05_pet_breeds.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MUKESHRAJMAHENDRAN/Deep-Learning--fastai/blob/main/05_pet_breeds.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVlFhrZ7uwRy",
        "outputId": "f8fea858-31cc-411c-c9b3-4cfb5628eb2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[?25hMounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "#hide\n",
        "! [ -e /content ] && pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cLGLmIZ4uwR5"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "from fastbook import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHo-Ok79uwR7"
      },
      "source": [
        "# Image Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcllzjnKuwR9"
      },
      "source": [
        "## From Dogs and Cats to Pet Breeds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "IXt76aZquwR9",
        "outputId": "e8231ae0-51b0-4e6c-b554-39afe3b273e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='811712512' class='' max='811706944' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [811712512/811706944 01:00<00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from fastai.vision.all import *\n",
        "path = untar_data(URLs.PETS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zO8tsTxKuwR-"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "Path.BASE_PATH = path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1Ktnq0ZuwR_",
        "outputId": "81951817-ee10-4005-dba4-3a0b619ae990"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#2) [Path('annotations'),Path('images')]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "path.ls()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4c8UCyEEuwSA",
        "outputId": "4a0a4ce3-ff01-4103-b77a-3c2e060f2706"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#7393) [Path('images/pug_34.jpg'),Path('images/Sphynx_124.jpg'),Path('images/Sphynx_58.jpg'),Path('images/havanese_191.jpg'),Path('images/Bombay_6.jpg'),Path('images/pomeranian_149.jpg'),Path('images/Egyptian_Mau_181.jpg'),Path('images/Birman_177.jpg'),Path('images/samoyed_155.jpg'),Path('images/saint_bernard_59.jpg')...]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "(path/\"images\").ls()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "5mgE45jbuwSB"
      },
      "outputs": [],
      "source": [
        "fname = (path/\"images\").ls()[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfgtWIs-uwSC",
        "outputId": "3a3827a6-e8dd-4f11-c8e2-5054801b82a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pug']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "re.findall(r'(.+)_\\d+.jpg$', fname.name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "a3Mk19HMuwSE"
      },
      "outputs": [],
      "source": [
        "pets = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
        "                 get_items=get_image_files, \n",
        "                 splitter=RandomSplitter(seed=42),\n",
        "                 get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'),\n",
        "                 item_tfms=Resize(460),\n",
        "                 batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",
        "dls = pets.dataloaders(path/\"images\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5A7GRr-juwSF"
      },
      "source": [
        "## Presizing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgCm-WNVuwSH"
      },
      "source": [
        "### Checking and Debugging a DataBlock"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "2DsQuzXiuwSH",
        "outputId": "599cf9c4-39fd-4bb5-dde6-5347a56b787a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x216 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dls.show_batch(nrows=1, ncols=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W8WA3LoAuwSI"
      },
      "outputs": [],
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "learn.fine_tune(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dt-uAQwquwSJ"
      },
      "source": [
        "## Cross-Entropy Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RByrRDxruwSJ"
      },
      "source": [
        "### Viewing Activations and Labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yW86ooSiuwSJ"
      },
      "outputs": [],
      "source": [
        "x,y = dls.one_batch()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Zv3TUA_uwSK"
      },
      "outputs": [],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zAQqCnbZuwSK"
      },
      "outputs": [],
      "source": [
        "preds,_ = learn.get_preds(dl=[(x,y)])\n",
        "preds[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "boc8JHwIuwSK"
      },
      "outputs": [],
      "source": [
        "len(preds[0]),preds[0].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Pnc20HmuwSL"
      },
      "source": [
        "### Softmax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NwUj_sEeuwSL"
      },
      "outputs": [],
      "source": [
        "plot_function(torch.sigmoid, min=-4,max=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zQ0K68d5uwSM"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "torch.random.manual_seed(42);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbrDkWcKuwSM"
