char_rnn_generate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZऀँंःऄअआइईउऊऋऌऍऎएऐऑऒओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळऴवशषसहऺऻ़ऽािीुूृॄॅॆेैॉॊोौ्ॎ .,;'-\n",
      "138\n",
      "# categories: 19 ['Spanish', 'Korean', 'French', 'Chinese', 'Italian', 'Russian', 'Nepali', 'Arabic', 'Dutch', 'Japanese', 'English', 'Polish', 'Vietnamese', 'German', 'Portuguese', 'Irish', 'Scottish', 'Greek', 'Czech']\n",
      "ओयश\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "    Extracted from PyTorch tutorial for Character Generation with RNN\n",
    "    Included to generate Nepali Names\n",
    "    \n",
    "    Date - 11/20/2018\n",
    "'''\n",
    "\n",
    "from __future__ import unicode_literals, print_function, division\n",
    "from io import open\n",
    "import glob\n",
    "import unicodedata\n",
    "import string\n",
    "\n",
    "# To include devnagari letters\n",
    "devnagari = \"\"\n",
    "for i in range(2304, 2383):\n",
    "    devnagari += chr(i)\n",
    "\n",
    "all_letters = string.ascii_letters + devnagari + \" .,;'-\"\n",
    "n_letters = len(all_letters) + 1 # Plus EOS marker\n",
    "\n",
    "print(all_letters)\n",
    "print(n_letters)\n",
    "\n",
    "def findFiles(path): return glob.glob(path)\n",
    "\n",
    "# Turn a Unicode string to plain ASCII, thanks to http://stackoverflow.com/a/518232/2809427\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(\n",
    "        c for c in unicodedata.normalize('NFD', s)\n",
    "        if unicodedata.category(c) != 'Mn'\n",
    "        and c in all_letters\n",
    "    )\n",
    "\n",
    "# Read a file and split into lines\n",
    "def readLines(filename):\n",
    "    lines = open(filename, encoding='utf-8').read().strip().split('\\n')\n",
    "    return [unicodeToAscii(line) for line in lines]\n",
    "\n",
    "# Build the category_lines dictionary, a list of lines per category\n",
    "category_lines = {}\n",
    "all_categories = []\n",
    "for filename in findFiles('data/names/*.txt'):\n",
    "    category = filename.split('/')[-1].split('.')[0]\n",
    "    all_categories.append(category)\n",
    "    lines = readLines(filename)\n",
    "    category_lines[category] = lines\n",
    "\n",
    "n_categories = len(all_categories)\n",
    "\n",
    "print('# categories:', n_categories, all_categories)\n",
    "print(unicodeToAscii('ओयेश'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.i2h = nn.Linear(n_categories + input_size + hidden_size, hidden_size)\n",
    "        self.i2o = nn.Linear(n_categories + input_size + hidden_size, output_size)\n",
    "        self.o2o = nn.Linear(hidden_size + output_size, output_size)\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "        self.softmax = nn.LogSoftmax(dim=1)\n",
    "\n",
    "    def forward(self, category, input, hidden):\n",
    "        input_combined = torch.cat((category, input, hidden), 1)\n",
    "        hidden = self.i2h(input_combined)\n",
    "        output = self.i2o(input_combined)\n",
    "        output_combined = torch.cat((hidden, output), 1)\n",
    "        output = self.o2o(output_combined)\n",
    "        output = self.dropout(output)\n",
    "        output = self.softmax(output)\n",
    "        return output, hidden\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, self.hidden_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Random item from a list\n",
    "def randomChoice(l):\n",
    "    return l[random.randint(0, len(l) - 1)]\n",
    "\n",
    "# Get a random category and random line from that category\n",
    "def randomTrainingPair():\n",
    "    category = randomChoice(all_categories)\n",
    "    line = randomChoice(category_lines[category])\n",
    "    return category, line\n",
    "\n",
    "# Get a random category and random line from that category\n",
    "def randomName(category='Nepali'):\n",
    "    line = randomChoice(category_lines[category])\n",
    "    return category, line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Nepali', 'सबिन')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomName()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot vector for category\n",
