modules/learning_rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimal learning rates\n",
    "\n",
    "In this tutorial, we'll use the MedNIST dataset to explore MONAI's `LearningRateFinder` and use it to get an initial estimate of a learning rate.\n",
    "\n",
    "We then employ one of Pytorch's cyclical learning rate schedulers to vary the learning rate over the course of the optimisation. This has been shown to give improved results: https://arxiv.org/abs/1506.01186. We'll compare this to the optimiser's (ADAM) default learning rate and the learning rate suggested by `LearningRateFinder`.\n",
    "\n",
    "This 2D classification is fairly easy, so to make it a little harder (and faster), we'll use a small network, only a subset of the images (~250 and ~25 for training and validation, respectively), we'll crop the images (from 64x64 to 20x20) and we won't use any random transformations. In a more difficult scenario, we probably wouldn't want to do any of these things.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/learning_rate.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "from monai.apps import MedNISTDataset\n",
    "from monai.config import print_config\n",
    "from monai.data import decollate_batch\n",
    "from monai.metrics import ROCAUCMetric\n",
    "from monai.networks.nets import DenseNet\n",
    "from monai.networks.utils import eval_mode\n",
    "from monai.optimizers import LearningRateFinder\n",
    "from monai.transforms import (\n",
    "    Activations,\n",
    "    AsDiscrete,\n",
    "    EnsureChannelFirstd,\n",
    "    CenterSpatialCropd,\n",
    "    Compose,\n",
    "    LoadImaged,\n",
    "    ScaleIntensityd,\n",
    "    EnsureTyped,\n",
    "    EnsureType,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import trange\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "if directory is not None:\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define MONAI transforms and get dataset and data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=\"image\"),\n",
    "        EnsureChannelFirstd(keys=\"image\"),\n",
    "        ScaleIntensityd(keys=\"image\"),\n",
    "        CenterSpatialCropd(keys=\"image\", roi_size=(20, 20)),\n",
    "        EnsureTyped(keys=\"image\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "file /home/rbrown/data/MONAI/MedNIST.tar.gz exists, skip downloading.\n",
      "extracted file /home/rbrown/data/MONAI/MedNIST exists, skip extracting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 249/249 [00:00<00:00, 913.43it/s]\n",
      "Loading dataset: 100%|██████████| 25/25 [00:00<00:00, 1242.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "file /home/rbrown/data/MONAI/MedNIST.tar.gz exists, skip downloading.\n",
      "extracted file /home/rbrown/data/MONAI/MedNIST exists, skip extracting.\n",
      "249\n",
      "25\n",
      "torch.Size([1, 20, 20])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Set fraction of images used for testing to be very high, then don't use it. In this way, we can reduce the number\n",
    "# of images in both train and val. Makes it faster and makes the training a little harder.\n",
    "def get_data(section):\n",
    "    ds = MedNISTDataset(\n",
    "        root_dir=root_dir,\n",
    "        transform=transforms,\n",
    "        section=section,\n",
    "        download=True,\n",
    "        num_workers=10,\n",
    "        val_frac=0.0005,\n",
    "        test_frac=0.995,\n",
    "    )\n",
    "    loader = DataLoader(ds, batch_size=30, shuffle=True, num_workers=10)\n",
    "    return ds, loader\n",
    "\n",
    "\n",
    "train_ds, train_loader = get_data(\"training\")\n",
    "val_ds, val_loader = get_data(\"validation\")\n",
    "\n",
    "print(len(train_ds))\n",
    "print(len(val_ds))\n",
    "print(train_ds[0][\"image\"].shape)\n",
    "num_classes = train_ds.get_num_classes()\n",
    "\n",
    "y_pred_trans = Compose([EnsureType(), Activations(softmax=True)])\n",
    "y_trans = Compose([EnsureType(), AsDiscrete(to_onehot=num_classes)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomly pick images from the dataset to visualize and check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, axes = plt.subplots(3, 3, figsize=(15, 15), facecolor=\"white\")\n",
    "for i, k in enumerate(np.random.randint(len(train_ds), size=9)):\n",
    "    data = train_ds[k]\n",
    "    im, title = data[\"image\"], data[\"class_name\"]\n",
    "    ax = axes[i // 3, i % 3]\n",
    "    im_show = ax.imshow(im[0])\n",
    "    ax.set_title(title, fontsize=25)\n",
    "    ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define loss function and network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "auc_metric = ROCAUCMetric()\n",
    "\n",
    "\n",
    "def get_new_net():\n",
    "    return DenseNet(\n",
    "        spatial_dims=2,\n",
    "        in_channels=1,\n",
    "        out_channels=num_classes,\n",
