convolutional_enhance_with_WideResNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXMPHoKIGbNa",
        "outputId": "4496c251-2f03-48fe-99dd-66ed81b9045f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using downloaded and verified file: ./cifar10.tgz\n",
            "['test', 'train', 'labels.txt']\n",
            "\n",
            "['truck', 'bird', 'deer', 'ship', 'horse', 'automobile', 'cat', 'frog', 'airplane', 'dog']\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import torch\n",
        "import torchvision\n",
        "import tarfile\n",
        "from torchvision.datasets.utils import download_url\n",
        "from torch.utils.data import random_split\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision.utils import make_grid\n",
        "from torchvision import transforms as tt\n",
        "from torchvision.transforms import ToTensor\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "from torch.utils.data import SubsetRandomSampler\n",
        "\n",
        "# WideResNet22 - 22 Convolutional layers: model with residual blocks and batch normalization\n",
        "# Residual Block adds teh original input back to the output feature map obtained by passing the input through >1 conv layers.\n",
        "# CNN Augmentation with ResNet and Regularization Techniques\n",
        "# 1. Data normalization - By subtracting the mean and dividing by the standard deviation across each channel.\n",
        "      # As a result the mean of the data across each channel is 0, and SD is 1. This prevents the values from any one channel\n",
        "      # from disproportionately affecting losses and gradients while training (due to higher or wider range of values than others)\n",
        "# 2. Data augmentation\n",
        "# 3. Residual Connections\n",
        "# 4. Batch normalization\n",
        "# 5. Learning Rate annealing\n",
        "# 6. Weight Decay\n",
        "# 7. Gradient Clipping\n",
        "\n",
        "dataset_url = \"http://files.fast.ai/data/examples/cifar10.tgz\"\n",
        "download_url(dataset_url, '.')\n",
        "\n",
        "with tarfile.open('./cifar10.tgz', 'r:gz') as tar:\n",
        "    tar.extractall(path='./data')\n",
        "data_dir = './data/cifar10'\n",
        "print(os.listdir(data_dir))\n",
        "print('')\n",
        "classes = os.listdir(data_dir + \"/train\")\n",
        "print(classes)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
        "train_tfms = tt.Compose([tt.RandomCrop(32, padding=4, padding_mode='reflect'),\n",
        "                         tt.RandomHorizontalFlip(),\n",
        "                         tt.ToTensor(),\n",
        "                         tt.Normalize(*stats)])\n",
        "valid_tfms = tt.Compose([tt.ToTensor(), tt.Normalize(*stats)])"
      ],
      "metadata": {
        "id": "jnTalyg5JXc1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = ImageFolder(data_dir+'/train', train_tfms)\n",
        "valid_ds = ImageFolder(data_dir+'/test', valid_tfms)"
      ],
      "metadata": {
        "id": "_F5LTZZuJ7EG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 256\n",
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=8, pin_memory=True)\n",
        "valid_dl = DataLoader(valid_ds, batch_size, shuffle=True, num_workers=8, pin_memory=True)\n",
        "# num_workers argument can be used to leverage multiple CPU cores and load images in parallel.\n",
        "# pin_memory arg avoid repeated memory allocation and deallocation by using same portion of memory (RAM) for\n",
        " # loading each batch of data."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5KfAqOxKC__",
        "outputId": "cc4c2278-0e8a-43b5-ceff-395e5c50a0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def show_batch(dl):\n",
        "    for images, labels in dl:\n",
        "        fig, ax = plt.subplots(figsize=(16, 16))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        ax.imshow(make_grid(images[:100], 10).permute(1, 2, 0))\n",
        "        break"
      ],
      "metadata": {
        "id": "EOkL9WeUKp_i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_batch(train_dl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "NNf8k8qyLAcn",
        "outputId": "441b9cc7-3b5c-4574-cf89-fbef1fa5c3d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.4290657..2.7537313].\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class SimpleResidualBlock(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu2 = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.conv1(x)\n",
        "        out = self.relu1(out)\n",
        "        out = self.conv2(out)\n",
        "        return self.relu2(out + x)"
      ],
      "metadata": {
        "id": "QqovDebvf7A1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "simple_resnet = SimpleResidualBlock()\n",
        "\n",
        "for images, labels in train_dl:\n",
        "    out = simple_resnet(images)\n",
