seq2seq_monitor_losses.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "id": "fELG9vDJuJdc"
   },
   "source": [
    "# seq2seq: Real Time Monitoring of Losses\n",
    "\n",
    "Quarks To Cosmos with AI Virtual Conference: July 12-16, 2021, Carnegie Mellon University\n",
    "\n",
    "## Contributors\n",
    "\n",
    "Abdulhakim Alnuqaydan, Ali Kadhim, Sergei Gleyzer, Harrison Prosper\n",
    "\n",
    "## Hackathon Contributors\n",
    "\n",
    "Andrew Roberts, Jessica Howard, Samuel Hori, Arvind Balasubramanian, Xiaosheng Zhao, Michael Andrews\n",
    "\n",
    "July 2021\n",
    "\n",
    "## Description\n",
    "\n",
    "Read specified losses file and plot loss curves in real time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "GwFXx5YluJde"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "XLIMITS = (0, 1200)\n",
    "YLIMITS = (2, 3)\n",
    "LOSS_FILE = 'seq2seq_losses_01.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_losses(loss_file):\n",
    "    losses = [a.split() for a in open(loss_file).readlines()]\n",
    "    train_losses = [float(z)  for z, _ in losses]\n",
    "    valid_losses = [float(z)  for _, z in losses]\n",
    "    epochs       = np.arange(len(train_losses))\n",
    "    return epochs, train_losses, valid_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_losses(i, ax):\n",
    "\n",
    "    epochs, train_losses, valid_losses = get_losses(LOSS_FILE)\n",
    "    \n",
    "    # use following code to smooth curves\n",
    "    #kernel_size  = 10\n",
    "    #kernel = np.ones(kernel_size) / kernel_size\n",
    "    #train_losses = np.convolve(train_losses, kernel, mode='same')\n",
    "    #valid_losses = np.convolve(valid_losses, kernel, mode='same')\n",
    "    #epochs       = np.arange(len(train_losses))\n",
    "\n",
    "    epoch = len(epochs)-1\n",
    "    \n",
    "    ax.clear()\n",
    "    ax.set_title('epoch: %5d' % epoch)\n",
    "    ax.plot(epochs, train_losses, c='red',  label='training')\n",
    "    ax.plot(epochs, valid_losses, c='blue', label='validation')\n",
    "    ax.set_xlim(XLIMITS)\n",
    "    ax.set_ylim(YLIMITS)\n",
    "    ax.set_xlabel('epoch', fontsize=16)\n",
    "    ax.set_ylabel('$\\overline{loss}$', fontsize=16)\n",
    "    ax.grid(True, which=\"both\", linestyle='-')\n",
    "    ax.legend()\n",
    "    \n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "};\n",
       "\n",
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       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
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       "    var cursor = msg['cursor'];\n",
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       "            break;\n",
