├── .gitignore
├── Network.py
├── README.md
├── Transformer.ipynb
├── data.py
├── doc
    ├── buildfig.png
    ├── compare.png
    ├── comparegraph.png
    ├── hyperparams.png
    ├── imports,png.PNG
    ├── init.png
    ├── lossgraph.png
    └── training.png
└── utils.py


/.gitignore:
--------------------------------------------------------------------------------
1 | .ipynb_checkpoints/
2 | __pycache__/
3 | *.py[cod]
4 | .git
5 | data/
6 | 


--------------------------------------------------------------------------------
/Network.py:
--------------------------------------------------------------------------------
 1 | import torch
 2 | import torch.nn as nn
 3 | import torch.nn.functional as F
 4 | import numpy as np
 5 | from utils import *
 6 | 
 7 | 
 8 | class EncoderLayer(torch.nn.Module):
 9 |     def __init__(self, dim_val, dim_attn, n_heads = 1):
10 |         super(EncoderLayer, self).__init__()
11 |         self.attn = MultiHeadAttentionBlock(dim_val, dim_attn , n_heads)
12 |         self.fc1 = nn.Linear(dim_val, dim_val)
13 |         self.fc2 = nn.Linear(dim_val, dim_val)
14 |         
15 |         self.norm1 = nn.LayerNorm(dim_val)
16 |         self.norm2 = nn.LayerNorm(dim_val)
17 |     
18 |     def forward(self, x):
19 |         a = self.attn(x)
20 |         x = self.norm1(x + a)
21 |         
22 |         a = self.fc1(F.elu(self.fc2(x)))
23 |         x = self.norm2(x + a)
24 |         
25 |         return x
26 | 
27 | class DecoderLayer(torch.nn.Module):
28 |     def __init__(self, dim_val, dim_attn, n_heads = 1):
29 |         super(DecoderLayer, self).__init__()
30 |         self.attn1 = MultiHeadAttentionBlock(dim_val, dim_attn, n_heads)
31 |         self.attn2 = MultiHeadAttentionBlock(dim_val, dim_attn, n_heads)
32 |         self.fc1 = nn.Linear(dim_val, dim_val)
33 |         self.fc2 = nn.Linear(dim_val, dim_val)
34 |         
35 |         self.norm1 = nn.LayerNorm(dim_val)
36 |         self.norm2 = nn.LayerNorm(dim_val)
37 |         self.norm3 = nn.LayerNorm(dim_val)
38 |         
39 |     def forward(self, x, enc):
40 |         a = self.attn1(x)
41 |         x = self.norm1(a + x)
42 |         
43 |         a = self.attn2(x, kv = enc)
44 |         x = self.norm2(a + x)
45 |         
46 |         a = self.fc1(F.elu(self.fc2(x)))
47 |         
48 |         x = self.norm3(x + a)
49 |         return x
50 | 
51 | class Transformer(torch.nn.Module):
52 |     def __init__(self, dim_val, dim_attn, input_size, dec_seq_len, out_seq_len, n_decoder_layers = 1, n_encoder_layers = 1, n_heads = 1):
53 |         super(Transformer, self).__init__()
54 |         self.dec_seq_len = dec_seq_len
55 |         
56 |         #Initiate encoder and Decoder layers
57 |         self.encs = []
58 |         for i in range(n_encoder_layers):
59 |             self.encs.append(EncoderLayer(dim_val, dim_attn, n_heads))
60 |         
61 |         self.decs = []
62 |         for i in range(n_decoder_layers):
63 |             self.decs.append(DecoderLayer(dim_val, dim_attn, n_heads))
64 |         
65 |         self.pos = PositionalEncoding(dim_val)
66 |         
67 |         #Dense layers for managing network inputs and outputs
68 |         self.enc_input_fc = nn.Linear(input_size, dim_val)
69 |         self.dec_input_fc = nn.Linear(input_size, dim_val)
70 |         self.out_fc = nn.Linear(dec_seq_len * dim_val, out_seq_len)
71 |     
72 |     def forward(self, x):
73 |         #encoder
74 |         e = self.encs[0](self.pos(self.enc_input_fc(x)))
75 |         for enc in self.encs[1:]:
76 |             e = enc(e)
77 |         
78 |         #decoder
79 |         d = self.decs[0](self.dec_input_fc(x[:,-self.dec_seq_len:]), e)
80 |         for dec in self.decs[1:]:
81 |             d = dec(d, e)
82 |             
83 |         #output
84 |         x = self.out_fc(d.flatten(start_dim=1))
85 |         
86 |         return x


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | This is an implementation of the Transformer algorithm on time series data in pytorch. In this case the modelling of the sigmoid function is used as a toy problem
 2 | 
 3 | Usage:  
 4 | First all the necessary imports as well as matplotlib for visualisation.  
 5 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/imports%2Cpng.PNG)  
 6 | Next we need to define some hyperparameters which will vary depending on the task.  
 7 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/hyperparams.png)  
 8 | We initilisise the Network and an optimizier, in this case Adam, as well as an empty list to track losses for visualisation.  
 9 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/init.png)  
10 | Using matplotlib in jupyter notebook we can graph losses in real time, first lets initialise a figure.  
11 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/buildfig.png)  
12 | We can now being training  
13 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/training.png)  
14 | You should see a live plot that looks similar to this tracking the ouput error  
15 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/lossgraph.png)  
16 | Now that the network is trained, lets give it the first few values of the sigmoid function and see how it approximates the rest.  
17 | We create another figure to visualise this.  
18 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/compare.png)  
19 | If all went well, the output should look something like this :  
20 | ![](https://github.com/LiamMaclean216/Pytorch-Transfomer/blob/master/doc/comparegraph.png)  
21 | Note that the network uses past values instead of the x axis for its predictions , so it makes sense that the output is offset.
22 | However it did succesfully captured the shape.    
23 | 
24 | Resources:  
25 | * Attention is all you need : https://arxiv.org/abs/1706.03762
26 | * Deep Transformer Models for Time Series Forecasting : https://arxiv.org/abs/2001.08317
27 | 
28 | 
29 | 


--------------------------------------------------------------------------------
/Transformer.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import torch\n",
  10 |     "import torch.nn as nn\n",
  11 |     "import torch.nn.functional as F\n",
  12 |     "import numpy as np\n",
  13 |     "from utils import *\n",
  14 |     "from Network import *\n",
  15 |     "from data import *\n",
  16 |     "\n",
  17 |     "%matplotlib notebook\n",
  18 |     "import matplotlib.pyplot as plt\n",
  19 |     "\n",
  20 |     "#hyperparams\n",
  21 |     "enc_seq_len = 6\n",
  22 |     "dec_seq_len = 2\n",
  23 |     "output_sequence_length = 1\n",
  24 |     "\n",
  25 |     "dim_val = 10\n",
  26 |     "dim_attn = 5\n",
  27 |     "lr = 0.002\n",
  28 |     "epochs = 20\n",
  29 |     "\n",
  30 |     "n_heads = 3 \n",
  31 |     "\n",
  32 |     "n_decoder_layers = 3\n",
  33 |     "n_encoder_layers = 3\n",
  34 |     "\n",
  35 |     "batch_size = 15\n",
  36 |     "\n",
  37 |     "#init network and optimizer\n",
  38 |     "t = Transformer(dim_val, dim_attn, 1,dec_seq_len,  output_sequence_length, n_decoder_layers, n_encoder_layers, n_heads)\n",
  39 |     "optimizer = torch.optim.Adam(t.parameters(), lr=lr)\n",
  40 |     "\n",
  41 |     "#keep track of loss for graph\n",
  42 |     "losses = []"
  43 |    ]
  44 |   },
  45 |   {
  46 |    "cell_type": "code",
  47 |    "execution_count": 3,
  48 |    "metadata": {},
  49 |    "outputs": [
  50 |     {
  51 |      "name": "stderr",
  52 |      "output_type": "stream",
  53 |      "text": [
  54 |       "D:\\OneDrive\\GitHub\\Pytorch-Chatbot\\data.py:52: FutureWarning: \n",
  55 |       "Passing list-likes to .loc or [] with any missing label will raise\n",
  56 |       "KeyError in the future, you can use .reindex() as an alternative.\n",
  57 |       "\n",
  58 |       "See the documentation here:\n",
  59 |       "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
  60 |       "  vectorized_targets.append(torch.tensor(word_vecs.loc[b[idx + 1]].values).type(dtype))\n",
  61 |       "D:\\OneDrive\\GitHub\\Pytorch-Chatbot\\data.py:51: FutureWarning: \n",
  62 |       "Passing list-likes to .loc or [] with any missing label will raise\n",
  63 |       "KeyError in the future, you can use .reindex() as an alternative.\n",
  64 |       "\n",
  65 |       "See the documentation here:\n",
  66 |       "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
  67 |       "  vectorized_inputs.append(torch.tensor(word_vecs.loc[b[idx]].values).type(dtype))\n"
  68 |      ]
  69 |     }
  70 |    ],
  71 |    "source": [
  72 |     "g = gen_data(4, gpu = True)\n",
  73 |     "vec_ins, vec_targs, ins, targs = next(g)"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "code",
  78 |    "execution_count": 2,
  79 |    "metadata": {
  80 |     "scrolled": false
  81 |    },
  82 |    "outputs": [
  83 |     {
  84 |      "data": {
  85 |       "application/javascript": [
  86 |        "/* Put everything inside the global mpl namespace */\n",
  87 |        "window.mpl = {};\n",
  88 |        "\n",
  89 |        "\n",
  90 |        "mpl.get_websocket_type = function() {\n",
  91 |        "    if (typeof(WebSocket) !== 'undefined') {\n",
  92 |        "        return WebSocket;\n",
  93 |        "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
  94 |        "        return MozWebSocket;\n",
  95 |        "    } else {\n",
  96 |        "        alert('Your browser does not have WebSocket support. ' +\n",
  97 |        "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
  98 |        "              'Firefox 4 and 5 are also supported but you ' +\n",
  99 |        "              'have to enable WebSockets in about:config.');\n",
 100 |        "    };\n",
 101 |        "}\n",
 102 |        "\n",
 103 |        "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
 104 |        "    this.id = figure_id;\n",
 105 |        "\n",
 106 |        "    this.ws = websocket;\n",
 107 |        "\n",
 108 |        "    this.supports_binary = (this.ws.binaryType != undefined);\n",
 109 |        "\n",
 110 |        "    if (!this.supports_binary) {\n",
 111 |        "        var warnings = document.getElementById(\"mpl-warnings\");\n",
 112 |        "        if (warnings) {\n",
 113 |        "            warnings.style.display = 'block';\n",
 114 |        "            warnings.textContent = (\n",
 115 |        "                \"This browser does not support binary websocket messages. \" +\n",
 116 |        "                    \"Performance may be slow.\");\n",
 117 |        "        }\n",
 118 |        "    }\n",
 119 |        "\n",
 120 |        "    this.imageObj = new Image();\n",
 121 |        "\n",
 122 |        "    this.context = undefined;\n",
 123 |        "    this.message = undefined;\n",
 124 |        "    this.canvas = undefined;\n",
 125 |        "    this.rubberband_canvas = undefined;\n",
 126 |        "    this.rubberband_context = undefined;\n",
 127 |        "    this.format_dropdown = undefined;\n",
 128 |        "\n",
 129 |        "    this.image_mode = 'full';\n",
 130 |        "\n",
 131 |        "    this.root = $('<div/>');\n",
 132 |        "    this._root_extra_style(this.root)\n",
 133 |        "    this.root.attr('style', 'display: inline-block');\n",
 134 |        "\n",
 135 |        "    $(parent_element).append(this.root);\n",
 136 |        "\n",
 137 |        "    this._init_header(this);\n",
 138 |        "    this._init_canvas(this);\n",
 139 |        "    this._init_toolbar(this);\n",
 140 |        "\n",
 141 |        "    var fig = this;\n",
 142 |        "\n",
 143 |        "    this.waiting = false;\n",
 144 |        "\n",
 145 |        "    this.ws.onopen =  function () {\n",
 146 |        "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
 147 |        "            fig.send_message(\"send_image_mode\", {});\n",
 148 |        "            if (mpl.ratio != 1) {\n",
 149 |        "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
 150 |        "            }\n",
 151 |        "            fig.send_message(\"refresh\", {});\n",
 152 |        "        }\n",
 153 |        "\n",
 154 |        "    this.imageObj.onload = function() {\n",
 155 |        "            if (fig.image_mode == 'full') {\n",
