\\nuse linear if possible\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Spend 20-minutes reading and clicking through the [CNN Explainer website](https://poloclub.github.io/cnn-explainer/).\n",
        "\n",
        "* Upload your own example image using the \"upload\" button on the website and see what happens in each layer of a CNN as your image passes through it."
      ],
      "metadata": {
        "id": "DKdEEFEqxM-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# done"
      ],
      "metadata": {
        "id": "TqZaJIRMbFtS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Load the [`torchvision.datasets.MNIST()`](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST) train and test datasets."
      ],
      "metadata": {
        "id": "lvf-3pODxXYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "\n",
        "train_data = datasets.MNIST(root=\".\",\n",
        "                            train=True,\n",
        "                            download=True,\n",
        "                            transform=transforms.ToTensor())\n",
        "\n",
        "test_data = datasets.MNIST(root=\".\",\n",
        "                            train=False,\n",
        "                            download=True,\n",
        "                            transform=transforms.ToTensor())\n",
        "\n",
        "train_data, train_data"
      ],
      "metadata": {
        "id": "SHjeuN81bHza",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5f27ef2-aa49-438d-ce3b-239b0ab31cf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset MNIST\n",
              "     Number of datapoints: 60000\n",
              "     Root location: .\n",
              "     Split: Train\n",
              "     StandardTransform\n",
              " Transform: ToTensor(),\n",
              " Dataset MNIST\n",
              "     Number of datapoints: 60000\n",
              "     Root location: .\n",
              "     Split: Train\n",
              "     StandardTransform\n",
              " Transform: ToTensor())"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data, len(train_data), len(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHVG3FVDWf5m",
        "outputId": "f34a4889-bb78-4322-f1b9-4e455a2a354f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset MNIST\n",
              "     Number of datapoints: 60000\n",
              "     Root location: .\n",
              "     Split: Train\n",
              "     StandardTransform\n",
              " Transform: ToTensor(),\n",
              " Dataset MNIST\n",
              "     Number of datapoints: 10000\n",
              "     Root location: .\n",
              "     Split: Test\n",
              "     StandardTransform\n",
              " Transform: ToTensor(),\n",
              " 60000,\n",
              " 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Visualize at least 5 different samples of the MNIST training dataset."
      ],
      "metadata": {
        "id": "qxZW-uAbxe_F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = test_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPRulD-kXz4F",
        "outputId": "e2a3d363-7f66-4bd8-892d-a9254ea7a6a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0 - zero',\n",
              " '1 - one',\n",
              " '2 - two',\n",
              " '3 - three',\n",
              " '4 - four',\n",
              " '5 - five',\n",
              " '6 - six',\n",
              " '7 - seven',\n",
              " '8 - eight',\n",
              " '9 - nine']"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "image, label = train_data[0]\n",
        "print(image.shape)\n",
        "plt.imshow(image.squeeze())\n",
        "plt.title(class_names[label])\n",
        "\n",
        "for i in range(5):\n",
        "  print(image.shape)\n",
        "  img = train_data[i][0]\n",
        "  label = train_data[i][1]\n",
        "  plt.figure(figsize=(3,3))\n",
        "  plt.imshow(img.squeeze())\n",
        "  plt.title(class_names[label])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ldVLa6saWYj3",
        "outputId": "3cb86c4f-f39b-4b61-e9b7-eb940c799dc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Turn the MNIST train and test datasets into dataloaders using `torch.utils.data.DataLoader`, set the `batch_size=32`."
      ],
      "metadata": {
        "id": "JAPDzW0wxhi3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(batch_size=32,\n",
        "                              dataset=train_data,\n",
        "                              shuffle=True)\n",
        "\n",
        "test_dataloader = DataLoader(batch_size=32,\n",
        "                              dataset=test_data,\n",
        "                              shuffle=False)\n",
        "\n",
        "len(train_dataloader), len(test_dataloader)\n"
      ],
      "metadata": {
        "id": "ALA6MPcFbJXQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "571a62cb-a621-4749-a111-ddab98ce6c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1875, 313)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Recreate `model_2` used in notebook 03 (the same model from the [CNN Explainer website](https://poloclub.github.io/cnn-explainer/), also known as TinyVGG) capable of fitting on the MNIST dataset."
