lesson3-planet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "The planet dataset isn't available on the fastai dataset page due to copyright restrictions. You can download it from Kaggle however. Let's see how to do this by using the Kaggle API as it's going to be pretty useful to you if you want to join a competition or use other Kaggle datasets later on.\n",
    "\n",
    "First, install the Kaggle API by uncommenting the following line and executing it, or by executing it in your terminal (depending on your platform you may need to modify this slightly to either add source activate fastai or similar, or prefix pip with a path. Have a look at how conda install is called for your platform in the appropriate Returning to work section of https://course.fast.ai/. (Depending on your environment, you may also need to append \"--user\" to the command.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install kaggle --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you need to upload your credentials from Kaggle on your instance. Login to kaggle and click on your profile picture on the top left corner, then 'My account'. Scroll down until you find a button named 'Create New API Token' and click on it. This will trigger the download of a file named 'kaggle.json'.\n",
    "\n",
    "Upload this file to the directory this notebook is running in, by clicking \"Upload\" on your main Jupyter page, then uncomment and execute the next two commands (or run them in a terminal). For Windows, uncomment the last two commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/Tim/.fastai/data/planet_tiny')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = Config.data_path()/'planet_tiny'\n",
    "path.mkdir(parents=True, exist_ok=True)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('C:/Users/Tim/.fastai/data/planet_tiny/labels.csv'),\n",
       " WindowsPath('C:/Users/Tim/.fastai/data/planet_tiny/train')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! kaggle competitions download -c planet-understanding-the-amazon-from-space -f train-jpg.tar.7z -p {path}  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "issue: SyntaxError: invalid syntax\n",
    "\n",
    "solution: https://github.com/Kaggle/kaggle-api/issues/12\n",
    "\n",
    "\n",
    "issue: 403 Forbidden:\n",
    "\n",
    "solution: Join Competition and Accept rules\n",
    "\n",
    "https://github.com/Kaggle/kaggle-api/issues/87 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! kaggle competitions download -c planet-understanding-the-amazon-from-space -f train_v2.csv -p {path} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! unzip -q -n {path}/train_v2.csv.zip -d {path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclassification\n",
    "Contrary to the pets dataset studied in last lesson, here each picture can have multiple labels. If we take a look at the csv file containing the labels (in 'labels.csv' here) we see that each 'image_name' is associated to several tags separated by spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_31112</td>\n",
       "      <td>clear primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_4300</td>\n",
       "      <td>partly_cloudy primary water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_39539</td>\n",
       "      <td>clear primary water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_12498</td>\n",
       "      <td>agriculture clear primary road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_9320</td>\n",
       "      <td>clear primary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    image_name                            tags\n",
       "0  train_31112                   clear primary\n",
       "1   train_4300     partly_cloudy primary water\n",
       "2  train_39539             clear primary water\n",
       "3  train_12498  agriculture clear primary road\n",
       "4   train_9320                   clear primary"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path/'labels.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To put this in a DataBunch while using the data block API(https://docs.fast.ai/data_block.html), we then need to using ImageList (and not ImageDataBunch). This will make sure the model created has the proper loss function to deal with the multiple classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use parentheses around the data block pipeline below, so that we can use a multiline statement without needing to add '\\'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "src = (ImageList.from_csv(path, 'labels.csv', folder='train', suffix='.jpg')\n",
    "       .split_by_rand_pct(0.2)\n",
    "       .label_from_df(label_delim=' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (src.transform(tfms, size=128)\n",
    "        .databunch(bs=12).normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=3, figsize=(12,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ImageDataBunch;\n",
       "\n",
       "Train: LabelList (160 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,partly_cloudy;primary;water,clear;primary;water,agriculture;clear;primary;road,clear;primary\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Valid: LabelList (40 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,agriculture;clear;primary;road,haze;primary,clear;primary,agriculture;habitation;partly_cloudy;primary;road\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Test: None"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a Learner we use the same function as in lesson 1. Our base architecture is resnet50 again, but the metrics are a little bit differeent: we use accuracy_thresh instead of accuracy. In lesson 1, we determined the predicition for a given class by picking the final activation that was the biggest, but here, each activation can be 0. or 1. accuracy_thresh selects the ones that are above a certain threshold (0.5 by default) and compares them to the ground truth.\n",
    "\n",
    "As for Fbeta, it's the metric that was used by Kaggle on this competition. See here https://en.wikipedia.org/wiki/F1_score for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "arch = models.resnet34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_02 = partial(accuracy_thresh, thresh=0.2)\n",
    "f_score = partial(fbeta, thresh=0.2)\n",
    "learn = cnn_learner(data, arch, metrics=[acc_02, f_score]).to_fp16()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner(data=ImageDataBunch;\n",
       "\n",
       "Train: LabelList (160 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,partly_cloudy;primary;water,clear;primary;water,agriculture;clear;primary;road,clear;primary\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Valid: LabelList (40 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,agriculture;clear;primary;road,haze;primary,clear;primary,agriculture;habitation;partly_cloudy;primary;road\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Test: None, model=Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (4): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (5): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Flatten()\n",
       "    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=14, bias=True)\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of BCEWithLogitsLoss(), metrics=[functools.partial(<function accuracy_thresh at 0x0000018AC79F3268>, thresh=0.2), functools.partial(<function fbeta at 0x0000018AC79F3158>, thresh=0.2)], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=WindowsPath('C:/Users/Tim/.fastai/data/planet_tiny'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[MixedPrecision\n",
       "learn: Learner(data=ImageDataBunch;\n",
       "\n",
       "Train: LabelList (160 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,partly_cloudy;primary;water,clear;primary;water,agriculture;clear;primary;road,clear;primary\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Valid: LabelList (40 items)\n",