 156 |        "                // Full images could contain transparency (where diff images\n",
 157 |        "                // almost always do), so we need to clear the canvas so that\n",
 158 |        "                // there is no ghosting.\n",
 159 |        "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
 160 |        "            }\n",
 161 |        "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
 162 |        "        };\n",
 163 |        "\n",
 164 |        "    this.imageObj.onunload = function() {\n",
 165 |        "        fig.ws.close();\n",
 166 |        "    }\n",
 167 |        "\n",
 168 |        "    this.ws.onmessage = this._make_on_message_function(this);\n",
 169 |        "\n",
 170 |        "    this.ondownload = ondownload;\n",
 171 |        "}\n",
 172 |        "\n",
 173 |        "mpl.figure.prototype._init_header = function() {\n",
 174 |        "    var titlebar = $(\n",
 175 |        "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
 176 |        "        'ui-helper-clearfix\"/>');\n",
 177 |        "    var titletext = $(\n",
 178 |        "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
 179 |        "        'text-align: center; padding: 3px;\"/>');\n",
 180 |        "    titlebar.append(titletext)\n",
 181 |        "    this.root.append(titlebar);\n",
 182 |        "    this.header = titletext[0];\n",
 183 |        "}\n",
 184 |        "\n",
 185 |        "\n",
 186 |        "\n",
 187 |        "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
 188 |        "\n",
 189 |        "}\n",
 190 |        "\n",
 191 |        "\n",
 192 |        "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
 193 |        "\n",
 194 |        "}\n",
 195 |        "\n",
 196 |        "mpl.figure.prototype._init_canvas = function() {\n",
 197 |        "    var fig = this;\n",
 198 |        "\n",
 199 |        "    var canvas_div = $('<div/>');\n",
 200 |        "\n",
 201 |        "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
 202 |        "\n",
 203 |        "    function canvas_keyboard_event(event) {\n",
 204 |        "        return fig.key_event(event, event['data']);\n",
 205 |        "    }\n",
 206 |        "\n",
 207 |        "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
 208 |        "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
 209 |        "    this.canvas_div = canvas_div\n",
 210 |        "    this._canvas_extra_style(canvas_div)\n",
 211 |        "    this.root.append(canvas_div);\n",
 212 |        "\n",
 213 |        "    var canvas = $('<canvas/>');\n",
 214 |        "    canvas.addClass('mpl-canvas');\n",
 215 |        "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
 216 |        "\n",
 217 |        "    this.canvas = canvas[0];\n",
 218 |        "    this.context = canvas[0].getContext(\"2d\");\n",
 219 |        "\n",
 220 |        "    var backingStore = this.context.backingStorePixelRatio ||\n",
 221 |        "\tthis.context.webkitBackingStorePixelRatio ||\n",
 222 |        "\tthis.context.mozBackingStorePixelRatio ||\n",
 223 |        "\tthis.context.msBackingStorePixelRatio ||\n",
 224 |        "\tthis.context.oBackingStorePixelRatio ||\n",
 225 |        "\tthis.context.backingStorePixelRatio || 1;\n",
 226 |        "\n",
 227 |        "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
 228 |        "\n",
 229 |        "    var rubberband = $('<canvas/>');\n",
 230 |        "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
 231 |        "\n",
 232 |        "    var pass_mouse_events = true;\n",
 233 |        "\n",
 234 |        "    canvas_div.resizable({\n",
 235 |        "        start: function(event, ui) {\n",
 236 |        "            pass_mouse_events = false;\n",
 237 |        "        },\n",
 238 |        "        resize: function(event, ui) {\n",
 239 |        "            fig.request_resize(ui.size.width, ui.size.height);\n",
 240 |        "        },\n",
 241 |        "        stop: function(event, ui) {\n",
 242 |        "            pass_mouse_events = true;\n",
 243 |        "            fig.request_resize(ui.size.width, ui.size.height);\n",
 244 |        "        },\n",
 245 |        "    });\n",
 246 |        "\n",
 247 |        "    function mouse_event_fn(event) {\n",
 248 |        "        if (pass_mouse_events)\n",
 249 |        "            return fig.mouse_event(event, event['data']);\n",
 250 |        "    }\n",
 251 |        "\n",
 252 |        "    rubberband.mousedown('button_press', mouse_event_fn);\n",
 253 |        "    rubberband.mouseup('button_release', mouse_event_fn);\n",
 254 |        "    // Throttle sequential mouse events to 1 every 20ms.\n",
 255 |        "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
 256 |        "\n",
 257 |        "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
 258 |        "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
 259 |        "\n",
 260 |        "    canvas_div.on(\"wheel\", function (event) {\n",
 261 |        "        event = event.originalEvent;\n",
 262 |        "        event['data'] = 'scroll'\n",
 263 |        "        if (event.deltaY < 0) {\n",
 264 |        "            event.step = 1;\n",
 265 |        "        } else {\n",
 266 |        "            event.step = -1;\n",
 267 |        "        }\n",
 268 |        "        mouse_event_fn(event);\n",
 269 |        "    });\n",
 270 |        "\n",
 271 |        "    canvas_div.append(canvas);\n",
 272 |        "    canvas_div.append(rubberband);\n",
 273 |        "\n",
 274 |        "    this.rubberband = rubberband;\n",
 275 |        "    this.rubberband_canvas = rubberband[0];\n",
 276 |        "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
 277 |        "    this.rubberband_context.strokeStyle = \"#000000\";\n",
 278 |        "\n",
 279 |        "    this._resize_canvas = function(width, height) {\n",
 280 |        "        // Keep the size of the canvas, canvas container, and rubber band\n",
 281 |        "        // canvas in synch.\n",
 282 |        "        canvas_div.css('width', width)\n",
 283 |        "        canvas_div.css('height', height)\n",
 284 |        "\n",
 285 |        "        canvas.attr('width', width * mpl.ratio);\n",
 286 |        "        canvas.attr('height', height * mpl.ratio);\n",
 287 |        "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
 288 |        "\n",
 289 |        "        rubberband.attr('width', width);\n",
 290 |        "        rubberband.attr('height', height);\n",
 291 |        "    }\n",
 292 |        "\n",
 293 |        "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
 294 |        "    // upon first draw.\n",
 295 |        "    this._resize_canvas(600, 600);\n",
 296 |        "\n",
 297 |        "    // Disable right mouse context menu.\n",
 298 |        "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
 299 |        "        return false;\n",
 300 |        "    });\n",
 301 |        "\n",
 302 |        "    function set_focus () {\n",
 303 |        "        canvas.focus();\n",
 304 |        "        canvas_div.focus();\n",
 305 |        "    }\n",
 306 |        "\n",
 307 |        "    window.setTimeout(set_focus, 100);\n",
 308 |        "}\n",
 309 |        "\n",
 310 |        "mpl.figure.prototype._init_toolbar = function() {\n",
 311 |        "    var fig = this;\n",
 312 |        "\n",
 313 |        "    var nav_element = $('<div/>');\n",
 314 |        "    nav_element.attr('style', 'width: 100%');\n",
 315 |        "    this.root.append(nav_element);\n",
 316 |        "\n",
 317 |        "    // Define a callback function for later on.\n",
 318 |        "    function toolbar_event(event) {\n",
 319 |        "        return fig.toolbar_button_onclick(event['data']);\n",
 320 |        "    }\n",
 321 |        "    function toolbar_mouse_event(event) {\n",
 322 |        "        return fig.toolbar_button_onmouseover(event['data']);\n",
 323 |        "    }\n",
 324 |        "\n",
 325 |        "    for(var toolbar_ind in mpl.toolbar_items) {\n",
 326 |        "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
 327 |        "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
 328 |        "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
 329 |        "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
 330 |        "\n",
 331 |        "        if (!name) {\n",
 332 |        "            // put a spacer in here.\n",
 333 |        "            continue;\n",
 334 |        "        }\n",
 335 |        "        var button = $('<button/>');\n",
 336 |        "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
 337 |        "                        'ui-button-icon-only');\n",
 338 |        "        button.attr('role', 'button');\n",
 339 |        "        button.attr('aria-disabled', 'false');\n",
 340 |        "        button.click(method_name, toolbar_event);\n",
 341 |        "        button.mouseover(tooltip, toolbar_mouse_event);\n",
 342 |        "\n",
 343 |        "        var icon_img = $('<span/>');\n",
 344 |        "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
 345 |        "        icon_img.addClass(image);\n",
 346 |        "        icon_img.addClass('ui-corner-all');\n",
 347 |        "\n",
 348 |        "        var tooltip_span = $('<span/>');\n",
 349 |        "        tooltip_span.addClass('ui-button-text');\n",
 350 |        "        tooltip_span.html(tooltip);\n",
 351 |        "\n",
 352 |        "        button.append(icon_img);\n",
 353 |        "        button.append(tooltip_span);\n",
 354 |        "\n",
 355 |        "        nav_element.append(button);\n",
 356 |        "    }\n",
 357 |        "\n",
 358 |        "    var fmt_picker_span = $('<span/>');\n",
 359 |        "\n",
 360 |        "    var fmt_picker = $('<select/>');\n",
 361 |        "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
 362 |        "    fmt_picker_span.append(fmt_picker);\n",
 363 |        "    nav_element.append(fmt_picker_span);\n",
 364 |        "    this.format_dropdown = fmt_picker[0];\n",
 365 |        "\n",
 366 |        "    for (var ind in mpl.extensions) {\n",
 367 |        "        var fmt = mpl.extensions[ind];\n",
 368 |        "        var option = $(\n",
 369 |        "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
 370 |        "        fmt_picker.append(option);\n",
 371 |        "    }\n",
 372 |        "\n",
 373 |        "    // Add hover states to the ui-buttons\n",
 374 |        "    $( \".ui-button\" ).hover(\n",
 375 |        "        function() { $(this).addClass(\"ui-state-hover\");},\n",
 376 |        "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
 377 |        "    );\n",
 378 |        "\n",
 379 |        "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
 380 |        "    nav_element.append(status_bar);\n",
 381 |        "    this.message = status_bar[0];\n",
 382 |        "}\n",
 383 |        "\n",
 384 |        "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
 385 |        "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
 386 |        "    // which will in turn request a refresh of the image.\n",
 387 |        "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
 388 |        "}\n",
 389 |        "\n",
 390 |        "mpl.figure.prototype.send_message = function(type, properties) {\n",
 391 |        "    properties['type'] = type;\n",
 392 |        "    properties['figure_id'] = this.id;\n",
 393 |        "    this.ws.send(JSON.stringify(properties));\n",
 394 |        "}\n",
 395 |        "\n",
 396 |        "mpl.figure.prototype.send_draw_message = function() {\n",
 397 |        "    if (!this.waiting) {\n",
 398 |        "        this.waiting = true;\n",
 399 |        "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
 400 |        "    }\n",
 401 |        "}\n",
 402 |        "\n",
 403 |        "\n",
 404 |        "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
 405 |        "    var format_dropdown = fig.format_dropdown;\n",
 406 |        "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
 407 |        "    fig.ondownload(fig, format);\n",
 408 |        "}\n",
 409 |        "\n",
 410 |        "\n",
 411 |        "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
