Computervision_Exrcise.ipynb

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            "right": null,
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            "width": null
          }
        },
        "3759d5374145491cbf44715b8507a536": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/homayun-chaab/Computer-Vison-MNIST-Dataset/blob/main/Computervision_Exrcise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uxFvs907TwHc"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "#import torchvision\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "from torchvision.datasets import MNIST\n",
        "#import mtplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#impeor utila\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "from tqdm.auto import tqdm\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "rQdXfpSWULjI",
        "outputId": "9b5ccb38-33c2-42af-cf3d-bdecd66528ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(f\"helper_function.py already exist, skiping download...\")\n",
        "else:\n",
        "  print(\"Downloading...\")\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60Yya9U-ksSM",
        "outputId": "0493d384-3a1c-468d-f4bb-ac293674d88d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")\n"
      ],
      "metadata": {
        "id": "yEXX0MLQVsRk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf35b117-f015-4407-eb00-8726873c6dbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 108941033.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 121989621.17it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 1648877/1648877 [00:00<00:00, 35683320.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 23035705.89it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data), len(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1Lykam_Wtzq",
        "outputId": "ea30aa64-93da-4972-be7b-fc33ffabe46e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlQ4aoFfXKWX",
        "outputId": "4fcaa035-24bc-41d3-e66e-6100f309c6a8"
      },
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsFkTaccXwAb",
        "outputId": "bea94eea-e027-45b2-fca7-5c2102e2addb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0 - zero': 0,\n",
              " '1 - one': 1,\n",
              " '2 - two': 2,\n",
              " '3 - three': 3,\n",
              " '4 - four': 4,\n",
              " '5 - five': 5,\n",
              " '6 - six': 6,\n",
              " '7 - seven': 7,\n",
              " '8 - eight': 8,\n",
              " '9 - nine': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize=(9, 9))\n",
        "rows, cols = 4, 4\n",
        "for i in range(1, rows*cols+1):\n",
        "  rand_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "  img, label = train_data[rand_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)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "C6lkcNM6XVeU",
        "outputId": "ffbeec32-88ae-4421-b224-3b4439c97dbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=True)\n",
        "\n",
        "train_dataloader, test_dataloader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPAN7i4fahSE",
        "outputId": "ba3d18f1-6d06-4580-f79f-4705ef48f4d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7d41e5265e70>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7d41e30a7fd0>)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CNN'S Model\n",
        "class MNISTV1(nn.Module):\n",
        "  def __init__(\n",
        "      self,\n",
        "      input_shape: int,\n",
        "      hidden_units: int,\n",
        "      output_shape: int\n",
        "  ):\n",
        "    super().__init__()\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "        nn.Conv2d(\n",
        "            in_channels=input_shape,\n",
        "            out_channels=hidden_units,\n",
        "            kernel_size=3,\n",
        "            stride=1,\n",
        "            padding=1\n",
        "        ),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(\n",
        "            in_channels=hidden_units,\n",
        "            out_channels=hidden_units,\n",
        "            kernel_size=3,\n",
        "            stride=1,\n",
        "            padding=1\n",
        "        ),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.conv_block_2 = nn.Sequential(\n",
        "        nn.Conv2d(\n",
        "            in_channels=hidden_units,\n",
        "            out_channels=hidden_units,\n",
        "            kernel_size=3,\n",
        "            stride=1,\n",
        "            padding=1\n",
        "        ),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(\n",
        "            in_channels=hidden_units,\n",
        "            out_channels=hidden_units,\n",
        "            kernel_size=3,\n",
        "            stride=1,\n",
        "            padding=1\n",
        "        ),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=hidden_units*7*7,\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    x = self.conv_block_1(x)\n",
        "    #print(x.shape)\n",
        "    x = self.conv_block_2(x)\n",
        "    #print(x.shape)\n",
        "    x = self.classifier(x)\n",
        "    #print(x.shape)\n",
        "    return x\n"
      ],
      "metadata": {
        "id": "MoNX_A5PedaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = MNISTV1(\n",
        "    input_shape=1,\n",
        "    hidden_units=10,\n",
        "    output_shape=len(class_names)\n",
        ").to(device)\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_Y41B4UilmN",
        "outputId": "f677da0b-d9fe-4525-c80c-7300cbbf2658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MNISTV1(\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): 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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import accuracy_fn\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(\n",
        "    model_2.parameters(),\n",
        "    lr=0.1\n",
        ")"
      ],
      "metadata": {
        "id": "1Sdh373vioBK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_step(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              optimizer: torch.optim.Optimizer,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device=device):\n",
        "\n",
        "  train_loss, train_acc = 0, 0\n",
        "  model.train()\n",
        "  for batch, (X, y) in enumerate(data_loader):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "\n",
        "    y_pred = model(X)\n",