      ],
      "metadata": {
        "id": "bCCVfXk5xjYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "\n",
        "class MNISTModelV2(nn.Module):\n",
        "  def __init__(self, input_channels, output_channels, hidden_units):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "      nn.Conv2d(in_channels=input_channels,out_channels=hidden_units, kernel_size=3, padding=1, stride=1),\n",
        "      nn.ReLU(),\n",
        "      nn.Conv2d(in_channels=hidden_units,out_channels=hidden_units, kernel_size=3, padding=1, stride=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,out_channels=hidden_units, kernel_size=3, padding=1, stride=1),\n",
        "      nn.ReLU(),\n",
        "      nn.Conv2d(in_channels=hidden_units,out_channels=hidden_units, kernel_size=3, padding=1, stride=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, out_features=output_channels)\n",
        "    )\n",
        "\n",
        "  def forward(self, X):\n",
        "    X = self.conv_block_1(X)\n",
        "    # print(X.shape)\n",
        "    X= self.conv_block_2(X)\n",
        "    # print(X.shape)\n",
        "    X = self.classifier(X)\n",
        "    return X"
      ],
      "metadata": {
        "id": "5IKNF22XbKYS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = MNISTModelV2(input_channels=1,\n",
        "                       output_channels=len(class_names),\n",
        "                       hidden_units=10)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIEnDlPWsKVZ",
        "outputId": "36f8dc40-586f-4240-cca1-2ddae259bc1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MNISTModelV2(\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": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdEAP_UpslR_",
        "outputId": "b850c8e7-ad3c-456e-a27a-9b417f27dab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.random\n",
        "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "# test_image = img[0]\n",
        "y = model_2(img.unsqueeze(dim=0))"
      ],
      "metadata": {
        "id": "16rSNzp-tJlT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Train the model you built in exercise 8. for 5 epochs on CPU and GPU and see how long it takes on each."
      ],
      "metadata": {
        "id": "sf_3zUr7xlhy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "\n",
        "# train n cpu\n",
        "model_cpu = MNISTModelV2(input_channels=1,\n",
        "                         hidden_units=10,\n",
        "                         output_channels=10).to(\"cuda\")\n",
        "\n",
        "# create a loss fn & optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_cpu.parameters(), lr=0.1)\n",
        "\n",
        "epochs=5\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss = 0\n",
        "  model_cpu.train()\n",
        "  for batch, (X, y) in enumerate(train_dataloader):\n",
        "    model_cpu.train()\n",
        "\n",
        "    X,y = X.to(\"cuda\"), y.to(\"cuda\")\n",
        "\n",
        "    y_pred = model_cpu(X)\n",
        "\n",
        "    loss = loss_fn(y_pred, y)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  test_loss_total = 0\n",
        "\n",
        "  model_cpu.eval()\n",
        "\n",
        "  with torch.inference_mode():\n",
        "    for batch, (X_test, y_test) in enumerate(test_dataloader):\n",
        "      X_test, y_test = X_test.to(\"cuda\"), y_test.to(\"cuda\")\n",
        "\n",
        "      test_pred = model_cpu(X_test)\n",
        "      test_loss = loss_fn(test_pred, y_test)\n",
        "\n",
        "      test_loss_total += test_loss\n",
        "\n",
        "    test_loss_total /= len(test_dataloader)\n",
        "\n",
        "  print(f\"Epoch: {epoch} | Loss: {train_loss:.3f} | Test loss: {test_loss_total:.3f}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jSo6vVWFbNLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "a44ef3f1c85a4d1c9f35f25f61506262",
            "6cbf11c2f00a48c793756e2138f83855",
            "f564408af0f44782bb3201a0c0c76204",
            "6355bfb9f88c4be48f8543e210917f8a",
            "794a57b51a2e434386526498579d38e6",
            "313a28221b3f4c128d6f79404f8053ec",
            "a432399f122a4767a91317f9c830cae4",
            "4825cc46e9a142dc9934a1e34e0c34c2",
            "8ab92dd5f65042d59b0a467863c32a58",
            "3709a5abf48a44d5af69435260046e78",
            "e026b1e658124f61bd764d99e868b839"
          ]
        },
        "outputId": "cd87464a-f798-46e3-d3b4-802045170feb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a44ef3f1c85a4d1c9f35f25f61506262"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.000 | Test loss: 0.061\n",
            "Epoch: 1 | Loss: 0.000 | Test loss: 0.051\n",
            "Epoch: 2 | Loss: 0.000 | Test loss: 0.048\n",
            "Epoch: 3 | Loss: 0.000 | Test loss: 0.045\n",