 412 |        "    var size = msg['size'];\n",
 413 |        "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
 414 |        "        fig._resize_canvas(size[0], size[1]);\n",
 415 |        "        fig.send_message(\"refresh\", {});\n",
 416 |        "    };\n",
 417 |        "}\n",
 418 |        "\n",
 419 |        "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
 420 |        "    var x0 = msg['x0'] / mpl.ratio;\n",
 421 |        "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
 422 |        "    var x1 = msg['x1'] / mpl.ratio;\n",
 423 |        "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
 424 |        "    x0 = Math.floor(x0) + 0.5;\n",
 425 |        "    y0 = Math.floor(y0) + 0.5;\n",
 426 |        "    x1 = Math.floor(x1) + 0.5;\n",
 427 |        "    y1 = Math.floor(y1) + 0.5;\n",
 428 |        "    var min_x = Math.min(x0, x1);\n",
 429 |        "    var min_y = Math.min(y0, y1);\n",
 430 |        "    var width = Math.abs(x1 - x0);\n",
 431 |        "    var height = Math.abs(y1 - y0);\n",
 432 |        "\n",
 433 |        "    fig.rubberband_context.clearRect(\n",
 434 |        "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
 435 |        "\n",
 436 |        "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
 437 |        "}\n",
 438 |        "\n",
 439 |        "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
 440 |        "    // Updates the figure title.\n",
 441 |        "    fig.header.textContent = msg['label'];\n",
 442 |        "}\n",
 443 |        "\n",
 444 |        "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
 445 |        "    var cursor = msg['cursor'];\n",
 446 |        "    switch(cursor)\n",
 447 |        "    {\n",
 448 |        "    case 0:\n",
 449 |        "        cursor = 'pointer';\n",
 450 |        "        break;\n",
 451 |        "    case 1:\n",
 452 |        "        cursor = 'default';\n",
 453 |        "        break;\n",
 454 |        "    case 2:\n",
 455 |        "        cursor = 'crosshair';\n",
 456 |        "        break;\n",
 457 |        "    case 3:\n",
 458 |        "        cursor = 'move';\n",
 459 |        "        break;\n",
 460 |        "    }\n",
 461 |        "    fig.rubberband_canvas.style.cursor = cursor;\n",
 462 |        "}\n",
 463 |        "\n",
 464 |        "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
 465 |        "    fig.message.textContent = msg['message'];\n",
 466 |        "}\n",
 467 |        "\n",
 468 |        "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
 469 |        "    // Request the server to send over a new figure.\n",
 470 |        "    fig.send_draw_message();\n",
 471 |        "}\n",
 472 |        "\n",
 473 |        "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
 474 |        "    fig.image_mode = msg['mode'];\n",
 475 |        "}\n",
 476 |        "\n",
 477 |        "mpl.figure.prototype.updated_canvas_event = function() {\n",
 478 |        "    // Called whenever the canvas gets updated.\n",
 479 |        "    this.send_message(\"ack\", {});\n",
 480 |        "}\n",
 481 |        "\n",
 482 |        "// A function to construct a web socket function for onmessage handling.\n",
 483 |        "// Called in the figure constructor.\n",
 484 |        "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
 485 |        "    return function socket_on_message(evt) {\n",
 486 |        "        if (evt.data instanceof Blob) {\n",
 487 |        "            /* FIXME: We get \"Resource interpreted as Image but\n",
 488 |        "             * transferred with MIME type text/plain:\" errors on\n",
 489 |        "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
 490 |        "             * to be part of the websocket stream */\n",
 491 |        "            evt.data.type = \"image/png\";\n",
 492 |        "\n",
 493 |        "            /* Free the memory for the previous frames */\n",
 494 |        "            if (fig.imageObj.src) {\n",
 495 |        "                (window.URL || window.webkitURL).revokeObjectURL(\n",
 496 |        "                    fig.imageObj.src);\n",
 497 |        "            }\n",
 498 |        "\n",
 499 |        "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
 500 |        "                evt.data);\n",
 501 |        "            fig.updated_canvas_event();\n",
 502 |        "            fig.waiting = false;\n",
 503 |        "            return;\n",
 504 |        "        }\n",
 505 |        "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
 506 |        "            fig.imageObj.src = evt.data;\n",
 507 |        "            fig.updated_canvas_event();\n",
 508 |        "            fig.waiting = false;\n",
 509 |        "            return;\n",
 510 |        "        }\n",
 511 |        "\n",
 512 |        "        var msg = JSON.parse(evt.data);\n",
 513 |        "        var msg_type = msg['type'];\n",
 514 |        "\n",
 515 |        "        // Call the  \"handle_{type}\" callback, which takes\n",
 516 |        "        // the figure and JSON message as its only arguments.\n",
 517 |        "        try {\n",
 518 |        "            var callback = fig[\"handle_\" + msg_type];\n",
 519 |        "        } catch (e) {\n",
 520 |        "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
 521 |        "            return;\n",
 522 |        "        }\n",
 523 |        "\n",
 524 |        "        if (callback) {\n",
 525 |        "            try {\n",
 526 |        "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
 527 |        "                callback(fig, msg);\n",
 528 |        "            } catch (e) {\n",
 529 |        "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
 530 |        "            }\n",
 531 |        "        }\n",
 532 |        "    };\n",
 533 |        "}\n",
 534 |        "\n",
 535 |        "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
 536 |        "mpl.findpos = function(e) {\n",
 537 |        "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
 538 |        "    var targ;\n",
 539 |        "    if (!e)\n",
 540 |        "        e = window.event;\n",
 541 |        "    if (e.target)\n",
 542 |        "        targ = e.target;\n",
 543 |        "    else if (e.srcElement)\n",
 544 |        "        targ = e.srcElement;\n",
 545 |        "    if (targ.nodeType == 3) // defeat Safari bug\n",
 546 |        "        targ = targ.parentNode;\n",
 547 |        "\n",
 548 |        "    // jQuery normalizes the pageX and pageY\n",
 549 |        "    // pageX,Y are the mouse positions relative to the document\n",
 550 |        "    // offset() returns the position of the element relative to the document\n",
 551 |        "    var x = e.pageX - $(targ).offset().left;\n",
 552 |        "    var y = e.pageY - $(targ).offset().top;\n",
 553 |        "\n",
 554 |        "    return {\"x\": x, \"y\": y};\n",
 555 |        "};\n",
 556 |        "\n",
 557 |        "/*\n",
 558 |        " * return a copy of an object with only non-object keys\n",
 559 |        " * we need this to avoid circular references\n",
 560 |        " * http://stackoverflow.com/a/24161582/3208463\n",
 561 |        " */\n",
 562 |        "function simpleKeys (original) {\n",
 563 |        "  return Object.keys(original).reduce(function (obj, key) {\n",
 564 |        "    if (typeof original[key] !== 'object')\n",
 565 |        "        obj[key] = original[key]\n",
 566 |        "    return obj;\n",
 567 |        "  }, {});\n",
 568 |        "}\n",
 569 |        "\n",
 570 |        "mpl.figure.prototype.mouse_event = function(event, name) {\n",
 571 |        "    var canvas_pos = mpl.findpos(event)\n",
 572 |        "\n",
 573 |        "    if (name === 'button_press')\n",
 574 |        "    {\n",
 575 |        "        this.canvas.focus();\n",
 576 |        "        this.canvas_div.focus();\n",
 577 |        "    }\n",
 578 |        "\n",
 579 |        "    var x = canvas_pos.x * mpl.ratio;\n",
 580 |        "    var y = canvas_pos.y * mpl.ratio;\n",
 581 |        "\n",
 582 |        "    this.send_message(name, {x: x, y: y, button: event.button,\n",
 583 |        "                             step: event.step,\n",
 584 |        "                             guiEvent: simpleKeys(event)});\n",
 585 |        "\n",
 586 |        "    /* This prevents the web browser from automatically changing to\n",
 587 |        "     * the text insertion cursor when the button is pressed.  We want\n",
 588 |        "     * to control all of the cursor setting manually through the\n",
 589 |        "     * 'cursor' event from matplotlib */\n",
 590 |        "    event.preventDefault();\n",
 591 |        "    return false;\n",
 592 |        "}\n",
 593 |        "\n",
 594 |        "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
 595 |        "    // Handle any extra behaviour associated with a key event\n",
 596 |        "}\n",
 597 |        "\n",
 598 |        "mpl.figure.prototype.key_event = function(event, name) {\n",
 599 |        "\n",
 600 |        "    // Prevent repeat events\n",
 601 |        "    if (name == 'key_press')\n",
 602 |        "    {\n",
 603 |        "        if (event.which === this._key)\n",
 604 |        "            return;\n",
 605 |        "        else\n",
 606 |        "            this._key = event.which;\n",
 607 |        "    }\n",
 608 |        "    if (name == 'key_release')\n",
 609 |        "        this._key = null;\n",
 610 |        "\n",
 611 |        "    var value = '';\n",
 612 |        "    if (event.ctrlKey && event.which != 17)\n",
 613 |        "        value += \"ctrl+\";\n",
 614 |        "    if (event.altKey && event.which != 18)\n",
 615 |        "        value += \"alt+\";\n",
 616 |        "    if (event.shiftKey && event.which != 16)\n",
 617 |        "        value += \"shift+\";\n",
 618 |        "\n",
 619 |        "    value += 'k';\n",
 620 |        "    value += event.which.toString();\n",
 621 |        "\n",
 622 |        "    this._key_event_extra(event, name);\n",
 623 |        "\n",
 624 |        "    this.send_message(name, {key: value,\n",
 625 |        "                             guiEvent: simpleKeys(event)});\n",
 626 |        "    return false;\n",
 627 |        "}\n",
 628 |        "\n",
 629 |        "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
 630 |        "    if (name == 'download') {\n",
 631 |        "        this.handle_save(this, null);\n",
 632 |        "    } else {\n",
 633 |        "        this.send_message(\"toolbar_button\", {name: name});\n",
 634 |        "    }\n",
 635 |        "};\n",
 636 |        "\n",
 637 |        "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
 638 |        "    this.message.textContent = tooltip;\n",
 639 |        "};\n",
 640 |        "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",
 641 |        "\n",
 642 |        "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
 643 |        "\n",
 644 |        "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
 645 |        "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
 646 |        "    // object with the appropriate methods. Currently this is a non binary\n",
 647 |        "    // socket, so there is still some room for performance tuning.\n",
 648 |        "    var ws = {};\n",
 649 |        "\n",
 650 |        "    ws.close = function() {\n",
 651 |        "        comm.close()\n",
 652 |        "    };\n",
 653 |        "    ws.send = function(m) {\n",
 654 |        "        //console.log('sending', m);\n",
 655 |        "        comm.send(m);\n",
 656 |        "    };\n",
 657 |        "    // Register the callback with on_msg.\n",
 658 |        "    comm.on_msg(function(msg) {\n",
 659 |        "        //console.log('receiving', msg['content']['data'], msg);\n",
 660 |        "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
 661 |        "        ws.onmessage(msg['content']['data'])\n",
 662 |        "    });\n",
 663 |        "    return ws;\n",
 664 |        "}\n",
 665 |        "\n",
 666 |        "mpl.mpl_figure_comm = function(comm, msg) {\n",
 667 |        "    // This is the function which gets called when the mpl process\n",
 668 |        "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