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       "\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.handle_history_buttons = function (fig, msg) {\n",
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       "        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",
       "\n",
       "mpl.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",
       "\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",
       "\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\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",
       "    }\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",
       " */\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",
       "        }\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",
       "        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",
       "\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",
       "    // 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",
       "\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\", \"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",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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 = 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.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.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",
       "\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",
       "\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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.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",
       "\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Wei8UaNG6eaEhITIX3/95VIdRiGIpmmyaNGiQufNmTPHcN7KlStdqsMso3PfcMMNpsKbfLm5uYahlpl3FsE8M0FQpUqVZPr06Yb3AnPHpUuXZPLkyXLXXXdJcHCw0/VUqVJFXnnlFdm9e7fbNdhsNt27FcuVK1fkO54IggAAAKxHEAQAAAB4UGZmpmFQsn79erfW3bx5s27NZ555psh5joKgXbt2uVxL165dTddiVEfHjh1drkFEpG7duro1n3322ULndOnSRTenSZMmbtVhhtFz8fbbb7u8Xrt27XTrLVy40MKKUZDZdwSFhITIAw884PT9n5xx6dIlmTFjhnTs2FHCwsKcriUsLEw6dOggc+fOdfvdSVOmTHEpnCYIAgAAsB5BEAAAAOBBa9eu1W1wVq1aVWw2m1vr5uTk6D4erl69ekXOMwpgGjVq5FYtRjetb9iwoanaNU2TOXPmuFXHf//7X92aN910k8Pjc3NzJSoqSjfnww8/dKsOM4w2wH/44QeX13vxxRd1602ZMsXCilGQKx8Nlz8efvhhSUxMdOv8M2bMkOuvv97lGvJHrVq1XP69l5CQIGXKlLFbr02bNk79OUcQBAAAYD2CIAAAAMCDRowYodvgfPDBBy1Zu2HDhrq1MzIyCp1jFAT17NnTrToOHTqkWzMoKMjhPYvi4+MNN3737dvnVh3ff/+9YR3nzp0zPH7Xrl2GdWzZssWtOswwOv+xY8dcXu+tt97Srfe///3PworhjPT0dDlx4oTExsbKmDFj5LbbbnMYwNSvX1+OHz9u+hzZ2dnyxBNPGK4ZFBQk999/v4wcOVKWLl0qcXFxsmrVKlm0aJG8+eabcvPNNzuc58r9oB566CG7dcLCwmTv3r1OzSUIAgAAsB5BEAAAAOBB3bt3121wduvWTTZu3Oj2aNq0qW7thISEQusxCoLGjx/v1jXabDYpXbq0bt3t27cbHv/111/rjo2KipK8vDy36jhx4oThhvKOHTsMj1+6dKnu2IiICMnOznarDjOM6k1PT3d5