03_Computer_Vision_PyTorch.ipynb

{
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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/barzansaeedpour/ANPR-YOLOv8/blob/main/03_Computer_Vision_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtYrvQDeNAXd"
      },
      "source": [
        "# PyTorch Computer Vision\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdkSiRLHNEmo"
      },
      "source": [
        "## 0. Computer vision libraries in PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82gwJ8hFOZkb"
      },
      "source": [
        "* `torchvision` - base domain library for PyTorch computer vision\n",
        "* `torchvision.datasets` - get datasets and data loading functions for computer vision\n",
        "* `torchvision.models` - get pretrained computer vision models that you can leverage for your own problems\n",
        "* `torchvision.transforms` - functions for manipulating your vision data (images) to be suitable for use with ML model\n",
        "* `torch.utils.data.Dataset` - Base dataset class for PyTorch   \n",
        "* `torch.utils.data.DataLoader` - Creates a Python iterable over a dataset    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrJYcR_tPp7e",
        "outputId": "b181decc-0a94-49d2-c2c2-be9d04531103"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.3.1+cu121\n",
            "0.18.1+cu121\n"
          ]
        }
      ],
      "source": [
        "# Import PyTorch\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Import torchvision\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "# Import matplotlib for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Check versions\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRyvg9mXpALc"
      },
      "source": [
        "## 1. Getting a dataset\n",
        "\n",
        "The dataset we'll be using is FashionMNIST from torchvision.dataset - https://pytorch.org/vision/stable/generated/torchvision.datasets.FashionMNIST.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "oj1an74UHDaP"
      },
      "outputs": [],
      "source": [
        "# Setup training data\n",
        "from torchvision import datasets\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root=\"data\", # where to download data to?\n",
        "    train= True, # do we want the training data?\n",
        "    download=True, # do we want to download yes/bo?\n",
        "    transform= torchvision.transforms.ToTensor(), # how do we want to transform the data?\n",
        "    target_transform=None # how do we want to transform the labels/targets?\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train= False,\n",
        "    download=True,\n",
        "    transform= torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "UQu7GNlLqc73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4088fda4-038f-48a4-fc87-6336e840c676"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "len(train_data), len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "JeD0HRICsNWV",
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        "outputId": "73986f0e-abdd-42dd-cc2f-c39dd841fdcd"
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              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# See the first training example\n",
        "image, label = train_data[0]\n",
        "image, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "T0_zm3BRsPWE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac3bbf04-4a24-4de2-8a09-e278a546783c"
      },
      "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": 41
        }
      ],
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "Y8k2Vb529cYF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57725087-5eb9-41e8-8f12-eb553a7d68fd"
      },
      "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": 42
        }
      ],
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "513rROW09onF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e67641d0-935f-4964-f088-5829a2b96c44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 0, 0,  ..., 3, 0, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "train_data.targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "jHIWR7Gc9zwW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f26d064-49fe-4722-eddf-2abf2b266c8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28]) -> [color_channels, height, width]\n",
            "Image label: Ankle boot\n"
          ]
        }
      ],
      "source": [
        "# Check the shape of our image\n",
        "print(f\"Image shape: {image.shape} -> [color_channels, height, width]\")\n",
        "print(f\"Image label: {class_names[label]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7n08oB-_oQo"
      },
      "source": [
        "### 1.2 Visulazing our data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "_O0aaaJd9-O-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "8d934fd4-32ce-4cc0-e961-d33e5115e999"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "image, label = train_data[0]\n",
        "print(f\"Image shape: {image.shape}\")\n",
        "plt.imshow(image.squeeze())\n",
        "plt.title(f\"{class_names[label]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "dbu-rvJz_yVu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "dfdebc71-32c9-47cc-9762-54722aec4652"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAWu0lEQVR4nO3da2yedf0/8M/d9bBuHTB2YFT2owibTEQgAzmOHRCUw8QgsvjAMIGIJgQhGJ/4gBiNykEkCIahxpCxZKAZJ+UgKEFlmDEMQgxEBhsKw43BNrd2bbf2+j8wfOIc0n6vvy1TX69kIffd7/v+Xr3u++6719Z+aFRVVQUARETTe30AAOw9lAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQC/3EWL14cHR0dQ66bN29ezJs379+277x58+JDH/rQv+3xYG+kFBgV3//+96PRaMTxxx//Xh/Kf6RvfvObcc8997zXh8H/AKXAqFi2bFl0dXXFqlWrYs2aNe/14fzHUQqMFqXAiFu7dm2sXLkybrjhhpgyZUosW7bsvT4k4F9QCoy4ZcuWxcSJE+Pss8+O888//x1LYd26ddFoNOL666+P2267LQ499NBoa2uL4447Lp566qkh93jmmWdiypQpMW/evNi+ffu/XNfX1xdXX311HHbYYdHW1hbTp0+Pr3zlK9HX1zfsz+fpp5+Ok046Kdrb2+OQQw6JW2+9dY81GzdujIsvvjgOOOCAGDt2bBx11FFx++2377Guu7s7rrrqqpg+fXq0tbXFBz7wgbj++uvjH4cXNxqN6O7ujttvvz0ajUY0Go1YvHjxsI8XilQwwg4//PDq4osvrqqqqn79619XEVGtWrVqtzVr166tIqI65phjqsMOO6y65pprqmuvvbaaPHlyddBBB1X9/f259sILL6zGjx+ft1etWlVNnDixOv3006uenp68f+7cudXcuXPz9sDAQHXGGWdU48aNq6644opqyZIl1WWXXVY1NzdX55577pCfx9y5c6vOzs5q6tSp1WWXXVbddNNN1SmnnFJFRPWjH/0o1/X09FSzZs2qWlpaqiuvvLK66aabqjlz5lQRUd144425bnBwsFqwYEHVaDSqSy65pLr55purhQsXVhFRXXHFFblu6dKlVVtbWzVnzpxq6dKl1dKlS6uVK1cOfeKhBqXAiFq9enUVEdUjjzxSVdXfvxAedNBB1Ze+9KXd1r1dCpMmTareeuutvP/ee++tIqK6//77875/LIXf/va31T777FOdffbZVW9v726P+c+lsHTp0qqpqan6zW9+s9u6W2+9tYqI6oknnnjXz2Xu3LlVRFTf+c538r6+vr7q6KOPrqZOnZrFdeONN1YRUd1xxx25rr+/vzrxxBOrjo6O6m9/+1tVVVV1zz33VBFRfeMb39htn/PPP79qNBrVmjVr8r7x48dXF1544bseH/w7+OsjRtSyZcvigAMOiPnz50fE3/8qZNGiRbF8+fIYGBjYY/2iRYti4sSJeXvOnDkREfHyyy/vsfaxxx6Lj33sY3HaaafFihUroq2t7V2P5Sc/+UnMmjUrDj/88Ni0aVP+WbBgQT7eUJqbm+PSSy/N262trXHppZfGxo0b4+mnn46IiAceeCCmTZsWn/nMZ3JdS0tLXH755bF9+/Z4/PHHc92YMWPi8ssv322Pq666KqqqigcffHDI44F/N6XAiBkYGIjly5fH/PnzY+3atbFmzZpYs2ZNHH/88bFhw4b45S9/uUfm//7v/3a7/XZBbN68ebf7e3t74+yzz45jjjkm7rrrrmhtbR3yeF588cX44x//GFOmTNntz8yZMyPi7/8OMJTOzs4YP378bve9nV+3bl1ERLzyyisxY8aMaGra/e01a9as/Pjb/+3s7IwJEya86zoYTc3v9QHw3+tXv/pVvP7667F8+fJYvnz5Hh9ftmxZnHHGGbvdN2bMmHd8rOqf/q+xbW1tcdZZZ8W9994bDz30UJxzzjlDHs/g4GAceeSRccMNN7zjx6dPnz7kY8B/O6XAiFm2bFlMnTo1brnllj0+tmLFirj77rvj1ltvjfb29uLHbjQasWzZsjj33HPj05/+dDz44IND/vbyoYceGn/4wx/itNNOi0ajUbxnRMT69euju7t7t6uFP/3pTxER0dXVFRERBx98cDz77LMxODi429XCCy+8kB9/+7+PPvpobNu2bberhX9e9/bnC6PBXx8xInbs2BErVqyIc845J84///w9/lx22WWxbdu2uO+++2rv0draGitWrIjjjjsuFi5cGKtWrXrX9RdccEG89tpr8YMf/OAdj7e7u3vIPXft2hVLlizJ2/39/bFkyZKYMmVKzJ49OyIizjrrrPjrX/8ad9555265733ve9HR0RFz587NdQMDA3HzzTfvtsd3v/vdaDQaceaZZ+Z948ePjy1btgx5fPD/y5UCI+K+++6Lbdu2xSc+8Yl3/PgJJ5yQv8i2aNGi2vu0t7fHz372s1iwYEGceeaZ8fjjj//L+USf/exn46677oovfOEL8dhjj8XJJ58cAwMD8cILL8Rdd90VDz/8cBx77LHvul9nZ2dcc801sW7dupg5c2bceeed8cwzz8Rtt90WLS0tERHx+c9/PpYsWRKLFy+Op59+Orq6uuKnP/1pPPHEE3HjjTfmVcHChQtj/vz58dWvfjXWrVsXRx11VPziF7+Ie++9N6644oo49NBDc9/Zs2fHo48+GjfccEN0dnbGIYccYmQII+O9/vEn/jstXLiwGjt2bNXd3f0v1yxevLhqaWmpNm3alD+Set111+2xLiKqq6++Om//8+8pVFVVbdq0qfrgBz9YTZs2rXrxxRerqtrzR1Kr6u8/GnrNNddURxxxRNXW1lZNnDixmj17dvW1r32t2rp167t+TnPnzq2OOOKIavXq1dWJJ55YjR07tjr44IOrm2++eY+1GzZsqD73uc9VkydPrlpbW6sjjzyy+vGPf7zHum3btlVXXnll1dnZWbW0tFQzZsyorrvuumpwcHC3dS+88EJ16qmnVu3t7VVE+PFURkyjqv7pX/AA+J/l3xQASEoBgKQUAEhKAYCkFABISgGANOxfXvNr9gD/2YbzGwiuFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASM3v9QHAUBqNRnGmqqoROJI9TZgwoThzyimn1NrrwQcfrJUrVed8jxkzpjiza9eu4szers65q2ukXuOuFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYBkIB57vaam8u9dBgYGijOHHXZYceaSSy4pzuzYsaM4ExHR3d1dnOnt7S3OrFq1qjgzmsPt6gydq/MaqrPPaJ6HOkMIh8OVAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJAMxGOvV2fwV52BeAsWLCjOfPSjHy3OvPrqq8WZiIi2trbizLhx44ozp59+enHmhz/8YXFmw4YNxZmIiKqqijN1Xg91dHR01MoNDg4WZ3p6emrtNRRXCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEAyEI+9Xn9//6jsc9xxxxVnurq6ijN1BvxFRDQ1lX8P9/DDDxdnjjnmmOLMtddeW5xZvXp1cSYi4rnnnivOPP/888WZj3zkI8WZOq+hiIiVK1cWZ5588slaew3FlQIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQDMRj1DQajVq5qqqKM6effnpx5thjjy3ObNu2rTgzfvz44kxExMyZM0cl89RTTxVn1qxZU5zp6OgozkREnHjiicWZ8847rzizc+fO4kydcxcRcckllxRn+vr6au01FFcKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKAKRGNcwRlHUnXLL329uf2zpTUn/3u98VZ7q6uoozddQ937t27SrO9Pf319qrVG9vb3FmcHCw1l6///3vizN1prjWOd8f//jHizMREe9///uLM+973/uKM8N5L7lSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAklIAICkFAFLze30AvPfqDJzb223evLk4c+CBBxZnduzYUZxpa2srzkRENDeXv107OjqKM3WG27W3txdn6g7EmzNnTnHmpJNOKs40NZV/zzx16tTiTETEQw89VCs3ElwpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAMlAPP4rjRs3rjhTZwBanUxPT09xJiJi69atxZk333yzONPV1VWcqTNUsdFoFGci6p3zOq+HgYGB4kzdIX/Tp0+vlRsJrhQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGAZCAetQaT1RlKVmfAWERER0dHcaazs7M409fXNyqZtra24kxERH9/f3GmzvC9/fbbrzhTZ/BenSF1ERGtra3FmW3bthVn9t133+LMs88+W5yJqPcaP/bYY2vtNRRXCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkU1KJqqqKM2PGjCnO1J2SumjRouLMtGnTijNvvPFGcaa9vb04Mzg4WJyJiBg/fnxxZvr06cWZOtNY60x+3blzZ3EmIqK5ufzLVp3nadKkScWZW265pTgTEXH00UcXZ+qch+FwpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgCkRjXMaWiNRmOkj4X3SJ3BWrt27RqBI3lnxx9/fHHm5z//eXFmx44dxZnRHAw4YcKE4kxvb29x5s033yzOtLS0jEomot5gwM2bN9faq1Sd8x0Rcd111xVn7rjjjuLMcL7cu1IAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAUvkktBFWd/BencFkTU3lnVjn+Hbu3FmcGRwcLM7UNZrD7ep44IEHijPd3d3FmToD8VpbW4szw5xBuYc33nijOFPnfTF27NjiTJ3XeF2j9X6qc+4+/OEPF2ciIrZu3VorNxJcKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBpRAfi1RkoNTAwUGuvvX2o297s1FNPLc586lOfKs6cfPLJxZmIiJ6enuLMm2++WZypM9yuubn8LVT3NV7nPNR5D7a1tRVn6gzRqzsYsM55qKPO62H79u219jrvvPOKM/fff3+tvYbiSgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABIjWqYU6kajcZIH8uo23///YsznZ2dxZkZM2aMyj4R9QZrzZw5szjT19dXnGlqqvc9yM6dO4sz7e3txZn169cXZ1paWoozdQatRURMmjSpONPf31+cGTduXHFm5cqVxZmOjo7iTES9AY6Dg4PFma1btxZn6rweIiI2bNhQnJk1a1ZxZjhf7l0pAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJBGdErqCSecUJz5+te/XpyJiJgyZUpxZr/99ivODAwMFGfGjBlTnNmyZUtxJiJi165dxZk6UzHrTN+sO2l3x44dxZnnn3++OHPBBRcUZ1avXl2cmTBhQnEmImLixInFma6urlp7lXr55ZeLM3XPw7Zt24ozPT09xZk6k3brTn7dZ599ijN13rempAJQRCkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQhj0Qr7m5ufjBn3zyyeLMgQceWJyJqDeork6mzmCtOuoM0YuoNzxutOy77761cpMnTy7OLF68uDhzxhlnFGe++MUvFmfWr19fnImI6O3tLc6sXbu2OFNnuN2MGTOKM5MmTSrORNQbxtjS0lKcqTOwr84+ERGDg4PFmYMPPrg4YyAeAEWUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAGnYA/Euuuii4gf/9re/XZx56aWXijMRER0dHaOSaWtrK87UUXewVp2hc3/5y1+KM3WGuk2ZMqU4ExHR1FT+vcu0adOKM5/85CeLM2PHji3OdHV1FWci6r1eZ8+ePSqZOs9RncF2dfdqbW2ttVepRqNRK1fn/X7CCScUZ/785z8PucaVAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCah7tw48aNxQ9eZ9DahAkTijMREX19fcWZOsdXZyhZnWFc++yzT3EmIuKtt94qzrzyyivFmTrnYceOHcWZiIje3t7izK5du4ozd999d3HmueeeK87UHYi3//77F2fqDJ3bsmVLcWbnzp3FmTrPUUTE4OBgcabOwLk6+9QdiFfna8TMmTNr7TUUVwoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAGvZAvNdee634wauqKs68+uqrxZmIiPHjxxdnJk+eXJypMyxs06ZNxZk33nijOBMR0dw87Kc0tbW1FWfqDBgbO3ZscSai3pDEpqby73fqPE+zZs0qznR3dxdnIuoNcNy8eXNxps7roc65qzNEL6LeIL06e7W3txdnpk2bVpyJiNi6dWtx5uijj66111BcKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQhj1S85lnnil+8BUrVhRnLrroouJMRMT69euLMy+//HJxpre3tzjT0dFRnKkzhTSi3mTH1tbW4syYMWOKM319fcWZiIiBgYHiTJ0JvT09PcWZ119/vThT59gi6p2HOlNzR+s13t/fX5yJqDepuE6mzmTVOhNcIyIOOeSQ4syGDRtq7TUUVwoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAalTDnM7VaDRG+lgiIuLMM8+slfvyl79cnJk6dWpxZtOmTcWZOsO46gw/i6g3qK7OQLw6g9bqHFtEvddenaFzdYYQ1snUOd919xqt922dfUZqoNs7qXPOBwcHizPTpk0rzkREPPvss8WZCy64oDgznPeFKwUAklIAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgDXsgXp1hZnUGSo2m+fPnF2e+9a1vFWfqDN7bd999izMREU1N5T1f57mtMxCv7pC/OjZu3FicqTNE77XXXivO1H1fbN++vThTdwhhqTrnbufOnbX26unpKc7UeV888sgjxZnnn3++OBMRsXLlylq5UgbiAVBEKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCGPRCv0WiM9LHwDw4//PBaucmTJxdntmzZUpw56KCDijPr1q0rzkTUG5z20ksv1doL/psZiAdAEaUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJFNSAf5HmJIKQBGlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKAKTm4S6sqmokjwOAvYArBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUA0v8DLIGL+5XJ9CsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label]);\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "A9sJ2BtJAxtY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "7dc14cf2-9d7b-4555-f9b1-e83d8b2a8aa8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot more images\n",
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize = (7,7))\n",
        "rows, cols = 4, 4\n",
        "for i in range(1, rows*cols+1):\n",
        "    random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "    img, label = train_data[random_idx]\n",
        "    fig.add_subplot(rows, cols, i)\n",
        "    plt.imshow(img.squeeze(), cmap='gray')\n",
        "    plt.title(class_names[label])\n",
        "    plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YztWFKTQD8pU"
      },
      "source": [
        "Do you think these items of clothing (images) could be modelled with pure linear lines? Or we'll need non-linearities?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "rh4nj8NUIgJk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db977b39-18fd-4c29-b7cf-a727220b3ee1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset FashionMNIST\n",
              "     Number of datapoints: 60000\n",
              "     Root location: data\n",
              "     Split: Train\n",
              "     StandardTransform\n",
              " Transform: ToTensor(),\n",
              " Dataset FashionMNIST\n",
              "     Number of datapoints: 10000\n",
              "     Root location: data\n",
              "     Split: Test\n",
              "     StandardTransform\n",
              " Transform: ToTensor())"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "train_data, test_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNbJenybIiIZ"
      },
      "source": [
        "## 2. Prepare DataLoader\n",
        "\n",
        "Right now, our data is in the form of PyTorch Datasets.\n",
        "\n",
        "DataLoader turns our dataset into a Python iterable.\n",
        "\n",
        "More specifically, we want to turn our data into batches (or mini-batches).\n",
        "\n",
        "Why would we do this?\n",
        "\n",
        "1. It is more computationally efficient, as in, your computig hardware may not be able to look (store in memory) at 60000 images in one hit. So we break it down to 32 images at a time (batch size of 32).\n",
        "2. It gives our neural network more chances to update its gradients per epoch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "NKTiu9wXBvr-"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Setup the batch size hyperparameter\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Turn datasets into iterables (batches)\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,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "GU1x-S0Gnpep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37988c12-937f-4fba-f517-57e371cc620f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7fcaa43c4670>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7fcaa43c4820>)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "train_dataloader, test_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "-xaad9WunVMS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdda66c8-129b-47df-8096-213ee530ec80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLoader: (<torch.utils.data.dataloader.DataLoader object at 0x7fcaa43c4670>, <torch.utils.data.dataloader.DataLoader object at 0x7fcaa43c4820>)\n",
            "Length of train_dataloader: 1875 batches of 32...\n",
            "Length of test_dataloader: 313 batches of 32...\n"
          ]
        }
      ],
      "source": [
        "# Let's check out what we've created\n",
        "print(f\"DataLoader: {train_dataloader, test_dataloader}\")\n",
        "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}...\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "HX5lcPyWowyM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10a881a1-a324-4c29-dc17-0a9974225f90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# Check out what's inside the training dataloader\n",
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "ISmhjdBtrjOY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "3d04b172-313c-4daa-8d03-b3b5618ce27f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image size: torch.Size([1, 28, 28])\n",
            "Label: 6, label size: torch.Size([])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAQuUlEQVR4nO3dX6gfdP3H8fd355ydv9vOGbZl6raT+QcmNmoqXRitGhJUkC5ICCyCCsu7ugh2mxcSQiRIXim7CDFEulCD6A+EyaJCisniKJktmW7u2DnH8z3/PL+L4E1Df+28P23f7Zw9Hpd6Xn6/+/o9PvfV7W1ndXV1NQAgIjZd7CcAwKVDFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFLgsdDqd+Pa3v33Or3v00Uej0+nE3/72twv/pOASJAqse3/+85/j0KFDsXv37hgaGoqrrroqDh48GD/60Y8u+GPff//98dRTT13wx4Fe6bh9xHr23HPPxYEDB2LXrl1xzz33xPvf//549dVX4/nnn4+XXnoppqamIuLfnxS+9a1vxUMPPfRf/3orKyuxtLQUg4OD0el0zvn4Y2NjcejQoXj00UfPxw8HLrr+i/0E4H/x/e9/P7Zt2xa///3vY3x8/Kw/9/rrr5f/en19fdHX1/dfv2Z1dTW63W4MDw+X//pwqfOvj1jXXnrppdi7d++7ghARsWPHjnf9saeeeipuuummGBwcjL1798azzz571p9/r/+msGfPnvjsZz8bP//5z2P//v0xPDwcP/7xj6PT6cTc3Fw89thj0el0otPpxFe+8pXz/COE3hIF1rXdu3fHH/7wh/jLX/5yzq/97W9/G/fee2986UtfigceeCC63W7cddddcfr06XNujx8/HnfffXccPHgwfvjDH8a+ffviyJEjMTg4GLfffnscOXIkjhw5Et/4xjfOxw8LLhr/+oh17Tvf+U585jOfiX379sWtt94at99+e3zqU5+KAwcOxMDAwFlf++KLL8axY8fi2muvjYiIAwcOxIc//OH4yU9+cs5fmTQ1NRXPPvts3HHHHWf98W9+85vxwQ9+ML785S+f3x8YXCQ+KbCuHTx4MH73u9/F5z//+XjhhRfigQceiDvuuCOuuuqq+NnPfnbW137605/OIERE3HzzzbF169Z4+eWXz/k4k5OT7woCbESiwLp3yy23xJNPPhlnzpyJo0ePxve+972YmZmJQ4cOxbFjx/Lrdu3a9a7txMREnDlz5pyPMTk5eV6fM1yqRIENY/PmzXHLLbfE/fffHw8//HAsLS3FE088kX/+//tVRWv5Vdl+pRGXC1FgQ9q/f39ERLz22msX9HHW8nsZYD0RBda1X/3qV+/5M/2nn346IiJuuOGGC/r4o6OjMT09fUEfA3rJrz5iXbvvvvvi7bffji984Qtx4403xuLiYjz33HPx+OOPx549e+KrX/3qBX38j370o/GLX/wiHnzwwfjABz4Qk5OTcdttt13Qx4QLSRRY137wgx/EE088EU8//XQ88sgjsbi4GLt27Yp77703Dh8+/J6/qe18evDBB+PrX/96HD58OObn5+Oee+4RBdY1t48ASP6bAgBJFABIogBAEgUAkigAkEQBgLTm36fgt/Nzsezevbu8+fjHP17e/PGPfyxv3ve+95U3v/71r8ubVi3ft36V+sa1lr+3PikAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCt+f/R7CBeb7W+3hvxmNnDDz9c3uzdu7e8+elPf1re3HnnneXNQw89VN5EtD2/jciRv3YO4gFQIgoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAKn/Yj8B3tulfsBr586d5c0nP/nJpsc6depUeTMyMlLefPe73y1vpqeny5uPfexj5U1ExOnTp8ub48ePlzf//Oc/y5teutS/N9Y7nxQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYDUWV3jycFOp3Ohnwv/4aabbmra7du3r7z50Ic+1PRYVZOTk027LVu2lDfXXXddedPymrdccH3++efLm4iIbdu2lTfPPPNMedPtdsubf/zjH+XN0aNHy5uIiFdeeaVpx9ouzPqkAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGA5CBeD9x8883lzRe/+MWmxzp27Fh5s7y8XN688cYb5c3+/fvLm4iIO++8s7x57LHHypuvfe1r5U3Lcbarr766vImI+Pvf/17ePPLII+XN+Ph4eXPFFVeUN9u3by9vItp+TKdPn256rI3GQTwASkQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACA5iNcD9913X3nz5ptvNj1Wy4G2sbGx8qa/v7+8ef3118ubiIjZ2dnyZuvWreXN3XffXd6cOHGivPnNb35T3kRErKyslDc7d+4sb7rdbnnT8s+HK6+8sryJiFhcXCxvHn/88abH2mgcxAOgRBQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAFL9qhlle/bsKW/OnDnT9FgTExNNu17YsWNH027Lli3lzTvvvFPeLC8vlzcvvvhieTMwMFDeRETs2rWrvGk5bjc0NFTetBzr27Sp7eek119/fdOOtfFJAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIAyUG8ohtvvLG8WV1dLW+2bdtW3kS0HUBrOQQ3Pz9f3nQ6nfImou3Y2vDwcHnTcoTw5MmT5U3Lgb+Itte8v7/+Ld7yfmh5v27durW8iYhYWFgob2644Yby5vjx4+XNRuCTAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkFxJLfrEJz5R3rRcW9y8eXN5ExExMTFR3szOzpY309PT5U1fX195ExGxtLRU3oyOjpY3r732WnmzaVPvfl41NzdX3uzYsaO8GRwcLG927txZ3pw4caK8iWh7j3/kIx8pb1xJBeCyJwoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAMlBvKLrrruuvPnTn/5U3kxNTZU3ERG33XZbeTM+Pl7e9PfX3zqnTp0qbyLajgMODAyUN2+++WZ50/LcxsbGypuIiIWFhfJm69at5U3L+6HlQOIrr7xS3kREXH/99eVNy5G/y5VPCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASJf1QbyWw1qzs7PlTV9fX3mzsrJS3kREdDqd8mZ5ebm8mZiYKG8WFxfLm4iIbrdb3rQcnWt5zbdt21betBypi2g76tZysK/lcVr+3o6MjJQ3ERFvvPFGedPy9/aaa64pb1599dXy5lLjkwIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFANJlfRDvyiuvLG9ajrO1HNZqPR539dVXlzdTU1PlzdzcXHnTquU1bzkE12JhYaG8aTmqGNH2OuzcubO8aTke13KAcGBgoLxp1fI67Nu3r7xxEA+ADUUUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQOqurq6tr+sJO50I/lw1r9+7d5c2WLVuaHutzn/tceTM4OFjenDhxoryZn58vbyIiZmZmypuWK6lr/FY4S6+u5ka0XRV95513ypvt27eXN9dee21588wzz5Q3EREnT54sb44dO9aTx7nUreU97pMCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSg3gbzMTERHlz+PDh8uavf/1refP222+XNxFtR91ajsetrKyUNy3PrWUTETE2NtaTTctr9+STT5Y3U1NT5Q3/GwfxACgRBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGA1H+xn8DF1HLkr1eHAVuPpnW73fJmjTcRz9LfX3/rtGwiIhYXF8ublqNuLcfjTp48Wd4MDQ2VNxERy8vL5U3La9fyOJf6cbuW79uW74uNwCcFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgCky/ogXouWQ3W9OqIXETE/P9+TTcvBuVYtB9pafkwtB9AGBwd78jgREZs3by5vRkdHy5uZmZny5lJ3uR63a+GTAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUA0mV9EK9XR7Iu9WNcCwsL5U1/f/2t09fXV95ERAwPD5c3Q0ND5U3L4cKWTctRxVYjIyPlzenTpy/AM2G98EkBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIl/WVVP5teXm5vGm5XDo7O1veRLRdcW25XtpyWfVf//pXebNpU9vPxXp1xXV6erq8YePwSQGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAMlBPJqOpvX31986fX195U1E26G6FktLS+VNy3Nreb0j2l7zlsOFLQcS2Th8UgAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQHIQj1hZWSlvNm2q/3yi9RBcy2ONjo6WNy3H7ebm5sqbxcXF8qZVyxHClvcDG4dPCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASA7iEUtLS+XNyMhIedPf3/Z263a75c3AwEB5s7y8XN5MT0+XN+Pj4+VNRNtxu9bXnMuXTwoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiuZdGk0+n0ZBPRdgjuzJkz5c0VV1xR3rQet+uVoaGhnmzYOHxSACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkiupxNLSUnnT319/6ywvL5c3EREDAwM92QwPD5c3c3Nz5U232y1vIiIGBwebdlUtrx0bh08KACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIDuIR8/Pz5U3L8bi+vr7yJiJidna2vOl0Oj15nJmZmfJmZGSkvImIWFlZ6cmm9XAhG4NPCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASA7i0XQ8bvPmzT3ZRLQd3xsfHy9vhoaGyptut9uTx2nV8linTp26AM/k3VredxERq6ur5/mZ8J98UgAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQHIQj6ajaQsLC+XNli1bypuIiL6+vvLmrbfeKm9anl8vj9u1GBsbK29aXjs2Dp8UAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGA5ErqJarT6TTtVldXy5uZmZny5tZbby1vfvnLX5Y3EREDAwPlTct10NHR0fKm2+2WNy2vd0TE8PBweTM+Pl7eTE9PlzdsHD4pAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgdVbXeEGt9UAbG9PevXvLm6WlpabHuuaaa8qbycnJ8mb79u3lzcmTJ8ub1u+lt956q7w5ceJEeXP06NHyhvVhLf+490kBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgCpf61fuMa7eQCsYz4pAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJD+DweYWJOnM3TKAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Show a sample\n",