 669 |        "\n",
 670 |        "    var id = msg.content.data.id;\n",
 671 |        "    // Get hold of the div created by the display call when the Comm\n",
 672 |        "    // socket was opened in Python.\n",
 673 |        "    var element = $(\"#\" + id);\n",
 674 |        "    var ws_proxy = comm_websocket_adapter(comm)\n",
 675 |        "\n",
 676 |        "    function ondownload(figure, format) {\n",
 677 |        "        window.open(figure.imageObj.src);\n",
 678 |        "    }\n",
 679 |        "\n",
 680 |        "    var fig = new mpl.figure(id, ws_proxy,\n",
 681 |        "                           ondownload,\n",
 682 |        "                           element.get(0));\n",
 683 |        "\n",
 684 |        "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
 685 |        "    // web socket which is closed, not our websocket->open comm proxy.\n",
 686 |        "    ws_proxy.onopen();\n",
 687 |        "\n",
 688 |        "    fig.parent_element = element.get(0);\n",
 689 |        "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
 690 |        "    if (!fig.cell_info) {\n",
 691 |        "        console.error(\"Failed to find cell for figure\", id, fig);\n",
 692 |        "        return;\n",
 693 |        "    }\n",
 694 |        "\n",
 695 |        "    var output_index = fig.cell_info[2]\n",
 696 |        "    var cell = fig.cell_info[0];\n",
 697 |        "\n",
 698 |        "};\n",
 699 |        "\n",
 700 |        "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
 701 |        "    var width = fig.canvas.width/mpl.ratio\n",
 702 |        "    fig.root.unbind('remove')\n",
 703 |        "\n",
 704 |        "    // Update the output cell to use the data from the current canvas.\n",
 705 |        "    fig.push_to_output();\n",
 706 |        "    var dataURL = fig.canvas.toDataURL();\n",
 707 |        "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
 708 |        "    // the notebook keyboard shortcuts fail.\n",
 709 |        "    IPython.keyboard_manager.enable()\n",
 710 |        "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
 711 |        "    fig.close_ws(fig, msg);\n",
 712 |        "}\n",
 713 |        "\n",
 714 |        "mpl.figure.prototype.close_ws = function(fig, msg){\n",
 715 |        "    fig.send_message('closing', msg);\n",
 716 |        "    // fig.ws.close()\n",
 717 |        "}\n",
 718 |        "\n",
 719 |        "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
 720 |        "    // Turn the data on the canvas into data in the output cell.\n",
 721 |        "    var width = this.canvas.width/mpl.ratio\n",
 722 |        "    var dataURL = this.canvas.toDataURL();\n",
 723 |        "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
 724 |        "}\n",
 725 |        "\n",
 726 |        "mpl.figure.prototype.updated_canvas_event = function() {\n",
 727 |        "    // Tell IPython that the notebook contents must change.\n",
 728 |        "    IPython.notebook.set_dirty(true);\n",
 729 |        "    this.send_message(\"ack\", {});\n",
 730 |        "    var fig = this;\n",
 731 |        "    // Wait a second, then push the new image to the DOM so\n",
 732 |        "    // that it is saved nicely (might be nice to debounce this).\n",
 733 |        "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
 734 |        "}\n",
 735 |        "\n",
 736 |        "mpl.figure.prototype._init_toolbar = function() {\n",
 737 |        "    var fig = this;\n",
 738 |        "\n",
 739 |        "    var nav_element = $('<div/>');\n",
 740 |        "    nav_element.attr('style', 'width: 100%');\n",
 741 |        "    this.root.append(nav_element);\n",
 742 |        "\n",
 743 |        "    // Define a callback function for later on.\n",
 744 |        "    function toolbar_event(event) {\n",
 745 |        "        return fig.toolbar_button_onclick(event['data']);\n",
 746 |        "    }\n",
 747 |        "    function toolbar_mouse_event(event) {\n",
 748 |        "        return fig.toolbar_button_onmouseover(event['data']);\n",
 749 |        "    }\n",
 750 |        "\n",
 751 |        "    for(var toolbar_ind in mpl.toolbar_items){\n",
 752 |        "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
 753 |        "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
 754 |        "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
 755 |        "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
 756 |        "\n",
 757 |        "        if (!name) { continue; };\n",
 758 |        "\n",
 759 |        "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
 760 |        "        button.click(method_name, toolbar_event);\n",
 761 |        "        button.mouseover(tooltip, toolbar_mouse_event);\n",
 762 |        "        nav_element.append(button);\n",
 763 |        "    }\n",
 764 |        "\n",
 765 |        "    // Add the status bar.\n",
 766 |        "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
 767 |        "    nav_element.append(status_bar);\n",
 768 |        "    this.message = status_bar[0];\n",
 769 |        "\n",
 770 |        "    // Add the close button to the window.\n",
 771 |        "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
 772 |        "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
 773 |        "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
 774 |        "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
 775 |        "    buttongrp.append(button);\n",
 776 |        "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
 777 |        "    titlebar.prepend(buttongrp);\n",
 778 |        "}\n",
 779 |        "\n",
 780 |        "mpl.figure.prototype._root_extra_style = function(el){\n",
 781 |        "    var fig = this\n",
 782 |        "    el.on(\"remove\", function(){\n",
 783 |        "\tfig.close_ws(fig, {});\n",
 784 |        "    });\n",
 785 |        "}\n",
 786 |        "\n",
 787 |        "mpl.figure.prototype._canvas_extra_style = function(el){\n",
 788 |        "    // this is important to make the div 'focusable\n",
 789 |        "    el.attr('tabindex', 0)\n",
 790 |        "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
 791 |        "    // off when our div gets focus\n",
 792 |        "\n",
 793 |        "    // location in version 3\n",
 794 |        "    if (IPython.notebook.keyboard_manager) {\n",
 795 |        "        IPython.notebook.keyboard_manager.register_events(el);\n",
 796 |        "    }\n",
 797 |        "    else {\n",
 798 |        "        // location in version 2\n",
 799 |        "        IPython.keyboard_manager.register_events(el);\n",
 800 |        "    }\n",
 801 |        "\n",
 802 |        "}\n",
 803 |        "\n",
 804 |        "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
 805 |        "    var manager = IPython.notebook.keyboard_manager;\n",
 806 |        "    if (!manager)\n",
 807 |        "        manager = IPython.keyboard_manager;\n",
 808 |        "\n",
 809 |        "    // Check for shift+enter\n",
 810 |        "    if (event.shiftKey && event.which == 13) {\n",
 811 |        "        this.canvas_div.blur();\n",
 812 |        "        event.shiftKey = false;\n",
 813 |        "        // Send a \"J\" for go to next cell\n",
 814 |        "        event.which = 74;\n",
 815 |        "        event.keyCode = 74;\n",
 816 |        "        manager.command_mode();\n",
 817 |        "        manager.handle_keydown(event);\n",
 818 |        "    }\n",
 819 |        "}\n",
 820 |        "\n",
 821 |        "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
 822 |        "    fig.ondownload(fig, null);\n",
 823 |        "}\n",
 824 |        "\n",
 825 |        "\n",
 826 |        "mpl.find_output_cell = function(html_output) {\n",
 827 |        "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
 828 |        "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
 829 |        "    // IPython event is triggered only after the cells have been serialised, which for\n",
 830 |        "    // our purposes (turning an active figure into a static one), is too late.\n",
 831 |        "    var cells = IPython.notebook.get_cells();\n",
 832 |        "    var ncells = cells.length;\n",
 833 |        "    for (var i=0; i<ncells; i++) {\n",
 834 |        "        var cell = cells[i];\n",
 835 |        "        if (cell.cell_type === 'code'){\n",
 836 |        "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
 837 |        "                var data = cell.output_area.outputs[j];\n",
 838 |        "                if (data.data) {\n",
 839 |        "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
 840 |        "                    data = data.data;\n",
 841 |        "                }\n",
 842 |        "                if (data['text/html'] == html_output) {\n",
 843 |        "                    return [cell, data, j];\n",
 844 |        "                }\n",
 845 |        "            }\n",
 846 |        "        }\n",
 847 |        "    }\n",
 848 |        "}\n",
 849 |        "\n",
 850 |        "// Register the function which deals with the matplotlib target/channel.\n",
 851 |        "// The kernel may be null if the page has been refreshed.\n",
 852 |        "if (IPython.notebook.kernel != null) {\n",
 853 |        "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
 854 |        "}\n"
 855 |       ],
 856 |       "text/plain": [
 857 |        "<IPython.core.display.Javascript object>"
 858 |       ]
 859 |      },
 860 |      "metadata": {},
 861 |      "output_type": "display_data"
 862 |     },
 863 |     {
 864 |      "data": {
 865 |       "text/html": [
 866 |        "<img 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\" width=\"640\">"
 867 |       ],
 868 |       "text/plain": [
 869 |        "<IPython.core.display.HTML object>"
 870 |       ]
 871 |      },
 872 |      "metadata": {},
 873 |      "output_type": "display_data"
 874 |     }
 875 |    ],
 876 |    "source": [
 877 |     "#build live matplotlib fig\n",
 878 |     "fig = plt.figure()\n",
 879 |     "\n",
 880 |     "ax = fig.add_subplot(111)\n",
 881 |     "plt.ion()\n",
 882 |     "\n",
 883 |     "fig.show()\n",
 884 |     "fig.canvas.draw()\n",
 885 |     "\n",
 886 |     "    \n",
 887 |     "for e in range(epochs):\n",
 888 |     "    out = []\n",
 889 |     "    \n",
 890 |     "    for b in range(-10- enc_seq_len, 10 - enc_seq_len):\n",
 891 |     "        optimizer.zero_grad()\n",
 892 |     "        X, Y = get_data(batch_size, enc_seq_len, output_sequence_length)\n",
 893 |     "        \n",
 894 |     "        #Forward pass and calculate loss\n",
 895 |     "        net_out = t(X)\n",
 896 |     "        #print(net_out.shape,Y.shape)\n",
 897 |     "        loss = torch.mean((net_out - Y) ** 2)\n",
 898 |     "\n",
 899 |     "        #backwards pass\n",
 900 |     "        loss.backward()\n",
 901 |     "        optimizer.step()\n",
 902 |     "\n",
 903 |     "        #Track losses and draw rgaph\n",
 904 |     "        out.append([net_out.detach().numpy(), Y])\n",
 905 |     "        losses.append(loss)\n",
 906 |     "\n",
 907 |     "        ax.clear()\n",
 908 |     "        ax.plot(losses)\n",
 909 |     "        ax.set_title(\"Mean Squared Error\")\n",
 910 |     "        fig.canvas.draw()\n",
 911 |     "\n"
 912 |    ]
 913 |   },
 914 |   {
 915 |    "cell_type": "code",
 916 |    "execution_count": 5,
 917 |    "metadata": {
 918 |     "scrolled": false
 919 |    },
 920 |    "outputs": [
 921 |     {
 922 |      "data": {
 923 |       "application/javascript": [
 924 |        "/* Put everything inside the global mpl namespace */\n",
 925 |        "window.mpl = {};\n",
 926 |        "\n",
 927 |        "\n",
 928 |        "mpl.get_websocket_type = function() {\n",
 929 |        "    if (typeof(WebSocket) !== 'undefined') {\n",
 930 |        "        return WebSocket;\n",
 931 |        "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
 932 |        "        return MozWebSocket;\n",
 933 |        "    } else {\n",
 934 |        "        alert('Your browser does not have WebSocket support. ' +\n",
 935 |        "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