    "def categoryTensor(category):\n",
    "    li = all_categories.index(category)\n",
    "    tensor = torch.zeros(1, n_categories)\n",
    "    tensor[0][li] = 1\n",
    "    return tensor\n",
    "\n",
    "# One-hot matrix of first to last letters (not including EOS) for input\n",
    "def inputTensor(line):\n",
    "    tensor = torch.zeros(len(line), 1, n_letters)\n",
    "    for li in range(len(line)):\n",
    "        letter = line[li]\n",
    "        tensor[li][0][all_letters.find(letter)] = 1\n",
    "    return tensor\n",
    "\n",
    "# LongTensor of second letter to end (EOS) for target\n",
    "def targetTensor(line):\n",
    "    letter_indexes = [all_letters.find(line[li]) for li in range(1, len(line))]\n",
    "    letter_indexes.append(n_letters - 1) # EOS\n",
    "    return torch.LongTensor(letter_indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make category, input, and target tensors from a random category, line pair\n",
    "def randomTrainingExample():\n",
    "    category, line = randomTrainingPair()\n",
    "    category_tensor = categoryTensor(category)\n",
    "    input_line_tensor = inputTensor(line)\n",
    "    target_line_tensor = targetTensor(line)\n",
    "    return category_tensor, input_line_tensor, target_line_tensor\n",
    "\n",
    "# Make category, input, and target tensors from a random category, line pair\n",
    "def mock_randomTrainingExample():\n",
    "    category, line = randomName()\n",
    "    print('Name: ',line)\n",
    "    category_tensor = categoryTensor(category)\n",
    "    input_line_tensor = inputTensor(line)\n",
    "    target_line_tensor = targetTensor(line)\n",
    "    return category_tensor, input_line_tensor, target_line_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  अशमा\n",
      "tensor([106,  98, 114, 137])\n",
      "138\n"
     ]
    }
   ],
   "source": [
    "category_tensor, input_line_tensor, target_line_tensor = mock_randomTrainingExample()\n",
    "print(target_line_tensor)\n",
    "print(n_letters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "\n",
    "def timeSince(since):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return '%dm %ds' % (m, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "\n",
    "learning_rate = 0.0005\n",
    "\n",
    "rnn = RNN(n_letters, 128, n_letters)\n",
    "\n",
    "def train(category_tensor, input_line_tensor, target_line_tensor):\n",
    "    target_line_tensor.unsqueeze_(-1)\n",
    "    hidden = rnn.initHidden()\n",
    "\n",
    "    loss = 0\n",
    "    \n",
    "    rnn.zero_grad()\n",
    "\n",
    "    for i in range(input_line_tensor.size(0)):\n",
    "        output, hidden = rnn(category_tensor, input_line_tensor[i], hidden)\n",
    "        loss += criterion(output, target_line_tensor[i])\n",
    "    \n",
    "    if loss == 0:\n",
    "        print(\"Input line tensor size = \", input_line_tensor.size(0))\n",
    "        print(\"Inside loss = \", loss)\n",
    "        print(category_tensor)\n",
    "        \n",
    "    loss.backward()\n",
    "   \n",
    "\n",
    "    for p in rnn.parameters():\n",
    "        p.data.add_(-learning_rate, p.grad.data)\n",
    "\n",
    "    return output, loss.item() / input_line_tensor.size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category Tensor: torch.Size([1, 19])\n",
      "Input shape: torch.Size([2, 1, 138])\n",
      "Target line tensor: torch.Size([2, 1])\n",
      "Output shape: torch.Size([1, 138])\n",
      "Loss: 4.895919322967529\n"
     ]
    }
   ],
   "source": [
    "category_tensor, input_line_tensor, target_line_tensor = randomTrainingExample()\n",
    "output, loss = train(category_tensor, input_line_tensor, target_line_tensor)\n",
    "print(\"Category Tensor:\", category_tensor.shape)\n",
    "print(\"Input shape:\",input_line_tensor.shape)\n",
    "print(\"Target line tensor:\",target_line_tensor.shape)\n",
    "print(\"Output shape:\",output.shape)\n",
    "print(\"Loss:\", loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0m 26s (5000 5%) 2.6343\n",
      "0m 56s (10000 10%) 2.1942\n",
      "1m 35s (15000 15%) 2.1304\n",
      "2m 14s (20000 20%) 2.7490\n",