      },
      "outputs": [],
      "source": [
        "acts = torch.randn((6,2))*2\n",
        "acts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N2FvX6niuwSM"
      },
      "outputs": [],
      "source": [
        "acts.sigmoid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AU3vT_uPuwSN"
      },
      "outputs": [],
      "source": [
        "(acts[:,0]-acts[:,1]).sigmoid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5eCRa7cduwSN"
      },
      "outputs": [],
      "source": [
        "sm_acts = torch.softmax(acts, dim=1)\n",
        "sm_acts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnURrDSluwSN"
      },
      "source": [
        "### Log Likelihood"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5yvwLQiiuwSO"
      },
      "outputs": [],
      "source": [
        "targ = tensor([0,1,0,1,1,0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eGi4WzpTuwSO"
      },
      "outputs": [],
      "source": [
        "sm_acts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wE2RkhSRuwSO"
      },
      "outputs": [],
      "source": [
        "idx = range(6)\n",
        "sm_acts[idx, targ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JYSXwxCQuwSP"
      },
      "outputs": [],
      "source": [
        "from IPython.display import HTML\n",
        "df = pd.DataFrame(sm_acts, columns=[\"3\",\"7\"])\n",
        "df['targ'] = targ\n",
        "df['idx'] = idx\n",
        "df['result'] = sm_acts[range(6), targ]\n",
        "t = df.style.hide_index()\n",
        "#To have html code compatible with our script\n",
        "html = t._repr_html_().split('</style>')[1]\n",
        "html = re.sub(r'<table id=\"([^\"]+)\"\\s*>', r'<table >', html)\n",
        "display(HTML(html))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z-GGKthOuwSP"
      },
      "outputs": [],
      "source": [
        "-sm_acts[idx, targ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0uxCQSc0uwSQ"
      },
      "outputs": [],
      "source": [
        "F.nll_loss(sm_acts, targ, reduction='none')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA-yxZx_uwSQ"
      },
      "source": [
        "#### Taking the Log\n",
        "\n",
        "Recall that cross entropy loss may involve the multiplication of many numbers.  Multiplying lots of negative numbers together can cause problems like [numerical underflow](https://en.wikipedia.org/wiki/Arithmetic_underflow) in computers.  Therefore, we want to transform these probabilities to larger values so we can perform mathematical operations on them.  There is a mathematical function that does exactly this: the *logarithm* (available as `torch.log`). It is not defined for numbers less than 0, and looks like this between 0 and 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQATwbW9uwSQ"
      },
      "outputs": [],
      "source": [
        "plot_function(torch.log, min=0,max=1, ty='log(x)', tx='x')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3S_BP6GuwSR"
      },
      "outputs": [],
      "source": [
        "plot_function(lambda x: -1*torch.log(x), min=0,max=1, tx='x', ty='- log(x)', title = 'Log Loss when true label = 1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sKU3z0yXuwSR"
      },
      "outputs": [],
      "source": [
        "from IPython.display import HTML\n",
        "df['loss'] = -torch.log(tensor(df['result']))\n",
        "t = df.style.hide_index()\n",
        "#To have html code compatible with our script\n",
        "html = t._repr_html_().split('</style>')[1]\n",
        "html = re.sub(r'<table id=\"([^\"]+)\"\\s*>', r'<table >', html)\n",
        "display(HTML(html))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kryq836kuwSR"
      },
      "source": [
        "### Negative Log Likelihood"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "osJKaBO5uwSS"
      },
      "outputs": [],
      "source": [
        "loss_func = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VCk7HYO7uwSV"
      },
      "outputs": [],
      "source": [
        "loss_func(acts, targ)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BC8pN6cAuwSV"
      },
      "outputs": [],
      "source": [
        "F.cross_entropy(acts, targ)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WFN38NOZuwSV"
      },
      "outputs": [],
      "source": [
        "nn.CrossEntropyLoss(reduction='none')(acts, targ)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdac_Aj9uwSV"
      },
      "source": [
        "## Model Interpretation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V3_8A1DKuwSW"
      },
      "outputs": [],
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZNTuovXuwSW"
      },
      "outputs": [],
      "source": [
        "interp.most_confused(min_val=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIW7a0wruwSW"
      },
      "source": [
        "## Improving Our Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnwP00vAuwSX"
      },
      "source": [
        "### The Learning Rate Finder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
            "f6f0e7454f544d609203b78a7e81053d",