vWHDhunWGz58uIUVw1VxcXHSoEEDw+e8WbNmpu4LZbPZ5LHHHjNc67777nMqhFm5cqXUrl3bcA0z4eEXX3yhmz906FCn5xMEAQAAWI8gCAAAAPCg9u3bO3wngIrhKPTIZxQELViwwO3rrF+/vm7d2NhYw2M/++wz3bF16tRxuwabzSZBQUG6tePi4gyPnzRpku7YmjVrul2HGUbPoTuMgiBX3uEBNdLT06V169aGz7uZwG7y5MmGa/Tr189UoHru3Dlp3ry5bp0SJUrIb7/9VuT8M2fOSKVKlezmNmjQQDIzM52ugSAIAADAegRBAOCD3LxNBADAhxltsqoca9euLbQeoyBo2bJlbl+n0UdfLV261PDY8ePH64695ZZb3K5BRHT3KdE0Tb7++mvDY0ePHq07tlmzZpbU4SyCoMCTmpoqlStX1j1P5cuXL/KjHUUuh0nlypXTzW/VqpXk5uaarufEiROG691zzz1Fzu3Ro4fTAbAjBEEAAADWIwgCAAAAPKhevXoeDYKK2oQ1CoJWr17t9nW2atVKt+78+fMNjzW6b1LLli3drkFEDDfYzdRx5513WlKHswiCAtOUKVMMn3tHoeW1pk6dajh33bp1Ltdj1Auapsnu3bsdzvnhhx90xz/77LOmz00QBAAAYD2CIAAAAMCDGjZs6PNB0DfffOP2dTZr1ky3rqN3Gn388ce6Y5s0aeJ2DSIiUVFRTtdh9PFajRo1sqQOZxEEBaYzZ85ISEiI7rkaNGhQkXON7g1Uu3Ztt+o5fPiw4e/Fjz76yOGc3r172x0bHh4uy5cvN32vs//+97+G5166dKnu2Pj4eLeuEwAAIFAQBAEAAAAedMcdd+g2OKdPn+61eoyCoLlz57q9rtFN53/99VfDY+fNm6c7tkaNGm7XkJOTY7ihvH79esPjFy5cqDu2atWqbtdhBkFQ4DJ6t+ATTzxR5DyjXuvRo4fb9VSrVk23bq9evRwe36tXL4+G3JqmSa1atdy+TgAAgEBAEAQAAAB4kNH/3h8yZIjX6jEKgt5//3231szOzpbQ0FDdunv27DE8/vvvv9cdGxISIpcuXXKrjj/++MNw83jv3r2Gx//000+6Y4ODg+X8+fNu1WEGQVDgatmype65ateuXZHzSpYsqZvnzDuJimJ0n68uXbo4PJ4gCAAAwHcRBAEAAAAeZPSxR506dfJaPUZBUNeuXd1a8/fff9etGR4eLllZWYbH//XXX4abvJs3b3arji+++MKwjszMTMPjExMTDeuw4p5JziIIClw333yz7rnq3LlzkfPCw8N1895++22367nzzjtN/VlFEAQAAOC7CIIAAAAAD1q+fLluMzMiIkLS09O9Uo9REFS5cmXJzc11ec1Jkybp1mzRokWhcypXrqybM3bsWJdrEBF5/vnndWveddddhc5p1KiRbo4n37FFEBSYbDablC9fXvdcPfvss0XONfoItz59+rhdU926dXXr9uzZ0+HxBEEAAAC+iyAIAAAA8KCLFy9KVFSUbkNz9uzZXqnHKAjSNE1++uknl9c0+oirV199tdA5Xbt21c255ZZbXK4hMzNTypUrp1vzjTfeKHRe//79dXMqVark8F1EViMICkybN282fO6dCUNvvfVW3bx//OMfbtWTnJwswcHBunWt+Mi5osyePdvwsTh8+LDycwMAABRXBEEAAACAhxn9z/latWp5LGy4lqMg6KGHHnJpvS1bthiut379+kLnLViwwHDemjVrXKrj008/NVxv48aNhc777bffDOdNmDDBpTrMIggKTH369DF87rdu3Vrk3L59++rmBQUFyV9//eVyPTNmzDCs58svv3R5TWcRBAEAAFiPIAgAAADwsB07dhj+b/vnn3/e47U4CoI0TZOVK1eaWstms8ldd92lW6dJkyZFzs3OzpYqVaro5jZt2lRycnJM1ZGcnGz4MVu33367U/Pvuece3dzIyEjZt2+fqTpc4WtBUGxsrGFNhEnWiY2NlaCgIN1j3KBBA6fmL1682PA5Kuxj3Apz6dIlqV27tm694OBgOXv2rEtrmkEQBAAAYD2CIAAAAMALnnvuOcPNznfeeUfy8vLcWjsjI0OmTJkiqampRR5bWBB0/fXXy8GDB50+74ABAwzXmTZtmlPzo6OjDef36NFDbDabU2tkZGTIP//5T8N1Fi5c6NQaq1evNpxfs2ZNl8Mgm80mp06dKvI4giD/0LNnT4mNjXV7nVWrVkmpUqUMH2NnPy7y/PnzhsGnpmkyc+ZMU/XYbDZ58sknDdfq1KmTC1doHkEQAACA9QiCAAAAAC9ITU2VWrVqGW543nPPPfL777+bWs9ms8mWLVvkrbfeurop7EzwYBQEXftupRo1akhcXFyha6Snp8uLL75oeC0tW7Z0OtjKzMyUm2++2XCdJ554QpKTkwud/+effzoMgTp37uxUDfkchVrly5eXGTNmSG5urlPrZGRkyNy5c6VJkyZOhScEQf4hv3dbtmwpH3/8sZw4ccLU/D179kivXr0M3wmkaZrcfffdpgLhcePGGa4THBws7777rmRlZRW5xqlTp6RDhw6G6wQFBUl8fLypa3QVQRAAAID1CIIAAAAAL9m1a5eUK1fO4Tty2rZtK2PGjJF169ZJQkKCZGRkSHZ2tpw5c0YOHToka9askQkTJsizzz4r1atX1813NQh69dVXdZvAjz32mCxZskQOHz4smZmZcubMGYmPj5f3339fatasaVh/WFiY7Nq1y9RjsnPnTgkPDzdcr2LFijJo0CBZv369nDp1SrKysuT48eOyevVqeeGFFyQqKspwXqVKleT06dOm6sjOzpaWLVs6fG4aNGgg//nPfyQuLk6OHj0qFy9elEuXLsnJkydl48aNMmnSJHnyySftaiIIKj4KhrhBQUHSpEkT+fe//y0xMTGycOFCWb58ufz666+yZs0a+fbbb2XKlCnSv39/adasmcPfV5qmSZ06dZzq3WtlZmbKbbfd5nDNqlWrynvvvSexsbFXeyc9PV2OHDkiS5Yskb59+0pERITD+a+99pqiR1KPIAgAAMB6BEEAAACAF8XHxxveG8eK4WoQFBsbK/3793fr3EFBQTJv3jyXHpPFixdLiRIlLHkMSpcuLRs3bnSpjuTk5EI3180OgqDiw9G7+dwdt956qxw/ftylmhITE6V+/fqW19S1a1e3P67SDIIgAAAA6xEEAQAAAF524sQJeeCBByzdvA0KCpKkpKQiz+0oCMrJyZEePXq4dO7Q0FCZM2eOW4/JTz/9JBUqVHDrMahTp47pj9grKD09XZ555hmCIIIgO1YHQZGRkTJ69GjJyclxq66TJ09Kx44dLakpODhYBg0a5NTHylmJIAgAAMB6BEEAAACAj1i6dKnb70C56aabZMSIEXL06FGnzukoCBK5fN+hCRMmSMmSJZ0+/y233CJbtmyx5PE4ffq09O7d2/S7g6KiomTw4MFy4cIFS+oQEfnxxx/l9ttvd+k5CQ0NlX/9619OhVIEQf4hLi5O3njjDWnUqJFb/VqnTh0ZOnSoHDt2zNL6PvvsM2ncuLFLNQUFBcn9998vv/76q6U1OYsgCAAAwHoEQQAAAICP2bJli/znP/+RVq1aFRrClCpVSpo3by49e/aUWbNmyZEjR0yfq7AgKN+ZM2dk9OjR0qJFCwkNDdUdX7p0aXnkkUdk6dKlSj5C6ujRozJq1Chp3bq1REZGGj4WpUuXlvbt28vEiRMlOTnZ8hrybdq0Sd544w259dZbDR+L/OCnYcOG8vTTT8vnn3/u1Duz4L+SkpJk2bJlMnToUOnevbu0bNlSqlevLmXLlpUSJUpIeHi4VKpUSerVqyctWrSQPn36yCeffCKbN28Wm82mtLbY2FgZOHCgtGrVyuE9tDRNkwoVKkinTp3k/fffl/379yutqSjbt2+XYcOG6UZKSopX6wIAAPBnBEEAAACA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width=\"499.79999999999995\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use following to cause animated plot to appear in notebook\n",
    "%matplotlib notebook   \n",
    "\n",
    "# set up an empty figure\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "\n",
    "# add a subplot to it\n",
    "nrows, ncols, index = 1,1,1\n",
    "ax  = fig.add_subplot(nrows, ncols, index)\n",
    "plot_losses(2, ax)\n",
    "\n",
    "def animate(i):\n",
    "    plot_losses(i, ax)\n",
    "    \n",
    "seconds = 30 # animate is called every 30 seconds\n",
    "anim = FuncAnimation(fig, animate,\n",
    "                     interval=1000*seconds,   # in ms\n",
    "                     frames=XLIMITS[-1],\n",
    "                     repeat=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# You can save the animated plot as an mp4 movie as follows:\n",
    "#anim.save('seq2seq_losses_01.mp4', writer='ffmpeg', fps=30) \n",
    "\n",
    "# This requires the module ffmpeg, which you can install as follows\n",
    "# (assuming you are working in a miniconda or anaconda environment):\n",
    "#\n",
    "#    conda install -c conda-forge ffmpeg\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "seq2seq_data_generator.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 - AI",
   "language": "python",
   "name": "python3-ai"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}