            "Epoch: 4 | Loss: 0.000 | Test loss: 0.039\n",
            "CPU times: user 1min 8s, sys: 673 ms, total: 1min 9s\n",
            "Wall time: 1min 21s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# Train on GPU\n",
        "model_gpu = MNISTModelV2(input_channels=1,\n",
        "                        hidden_units=10,\n",
        "                        output_channels=10).to(device)\n",
        "\n",
        "# Create a loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_gpu.parameters(), lr=0.1)\n",
        "\n",
        "# Training loop\n",
        "epochs = 5\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss = 0\n",
        "  model_gpu.train()\n",
        "  for batch, (X, y) in enumerate(train_dataloader):\n",
        "    # Put data on target device\n",
        "    X, y = X.to(device), y.to(device)\n",
        "\n",
        "    # Forward pass\n",
        "    y_pred = model_gpu(X)\n",
        "\n",
        "    # Loss calculation\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss\n",
        "\n",
        "    # Optimizer zero grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    # Step the optimizer\n",
        "    optimizer.step()\n",
        "\n",
        "  # Adjust train loss to number of batches\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  ### Testing loop\n",
        "  test_loss_total = 0\n",
        "  # Put model in eval mode and turn on inference mode\n",
        "  model_gpu.eval()\n",
        "  with torch.inference_mode():\n",
        "    for batch, (X_test, y_test) in enumerate(test_dataloader):\n",
        "      # Make sure test data on target device\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "      test_pred = model_gpu(X_test)\n",
        "      test_loss = loss_fn(test_pred, y_test)\n",
        "\n",
        "      test_loss_total += test_loss\n",
        "\n",
        "    # Adjust test loss total for number of batches\n",
        "    test_loss_total /= len(test_dataloader)\n",
        "\n",
        "  # Print out what's happening\n",
        "  print(f\"Epoch: {epoch} | Loss: {train_loss:.3f} | Test loss: {test_loss_total:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "e43d1e5921b246c1a3a1bbd01f93ec02",
            "73fa8e8a5366471899ea85b7c0c19982",
            "ed6aa457fd5446afaa0d23e8de0d268a",
            "04c3a82eb3bf4c1482f88bc3972b6449",
            "becad8b38b1d4b258831a3a795396f9e",
            "09ca940bd9f54be8be00049836b52c14",
            "4bcdc6846cbd4a5eb5133b30a3932b7d",
            "f16bd5e960294e0bb2cc28290341069e",
            "bcef53c644c048589c200555eeaf2d8e",
            "1df224b1ed084db9b6c179abcf74058f",
            "6af785f30dff44968be6589bbc24385b"
          ]
        },
        "id": "ZueQ-x1Uuorf",
        "outputId": "e1c5064e-e2de-47d7-af94-d38c72ac1d97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e43d1e5921b246c1a3a1bbd01f93ec02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.284 | Test loss: 0.076\n",
            "Epoch: 1 | Loss: 0.070 | Test loss: 0.060\n",
            "Epoch: 2 | Loss: 0.055 | Test loss: 0.048\n",
            "Epoch: 3 | Loss: 0.047 | Test loss: 0.045\n",
            "Epoch: 4 | Loss: 0.041 | Test loss: 0.045\n",
            "CPU times: user 1min 3s, sys: 666 ms, total: 1min 4s\n",
            "Wall time: 1min 7s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10. Make predictions using your trained model and visualize at least 5 of them comparing the prediciton to the target label."
      ],
      "metadata": {
        "id": "w1CsHhPpxp1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.imshow(test_data[0][0].squeeze())\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(test_data[0][0].shape)\n",
        "y_logits = model_cpu(test_data[0][0].unsqueeze(dim=0).to(device)).to(device)\n",
        "y_prob_pred = torch.softmax(y_logits,dim=1)\n",
        "y_pred = torch.argmax(y_prob_pred)\n",
        "y_pred\n",
        "\n",
        "\n",
        "num_to_plot = 5\n",
        "\n",
        "for i in range(num_to_plot):\n",
        "  img = test_data[i][0]\n",
        "  label = test_data[i][1]\n",
        "  y_logits = model_cpu(img.unsqueeze(dim=0).to(device)).to(device)\n",
        "  y_prob_pred = torch.softmax(y_logits,dim=1)\n",
        "  y_pred = torch.argmax(y_prob_pred)\n",
        "\n",
        "  plt.figure()\n",
        "  plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "  plt.title(f\"Truth: {label} | Pred: {y_pred}\")\n",
        "  plt.axis(False)\n",
        "\n"
      ],
      "metadata": {
        "id": "_YGgZvSobNxu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9bee0bf8-abc2-4700-c19b-21aa016cc33a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Plot a confusion matrix comparing your model's predictions to the truth labels."