        "torch.manual_seed(42)\n",
        "random_idx = torch.randint(0, len(train_features_batch), size = [1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap='gray')\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);\n",
        "\n",
        "print(f\"Image size: {img.shape}\")\n",
        "print(f\"Label: {label}, label size: {label.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir-h9-RsvLWu"
      },
      "source": [
        "## 3. Model 0: Build a baseline model\n",
        "\n",
        "when start to build a series of machine learning 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 whe necessary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "KeY9RAPStlob",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89a0054e-f3e3-466f-aeb2-76ee981cfb3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening: torch.Size([1, 28, 28])\n",
            "Shape after flattening: torch.Size([1, 784])\n"
          ]
        }
      ],
      "source": [
        "# Create a flatten layer\n",
        "flatten_model = nn.Flatten()\n",
        "\n",
        "# Get a single sample\n",
        "x = train_features_batch[0]\n",
        "\n",
        "# Flatten the sample\n",
        "output = flatten_model(x) # perform forward pass\n",
        "\n",
        "# Print out what happend\n",
        "print(f\"Shape before flattening: {x.shape}\")\n",
        "print(f\"Shape after flattening: {output.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "-afPyIr_3L_b"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "class FashionMNISTModelV0(nn.Module):\n",
        "    def __init__(self,\n",
        "                 input_shape: int,\n",
        "                 hidden_units: int,\n",
        "                 output_shape: int):\n",
        "        super().__init__()\n",
        "        self.layer_stack = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(in_features= input_shape,\n",
        "                      out_features = hidden_units),\n",
        "            nn.Linear(in_features= hidden_units,\n",
        "                      out_features= output_shape)\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        return self.layer_stack(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "EghO5Hks-VBu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "216d1ed6-d0fe-42cf-caab-560e4ded4849"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV0(\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": 56
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "# Setup mode with input parameters\n",
        "model_0 = FashionMNISTModelV0(\n",
        "    input_shape= 784, # this is 28*28\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "bV3FePuf-U_K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30a057ef-16ca-4f97-ec57-b61951495cd7"
      },
      "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": 57
        }
      ],
      "source": [
        "dummy_x = torch.rand([1, 1, 28, 28])\n",
        "model_0(dummy_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "iyorDzWV-U8u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e5e61d-4120-44cf-c9c3-70c47bdbd5d1"
      },
      "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": 58
        }
      ],
      "source": [
        "model_0.state_dict()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xETFCBx6A7iF"
      },
      "source": [
        "### 3.1 Setup loss, optimizer and evaluation metrics\n",
        "\n",
        "* Loss function - since we're working with multi-class data, our loss function will be `nn.CrossEntropyLoss()`\n",
        "* Optimizer - our optimizer `torch.optim.SGD()` (Stochastic gradient descent)\n",
        "* Evaluation metirc - since we're working on a classification problem, let's use accuracy as our evaluation metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_jvsa9uCrtJ"
      },
      "source": [
        "Adding some helper functions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "6l8eyrRZ-U6Q"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "A series of helper functions used throughout the course.\n",
        "\n",
        "If a function gets defined once and could be used over and over, it'll go in here.\n",
        "\"\"\"\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from torch import nn\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "import requests\n",
        "\n",
        "# Walk through an image classification directory and find out how many files (images)\n",
        "# are in each subdirectory.\n",
        "import os\n",
        "\n",
        "# def walk_through_dir(dir_path):\n",
        "#     \"\"\"\n",
        "#     Walks through dir_path returning its contents.\n",
        "#     Args:\n",
        "#     dir_path (str): target directory\n",
        "\n",
        "#     Returns:\n",
        "#     A print out of:\n",
        "#       number of subdiretories in dir_path\n",
        "#       number of images (files) in each subdirectory\n",
        "#       name of each subdirectory\n",
        "#     \"\"\"\n",
        "#     for dirpath, dirnames, filenames in os.walk(dir_path):\n",
        "#         print(f\"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")\n",
        "\n",
        "# def plot_decision_boundary(model: torch.nn.Module, X: torch.Tensor, y: torch.Tensor):\n",
        "#     \"\"\"Plots decision boundaries of model predicting on X in comparison to y.\n",
        "\n",
        "#     Source - https://madewithml.com/courses/foundations/neural-networks/ (with modifications)\n",
        "#     \"\"\"\n",
        "#     # Put everything to CPU (works better with NumPy + Matplotlib)\n",
        "#     model.to(\"cpu\")\n",
        "#     X, y = X.to(\"cpu\"), y.to(\"cpu\")\n",
        "\n",
        "#     # Setup prediction boundaries and grid\n",
        "#     x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "#     y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "#     xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n",
        "\n",
        "#     # Make features\n",
        "#     X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()\n",
        "\n",
        "#     # Make predictions\n",
        "#     model.eval()\n",
        "#     with torch.inference_mode():\n",
        "#         y_logits = model(X_to_pred_on)\n",
        "\n",
        "#     # Test for multi-class or binary and adjust logits to prediction labels\n",
        "#     if len(torch.unique(y)) > 2:\n",
        "#         y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1)  # mutli-class\n",
        "#     else:\n",
        "#         y_pred = torch.round(torch.sigmoid(y_logits))  # binary\n",
        "\n",
        "#     # Reshape preds and plot\n",
        "#     y_pred = y_pred.reshape(xx.shape).detach().numpy()\n",
        "#     plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "#     plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "#     plt.xlim(xx.min(), xx.max())\n",
        "#     plt.ylim(yy.min(), yy.max())\n",
        "\n",
        "\n",
        "# # Plot linear data or training and test and predictions (optional)\n",
        "# def plot_predictions(\n",
        "#     train_data, train_labels, test_data, test_labels, predictions=None\n",
        "# ):\n",
        "#     \"\"\"\n",
        "#   Plots linear training data and test data and compares predictions.\n",
        "#   \"\"\"\n",
        "#     plt.figure(figsize=(10, 7))\n",
        "\n",
        "#     # Plot training data in blue\n",
        "#     plt.scatter(train_data, train_labels, c=\"b\", s=4, label=\"Training data\")\n",
        "\n",
        "#     # Plot test data in green\n",
        "#     plt.scatter(test_data, test_labels, c=\"g\", s=4, label=\"Testing data\")\n",
        "\n",
        "#     if predictions is not None:\n",
        "#         # Plot the predictions in red (predictions were made on the test data)\n",
        "#         plt.scatter(test_data, predictions, c=\"r\", s=4, label=\"Predictions\")\n",
        "\n",
        "#     # Show the legend\n",
        "#     plt.legend(prop={\"size\": 14})\n",
        "\n",
        "\n",
        "# Calculate accuracy (a classification metric)\n",
        "def accuracy_fn(y_true, y_pred):\n",
        "    \"\"\"Calculates accuracy between truth labels and predictions.\n",
        "\n",
        "    Args:\n",
        "        y_true (torch.Tensor): Truth labels for predictions.\n",
        "        y_pred (torch.Tensor): Predictions to be compared to predictions.\n",
        "\n",
        "    Returns:\n",
        "        [torch.float]: Accuracy value between y_true and y_pred, e.g. 78.45\n",
        "    \"\"\"\n",
        "    correct = torch.eq(y_true, y_pred).sum().item()\n",
        "    acc = (correct / len(y_pred)) * 100\n",
        "    return acc\n",
        "\n",
        "\n",
        "# def print_train_time(start, end, device=None):\n",
        "#     \"\"\"Prints difference between start and end time.\n",
        "\n",
        "#     Args:\n",
        "#         start (float): Start time of computation (preferred in timeit format).\n",
        "#         end (float): End time of computation.\n",
        "#         device ([type], optional): Device that compute is running on. Defaults to None.\n",
        "\n",
        "#     Returns:\n",
        "#         float: time between start and end in seconds (higher is longer).\n",
        "#     \"\"\"\n",
        "#     total_time = end - start\n",
        "#     print(f\"\\nTrain time on {device}: {total_time:.3f} seconds\")\n",
        "#     return total_time\n",
        "\n",
        "\n",
        "# # Plot loss curves of a model\n",
        "# def plot_loss_curves(results):\n",
        "#     \"\"\"Plots training curves of a results dictionary.\n",
        "\n",
        "#     Args:\n",
        "#         results (dict): dictionary containing list of values, e.g.\n",
        "#             {\"train_loss\": [...],\n",
        "#              \"train_acc\": [...],\n",
        "#              \"test_loss\": [...],\n",
        "#              \"test_acc\": [...]}\n",
        "#     \"\"\"\n",
        "#     loss = results[\"train_loss\"]\n",
        "#     test_loss = results[\"test_loss\"]\n",
        "\n",
        "#     accuracy = results[\"train_acc\"]\n",
        "#     test_accuracy = results[\"test_acc\"]\n",
        "\n",
        "#     epochs = range(len(results[\"train_loss\"]))\n",
        "\n",
        "#     plt.figure(figsize=(15, 7))\n",
        "\n",
        "#     # Plot loss\n",
        "#     plt.subplot(1, 2, 1)\n",
        "#     plt.plot(epochs, loss, label=\"train_loss\")\n",
        "#     plt.plot(epochs, test_loss, label=\"test_loss\")\n",
        "#     plt.title(\"Loss\")\n",
        "#     plt.xlabel(\"Epochs\")\n",
        "#     plt.legend()\n",
        "\n",
        "#     # Plot accuracy\n",
        "#     plt.subplot(1, 2, 2)\n",
        "#     plt.plot(epochs, accuracy, label=\"train_accuracy\")\n",
        "#     plt.plot(epochs, test_accuracy, label=\"test_accuracy\")\n",
        "#     plt.title(\"Accuracy\")\n",
        "#     plt.xlabel(\"Epochs\")\n",
        "#     plt.legend()\n",
        "\n",
        "\n",
        "# # Pred and plot image function from notebook 04\n",
        "# # See creation: https://www.learnpytorch.io/04_pytorch_custom_datasets/#113-putting-custom-image-prediction-together-building-a-function\n",
        "# from typing import List\n",
        "# import torchvision\n",
        "\n",
        "\n",
        "# def pred_and_plot_image(\n",
        "#     model: torch.nn.Module,\n",
        "#     image_path: str,\n",
        "#     class_names: List[str] = None,\n",
        "#     transform=None,\n",
        "#     device: torch.device = \"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
        "# ):\n",
        "#     \"\"\"Makes a prediction on a target image with a trained model and plots the image.\n",
        "\n",
        "#     Args:\n",
        "#         model (torch.nn.Module): trained PyTorch image classification model.\n",
        "#         image_path (str): filepath to target image.\n",
        "#         class_names (List[str], optional): different class names for target image. Defaults to None.\n",
        "#         transform (_type_, optional): transform of target image. Defaults to None.\n",
        "#         device (torch.device, optional): target device to compute on. Defaults to \"cuda\" if torch.cuda.is_available() else \"cpu\".\n",
        "\n",
        "#     Returns:\n",
        "#         Matplotlib plot of target image and model prediction as title.\n",
        "\n",
        "#     Example usage:\n",
        "#         pred_and_plot_image(model=model,\n",
        "#                             image=\"some_image.jpeg\",\n",
        "#                             class_names=[\"class_1\", \"class_2\", \"class_3\"],\n",
        "#                             transform=torchvision.transforms.ToTensor(),\n",
        "#                             device=device)\n",
        "#     \"\"\"\n",
        "\n",
        "#     # 1. Load in image and convert the tensor values to float32\n",
        "#     target_image = torchvision.io.read_image(str(image_path)).type(torch.float32)\n",
        "\n",
        "#     # 2. Divide the image pixel values by 255 to get them between [0, 1]\n",
        "#     target_image = target_image / 255.0\n",
        "\n",
        "#     # 3. Transform if necessary\n",
        "#     if transform:\n",
        "#         target_image = transform(target_image)\n",
        "\n",
        "#     # 4. Make sure the model is on the target device\n",
        "#     model.to(device)\n",
        "\n",
        "#     # 5. Turn on model evaluation mode and inference mode\n",
        "#     model.eval()\n",
        "#     with torch.inference_mode():\n",
        "#         # Add an extra dimension to the image\n",
        "#         target_image = target_image.unsqueeze(dim=0)\n",
        "\n",
        "#         # Make a prediction on image with an extra dimension and send it to the target device\n",
        "#         target_image_pred = model(target_image.to(device))\n",
        "\n",
        "#     # 6. Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)\n",
        "#     target_image_pred_probs = torch.softmax(target_image_pred, dim=1)\n",
        "\n",
        "#     # 7. Convert prediction probabilities -> prediction labels\n",
        "#     target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)\n",
        "\n",
        "#     # 8. Plot the image alongside the prediction and prediction probability\n",
        "#     plt.imshow(\n",
        "#         target_image.squeeze().permute(1, 2, 0)\n",
        "#     )  # make sure it's the right size for matplotlib\n",
        "#     if class_names:\n",
        "#         title = f\"Pred: {class_names[target_image_pred_label.cpu()]} | Prob: {target_image_pred_probs.max().cpu():.3f}\"\n",
        "#     else:\n",
        "#         title = f\"Pred: {target_image_pred_label} | Prob: {target_image_pred_probs.max().cpu():.3f}\"\n",
        "#     plt.title(title)\n",
        "#     plt.axis(False)\n",
        "\n",
        "# def set_seeds(seed: int=42):\n",
        "#     \"\"\"Sets random sets for torch operations.\n",
        "\n",
        "#     Args:\n",
        "#         seed (int, optional): Random seed to set. Defaults to 42.\n",
        "#     \"\"\"\n",
        "#     # Set the seed for general torch operations\n",
        "#     torch.manual_seed(seed)\n",
        "#     # Set the seed for CUDA torch operations (ones that happen on the GPU)\n",
        "#     torch.cuda.manual_seed(seed)\n",
        "\n",
        "# def download_data(source: str,\n",
        "#                   destination: str,\n",
        "#                   remove_source: bool = True) -> Path:\n",
        "#     \"\"\"Downloads a zipped dataset from source and unzips to destination.\n",
        "\n",
        "#     Args:\n",
        "#         source (str): A link to a zipped file containing data.\n",
        "#         destination (str): A target directory to unzip data to.\n",
        "#         remove_source (bool): Whether to remove the source after downloading and extracting.\n",
        "\n",
        "#     Returns:\n",
        "#         pathlib.Path to downloaded data.\n",
        "\n",
        "#     Example usage:\n",
        "#         download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
        "#                       destination=\"pizza_steak_sushi\")\n",
        "#     \"\"\"\n",
        "#     # Setup path to data folder\n",
        "#     data_path = Path(\"data/\")\n",
        "#     image_path = data_path / destination\n",
        "\n",
        "#     # If the image folder doesn't exist, download it and prepare it...\n",
        "#     if image_path.is_dir():\n",
        "#         print(f\"[INFO] {image_path} directory exists, skipping download.\")\n",
        "#     else:\n",
        "#         print(f\"[INFO] Did not find {image_path} directory, creating one...\")\n",
        "#         image_path.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "#         # Download pizza, steak, sushi data\n",
        "#         target_file = Path(source).name\n",
        "#         with open(data_path / target_file, \"wb\") as f:\n",
        "#             request = requests.get(source)\n",
        "#             print(f\"[INFO] Downloading {target_file} from {source}...\")\n",
        "#             f.write(request.content)\n",
        "\n",
        "#         # Unzip pizza, steak, sushi data\n",
        "#         with zipfile.ZipFile(data_path / target_file, \"r\") as zip_ref:\n",
        "#             print(f\"[INFO] Unzipping {target_file} data...\")\n",
        "#             zip_ref.extractall(image_path)\n",
        "\n",
        "#         # Remove .zip file\n",
        "#         if remove_source:\n",
