{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting and Generating Texts\n",
    "\n",
    "This notebook explores the idea of predicting items in a sequence, and then using those predictions to generate new sequences based on the probabilities.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "ConX, version 3.6.0\n"
     ]
    }
   ],
   "source": [
    "import conx as cx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EmbeddingLayer\n",
    "\n",
    "An EmbeddingLayer allows the system to find (or use) distributed representations for words or letters. \n",
    "\n",
    "First, we need a method of encoding and decoding our sequenced data. We'll begin with characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(s):\n",
    "    \"\"\"Convert string or char into integers\"\"\"\n",
    "    if len(s) == 1:\n",
    "        return (1 + ord(s.lower()) - ord('a')) if s.isalpha() else 0\n",
    "    else:\n",
    "        return cleanup([encode(c) for c in s])\n",
    "\n",
    "def cleanup(items):\n",
    "    \"\"\"Remove repeated zeros\"\"\"\n",
    "    retval = []\n",
    "    for i in items:\n",
    "        if ((i != 0) or \n",
    "            (len(retval) == 0) or \n",
    "            (retval[-1] != 0)):\n",
    "            retval.append(i)\n",
    "    return retval\n",
    "\n",
    "def decode(n):\n",
    "    \"\"\"Convert integers into characters\"\"\"\n",
    "    if isinstance(n, (list, tuple)):\n",
    "        return [decode(v) for v in n]\n",
    "    elif n == 0:\n",
    "        return ' '\n",
    "    else:\n",
    "        return chr(ord('a') + int(n) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode(\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 5, 12, 12, 15, 0, 23, 15, 18, 12, 4, 0]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode(\"Hello, world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode(\"AaaA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'h'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' ', 'w', 'h', 'a', 't', ' ', 's', ' ', 'u', 'p', ' ', 'd', 'o', 'c', ' ']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode(encode(\"   what's     up  doc?   \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' what s up doc '"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\".join(decode(encode(\"   what's     up  doc?   \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given 1 - Predict 1\n",
    "\n",
    "Let's start out with sequence of characers of length 1. We'll just try to predict what the next character is given a single letter. We'll start with a fairly small corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = \"\"\"Four score and seven years ago our fathers brought forth on this continent, \n",
    "a new nation, conceived in Liberty, and dedicated to the proposition that all men are \n",
    "created equal. Now we are engaged in a great civil war, testing whether that nation, or \n",
    "any nation so conceived and so dedicated, can long endure. We are met on a great battle-field \n",
    "of that war. We have come to dedicate a portion of that field, as a final resting place \n",
    "for those who here gave their lives that that nation might live. It is altogether fitting \n",
    "and proper that we should do this. But, in a larger sense, we can not dedicate — we can not \n",
    "consecrate — we can not hallow — this ground. The brave men, living and dead, who struggled \n",
    "here, have consecrated it, far above our poor power to add or detract. The world will little \n",
    "note, nor long remember what we say here, but it can never forget what they did here. It is \n",
    "for us the living, rather, to be dedicated here to the unfinished work which they who fought \n",
    "here have thus far so nobly advanced. It is rather for us to be here dedicated to the great \n",
    "task remaining before us — that from these honored dead we take increased devotion to that \n",
    "cause for which they gave the last full measure of devotion — that we here highly resolve that \n",
    "these dead shall not have died in vain — that this nation, under God, shall have a new birth of \n",
    "freedom — and that government of the people, by the people, for the people, shall not perish \n",
    "from the earth.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four score and seven years ago our fathers brought forth on this continent a new nation conceived in liberty and dedicated to the proposition that all men are created equal now we are engaged in a great civil war testing whether that nation or any nation so conceived and so dedicated can long endure we are met on a great battle field of that war we have come to dedicate a portion of that field as a final resting place for those who here gave their lives that that nation might live it is altogether fitting and proper that we should do this but in a larger sense we can not dedicate we can not consecrate we can not hallow this ground the brave men living and dead who struggled here have consecrated it far above our poor power to add or detract the world will little note nor long remember what we say here but it can never forget what they did here it is for us the living rather to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced it is rather for us to be here dedicated to the great task remaining before us that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion that we here highly resolve that these dead shall not have died in vain that this nation under god shall have a new birth of freedom and that government of the people by the people for the people shall not perish from the earth '"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\".join(decode(encode(corpus)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_vocab = max(encode(corpus)) + 1\n",
    "len_vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = []\n",
    "encoded_corpus = encode(corpus)\n",
    "for i in range(len(encoded_corpus) - 1):\n",
    "    code = encoded_corpus[i]\n",
    "    next_code = encoded_corpus[i + 1]\n",
    "    dataset.append([[code], cx.onehot(next_code, len_vocab)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = cx.Network(\"Given 1 - Predict 1\")\n",
    "net.add(cx.Layer(\"input\", 1), \n",
    "        cx.EmbeddingLayer(\"embed\", 26, 64),  # in, out\n",
    "        cx.FlattenLayer(\"flatten\"),\n",