      ],
      "metadata": {
        "id": "qQwzqlBWxrpG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torchmetrics -U mlxtend"
      ],
      "metadata": {
        "id": "vSrXiT_AbQ6e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mlxtend\n",
        "print(mlxtend.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQ8nKunJ0suE",
        "outputId": "e0f5995a-e8f5-4b32-a3db-b2e1b2731e2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.23.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "model_cpu.eval()\n",
        "y_preds = []\n",
        "\n",
        "with torch.inference_mode():\n",
        "  for batch, (X,y) in tqdm(enumerate(test_dataloader)):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    y_pred_logits = model_cpu(X)\n",
        "    y_pred_probs = torch.softmax(y_pred_logits, dim=1)\n",
        "    y_pred_label = torch.argmax(y_pred_probs, dim=1)\n",
        "\n",
        "    y_preds.append(y_pred_label)\n",
        "\n",
        "  y_preds = torch.cat(y_preds).cpu()\n",
        "len(y_preds)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "d2f7bc8283ad4db3ac3f1ddddb1fabf5",
            "ebe98317ea1142069ff80142f52d1451",
            "1db15bb06d0a43809de8b1a43626aa19",
            "54dca0106d0747058927be2670de1828",
            "95d01cb061c743c7ab6009df1b9bcc2e",
            "553cca3402bf4fdfbbcce54d79802e55",
            "1a2588bcdf6d4f0e9233a3cd4a690cac",
            "f5e8c55e20954973a2b421e1023b3e03",
            "0beb21d017bb4ac6858e2ca59e661f26",
            "7fcdde511bd3430e87ab421d82b493e2",
            "b143c6593262481fb08b8780239c3392"
          ]
        },
        "id": "plSPXgGr0tgq",
        "outputId": "48952c3d-d351-47e7-f44b-fa64d41dce37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2f7bc8283ad4db3ac3f1ddddb1fabf5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.targets[:10], y_preds[:10]\n",
        "\n",
        "\n",
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "comfmat = ConfusionMatrix(task=\"multiclass\", num_classes=len(class_names))\n",
        "comfmat_tensor = comfmat(preds=y_preds,\n",
        "                         target=test_data.targets)\n",
        "\n",
        "fix, ax = plot_confusion_matrix(\n",
        "    conf_mat=comfmat_tensor.numpy(),\n",
        "    class_names=class_names,\n",
        "    figsize=(10, 7)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "scA8e6ar4e4G",
        "outputId": "20dd9c9f-609e-493b-acb4-1ddcdc9bdfdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 12. Create a random tensor of shape `[1, 3, 64, 64]` and pass it through a `nn.Conv2d()` layer with various hyperparameter settings (these can be any settings you choose), what do you notice if the `kernel_size` parameter goes up and down?"
      ],
      "metadata": {
        "id": "lj6bDhoWxt2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A_tensor = torch.rand([1,3,64,64])\n",
        "A_tensor.shape\n",
        "\n",
        "conv2_layer = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=2, padding=1, stride=2)\n",
        "\n",
        "print(f\"Random tensor original shape: {A_tensor.shape}\")\n",
        "random_tensor_through_conv_layer = conv2_layer(A_tensor)\n",
        "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")\n"
      ],
      "metadata": {
        "id": "leCTsqtSbR5P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dbce1a2-0d75-4dfb-bddd-768091155980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
            "Random tensor through conv layer shape: torch.Size([1, 64, 33, 33])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 13. Use a model similar to the trained `model_2` from notebook 03 to make predictions on the test [`torchvision.datasets.FashionMNIST`](https://pytorch.org/vision/main/generated/torchvision.datasets.FashionMNIST.html) dataset.\n",
        "* Then plot some predictions where the model was wrong alongside what the label of the image should've been.\n",
        "* After visualing these predictions do you think it's more of a modelling error or a data error?\n",
        "* As in, could the model do better or are the labels of the data too close to each other (e.g. a \"Shirt\" label is too close to \"T-shirt/top\")?"