        "#             os.remove(data_path / target_file)\n",
        "\n",
        "#     return image_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "1GQha-td-U3L"
      },
      "outputs": [],
      "source": [
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXIuTcGssflm"
      },
      "source": [
        "### 3.2 Crating a function to time our experiments\n",
        "\n",
        "Machine learning is very experimental.\n",
        "\n",
        "Two of the main things you'll often wnat to track are:\n",
        "\n",
        "1. Model's performance (loss and accuracy values etc)\n",
        "2. How fast it runs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "t7fXi6djDsH0"
      },
      "outputs": [],
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start:float,\n",
        "                     end: float,\n",
        "                     device: torch.device= None):\n",
        "    \"\"\" 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "m2RJnVt2uT3W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf0bf821-016c-4287-a3cc-7dd4c11cdbf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train time on cpu: 0.000 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.324300011328887e-05"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "# How to use:\n",
        "\n",
        "start_time = timer()\n",
        "# some code\n",
        "end_time = timer()\n",
        "print_train_time(start=start_time, end = end_time, device='cpu')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jK6MkKaKwvxx"
      },
      "source": [
        "### 3.3 Creating a trainig loop and training a model on batches of data\n",
        "\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 the test loss *per batch*.\n",
        "4. Print out what's happening.\n",
        "5. Time it all(for fun)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
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        "id": "2Pgt5rg2u6Gq",
        "outputId": "d2aacede-6fe7-44af-b2dd-614eb86711cc"
      },
      "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": "535faa5ad3064f1eacb99adf3d513b29"
            }
          },
          "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",
            "\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",
            "\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",
            "\n",
            "Train time on cpu: 38.764 seconds\n"
          ]
        }
      ],
      "source": [
        "# Import tqdm for progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Set the seed and start the timer\n",
        "torch.manual_seed(42)\n",
        "train_time_start_on_cpu = timer()\n",
        "\n",
        "# Set the number of epochs (we'll keep this 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",
        "    ### Training\n",
        "    train_loss = 0\n",
        "    # Add a loop to loop through the 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. Calculate loss (per batch)\n",
        "        loss = loss_fn(y_pred, y)\n",
        "        train_loss += loss    # accumulate train loss\n",
        "\n",
        "        # 3. Optimizer zero grad\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # 4. Loss backward\n",
        "        loss.backward()\n",
        "\n",
        "        # 5. Optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "        # Print our what's happening\n",
        "        if batch % 400 == 0:\n",
        "            print(f\"Looked at {batch * len(X)} / {len(train_dataloader.dataset)} samples.\")\n",
        "\n",
        "    # Divide 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",
        "            # 1. Forward pass\n",
        "            test_pred = model_0(X_test)\n",
        "\n",
        "            # 2. Calculate the loss (accumulatively)\n",
        "            test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "            # 3. Calculate 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",
        "\n",
        "    # Print out what's happening\n",
        "    print(f\"\\nTrain loss: {train_loss:.4f} | Test loss: {test_loss:.4f}, Test acc: {test_acc:.2f}%\\n\")\n",
        "\n",
        "# Calculate  training time\n",
        "train_time_end_on_cpu = timer()\n",
        "total_train_time_model_0 = print_train_time(start= train_time_start_on_cpu,\n",
        "                                            end= train_time_end_on_cpu,\n",
        "                                            device= str(next(model_0.parameters()).device))\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTscUs4e-LQ8"
      },
      "source": [
        "## 4. Make predictions and get Model 0 results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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            "35d36257d77e4f34aeaf2224ea10d148",
            "977ffe3c000844ca85181eb8a6807d6c",
            "0d4ca085639149c59dec8d3516e3cfbe",
            "ead9aed4a04d4b438b997ca09b8e7f13"
          ]
        },
        "id": "JjG69sMOCNrT",
        "outputId": "680cd786-580d-41e8-e138-06afa1cb576d"
      },
      "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": "35a0881b18b44c7ea5cf7ba2ea938962"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name: ': 'FashionMNISTModelV0',\n",
              " 'model_loss: ': 0.47663894295692444,\n",
              " 'model_acc': 83.42651757188499}"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn):\n",
        "    \"\"\"Returns a dictionary containing the results of model 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",
        "            # 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",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name: \": model.__class__.__name__, # only works when model was created with a class\n",
        "            \"model_loss: \": loss.item(),\n",
        "            \"model_acc\": acc}\n",
        "\n",
        "# Calculate model_0 results on test dataset\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",
        "model_0_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3G8yJs1cAr8"
      },
      "source": [
        "## 5. Setup device agnostic-code (for using a GPU if there is one)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtsIHvPrdTyl",
        "outputId": "5f9b370e-666d-45e2-f896-9e8798b5b6fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqA6fu2za1Pf",
        "outputId": "d7b6ac49-dd32-4aa1-9f6a-1c5eb630be6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "# Setup device-agnostic code\n",
        "import torch\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(device)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OGl2NY7eZkc"
      },
      "source": [
        "## 6. Model 1: Building a better model with non-linearity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "litFAFFuc25s"
      },
      "outputs": [],
      "source": [
        "# Create a model with non-linear and linear layers\n",
        "class FashionMNISTModelV1(nn.Module):\n",
        "    def __init__(self,\n",
        "                 input_shape: int,\n",
        "                 hidden_units: int,\n",
        "                 output_shape: int,):\n",
        "\n",
        "        super().__init__()\n",
        "        self.layer_stack = nn.Sequential(\n",
        "            nn.Flatten(), # flattern 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:torch.Tensor):\n",
        "        return self.layer_stack(x)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9Nfi8F9e-YN",
        "outputId": "dc7f966f-f411-48a7-dd05-2ef6bd559a34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "# Create an instance of model_1\n",
        "torch.manual_seed(42)\n",
        "model_1 = FashionMNISTModelV1(input_shape=784, # this is the ouput of the flatten layer after our 28*28 image goes in\n",
        "                              hidden_units=10,\n",
        "                              output_shape = len(class_names)\n",
        "                              ).to(device) # send to the GPU if it's available\n",
        "next(model_1.parameters()).device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYxw9y26jlBU"
      },
      "source": [
        "### 6.1 Setup loss, optimizer and evaluation metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "lBjRpbY5e_md"
      },
      "outputs": [],
      "source": [
        "loss_fn = nn.CrossEntropyLoss() # measurs how wrong our model is\n",
        "optimizer = torch.optim.SGD(params=model_1.parameters(), # tries to update our model's parameters to reduce the loss\n",
        "                            lr=0.1,)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deC7jOZcka2J"
      },
      "source": [
        "### 6.2 Functionizing training and evaluation/testing loops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "Z5fv1hS7e_vq"
      },
      "outputs": [],
      "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",
        "    train_loss, train_acc = 0, 0\n",
        "\n",
        "    # Put model into training mode\n",
        "    model.train()\n",
        "\n",
        "    # Add a loop to loop through the 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. Calculate loss and accuracy (per batch)\n",
        "        loss = loss_fn(y_pred, y)\n",
        "        train_loss += loss    # accumulate train 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 zero grad\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # 4. Loss backward\n",
        "        loss.backward()\n",
        "\n",
        "        # 5. Optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "    # Divide total train loss and acc by length of train dataloader\n",
        "    train_loss /= len(data_loader)\n",
        "    train_acc /= len(data_loader)\n",
        "\n",
        "    # Print out what's happening\n",
        "    print(f\"Train loss: {train_loss:.5f} | Train acc: {train_acc:.2f}%\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "nm0uGtuxkMuL"
      },
      "outputs": [],
      "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",
        "    # Put the model in eval mode\n",
        "    model.eval()\n",
        "\n",
        "    # Turn on inference mode context manager\n",
        "    with torch.inference_mode(): # inference is another word for predicion\n",
        "        for X,y in data_loader:\n",
        "            # Send the data to the target device\n",
        "            X, y = X.to(device), y.to(device)\n",
        "\n",
        "            # 1. Forward pass (outputs raw logits)\n",
        "            test_pred = model(X)\n",
        "\n",
        "            # 2. Calculate the loss/acc\n",
        "            test_loss += loss_fn(test_pred, y)\n",
        "            test_acc += accuracy_fn(y_true= y, y_pred=test_pred.argmax(dim=1)) # go from raw logits to prediction labels\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344,
          "referenced_widgets": [
            "9ec7061cbe1f4b1182572c84f7c02a45",
            "8180bb98d16d41e88b9cea180ad3619b",
            "06b75eab627f48eaa7885b90f23fb012",
            "7a381515ccf3442e8e385932c98ec296",
            "293ab92c5a58485daf30ddb35c2ff5d0",
            "eba140b1f17e489c9a460ba977440719",
            "a99363ab9cce443ca664bc22d07c5d70",
            "bc518e2922da4b16ba9eb3881f8f9d4a",
            "f0677fa541b740888082b42337aefab9",
            "383c496bee0e4afc8ed5429706094175",
            "a23c6e387f7f4c57a4d5a1769ab4bac5"
          ]
        },
        "id": "pCzJVflYe_2S",
        "outputId": "34ef9b62-c010-4f48-a20b-0891bb3717cf"
      },
      "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": "9ec7061cbe1f4b1182572c84f7c02a45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "------------\n",
            "Train loss: 0.64302 | Train acc: 76.43%\n",
            "Test loss:  0.68500 | Test acc: 75.02%\n",
            "\n",
            "Epoch: 1\n",
            "------------\n",
            "Train loss: 0.64302 | Train acc: 76.43%\n",
            "Test loss:  0.68500 | Test acc: 75.02%\n",
            "\n",
            "Epoch: 2\n",
            "------------\n",
            "Train loss: 0.64302 | Train acc: 76.43%\n",
            "Test loss:  0.68500 | Test acc: 75.02%\n",
            "\n",
            "Train time on cpu: 34.016 seconds\n"
          ]
        }
      ],
      "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",
        "# Set epochs\n",
        "epochs = 3\n",
        "\n",
        "# Create training and test loop\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n------------\")\n",
        "    train_step(model=model_1,\n",
        "               data_loader=train_dataloader,\n",
        "               loss_fn=loss_fn,\n",
        "               optimizer=optimizer,\n",
        "               accuracy_fn=accuracy_fn,\n",
        "               device=device)\n",
        "    test_step(model=model_1,\n",
        "               data_loader=test_dataloader,\n",
        "               loss_fn=loss_fn,\n",
        "               accuracy_fn=accuracy_fn,\n",
        "               device=device)\n",
        "train_time_end_on_gpu = timer()\n",
        "total_train_time_model_1 = print_train_time(start= train_time_start_on_gpu,\n",
        "                                             end=train_time_end_on_gpu,\n",
        "                                             device=device,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "su-LEo4R8Klc",
        "outputId": "5d155c9a-b18d-4c65-9f2b-31d0ee38ca7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'model_name: ': 'FashionMNISTModelV0', 'model_loss: ': 0.47663894295692444, 'model_acc': 83.42651757188499}\n"
          ]
        }
      ],
      "source": [
        "print(model_0_results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "ISEtBukae_5G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "Qgg0eWZ36Z32"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn=accuracy_fn,\n",
        "               device = device):\n",
        "    \"\"\"Returns a dictionary containing the results of model 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",
        "            # Make our data device agnostic\n",
        "            X, y = X.to(device), y.to(device)\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",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name: \": model.__class__.__name__, # only works when model was created with a class\n",
        "            \"model_loss: \": loss.item(),\n",
        "            \"model_acc\": acc}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "f82431024efb4a60a7157587e1a61cf5",
            "0ba0f7f34e5d4ac9bc858dfef73b4950",
            "f4110f4615ae4b13be7a991d7fba2dc8",
            "8f2ff8155fdb41eab65a6bb9280ec6ce",
            "ca1255ac2ec04c9db3149e3029b54e32",
            "87b6b6587482423589e9a8f736153fc0",
            "51ed191b40e441b7882d229b267aa365",
            "acfb4086867c47cc9d0a111aca175540",
            "8c574615193a4883a708234f217d27bd",
            "0c7fa729217c4ae5a75ed165d8ee744c",
            "f9c3a3d96b524266abef1c67fc0913ef"
          ]
        },
        "id": "-7RoVFLX6Z0n",
        "outputId": "1a964524-78f6-4320-9cce-69f9196a92d6"
      },
      "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": "f82431024efb4a60a7157587e1a61cf5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name: ': 'FashionMNISTModelV1',\n",
              " 'model_loss: ': 0.6850009560585022,\n",
              " 'model_acc': 75.01996805111821}"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVszV81K6Zx8",
        "outputId": "b003b873-d71f-4763-e6c2-93ac795680ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name: ': 'FashionMNISTModelV0',\n",
              " 'model_loss: ': 0.47663894295692444,\n",
              " 'model_acc': 83.42651757188499}"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "model_0_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SgfML_qRSA3"
      },
      "source": [
        "## 7.  Model 2: Building a Convelutional Neural Network (CNN)\n",
        "\n",
        "CNNs are also known as ConvNets.\n",
        "\n",
        "CNNs are known for their capabilities to find patterns in visual data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "XfcZ40476Zt5"
      },
      "outputs": [],
      "source": [
        "# Create a convolutional neural network\n",
        "class FashionMNISTModelV2(nn.Module):\n",
        "    \"\"\"\n",
        "    Model architecture the replicates the TinyVGG\n",
        "    model from CNN explainer website.\n",
        "    \"\"\"\n",
        "    def __init__(self, input_shape: int, hidden_units: int, 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), # Values we can set ourselves in our NN's are called 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(\n",
        "                      kernel_size=2,\n",
        "            ),\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(\n",
        "                      kernel_size=2,\n",
        "            ),\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(in_features=hidden_units * 7*7, # After flattening, we should consider the size!\n",
        "                      out_features=output_shape,\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mRw5J4RzOXM",
        "outputId": "ffa0ab92-4824-48ba-c3b6-a5a9cf5a5177"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "RoeudQA36ZgM"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV2(input_shape=1,\n",
        "                              hidden_units=10,\n",
        "                              output_shape=len(class_names)).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "-LG0c_Y2OtJN",
        "outputId": "dd27a83a-27b0-496c-c65a-dcc6613f59a3"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcaa7e32fb0>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pass image through model\n",
        "# model_2(image.to(device))"
      ],
      "metadata": {
        "id": "lyCeE849O2OU"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rand_image_tensor = torch.randn(size=(1, 28, 28))\n",
        "rand_image_tensor.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXaSPR2KPO3F",
        "outputId": "2d101efe-3fc5-4d90-9e8f-f939f54cc893"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2(rand_image_tensor.unsqueeze(0).to(device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnoSkphFPXFV",
        "outputId": "a139f534-18fa-4697-a2c0-1acf7ecb2f85"
      },
      "execution_count": 105,
      "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]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsbGKGZA1YT2"
      },
      "source": [
        "### 7.1 Stepping through `nn.Conv2d()`\n",
        "\n",
        "See the documentation for `nn.Conv2d` here - https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jobckBSNz0Dx",
        "outputId": "d20c6c99-8c8f-48dd-8351-d42dd657b75b"
      },
      "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"
          ]
        }
      ],
      "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] # select the first image\n",
        "\n",
        "print(f\"Image batch shape: {images.shape}\")\n",
        "print(f\"Single image shape: {test_image.shape}\")\n",
        "print(f\"Test image:\\n {test_image}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy-DSIzE2tsl",
        "outputId": "d0bddd73-e434-40cc-988d-f80d9ea38244"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "test_image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sutluoauw2Jg",
        "outputId": "fff7a01f-c401-4057-8c11-a5415664f586"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[-2.8778e-01, -6.0596e-02, -5.6306e-02,  ...,  2.8654e-01,\n",
              "           6.6224e-01, -2.3216e-01],\n",
              "         [-9.8911e-01, -4.0099e-01,  4.1832e-01,  ...,  4.7459e-01,\n",
              "          -1.8552e-01, -5.7622e-01],\n",
              "         [-4.1340e-02, -2.3277e-01,  3.7418e-01,  ...,  2.8255e-02,\n",
              "           1.4923e-01,  1.4236e-01],\n",
              "         ...,\n",
              "         [-8.0374e-01, -7.6687e-01, -5.9457e-02,  ...,  1.7452e-01,\n",
              "           4.2594e-01, -4.8341e-01],\n",
              "         [-1.4512e-01, -1.1566e-01,  6.1783e-01,  ...,  2.4126e-01,\n",
              "          -3.6626e-01,  3.5645e-01],\n",
              "         [ 3.6096e-02,  1.5214e-01,  2.3123e-01,  ...,  3.0904e-01,\n",
              "          -4.9680e-01, -7.2258e-01]],\n",
              "\n",
              "        [[-1.0853e+00, -1.6079e+00,  1.3346e-01,  ...,  2.1698e-01,\n",