      "2m 46s (25000 25%) 2.7622\n",
      "3m 20s (30000 30%) 2.0742\n",
      "3m 52s (35000 35%) 2.1995\n",
      "4m 20s (40000 40%) 1.9509\n",
      "5m 8s (45000 45%) 4.2864\n",
      "5m 43s (50000 50%) 1.7812\n",
      "6m 22s (55000 55%) 1.7052\n",
      "6m 59s (60000 60%) 2.2467\n",
      "7m 31s (65000 65%) 3.3301\n",
      "8m 8s (70000 70%) 1.7843\n",
      "8m 40s (75000 75%) 2.1439\n",
      "9m 14s (80000 80%) 2.2198\n",
      "9m 47s (85000 85%) 3.5125\n",
      "10m 17s (90000 90%) 2.2364\n",
      "10m 47s (95000 95%) 2.8402\n",
      "11m 23s (100000 100%) 2.1846\n"
     ]
    }
   ],
   "source": [
    "rnn = RNN(n_letters, 100, n_letters)\n",
    "\n",
    "n_iters = 100000\n",
    "print_every = 5000\n",
    "plot_every = 500\n",
    "all_losses = []\n",
    "total_loss = 0\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for iter in range(1, n_iters+1):\n",
    "    output, loss = train(*randomTrainingExample())\n",
    "    total_loss += loss\n",
    "    \n",
    "    if iter % print_every == 0:\n",
    "        print('%s (%d %d%%) %.4f' % (timeSince(start), iter, iter/n_iters * 100, loss))\n",
    "        \n",
    "    if iter % plot_every == 0:\n",
    "        all_losses.append(total_loss / plot_every)\n",
    "        total_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save torch model\n",
    "torch.save(rnn.state_dict(),f='char_rnn_gen.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "    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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    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",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\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",
       "    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, 0, fig.canvas.width, fig.canvas.height);\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": {
      "text/html": [
       "<img 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\" width=\"639.9333333333333\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3030b745c0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(all_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model\n",
    "rnn.load_state_dict(torch.load('char_rnn_gen.model'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "\n",
    "max_length = 20\n",
    "\n",
    "#Sample from a category and  starting letter\n",
    "def sample(category, start_letter = 'A'):\n",
    "    category_tensor = Variable(categoryTensor(category))\n",
    "    input = Variable(inputTensor(start_letter))\n",
    "    hidden = rnn.initHidden()\n",
    "    \n",
    "    output_name = start_letter\n",
    "    \n",
    "    for i in range(max_length):\n",
    "        output, hidden = rnn(category_tensor, input[0], hidden)\n",
    "        topv, topi = output.data.topk(1)\n",
    "        topi = topi[0][0]\n",
    "        if topi == n_letters-1:\n",
    "            break\n",
    "        else:\n",
    "            letter = all_letters[topi]\n",
    "            output_name += letter\n",
    "            \n",
    "        input = Variable(inputTensor(letter))\n",
    "        \n",
    "    return output_name\n",
    "\n",
    "# Get multiple samples from one category and multiple starting letters\n",
    "def samples(category, start_letters = 'ABCDEF'):\n",
    "    for start_letter in start_letters:\n",
    "        print(sample(category, start_letter))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "कििा\n",
      "खििा\n",
      "गििा\n",
      "घनिा\n",
      "चििा\n",
      "छििा\n",
      "जििा\n",
      "तििना\n",
      "थििा\n",
      "दिना\n",
      "धरिा\n",
      "नििा\n",
      "पििा\n",
      "फििा\n",
      "बििा\n",
      "भििा\n",
      "मििा\n",
      "यििा\n",
      "रििा\n",
      "लििा\n",
      "वििा\n",
      "शििा\n",
      "सििाा\n",
      "हििा\n",
      "करना\n",
      "्ििा\n",
      "षििा\n"
     ]
    }
   ],
   "source": [
    "# nepali_letter = 'कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहक्षत्रज्ञ'\n",
    "samples('Nepali', 'कखगघचछजतथदधनपफबभमयरलवशसहक्ष')"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}