    "        cx.Layer(\"output\", 26, activation=\"softmax\"))\n",
    "net.connect()\n",
    "net.compile(error=\"categorical_crossentropy\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dashboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " <defs>\n",
       "  <style type=\"text/css\">\n",
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       "  </style>\n",
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       " <g id=\"figure_1\">\n",
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    {
     "name": "stdout",
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     "text": [
      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#   30 |   2.09850 |   0.32324 \n",
      "Saving network... Saved!\n"
     ]
    }
   ],
   "source": [
    "if net.saved():\n",
    "    net.load()\n",
    "    net.plot_results()\n",
    "else:\n",
    "    net.train(30, accuracy=.95, save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(net, count, len_vocab):\n",
    "    retval = \"\"\n",
    "    # start at a random point:\n",
    "    inputs = cx.choice(net.dataset.inputs)\n",
    "    # now we get the next, and the next, ...\n",
    "    for i in range(count):\n",
    "        # use the outputs as a prob distrbution\n",
    "        outputs = net.propagate(inputs)\n",
    "        code = cx.choice(p=outputs)\n",
    "        c = decode(code)\n",
    "        print(c, end=\"\")\n",
    "        retval += c\n",
    "    return retval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fhdhaittw twatbwdsnfhtapuyndodntwicnboiptndtafptnwnditiahtnyfpwiwsfthscocbnwqtlhnwwawpnnitaonejhtwagtlndathftptowdclwfawtpaafdohwioawnlwtddcwaaclettgocctehnltpttthttdoatttdgwbtodtapowviwtfacsfdvbiphbdftaltffbwttndwnardnfpcwttrifchtbfahiboatthtttoapbaowrogcgshtdahwiahtmtatfptsftstlwotgmnphaowwhaathlmcctcapaftqtttguwwwsbgcbwitphttvcwwitatfdset btirabdatawtaftbafbstfgfnataattctdrottnsaahbaagttwtdimdwttsyddgwtatgndgtttdsthsctfswhofwhgtgmctfbhoorcxsaatcahtndotpthnoalttatbwnwisobtwwdthpgalbtafhctsfedp"
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     "data": {
      "text/plain": [
       "'fhdhaittw twatbwdsnfhtapuyndodntwicnboiptndtafptnwnditiahtnyfpwiwsfthscocbnwqtlhnwwawpnnitaonejhtwagtlndathftptowdclwfawtpaafdohwioawnlwtddcwaaclettgocctehnltpttthttdoatttdgwbtodtapowviwtfacsfdvbiphbdftaltffbwttndwnardnfpcwttrifchtbfahiboatthtttoapbaowrogcgshtdahwiahtmtatfptsftstlwotgmnphaowwhaathlmcctcapaftqtttguwwwsbgcbwitphttvcwwitatfdset btirabdatawtaftbafbstfgfnataattctdrottnsaahbaagttwtdimdwttsyddgwtatgndgtttdsthsctfswhofwhgtgmctfbhoorcxsaatcahtndotpthnoalttatbwnwisobtwwdthpgalbtafhctsfedp'"
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     "execution_count": 24,
     "metadata": {},
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    }
   ],
   "source": [
    "generate(net, 500, len_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given 5 - Predict 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "net2 = cx.Network(\"Given 5 - Predict 1\")\n",
    "net2.add(cx.Layer(\"input\", 5),\n",
    "         cx.EmbeddingLayer(\"embed\", 26, 64),\n",
    "         cx.FlattenLayer(\"flatten\"),\n",
    "         cx.Layer(\"output\", 26, activation=\"softmax\"))\n",
    "net2.connect()\n",
    "net2.compile(error=\"categorical_crossentropy\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = []\n",
    "encoded_corpus = encode(corpus)\n",
    "for i in range(len(encoded_corpus) - 5):\n",
    "    code = encoded_corpus[i:i+5]\n",
    "    next_code = encoded_corpus[i + 5]\n",
    "    if len(code) == 5:\n",
    "        dataset.append([code, cx.onehot(next_code, len_vocab)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "net2.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ['f', 'o', 'u', 'r', ' '] s\n",
      "1 ['o', 'u', 'r', ' ', 's'] c\n",
      "2 ['u', 'r', ' ', 's', 'c'] o\n",
      "3 ['r', ' ', 's', 'c', 'o'] r\n",
      "4 [' ', 's', 'c', 'o', 'r'] e\n",
      "5 ['s', 'c', 'o', 'r', 'e']  \n",
      "6 ['c', 'o', 'r', 'e', ' '] a\n",
      "7 ['o', 'r', 'e', ' ', 'a'] n\n",
      "8 ['r', 'e', ' ', 'a', 'n'] d\n",
      "9 ['e', ' ', 'a', 'n', 'd']  \n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i, decode(net2.dataset.inputs[i]), decode(cx.argmax(net2.dataset.targets[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, options=('Test', 'Train'), rows=1, value='Train'), FloatSlider(value=0.5, continuous_update=False, description='Zoom', layout=Layout(width='65%'), max=1.0, style=SliderStyle(description_width='initial')), IntText(value=150, description='Horizontal space between banks:', style=DescriptionStyle(description_width='initial')), IntText(value=30, description='Vertical space between layers:', style=DescriptionStyle(description_width='initial')), HBox(children=(Checkbox(value=False, description='Show Targets', style=DescriptionStyle(description_width='initial')), Checkbox(value=False, description='Errors', style=DescriptionStyle(description_width='initial')))), Select(description='Features:', options=('',), rows=1, value=''), IntText(value=3, description='Feature columns:', style=DescriptionStyle(description_width='initial')), FloatText(value=1.0, description='Feature scale:', style=DescriptionStyle(description_width='initial'))), layout=Layout(width='100%')), VBox(children=(Select(description='Layer:', index=3, options=('input', 'embed', 'flatten', 'output'), rows=1, value='output'), Checkbox(value=True, description='Visible'), Select(description='Colormap:', options=('', 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'seismic', 'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r', 'winter', 'winter_r'), rows=1, value=''), HTML(value='<img 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FloatText(value=0.0, description='Leftmost color maps to:', style=DescriptionStyle(description_width='initial')), FloatText(value=1.0, description='Rightmost color maps to:', style=DescriptionStyle(description_width='initial')), IntText(value=0, description='Feature to show:', style=DescriptionStyle(description_width='initial')), HBox(children=(Checkbox(value=False, description='Rotate network', layout=Layout(width='52%'), style=DescriptionStyle(description_width='initial')), Button(icon='save', layout=Layout(width='10%'), style=ButtonStyle())))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'Given 5 - Predict 