              "          -1.7643e+00,  2.5263e-01],\n",
              "         [-8.2507e-01,  6.3866e-01,  1.8845e-01,  ..., -1.0936e-01,\n",
              "           4.8068e-01,  8.4869e-01],\n",
              "         [ 6.4927e-01, -4.2061e-03, -4.9991e-01,  ...,  5.8356e-01,\n",
              "           2.4611e-01,  6.6233e-01],\n",
              "         ...,\n",
              "         [ 9.8860e-02,  1.1661e+00,  3.1532e-01,  ..., -6.5450e-01,\n",
              "          -1.9585e-02,  2.4397e-01],\n",
              "         [-5.9820e-01,  3.7339e-01, -7.2705e-01,  ..., -3.1185e-02,\n",
              "          -8.9892e-01,  2.9192e-01],\n",
              "         [-1.8412e-01,  2.2085e-01,  1.6990e-01,  ...,  4.7418e-01,\n",
              "          -9.0971e-01,  7.9518e-01]],\n",
              "\n",
              "        [[ 3.9605e-02,  6.7841e-01, -9.6515e-01,  ..., -2.8578e-01,\n",
              "          -2.9320e-01, -1.8889e-01],\n",
              "         [-1.5027e+00, -1.1401e+00, -2.0217e-01,  ...,  2.5681e-01,\n",
              "          -9.3105e-01,  6.8724e-02],\n",
              "         [-4.1801e-01,  2.6299e-01,  3.3114e-02,  ...,  2.7162e-01,\n",
              "          -6.0643e-01, -7.4065e-01],\n",
              "         ...,\n",
              "         [ 5.3659e-02, -1.6118e-01, -2.2006e-01,  ...,  4.0253e-01,\n",
              "           2.1769e-01,  2.0759e-01],\n",
              "         [ 8.3423e-02,  2.5296e-01, -4.3232e-01,  ...,  1.1411e+00,\n",
              "           9.1973e-01,  3.2513e-02],\n",
              "         [ 2.0770e-01,  6.7494e-01,  1.7256e-01,  ..., -4.1328e-01,\n",
              "          -1.6368e-01, -1.3886e-01]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[-6.5893e-01, -1.0476e+00,  1.0067e+00,  ..., -1.7439e-01,\n",
              "           3.5072e-01,  2.5077e-01],\n",
              "         [ 1.3226e+00,  1.9803e+00, -6.4183e-01,  ...,  3.2681e-01,\n",
              "           1.5939e+00, -1.0821e-01],\n",
              "         [ 8.7122e-01, -1.0276e+00,  4.5702e-01,  ...,  5.1113e-01,\n",
              "           1.0244e+00,  8.7522e-01],\n",
              "         ...,\n",
              "         [ 1.3555e+00,  1.3009e+00,  4.9802e-01,  ..., -1.0216e-01,\n",
              "          -5.6769e-01,  8.4543e-02],\n",
              "         [ 1.5033e-01, -3.4481e-01,  1.0851e+00,  ..., -1.6738e-01,\n",
              "          -5.1884e-01,  1.9113e-01],\n",
              "         [-2.3805e-02, -4.3101e-01,  6.8124e-02,  ...,  1.0441e+00,\n",
              "           4.1791e-01,  6.0961e-01]],\n",
              "\n",
              "        [[-5.7891e-02,  6.3499e-02, -3.7689e-01,  ..., -7.3454e-01,\n",
              "           7.6985e-01, -6.7518e-01],\n",
              "         [ 2.9175e-01,  1.1631e-04, -6.0476e-01,  ..., -1.1983e+00,\n",
              "           6.5636e-01, -5.4662e-01],\n",
              "         [ 2.1523e-01, -1.9851e-01, -1.3285e-01,  ...,  7.5264e-01,\n",
              "          -1.5505e+00,  4.5457e-01],\n",
              "         ...,\n",
              "         [-2.0348e-01, -4.8311e-01, -3.0158e-02,  ..., -5.2867e-01,\n",
              "          -8.0491e-01,  7.7403e-01],\n",
              "         [ 7.5117e-01, -1.5956e-01,  2.5034e-01,  ..., -4.0541e-01,\n",
              "           2.8248e-01, -4.3361e-01],\n",
              "         [-2.1132e-01, -4.4753e-01, -1.0997e-01,  ..., -7.6029e-02,\n",
              "           7.9822e-01, -6.3137e-01]],\n",
              "\n",
              "        [[ 8.6642e-01,  1.6339e+00,  5.4498e-02,  ...,  2.3448e-01,\n",
              "           5.0939e-01,  8.1898e-01],\n",
              "         [ 1.0650e-01,  3.0274e-01, -7.4020e-01,  ...,  8.3167e-01,\n",
              "          -4.6203e-01,  3.4506e-01],\n",
              "         [ 1.1760e-02, -6.1708e-02, -7.6054e-01,  ..., -8.1317e-01,\n",
              "           1.0693e+00, -9.6907e-01],\n",
              "         ...,\n",
              "         [-1.1468e+00, -1.3791e-01, -3.6390e-01,  ...,  5.5073e-01,\n",
              "           2.0598e-01,  1.0710e-01],\n",
              "         [-3.4261e-01, -1.6038e-02, -6.6151e-01,  ..., -4.2419e-01,\n",
              "           4.4527e-01, -1.0111e-01],\n",
              "         [ 4.4487e-02,  9.7506e-02, -3.1829e-01,  ...,  6.0565e-02,\n",
              "           5.3990e-01, -4.2942e-01]]], grad_fn=<SqueezeBackward1>)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "# Create a single conv2d layer\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_output = conv_layer(test_image)\n",
        "conv_output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZGAUhoJAHVx",
        "outputId": "8db7132f-c039-485f-d703-6c31f20a230f"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 62, 62])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xq39J0KT1HOq"
      },
      "source": [
        "### 7.2 Stepping througth `nn.MaxPool2d`\n",
        "\n",
        "documentation : https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "_8URr3TGzKUi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38422570-a710-4828-cf0e-1364dfd2175e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "test_image.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Test image original shape: {test_image.shape}\")\n",
        "print(f\"Test image with unsqueezed dimention: {test_image.unsqueeze(0).shape}\")\n",
        "\n",
        "# Create a sample nn.MaxPool2d layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "# Pass the data through the conv layer\n",
        "test_image_through_conv = conv_layer(test_image.unsqueeze(dim=0))\n",
        "print(f\"Shape after going through conv. layer: {test_image_through_conv.shape}\")\n",
        "\n",
        "# Pass the data through the max pooling layer\n",
        "test_image_through_conv_and_max_pool = max_pool_layer(test_image_through_conv)\n",
        "print(f\"Shape after going through conv and max pooling layer: {test_image_through_conv_and_max_pool.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olqJra58BKsp",
        "outputId": "cb64ecc3-a2e9-4058-e35d-279171ef4873"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test image original shape: torch.Size([3, 64, 64])\n",
            "Test image with unsqueezed dimention: torch.Size([1, 3, 64, 64])\n",
            "Shape after going through conv. layer: torch.Size([1, 10, 62, 62])\n",
            "Shape after going through conv and max pooling layer: torch.Size([1, 10, 31, 31])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### An Example to see what is going on in maxpool layer:"
      ],
      "metadata": {
        "id": "nteUk62cHvST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a random tensor with a similar number of dimensions to our images\n",
        "\n",
        "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
        "print(f'\\nRandom tensor: \\n{random_tensor}')\n",
        "print(f'Random tensor shape: {random_tensor.shape}')\n",
        "\n",
        "# Create a max pool layer\n",
        "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "# Pass the random tensor through the max pooling layer\n",
        "max_pool_tensor = max_pool_layer(random_tensor)\n",
        "print(f'\\nMax pool tensor: \\n{max_pool_tensor}')\n",
        "print(f'Max pool tensor shape: {max_pool_tensor.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn05zUzXCgJS",
        "outputId": "849cd1b5-fcfe-496e-ba6a-e4e3870fa9b6"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random tensor: \n",
            "tensor([[[[0.4963, 0.0045],\n",
            "          [0.5534, 0.1379]]]])\n",
            "Random tensor shape: torch.Size([1, 1, 2, 2])\n",
            "\n",
            "Max pool tensor: \n",
            "tensor([[[[0.5534]]]])\n",
            "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.3  Setup a loss function and an optimizer for `model_2`"
      ],
      "metadata": {
        "id": "_yJG-s_tiHFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss function/eval metrics/optimizer\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model_2.parameters(), lr=0.1)"
      ],
      "metadata": {
        "id": "DQ5g2IdZHk9V"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmOjJsV9iDcZ",
        "outputId": "1ca6ba86-407f-4892-b5b6-138bc0dcb076"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('conv_block_1.0.weight',\n",
              "              tensor([[[[ 0.2548,  0.2767, -0.0781],\n",
              "                        [ 0.3062, -0.0730,  0.0673],\n",
              "                        [-0.1623,  0.1958,  0.2938]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2445,  0.2897,  0.0624],\n",
              "                        [ 0.2463,  0.0451,  0.1607],\n",
              "                        [-0.0471,  0.2570,  0.0493]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1556,  0.0850, -0.1536],\n",
              "                        [-0.0391, -0.1354,  0.2211],\n",
              "                        [-0.2631, -0.1537, -0.0941]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2004,  0.0315, -0.3292],\n",
              "                        [ 0.3010, -0.2832,  0.2573],\n",
              "                        [ 0.0555, -0.1082,  0.2060]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0520,  0.2693,  0.0364],\n",
              "                        [-0.1051,  0.0896, -0.0904],\n",
              "                        [ 0.1403,  0.2976,  0.1927]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1457,  0.1924,  0.0596],\n",
              "                        [ 0.1693, -0.2032, -0.3300],\n",
              "                        [-0.1288, -0.2557,  0.2735]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0960,  0.1381,  0.1054],\n",
              "                        [-0.0058,  0.2609, -0.2368],\n",
              "                        [ 0.0210, -0.2275,  0.1028]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1148,  0.1021, -0.0694],\n",
              "                        [ 0.2765, -0.1976, -0.1988],\n",
              "                        [-0.1988,  0.2998,  0.1111]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.3208, -0.2751, -0.3306],\n",
              "                        [-0.2608, -0.2242,  0.1350],\n",
              "                        [ 0.1194,  0.2770, -0.1721]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.2272,  0.1769, -0.1347],\n",
              "                        [ 0.2023, -0.0791,  0.1907],\n",
              "                        [-0.2590, -0.1682,  0.1016]]]])),\n",
              "             ('conv_block_1.0.bias',\n",
              "              tensor([ 0.0705, -0.0850,  0.1987,  0.2266, -0.2417, -0.1780,  0.3052, -0.1125,\n",
              "                      -0.1182, -0.3225])),\n",
              "             ('conv_block_1.2.weight',\n",
              "              tensor([[[[-0.0604,  0.0263, -0.0139],\n",
              "                        [-0.0765,  0.0025, -0.0720],\n",
              "                        [-0.0894, -0.0580, -0.0923]],\n",
              "              \n",
              "                       [[-0.0671,  0.1054,  0.0199],\n",
              "                        [ 0.0325, -0.0983, -0.0692],\n",
              "                        [-0.0351,  0.0165, -0.0928]],\n",
              "              \n",
              "                       [[-0.0454, -0.0631,  0.0003],\n",
              "                        [-0.0392, -0.0073, -0.0714],\n",
              "                        [-0.0724, -0.0615, -0.0361]],\n",
              "              \n",
              "                       [[-0.0832,  0.0884, -0.0209],\n",
              "                        [ 0.0907,  0.0328, -0.0893],\n",
              "                        [ 0.0729, -0.0290, -0.0404]],\n",
              "              \n",
              "                       [[-0.0875, -0.1048,  0.0302],\n",
              "                        [-0.0230,  0.0410, -0.0865],\n",
              "                        [ 0.0783, -0.0774, -0.0182]],\n",
              "              \n",
              "                       [[ 0.0220,  0.0544,  0.0851],\n",
              "                        [ 0.0960, -0.0836,  0.0265],\n",
              "                        [-0.0453, -0.0116, -0.0789]],\n",
              "              \n",
              "                       [[ 0.0960, -0.0774,  0.0563],\n",
              "                        [ 0.0370,  0.0343, -0.0570],\n",
              "                        [ 0.0958,  0.0232,  0.0136]],\n",
              "              \n",
              "                       [[-0.0929,  0.0442, -0.0158],\n",
              "                        [-0.0483,  0.0905,  0.0235],\n",
              "                        [-0.0583, -0.0534, -0.0050]],\n",
              "              \n",
              "                       [[ 0.0589, -0.0269, -0.0601],\n",
              "                        [-0.0361, -0.0787,  0.0376],\n",
              "                        [ 0.0816, -0.0992,  0.0245]],\n",
              "              \n",
              "                       [[ 0.0545,  0.0191, -0.0375],\n",
              "                        [ 0.0550,  0.0554,  0.0394],\n",
              "                        [-0.0185, -0.0279,  0.0113]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0186, -0.0314,  0.0674],\n",
              "                        [ 0.0906, -0.0104, -0.0236],\n",
              "                        [ 0.0015, -0.0063,  0.0253]],\n",
              "              \n",
              "                       [[ 0.0295, -0.0957, -0.0389],\n",
              "                        [ 0.0888,  0.0411, -0.0052],\n",
              "                        [-0.0636, -0.0645, -0.0944]],\n",
              "              \n",
              "                       [[-0.0344,  0.0356,  0.0672],\n",
              "                        [ 0.0487, -0.0932, -0.0634],\n",
              "                        [-0.0166,  0.1020,  0.0152]],\n",
              "              \n",
              "                       [[-0.0273,  0.0436, -0.0401],\n",
              "                        [-0.0682,  0.0769, -0.0479],\n",
              "                        [-0.0211, -0.1049,  0.0705]],\n",
              "              \n",
              "                       [[ 0.0799,  0.0384, -0.0735],\n",
              "                        [-0.1040, -0.0856,  0.0786],\n",
              "                        [ 0.0506,  0.0887,  0.0552]],\n",
              "              \n",
              "                       [[ 0.0267, -0.0010, -0.0802],\n",
              "                        [-0.0903, -0.0986,  0.0432],\n",
              "                        [-0.0518, -0.0212, -0.0607]],\n",
              "              \n",
              "                       [[-0.0192, -0.0742, -0.0689],\n",
              "                        [ 0.0350, -0.0313,  0.0651],\n",
              "                        [-0.0338, -0.0773, -0.0186]],\n",
              "              \n",
              "                       [[-0.0511, -0.0322, -0.1003],\n",
              "                        [ 0.0590, -0.0734,  0.0530],\n",
              "                        [ 0.0478,  0.0753, -0.0809]],\n",
              "              \n",
              "                       [[ 0.0758, -0.0498,  0.0391],\n",
              "                        [ 0.0990, -0.0149, -0.0008],\n",
              "                        [-0.0243, -0.0880,  0.0506]],\n",
              "              \n",
              "                       [[-0.1046,  0.0654,  0.0789],\n",
              "                        [ 0.0997, -0.0249, -0.0866],\n",
              "                        [ 0.0237,  0.0582, -0.1049]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0239, -0.0632, -0.0092],\n",
              "                        [-0.0519, -0.0431, -0.0335],\n",
              "                        [-0.1002,  0.0865,  0.0884]],\n",
              "              \n",
              "                       [[-0.0165, -0.0120, -0.0430],\n",
              "                        [-0.0952, -0.1026,  0.0392],\n",
              "                        [-0.0579, -0.0678, -0.0082]],\n",
              "              \n",
              "                       [[-0.0351, -0.0341,  0.0034],\n",
              "                        [-0.0224, -0.0363, -0.0505],\n",
              "                        [-0.0858,  0.0884, -0.0422]],\n",
              "              \n",
              "                       [[ 0.0279, -0.0366,  0.0086],\n",
              "                        [ 0.0983,  0.0486, -0.0913],\n",
              "                        [ 0.0418,  0.1001,  0.0277]],\n",
              "              \n",
              "                       [[ 0.0707,  0.1039, -0.0162],\n",
              "                        [ 0.0219, -0.0733, -0.0217],\n",
              "                        [ 0.0781,  0.0540, -0.0667]],\n",
              "              \n",
              "                       [[-0.0845, -0.0720, -0.1040],\n",
              "                        [-0.0813, -0.0261,  0.0711],\n",
              "                        [ 0.0176, -0.0802, -0.0846]],\n",
              "              \n",
              "                       [[ 0.0524, -0.0784, -0.0130],\n",
              "                        [ 0.0506, -0.0488, -0.0115],\n",
              "                        [-0.0092, -0.0249, -0.0534]],\n",
              "              \n",
              "                       [[-0.0940, -0.0852, -0.0564],\n",
              "                        [ 0.1018, -0.0509, -0.0708],\n",
              "                        [ 0.0256,  0.0291,  0.0578]],\n",
              "              \n",
              "                       [[ 0.0801,  0.0587, -0.1045],\n",
              "                        [ 0.0093,  0.0639, -0.0097],\n",
              "                        [-0.0621,  0.1005, -0.0394]],\n",
              "              \n",
              "                       [[-0.0600, -0.0950,  0.0047],\n",
              "                        [ 0.0467,  0.0233,  0.0208],\n",
              "                        [-0.0799, -0.0984,  0.0019]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0961,  0.0608, -0.0614],\n",
              "                        [-0.0137, -0.0777, -0.0509],\n",
              "                        [ 0.0191,  0.0574,  0.0873]],\n",
              "              \n",
              "                       [[-0.0968,  0.0705, -0.0743],\n",
              "                        [ 0.0395,  0.0892,  0.0015],\n",
              "                        [ 0.0959, -0.0898, -0.0403]],\n",
              "              \n",
              "                       [[ 0.0615, -0.0230, -0.0216],\n",
              "                        [-0.0439,  0.0727,  0.0517],\n",
              "                        [ 0.0338, -0.0592, -0.0856]],\n",
              "              \n",
              "                       [[ 0.0114,  0.0312, -0.0487],\n",
              "                        [-0.0295,  0.0712,  0.0084],\n",
              "                        [ 0.0048, -0.0259, -0.0955]],\n",
              "              \n",
              "                       [[-0.0991, -0.0504, -0.0536],\n",
              "                        [ 0.0328, -0.0307, -0.0412],\n",
              "                        [ 0.1005,  0.0367,  0.0751]],\n",
              "              \n",
              "                       [[-0.0510, -0.0431,  0.0387],\n",
              "                        [-0.0702, -0.0689, -0.0051],\n",
              "                        [-0.0386, -0.0790,  0.0625]],\n",
              "              \n",
              "                       [[ 0.0848,  0.0171, -0.0184],\n",
              "                        [-0.0976, -0.0384,  0.0268],\n",
              "                        [ 0.0497, -0.0133, -0.0417]],\n",
              "              \n",
              "                       [[ 0.0587, -0.0839,  0.0666],\n",
              "                        [-0.0409,  0.0016, -0.0208],\n",
              "                        [ 0.0128, -0.0319,  0.0766]],\n",
              "              \n",
              "                       [[-0.0027,  0.0823,  0.1013],\n",
              "                        [-0.0514, -0.0769,  0.0846],\n",
              "                        [ 0.0826, -0.0805, -0.0081]],\n",
              "              \n",