 936 |        "              'Firefox 4 and 5 are also supported but you ' +\n",
 937 |        "              'have to enable WebSockets in about:config.');\n",
 938 |        "    };\n",
 939 |        "}\n",
 940 |        "\n",
 941 |        "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
 942 |        "    this.id = figure_id;\n",
 943 |        "\n",
 944 |        "    this.ws = websocket;\n",
 945 |        "\n",
 946 |        "    this.supports_binary = (this.ws.binaryType != undefined);\n",
 947 |        "\n",
 948 |        "    if (!this.supports_binary) {\n",
 949 |        "        var warnings = document.getElementById(\"mpl-warnings\");\n",
 950 |        "        if (warnings) {\n",
 951 |        "            warnings.style.display = 'block';\n",
 952 |        "            warnings.textContent = (\n",
 953 |        "                \"This browser does not support binary websocket messages. \" +\n",
 954 |        "                    \"Performance may be slow.\");\n",
 955 |        "        }\n",
 956 |        "    }\n",
 957 |        "\n",
 958 |        "    this.imageObj = new Image();\n",
 959 |        "\n",
 960 |        "    this.context = undefined;\n",
 961 |        "    this.message = undefined;\n",
 962 |        "    this.canvas = undefined;\n",
 963 |        "    this.rubberband_canvas = undefined;\n",
 964 |        "    this.rubberband_context = undefined;\n",
 965 |        "    this.format_dropdown = undefined;\n",
 966 |        "\n",
 967 |        "    this.image_mode = 'full';\n",
 968 |        "\n",
 969 |        "    this.root = $('<div/>');\n",
 970 |        "    this._root_extra_style(this.root)\n",
 971 |        "    this.root.attr('style', 'display: inline-block');\n",
 972 |        "\n",
 973 |        "    $(parent_element).append(this.root);\n",
 974 |        "\n",
 975 |        "    this._init_header(this);\n",
 976 |        "    this._init_canvas(this);\n",
 977 |        "    this._init_toolbar(this);\n",
 978 |        "\n",
 979 |        "    var fig = this;\n",
 980 |        "\n",
 981 |        "    this.waiting = false;\n",
 982 |        "\n",
 983 |        "    this.ws.onopen =  function () {\n",
 984 |        "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
 985 |        "            fig.send_message(\"send_image_mode\", {});\n",
 986 |        "            if (mpl.ratio != 1) {\n",
 987 |        "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
 988 |        "            }\n",
 989 |        "            fig.send_message(\"refresh\", {});\n",
 990 |        "        }\n",
 991 |        "\n",
 992 |        "    this.imageObj.onload = function() {\n",
 993 |        "            if (fig.image_mode == 'full') {\n",
 994 |        "                // Full images could contain transparency (where diff images\n",
 995 |        "                // almost always do), so we need to clear the canvas so that\n",
 996 |        "                // there is no ghosting.\n",
 997 |        "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
 998 |        "            }\n",
 999 |        "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
1000 |        "        };\n",
1001 |        "\n",
1002 |        "    this.imageObj.onunload = function() {\n",
1003 |        "        fig.ws.close();\n",
1004 |        "    }\n",
1005 |        "\n",
1006 |        "    this.ws.onmessage = this._make_on_message_function(this);\n",
1007 |        "\n",
1008 |        "    this.ondownload = ondownload;\n",
1009 |        "}\n",
1010 |        "\n",
1011 |        "mpl.figure.prototype._init_header = function() {\n",
1012 |        "    var titlebar = $(\n",
1013 |        "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
1014 |        "        'ui-helper-clearfix\"/>');\n",
1015 |        "    var titletext = $(\n",
1016 |        "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
1017 |        "        'text-align: center; padding: 3px;\"/>');\n",
1018 |        "    titlebar.append(titletext)\n",
1019 |        "    this.root.append(titlebar);\n",
1020 |        "    this.header = titletext[0];\n",
1021 |        "}\n",
1022 |        "\n",
1023 |        "\n",
1024 |        "\n",
1025 |        "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
1026 |        "\n",
1027 |        "}\n",
1028 |        "\n",
1029 |        "\n",
1030 |        "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
1031 |        "\n",
1032 |        "}\n",
1033 |        "\n",
1034 |        "mpl.figure.prototype._init_canvas = function() {\n",
1035 |        "    var fig = this;\n",
1036 |        "\n",
1037 |        "    var canvas_div = $('<div/>');\n",
1038 |        "\n",
1039 |        "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
1040 |        "\n",
1041 |        "    function canvas_keyboard_event(event) {\n",
1042 |        "        return fig.key_event(event, event['data']);\n",
1043 |        "    }\n",
1044 |        "\n",
1045 |        "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
1046 |        "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
1047 |        "    this.canvas_div = canvas_div\n",
1048 |        "    this._canvas_extra_style(canvas_div)\n",
1049 |        "    this.root.append(canvas_div);\n",
1050 |        "\n",
1051 |        "    var canvas = $('<canvas/>');\n",
1052 |        "    canvas.addClass('mpl-canvas');\n",
1053 |        "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
1054 |        "\n",
1055 |        "    this.canvas = canvas[0];\n",
1056 |        "    this.context = canvas[0].getContext(\"2d\");\n",
1057 |        "\n",
1058 |        "    var backingStore = this.context.backingStorePixelRatio ||\n",
1059 |        "\tthis.context.webkitBackingStorePixelRatio ||\n",
1060 |        "\tthis.context.mozBackingStorePixelRatio ||\n",
1061 |        "\tthis.context.msBackingStorePixelRatio ||\n",
1062 |        "\tthis.context.oBackingStorePixelRatio ||\n",
1063 |        "\tthis.context.backingStorePixelRatio || 1;\n",
1064 |        "\n",
1065 |        "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
1066 |        "\n",
1067 |        "    var rubberband = $('<canvas/>');\n",
1068 |        "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
1069 |        "\n",
1070 |        "    var pass_mouse_events = true;\n",
1071 |        "\n",
1072 |        "    canvas_div.resizable({\n",
1073 |        "        start: function(event, ui) {\n",
1074 |        "            pass_mouse_events = false;\n",
1075 |        "        },\n",
1076 |        "        resize: function(event, ui) {\n",
1077 |        "            fig.request_resize(ui.size.width, ui.size.height);\n",
1078 |        "        },\n",
1079 |        "        stop: function(event, ui) {\n",
1080 |        "            pass_mouse_events = true;\n",
1081 |        "            fig.request_resize(ui.size.width, ui.size.height);\n",
1082 |        "        },\n",
1083 |        "    });\n",
1084 |        "\n",
1085 |        "    function mouse_event_fn(event) {\n",
1086 |        "        if (pass_mouse_events)\n",
1087 |        "            return fig.mouse_event(event, event['data']);\n",
1088 |        "    }\n",
1089 |        "\n",
1090 |        "    rubberband.mousedown('button_press', mouse_event_fn);\n",
1091 |        "    rubberband.mouseup('button_release', mouse_event_fn);\n",
1092 |        "    // Throttle sequential mouse events to 1 every 20ms.\n",
1093 |        "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
1094 |        "\n",
1095 |        "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
1096 |        "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
1097 |        "\n",
1098 |        "    canvas_div.on(\"wheel\", function (event) {\n",
1099 |        "        event = event.originalEvent;\n",
1100 |        "        event['data'] = 'scroll'\n",
1101 |        "        if (event.deltaY < 0) {\n",
1102 |        "            event.step = 1;\n",
1103 |        "        } else {\n",
1104 |        "            event.step = -1;\n",
1105 |        "        }\n",
1106 |        "        mouse_event_fn(event);\n",
1107 |        "    });\n",
1108 |        "\n",
1109 |        "    canvas_div.append(canvas);\n",
1110 |        "    canvas_div.append(rubberband);\n",
1111 |        "\n",
1112 |        "    this.rubberband = rubberband;\n",
1113 |        "    this.rubberband_canvas = rubberband[0];\n",
1114 |        "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
1115 |        "    this.rubberband_context.strokeStyle = \"#000000\";\n",
1116 |        "\n",
1117 |        "    this._resize_canvas = function(width, height) {\n",
1118 |        "        // Keep the size of the canvas, canvas container, and rubber band\n",
1119 |        "        // canvas in synch.\n",
1120 |        "        canvas_div.css('width', width)\n",
1121 |        "        canvas_div.css('height', height)\n",
1122 |        "\n",
1123 |        "        canvas.attr('width', width * mpl.ratio);\n",
1124 |        "        canvas.attr('height', height * mpl.ratio);\n",
1125 |        "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
1126 |        "\n",
1127 |        "        rubberband.attr('width', width);\n",
1128 |        "        rubberband.attr('height', height);\n",
1129 |        "    }\n",
1130 |        "\n",
1131 |        "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
1132 |        "    // upon first draw.\n",
1133 |        "    this._resize_canvas(600, 600);\n",
1134 |        "\n",
1135 |        "    // Disable right mouse context menu.\n",
1136 |        "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
1137 |        "        return false;\n",
1138 |        "    });\n",
1139 |        "\n",
1140 |        "    function set_focus () {\n",
1141 |        "        canvas.focus();\n",
1142 |        "        canvas_div.focus();\n",
1143 |        "    }\n",
1144 |        "\n",
1145 |        "    window.setTimeout(set_focus, 100);\n",
1146 |        "}\n",
1147 |        "\n",
1148 |        "mpl.figure.prototype._init_toolbar = function() {\n",
1149 |        "    var fig = this;\n",
1150 |        "\n",
1151 |        "    var nav_element = $('<div/>');\n",
1152 |        "    nav_element.attr('style', 'width: 100%');\n",
1153 |        "    this.root.append(nav_element);\n",
1154 |        "\n",
1155 |        "    // Define a callback function for later on.\n",
1156 |        "    function toolbar_event(event) {\n",
1157 |        "        return fig.toolbar_button_onclick(event['data']);\n",
1158 |        "    }\n",
1159 |        "    function toolbar_mouse_event(event) {\n",
1160 |        "        return fig.toolbar_button_onmouseover(event['data']);\n",
1161 |        "    }\n",
1162 |        "\n",
1163 |        "    for(var toolbar_ind in mpl.toolbar_items) {\n",
1164 |        "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
1165 |        "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
1166 |        "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
1167 |        "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
1168 |        "\n",
1169 |        "        if (!name) {\n",
1170 |        "            // put a spacer in here.\n",
1171 |        "            continue;\n",
1172 |        "        }\n",
1173 |        "        var button = $('<button/>');\n",
1174 |        "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
1175 |        "                        'ui-button-icon-only');\n",
1176 |        "        button.attr('role', 'button');\n",
1177 |        "        button.attr('aria-disabled', 'false');\n",
1178 |        "        button.click(method_name, toolbar_event);\n",
1179 |        "        button.mouseover(tooltip, toolbar_mouse_event);\n",
1180 |        "\n",
1181 |        "        var icon_img = $('<span/>');\n",
1182 |        "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
1183 |        "        icon_img.addClass(image);\n",
1184 |        "        icon_img.addClass('ui-corner-all');\n",
1185 |        "\n",
1186 |        "        var tooltip_span = $('<span/>');\n",
1187 |        "        tooltip_span.addClass('ui-button-text');\n",
1188 |        "        tooltip_span.html(tooltip);\n",
1189 |        "\n",
1190 |        "        button.append(icon_img);\n",