        "    print(out.shape)\n",
        "    break\n",
        "# torch.Size([256, 3, 32, 32])\n",
        "# https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec\n",
        "# After each convolutional layer, adding a batch normalization layer."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJFbzMnAgZ0q",
        "outputId": "f09bc1c9-4df5-4285-cdc9-08c3e9adeed0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([256, 3, 32, 32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_2d(ni, nf, stride=1, ks=3):\n",
        "    return nn.Conv2d(in_channels=ni, out_channels=nf, kernel_size=ks, stride=stride, padding=ks//2, bias=False)\n",
        "\n",
        "def bn_relu_conv(ni, nf):\n",
        "    return nn.Sequential(nn.BatchNorm2d(ni),\n",
        "                         nn.ReLU(inplace=True),\n",
        "                         conv_2d(ni, nf))\n",
        "\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, ni, nf, stride=1):\n",
        "        super().__init__()\n",
        "        self.bn = nn.BatchNorm2d(ni)\n",
        "        self.conv1 = conv_2d(ni, nf, stride)\n",
        "        self.conv2 = bn_relu_conv(nf, nf)\n",
        "        self.shortcut = lambda x: x\n",
        "        if ni != nf:\n",
        "            self.shortcut = conv_2d(ni, nf, stride, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.bn(x), inplace=True)\n",
        "        r = self.shortcut(x)\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2(x) * 0.2\n",
        "        return x.add_(r)\n",
        "\n"
      ],
      "metadata": {
        "id": "Du9cNHWxx_G0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_group(N, ni, nf, stride):\n",
        "    start = ResidualBlock(ni, nf, stride)\n",
        "    rest = [ResidualBlock(nf, nf) for j in range(1, N)]\n",
        "    return [start] + rest\n",
        "\n",
        "class Flatten(nn.Module):\n",
        "    def __init__(self): super().__init__()\n",
        "    def forward(self, x): return x.view(x.size(0), -1)\n",
        "\n",
        "class WideResNet(nn.Module):\n",
        "    def __init__(self, n_groups, N, n_classes, k=1, n_start=16):\n",
        "        super().__init__()  # Increase channels to n_start using conv layer\n",
        "        layers = [conv_2d(3, n_start)]\n",
        "        n_channels = [n_start]\n",
        "\n",
        "        for i in range(n_groups): # add Groups of BasicBlock(increase channels & downsample)\n",
        "            n_channels.append( n_start * (2**i) *k)\n",
        "            stride = 2 if i>0 else 1\n",
        "            layers += make_group(N, n_channels[i], n_channels[i+1], stride)\n",
        "\n",
        "        layers += [nn.BatchNorm2d(n_channels[3]),\n",
        "                   nn.ReLU(inplace=True),\n",
        "                   nn.AdaptiveAvgPool2d(1),\n",
        "                   Flatten(),\n",
        "                   nn.Linear(n_channels[3], n_classes)]\n",
        "\n",
        "        self.features = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x): return self.features(x)\n",
        "\n",
        "def wrn_22():\n",
        "    return WideResNet(n_groups=3, N=3, n_classes=10, k=6)\n"
      ],
      "metadata": {
        "id": "f0VZExCyzLmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = wrn_22()"
      ],
      "metadata": {
        "id": "kqYRBZlECWCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Nup-qXWCx3V",
        "outputId": "4b443e26-050e-44c5-dbd0-70e51602d362"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WideResNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (1): ResidualBlock(\n",
              "      (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(16, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "      (shortcut): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    )\n",
              "    (2): ResidualBlock(\n",
              "      (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (3): ResidualBlock(\n",
              "      (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (4): ResidualBlock(\n",
              "      (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(96, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "      (shortcut): Conv2d(96, 192, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "    )\n",
              "    (5): ResidualBlock(\n",
              "      (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (6): ResidualBlock(\n",
              "      (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (7): ResidualBlock(\n",
              "      (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(192, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "      (shortcut): Conv2d(192, 384, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "    )\n",
              "    (8): ResidualBlock(\n",
              "      (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (9): ResidualBlock(\n",