      ],
      "metadata": {
        "id": "VHS20cNTxwSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "\n",
        "train_fmnist_data = datasets.FashionMNIST(root=\"'\",download=True,train=True,transform=transforms.ToTensor())\n",
        "\n",
        "test_fmnist_data = datasets.FashionMNIST(root=\"'\",download=True,train=False,transform=transforms.ToTensor())\n",
        "\n",
        "len(train_fmnist_data), len(test_fmnist_data)"
      ],
      "metadata": {
        "id": "78a8LjtdbSZj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa57ea34-90ce-4660-bbd6-d0bbdb6077b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_fmnist_dataloader = DataLoader(batch_size=32, shuffle=True,dataset=train_fmnist_data)\n",
        "test_fmnist_dataloader = DataLoader(batch_size=32, shuffle=False,dataset=test_fmnist_data)\n",
        "len(train_fmnist_dataloader), len(test_fmnist_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0YAYG_d-dRR",
        "outputId": "427a8bb3-6acd-4fc0-fbd1-2f8de27837a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1875, 313)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fmnist_class_names = train_fmnist_data.classes\n",
        "fmnist_class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fucqak5i-eYM",
        "outputId": "af221fc5-0efd-4fe7-e0f5-2559ebbf0e8f"
      },
      "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": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_FMNIST = MNISTModelV2(hidden_units=10, input_channels=1, output_channels=len(fmnist_class_names)).to(device)\n",
        "model_FMNIST"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2rJVsGj-eVW",
        "outputId": "6738be7c-cac3-47e5-f4b9-069836f4296c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MNISTModelV2(\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": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_FMNIST.parameters(), lr=0.1)"
      ],
      "metadata": {
        "id": "o5W7hVu7_chr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "# acc_fn = Accuracy(num_classes=len(fmnist_class_names)).to(device)\n",
        "acc_fn = Accuracy(task=\"multiclass\" ,num_classes=len(fmnist_class_names)).to(device)\n",
        "\n",
        "epochs = 5\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss, test_loss_total = 0, 0\n",
        "  train_acc, test_acc = 0, 0\n",
        "\n",
        "  model_FMNIST.train()\n",
        "\n",
        "  for batch, (X_train,y_train) in enumerate(train_fmnist_dataloader):\n",
        "    X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "\n",
        "    y_pred = model_FMNIST(X_train).to(device)\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "\n",
        "    train_loss += loss\n",
        "    train_acc = acc_fn(y_pred, y_train)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "  train_loss /= len(train_fmnist_dataloader)\n",
        "  train_acc /= len(train_fmnist_dataloader)\n",
        "\n",
        "  model_FMNIST.eval()\n",
        "  with torch.inference_mode():\n",
        "    for batch, (X_test,y_test) in enumerate(test_fmnist_dataloader):\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "      y_pred_test = model_FMNIST(X_test)\n",
        "      loss = loss_fn(y_pred_test, y_test)\n",
        "\n",
        "      test_loss_total += loss\n",
        "      test_acc += acc_fn(y_pred_test, y_test)\n",
        "\n",
        "    test_loss_total /= len(test_fmnist_dataloader)\n",
        "    test_acc /= len(test_fmnist_dataloader)\n",
        "\n",
        "  print(f\"Epoch: {epoch} | Train loss: {train_loss:.3f} | Train acc: {train_acc:.2f} | Test loss: {test_loss_total:.3f} | Test acc: {test_acc:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "a236cd6a71ba478e9b659c49de3b25fb",
            "ff954c6da1da4c83b629220c4050798b",
            "193c7b2c82f94e249ccc72e8d1d077ae",
            "a7f4392b8aad4716b077ad943f0a4c3d",
            "232ddb75b413496a9c9d150ef6169f35",
            "5a5bea1b715b46f69dcf40a2a4813be1",
            "bfa260e8d3ba4d799b397d1b89bda444",
            "515b381cf9734129bcfdcc9f2589e8ac",
            "1c5323d655d04722b0e96ca0710495a8",
            "aa639efb00ed4d9f971a11fed818399d",
            "626c0b42fc8d4ec2aaa93c2fd65a5c73"
          ]
        },
        "id": "mXc4dwJm_ceM",
        "outputId": "44aa7de6-af81-4ca3-ebb4-1f3863f76b71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a236cd6a71ba478e9b659c49de3b25fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Train loss: 0.717 | Train acc: 0.00 | Test loss: 0.394 | Test acc: 0.86\n",