              "                       [[-0.1039, -0.0863,  0.0204],\n",
              "                        [ 0.0280,  0.0223, -0.0287],\n",
              "                        [ 0.0972,  0.0151, -0.0622]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0060,  0.0253,  0.0369],\n",
              "                        [-0.0745,  0.0395, -0.0539],\n",
              "                        [-0.0876, -0.0576,  0.1017]],\n",
              "              \n",
              "                       [[ 0.0901,  0.0944,  0.0619],\n",
              "                        [ 0.0796, -0.0141, -0.0580],\n",
              "                        [ 0.0527, -0.0546, -0.0711]],\n",
              "              \n",
              "                       [[-0.0337,  0.0221,  0.0543],\n",
              "                        [-0.0409, -0.0620,  0.0142],\n",
              "                        [-0.0621, -0.0686,  0.0549]],\n",
              "              \n",
              "                       [[-0.0177,  0.0963,  0.1025],\n",
              "                        [ 0.0315,  0.0363,  0.0243],\n",
              "                        [ 0.0017, -0.0077,  0.0014]],\n",
              "              \n",
              "                       [[ 0.0394,  0.0980, -0.0273],\n",
              "                        [-0.0446, -0.0255, -0.0509],\n",
              "                        [ 0.0179,  0.0787,  0.0824]],\n",
              "              \n",
              "                       [[ 0.0484, -0.0776, -0.0566],\n",
              "                        [-0.0232, -0.0194,  0.0087],\n",
              "                        [-0.0968,  0.0328, -0.0804]],\n",
              "              \n",
              "                       [[-0.0667, -0.0876,  0.0918],\n",
              "                        [-0.0998,  0.0795, -0.0035],\n",
              "                        [-0.0123,  0.0659, -0.0097]],\n",
              "              \n",
              "                       [[ 0.0661,  0.0762, -0.0915],\n",
              "                        [ 0.0406,  0.0199,  0.0227],\n",
              "                        [ 0.0154,  0.0288, -0.0507]],\n",
              "              \n",
              "                       [[-0.0135,  0.1002,  0.0708],\n",
              "                        [-0.0040, -0.0991,  0.0046],\n",
              "                        [-0.0718,  0.0857, -0.0640]],\n",
              "              \n",
              "                       [[-0.0076, -0.0234,  0.0188],\n",
              "                        [ 0.0992,  0.0100,  0.0610],\n",
              "                        [ 0.0818,  0.0851, -0.0364]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0236,  0.0508, -0.0288],\n",
              "                        [ 0.0494, -0.0230, -0.0715],\n",
              "                        [ 0.0429,  0.0162,  0.0470]],\n",
              "              \n",
              "                       [[ 0.1047,  0.0720,  0.0999],\n",
              "                        [ 0.0056, -0.0907, -0.0739],\n",
              "                        [-0.0655, -0.0929, -0.0528]],\n",
              "              \n",
              "                       [[-0.0970, -0.0973, -0.0630],\n",
              "                        [-0.1039, -0.0647,  0.0402],\n",
              "                        [ 0.0879, -0.0314, -0.0307]],\n",
              "              \n",
              "                       [[ 0.0563, -0.0520, -0.0498],\n",
              "                        [ 0.0649, -0.0918,  0.0129],\n",
              "                        [ 0.0931,  0.0181,  0.0287]],\n",
              "              \n",
              "                       [[-0.0614, -0.0015,  0.0058],\n",
              "                        [ 0.0259,  0.0410,  0.0916],\n",
              "                        [-0.0805,  0.0032, -0.0527]],\n",
              "              \n",
              "                       [[-0.0834, -0.0084, -0.0928],\n",
              "                        [ 0.0736,  0.0122, -0.0568],\n",
              "                        [ 0.0551, -0.0998, -0.0408]],\n",
              "              \n",
              "                       [[-0.0205, -0.0896, -0.0670],\n",
              "                        [-0.0172,  0.0800,  0.1018],\n",
              "                        [ 0.0671, -0.0629, -0.0690]],\n",
              "              \n",
              "                       [[ 0.0920,  0.0373,  0.0028],\n",
              "                        [ 0.0143, -0.0847, -0.0352],\n",
              "                        [ 0.1015, -0.0260, -0.0053]],\n",
              "              \n",
              "                       [[-0.0875, -0.0590, -0.0022],\n",
              "                        [-0.0655, -0.0131,  0.0429],\n",
              "                        [-0.1031,  0.0313, -0.0697]],\n",
              "              \n",
              "                       [[-0.0514,  0.0405,  0.0838],\n",
              "                        [-0.0288, -0.0433, -0.0953],\n",
              "                        [-0.0544, -0.0923, -0.0241]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0215, -0.0988,  0.0920],\n",
              "                        [ 0.0661, -0.1032, -0.0503],\n",
              "                        [ 0.0344, -0.0217, -0.0115]],\n",
              "              \n",
              "                       [[-0.0476,  0.0847, -0.0589],\n",
              "                        [ 0.0874,  0.0068,  0.0212],\n",
              "                        [ 0.0822, -0.0174, -0.0600]],\n",
              "              \n",
              "                       [[-0.0170,  0.0855, -0.0782],\n",
              "                        [ 0.0239, -0.1036,  0.0553],\n",
              "                        [ 0.0389,  0.0045,  0.0452]],\n",
              "              \n",
              "                       [[ 0.0001,  0.0583, -0.0834],\n",
              "                        [-0.0155,  0.0468,  0.1050],\n",
              "                        [ 0.0537, -0.0767,  0.0811]],\n",
              "              \n",
              "                       [[-0.0235, -0.0225, -0.0958],\n",
              "                        [-0.0166,  0.0746,  0.0147],\n",
              "                        [-0.0614,  0.0324, -0.0338]],\n",
              "              \n",
              "                       [[ 0.0962, -0.0915, -0.0333],\n",
              "                        [-0.1018, -0.0415,  0.0332],\n",
              "                        [ 0.1015,  0.0177,  0.1033]],\n",
              "              \n",
              "                       [[ 0.0206,  0.0609,  0.0845],\n",
              "                        [ 0.0881, -0.0590,  0.0969],\n",
              "                        [ 0.0639, -0.0493, -0.0503]],\n",
              "              \n",
              "                       [[-0.0884,  0.0265, -0.0854],\n",
              "                        [ 0.0445,  0.0333, -0.0916],\n",
              "                        [ 0.0287, -0.0086,  0.0482]],\n",
              "              \n",
              "                       [[ 0.0605, -0.1048,  0.0967],\n",
              "                        [ 0.0884,  0.0419, -0.0963],\n",
              "                        [-0.0377, -0.0305, -0.0271]],\n",
              "              \n",
              "                       [[ 0.0594,  0.0383,  0.0835],\n",
              "                        [-0.0395,  0.0355,  0.0375],\n",
              "                        [-0.0878, -0.1022, -0.0547]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0722, -0.0992, -0.0918],\n",
              "                        [ 0.0591,  0.0569,  0.0867],\n",
              "                        [-0.0796, -0.0771,  0.0541]],\n",
              "              \n",
              "                       [[ 0.0917,  0.0631,  0.0165],\n",
              "                        [ 0.0347,  0.1000, -0.0680],\n",
              "                        [-0.0479,  0.0737, -0.0721]],\n",
              "              \n",
              "                       [[-0.0581,  0.0769,  0.0333],\n",
              "                        [ 0.0341, -0.0447, -0.0015],\n",
              "                        [ 0.0965, -0.0633,  0.0008]],\n",
              "              \n",
              "                       [[ 0.0501, -0.0728,  0.1024],\n",
              "                        [-0.0527, -0.0253, -0.0285],\n",
              "                        [-0.0687, -0.1034,  0.0594]],\n",
              "              \n",
              "                       [[ 0.0280, -0.0987, -0.0678],\n",
              "                        [ 0.1042,  0.0403,  0.0423],\n",
              "                        [-0.0631, -0.0462, -0.0159]],\n",
              "              \n",
              "                       [[-0.0193, -0.0722,  0.0087],\n",
              "                        [ 0.0105, -0.0133,  0.0146],\n",
              "                        [-0.0418,  0.0274,  0.0398]],\n",
              "              \n",
              "                       [[-0.0555, -0.1045,  0.0552],\n",
              "                        [ 0.0251, -0.0536,  0.1016],\n",
              "                        [-0.0477,  0.0712,  0.0535]],\n",
              "              \n",
              "                       [[-0.0884,  0.0680, -0.0969],\n",
              "                        [-0.0584, -0.0176, -0.0711],\n",
              "                        [ 0.1030, -0.0211,  0.0419]],\n",
              "              \n",
              "                       [[-0.0941,  0.0607, -0.0328],\n",
              "                        [-0.0802,  0.0154,  0.0511],\n",
              "                        [ 0.0912, -0.0644, -0.0519]],\n",
              "              \n",
              "                       [[ 0.0203,  0.0286,  0.0405],\n",
              "                        [ 0.0579, -0.0239,  0.0586],\n",
              "                        [ 0.0777, -0.0275,  0.0750]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0515,  0.0930, -0.0599],\n",
              "                        [-0.0521, -0.0305,  0.0053],\n",
              "                        [ 0.0633, -0.0602,  0.0528]],\n",
              "              \n",
              "                       [[-0.0378,  0.0637, -0.0050],\n",
              "                        [-0.0923, -0.0580, -0.0763],\n",
              "                        [ 0.0523, -0.0707, -0.0088]],\n",
              "              \n",
              "                       [[ 0.0227, -0.0578,  0.0304],\n",
              "                        [-0.1029, -0.0754, -0.0955],\n",
              "                        [-0.0319, -0.0384,  0.0151]],\n",
              "              \n",
              "                       [[-0.0195,  0.0496,  0.0966],\n",
              "                        [ 0.0378, -0.0415, -0.0987],\n",
              "                        [ 0.0382, -0.0522,  0.0536]],\n",
              "              \n",
              "                       [[ 0.0705,  0.0407,  0.0989],\n",
              "                        [ 0.1001,  0.0223, -0.0768],\n",
              "                        [ 0.0942, -0.0500, -0.0498]],\n",
              "              \n",
              "                       [[ 0.0882,  0.0817,  0.0318],\n",
              "                        [ 0.0066, -0.0887, -0.0109],\n",
              "                        [ 0.1011,  0.0268,  0.0090]],\n",
              "              \n",
              "                       [[-0.0219, -0.0368,  0.0628],\n",
              "                        [ 0.0065,  0.0686, -0.0187],\n",
              "                        [ 0.0461,  0.0435,  0.0168]],\n",
              "              \n",
              "                       [[ 0.0662,  0.0661,  0.0977],\n",
              "                        [ 0.0810, -0.0270, -0.0892],\n",
              "                        [ 0.0193, -0.0009, -0.0275]],\n",
              "              \n",
              "                       [[-0.0177,  0.0050,  0.0769],\n",
              "                        [ 0.0329, -0.0374, -0.0433],\n",
              "                        [-0.0261, -0.0407,  0.0948]],\n",
              "              \n",
              "                       [[ 0.0558,  0.0952,  0.0003],\n",
              "                        [ 0.0213,  0.0366, -0.0998],\n",
              "                        [ 0.0094, -0.0071, -0.0591]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0818,  0.0933,  0.0857],\n",
              "                        [ 0.0489,  0.1006, -0.0428],\n",
              "                        [-0.0182,  0.0399, -0.0174]],\n",
              "              \n",
              "                       [[-0.0207, -0.0871,  0.0283],\n",
              "                        [-0.0637,  0.0038,  0.1028],\n",
              "                        [-0.0324, -0.0332,  0.0636]],\n",
              "              \n",
              "                       [[-0.0388, -0.0091,  0.0984],\n",
              "                        [-0.0432, -0.0754, -0.0590],\n",
              "                        [-0.0292, -0.0500, -0.0547]],\n",
              "              \n",
              "                       [[ 0.0426,  0.0179, -0.0337],\n",
              "                        [-0.0819, -0.0332, -0.0445],\n",
              "                        [-0.0343, -0.0951,  0.0227]],\n",
              "              \n",
              "                       [[-0.0774, -0.0821, -0.0861],\n",
              "                        [ 0.0440, -0.0635, -0.0435],\n",
              "                        [ 0.0826,  0.0560,  0.0604]],\n",
              "              \n",
              "                       [[-0.1001, -0.0756, -0.0398],\n",
              "                        [ 0.0871,  0.0108, -0.0788],\n",
              "                        [ 0.0007, -0.0819, -0.0231]],\n",
              "              \n",
              "                       [[-0.0290,  0.0912,  0.0326],\n",
              "                        [-0.0184,  0.0178, -0.0304],\n",
              "                        [ 0.0414,  0.0417,  0.0283]],\n",
              "              \n",
              "                       [[-0.0411,  0.0899, -0.0152],\n",
              "                        [-0.0410,  0.0660,  0.0859],\n",
              "                        [ 0.1049,  0.0312, -0.0359]],\n",
              "              \n",
              "                       [[ 0.0535,  0.0904, -0.1034],\n",
              "                        [-0.0131, -0.0719,  0.0196],\n",
              "                        [ 0.0436, -0.0218, -0.0088]],\n",
              "              \n",
              "                       [[ 0.0474, -0.0177, -0.0885],\n",
              "                        [ 0.0843, -0.0531, -0.0116],\n",
              "                        [ 0.0099, -0.0063, -0.0992]]]])),\n",
              "             ('conv_block_1.2.bias',\n",
              "              tensor([ 0.0484, -0.0479, -0.0547,  0.0252, -0.0550, -0.0487, -0.0355, -0.0396,\n",
              "                      -0.0440, -0.0284])),\n",
              "             ('conv_block_2.0.weight',\n",
              "              tensor([[[[ 2.7393e-02, -8.5299e-02, -6.3802e-02],\n",
              "                        [ 1.5381e-03,  1.4659e-02,  5.8217e-02],\n",
              "                        [-7.4044e-02,  3.3646e-02,  5.9914e-02]],\n",
              "              \n",
              "                       [[ 5.8530e-02, -9.8180e-02, -4.0225e-02],\n",
              "                        [-9.0606e-02, -6.6704e-02,  5.8711e-02],\n",
              "                        [-1.5740e-02,  4.4769e-02, -6.1876e-02]],\n",
              "              \n",
              "                       [[ 1.6018e-02, -6.3758e-02,  5.2693e-02],\n",
              "                        [-4.6104e-02, -2.6432e-02, -9.1456e-02],\n",
              "                        [ 3.4822e-04,  1.0008e-01,  5.1163e-02]],\n",
              "              \n",
              "                       [[-5.6240e-02,  1.4176e-03, -1.1558e-02],\n",
              "                        [-8.4862e-02,  8.2650e-02,  1.6993e-03],\n",
              "                        [ 2.2199e-02, -4.2567e-02, -4.9323e-02]],\n",
              "              \n",
              "                       [[ 1.7381e-02,  3.8971e-02,  2.3643e-02],\n",
              "                        [-5.0801e-02,  1.0234e-01, -1.5517e-02],\n",
              "                        [-6.4554e-02, -4.9301e-02,  1.0377e-01]],\n",
              "              \n",
              "                       [[ 5.0766e-06, -1.4309e-02, -4.3867e-02],\n",
              "                        [-2.7633e-02, -8.8779e-02, -8.3767e-02],\n",
              "                        [ 6.1695e-02,  9.0172e-02,  1.0059e-01]],\n",
              "              \n",
              "                       [[-7.6099e-02,  5.7012e-02, -6.5245e-02],\n",
              "                        [ 6.2883e-02,  7.6058e-02,  8.1573e-02],\n",
              "                        [ 7.5900e-02,  6.5941e-02,  2.0516e-03]],\n",
              "              \n",
              "                       [[ 4.8434e-02, -3.7712e-02,  4.5899e-02],\n",
              "                        [-3.3879e-02, -1.7700e-03, -9.1746e-02],\n",
              "                        [-2.7562e-02, -5.5432e-02, -3.5557e-02]],\n",
              "              \n",
              "                       [[-6.7313e-02, -9.4810e-02,  6.8639e-03],\n",
              "                        [ 6.8408e-02,  9.6001e-02,  6.1512e-02],\n",
              "                        [-5.4638e-02, -1.0425e-01,  3.9983e-02]],\n",
              "              \n",
              "                       [[ 5.9062e-02, -9.0495e-02,  3.7798e-02],\n",
              "                        [ 8.9121e-02,  6.3853e-03, -6.3505e-02],\n",
              "                        [ 8.6423e-02,  4.5011e-02,  6.9802e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-7.1287e-02,  6.1342e-02, -7.2002e-02],\n",
              "                        [ 1.0430e-01, -4.4662e-02,  6.3516e-02],\n",
              "                        [ 2.1107e-02,  2.7935e-02, -1.6165e-02]],\n",
              "              \n",
              "                       [[ 4.3295e-02, -4.3932e-02, -9.9357e-02],\n",
              "                        [-4.0499e-02,  8.2592e-02, -2.7751e-02],\n",
              "                        [ 3.3132e-02, -3.8973e-02,  7.9073e-02]],\n",
              "              \n",
              "                       [[ 6.3086e-02,  3.7211e-02, -5.3881e-02],\n",
              "                        [-8.6133e-02,  3.9686e-03, -6.1839e-02],\n",
              "                        [ 8.6667e-02, -1.0130e-01,  4.7104e-02]],\n",
              "              \n",
              "                       [[ 1.0508e-01,  5.2792e-02,  3.5942e-02],\n",
              "                        [-1.0142e-01,  1.0139e-01, -1.8030e-02],\n",
              "                        [-9.8495e-02,  1.0406e-01, -4.2894e-02]],\n",
              "              \n",
              "                       [[-7.4575e-03,  9.6479e-02, -7.3070e-02],\n",
              "                        [-7.4576e-02,  1.7141e-02, -1.4109e-02],\n",
              "                        [ 2.4280e-02, -8.8407e-02,  3.1524e-03]],\n",
              "              \n",
              "                       [[-4.6882e-02, -5.1820e-02, -9.6517e-02],\n",
              "                        [ 5.5890e-02,  2.0306e-02, -8.9118e-02],\n",
              "                        [ 8.3648e-02,  3.1794e-02,  1.9560e-02]],\n",
              "              \n",
              "                       [[-6.1890e-02,  1.5896e-02,  1.0157e-01],\n",
              "                        [ 7.2299e-02, -8.2100e-02,  9.6220e-02],\n",
              "                        [ 8.1702e-03,  5.0698e-02,  8.1869e-02]],\n",
              "              \n",
              "                       [[ 8.9862e-02, -8.2170e-02,  9.2303e-02],\n",
              "                        [-7.1591e-02,  7.9021e-03, -7.3656e-02],\n",
              "                        [-2.3109e-02, -4.7901e-03, -1.2611e-02]],\n",
              "              \n",
              "                       [[-1.6652e-02,  8.3137e-03,  1.0398e-01],\n",
              "                        [ 6.1244e-02,  5.8973e-02,  4.2190e-02],\n",