1'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=1415), Label(value='of 1416', layout=Layout(width='100px'))), layout=Layout(height='40px')), HBox(children=(Button(icon='fast-backward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='backward', layout=Layout(width='100%'), style=ButtonStyle()), IntText(value=0, layout=Layout(width='100%')), Button(icon='forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='fast-forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(description='Play', icon='play', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='refresh', layout=Layout(width='25%'), style=ButtonStyle())), layout=Layout(height='50px', width='100%'))), layout=Layout(width='100%')), HTML(value='<p style=\"text-align:center\"></p>', layout=Layout(justify_content='center', overflow_x='auto', overflow_y='auto', width='95%')), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net2.dashboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 720 288\" width=\"720pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 288  L 720 288  L 720 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 90 252  L 343.636364 252  L 343.636364 34.56  L 90 34.56  z \" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\">\n",
       "      <defs>\n",
       "       <path d=\"M 0 0  L 0 3.5  \" id=\"me667894f93\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n",
       "      </defs>\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"101.528926\" xlink:href=\"#me667894f93\" y=\"252\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_1\">\n",
       "      <!-- 0 -->\n",
       "      <defs>\n",
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       "      </defs>\n",
       "      <g transform=\"translate(98.347676 266.598437)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "    </g>\n",
       "    <g id=\"xtick_2\">\n",
       "     <g id=\"line2d_2\">\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"159.173554\" xlink:href=\"#me667894f93\" y=\"252\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_2\">\n",
       "      <!-- 20 -->\n",
       "      <defs>\n",
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       "      <g transform=\"translate(152.811054 266.598437)scale(0.1 -0.1)\">\n",
       "       <use xlink:href=\"#DejaVuSans-32\"/>\n",
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     "text": [
      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#   80 |   1.12873 |   0.63983 \n",
      "Saving network... Saved!\n"
     ]
    }
   ],
   "source": [
    "if net2.saved():\n",
    "    net2.load()\n",
    "    net2.plot_results()\n",
    "else:\n",
    "    net2.train(80, accuracy=.95, plot=True, save=True)"
   ]
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   "metadata": {},
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   "source": [
    "def generate2(net, count, len_vocab):\n",
    "    # start at a random point:\n",
    "    inputs = cx.choice(net.dataset.inputs)\n",
    "    retval = \"\".join(decode(inputs))\n",
    "    print(retval, end=\"\")\n",
    "    # now we get the next, and the next, ...\n",
    "    for i in range(count):\n",
    "        # use the outputs as a prob distrbution\n",
    "        outputs = net.propagate(inputs)\n",
    "        pickone = cx.choice(p=outputs)\n",
    "        inputs = inputs[1:] + [pickone]\n",
    "        c = decode(pickone)\n",
    "        print(c, end=\"\")\n",
    "        retval += c\n",
    "    return retval"
   ]
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate2(net2, 1000, 26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTMLayer\n",
    "\n",
    "\n",
    "\n",
    "### Many to One Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dblank/.local/lib/python3.6/site-packages/keras/layers/recurrent.py:1993: UserWarning: RNN dropout is no longer supported with the Theano backend due to technical limitations. You can either set `dropout` and `recurrent_dropout` to 0, or use the TensorFlow backend.\n",
      "  'RNN dropout is no longer supported with the Theano backend '\n"
     ]
    }
   ],
   "source": [
    "net3 = cx.Network(\"LSTM - Many to One\")\n",
    "net3.add(cx.Layer(\"input\", 40), # sequence length\n",
    "         cx.EmbeddingLayer(\"embed\", 26, 64), # sequence_length from input\n",
    "         cx.LSTMLayer(\"lstm\", 64),\n",
    "         cx.Layer(\"output\", 26, activation=\"softmax\"))\n",
    "net3.connect()\n",
    "net3.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = []\n",
    "encoded_corpus = encode(corpus)\n",
    "for i in range(len(encoded_corpus) - 40):\n",
    "    code = encoded_corpus[i:i+40]\n",
    "    next_code = encoded_corpus[i + 40]\n",
    "    if len(code) == 40:\n",
    "        dataset.append([code, cx.onehot(next_code, len_vocab)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "net3.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dash = net3.dashboard()\n",
    "dash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.038273777812719345,\n",
       " 0.03815032169222832,\n",
       " 0.03845784068107605,\n",
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       " 0.03870994597673416,\n",
       " 0.03826727718114853,\n",
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       " 0.03865499049425125,\n",
       " 0.038360513746738434,\n",
       " 0.03830300644040108,\n",
       " 0.038377050310373306,\n",
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       " 0.03817221522331238,\n",
       " 0.03834393247961998,\n",
       " 0.038742661476135254,\n",
       " 0.038073718547821045,\n",
       " 0.03857533633708954,\n",
       " 0.03845741227269173,\n",
       " 0.038555778563022614,\n",
       " 0.03872159868478775,\n",
       " 0.03873656317591667,\n",
       " 0.03813197463750839,\n",
       " 0.03850744664669037,\n",
       " 0.039117053151130676]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dash.propagate(net3.dataset.inputs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9e0fc9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if net3.saved():\n",
    "    net.load()\n",
    "    net.plot_results()\n",
    "else:\n",
    "    net3.train(150, save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate3(net, count, len_vocab):\n",
    "    # start with a full sentence:\n",
    "    inputs = cx.choice(net.dataset.inputs)\n",
    "    print(\"\".join(decode(inputs)), end=\"\")\n",