        "\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    train_loss += loss\n",
        "    train_acc += accuracy_fn(y_true=y,\n",
        "                            y_pred=y_pred.argmax(dim=1))\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "  print(f\"Train Loss: {train_loss:.2f}% | Train Acc: {train_acc:.2f}%\")"
      ],
      "metadata": {
        "id": "MrfgCEAslcqP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_step(\n",
        "    model: torch.nn.Module,\n",
        "    data_loader: torch.utils.data.DataLoader,\n",
        "    loss_fn: torch.nn.Module,\n",
        "    accuracy_fn,\n",
        "    devie: torch.device=device\n",
        "):\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model.eval\n",
        "  with torch.inference_mode():\n",
        "    for X, y in data_loader:\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      test_pred = model(X)\n",
        "      test_loss += loss_fn(test_pred,\n",
        "                     y)\n",
        "      test_acc += accuracy_fn(y_true=y,\n",
        "                              y_pred=test_pred.argmax(dim=1))\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "    test_acc /= len(test_dataloader)\n",
        "    print(f\"Test Loss: {test_loss:.2f}% | Test Acc: {test_acc:.2f}%\")\n"
      ],
      "metadata": {
        "id": "pf-A4AwIrNra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start:float,\n",
        "                     end: float,\n",
        "                     device: torch.device = None):\n",
        "\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} secounds\")\n",
        "  return total_time"
      ],
      "metadata": {
        "id": "ejkR8DWLuBJQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_start_time_on_gpu = timer()\n",
        "epochs = 3\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n-----------\")\n",
        "  train_step(\n",
        "      model_2,\n",
        "      train_dataloader,\n",
        "      optimizer,\n",
        "      loss_fn,\n",
        "      accuracy_fn,\n",
        "      device\n",
        "  )\n",
        "  test_step(\n",
        "      model_2,\n",
        "      test_dataloader,\n",
        "      loss_fn,\n",
        "      accuracy_fn,\n",
        "      device)\n",
        "\n",
        "\n",
        "train_end_time_on_gpu = timer()\n",
        "\n",
        "total_train_time_model_1 = print_train_time(start=train_start_time_on_gpu,\n",
        "                                            end=train_end_time_on_gpu,\n",
        "                                            device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285,
          "referenced_widgets": [
            "4cafed33aec84c889e8c3fc0d5fc0f17",
            "dc6b58c4b6a640c991494e0602cd7908",
            "c59af25ae1ec40d2bdc6fda930cc9a13",
            "b95e6f23d8354713baf08bcf3f3ce740",
            "65e1d5cc533d41f2b70f977a69690af0",
            "acab050a92994a4c87c091c707061e7c",
            "fa1d1486dc0b463db08ce02135b85c90",
            "ad77d4c4e8c4462b984ab60711b60776",
            "87de58830a17489ea03447151e98215f",
            "5fe51af9a36a4134af148e73d9ba17b7",
            "78289fdc7cfb4c4291c2402b19a4d3a8"
          ]
        },
        "id": "lvDyab8dpbyi",
        "outputId": "49bf16c1-4adb-4bea-9c19-acb3b90dc14c"
      },
      "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": "4cafed33aec84c889e8c3fc0d5fc0f17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "-----------\n",
            "Train Loss: 0.05% | Train Acc: 98.53%\n",
            "Test Loss: 0.04% | Test Acc: 98.69%\n",
            "Epoch: 1\n",
            "-----------\n",
            "Train Loss: 0.04% | Train Acc: 98.73%\n",
            "Test Loss: 0.04% | Test Acc: 98.80%\n",
            "Epoch: 2\n",
            "-----------\n",
            "Train Loss: 0.04% | Train Acc: 98.82%\n",
            "Test Loss: 0.03% | Test Acc: 98.93%\n",
            "Train time on cuda: 41.878 secounds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = []\n",
        "model_2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X, y in tqdm(test_dataloader, desc=\"Making predections...\"):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    y_logits = model_2(X)\n",
        "\n",
        "    y_pred = torch.softmax(y_logits.squeeze(), dim=0).argmax(dim=1)\n",
        "    y_preds.append(y_pred.cpu())\n",
        "\n",
        "y_pred_tensor = torch.cat(y_preds)\n",
        "y_pred_tensor[:10]\n"
      ],
      "metadata": {
        "id": "XMzmpBCRuCSb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "ce8e6b681eab47c39e2f40ac464bbcd5",
            "fceeeb45f9b6424a92847e1fd172fb43",
            "74becd602aac489db654f90da71aab7d",
            "d4ea2cea83264d8aa18def4cca6ddcc8",
            "40a52e1629954dd8b475137cceeba29e",
            "8ede6dbd63c44e1dbb690228baaaaffc",
            "4635742cd9584fafbb3f856d319976c3",
            "e789688a2d6c46188cd6a4db6440f224",
            "180f214fb98e4913b0dd406c8df1cf71",
            "43925f0661344eb4ae01df5d5c8c34ba",
            "3759d5374145491cbf44715b8507a536"
          ]
        },
        "outputId": "a47a1bf8-f988-44fb-beb0-cf94ccb01464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Making predections...:   0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce8e6b681eab47c39e2f40ac464bbcd5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3, 3, 5, 1, 0, 2, 7, 4, 0, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predections(model: torch.nn.Module,\n",
        "                     data: torch.utils.data,\n",
        "                     device: torch.device=device):\n",
        "  pred_prob = []\n",
        "  with torch.inference_mode():\n",
        "    for X, y in tqdm(test_dataloader, desc=\"Making Predctions...\"):\n",
        "      X, y =X.to(device), y.to(device)\n",
        "      y_logits = model_2(X)\n",
        "      y_pred = torch.softmax(y_logits.squeeze(), dim=0).argmax(dim=1)\n",
        "      y_preds.append(y_pred.cpu())\n",
        "\n",
        "  return torch.stack(y_preds)"
      ],
      "metadata": {
        "id": "MGEWGR_LJnL0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9, 9))\n",
        "rows, cols = 0, 0\n",
        "for i, sample in enumerate(rwos, cols+1):\n",
        "  plt.subplot(rows, cols)\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "  pred_label = class_names[pred_classes[i]"
      ],
      "metadata": {
        "id": "Jbq4EtIrG6Gw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}