notebook/Xception train val curves.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Video Face Manipulation Detection Through Ensemble of CNNs\n",
    "Image and Sound Processing Lab - Politecnico di Milano\n",
    "- Nicolò Bonettini\n",
    "- Edoardo Daniele Cannas\n",
    "- Sara Mandelli\n",
    "- Luca Bondi\n",
    "- Paolo Bestagini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "matplotlib.rcParams['mathtext.fontset'] = 'stix'\n",
    "matplotlib.rcParams['font.family'] = 'STIXGeneral'\n",
    "matplotlib.pyplot.title(r'ABC123 vs $\\mathrm{ABC123}^{123}$')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for path in Path('fpv').glob('*.json'):\n",
    "    try:\n",
    "        fpv = int(path.name.split('ff-c23-720-140-140-')[1].split('fpv')[0])\n",
    "    except IndexError:\n",
    "        fpv = 32\n",
    "    split = path.name.split('tag-')[1].split('_loss')[0]\n",
    "    df = pd.read_json(path,)\n",
    "    df.rename(columns={0:'timestamp',1:'iter',2:'loss'},inplace=True)\n",
    "    df['split'] = split\n",
    "    df['fpv'] = fpv\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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       "\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>timestamp</th>\n",
       "      <th>iter</th>\n",
       "      <th>loss</th>\n",
       "      <th>split</th>\n",
       "      <th>fpv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.586856e+09</td>\n",
       "      <td>100</td>\n",
       "      <td>0.664077</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.586856e+09</td>\n",
       "      <td>200</td>\n",
       "      <td>0.602064</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.586856e+09</td>\n",
       "      <td>300</td>\n",
       "      <td>0.548841</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.586856e+09</td>\n",
       "      <td>400</td>\n",
       "      <td>0.517333</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.586856e+09</td>\n",
       "      <td>500</td>\n",
       "      <td>0.501915</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>1.586862e+09</td>\n",
       "      <td>28000</td>\n",
       "      <td>0.484979</td>\n",
       "      <td>val</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>1.586862e+09</td>\n",
       "      <td>28500</td>\n",
       "      <td>0.471129</td>\n",
       "      <td>val</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>1.586862e+09</td>\n",
       "      <td>29000</td>\n",
       "      <td>0.527016</td>\n",
       "      <td>val</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>1.586862e+09</td>\n",
       "      <td>29500</td>\n",
       "      <td>0.471535</td>\n",
       "      <td>val</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>1.586862e+09</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.531895</td>\n",
       "      <td>val</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp   iter      loss  split  fpv\n",
       "0     1.586856e+09    100  0.664077  train   10\n",
       "1     1.586856e+09    200  0.602064  train   10\n",
       "2     1.586856e+09    300  0.548841  train   10\n",
       "3     1.586856e+09    400  0.517333  train   10\n",
       "4     1.586856e+09    500  0.501915  train   10\n",
       "...            ...    ...       ...    ...  ...\n",
       "2155  1.586862e+09  28000  0.484979    val   15\n",
       "2156  1.586862e+09  28500  0.471129    val   15\n",
       "2157  1.586862e+09  29000  0.527016    val   15\n",
       "2158  1.586862e+09  29500  0.471535    val   15\n",
       "2159  1.586862e+09  30000  0.531895    val   15\n",
       "\n",
       "[2160 rows x 5 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat(dfs,ignore_index=True)\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "                        'ui-button-icon-only');\n",
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       "\n",
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       "\n",
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       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.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",
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       "}\n",
       "\n",
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       "    properties['type'] = type;\n",
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       "\n",
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       "    if (!this.waiting) {\n",
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       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.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",
       "\n",
       "\n",
       "mpl.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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
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       "    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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\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",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.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.\n",
       "mpl.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var 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",
       "\n",
       "mpl.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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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",
       "    // 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",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.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.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "markers = ['o','+','d','*','s','1']\n",
    "linestyles = [':','--',':','--',':','--']\n",
    "\n",
    "fig,ax = plt.subplots(2,1,figsize=(6,4))\n",
    "\n",
    "df_train = df_all[df_all['split']=='train']\n",
    "for idx,fpv in enumerate(sorted(df_train['fpv'].unique())):\n",
    "    df_fpv = df_train[df_train['fpv']==fpv]\n",
    "    ax[0].plot(df_fpv['iter'][::4],df_fpv['loss'][::4],label='{:d} FPV'.format(fpv),\n",
    "               marker=markers[idx],linestyle=linestyles[idx],fillstyle='none',linewidth=1)\n",
    "\n",
    "ax[0].grid(True)\n",
    "ax[0].set_xlabel('Iteration')\n",
    "ax[0].set_ylabel('Training loss')\n",
    "ax[0].legend(loc='upper right',ncol=3)\n",
    "ax[0].set_xlim([1000,10000])\n",
    "ax[0].set_yticks(np.arange(0,1,0.1))\n",
    "ax[0].set_ylim([0.1,0.4])\n",
    "\n",
    "\n",
    "df_val = df_all[df_all['split']=='val']\n",
    "for idx,fpv in enumerate(sorted(df_train['fpv'].unique())):\n",
    "    df_fpv = df_val[df_val['fpv']==fpv]\n",
    "    ax[1].plot(df_fpv['iter'],df_fpv['loss'],label='{:d} FPV'.format(fpv),\n",
    "               marker=markers[idx],linestyle=linestyles[idx],fillstyle='none',linewidth=1)\n",
    "\n",
    "ax[1].grid(True)\n",
    "ax[1].set_xlabel('Iteration')\n",
    "ax[1].set_ylabel('Validation loss')\n",
    "ax[1].set_xlim([1000,10000])\n",
    "ax[1].set_yticks(np.arange(0,1,0.1))\n",
    "ax[1].set_ylim([0.25,0.55])\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('xception_train_val.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}