              "                        [ 8.1606e-02, -4.8645e-03,  8.3813e-03]],\n",
              "              \n",
              "                       [[ 2.1693e-02, -9.1931e-02, -8.4913e-02],\n",
              "                        [ 1.2923e-02, -4.1241e-02, -1.9342e-03],\n",
              "                        [-2.4187e-02,  1.6408e-02,  6.8581e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.4958e-02,  8.4418e-02,  8.3227e-02],\n",
              "                        [-8.0901e-02, -8.1400e-02, -8.5284e-02],\n",
              "                        [-5.7766e-02, -4.1033e-02, -7.9341e-03]],\n",
              "              \n",
              "                       [[-2.5635e-02, -5.3258e-02, -3.3488e-02],\n",
              "                        [-3.8131e-02,  1.0341e-01, -3.9068e-02],\n",
              "                        [-7.5473e-02,  4.3818e-02, -6.0886e-03]],\n",
              "              \n",
              "                       [[ 8.0698e-02,  6.5863e-02,  9.6843e-02],\n",
              "                        [-7.7197e-02,  6.7764e-02,  8.8464e-02],\n",
              "                        [-5.2054e-02,  9.6890e-02,  7.9019e-02]],\n",
              "              \n",
              "                       [[ 1.1544e-03,  5.0823e-02, -3.6853e-02],\n",
              "                        [-9.1936e-02,  2.6645e-02,  3.1425e-02],\n",
              "                        [-6.8891e-02,  5.1123e-02, -9.0043e-02]],\n",
              "              \n",
              "                       [[ 9.0718e-02,  1.0208e-01,  2.8699e-02],\n",
              "                        [-6.6137e-02,  5.1300e-02,  1.7963e-02],\n",
              "                        [ 2.8663e-02,  3.4643e-02,  8.0254e-02]],\n",
              "              \n",
              "                       [[-4.5309e-02, -2.3711e-02,  2.8746e-02],\n",
              "                        [ 1.1486e-02,  8.5000e-02, -5.5365e-02],\n",
              "                        [-3.8387e-03,  1.9696e-02, -2.7996e-02]],\n",
              "              \n",
              "                       [[ 7.1859e-02,  1.1530e-02, -9.7422e-02],\n",
              "                        [-1.1420e-02, -4.7809e-02,  1.0243e-02],\n",
              "                        [-1.2250e-02, -1.0456e-01, -1.9208e-02]],\n",
              "              \n",
              "                       [[-1.0096e-02, -3.1083e-02,  9.6848e-02],\n",
              "                        [-2.3000e-02,  6.7717e-02,  2.6112e-02],\n",
              "                        [-8.8979e-02,  2.4770e-02,  8.7356e-02]],\n",
              "              \n",
              "                       [[-6.8948e-02, -6.8134e-02,  1.0318e-01],\n",
              "                        [ 8.4697e-02, -5.8807e-02,  6.3429e-02],\n",
              "                        [-1.3485e-02, -1.0393e-01,  7.9198e-03]],\n",
              "              \n",
              "                       [[ 3.4057e-02, -3.1619e-02,  3.6670e-02],\n",
              "                        [-9.0136e-02,  7.3050e-02,  8.9865e-02],\n",
              "                        [ 5.8130e-02,  1.7866e-02,  3.4716e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-7.6269e-02, -2.6339e-02, -1.0063e-02],\n",
              "                        [-5.8659e-02, -7.7857e-02,  7.0900e-02],\n",
              "                        [ 7.1535e-02, -9.5731e-02,  3.3542e-02]],\n",
              "              \n",
              "                       [[ 4.2881e-02,  1.0014e-01,  6.0985e-02],\n",
              "                        [ 9.6907e-02, -3.4510e-02,  7.3827e-02],\n",
              "                        [ 8.5740e-02, -9.9541e-02, -8.4613e-02]],\n",
              "              \n",
              "                       [[ 2.1335e-02,  5.7557e-02, -5.2369e-02],\n",
              "                        [ 1.1609e-02, -1.5303e-04,  2.6680e-02],\n",
              "                        [-5.6642e-02,  5.9455e-02,  7.0098e-02]],\n",
              "              \n",
              "                       [[-7.3139e-02,  1.0211e-03,  2.9247e-04],\n",
              "                        [ 3.3849e-02,  9.8198e-02,  3.0913e-02],\n",
              "                        [-2.3951e-02,  9.4672e-02, -4.0112e-02]],\n",
              "              \n",
              "                       [[-3.0608e-02,  7.1969e-03, -8.0270e-02],\n",
              "                        [ 1.1470e-02, -7.1518e-02,  1.0838e-02],\n",
              "                        [ 1.0099e-02,  1.4591e-02, -8.8891e-02]],\n",
              "              \n",
              "                       [[-1.0012e-01,  4.8501e-02,  9.0399e-02],\n",
              "                        [-9.3537e-02,  3.9043e-02, -7.7594e-02],\n",
              "                        [ 6.6082e-03,  9.8068e-02,  7.9965e-02]],\n",
              "              \n",
              "                       [[-7.7069e-02,  6.5203e-02,  5.5057e-02],\n",
              "                        [-1.6168e-04,  1.0211e-01, -4.1866e-02],\n",
              "                        [-2.4530e-02, -5.3275e-02,  1.5168e-02]],\n",
              "              \n",
              "                       [[ 2.7911e-02,  8.3990e-03, -5.9307e-02],\n",
              "                        [-4.7452e-02,  3.5855e-02, -9.2426e-02],\n",
              "                        [-1.6416e-02, -2.3350e-03, -4.2708e-02]],\n",
              "              \n",
              "                       [[ 3.8360e-02,  6.7940e-03,  7.4004e-02],\n",
              "                        [-9.3616e-03, -6.6528e-02,  7.4477e-02],\n",
              "                        [ 1.4720e-02, -3.0189e-02, -6.9476e-02]],\n",
              "              \n",
              "                       [[ 2.4707e-02, -1.0053e-01,  2.7762e-02],\n",
              "                        [ 5.2119e-02, -9.2465e-02, -6.9009e-02],\n",
              "                        [-7.5781e-02,  8.8597e-02,  8.9611e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.5987e-03,  9.8959e-02, -3.5239e-02],\n",
              "                        [-1.0233e-01,  3.6819e-02,  3.7343e-02],\n",
              "                        [ 1.0334e-01, -3.0510e-05,  8.0785e-02]],\n",
              "              \n",
              "                       [[ 6.4612e-02,  7.6292e-02, -1.0460e-01],\n",
              "                        [ 8.6800e-02, -8.9856e-02,  9.4501e-02],\n",
              "                        [-4.3682e-03, -9.3415e-02,  2.9314e-02]],\n",
              "              \n",
              "                       [[-2.1456e-02, -9.4678e-02, -3.8215e-02],\n",
              "                        [ 1.0868e-02,  8.2098e-02, -3.2406e-02],\n",
              "                        [ 6.2610e-02,  1.3200e-02,  3.5531e-03]],\n",
              "              \n",
              "                       [[ 2.0170e-02, -6.9177e-02, -8.7616e-02],\n",
              "                        [-3.3121e-02, -9.8226e-02, -4.9158e-02],\n",
              "                        [ 4.8494e-03, -6.9424e-02, -4.3723e-02]],\n",
              "              \n",
              "                       [[-1.8941e-02, -1.2144e-02, -5.8187e-02],\n",
              "                        [ 5.0650e-03, -1.4795e-02,  3.0147e-02],\n",
              "                        [ 4.7611e-03, -5.2638e-02, -3.6291e-02]],\n",
              "              \n",
              "                       [[-1.2149e-03, -6.5774e-02,  8.2520e-03],\n",
              "                        [-7.4425e-03,  4.0897e-02,  2.4947e-02],\n",
              "                        [ 7.8887e-02, -3.4749e-03, -7.7887e-02]],\n",
              "              \n",
              "                       [[ 4.7119e-02, -7.1240e-02, -1.4489e-02],\n",
              "                        [-3.4132e-02, -3.9997e-02, -3.9000e-02],\n",
              "                        [ 9.6863e-02,  6.0342e-02,  2.9213e-02]],\n",
              "              \n",
              "                       [[ 9.8975e-02, -9.5524e-02,  1.7010e-02],\n",
              "                        [ 6.7481e-02,  7.0022e-02, -8.3890e-02],\n",
              "                        [ 3.7514e-02, -6.0050e-02, -4.1187e-03]],\n",
              "              \n",
              "                       [[-2.1996e-02, -8.8013e-02, -1.0055e-01],\n",
              "                        [-6.9349e-02,  4.7832e-02,  4.8218e-02],\n",
              "                        [-9.1681e-02, -3.9586e-02,  1.7218e-03]],\n",
              "              \n",
              "                       [[-9.1135e-02,  5.9393e-02,  9.5473e-02],\n",
              "                        [ 1.8643e-02, -7.8321e-02,  2.4580e-02],\n",
              "                        [ 3.8265e-02,  8.3468e-02, -5.6085e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-9.4437e-02,  4.6312e-02,  6.5624e-03],\n",
              "                        [-3.4345e-02, -4.4169e-02, -5.4351e-02],\n",
              "                        [ 8.5328e-02, -1.8187e-02,  7.6022e-02]],\n",
              "              \n",
              "                       [[ 9.4094e-02,  1.3353e-02,  2.2454e-02],\n",
              "                        [-7.1789e-03,  7.2397e-02, -9.4983e-02],\n",
              "                        [ 4.1919e-02, -1.7174e-02,  4.8132e-02]],\n",
              "              \n",
              "                       [[-4.6949e-04, -3.9029e-02, -1.1379e-02],\n",
              "                        [ 5.6920e-02, -7.3210e-02, -6.6629e-02],\n",
              "                        [-2.3611e-02, -3.8235e-02,  4.1409e-02]],\n",
              "              \n",
              "                       [[ 7.0937e-02, -1.1289e-02,  9.9672e-02],\n",
              "                        [-4.4042e-02, -5.9151e-02, -4.7191e-02],\n",
              "                        [-7.2624e-02, -7.3885e-02, -9.3921e-02]],\n",
              "              \n",
              "                       [[-9.3422e-02,  2.7512e-02,  6.4284e-02],\n",
              "                        [ 9.8963e-02,  8.9787e-02, -6.0709e-03],\n",
              "                        [ 2.0454e-02, -6.3068e-02,  4.0743e-02]],\n",
              "              \n",
              "                       [[-1.0107e-01,  4.9719e-02,  1.9334e-02],\n",
              "                        [ 3.2393e-02,  3.8595e-02, -4.8394e-02],\n",
              "                        [ 9.0452e-02,  5.0307e-02,  6.9243e-02]],\n",
              "              \n",
              "                       [[ 1.3922e-02,  6.6196e-02,  7.0941e-02],\n",
              "                        [ 4.7775e-02,  8.0297e-02, -1.9119e-02],\n",
              "                        [ 6.9310e-02,  2.4286e-02,  6.3424e-02]],\n",
              "              \n",
              "                       [[ 1.0267e-01,  2.3869e-02, -3.9124e-02],\n",
              "                        [-1.0488e-02,  2.9676e-02,  1.7773e-02],\n",
              "                        [-2.8795e-02,  8.2590e-02,  6.3331e-02]],\n",
              "              \n",
              "                       [[-6.5475e-02, -8.5889e-03, -1.0119e-02],\n",
              "                        [-6.6063e-02,  1.5374e-02, -3.2360e-02],\n",
              "                        [-5.4419e-02, -3.3894e-02, -3.7584e-02]],\n",
              "              \n",
              "                       [[ 1.0084e-01,  4.0432e-02,  1.0373e-01],\n",
              "                        [ 2.8903e-02,  2.3868e-02,  4.3333e-02],\n",
              "                        [ 1.8092e-02, -8.2722e-02, -6.2334e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.5538e-02,  1.5846e-03,  3.9709e-02],\n",
              "                        [ 4.0588e-02,  8.3623e-02,  2.1458e-02],\n",
              "                        [-3.5975e-02, -7.9271e-02, -7.7203e-02]],\n",
              "              \n",
              "                       [[-6.2965e-02,  3.1792e-02,  5.6950e-02],\n",
              "                        [ 9.2224e-02, -3.3342e-02, -8.3150e-03],\n",
              "                        [-3.1303e-02, -3.8517e-04,  3.3837e-02]],\n",
              "              \n",
              "                       [[-2.3160e-03,  4.8799e-03,  1.3354e-02],\n",
              "                        [ 3.9256e-02, -3.1981e-02, -6.2855e-02],\n",
              "                        [ 2.4869e-02, -1.2481e-02, -4.7753e-02]],\n",
              "              \n",
              "                       [[ 4.4268e-02,  9.5597e-04, -1.5333e-02],\n",
              "                        [-5.1027e-02, -1.3868e-02, -8.9632e-02],\n",
              "                        [ 2.3980e-02,  1.5818e-03,  6.3966e-02]],\n",
              "              \n",
              "                       [[ 6.8063e-03,  8.4277e-03,  2.8715e-02],\n",
              "                        [ 8.0210e-02, -4.9812e-02,  6.2930e-02],\n",
              "                        [ 2.5779e-02, -7.0320e-02,  3.6702e-02]],\n",
              "              \n",
              "                       [[-6.3217e-02, -3.3181e-02, -5.0245e-02],\n",
              "                        [-7.1711e-02,  8.3017e-02, -9.4217e-02],\n",
              "                        [ 5.2706e-02, -9.4870e-02, -1.2829e-02]],\n",
              "              \n",
              "                       [[ 6.2868e-03,  7.4937e-02, -3.8147e-02],\n",
              "                        [ 3.0340e-02,  1.6329e-02,  6.2021e-02],\n",
              "                        [ 6.2667e-03,  3.9470e-02, -6.3677e-02]],\n",
              "              \n",
              "                       [[-7.3250e-02,  9.3928e-02, -7.6808e-02],\n",
              "                        [-1.7945e-02, -1.2742e-02,  1.0308e-01],\n",
              "                        [-2.2780e-02, -8.0249e-02, -2.6721e-02]],\n",
              "              \n",
              "                       [[ 5.4372e-02,  4.1773e-02,  8.7204e-02],\n",
              "                        [-2.1579e-02,  4.9653e-02, -9.9194e-02],\n",
              "                        [ 4.0787e-02,  4.8432e-02,  6.7998e-02]],\n",
              "              \n",
              "                       [[-6.0446e-02, -2.8142e-02,  2.5502e-02],\n",
              "                        [-7.4905e-02, -8.3851e-02, -1.0141e-01],\n",
              "                        [ 5.8842e-03,  6.5458e-02,  2.7075e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.4263e-03,  3.6727e-02, -6.6240e-02],\n",
              "                        [ 1.1113e-02, -2.6186e-02, -5.2193e-02],\n",
              "                        [ 9.0902e-02, -8.1550e-02,  1.5448e-02]],\n",
              "              \n",
              "                       [[-9.2624e-02, -3.5762e-03, -4.6840e-02],\n",
              "                        [ 3.4695e-02, -5.9191e-02,  6.7466e-02],\n",
              "                        [-8.5536e-02,  6.3313e-02, -7.9181e-02]],\n",
              "              \n",
              "                       [[ 5.6456e-02, -4.4384e-02, -2.4556e-04],\n",
              "                        [-1.9238e-02,  6.8414e-02,  3.4546e-02],\n",
              "                        [-9.2887e-02,  9.6914e-03, -7.2718e-02]],\n",
              "              \n",
              "                       [[ 7.8800e-02,  1.7319e-02, -2.7109e-02],\n",
              "                        [-5.3777e-02,  3.6485e-02, -6.3129e-02],\n",
              "                        [ 4.9992e-02,  5.7519e-02,  6.4701e-02]],\n",
              "              \n",
              "                       [[ 2.7537e-02, -9.2272e-02,  7.5823e-02],\n",
              "                        [-3.2700e-02, -3.1163e-02, -1.1325e-02],\n",
              "                        [ 7.7068e-02,  8.1052e-02,  1.6276e-02]],\n",
              "              \n",
              "                       [[ 5.0296e-02, -9.8241e-02,  2.4900e-04],\n",
              "                        [-9.3254e-02,  3.5876e-02, -7.5099e-02],\n",
              "                        [-3.7568e-02,  7.3684e-02,  1.0074e-01]],\n",
              "              \n",
              "                       [[-6.3286e-02, -5.8503e-02,  1.3055e-02],\n",
              "                        [ 4.1437e-02, -1.7168e-02, -3.2918e-02],\n",
              "                        [-6.9237e-02,  4.4997e-02,  1.0328e-01]],\n",
              "              \n",
              "                       [[-5.1026e-02,  4.9718e-02,  5.1481e-02],\n",
              "                        [ 8.4728e-02, -1.2001e-02,  3.3202e-03],\n",
              "                        [ 7.7444e-02,  6.6631e-02,  1.0411e-01]],\n",
              "              \n",
              "                       [[-3.0207e-02,  4.1709e-02,  7.3605e-02],\n",
              "                        [-7.1553e-02,  2.0940e-02, -2.3586e-02],\n",
              "                        [ 6.7760e-02, -4.7342e-02,  7.3933e-03]],\n",
              "              \n",
              "                       [[ 6.3067e-02, -9.6567e-02, -8.9004e-02],\n",
              "                        [-5.3989e-02,  6.7611e-02,  7.0680e-02],\n",
              "                        [-7.1991e-02,  2.0100e-02, -5.5854e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-4.8926e-02,  9.0907e-02,  5.0914e-02],\n",
              "                        [-2.8828e-02,  1.5516e-02,  2.0424e-02],\n",
              "                        [ 2.4691e-02, -3.6079e-02, -6.2074e-02]],\n",
              "              \n",
              "                       [[ 6.9788e-02,  1.4164e-02,  4.4119e-02],\n",
              "                        [-3.9922e-02,  5.1057e-02,  7.6713e-02],\n",
              "                        [ 6.4107e-02,  2.8660e-02,  1.0371e-01]],\n",
              "              \n",
              "                       [[-2.3053e-04,  2.2441e-02,  1.0015e-01],\n",
              "                        [ 1.0245e-01, -4.4506e-02,  9.4953e-02],\n",
              "                        [ 3.8902e-02, -1.1799e-02,  9.2038e-02]],\n",
              "              \n",
              "                       [[-5.4605e-02,  6.8490e-02,  1.0445e-01],\n",
              "                        [-7.2701e-02, -6.2201e-02, -1.0445e-01],\n",
              "                        [-1.8970e-02, -9.5733e-02, -3.5304e-02]],\n",
              "              \n",
              "                       [[ 3.2002e-02,  7.4511e-02,  5.8717e-02],\n",
              "                        [ 5.8511e-02,  4.3730e-02, -6.5378e-02],\n",
              "                        [-8.3694e-02,  4.3696e-03,  1.0009e-01]],\n",
              "              \n",
              "                       [[ 5.9351e-03, -9.0662e-03, -7.1545e-02],\n",
              "                        [-5.2266e-02, -8.1256e-02,  8.4398e-02],\n",
              "                        [-1.7174e-02, -9.3119e-02,  1.1308e-02]],\n",
              "              \n",
              "                       [[ 7.6494e-03, -1.3023e-02,  3.7733e-02],\n",
              "                        [ 5.6687e-02, -9.9128e-02, -8.0753e-02],\n",
              "                        [-5.0639e-03, -9.7729e-02, -9.5750e-02]],\n",
              "              \n",
              "                       [[ 9.3067e-02, -8.0174e-03, -5.2113e-02],\n",
              "                        [-3.6157e-02, -8.2295e-02,  8.2258e-02],\n",
              "                        [-2.2857e-02, -5.9265e-02, -7.9944e-02]],\n",
              "              \n",
              "                       [[ 6.1611e-02, -1.4571e-02, -1.1074e-02],\n",
              "                        [-2.7473e-02, -5.0883e-02,  1.8751e-02],\n",
              "                        [ 8.1099e-02, -6.1093e-02,  5.0504e-03]],\n",
              "              \n",
              "                       [[-8.0165e-02, -4.9426e-02,  9.2525e-02],\n",
              "                        [ 1.1052e-03,  1.0154e-01, -1.8468e-02],\n",
              "                        [-5.7453e-02, -6.2981e-02,  9.3426e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-8.1058e-02,  5.5318e-02,  2.6203e-02],\n",
              "                        [ 3.1107e-02,  5.9476e-02, -2.7577e-02],\n",
              "                        [ 6.5223e-02, -8.3982e-02, -3.7087e-02]],\n",
              "              \n",
              "                       [[ 7.7164e-02,  3.1283e-02, -1.4038e-02],\n",
              "                        [-2.4616e-02, -6.4364e-02,  6.4098e-02],\n",
              "                        [-3.3520e-03, -3.5664e-03,  2.4929e-02]],\n",