              "      (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv1): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (conv2): Sequential(\n",
              "        (0): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (1): ReLU(inplace=True)\n",
              "        (2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      )\n",
              "    )\n",
              "    (10): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): AdaptiveAvgPool2d(output_size=1)\n",
              "    (13): Flatten()\n",
              "    (14): Linear(in_features=384, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for images, labels in train_dl:\n",
        "    print('images.shape:', images.shape)\n",
        "    out = model(images)\n",
        "    print('out.shape:', out.shape)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJyXT-v8CYaa",
        "outputId": "a5dd5f7e-8150-4ae8-d2fd-c0f5ee557e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "images.shape: torch.Size([256, 3, 32, 32])\n",
            "out.shape: torch.Size([256, 10])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Learning rate scheduling refers to the process of dynamically changing the learning rate while the model is being trained.\n",
        "# Among many strategies, this is 1-cycle policy. Starts with low learning rate, then gradually increases linearly to high value for about\n",
        "# half of the training, then slowly brings it to original value. At end, training for few iterations with a very low learning rate\n",
        "# 1-cycle: https://sgugger.github.io/the-1cycle-policy.html"
      ],
      "metadata": {
        "id": "HD7lU2GFCztz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastai --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tckxQLRvK6Px",
        "outputId": "bc83234d-c2eb-425b-d8d4-1a4f94ae5e2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install fastai\n",
        "\n",
        "\n",
        "from fastai.data.core import DataLoaders\n",
        "from fastai.learner import Learner\n",
        "from fastai.metrics import accuracy\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Assuming train_ds and valid_ds are defined\n",
        "batch_size = 64\n",
        "path='./data/cifar10'\n",
        "# Create DataLoaders\n",
        "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
        "valid_dl = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# # Create DataLoaders object\n",
        "dls = DataLoaders(train_dl, valid_dl)\n",
        "\n",
        "# # Define learner with correct loss function argument\n",
        "learner = Learner(dls, model, loss_func=F.cross_entropy, metrics=[accuracy])#, path=path)\n",
        "\n",
        "# # Set gradient clipping\n",
        "learner.clip = 0.1\n",
        "learner.save('cifar10_trained_model')\n",
        "# data = DataLoader(train_ds, valid_ds, bs=batch_size, path='./data/cifar10')\n",
        "# learner = Learner(data, model, loss_func=F.cross_entropy, metrics=[accuracy])\n",
        "# learner.clip = 0.1\n",
        "# The statement learner.clip = 0.1 is used to perform gradient clipping, i.e., it limits the values of gradients to the range [-1,1],\n",
        "# preventing the undesirable changes in the parameters (weights & biases) due to large gradient values.\n",
        "\n",
        "# FastAI also provides learning rate finder, which tries a range of learning rates, and helps you select a good learning rate\n",
        "# by looking at the graph of loss vs. learning rate."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyoz5aCMECU6",
        "outputId": "84ea2a5a-9da9-4912-c9fe-539a17aa73c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/cifar10_trained_model.pth')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learner.lr_find()\n",
        "\n",
        "learner.recorder.plot_loss()\n",
        "# learner.fit_one_cycle(9, 5e-3, wd=1e-4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uH29prTl1uaP",
        "outputId": "5dbd4088-f996-40a0-b9e0-a480f05a7192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'WideResNet' object has no attribute 'lr_find'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-2077a2b6e4dd>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecorder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# learner.fit_one_cycle(9, 5e-3, wd=1e-4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    537\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1929\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1930\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1931\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m   1932\u001b[0m             \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1933\u001b[0m         )\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'WideResNet' object has no attribute 'lr_find'"
          ]
        }
      ]
    }
  ]
}