    "        init_features=2,\n",
    "        growth_rate=2,\n",
    "        block_config=(2,),\n",
    "    ).to(device)\n",
    "\n",
    "\n",
    "model = get_new_net()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate optimal learning rate\n",
    "\n",
    "Use MONAI's `LearningRateFinder` to get an initial estimate of a learning rate. Assume that it's in the range 1e-5, 1e0. If that weren't the case (which we'd notice in the plot), we could just try again over a larger/different window.\n",
    "\n",
    "We can plot the results and extract the learning rate with the steepest gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing optimal learning rate:  90%|█████████ | 18/20 [00:14<00:01,  1.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping early, the loss has diverged\n",
      "Resetting model and optimizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "lower_lr, upper_lr = 1e-3, 1e-0\n",
    "optimizer = torch.optim.Adam(model.parameters(), lower_lr)\n",
    "lr_finder = LearningRateFinder(model, optimizer, loss_function, device=device)\n",
    "lr_finder.range_test(train_loader, val_loader, end_lr=upper_lr, num_iter=20)\n",
    "steepest_lr, _ = lr_finder.get_steepest_gradient()\n",
    "ax = plt.subplots(1, 1, figsize=(15, 15), facecolor=\"white\")[1]\n",
    "_ = lr_finder.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Live plotting\n",
    "\n",
    "This function is just a wrapper around `range`/`trange` such that the plots are updated on every iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_range(data, wrapped_generator):\n",
    "    plt.ion()\n",
    "    for q in data.values():\n",
    "        for d in q.values():\n",
    "            if isinstance(d, dict):\n",
    "                ax = d[\"line\"].axes\n",
    "                ax.legend()\n",
    "                fig = ax.get_figure()\n",
    "    fig.show()\n",
    "\n",
    "    for i in wrapped_generator:\n",
    "        yield i\n",
    "        for q in data.values():\n",
    "            for d in q.values():\n",
    "                if isinstance(d, dict):\n",
    "                    d[\"line\"].set_data(d[\"x\"], d[\"y\"])\n",
    "                    ax = d[\"line\"].axes\n",
    "                    ax.legend()\n",
    "                    ax.relim()\n",
    "                    ax.autoscale_view()\n",
    "        fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "The training looks slightly different from a vanilla loop, but this is only because it loops across each of the different learning rate methods (standard, steepest and cyclical), such that they can be updated simultaneously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model_optimizer_scheduler(d):\n",
    "    d[\"model\"] = get_new_net()\n",
    "\n",
    "    if \"lr_lims\" in d:\n",
    "        d[\"optimizer\"] = torch.optim.Adam(d[\"model\"].parameters(), d[\"lr_lims\"][0])\n",
    "        d[\"scheduler\"] = torch.optim.lr_scheduler.CyclicLR(\n",
    "            d[\"optimizer\"],\n",
    "            base_lr=d[\"lr_lims\"][0],\n",
    "            max_lr=d[\"lr_lims\"][1],\n",
    "            step_size_up=d[\"step\"],\n",
    "            cycle_momentum=False,\n",
    "        )\n",
    "    elif \"lr_lim\" in d:\n",
    "        d[\"optimizer\"] = torch.optim.Adam(d[\"model\"].parameters(), d[\"lr_lim\"])\n",
    "    else:\n",
    "        d[\"optimizer\"] = torch.optim.Adam(d[\"model\"].parameters())\n",
    "\n",
    "\n",
    "def train(max_epochs, axes, data):\n",
    "    for d in data.keys():\n",
    "        get_model_optimizer_scheduler(data[d])\n",
    "\n",
    "        for q, i in enumerate([\"train\", \"auc\", \"acc\"]):\n",
    "            data[d][i] = {\"x\": [], \"y\": []}\n",
    "            (data[d][i][\"line\"],) = axes[q].plot(data[d][i][\"x\"], data[d][i][\"y\"], label=d)\n",
    "\n",
    "        val_interval = 1\n",
    "\n",
    "    for epoch in plot_range(data, trange(max_epochs)):\n",
    "        for d in data.keys():\n",
    "            data[d][\"epoch_loss\"] = 0\n",
    "        for batch_data in train_loader:\n",
    "            inputs = batch_data[\"image\"].to(device)\n",
    "            labels = batch_data[\"label\"].to(device)\n",
    "\n",
    "            for d in data.keys():\n",
    "                data[d][\"optimizer\"].zero_grad()\n",
    "                outputs = data[d][\"model\"](inputs)\n",
    "                loss = loss_function(outputs, labels)\n",
    "                loss.backward()\n",
    "                data[d][\"optimizer\"].step()\n",
    "                if \"scheduler\" in data[d]:\n",
    "                    data[d][\"scheduler\"].step()\n",
    "                data[d][\"epoch_loss\"] += loss.item()\n",
    "        for d in data.keys():\n",
    "            data[d][\"epoch_loss\"] /= len(train_loader)\n",
    "            data[d][\"train\"][\"x\"].append(epoch + 1)\n",