              "              \n",
              "                       [[ 7.7787e-02, -5.3778e-02, -3.6303e-02],\n",
              "                        [ 7.1429e-02,  5.9532e-02, -5.1855e-02],\n",
              "                        [-1.0428e-01,  1.9555e-02,  5.5434e-02]],\n",
              "              \n",
              "                       [[ 2.5178e-02,  7.4768e-02, -8.3640e-02],\n",
              "                        [ 5.3156e-02, -6.5531e-02,  5.9325e-02],\n",
              "                        [ 7.8394e-02,  3.3385e-02,  8.5284e-02]],\n",
              "              \n",
              "                       [[-6.9481e-02, -9.4275e-02, -1.0135e-01],\n",
              "                        [ 6.6179e-02,  3.6926e-02, -7.7188e-02],\n",
              "                        [ 5.1048e-02,  9.6177e-02, -1.0394e-01]],\n",
              "              \n",
              "                       [[ 7.6466e-02,  1.6167e-02,  9.8053e-03],\n",
              "                        [ 9.4847e-02,  9.5458e-02,  4.4414e-02],\n",
              "                        [ 8.3288e-02,  4.3853e-02,  1.7176e-02]],\n",
              "              \n",
              "                       [[-9.2656e-02,  1.9689e-02, -7.4993e-02],\n",
              "                        [ 3.2452e-02,  1.8598e-02,  2.3681e-03],\n",
              "                        [-7.2071e-02, -6.3899e-02,  7.7912e-02]],\n",
              "              \n",
              "                       [[ 5.1336e-02,  5.5576e-02, -3.1410e-02],\n",
              "                        [-1.8151e-02, -2.7014e-02,  7.2489e-02],\n",
              "                        [-4.5504e-02,  6.6394e-02,  7.2679e-02]],\n",
              "              \n",
              "                       [[-9.6403e-02,  6.4369e-04, -2.0076e-02],\n",
              "                        [-5.8273e-02,  4.5507e-02, -1.2807e-02],\n",
              "                        [ 9.2287e-02, -6.5976e-02,  4.8976e-02]],\n",
              "              \n",
              "                       [[-8.9998e-02, -5.2833e-02,  7.1903e-03],\n",
              "                        [ 8.3283e-02,  5.5521e-02, -8.6550e-02],\n",
              "                        [ 1.1676e-02, -6.2138e-02,  4.5674e-03]]]])),\n",
              "             ('conv_block_2.0.bias',\n",
              "              tensor([-0.0878, -0.0309,  0.0723, -0.0967, -0.1005,  0.0192,  0.0144, -0.0193,\n",
              "                       0.0920, -0.0635])),\n",
              "             ('conv_block_2.2.weight',\n",
              "              tensor([[[[-6.3992e-02, -7.8791e-02, -1.9619e-02],\n",
              "                        [-2.6901e-02,  6.5222e-02, -5.9186e-03],\n",
              "                        [ 3.3663e-02, -4.3804e-02,  8.5507e-02]],\n",
              "              \n",
              "                       [[ 8.8862e-02, -9.4401e-02, -2.7090e-02],\n",
              "                        [-8.9439e-02,  4.4781e-02, -9.2094e-02],\n",
              "                        [-4.9839e-02,  1.0532e-01, -1.0066e-01]],\n",
              "              \n",
              "                       [[ 7.7771e-02,  8.9049e-03,  8.4289e-02],\n",
              "                        [-5.3494e-02,  6.9236e-02,  1.2718e-02],\n",
              "                        [ 8.1073e-03,  7.1945e-02, -1.0019e-01]],\n",
              "              \n",
              "                       [[-8.4902e-02,  1.0180e-01, -6.3298e-02],\n",
              "                        [-7.5980e-02, -5.1539e-03, -3.3742e-02],\n",
              "                        [-1.4421e-02, -7.0623e-02,  3.8034e-02]],\n",
              "              \n",
              "                       [[-9.0703e-02,  8.5374e-03,  6.1510e-02],\n",
              "                        [ 2.0253e-02,  1.4006e-02,  1.5418e-02],\n",
              "                        [-3.0880e-02, -2.0080e-02, -4.4450e-02]],\n",
              "              \n",
              "                       [[-7.1207e-02, -5.5810e-02,  1.0420e-01],\n",
              "                        [-1.7641e-02,  3.6924e-02,  7.2896e-02],\n",
              "                        [-8.2343e-03, -5.6707e-02, -7.1419e-02]],\n",
              "              \n",
              "                       [[-3.8833e-02,  3.7624e-02, -8.8771e-02],\n",
              "                        [-1.2870e-02,  4.0096e-02,  8.5999e-02],\n",
              "                        [ 3.1721e-02,  2.0846e-02,  7.2162e-02]],\n",
              "              \n",
              "                       [[ 4.8708e-02,  3.5661e-02, -3.2682e-02],\n",
              "                        [-8.4528e-02, -2.2769e-02, -1.9117e-02],\n",
              "                        [ 7.7410e-03, -1.1593e-02,  4.2616e-02]],\n",
              "              \n",
              "                       [[ 7.0050e-02, -4.2735e-02, -1.0002e-01],\n",
              "                        [-5.4081e-02, -5.0436e-02,  5.9750e-02],\n",
              "                        [-6.7994e-02, -9.9145e-03, -2.2340e-02]],\n",
              "              \n",
              "                       [[-6.3976e-02,  4.7780e-02, -4.3909e-02],\n",
              "                        [-5.4531e-03, -7.4112e-02, -1.0632e-02],\n",
              "                        [ 1.4977e-02, -4.2894e-03, -3.9386e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 3.1315e-02, -2.7311e-02, -5.8439e-02],\n",
              "                        [-7.7732e-02, -2.2329e-02, -9.9578e-02],\n",
              "                        [ 8.7492e-02, -5.0357e-02, -4.3684e-02]],\n",
              "              \n",
              "                       [[ 9.7439e-03,  2.7326e-02, -9.9393e-03],\n",
              "                        [ 7.2313e-02, -6.1448e-02,  3.7777e-02],\n",
              "                        [-2.3773e-04, -8.5747e-02, -4.0824e-02]],\n",
              "              \n",
              "                       [[ 2.6825e-02,  2.0138e-02,  7.6647e-02],\n",
              "                        [ 7.0518e-02, -5.7493e-02, -4.5013e-02],\n",
              "                        [-2.2351e-02, -7.5517e-02, -2.8459e-02]],\n",
              "              \n",
              "                       [[-8.6258e-02,  4.0092e-02,  7.4583e-02],\n",
              "                        [ 8.3459e-03, -7.5460e-02, -7.9827e-02],\n",
              "                        [-4.1036e-02,  3.0659e-02,  2.5711e-03]],\n",
              "              \n",
              "                       [[ 1.9166e-02,  9.9346e-02,  4.8956e-02],\n",
              "                        [ 2.2665e-02, -2.1327e-02,  4.9864e-02],\n",
              "                        [ 3.8563e-02, -9.4879e-02, -6.2266e-02]],\n",
              "              \n",
              "                       [[ 3.5381e-03,  3.9997e-02,  5.1282e-02],\n",
              "                        [-6.2748e-02, -1.0458e-01, -5.4909e-03],\n",
              "                        [-1.2050e-02,  3.0588e-02, -2.8988e-02]],\n",
              "              \n",
              "                       [[ 8.0588e-02,  7.0333e-03,  7.6975e-02],\n",
              "                        [-7.3398e-02,  4.2167e-02,  1.2560e-02],\n",
              "                        [-5.2720e-02,  5.2256e-02, -1.0372e-01]],\n",
              "              \n",
              "                       [[ 8.5220e-02,  8.4947e-03,  1.0178e-02],\n",
              "                        [ 4.8746e-02,  8.7503e-03,  4.5184e-02],\n",
              "                        [ 6.7063e-02, -8.2268e-02,  6.9735e-02]],\n",
              "              \n",
              "                       [[-1.5784e-02, -2.4513e-02,  2.1217e-02],\n",
              "                        [ 8.2446e-02, -5.7302e-02, -7.1039e-02],\n",
              "                        [ 6.5418e-02, -4.9507e-02,  3.3937e-02]],\n",
              "              \n",
              "                       [[-1.5530e-02,  2.9014e-02,  8.0439e-02],\n",
              "                        [-5.3421e-02, -5.1151e-02,  5.1716e-02],\n",
              "                        [ 5.7714e-03, -1.1601e-02, -9.2590e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 8.9309e-02, -3.9919e-03, -1.9415e-02],\n",
              "                        [-4.3269e-02, -2.0801e-02,  5.1233e-02],\n",
              "                        [-2.4227e-03,  9.0147e-02, -6.0858e-03]],\n",
              "              \n",
              "                       [[-1.5122e-02,  5.9498e-02, -2.7275e-03],\n",
              "                        [-2.1039e-02,  3.5231e-02,  8.3129e-02],\n",
              "                        [ 2.6305e-02,  7.3398e-02,  6.8309e-02]],\n",
              "              \n",
              "                       [[ 2.9810e-02,  3.6650e-02,  3.4014e-02],\n",
              "                        [ 1.0934e-02,  8.9675e-02,  9.7308e-02],\n",
              "                        [ 3.7524e-02, -5.2640e-03,  9.4509e-02]],\n",
              "              \n",
              "                       [[-8.2042e-02,  7.7453e-02,  5.5849e-02],\n",
              "                        [ 6.7687e-02, -8.0992e-03, -7.8646e-02],\n",
              "                        [ 7.5193e-02, -4.6091e-02,  2.7734e-02]],\n",
              "              \n",
              "                       [[ 5.9719e-02, -9.8508e-02,  6.9954e-03],\n",
              "                        [-3.7444e-02,  7.4815e-02, -6.7114e-02],\n",
              "                        [ 6.4001e-02,  6.5730e-02,  5.8156e-02]],\n",
              "              \n",
              "                       [[ 1.0119e-01,  1.5964e-02, -9.5541e-02],\n",
              "                        [ 7.5248e-02,  9.6499e-03,  2.0918e-03],\n",
              "                        [-1.0041e-01, -2.3691e-02, -5.1162e-02]],\n",
              "              \n",
              "                       [[ 1.0324e-01,  7.5054e-02,  7.8634e-02],\n",
              "                        [ 7.2188e-02, -6.5340e-02, -4.5270e-02],\n",
              "                        [-4.1252e-02, -4.2257e-02,  8.2054e-02]],\n",
              "              \n",
              "                       [[ 3.5815e-02,  8.4470e-02, -4.9309e-03],\n",
              "                        [-9.3965e-02, -3.0582e-02,  7.4081e-02],\n",
              "                        [ 6.4174e-02,  3.2632e-02, -3.0919e-02]],\n",
              "              \n",
              "                       [[-9.8386e-02, -5.6639e-02,  5.4958e-02],\n",
              "                        [-4.2518e-02,  5.0421e-02,  2.8781e-02],\n",
              "                        [-4.0486e-02,  6.4202e-02, -3.3871e-02]],\n",
              "              \n",
              "                       [[-3.5020e-03, -4.0152e-02, -9.9988e-02],\n",
              "                        [ 1.6996e-02,  3.0460e-02, -5.3072e-02],\n",
              "                        [ 6.4663e-02, -9.4558e-02, -1.0161e-01]]],\n",
              "              \n",
              "              \n",
              "                      [[[-6.5106e-02, -3.6430e-02, -1.1707e-02],\n",
              "                        [-2.0370e-02,  4.8108e-02, -9.2510e-02],\n",
              "                        [ 1.5521e-02,  1.8254e-03,  2.7842e-02]],\n",
              "              \n",
              "                       [[ 1.0479e-01,  6.4874e-02, -5.8366e-02],\n",
              "                        [-8.6378e-02, -2.5520e-02, -5.2876e-02],\n",
              "                        [ 3.6820e-02,  9.6628e-04,  8.4783e-02]],\n",
              "              \n",
              "                       [[ 4.1405e-02, -1.9382e-02,  3.6229e-03],\n",
              "                        [ 2.5244e-02, -1.3080e-02,  8.5058e-02],\n",
              "                        [-8.2420e-02,  5.1377e-02, -6.7192e-02]],\n",
              "              \n",
              "                       [[-9.2347e-02, -2.1640e-02,  5.1366e-02],\n",
              "                        [ 7.4478e-02,  2.6452e-02, -9.1104e-03],\n",
              "                        [-5.9092e-03, -4.2731e-02, -9.4592e-03]],\n",
              "              \n",
              "                       [[-7.2831e-03,  8.9699e-02,  6.1690e-02],\n",
              "                        [-8.4351e-02,  4.3604e-04, -6.4834e-02],\n",
              "                        [-1.6733e-02, -8.3776e-02,  2.7402e-02]],\n",
              "              \n",
              "                       [[-7.6008e-02,  1.0406e-01,  7.9605e-02],\n",
              "                        [-7.2559e-02, -9.9239e-02,  4.1128e-03],\n",
              "                        [-2.9425e-02,  3.0945e-02, -7.1353e-02]],\n",
              "              \n",
              "                       [[ 4.3148e-02, -9.1047e-02, -5.5632e-02],\n",
              "                        [-5.5414e-02,  5.1007e-02, -2.7597e-03],\n",
              "                        [-1.0130e-01, -6.0201e-02, -4.8781e-02]],\n",
              "              \n",
              "                       [[-9.7802e-02,  1.3497e-02,  3.7561e-02],\n",
              "                        [-1.9340e-02, -4.1947e-02, -6.3926e-04],\n",
              "                        [-8.3725e-02, -6.4184e-02, -2.4040e-03]],\n",
              "              \n",
              "                       [[ 9.3643e-02, -3.2414e-02,  5.2247e-02],\n",
              "                        [-4.1484e-02, -2.8060e-02, -1.0034e-01],\n",
              "                        [ 8.7330e-02,  1.0264e-01, -2.2139e-03]],\n",
              "              \n",
              "                       [[ 6.6974e-02,  8.6219e-02,  5.2359e-02],\n",
              "                        [ 5.4288e-02, -1.0035e-01, -9.9050e-02],\n",
              "                        [-8.0906e-02,  3.2970e-02, -9.1177e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-8.0464e-02, -5.1092e-02, -9.7154e-02],\n",
              "                        [ 1.4203e-04,  1.5207e-02, -6.1686e-02],\n",
              "                        [ 6.9018e-02, -4.0018e-02, -2.9676e-02]],\n",
              "              \n",
              "                       [[ 8.0309e-02,  9.0499e-02, -1.2093e-02],\n",
              "                        [-7.5671e-02, -5.2881e-02,  1.3423e-02],\n",
              "                        [ 6.1790e-02,  5.2477e-02, -4.6547e-02]],\n",
              "              \n",
              "                       [[-9.9650e-02, -9.2249e-02, -3.3537e-02],\n",
              "                        [ 1.3223e-03, -4.7347e-02, -8.3348e-02],\n",
              "                        [ 1.1109e-02, -8.3668e-02, -8.0946e-02]],\n",
              "              \n",
              "                       [[-8.5692e-02, -2.8563e-02,  9.3104e-02],\n",
              "                        [ 4.1207e-02, -1.2498e-02,  2.1694e-02],\n",
              "                        [ 4.1975e-02,  6.1414e-04, -8.5020e-02]],\n",
              "              \n",
              "                       [[-6.4944e-02, -7.1610e-02, -2.6766e-03],\n",
              "                        [-9.6492e-02, -1.9166e-02, -3.8545e-02],\n",
              "                        [ 1.0345e-01,  8.5679e-02,  6.1227e-02]],\n",
              "              \n",
              "                       [[ 5.9116e-03, -3.4129e-02,  2.6887e-02],\n",
              "                        [-7.2830e-02, -4.4957e-02, -2.1175e-02],\n",
              "                        [-2.4766e-02, -9.9854e-02,  4.1903e-02]],\n",
              "              \n",
              "                       [[ 8.6803e-02, -5.8141e-02,  2.8415e-02],\n",
              "                        [-1.2225e-02, -3.8445e-03,  6.1443e-03],\n",
              "                        [ 9.1346e-02,  1.4124e-02, -6.6690e-02]],\n",
              "              \n",
              "                       [[-3.7917e-02,  5.1495e-02,  3.2893e-02],\n",
              "                        [ 2.0487e-03, -1.3912e-02, -4.1012e-02],\n",
              "                        [-3.7413e-02, -5.5602e-02,  1.7273e-02]],\n",
              "              \n",
              "                       [[ 2.9603e-02,  8.0717e-02, -2.3813e-02],\n",
              "                        [ 7.5461e-03,  6.8125e-02,  4.5852e-02],\n",
              "                        [ 1.3544e-02,  3.2390e-02,  5.4714e-03]],\n",
              "              \n",
              "                       [[-9.0419e-02,  4.0636e-03, -2.3040e-02],\n",
              "                        [ 9.5123e-02,  9.5145e-02,  2.0912e-02],\n",
              "                        [ 9.4215e-02, -5.4288e-02,  9.1619e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 9.0756e-02, -4.0288e-03, -8.4592e-02],\n",
              "                        [-3.4015e-02, -2.8189e-02,  1.7411e-03],\n",
              "                        [-9.5569e-02,  1.9535e-02, -4.3839e-02]],\n",
              "              \n",
              "                       [[-2.6989e-02, -5.4443e-02, -2.2255e-02],\n",
              "                        [-9.7896e-02, -5.5885e-02,  9.7108e-03],\n",
              "                        [ 6.9072e-02,  9.5790e-02, -7.9737e-02]],\n",
              "              \n",
              "                       [[ 4.4264e-02, -5.9419e-02, -8.1498e-02],\n",
              "                        [-4.6417e-03, -6.0468e-02, -9.0783e-02],\n",
              "                        [-9.8509e-02, -7.0556e-02,  8.6619e-02]],\n",
              "              \n",
              "                       [[ 5.8788e-02, -4.1726e-02, -7.0553e-02],\n",
              "                        [-8.1085e-02, -6.2246e-02, -4.3376e-02],\n",
              "                        [ 6.3308e-02,  3.4496e-02, -4.0622e-02]],\n",
              "              \n",
              "                       [[ 7.2567e-02, -6.5484e-02, -8.5876e-02],\n",
              "                        [ 2.3006e-02, -5.8123e-02,  2.9987e-02],\n",
              "                        [ 8.9306e-02, -4.9849e-02, -7.3556e-02]],\n",
              "              \n",
              "                       [[ 3.9676e-02, -9.5200e-02,  9.4044e-02],\n",
              "                        [-4.9780e-02,  5.0961e-02, -8.3818e-02],\n",
              "                        [-7.1348e-02,  1.1611e-02,  3.7463e-02]],\n",
              "              \n",
              "                       [[ 8.1734e-02,  8.8158e-02, -6.0623e-03],\n",
              "                        [-1.3552e-02,  1.7424e-02, -2.4486e-02],\n",
              "                        [ 3.5882e-03, -9.9828e-02, -8.6531e-02]],\n",
              "              \n",
              "                       [[ 7.2233e-02, -6.1597e-02,  8.3008e-02],\n",
              "                        [ 1.1568e-02,  2.5676e-02,  9.5804e-02],\n",
              "                        [-5.8628e-02, -1.6640e-02,  1.8675e-02]],\n",
              "              \n",
              "                       [[ 3.6012e-02, -1.0259e-01,  3.7464e-02],\n",
              "                        [-6.2163e-02,  1.3846e-02,  7.1315e-02],\n",
              "                        [-1.0500e-02, -3.3346e-03, -7.8757e-03]],\n",
              "              \n",
              "                       [[ 8.7962e-02,  5.9907e-02,  1.7727e-02],\n",
              "                        [-6.3437e-02, -5.7241e-02,  8.3964e-02],\n",
              "                        [ 7.5834e-02,  6.1033e-02, -8.2189e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 8.2092e-02, -1.0076e-02,  7.7661e-02],\n",
              "                        [ 9.1553e-02,  1.1554e-02, -4.3863e-02],\n",
              "                        [ 9.9153e-02, -5.4931e-02,  6.8876e-02]],\n",
              "              \n",
              "                       [[-1.0108e-01, -3.3153e-02, -9.1902e-02],\n",
              "                        [-4.7284e-02,  4.4759e-02, -7.5529e-02],\n",
              "                        [-9.1158e-02,  7.5371e-02,  5.6270e-02]],\n",
              "              \n",
              "                       [[-1.1527e-03, -7.4309e-02, -2.7927e-02],\n",
              "                        [-3.4129e-02,  6.5100e-02, -3.4478e-02],\n",
              "                        [-3.0360e-02, -7.4720e-02, -4.9646e-02]],\n",
              "              \n",
              "                       [[ 5.7074e-02,  6.7914e-02,  1.5315e-02],\n",