            "2c999b3ef9c546c38afa0ee2d448629d",
            "1680c9b65cbe45ae80abe303067b81ae",
            "54a76d8223e148149b97d29a8d37caaa",
            "3aa88bc373ee417e856ab40e9cba0103",
            "75837d43b5e04be5ad880abe62f83c26",
            "8cefc97069b044b2924f7faa11de53c4",
            "1d0cacf5864c44758a3a664570726dc5",
            "a377f7b3851f4a10a08d35f0c336c068",
            "1b23cfc42cb34a4b8b880bb0f3496123",
            "d96def782d5a443e961628841966ffc2"
          ]
        },
        "id": "wjXd6DiXuwSX",
        "outputId": "1be0e359-797d-4ff1-e798-281a5a1b9e33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-b627a593.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-b627a593.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/83.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6f0e7454f544d609203b78a7e81053d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>2.664156</td>\n",
              "      <td>5.822745</td>\n",
              "      <td>0.509472</td>\n",
              "      <td>01:14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>3.762541</td>\n",
              "      <td>2.335304</td>\n",
              "      <td>0.657645</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "learn.fine_tune(1, base_lr=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "zkT7p05yuwSX",
        "outputId": "dd096513-cba9-494f-e3fa-eeaed002ca76"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "lr_min,lr_steep = learn.lr_find(suggest_funcs=(minimum, steep))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsyzpiH8uwSX",
        "outputId": "f1906ed2-a25e-44cf-94b9-538f4299552e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum/10: 1.00e-02, steepest point: 2.09e-03\n"
          ]
        }
      ],
      "source": [
        "print(f\"Minimum/10: {lr_min:.2e}, steepest point: {lr_steep:.2e}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "learn.fine_tune(2, base_lr=3e-3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "KbSUbCrUxPwf",
        "outputId": "26b878d9-d5f8-44d4-dfa4-4376962ce19d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.334879</td>\n",
              "      <td>0.371053</td>\n",
              "      <td>0.119756</td>\n",
              "      <td>01:22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.547440</td>\n",
              "      <td>0.392177</td>\n",
              "      <td>0.119080</td>\n",
              "      <td>01:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.341453</td>\n",
              "      <td>0.288694</td>\n",
              "      <td>0.085927</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBK0Ihm1uwSY"
      },
      "source": [
        "### Unfreezing and Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Bv8J8exbuwSY"
      },
      "outputs": [],
      "source": [
        "#learn.fine_tune??"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "lanH8N-BuwSZ",
        "outputId": "ced0e080-7ea8-4d11-db7d-c87cb88f1a14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.156467</td>\n",
              "      <td>0.377496</td>\n",
              "      <td>0.116373</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.520440</td>\n",
              "      <td>0.294080</td>\n",
              "      <td>0.084574</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.314360</td>\n",
              "      <td>0.258578</td>\n",
              "      <td>0.077131</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "learn.fit_one_cycle(3, 3e-3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "zHAhF6L8uwSa"
      },
      "outputs": [],
      "source": [
        "learn.unfreeze()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hmgOyTWUuwSa",
        "outputId": "d91cd166-8ea0-43f6-a3e6-e9aaecd74e4b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(valley=2.511886486900039e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "learn.lr_find()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "5DnWSD_wuwSa",
        "outputId": "5fdf3647-53d5-4654-c65e-a65a8534b9d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.256544</td>\n",
              "      <td>0.259017</td>\n",
              "      <td>0.076455</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.228519</td>\n",
              "      <td>0.246978</td>\n",
              "      <td>0.074425</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.214960</td>\n",
              "      <td>0.240534</td>\n",
              "      <td>0.071042</td>\n",
              "      <td>01:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.201456</td>\n",
              "      <td>0.237532</td>\n",
              "      <td>0.071042</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.176188</td>\n",
              "      <td>0.235368</td>\n",
              "      <td>0.073072</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.180512</td>\n",
              "      <td>0.238139</td>\n",
              "      <td>0.071719</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "learn.fit_one_cycle(6, lr_max=1e-5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3k6PK54juwSa"
      },
      "source": [