            "Epoch: 1 | Train loss: 0.356 | Train acc: 0.00 | Test loss: 0.349 | Test acc: 0.87\n",
            "Epoch: 2 | Train loss: 0.322 | Train acc: 0.00 | Test loss: 0.340 | Test acc: 0.88\n",
            "Epoch: 3 | Train loss: 0.300 | Train acc: 0.00 | Test loss: 0.299 | Test acc: 0.89\n",
            "Epoch: 4 | Train loss: 0.288 | Train acc: 0.00 | Test loss: 0.314 | Test acc: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with trained model_2\n",
        "test_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X_test, y_test in tqdm(test_fmnist_dataloader):\n",
        "    y_logits = model_FMNIST(X_test.to(device))\n",
        "    y_pred_probs = torch.softmax(y_logits, dim=1)\n",
        "    y_pred_labels = torch.argmax(y_pred_probs, dim=1)\n",
        "    test_preds.append(y_pred_labels)\n",
        "test_preds = torch.cat(test_preds).cpu() # matplotlib likes CPU\n",
        "test_preds[:100], len(test_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "d060d369a1eb4427b5e8be32c4f3463c",
            "44c8675acb8c4b7fa60641054016929b",
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            "ee8c35bd69f74834ace459c677383488",
            "67998acf8a4f45b39467be0039e4f602",
            "948ca226103c43d5ba5cf96b4cf7d482",
            "e4fce8ac01014d7a9eb3c3f8a1af0f0a",
            "336a845246e348abb4ce712a275058e1",
            "03fccd8bd38a4f498a4d39dde64c0212",
            "ae9126c924dc4dc7bd77a5ce9aa3126a",
            "9c7a3e69bf7c4b01a8d72b7019b565fe"
          ]
        },
        "id": "b3CW_-wnLTAb",
        "outputId": "65e0f231-51b6-4d9d-cee1-7d176d2ac354"
      },
      "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": "d060d369a1eb4427b5e8be32c4f3463c"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5]),\n",
              " 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_preds = []\n",
        "model_FMNIST.eval()\n",
        "with torch.inference_mode():\n",
        "  for (X_test, y_test) in tqdm(test_fmnist_dataloader):\n",
        "    X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "    y_logits = model_FMNIST(X_test)\n",
        "    y_prob = torch.softmax(y_logits, dim=1)\n",
        "    y_label = torch.argmax(y_prob, dim=1)\n",
        "\n",
        "    test_preds.append(y_label)\n",
        "test_preds = torch.cat(test_preds).cpu()\n",
        "\n",
        "test_preds[:100], len(test_preds)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "eb25a26588434ae7a02c76d44bdb30b1",
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            "170ee52f08ec4126b65dcd60ab9c52f5",
            "d5cd3b3a658d499b82448d7c1f371a1c",
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        "id": "HdA9xZnm_EWW",
        "outputId": "b3381c5d-3584-4c19-b5e0-4acec95e84f5"
      },
      "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": "eb25a26588434ae7a02c76d44bdb30b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "         5, 5, 5, 5]),\n",
              " 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "wrong_pred_indexes = np.where(test_preds != test_fmnist_data.targets)[0]\n",
        "len(wrong_pred_indexes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrsDNSi4_ETo",
        "outputId": "99229633-f616-4aa8-8207-b64d1187fdf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9000"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random_selection = random.sample(list(wrong_pred_indexes), k=9)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i, idx in enumerate(random_selection):\n",
        "\n",
        "  true_label = fmnist_class_names[test_fmnist_data[idx][1]]\n",
        "  pred_label = fmnist_class_names[test_preds[idx]]\n",
        "\n",
        "  plt.subplot(3,3,i+1)\n",
        "  plt.imshow(test_fmnist_data[idx][0].squeeze(), cmap=\"gray\")\n",
        "  plt.title(f\"True: {true_label} | Pred: {pred_label}\", c=\"r\")\n",
        "  plt.axis(False);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "A--kpzre_EQv",
        "outputId": "19b02537-4aa1-4b3a-d23a-1f1beaac8665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RB6hApQa-eSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U-mwEZZQ-eQk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VpEF0Jg4-eNB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OXCpL4k1-eKV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}