1191 |        "        button.append(tooltip_span);\n",
1192 |        "\n",
1193 |        "        nav_element.append(button);\n",
1194 |        "    }\n",
1195 |        "\n",
1196 |        "    var fmt_picker_span = $('<span/>');\n",
1197 |        "\n",
1198 |        "    var fmt_picker = $('<select/>');\n",
1199 |        "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
1200 |        "    fmt_picker_span.append(fmt_picker);\n",
1201 |        "    nav_element.append(fmt_picker_span);\n",
1202 |        "    this.format_dropdown = fmt_picker[0];\n",
1203 |        "\n",
1204 |        "    for (var ind in mpl.extensions) {\n",
1205 |        "        var fmt = mpl.extensions[ind];\n",
1206 |        "        var option = $(\n",
1207 |        "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
1208 |        "        fmt_picker.append(option);\n",
1209 |        "    }\n",
1210 |        "\n",
1211 |        "    // Add hover states to the ui-buttons\n",
1212 |        "    $( \".ui-button\" ).hover(\n",
1213 |        "        function() { $(this).addClass(\"ui-state-hover\");},\n",
1214 |        "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
1215 |        "    );\n",
1216 |        "\n",
1217 |        "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
1218 |        "    nav_element.append(status_bar);\n",
1219 |        "    this.message = status_bar[0];\n",
1220 |        "}\n",
1221 |        "\n",
1222 |        "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
1223 |        "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
1224 |        "    // which will in turn request a refresh of the image.\n",
1225 |        "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
1226 |        "}\n",
1227 |        "\n",
1228 |        "mpl.figure.prototype.send_message = function(type, properties) {\n",
1229 |        "    properties['type'] = type;\n",
1230 |        "    properties['figure_id'] = this.id;\n",
1231 |        "    this.ws.send(JSON.stringify(properties));\n",
1232 |        "}\n",
1233 |        "\n",
1234 |        "mpl.figure.prototype.send_draw_message = function() {\n",
1235 |        "    if (!this.waiting) {\n",
1236 |        "        this.waiting = true;\n",
1237 |        "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
1238 |        "    }\n",
1239 |        "}\n",
1240 |        "\n",
1241 |        "\n",
1242 |        "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
1243 |        "    var format_dropdown = fig.format_dropdown;\n",
1244 |        "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
1245 |        "    fig.ondownload(fig, format);\n",
1246 |        "}\n",
1247 |        "\n",
1248 |        "\n",
1249 |        "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
1250 |        "    var size = msg['size'];\n",
1251 |        "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
1252 |        "        fig._resize_canvas(size[0], size[1]);\n",
1253 |        "        fig.send_message(\"refresh\", {});\n",
1254 |        "    };\n",
1255 |        "}\n",
1256 |        "\n",
1257 |        "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
1258 |        "    var x0 = msg['x0'] / mpl.ratio;\n",
1259 |        "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
1260 |        "    var x1 = msg['x1'] / mpl.ratio;\n",
1261 |        "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
1262 |        "    x0 = Math.floor(x0) + 0.5;\n",
1263 |        "    y0 = Math.floor(y0) + 0.5;\n",
1264 |        "    x1 = Math.floor(x1) + 0.5;\n",
1265 |        "    y1 = Math.floor(y1) + 0.5;\n",
1266 |        "    var min_x = Math.min(x0, x1);\n",
1267 |        "    var min_y = Math.min(y0, y1);\n",
1268 |        "    var width = Math.abs(x1 - x0);\n",
1269 |        "    var height = Math.abs(y1 - y0);\n",
1270 |        "\n",
1271 |        "    fig.rubberband_context.clearRect(\n",
1272 |        "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
1273 |        "\n",
1274 |        "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
1275 |        "}\n",
1276 |        "\n",
1277 |        "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
1278 |        "    // Updates the figure title.\n",
1279 |        "    fig.header.textContent = msg['label'];\n",
1280 |        "}\n",
1281 |        "\n",
1282 |        "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
1283 |        "    var cursor = msg['cursor'];\n",
1284 |        "    switch(cursor)\n",
1285 |        "    {\n",
1286 |        "    case 0:\n",
1287 |        "        cursor = 'pointer';\n",
1288 |        "        break;\n",
1289 |        "    case 1:\n",
1290 |        "        cursor = 'default';\n",
1291 |        "        break;\n",
1292 |        "    case 2:\n",
1293 |        "        cursor = 'crosshair';\n",
1294 |        "        break;\n",
1295 |        "    case 3:\n",
1296 |        "        cursor = 'move';\n",
1297 |        "        break;\n",
1298 |        "    }\n",
1299 |        "    fig.rubberband_canvas.style.cursor = cursor;\n",
1300 |        "}\n",
1301 |        "\n",
1302 |        "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
1303 |        "    fig.message.textContent = msg['message'];\n",
1304 |        "}\n",
1305 |        "\n",
1306 |        "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
1307 |        "    // Request the server to send over a new figure.\n",
1308 |        "    fig.send_draw_message();\n",
1309 |        "}\n",
1310 |        "\n",
1311 |        "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
1312 |        "    fig.image_mode = msg['mode'];\n",
1313 |        "}\n",
1314 |        "\n",
1315 |        "mpl.figure.prototype.updated_canvas_event = function() {\n",
1316 |        "    // Called whenever the canvas gets updated.\n",
1317 |        "    this.send_message(\"ack\", {});\n",
1318 |        "}\n",
1319 |        "\n",
1320 |        "// A function to construct a web socket function for onmessage handling.\n",
1321 |        "// Called in the figure constructor.\n",
1322 |        "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
1323 |        "    return function socket_on_message(evt) {\n",
1324 |        "        if (evt.data instanceof Blob) {\n",
1325 |        "            /* FIXME: We get \"Resource interpreted as Image but\n",
1326 |        "             * transferred with MIME type text/plain:\" errors on\n",
1327 |        "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
1328 |        "             * to be part of the websocket stream */\n",
1329 |        "            evt.data.type = \"image/png\";\n",
1330 |        "\n",
1331 |        "            /* Free the memory for the previous frames */\n",
1332 |        "            if (fig.imageObj.src) {\n",
1333 |        "                (window.URL || window.webkitURL).revokeObjectURL(\n",
1334 |        "                    fig.imageObj.src);\n",
1335 |        "            }\n",
1336 |        "\n",
1337 |        "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
1338 |        "                evt.data);\n",
1339 |        "            fig.updated_canvas_event();\n",
1340 |        "            fig.waiting = false;\n",
1341 |        "            return;\n",
1342 |        "        }\n",
1343 |        "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
1344 |        "            fig.imageObj.src = evt.data;\n",
1345 |        "            fig.updated_canvas_event();\n",
1346 |        "            fig.waiting = false;\n",
1347 |        "            return;\n",
1348 |        "        }\n",
1349 |        "\n",
1350 |        "        var msg = JSON.parse(evt.data);\n",
1351 |        "        var msg_type = msg['type'];\n",
1352 |        "\n",
1353 |        "        // Call the  \"handle_{type}\" callback, which takes\n",
1354 |        "        // the figure and JSON message as its only arguments.\n",
1355 |        "        try {\n",
1356 |        "            var callback = fig[\"handle_\" + msg_type];\n",
1357 |        "        } catch (e) {\n",
1358 |        "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
1359 |        "            return;\n",
1360 |        "        }\n",
1361 |        "\n",
1362 |        "        if (callback) {\n",
1363 |        "            try {\n",
1364 |        "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
1365 |        "                callback(fig, msg);\n",
1366 |        "            } catch (e) {\n",
1367 |        "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
1368 |        "            }\n",
1369 |        "        }\n",
1370 |        "    };\n",
1371 |        "}\n",
1372 |        "\n",
1373 |        "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
1374 |        "mpl.findpos = function(e) {\n",
1375 |        "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
1376 |        "    var targ;\n",
1377 |        "    if (!e)\n",
1378 |        "        e = window.event;\n",
1379 |        "    if (e.target)\n",
1380 |        "        targ = e.target;\n",
1381 |        "    else if (e.srcElement)\n",
1382 |        "        targ = e.srcElement;\n",
1383 |        "    if (targ.nodeType == 3) // defeat Safari bug\n",
1384 |        "        targ = targ.parentNode;\n",
1385 |        "\n",
1386 |        "    // jQuery normalizes the pageX and pageY\n",
1387 |        "    // pageX,Y are the mouse positions relative to the document\n",
1388 |        "    // offset() returns the position of the element relative to the document\n",
1389 |        "    var x = e.pageX - $(targ).offset().left;\n",
1390 |        "    var y = e.pageY - $(targ).offset().top;\n",
1391 |        "\n",
1392 |        "    return {\"x\": x, \"y\": y};\n",
1393 |        "};\n",
1394 |        "\n",
1395 |        "/*\n",
1396 |        " * return a copy of an object with only non-object keys\n",
1397 |        " * we need this to avoid circular references\n",
1398 |        " * http://stackoverflow.com/a/24161582/3208463\n",
1399 |        " */\n",
1400 |        "function simpleKeys (original) {\n",
1401 |        "  return Object.keys(original).reduce(function (obj, key) {\n",
1402 |        "    if (typeof original[key] !== 'object')\n",
1403 |        "        obj[key] = original[key]\n",
1404 |        "    return obj;\n",
1405 |        "  }, {});\n",
1406 |        "}\n",
1407 |        "\n",
1408 |        "mpl.figure.prototype.mouse_event = function(event, name) {\n",
1409 |        "    var canvas_pos = mpl.findpos(event)\n",
1410 |        "\n",
1411 |        "    if (name === 'button_press')\n",
1412 |        "    {\n",
1413 |        "        this.canvas.focus();\n",
1414 |        "        this.canvas_div.focus();\n",
1415 |        "    }\n",
1416 |        "\n",
1417 |        "    var x = canvas_pos.x * mpl.ratio;\n",
1418 |        "    var y = canvas_pos.y * mpl.ratio;\n",
1419 |        "\n",
1420 |        "    this.send_message(name, {x: x, y: y, button: event.button,\n",
1421 |        "                             step: event.step,\n",
1422 |        "                             guiEvent: simpleKeys(event)});\n",
1423 |        "\n",
1424 |        "    /* This prevents the web browser from automatically changing to\n",
1425 |        "     * the text insertion cursor when the button is pressed.  We want\n",
1426 |        "     * to control all of the cursor setting manually through the\n",
1427 |        "     * 'cursor' event from matplotlib */\n",
1428 |        "    event.preventDefault();\n",
1429 |        "    return false;\n",
1430 |        "}\n",
1431 |        "\n",
1432 |        "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
1433 |        "    // Handle any extra behaviour associated with a key event\n",
1434 |        "}\n",
1435 |        "\n",
1436 |        "mpl.figure.prototype.key_event = function(event, name) {\n",
1437 |        "\n",
1438 |        "    // Prevent repeat events\n",
1439 |        "    if (name == 'key_press')\n",
1440 |        "    {\n",
1441 |        "        if (event.which === this._key)\n",
1442 |        "            return;\n",
1443 |        "        else\n",
1444 |        "            this._key = event.which;\n",
1445 |        "    }\n",
1446 |        "    if (name == 'key_release')\n",
1447 |        "        this._key = null;\n",
1448 |        "\n",
1449 |        "    var value = '';\n",