        "### Discriminative Learning Rates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "maZmyRxTuwSa",
        "outputId": "59e6e0f5-b202-40ae-9ff6-16a325b12970"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.136785</td>\n",
              "      <td>0.380223</td>\n",
              "      <td>0.110284</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.523489</td>\n",
              "      <td>0.310369</td>\n",
              "      <td>0.095399</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.329098</td>\n",
              "      <td>0.261912</td>\n",
              "      <td>0.078484</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.261475</td>\n",
              "      <td>0.255393</td>\n",
              "      <td>0.076455</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.244022</td>\n",
              "      <td>0.251076</td>\n",
              "      <td>0.079161</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.216754</td>\n",
              "      <td>0.241401</td>\n",
              "      <td>0.074425</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.211632</td>\n",
              "      <td>0.245860</td>\n",
              "      <td>0.070365</td>\n",
              "      <td>01:16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.190057</td>\n",
              "      <td>0.240090</td>\n",
              "      <td>0.071042</td>\n",
              "      <td>01:16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.172886</td>\n",
              "      <td>0.246063</td>\n",
              "      <td>0.071042</td>\n",
              "      <td>01:16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.147288</td>\n",
              "      <td>0.243775</td>\n",
              "      <td>0.074425</td>\n",
              "      <td>01:16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.158478</td>\n",
              "      <td>0.241310</td>\n",
              "      <td>0.066306</td>\n",
              "      <td>01:16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
        "learn.fit_one_cycle(3, 3e-3)\n",
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(8, lr_max=slice(1e-6,1e-4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "znN7DoeWuwSb",
        "outputId": "210c63d7-fad0-4ee2-ab0f-8eace90c501f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "learn.recorder.plot_loss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzaWEaiNuwSb"
      },
      "source": [
        "### Selecting the Number of Epochs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUUV4Yk3uwSb"
      },
      "source": [
        "### Deeper Architectures"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413,
          "referenced_widgets": [
            "2d39e063540b4e39b27a9c2f1712b842",
            "726e355d8c50428692b688894a42010f",
            "00a237b16e9d4044ae841c8b94e52e60",
            "22d0a3a3736a49c1965c5013465b32c5",
            "dc9cf8437e1e43e9be47dd13118e5339",
            "fe11d27f9e0c40ecb4d9334533c25e32",
            "ee84f2400fb841b29284bee23427181f",
            "53edb1804885436e930c79fe636e75fa",
            "f20bb1e2f3944f25b864228b00f6f89c",
            "7d5766faac7a481abcded2fb0b3f435a",
            "6a73421c97bd449fba73992d98cfc8f2"
          ]
        },
        "id": "x-G57RaNuwSb",
        "outputId": "abc42a99-5956-4ad6-cc22-e2ea61cfe576"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d39e063540b4e39b27a9c2f1712b842"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.302042</td>\n",
              "      <td>0.320924</td>\n",
              "      <td>0.100135</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.596784</td>\n",
              "      <td>0.282380</td>\n",
              "      <td>0.098782</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.408727</td>\n",
              "      <td>0.304738</td>\n",
              "      <td>0.092693</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.288165</td>\n",
              "      <td>0.268172</td>\n",
              "      <td>0.079161</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.313125</td>\n",
              "      <td>0.380513</td>\n",
              "      <td>0.109608</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.245671</td>\n",
              "      <td>0.310210</td>\n",
              "      <td>0.091340</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.170365</td>\n",
              "      <td>0.270297</td>\n",
              "      <td>0.073748</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.100788</td>\n",
              "      <td>0.235597</td>\n",
              "      <td>0.059540</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.063954</td>\n",
              "      <td>0.226725</td>\n",
              "      <td>0.058187</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from fastai.callback.fp16 import *\n",
        "learn = vision_learner(dls, resnet50, metrics=error_rate).to_fp16()\n",
        "learn.fine_tune(6, freeze_epochs=3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CEecINOgvhKv"
      },
      "execution_count": 29,
      "outputs": []
    }
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