    "    for i in range(count):\n",
    "        outputs = net.propagate(inputs)\n",
    "        pickone = cx.choice(p=outputs)\n",
    "        inputs = inputs[1:] + [pickone]\n",
    "        print(decode(pickone), end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highly resolve that these dead shall not at cate forg tan thate ond or we ive cave we rive polly whls eor wit sas lore cane niter niand deat of the hereg dead pefpyate hipnl unsg inglinginreminon dored toicon fir lony dad divhe heage ralgle ar reos id do hur lony ar bbere brare nmated wo dedidad wo af herer here vashe that late dedicathre leather that or kecamen ant genont ferecond dedicat bere the bislic thall to uk ur borke thkal therto the hieg to dishe dediondgiin ur lorcem berey od cavt thath we alled onicatint inng keveretin seo"
     ]
    }
   ],
   "source": [
    "generate3(net3, 500, len_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Many to Many LSTM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dblank/.local/lib/python3.6/site-packages/keras/layers/recurrent.py:1993: UserWarning: RNN dropout is no longer supported with the Theano backend due to technical limitations. You can either set `dropout` and `recurrent_dropout` to 0, or use the TensorFlow backend.\n",
      "  'RNN dropout is no longer supported with the Theano backend '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, None)              0         \n",
      "_________________________________________________________________\n",
      "embed (Embedding)            (None, None, 64)          1664      \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, None, 256)         328704    \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, None, 26)          6682      \n",
      "=================================================================\n",
      "Total params: 337,050\n",
      "Trainable params: 337,050\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "net4 = cx.Network(\"Many-to-Many LSTM\")\n",
    "net4.add(cx.Layer(\"input\", None),  # None for variable number \n",
    "         cx.EmbeddingLayer(\"embed\", 26, 64),\n",
    "         cx.LSTMLayer(\"lstm\", 256, return_sequences=True), # , stateful=True\n",
    "         cx.Layer(\"output\", 26, activation='softmax', time_distributed=True))\n",
    "net4.connect()\n",
    "net4.compile(error=\"categorical_crossentropy\", optimizer=\"adam\")\n",
    "net4.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = []\n",
    "encoded_corpus = ([0] * 39) + encode(corpus)\n",
    "for i in range(len(encoded_corpus) - 40):\n",
    "    code = encoded_corpus[i:i+40]\n",
    "    next_code = encoded_corpus[i+1:i+40+1]\n",
    "    if len(code) == 40:\n",
    "        dataset.append([code, list(map(lambda n: cx.onehot(n, len_vocab), next_code))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 26)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.shape(dataset[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: network 'Many-to-Many LSTM' target bank #0 has a multi-dimensional shape, which is not allowed\n"
     ]
    }
   ],
   "source": [
    "net4.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3fc188c410f483aba2ed16d55303933",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Dashboard</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, options=('Test', 'Train'), rows=1, value='Train'), FloatSlider(value=0.5, continuous_update=False, description='Zoom', layout=Layout(width='65%'), max=1.0, style=SliderStyle(description_width='initial')), IntText(value=150, description='Horizontal space between banks:', style=DescriptionStyle(description_width='initial')), IntText(value=30, description='Vertical space between layers:', style=DescriptionStyle(description_width='initial')), HBox(children=(Checkbox(value=False, description='Show Targets', style=DescriptionStyle(description_width='initial')), Checkbox(value=False, description='Errors', style=DescriptionStyle(description_width='initial')))), Select(description='Features:', options=('',), rows=1, value=''), IntText(value=3, description='Feature columns:', style=DescriptionStyle(description_width='initial')), FloatText(value=1.0, description='Feature scale:', style=DescriptionStyle(description_width='initial'))), layout=Layout(width='100%')), VBox(children=(Select(description='Layer:', index=3, options=('input', 'embed', 'lstm', 'output'), rows=1, value='output'), Checkbox(value=True, description='Visible'), Select(description='Colormap:', options=('', 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'seismic', 'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r', 'winter', 'winter_r'), rows=1, value=''), HTML(value='<img 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FloatText(value=0.0, description='Leftmost color maps to:', style=DescriptionStyle(description_width='initial')), FloatText(value=1.0, description='Rightmost color maps to:', style=DescriptionStyle(description_width='initial')), IntText(value=0, description='Feature to show:', style=DescriptionStyle(description_width='initial')), HBox(children=(Checkbox(value=False, description='Rotate network', layout=Layout(width='52%'), style=DescriptionStyle(description_width='initial')), Button(icon='save', layout=Layout(width='10%'), style=ButtonStyle())))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'Many-to-Many LSTM'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=1419), Label(value='of 1420', layout=Layout(width='100px'))), layout=Layout(height='40px')), HBox(children=(Button(icon='fast-backward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='backward', layout=Layout(width='100%'), style=ButtonStyle()), IntText(value=0, layout=Layout(width='100%')), Button(icon='forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='fast-forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(description='Play', icon='play', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='refresh', layout=Layout(width='25%'), style=ButtonStyle())), layout=Layout(height='50px', width='100%'))), layout=Layout(width='100%')), HTML(value='<p style=\"text-align:center\"></p>', layout=Layout(justify_content='center', overflow_x='auto', overflow_y='auto', width='95%')), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dash4 = net4.dashboard()\n",
    "dash4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.038582801818847656,\n",
       " 0.03854098543524742,\n",