1450 |        "    if (event.ctrlKey && event.which != 17)\n",
1451 |        "        value += \"ctrl+\";\n",
1452 |        "    if (event.altKey && event.which != 18)\n",
1453 |        "        value += \"alt+\";\n",
1454 |        "    if (event.shiftKey && event.which != 16)\n",
1455 |        "        value += \"shift+\";\n",
1456 |        "\n",
1457 |        "    value += 'k';\n",
1458 |        "    value += event.which.toString();\n",
1459 |        "\n",
1460 |        "    this._key_event_extra(event, name);\n",
1461 |        "\n",
1462 |        "    this.send_message(name, {key: value,\n",
1463 |        "                             guiEvent: simpleKeys(event)});\n",
1464 |        "    return false;\n",
1465 |        "}\n",
1466 |        "\n",
1467 |        "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
1468 |        "    if (name == 'download') {\n",
1469 |        "        this.handle_save(this, null);\n",
1470 |        "    } else {\n",
1471 |        "        this.send_message(\"toolbar_button\", {name: name});\n",
1472 |        "    }\n",
1473 |        "};\n",
1474 |        "\n",
1475 |        "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
1476 |        "    this.message.textContent = tooltip;\n",
1477 |        "};\n",
1478 |        "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",
1479 |        "\n",
1480 |        "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
1481 |        "\n",
1482 |        "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
1483 |        "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
1484 |        "    // object with the appropriate methods. Currently this is a non binary\n",
1485 |        "    // socket, so there is still some room for performance tuning.\n",
1486 |        "    var ws = {};\n",
1487 |        "\n",
1488 |        "    ws.close = function() {\n",
1489 |        "        comm.close()\n",
1490 |        "    };\n",
1491 |        "    ws.send = function(m) {\n",
1492 |        "        //console.log('sending', m);\n",
1493 |        "        comm.send(m);\n",
1494 |        "    };\n",
1495 |        "    // Register the callback with on_msg.\n",
1496 |        "    comm.on_msg(function(msg) {\n",
1497 |        "        //console.log('receiving', msg['content']['data'], msg);\n",
1498 |        "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
1499 |        "        ws.onmessage(msg['content']['data'])\n",
1500 |        "    });\n",
1501 |        "    return ws;\n",
1502 |        "}\n",
1503 |        "\n",
1504 |        "mpl.mpl_figure_comm = function(comm, msg) {\n",
1505 |        "    // This is the function which gets called when the mpl process\n",
1506 |        "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
1507 |        "\n",
1508 |        "    var id = msg.content.data.id;\n",
1509 |        "    // Get hold of the div created by the display call when the Comm\n",
1510 |        "    // socket was opened in Python.\n",
1511 |        "    var element = $(\"#\" + id);\n",
1512 |        "    var ws_proxy = comm_websocket_adapter(comm)\n",
1513 |        "\n",
1514 |        "    function ondownload(figure, format) {\n",
1515 |        "        window.open(figure.imageObj.src);\n",
1516 |        "    }\n",
1517 |        "\n",
1518 |        "    var fig = new mpl.figure(id, ws_proxy,\n",
1519 |        "                           ondownload,\n",
1520 |        "                           element.get(0));\n",
1521 |        "\n",
1522 |        "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
1523 |        "    // web socket which is closed, not our websocket->open comm proxy.\n",
1524 |        "    ws_proxy.onopen();\n",
1525 |        "\n",
1526 |        "    fig.parent_element = element.get(0);\n",
1527 |        "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
1528 |        "    if (!fig.cell_info) {\n",
1529 |        "        console.error(\"Failed to find cell for figure\", id, fig);\n",
1530 |        "        return;\n",
1531 |        "    }\n",
1532 |        "\n",
1533 |        "    var output_index = fig.cell_info[2]\n",
1534 |        "    var cell = fig.cell_info[0];\n",
1535 |        "\n",
1536 |        "};\n",
1537 |        "\n",
1538 |        "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
1539 |        "    var width = fig.canvas.width/mpl.ratio\n",
1540 |        "    fig.root.unbind('remove')\n",
1541 |        "\n",
1542 |        "    // Update the output cell to use the data from the current canvas.\n",
1543 |        "    fig.push_to_output();\n",
1544 |        "    var dataURL = fig.canvas.toDataURL();\n",
1545 |        "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
1546 |        "    // the notebook keyboard shortcuts fail.\n",
1547 |        "    IPython.keyboard_manager.enable()\n",
1548 |        "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
1549 |        "    fig.close_ws(fig, msg);\n",
1550 |        "}\n",
1551 |        "\n",
1552 |        "mpl.figure.prototype.close_ws = function(fig, msg){\n",
1553 |        "    fig.send_message('closing', msg);\n",
1554 |        "    // fig.ws.close()\n",
1555 |        "}\n",
1556 |        "\n",
1557 |        "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
1558 |        "    // Turn the data on the canvas into data in the output cell.\n",
1559 |        "    var width = this.canvas.width/mpl.ratio\n",
1560 |        "    var dataURL = this.canvas.toDataURL();\n",
1561 |        "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
1562 |        "}\n",
1563 |        "\n",
1564 |        "mpl.figure.prototype.updated_canvas_event = function() {\n",
1565 |        "    // Tell IPython that the notebook contents must change.\n",
1566 |        "    IPython.notebook.set_dirty(true);\n",
1567 |        "    this.send_message(\"ack\", {});\n",
1568 |        "    var fig = this;\n",
1569 |        "    // Wait a second, then push the new image to the DOM so\n",
1570 |        "    // that it is saved nicely (might be nice to debounce this).\n",
1571 |        "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
1572 |        "}\n",
1573 |        "\n",
1574 |        "mpl.figure.prototype._init_toolbar = function() {\n",
1575 |        "    var fig = this;\n",
1576 |        "\n",
1577 |        "    var nav_element = $('<div/>');\n",
1578 |        "    nav_element.attr('style', 'width: 100%');\n",
1579 |        "    this.root.append(nav_element);\n",
1580 |        "\n",
1581 |        "    // Define a callback function for later on.\n",
1582 |        "    function toolbar_event(event) {\n",
1583 |        "        return fig.toolbar_button_onclick(event['data']);\n",
1584 |        "    }\n",
1585 |        "    function toolbar_mouse_event(event) {\n",
1586 |        "        return fig.toolbar_button_onmouseover(event['data']);\n",
1587 |        "    }\n",
1588 |        "\n",
1589 |        "    for(var toolbar_ind in mpl.toolbar_items){\n",
1590 |        "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
1591 |        "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
1592 |        "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
1593 |        "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
1594 |        "\n",
1595 |        "        if (!name) { continue; };\n",
1596 |        "\n",
1597 |        "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
1598 |        "        button.click(method_name, toolbar_event);\n",
1599 |        "        button.mouseover(tooltip, toolbar_mouse_event);\n",
1600 |        "        nav_element.append(button);\n",
1601 |        "    }\n",
1602 |        "\n",
1603 |        "    // Add the status bar.\n",
1604 |        "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
1605 |        "    nav_element.append(status_bar);\n",
1606 |        "    this.message = status_bar[0];\n",
1607 |        "\n",
1608 |        "    // Add the close button to the window.\n",
1609 |        "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
1610 |        "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
1611 |        "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
1612 |        "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
1613 |        "    buttongrp.append(button);\n",
1614 |        "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
1615 |        "    titlebar.prepend(buttongrp);\n",
1616 |        "}\n",
1617 |        "\n",
1618 |        "mpl.figure.prototype._root_extra_style = function(el){\n",
1619 |        "    var fig = this\n",
1620 |        "    el.on(\"remove\", function(){\n",
1621 |        "\tfig.close_ws(fig, {});\n",
1622 |        "    });\n",
1623 |        "}\n",
1624 |        "\n",
1625 |        "mpl.figure.prototype._canvas_extra_style = function(el){\n",
1626 |        "    // this is important to make the div 'focusable\n",
1627 |        "    el.attr('tabindex', 0)\n",
1628 |        "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
1629 |        "    // off when our div gets focus\n",
1630 |        "\n",
1631 |        "    // location in version 3\n",
1632 |        "    if (IPython.notebook.keyboard_manager) {\n",
1633 |        "        IPython.notebook.keyboard_manager.register_events(el);\n",
1634 |        "    }\n",
1635 |        "    else {\n",
1636 |        "        // location in version 2\n",
1637 |        "        IPython.keyboard_manager.register_events(el);\n",
1638 |        "    }\n",
1639 |        "\n",
1640 |        "}\n",
1641 |        "\n",
1642 |        "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
1643 |        "    var manager = IPython.notebook.keyboard_manager;\n",
1644 |        "    if (!manager)\n",
1645 |        "        manager = IPython.keyboard_manager;\n",
1646 |        "\n",
1647 |        "    // Check for shift+enter\n",
1648 |        "    if (event.shiftKey && event.which == 13) {\n",
1649 |        "        this.canvas_div.blur();\n",
1650 |        "        event.shiftKey = false;\n",
1651 |        "        // Send a \"J\" for go to next cell\n",
1652 |        "        event.which = 74;\n",
1653 |        "        event.keyCode = 74;\n",
1654 |        "        manager.command_mode();\n",
1655 |        "        manager.handle_keydown(event);\n",
1656 |        "    }\n",
1657 |        "}\n",
1658 |        "\n",
1659 |        "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
1660 |        "    fig.ondownload(fig, null);\n",
1661 |        "}\n",
1662 |        "\n",
1663 |        "\n",
1664 |        "mpl.find_output_cell = function(html_output) {\n",
1665 |        "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
1666 |        "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
1667 |        "    // IPython event is triggered only after the cells have been serialised, which for\n",
1668 |        "    // our purposes (turning an active figure into a static one), is too late.\n",
1669 |        "    var cells = IPython.notebook.get_cells();\n",
1670 |        "    var ncells = cells.length;\n",
1671 |        "    for (var i=0; i<ncells; i++) {\n",
1672 |        "        var cell = cells[i];\n",
1673 |        "        if (cell.cell_type === 'code'){\n",
1674 |        "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
1675 |        "                var data = cell.output_area.outputs[j];\n",
1676 |        "                if (data.data) {\n",
1677 |        "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
1678 |        "                    data = data.data;\n",
1679 |        "                }\n",
1680 |        "                if (data['text/html'] == html_output) {\n",
1681 |        "                    return [cell, data, j];\n",
1682 |        "                }\n",
1683 |        "            }\n",
1684 |        "        }\n",
1685 |        "    }\n",
1686 |        "}\n",
1687 |        "\n",
1688 |        "// Register the function which deals with the matplotlib target/channel.\n",
1689 |        "// The kernel may be null if the page has been refreshed.\n",
1690 |        "if (IPython.notebook.kernel != null) {\n",
1691 |        "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
1692 |        "}\n"
1693 |       ],
1694 |       "text/plain": [
1695 |        "<IPython.core.display.Javascript object>"