    "            data[d][\"train\"][\"y\"].append(data[d][\"epoch_loss\"])\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            with eval_mode(*[data[d][\"model\"] for d in data.keys()]):\n",
    "                for d in data:\n",
    "                    data[d][\"y_pred\"] = torch.tensor([], dtype=torch.float32, device=device)\n",
    "                y = torch.tensor([], dtype=torch.long, device=device)\n",
    "                for val_data in val_loader:\n",
    "                    val_images = val_data[\"image\"].to(device)\n",
    "                    val_labels = val_data[\"label\"].to(device)\n",
    "                    for d in data:\n",
    "                        data[d][\"y_pred\"] = torch.cat(\n",
    "                            [data[d][\"y_pred\"], data[d][\"model\"](val_images)],\n",
    "                            dim=0,\n",
    "                        )\n",
    "                    y = torch.cat([y, val_labels], dim=0)\n",
    "\n",
    "                for d in data:\n",
    "                    y_onehot = [y_trans(i) for i in decollate_batch(y)]\n",
    "                    y_pred_act = [y_pred_trans(i).cpu() for i in decollate_batch(data[d][\"y_pred\"])]\n",
    "                    auc_metric(y_pred_act, y_onehot)\n",
    "                    auc_result = auc_metric.aggregate()\n",
    "                    auc_metric.reset()\n",
    "                    del y_pred_act, y_onehot\n",
    "                    data[d][\"auc\"][\"x\"].append(epoch + 1)\n",
    "                    data[d][\"auc\"][\"y\"].append(auc_result)\n",
    "\n",
    "                    acc_value = torch.eq(data[d][\"y_pred\"].argmax(dim=1), y)\n",
    "                    acc_metric = acc_value.sum().item() / len(acc_value)\n",
    "                    data[d][\"acc\"][\"x\"].append(epoch + 1)\n",
    "                    data[d][\"acc\"][\"y\"].append(acc_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute('style', 'box-sizing: content-box;');\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n    if (this.ratio !== 1) {\n        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n    }\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            canvas.setAttribute(\n                'style',\n                'width: ' + width + 'px; height: ' + height + 'px;'\n            );\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    var cursor = msg['cursor'];\n    switch (cursor) {\n        case 0:\n            cursor = 'pointer';\n            break;\n        case 1:\n            cursor = 'default';\n            break;\n        case 2:\n            cursor = 'crosshair';\n            break;\n        case 3:\n            cursor = 'move';\n            break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = 'image/png';\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e) {\n        e = window.event;\n    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n    return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.which === this._key) {\n            return;\n        } else {\n            this._key = event.which;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.which !== 17) {\n        value += 'ctrl+';\n    }\n    if (event.altKey && event.which !== 18) {\n        value += 'alt+';\n    }\n    if (event.shiftKey && event.which !== 16) {\n        value += 'shift+';\n    }\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data']);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager) {\n        manager = IPython.keyboard_manager;\n    }\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [03:11<00:00,  1.91s/it]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 10), facecolor=\"white\")\n",
    "for ax in axes:\n",
    "    ax.set_xlabel(\"Epoch\")\n",
    "axes[0].set_ylabel(\"Train loss\")\n",
    "axes[1].set_ylabel(\"AUC\")\n",
    "axes[2].set_ylabel(\"ACC\")\n",
    "\n",
    "# In the paper referenced at the top of this notebook, a step\n",
    "# size of 8 times the number of iterations per epoch is suggested.\n",
    "step_size = 8 * len(train_loader)\n",
    "\n",
    "max_epochs = 100\n",
    "data = {}\n",
    "data[\"Default LR\"] = {}\n",
    "data[\"Steepest LR\"] = {\"lr_lim\": steepest_lr}\n",
    "data[\"Cyclical LR\"] = {\n",
    "    \"lr_lims\": (0.8 * steepest_lr, 1.2 * steepest_lr),\n",
    "    \"step\": step_size,\n",
    "}\n",
    "\n",
    "train(max_epochs, axes, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Unsurprisingly, both `Steepest LR` and `Cyclical LR` show quicker convergence of the loss function than `Default LR`.\n",
    "\n",
    "There's not much of a difference in this example between `Steepest LR` and `Cyclical LR`. A bigger difference may be apparent in a more complex optimisation problem, but feel free to play with the step size, and the lower and upper cyclical limits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
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