       " 0.03847697377204895,\n",
       " 0.03838202357292175,\n",
       " 0.03834330290555954,\n",
       " 0.038367994129657745,\n",
       " 0.03852173313498497,\n",
       " 0.038350626826286316,\n",
       " 0.03852957487106323,\n",
       " 0.03840670734643936,\n",
       " 0.03827265650033951,\n",
       " 0.03853010758757591,\n",
       " 0.038478899747133255,\n",
       " 0.03837151452898979,\n",
       " 0.03848860412836075,\n",
       " 0.03852129355072975,\n",
       " 0.038462840020656586,\n",
       " 0.03820224106311798,\n",
       " 0.03863661736249924,\n",
       " 0.03847706317901611,\n",
       " 0.03856559842824936,\n",
       " 0.03870689496397972,\n",
       " 0.038411181420087814,\n",
       " 0.03848462179303169,\n",
       " 0.038547102361917496,\n",
       " 0.03834003955125809]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dash4.propagate([13])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.038582801818847656,\n",
       "  0.03854098543524742,\n",
       "  0.03847697377204895,\n",
       "  0.03838202357292175,\n",
       "  0.03834330290555954,\n",
       "  0.038367994129657745,\n",
       "  0.03852173313498497,\n",
       "  0.038350626826286316,\n",
       "  0.03852957487106323,\n",
       "  0.03840670734643936,\n",
       "  0.03827265650033951,\n",
       "  0.03853010758757591,\n",
       "  0.038478899747133255,\n",
       "  0.03837151452898979,\n",
       "  0.03848860412836075,\n",
       "  0.03852129355072975,\n",
       "  0.038462840020656586,\n",
       "  0.03820224106311798,\n",
       "  0.03863661736249924,\n",
       "  0.03847706317901611,\n",
       "  0.03856559842824936,\n",
       "  0.03870689496397972,\n",
       "  0.038411181420087814,\n",
       "  0.03848462179303169,\n",
       "  0.038547102361917496,\n",
       "  0.03834003955125809],\n",
       " [0.038359951227903366,\n",
       "  0.03863690048456192,\n",
       "  0.038495197892189026,\n",
       "  0.03845957666635513,\n",
       "  0.03829522803425789,\n",
       "  0.03846903145313263,\n",
       "  0.03871066868305206,\n",
       "  0.03860457241535187,\n",
       "  0.03868010640144348,\n",
       "  0.0382499098777771,\n",
       "  0.038345471024513245,\n",
       "  0.03847820684313774,\n",
       "  0.03828677162528038,\n",
       "  0.038221828639507294,\n",
       "  0.03835667297244072,\n",
       "  0.038571350276470184,\n",
       "  0.03840469941496849,\n",
       "  0.03841114416718483,\n",
       "  0.03850405290722847,\n",
       "  0.038442227989435196,\n",
       "  0.038479533046483994,\n",
       "  0.038574136793613434,\n",
       "  0.03831023722887039,\n",
       "  0.038695789873600006,\n",
       "  0.03851288557052612,\n",
       "  0.038443852216005325]]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dash4.propagate([13, 21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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      "Interrupted! Cleaning up...\n",
      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#    3 |   2.64581 |   0.23125 \n"
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m~/.local/lib/python3.6/site-packages/conx/network.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, epochs, accuracy, error, batch_size, report_rate, verbose, kverbose, shuffle, tolerance, class_weight, sample_weight, use_validation_to_stop, plot, record, callbacks, save)\u001b[0m\n\u001b[1;32m   1337\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Saved!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minterrupted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1340\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1341\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "net4.train(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate4(net, count, len_vocab):\n",
    "    letters = [cx.choice(len_vocab)] # choose a random letter\n",
    "    for i in range(count):\n",
    "        print(decode(letters[-1]), end=\"\")\n",
    "        outputs = net.propagate(letters)\n",
    "        if len(cx.shape(outputs)) == 1:\n",
    "            p = outputs\n",
    "        else:\n",
    "            p = outputs[-1]\n",
    "        letters.append(cx.choice(p=p))\n",
    "        letters = letters[-40:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " cie a ccl ltnnndeririwsaosdie pthhhar o tctgedr fde loch hgotnhat cer encrccniscttee nurte oto n s l datnaryiearlidnttshaerr eihent no e r d thh  cv  herede rdh ighgcdltd vedlotuotss iee sbewita nde niwro iidwto fvrt ooretedprau leneofneigovn dedghr rccerdefnglog hnieianfn trchhc eutd cervo oletahdt  uin eepfogfiwga ced iihgg ihirthtetatuteoneeonhanf tthbyt tu eawrte at w  afsdltavovrrn gbvpn eh nehneruno  yearsosteirishtd rrncitt ctihgoftscd weoef ra tew waroraodvgeaaie a antdcpd fnnedagt have"
     ]
    }
   ],
   "source": [
    "generate4(net4, 500, len_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"397.72727272727275px\" height=\"400.00000000000006px\">\n",
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\"><title>Layer: input (input)\n",
       " output range: (0, +Infinity)\n",
       " shape = (None,)\n",
       " Keras class = Input\n",
       " dtype = int32</title></image><text x=\"280.0\" y=\"298.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >Many-to-Many LSTM</text></svg></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net4.picture([1, 100, 4, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"400.0px\" height=\"388.57142857142856px\">\n",
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       " lstm/kernel has shape (64, 1024)\n",
       " lstm/recurrent_kernel has shape (256, 1024)\n",
       " lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-static-Many-to-Many LSTM-468091_embed_27\" class=\"picture-static-Many-to-Many LSTM-468091_embed\" x=\"75.0\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhZAABAIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAZAABAAAIEgABCBxIsKDBgwgTKlzIsGHCgAA7\"><title>Layer: embed (hidden)\n",
       " output range: (-Infinity, +Infinity)\n",
       " shape = (None, 64)\n",
       " Keras class = Dense</title></image><text x=\"280.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >embed</text><line x1=\"175.0\" y1=\"264\" x2=\"175.0\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n",
       " embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"264\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-static-Many-to-Many LSTM-468091_input_28\" class=\"picture-static-Many-to-Many LSTM-468091_input\" x=\"75.0\" y=\"265\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: input (input)\n",
       " output range: (0, +Infinity)\n",
       " shape = (None,)\n",
       " Keras class = Input\n",
       " dtype = int32</title></image><text x=\"280.0\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >Many-to-Many LSTM</text></svg></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net4.picture([3, 69, 200, 10, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = net4.propagate(range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 40)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.shape(net4.dataset.inputs[43:47])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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Predict 1_embed_3\" class=\"Given 1 - Predict 1_embed\" x=\"75.0\" y=\"140\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAPv3+/Tq9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIOgABCBQYAIDBAAACAFgYAEAAAAEAAAgAIACAiwACANjIsWOAAAAABABAEkAAAChRBgAQIACAAABiBgQAOw==\"><title>Layer: embed (hidden)\n shape = (1, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"154.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"194\" x2=\"175.0\" y2=\"167\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"149.0\" y=\"194\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 1 - 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      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_6aff01cf33524d11abec846d95a8cb75",
       "style": "IPY_MODEL_797521a2cfb74237aa8aaebb6c35cfc1",
       "value": "<p style=\"text-align:center\"><svg id='Predict 1 Given 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 270\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Predict 1 Given 1</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_output_1\" class=\"Predict 1 Given 1_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAICwABCBxIsKDBggEBADs=\"><title>Layer: 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Predict 1_embed_3\" class=\"Given 5 - Predict 1_embed\" x=\"75.0\" y=\"140\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAAFAIcAAPv2+vbp8QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAAFAAAI/wADABgYAIDBAAASKlyYMIBDAAEABABAMQCAAAACAAAQIACAACADAAAQAEAAAChTpgwAAECAlwEAAAgAIACAmzhvBgAQAIBPnwECAAAQoCiAAAEAKA0AoCmAAAACBABAFUAAAFgDAAgAAECAAAACAAAQIACAAAEAqA0AoC2AAHABAAhAF0CAAADyBggAIACAAAEACA4AoDCAAAASAwjAuDGAxwEAAAgAIAAAAAEyBwAQAIDnAKBBAxgNIACA0wEABAAAIAAAAAFiA5hNG0AAAAACBAAQAIBvAAEAAAgQAIBxAAGSJwfAHEAAANADAJgOIACA69gBBNgOoDuAAOABiBYPAKC8+QAA0gMIAKC9+/cAAgCYHyAgADs=\"><title>Layer: embed (hidden)\n shape = (5, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"154.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"194\" x2=\"175.0\" y2=\"167\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"194\" width=\"202\" height=\"42\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_input_4\" class=\"Given 5 - Predict 1_input\" x=\"75.0\" y=\"195\" height=\"40\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhBQABAIcAAAAA2D09/38AALGx//MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAABQABAAAICAAJDAAQQEBAADs=\"><title>Layer: input (input)\n shape = (5,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"217.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
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      "model_module": "@jupyter-widgets/controls",
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       "style": "IPY_MODEL_25810063d3ab4a13a9741d5462ff8157",
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      "model_name": "HTMLModel",
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     "21e0c953936f43fd968864dd2c3b4397": {
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       "style": "IPY_MODEL_bbcc31c8165c46d1bb8d9a7dc6343cf9",
       "value": "<p style=\"text-align:center\"><svg id='LSTM - Many to One' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 345\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">LSTM - Many to One</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_output_1\" class=\"LSTM - Many to One_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAICwABCBxIsKDBggEBADs=\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (64, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_lstm_2\" class=\"LSTM - Many to One_lstm\" x=\"75.0\" y=\"85\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAP77+wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIDwABCBxIsKDBgwgTKkwYEAA7\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"99.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"139\" x2=\"175.0\" y2=\"112\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 256)\n lstm/recurrent_kernel has shape (64, 256)\n lstm/bias has shape (256,)</title></line><rect x=\"74.0\" y=\"139\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_embed_3\" class=\"LSTM - Many to One_embed\" x=\"75.0\" y=\"140\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAAoAIcAAPv3+/bp8gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAAoAEAI/wABAAgQAEAAAAACAAjAEIBDAAECAJgIIACAAAAyagQQAIBHjwECABg5MgCAkygDBADAEkAAADABBAgAIACAAAByBgDAMwCAAAACCAUAIACAo0cDAFjKtCnTAACiRg0QAIDVAAACaNUKoKtXAAEAAAgAIAAAAAEAAAgAIACAtwEAAAgAoG4AAAEA6A3AF0CAvwACAAhAGEAAAAESBwAQAIDjAAAABJg8GUAAAAEAaA4AoHMAAAEABBgNAEAAAKhRBwDAurXr1gEAyJYdIACA2wEABNi9G4Dv374DAAgAIEAAAMgDBAAQIAAAAAEABABAPUAAAAACAAgAIACAAOABiP8XHyAAgAAAAgQAACAAgPcBAgAAEABAgAAA8usPACAAAIABAgAAEABAgAAAAAQAACAAAAABJAYAUDFAAAAZA2wE0BFAAJAARAYIAMCkyQAAAARgCSAAAAABAASgWRPATQABAgAIAABAAAABAAwlGgDAUaRHAwBg2rRpAAAAAgAAEMAqAKwBAgDg2jUAgAAAAgQAACAAgAABAAAIAABAAAAAAswNAMBugAAA9AbgC8AvgACBAQwOEADA4cMBAAAI0DgAAAABAASgXBkAgAABAAAIAMAzgAChAwAgHQDAaQABVK8GACAAANgBAgCgHQDA7QAAdAcAEADAb+DBAQQAEAD/QAAAyQEECAAgAAAAAQAECADAegDsALQHABAAQIAAAMSPHx8gAAAAAdQDABAAwPv3AQAEABAgAAD8AQIACBAAAEAAAQAEAGAwQAAAAAIACAAgAIAAEgFQpBggAIAAAAIEAAAgAICQAQIAABAAQAAAAVYGAODyJYAAAQAACAAgAE4AAAIA6OkTQAAAQgEEABAAQIAAAJYGAOD0KYAAUgEEqAogAAAAAQBw7RoAQAAAAQIAABAAQIAAAAAEAAAgAAAAAeYGAGA3QAAAegPwBeAXQIDAAAYHCADg8OEAAAAEaAzgMeQAAAIACBAAAIAAAAIEAAAgAAAAAQAACGA6AIDU/wECAGgd4DWA2AAC0AZgO0AAALp1BwAAIEAAAMIDAAAQAEAAAAACAAAQAEAAANIDAAAQAAD2AAACAOge4DuAAOIBBAAQ4DyAAAACsA8AIACA+AEAAAhgHwD+AAAABAAQACAAAAEAAAgAIAAAhQEAAAgAAGIAAAEAVAxwEUAAjQACAAjwEUAAAAFIBgAQAEDKAAAABHAZAEBMmQEABAAQIAAAAAEABAgAAEAAAAACAAAQAGkAAEsDBADwNEBUAFMBBLAKAGuAAAC4cg0AAEAAsQDIlg0AIACAAAEAAAgAIEAAAAACAAAQAACAAHsDAPAbIAAAwQEIAzAMIEBiAIsDBP8A8PhxAAAAAlQGcBlzAAABAAQIAABAAAABAgAAEAAAgAAAAARwHQBA7AABANQOcBtAbgABeAPwHSAAAOHCAwAAECAAAOUBAAAIEAAAgAAAAgAAEAB7AADbAQQAEABAgAAAAgQAcP58AAABAgBw7z4AAAAB6AMIAAA/gAABAAQAADAAgIEECxosGCAAAAABGjoEACCARAAAAgC4eDFAAAAAAgAIAABAgJEkAZgEEABAgJUAAgAIACBmAAA0AwAIACCATgAAAgD4+TMAgKFEixINACBp0gABADgNACCAVKkAqgIIACAAgK1bAwAIAABAAAAAAgAIACAAAAABAgB4C/f/bYC5AQDYvQsgAIAAAAAECAAAQIDBgwEACBAAgOLFjBs7DhAAAIAAlCsDABAgMwAAAQB49hwgAAAAAQAEAAAggOrVAFoDCAAgAIDZAQAACAAgAAAAAQAACAAgAIDhAQAACAAgeQAAAQA4DwAdQIDpAAIACIAdQAAAAboHABAAgPgAAAAEOA8gvfr1AQAEABAAgHwAAQIACAAAQAAAAQIAAAgAQACCAAwGABAAQIAAABw+fBggAAAAASwCABAAQAAAAAIAABkyZAAAAQIAQAkgAIAAAQAACAAAQAAAAAIAABAAQACeAQAECABAaIAAAAAECABA6VKmAQAEABA1agAA/wEAAAgAAEAAAAEABAAAIEAAAGXNlg2QNgAAtm0BBAAQAACAAAEAAAiQNy8AAAECAAAcAEAAAAEABACQGECAAAACPAYAIAAAAAECAAgQAMDmzQE8BwAAIEAAAKUBBAAQAEAAAAACvAYQG0AAAAACAAgAIAAAAAEABAAeHMBwAAECAAgAAEAAAAEAPIceAMB06tMDAMCePXsAAAACAAAQQDwA8gECAEAfAMD69QHcAwgAAEAAAAEABAAAIEAAAAEAAAwAYCDBAAACAAgAYGEAAAEAQAwAAECAAAAuAggAYCPHjh0DAAgAIACAkgACBAAQAACAAAACBAAgMwBNADYDAP8IACBAAAA+f/4MEAAAgABGAQAIACAAgAAAnj4NACAAAAABAAAIACAAgAAAAAQIAGAs2bEBzgYAoHYtgAAAAgAAECAAAAAB7t4FACBAAAB+/wL2GwBAAAABACAGECAAgAAAAAQAECAAgMoBLgPIHABAAAABAgAILVp0gAAAAARIDQBAAACuX8OOHTtAAAAAAuDODQBAgN4AAAQAIFx4gAAAAAQAEAAAgADOnwOIDiAAgAAArmPPrj17gAAAAAQILx4AgADmAQAIAGD9+gABAAAIACAAAAAB7uMHoB9AAAABAAYIACAAgAAAEAYAsDAAgAAAAkQEACAAAIsWAwDQuJG+48YAAECCDBAAQMkAAAKkTAmAZcsAAQAEAAAgAIAANwHkBBAgAACfAAIACACAaFEAAQAkTRogAACnTgMAkDo1QAAAVwEEALAVQIAAAMCGBRuALIAAAQCkVRsAQIAAAAIEABAAQAAAAfACABCAL4AAAQAEBhAAQOEAhwEACBAAQGPHAAIAABAAQAAAAAIAABAAQAAAnwMAABAAQOkAAAIAUB2ANYAArwEEABCANoAAAALkDgAgAADfAQAACDA8IAA7\"><title>Layer: embed (hidden)\n shape = (40, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"204.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"294\" x2=\"175.0\" y2=\"267\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"294\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - 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href=\"data:image/gif;base64,R0lGODdhAAEoAIcAAP36+wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAEoAEAIpAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjqwEBADs=\"><title>Layer: lstm (hidden)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"272.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"315\" x2=\"175.0\" y2=\"288\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"315\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_embed_3\" class=\"Many-to-Many LSTM_embed\" x=\"75.0\" y=\"316\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: embed (hidden)\n shape = (None, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"380.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"470\" x2=\"175.0\" y2=\"443\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"470\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_input_4\" class=\"Many-to-Many LSTM_input\" x=\"75.0\" y=\"471\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKAABAIcAAH8AAPMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIDgABCBxIsKDBgwgHBggIADs=\"><title>Layer: input (input)\n shape = (None,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"485.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
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       "style": "IPY_MODEL_47933188000f406188ccee84d411236d",
       "value": "<img 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       "step": null,
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href=\"data:image/gif;base64,R0lGODdhGgAoAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgAoAEAINgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsmXBgAA7\"><title>Layer: 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href=\"data:image/gif;base64,R0lGODdhKAABAIcAAP/19QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIDQABCBxIsKDBgwgJBgQAOw==\"><title>Layer: 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href=\"data:image/gif;base64,R0lGODdhGgAoAIcAAJEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgAoAEAINgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsmXBgAA7\"><title>Layer: output (output)\n output range: (0, 1)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >output</text><line x1=\"175.0\" y1=\"104\" x2=\"175.0\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (256, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"104\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-644627_lstm_6\" class=\"picture-dashboard-Many-to-Many LSTM-644627_lstm\" x=\"75.0\" y=\"105\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAEoAIcAAP78/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAEoAEAIpAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjqwEBADs=\"><title>Layer: lstm (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >lstm</text><line x1=\"175.0\" y1=\"184\" x2=\"175.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-644627_embed_7\" class=\"picture-dashboard-Many-to-Many LSTM-644627_embed\" x=\"75.0\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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       "value": "<p style=\"text-align:center\"><svg id='Given 5 - Predict 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 260\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Given 5 - Predict 1</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_output_1\" class=\"Given 5 - Predict 1_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t//Hx/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAIDQABBABAsKDBgwgLBgQAOw==\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flatten to output\n output/kernel has shape (320, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_flatten_2\" class=\"Given 5 - Predict 1_flatten\" x=\"75.0\" y=\"85\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAEBAIcAAPr2+/fp8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAEBAAAI/wADAAgAoGAAAAESJgQQAACAAAEASAQQAIDFixgBBAAQIACAAAEAiBQZoCSAkycDAAAQAEAAAAEABAgAIEAAADgBBAgAAECAAAACAAgAIACAAAEABADAtGmAAACiBgAAIEAAAAEAaN0aoCuArwEABABAFkAAAGgDBAAAIEAAAHABBAAQoC6AAAAC6AXAF0CAAAACAwgAIEAAAAESAwAQIACAAAAiBwBAGUCAAAAyBwDAOUAAAKADAAgQAECAAABSBwDAOgCAAAAABAAAIACAAAEABAgAoHcAAMADBAAQoDiAAAEAKF+uPAAAAAGiBwAAIEAAAAACANgeoHsAAAHCAy0AEACA+QABAAQAACAAAAABAMgHEACA/QAA8gMIACBAAIAAAgwEACAAAIQAAgIAOw==\"><title>Layer: flatten (hidden)\n Keras class = Flatten</title></image><text x=\"280.0\" y=\"99.