       "x: ImageList\n",
       "Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128),Image (3, 128, 128)\n",
       "y: MultiCategoryList\n",
       "clear;primary,agriculture;clear;primary;road,haze;primary,clear;primary,agriculture;habitation;partly_cloudy;primary;road\n",
       "Path: C:\\Users\\Tim\\.fastai\\data\\planet_tiny;\n",
       "\n",
       "Test: None, model=Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (3): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (4): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (5): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (2): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Flatten()\n",
       "    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=14, bias=True)\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of BCEWithLogitsLoss(), metrics=[functools.partial(<function accuracy_thresh at 0x0000018AC79F3268>, thresh=0.2), functools.partial(<function fbeta at 0x0000018AC79F3158>, thresh=0.2)], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=WindowsPath('C:/Users/Tim/.fastai/data/planet_tiny'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[...], layer_groups=[Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (11): ReLU(inplace)\n",
       "  (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (15): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (16): ReLU(inplace)\n",
       "  (17): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (21): ReLU(inplace)\n",
       "  (22): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (26): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (27): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (28): ReLU(inplace)\n",
       "  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (31): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (32): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (33): ReLU(inplace)\n",
       "  (34): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (35): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (38): ReLU(inplace)\n",
       "  (39): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (5): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): ReLU(inplace)\n",
       "  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (11): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): ReLU(inplace)\n",
       "  (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (16): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): ReLU(inplace)\n",
       "  (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): ReLU(inplace)\n",
       "  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): ReLU(inplace)\n",
       "  (30): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (33): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (34): ReLU(inplace)\n",
       "  (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (36): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (37): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (39): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (41): ReLU(inplace)\n",
       "  (42): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (43): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (44): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (46): ReLU(inplace)\n",
       "  (47): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): AdaptiveAvgPool2d(output_size=1)\n",
       "  (1): AdaptiveMaxPool2d(output_size=1)\n",
       "  (2): Flatten()\n",
       "  (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Dropout(p=0.25)\n",
       "  (5): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Dropout(p=0.5)\n",
       "  (9): Linear(in_features=512, out_features=14, bias=True)\n",
       ")], add_time=True, silent=False)\n",
       "loss_scale: 65536\n",
       "max_noskip: 1000\n",
       "dynamic: True\n",
       "clip: None\n",
       "flat_master: False\n",
       "max_scale: 16777216], layer_groups=[Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (11): ReLU(inplace)\n",
       "  (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (15): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (16): ReLU(inplace)\n",
       "  (17): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (21): ReLU(inplace)\n",
       "  (22): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (26): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (27): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (28): ReLU(inplace)\n",
       "  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (31): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (32): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (33): ReLU(inplace)\n",
       "  (34): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (35): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (38): ReLU(inplace)\n",
       "  (39): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (5): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): ReLU(inplace)\n",
       "  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (11): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (14): ReLU(inplace)\n",
       "  (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (16): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): ReLU(inplace)\n",
       "  (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): ReLU(inplace)\n",
       "  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): ReLU(inplace)\n",
       "  (30): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (33): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (34): ReLU(inplace)\n",
       "  (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (36): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (37): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (39): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (41): ReLU(inplace)\n",
       "  (42): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (43): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (44): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (46): ReLU(inplace)\n",
       "  (47): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "), Sequential(\n",
       "  (0): AdaptiveAvgPool2d(output_size=1)\n",
       "  (1): AdaptiveMaxPool2d(output_size=1)\n",
       "  (2): Flatten()\n",
       "  (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Dropout(p=0.25)\n",
       "  (5): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Dropout(p=0.5)\n",
       "  (9): Linear(in_features=512, out_features=14, bias=True)\n",
       ")], add_time=True, silent=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the LR Finder to pick a good learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Don't train on local PC if GPU is not powerful enough\n",
    "my 980MX laptop GPU takes nearly 30min to finish one epoch, too slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1e-2/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>accuracy_thresh</th>\n",
       "      <th>fbeta</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.588179</td>\n",
       "      <td>0.192919</td>\n",
       "      <td>0.894584</td>\n",
       "      <td>0.793050</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.340343</td>\n",
       "      <td>0.177051</td>\n",
       "      <td>0.930111</td>\n",
       "      <td>0.849066</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.255920</td>\n",
       "      <td>0.180563</td>\n",
       "      <td>0.915550</td>\n",
       "      <td>0.815254</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.204114</td>\n",
       "      <td>0.163563</td>\n",
       "      <td>0.930111</td>\n",
       "      <td>0.822619</td>\n",
       "      <td>01:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.175613</td>\n",
       "      <td>0.156727</td>\n",
       "      <td>0.930111</td>\n",
       "      <td>0.821839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, slice(lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('stage-1-rn50')"
   ]
  }
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