              "                        [-3.9549e-02,  1.0124e-01,  2.0806e-02],\n",
              "                        [-4.0688e-02, -3.6535e-02, -1.4752e-02]],\n",
              "              \n",
              "                       [[ 4.9974e-02,  3.8555e-02,  7.6418e-02],\n",
              "                        [-4.7494e-03,  8.7183e-02, -4.2816e-02],\n",
              "                        [-4.8547e-02, -3.8927e-02, -9.8896e-02]],\n",
              "              \n",
              "                       [[-6.9195e-02, -9.5382e-02, -6.2294e-03],\n",
              "                        [ 9.9374e-04, -2.7358e-02, -7.2035e-02],\n",
              "                        [ 9.5637e-02, -3.4926e-02,  5.0233e-02]],\n",
              "              \n",
              "                       [[ 7.3408e-02, -6.9291e-02, -1.3179e-02],\n",
              "                        [ 6.0923e-02,  1.0218e-01, -1.3299e-02],\n",
              "                        [ 7.6382e-02, -8.2732e-02, -6.8489e-02]],\n",
              "              \n",
              "                       [[ 8.6682e-02, -9.9801e-03,  1.0414e-01],\n",
              "                        [ 7.6651e-03, -4.3714e-02,  1.0011e-01],\n",
              "                        [ 9.2179e-02,  9.7826e-03, -6.3900e-02]],\n",
              "              \n",
              "                       [[-4.5639e-03, -5.0693e-02,  7.6810e-02],\n",
              "                        [ 4.8829e-03,  2.2191e-02,  6.3927e-02],\n",
              "                        [ 3.4916e-02, -6.5803e-02,  8.7566e-02]],\n",
              "              \n",
              "                       [[ 6.4758e-02, -6.5073e-02,  7.9700e-02],\n",
              "                        [ 2.9905e-02, -2.0750e-02, -7.5385e-02],\n",
              "                        [-1.7490e-02, -1.0335e-01,  6.0163e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 7.6343e-02, -3.0347e-02,  9.7720e-02],\n",
              "                        [-3.9032e-02,  1.8051e-02, -7.3459e-02],\n",
              "                        [-4.4565e-03,  4.2610e-02,  4.5403e-02]],\n",
              "              \n",
              "                       [[-3.5346e-03, -5.3154e-02,  7.3680e-02],\n",
              "                        [ 6.9788e-02,  1.6916e-02, -4.8475e-02],\n",
              "                        [ 2.2349e-02,  2.8186e-04,  9.6302e-02]],\n",
              "              \n",
              "                       [[ 1.5621e-02,  8.1301e-03,  7.2057e-03],\n",
              "                        [ 5.6079e-02, -1.3024e-03,  9.0351e-02],\n",
              "                        [ 5.4917e-02, -7.9650e-02, -1.2063e-06]],\n",
              "              \n",
              "                       [[-8.9472e-02, -8.0934e-02,  2.0480e-02],\n",
              "                        [ 2.3687e-02, -9.2246e-03,  1.0019e-01],\n",
              "                        [-5.6627e-02, -4.4176e-02, -1.6881e-02]],\n",
              "              \n",
              "                       [[ 6.3911e-04, -8.9284e-03,  9.4909e-02],\n",
              "                        [-4.4519e-02, -5.5137e-02,  9.0599e-03],\n",
              "                        [ 7.9171e-02,  2.5019e-02,  5.6787e-02]],\n",
              "              \n",
              "                       [[ 2.0406e-02,  8.9839e-02,  6.3311e-02],\n",
              "                        [ 7.5428e-02, -1.4198e-02, -8.7268e-02],\n",
              "                        [-5.0002e-02,  3.5910e-02,  7.3950e-02]],\n",
              "              \n",
              "                       [[-4.1184e-02,  8.7218e-02,  1.5150e-02],\n",
              "                        [ 4.1869e-04,  4.1093e-03, -1.8623e-02],\n",
              "                        [ 9.8683e-02,  4.5784e-03,  6.4564e-02]],\n",
              "              \n",
              "                       [[-8.8967e-02, -5.4309e-02,  1.1852e-02],\n",
              "                        [ 8.4169e-02,  5.0184e-02,  2.0076e-02],\n",
              "                        [-1.0414e-01,  1.9816e-03, -6.9581e-02]],\n",
              "              \n",
              "                       [[-9.0006e-02,  1.4414e-02, -6.6693e-02],\n",
              "                        [ 9.5674e-02, -5.7294e-02,  3.3970e-02],\n",
              "                        [ 6.1871e-02, -8.1928e-02,  5.3946e-02]],\n",
              "              \n",
              "                       [[-1.4114e-02,  5.4619e-02,  1.0201e-01],\n",
              "                        [-4.4922e-02, -4.5653e-02,  8.3753e-02],\n",
              "                        [ 1.1722e-02, -1.0513e-02,  7.9971e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-5.0928e-02, -5.2047e-03,  7.2403e-02],\n",
              "                        [ 4.1195e-02, -6.8180e-02,  2.7398e-02],\n",
              "                        [-8.0368e-02, -5.7245e-02,  6.7779e-02]],\n",
              "              \n",
              "                       [[-2.8093e-02, -5.3691e-02,  7.4717e-03],\n",
              "                        [ 2.5759e-02, -6.5524e-02, -7.1084e-02],\n",
              "                        [-1.0209e-01,  2.7236e-02, -6.8013e-02]],\n",
              "              \n",
              "                       [[ 8.0331e-03, -2.3576e-02, -6.8923e-02],\n",
              "                        [-3.3636e-02, -8.1027e-02, -5.5797e-02],\n",
              "                        [-3.2857e-03, -9.0116e-02, -9.2447e-02]],\n",
              "              \n",
              "                       [[ 7.8958e-02,  9.9188e-03, -4.6618e-02],\n",
              "                        [-3.5047e-03,  7.8168e-02, -8.7939e-02],\n",
              "                        [-5.5886e-02, -7.6226e-02, -7.6634e-03]],\n",
              "              \n",
              "                       [[-3.6274e-03, -8.2146e-02,  7.3163e-02],\n",
              "                        [-8.0946e-02,  9.8414e-02, -7.2560e-02],\n",
              "                        [-1.4446e-02,  1.9710e-02, -4.6852e-02]],\n",
              "              \n",
              "                       [[ 9.6939e-02, -7.2673e-02, -5.8427e-03],\n",
              "                        [-7.7398e-02,  2.9261e-02,  8.9871e-02],\n",
              "                        [ 9.7776e-02,  1.2514e-02, -5.2773e-02]],\n",
              "              \n",
              "                       [[ 1.0244e-01,  7.8667e-03,  7.1317e-02],\n",
              "                        [-5.4751e-02, -4.8920e-02, -8.7504e-02],\n",
              "                        [ 9.6990e-02,  1.7486e-02, -7.5704e-02]],\n",
              "              \n",
              "                       [[ 9.0535e-03, -4.5211e-02,  5.2659e-03],\n",
              "                        [ 3.4988e-02, -5.2308e-02,  1.8394e-02],\n",
              "                        [-6.6553e-02,  2.0312e-02, -1.0178e-01]],\n",
              "              \n",
              "                       [[ 1.6797e-02,  1.0473e-01,  9.7094e-02],\n",
              "                        [ 3.8451e-02,  7.7563e-02,  1.0248e-01],\n",
              "                        [ 2.9870e-02,  3.5156e-02,  1.3707e-02]],\n",
              "              \n",
              "                       [[ 9.3322e-02,  9.0551e-02, -4.9570e-02],\n",
              "                        [-4.3333e-03, -5.3110e-02,  3.7824e-02],\n",
              "                        [-1.0214e-01,  3.7301e-02, -2.8929e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 3.8227e-02,  3.2899e-02, -5.2454e-02],\n",
              "                        [ 5.4687e-02,  4.4762e-02, -8.9602e-02],\n",
              "                        [ 1.0517e-01,  9.0731e-02,  6.5584e-02]],\n",
              "              \n",
              "                       [[-1.0699e-02,  3.7345e-02, -5.7028e-02],\n",
              "                        [-3.5818e-02,  4.9749e-02,  4.6925e-02],\n",
              "                        [ 4.1741e-02, -1.0053e-01,  8.7350e-02]],\n",
              "              \n",
              "                       [[-4.4028e-02,  9.1223e-02,  8.6852e-02],\n",
              "                        [ 3.9070e-02,  1.0502e-01,  6.0528e-02],\n",
              "                        [ 6.1821e-02, -3.5794e-02,  9.7766e-02]],\n",
              "              \n",
              "                       [[ 2.7627e-02,  6.2280e-02, -2.3834e-02],\n",
              "                        [ 7.6340e-02,  9.3509e-02, -8.0770e-02],\n",
              "                        [ 8.6415e-02, -6.9664e-02, -7.2571e-02]],\n",
              "              \n",
              "                       [[-8.8089e-02,  3.0459e-02, -7.9144e-02],\n",
              "                        [-3.9680e-02, -5.2988e-02,  2.8172e-02],\n",
              "                        [-1.0349e-01, -4.8324e-02,  7.7112e-04]],\n",
              "              \n",
              "                       [[ 9.4660e-03, -4.7605e-02,  3.7764e-02],\n",
              "                        [-6.9544e-02, -8.9270e-02, -1.4986e-02],\n",
              "                        [-5.6989e-02,  6.6443e-02, -7.2049e-02]],\n",
              "              \n",
              "                       [[-8.8494e-03,  4.3782e-02, -9.2311e-02],\n",
              "                        [ 8.1599e-02, -4.7895e-02, -2.8684e-02],\n",
              "                        [-6.4480e-02, -3.9279e-02, -4.0645e-02]],\n",
              "              \n",
              "                       [[-9.3801e-02,  3.6019e-02, -3.3768e-04],\n",
              "                        [ 1.0311e-01,  7.1117e-02,  9.1699e-02],\n",
              "                        [ 3.1014e-02,  5.5388e-02,  9.8704e-02]],\n",
              "              \n",
              "                       [[ 8.6545e-02, -8.0996e-02, -2.3636e-02],\n",
              "                        [-1.0166e-01,  3.9877e-03, -3.7229e-02],\n",
              "                        [ 9.1486e-02,  1.6666e-02,  1.1601e-03]],\n",
              "              \n",
              "                       [[-7.6248e-02, -8.2718e-02,  1.6594e-02],\n",
              "                        [-5.2376e-02, -4.8409e-02,  7.3938e-02],\n",
              "                        [-5.4952e-02, -4.6918e-02,  8.0934e-02]]]])),\n",
              "             ('conv_block_2.2.bias',\n",
              "              tensor([ 0.0412, -0.0599,  0.0319,  0.0531, -0.0936,  0.0197,  0.0241, -0.0041,\n",
              "                       0.1011, -0.0697])),\n",
              "             ('classifier.1.weight',\n",
              "              tensor([[ 0.0245, -0.0240, -0.0387,  ...,  0.0094, -0.0015, -0.0225],\n",
              "                      [ 0.0228,  0.0067, -0.0439,  ..., -0.0302,  0.0368,  0.0293],\n",
              "                      [ 0.0303,  0.0347, -0.0211,  ...,  0.0207, -0.0423, -0.0240],\n",
              "                      ...,\n",
              "                      [-0.0359, -0.0343,  0.0166,  ...,  0.0324,  0.0113, -0.0143],\n",
              "                      [-0.0294, -0.0316,  0.0251,  ..., -0.0056,  0.0300, -0.0396],\n",
              "                      [-0.0246, -0.0035, -0.0046,  ..., -0.0146, -0.0358,  0.0175]])),\n",
              "             ('classifier.1.bias',\n",
              "              tensor([ 0.0320, -0.0445,  0.0246, -0.0357, -0.0442,  0.0156, -0.0010, -0.0277,\n",
              "                       0.0404,  0.0037]))])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.4 Training and Testing `model_2` using our training and testing functions"
      ],
      "metadata": {
        "id": "P5zvhRKsmiZq"
      }
    },
    {
      "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",
        "train_time_start_model_2 = timer()\n",
        "\n",
        "# Train and test model\n",
        "epochs = 3\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n ------\")\n",
        "    train_step(model=model_2,\n",
        "               data_loader=train_dataloader,\n",
        "               loss_fn=loss_fn,\n",
        "               optimizer=optimizer,\n",
        "               accuracy_fn=accuracy_fn,\n",
        "               device=device)\n",
        "    test_step(model=model_2,\n",
        "                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",
        "total_train_time_model_2 = print_train_time(start= train_time_start_model_2,\n",
        "                                             end=train_time_end_model_2,\n",
        "                                             device=device,)\n"
      ],
      "metadata": {
        "id": "vfisjbEumZAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344,
          "referenced_widgets": [
            "3557a81caa264c499d666487e593d6c9",
            "d44408b092fa4ab9af41d5a11eb2ba4a",
            "0e96712e49034f9899d509079326b8ba",
            "ee7a159929ff40a1bced441db0b43ba3",
            "d776f43b438b4c75afadfa3952fd2a18",
            "d64c9371fa8745829877e5372a41a453",
            "e9546351f44a48e58dd07d1a2ce1bd8d",
            "82fb77d1b93e4df8801e230c71babe1a",
            "9507333a64b14342a6c8b543d5bd710b",
            "f8e28d1e6d0a48939e455f87225811f5",
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        },
        "outputId": "54e29ba6-add1-41cd-87a6-0193333349ee"
      },
      "execution_count": 115,
      "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": "3557a81caa264c499d666487e593d6c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            " ------\n",
            "Train loss: 0.59518 | Train acc: 78.38%\n",
            "Test loss:  0.39500 | Test acc: 85.99%\n",
            "\n",
            "Epoch: 1\n",
            " ------\n",
            "Train loss: 0.36536 | Train acc: 86.90%\n",
            "Test loss:  0.35244 | Test acc: 86.91%\n",
            "\n",
            "Epoch: 2\n",
            " ------\n",
            "Train loss: 0.32588 | Train acc: 88.13%\n",
            "Test loss:  0.32719 | Test acc: 88.07%\n",
            "\n",
            "Train time on cpu: 161.437 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",
        "model_2_results\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
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        "outputId": "452f8abc-16fe-4f49-c916-aff79adfbf36"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
              "model_id": "8d502412f21446cbb9cd95b32d18d468"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name: ': 'FashionMNISTModelV2',\n",
              " 'model_loss: ': 0.32718968391418457,\n",
              " 'model_acc': 88.06908945686901}"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwpokbktgNSm",
        "outputId": "7215d5c6-9452-4b95-cc84-d6959f212922"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name: ': 'FashionMNISTModelV0',\n",
              " 'model_loss: ': 0.47663894295692444,\n",
              " 'model_acc': 83.42651757188499}"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Compare model results and training time"
      ],
      "metadata": {
        "id": "FfFX8Q1Th3vv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([\n",
        "    model_0_results,\n",
        "    model_1_results,\n",
        "    model_2_results,\n",
        "])\n",
        "compare_results"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "GySrg0Gth20m",
        "outputId": "8644e60d-21b2-4946-df4f-348ad142c825"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          model_name:   model_loss:   model_acc\n",
              "0  FashionMNISTModelV0      0.476639  83.426518\n",
              "1  FashionMNISTModelV1      0.685001  75.019968\n",
              "2  FashionMNISTModelV2      0.327190  88.069089"
            ],
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              "      <th>model_name:</th>\n",
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              "      <td>0.476639</td>\n",
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              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModelV2</td>\n",
              "      <td>0.327190</td>\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('compare_results');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "compare_results",
              "summary": "{\n  \"name\": \"compare_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model_name: \",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"FashionMNISTModelV0\",\n          \"FashionMNISTModelV1\",\n          \"FashionMNISTModelV2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss: \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17971213834245262,\n        \"min\": 0.32718968391418457,\n        \"max\": 0.6850009560585022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.47663894295692444,\n          0.6850009560585022,\n          0.32718968391418457\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.614417560833454,\n        \"min\": 75.01996805111821,\n        \"max\": 88.06908945686901,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          83.42651757188499,\n          75.01996805111821,\n          88.06908945686901\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add training time to results comparison\n",
        "compare_results[\"training_time\"] = [\n",
        "    f\"{total_train_time_model_0:.3f}\",\n",
        "    f\"{total_train_time_model_1:.3f}\",\n",
        "    f\"{total_train_time_model_2:.3f}\",\n",
        "]\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "1BnAFnK_iZTZ",
        "outputId": "09f85295-dfc8-4b92-f53d-da85ecb1e846"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          model_name:   model_loss:   model_acc training_time\n",
              "0  FashionMNISTModelV0      0.476639  83.426518        38.764\n",
              "1  FashionMNISTModelV1      0.685001  75.019968        34.016\n",
              "2  FashionMNISTModelV2      0.327190  88.069089       161.437"
            ],
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              "      <th>model_name:</th>\n",
              "      <th>model_loss:</th>\n",
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              "      <th>0</th>\n",
              "      <td>FashionMNISTModelV0</td>\n",
              "      <td>0.476639</td>\n",
              "      <td>83.426518</td>\n",
              "      <td>38.764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModelV1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "      <td>34.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModelV2</td>\n",
              "      <td>0.327190</td>\n",
              "      <td>88.069089</td>\n",
              "      <td>161.437</td>\n",
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              "variable_name": "compare_results",
              "summary": "{\n  \"name\": \"compare_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model_name: \",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"FashionMNISTModelV0\",\n          \"FashionMNISTModelV1\",\n          \"FashionMNISTModelV2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss: \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17971213834245262,\n        \"min\": 0.32718968391418457,\n        \"max\": 0.6850009560585022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.47663894295692444,\n          0.6850009560585022,\n          0.32718968391418457\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.614417560833454,\n        \"min\": 75.01996805111821,\n        \"max\": 88.06908945686901,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          83.42651757188499,\n          75.01996805111821,\n          88.06908945686901\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"training_time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"38.764\",\n          \"34.016\",\n          \"161.437\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize our model results\n",
        "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": 451
        },
        "id": "ity9797NklRA",
        "outputId": "4b171958-94e5-47ef-e86b-7bba9af35be9"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Model')"
            ]
          },
          "metadata": {},
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wDcwKB71nCed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qafl_WDUnCRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3JVtdrEXnCFW"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyORvGJH0GZLr2n6jVBCPecx",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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