1696 |       ]
1697 |      },
1698 |      "metadata": {},
1699 |      "output_type": "display_data"
1700 |     },
1701 |     {
1702 |      "data": {
1703 |       "text/html": [
1704 |        "<img 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\" width=\"640\">"
1705 |       ],
1706 |       "text/plain": [
1707 |        "<IPython.core.display.HTML object>"
1708 |       ]
1709 |      },
1710 |      "metadata": {},
1711 |      "output_type": "display_data"
1712 |     },
1713 |     {
1714 |      "data": {
1715 |       "text/plain": [
1716 |        "<matplotlib.legend.Legend at 0x1d25a254ec8>"
1717 |       ]
1718 |      },
1719 |      "execution_count": 5,
1720 |      "metadata": {},
1721 |      "output_type": "execute_result"
1722 |     }
1723 |    ],
1724 |    "source": [
1725 |     "fig = plt.figure()\n",
1726 |     "ax = fig.add_subplot(111)\n",
1727 |     "plt.ion()\n",
1728 |     "\n",
1729 |     "fig.show()\n",
1730 |     "fig.canvas.draw()\n",
1731 |     "\n",
1732 |     "o = []\n",
1733 |     "x = [torch.sigmoid(torch.arange(-10,-1).float()).unsqueeze(-1).numpy().tolist()]\n",
1734 |     "\n",
1735 |     "#Draw graph comparing to sigmoid\n",
1736 |     "for i in range(-10, 10, output_sequence_length):\n",
1737 |     "    o.append([torch.sigmoid(torch.tensor(i).float())])\n",
1738 |     "    q = torch.tensor(x).float()\n",
1739 |     "    \n",
1740 |     "    if(output_sequence_length == 1):\n",
1741 |     "        x[0].append([t(q).detach().squeeze().numpy()])\n",
1742 |     "    else:\n",
1743 |     "        for a in t(q).detach().squeeze().numpy():\n",
1744 |     "            x[0].append([a])\n",
1745 |     "            \n",
1746 |     "ax.clear()\n",
1747 |     "ax.plot(x[0], label='Network output')\n",
1748 |     "ax.plot(o, label='Sigmoid function')\n",
1749 |     "ax.set_title(\"\")\n",
1750 |     "ax.legend(loc='upper left', frameon=False)\n"
1751 |    ]
1752 |   },
1753 |   {
1754 |    "cell_type": "code",
1755 |    "execution_count": null,
1756 |    "metadata": {},
1757 |    "outputs": [],
1758 |    "source": []
1759 |   }
1760 |  ],
1761 |  "metadata": {
1762 |   "kernelspec": {
1763 |    "display_name": "Python 3",
1764 |    "language": "python",
1765 |    "name": "python3"
1766 |   },
1767 |   "language_info": {
1768 |    "codemirror_mode": {
1769 |     "name": "ipython",
1770 |     "version": 3
1771 |    },
1772 |    "file_extension": ".py",
1773 |    "mimetype": "text/x-python",
1774 |    "name": "python",
1775 |    "nbconvert_exporter": "python",
1776 |    "pygments_lexer": "ipython3",
1777 |    "version": "3.7.4"
1778 |   }
1779 |  },
1780 |  "nbformat": 4,
1781 |  "nbformat_minor": 2
1782 | }
1783 | 


--------------------------------------------------------------------------------
/data.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | import csv
 3 | import torch
 4 | import random
 5 | import numpy as np
 6 | from torch.nn.utils.rnn import pad_sequence
 7 | 
 8 | def gen_data(batch_size, gpu = True):
 9 |     
10 |     #https://www.kaggle.com/shashankasubrahmanya/preprocessing-cornell-movie-dialogue-corpus
11 |     movie_lines = pd.read_csv('cornell_data/movie_lines.txt', sep = "\+\+\+\$\+\+\+", engine = "python", 
12 |                               index_col = False, names = ["LineID", "Character", "Movie", "Name", "Line"])
13 |     movie_lines = movie_lines[["LineID", "Line"]]
14 | 
15 |     movie_lines["Line"] = movie_lines['Line'].str.replace('.','')
16 |     movie_lines["Line"] = movie_lines['Line'].str.replace('!','')
17 |     movie_lines["Line"] = movie_lines['Line'].str.replace('?','')
18 |     movie_lines["Line"] = movie_lines['Line'].str.replace('  ',' ')
19 |     movie_lines["Line"] = movie_lines['Line'].str.replace('[^\w\s.!?]','')
20 |     movie_lines["Line"] = movie_lines["Line"].str.lower()
21 | 
22 |     movie_lines["LineID"] = movie_lines["LineID"].apply(str.strip)
23 |     movie_lines["Line"] = movie_lines["Line"].apply(lambda x : str(x).split(" ")[1:])
24 | 
25 |     movie_conversations = pd.read_csv("cornell_data/movie_conversations.txt", sep = "\+\+\+\$\+\+\+", 
26 |                                       engine = "python", index_col = False, names =  ["Character1", "Character2", "Movie", "Conversation"])
27 |     movie_conversations = movie_conversations["Conversation"]
28 | 
29 |     #convert from strings of lists to actual lists
30 |     movie_conversations = movie_conversations.apply(eval)
31 | 
32 |     word_vecs = pd.read_table("glove.6B.50d.txt", sep=" ", index_col=0, header=None, quoting=csv.QUOTE_NONE)
33 |     if(not gpu):
34 |         dtype = torch.FloatTensor
35 |     else:
36 |         dtype = torch.cuda.FloatTensor
37 |     
38 |     ba = 0
39 |     vectorized_inputs = []
40 |     vectorized_targets = []
41 |     
42 |     inputs = []
43 |     targets = []
44 |     while True:
45 |         i = random.randint(0, movie_conversations.size - (batch_size + 1))
46 |         batch = movie_conversations.loc[i:i+batch_size].apply(lambda x : movie_lines.loc[(movie_lines['LineID'].isin(x))])
47 |         batch = batch.apply(lambda x : x['Line'].values).values
48 | 
49 |         for b in batch:
50 |             for idx in range(len(b) - 1):
51 |                 vectorized_inputs.append(torch.tensor(word_vecs.loc[b[idx]].values).type(dtype))
52 |                 vectorized_targets.append(torch.tensor(word_vecs.loc[b[idx + 1]].values).type(dtype))
53 |                
54 |                 inputs.append(b[idx])
55 |                 targets.append(b[idx + 1])
56 |             
57 |                 ba += 1
58 |                 if ba >= batch_size:
59 |                     ba = 0
60 |                     yield (pad_sequence(vectorized_inputs, batch_first = True)
61 |                            , pad_sequence(vectorized_targets, batch_first = True), inputs, targets)
62 |                     vectorized_inputs = []
63 |                     vectorized_targets = []
64 |                     inputs = []
65 |                     targets = []


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/doc/buildfig.png:
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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/buildfig.png


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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/compare.png


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/doc/comparegraph.png:
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/doc/imports,png.PNG:
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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/imports,png.PNG


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/doc/init.png:
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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/init.png


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/doc/lossgraph.png:
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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/lossgraph.png


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/doc/training.png:
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https://raw.githubusercontent.com/LiamMaclean216/Pytorch-Chatbot/d93639049fc9227bf99be14df5bd272b5f3452d1/doc/training.png


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/utils.py:
--------------------------------------------------------------------------------
  1 | import torch
  2 | import torch.nn as nn
  3 | import torch.nn.functional as F
  4 | import numpy as np
  5 | import math
  6 | 
  7 | def a_norm(Q, K):
  8 |     m = torch.matmul(Q, K.transpose(2,1).float())
  9 |     m /= torch.sqrt(torch.tensor(Q.shape[-1]).float())
 10 |     
 11 |     return torch.softmax(m , -1)
 12 | 
 13 | 
 14 | def attention(Q, K, V):
 15 |     #Attention(Q, K, V) = norm(QK)V
 16 |     a = a_norm(Q, K) #(batch_size, dim_attn, seq_length)
 17 |     
 18 |     return  torch.matmul(a,  V) #(batch_size, seq_length, seq_length)
 19 | 
 20 | class AttentionBlock(torch.nn.Module):
 21 |     def __init__(self, dim_val, dim_attn):
 22 |         super(AttentionBlock, self).__init__()
 23 |         self.value = Value(dim_val, dim_val)
 24 |         self.key = Key(dim_val, dim_attn)
 25 |         self.query = Query(dim_val, dim_attn)
 26 |     
 27 |     def forward(self, x, kv = None):
 28 |         if(kv is None):
 29 |             #Attention with x connected to Q,K and V (For encoder)
 30 |             return attention(self.query(x), self.key(x), self.value(x))
 31 |         
 32 |         #Attention with x as Q, external vector kv as K an V (For decoder)
 33 |         return attention(self.query(x), self.key(kv), self.value(kv))
 34 |     
 35 | class MultiHeadAttentionBlock(torch.nn.Module):
 36 |     def __init__(self, dim_val, dim_attn, n_heads):
 37 |         super(MultiHeadAttentionBlock, self).__init__()
 38 |         self.heads = []
 39 |         for i in range(n_heads):
 40 |             self.heads.append(AttentionBlock(dim_val, dim_attn))
 41 |         
 42 |         self.fc = nn.Linear(n_heads * dim_val, dim_val, bias = False)
 43 |                       
 44 |         
 45 |     def forward(self, x, kv = None):
 46 |         a = []
 47 |         for h in self.heads:
 48 |             a.append(h(x, kv = kv))
 49 |             
 50 |         a = torch.stack(a, dim = -1) #combine heads
 51 |         a = a.flatten(start_dim = 2) #flatten all head outputs
 52 |         
 53 |         x = self.fc(a)
 54 |         
 55 |         return x
 56 |     
 57 | class Value(torch.nn.Module):
 58 |     def __init__(self, dim_input, dim_val):
 59 |         super(Value, self).__init__()
 60 |         self.dim_val = dim_val
 61 |         
 62 |         self.fc1 = nn.Linear(dim_input, dim_val, bias = False)
 63 |         #self.fc2 = nn.Linear(5, dim_val)
 64 |     
 65 |     def forward(self, x):
 66 |         x = self.fc1(x)
 67 |         #x = self.fc2(x)
 68 |         
 69 |         return x
 70 | 
 71 | class Key(torch.nn.Module):
 72 |     def __init__(self, dim_input, dim_attn):
 73 |         super(Key, self).__init__()
 74 |         self.dim_attn = dim_attn
 75 |         
 76 |         self.fc1 = nn.Linear(dim_input, dim_attn, bias = False)
 77 |         #self.fc2 = nn.Linear(5, dim_attn)
 78 |     
 79 |     def forward(self, x):
 80 |         x = self.fc1(x)
 81 |         #x = self.fc2(x)
 82 |         
 83 |         return x
 84 | 
 85 | class Query(torch.nn.Module):
 86 |     def __init__(self, dim_input, dim_attn):
 87 |         super(Query, self).__init__()
 88 |         self.dim_attn = dim_attn
 89 |         
 90 |         self.fc1 = nn.Linear(dim_input, dim_attn, bias = False)
 91 |         #self.fc2 = nn.Linear(5, dim_attn)
 92 |     
 93 |     def forward(self, x):
 94 |         
 95 |         x = self.fc1(x)
 96 |         #print(x.shape)
 97 |         #x = self.fc2(x)
 98 |         
 99 |         return x
100 | 
101 | # https://pytorch.org/tutorials/beginner/transformer_tutorial.html
102 | class PositionalEncoding(nn.Module):
103 |     def __init__(self, d_model, dropout=0.1, max_len=5000):
104 |         super(PositionalEncoding, self).__init__()
105 | 
106 |         pe = torch.zeros(max_len, d_model)
107 |         position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
108 |         
109 |         div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
110 |         
111 |         pe[:, 0::2] = torch.sin(position * div_term)
112 |         pe[:, 1::2] = torch.cos(position * div_term)
113 |         
114 |         pe = pe.unsqueeze(0).transpose(0, 1)
115 |         
116 |         self.register_buffer('pe', pe)
117 | 
118 |     def forward(self, x):
119 |         x = x + self.pe[:x.size(1), :]. squeeze(1)
120 |         return x     
121 |     
122 | def get_data(batch_size, input_sequence_length, output_sequence_length):
123 |     i = input_sequence_length + output_sequence_length
124 |     
125 |     t = torch.zeros(batch_size,1).uniform_(0,20 - i).int()
126 |     b = torch.arange(-10, -10 + i).unsqueeze(0).repeat(batch_size,1) + t
127 |     
128 |     s = torch.sigmoid(b.float())
129 |     return s[:, :input_sequence_length].unsqueeze(-1), s[:,-output_sequence_length:]


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