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">flatten</text><line x1=\"175.0\" y1=\"139\" x2=\"175.0\" y2=\"112\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to flatten</title></line><rect x=\"74.0\" y=\"139\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_embed_3\" class=\"Given 5 - Predict 1_embed\" x=\"75.0\" y=\"140\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAAFAIcAAPr2+/fp8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAAFAAAI/wADAAgAoGAAAAESJgQQAACAAAEASAQQAIDFixgBBAAQIACAAAEAiBQZoCSAkycDAAAQAEAAAAEABAgAIEAAADgBBAgAAECAAAACAAgAIACAAAEABADAtGmAAACiBgAAIEAAAAEAaN0aoCuArwEABABAFkAAAGgDBAAAIEAAAHABBAAQoC6AAAAC6AXAF0CAAAACAwgAIEAAAAESAwAQIACAAAAiBwBAGUCAAAAyBwDAOUAAAKADAAgQAECAAABSBwDAOgCAAAAABAAAIACAAAEABAgAoHcAAMADBAAQoDiAAAEAKF+uPAAAAAGiBwAAIEAAAAACANgeoHsAAAHCAy0AEACA+QABAAQAACAAAAABAMgHEACA/QAA8gMIACBAAIAAAgwEACAAAIQAAgIAOw==\"><title>Layer: embed (hidden)\n shape = (5, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"154.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"194\" x2=\"175.0\" y2=\"167\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"194\" width=\"202\" height=\"42\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - 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       "value": "<p style=\"text-align:center\"><svg id='Given 5 - Predict 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 255\" max-width=\"100%\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Given 5 - Predict 1</text><rect x=\"74.0\" y=\"24\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_output_1\" class=\"Given 5 - Predict 1_output\" x=\"75.0\" y=\"25\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAICwABCBxIsKDBggEBADs=\"><title>Layer: 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href=\"data:image/gif;base64,R0lGODdhQAEBAIcAAPv3+/fq8QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAEBAAAI/wABAAgAIACAAAASBggAIIBDhwACAJgYAIDFiwEyAtgIIEAAAAECABgZIAAAAAECAFgZAIDLly4DBAAQAIBNAAEABAgAoGcAAECDAggAAEAAAAEAKF0aIACAAFChAgAQAACAAFgBAAgAoGsAAAEAiAUQIACAAAEABAgAoG0AAAACAAAQAEAAAHgDAAAQIAAAAAEACA4QAIBhAAEAKF4cIACAAAACSAZAOUAAAJgDBADAmXOAAABCAwhAOgAAAAEAqA4AAEAAAAEAAAgAoHYAAAFyA9i9OwCA3wECABgOIACAAACSBwgAIEAAAAECAJgOIEAAANizBwAAIEAAAAEAACYIEABAgAAA0qcPAABAgPcA4scPACAAgAAA8ucPwB9AAIAABAIICAA7\"><title>Layer: flatten (hidden)\n Keras class = Flatten</title></image><text x=\"280.0\" y=\"94.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">flatten</text><line x1=\"175.0\" y1=\"134\" x2=\"175.0\" y2=\"107\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to flatten</title></line><rect x=\"74.0\" y=\"134\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_embed_3\" class=\"Given 5 - Predict 1_embed\" x=\"75.0\" y=\"135\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAAFAIcAAPv3+/fq8QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAAFAAAI/wABAAgAIACAAAASBggAIIBDhwACAJgYAIDFiwEyAtgIIEAAAAECABgZIAAAAAECAFgZAIDLly4DBAAQAIBNAAEABAgAoGcAAECDAggAAEAAAAEAKF0aIACAAFChAgAQAACAAFgBAAgAoGsAAAEAiAUQIACAAAEABAgAoG0AAAACAAAQAEAAAHgDAAAQIAAAAAEACA4QAIBhAAEAKF4cIACAAAACSAZAOUAAAJgDBADAmXOAAABCAwhAOgAAAAEAqA4AAEAAAAEAAAgAoHYAAAFyA9i9OwCA3wECABgOIACAAACSBwgAIEAAAAECAJgOIEAAANizBwAAIEAAAAEAACYIEABAgAAA0qcPAABAgPcA4scPACAAgAAA8ucPwB9AAIAABAIICAA7\"><title>Layer: embed (hidden)\n shape = (5, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"149.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"189\" x2=\"175.0\" y2=\"162\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"189\" width=\"202\" height=\"42\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 5 - Predict 1_input_4\" class=\"Given 5 - Predict 1_input\" x=\"75.0\" y=\"190\" height=\"40\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (5,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "654ab9fb4ce84a12a0a519f5dd760b50": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_c39e54129e054ef8a069019e7ce56877",
       "style": "IPY_MODEL_96f0ea3b216d469fb7b2bc3c6a2a8c8c",
       "value": "<p style=\"text-align:center\"><svg id='LSTM - Many to One' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 345\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">LSTM - Many to One</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_output_1\" class=\"LSTM - Many to One_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAICwABCBxIsKDBggEBADs=\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (64, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_lstm_2\" class=\"LSTM - Many to One_lstm\" x=\"75.0\" y=\"85\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAP77/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIDwABCBxIsKDBgwgTKkwYEAA7\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"99.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"139\" x2=\"175.0\" y2=\"112\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 256)\n lstm/recurrent_kernel has shape (64, 256)\n lstm/bias has shape (256,)</title></line><rect x=\"74.0\" y=\"139\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_embed_3\" class=\"LSTM - Many to One_embed\" x=\"75.0\" y=\"140\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: embed (hidden)\n shape = (40, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"204.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"294\" x2=\"175.0\" y2=\"267\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"294\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - Many to One_input_4\" class=\"LSTM - Many to One_input\" x=\"75.0\" y=\"295\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKAABAIcAAH8AAOYAAD09/5EAALGx/xUV/wAA2NnZ/wAAaAAAvAAA8bkAAM0AAP8REf85OQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIMAADEDAgAECBBQQEBAAw4AADAAUCJAhwAACCAAMEFAAwoAEBAAQMCAAQYIACBwECAgA7\"><title>Layer: input (input)\n shape = (40,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"309.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "6552533844aa4b7194e97058a47b36ee": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "95%"
      }
     },
     "655a5dab11cb4c9498a43e8bbd80ef4b": {
      "model_module": "@jupyter-widgets/controls",
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      "model_module": "@jupyter-widgets/controls",
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       "style": "IPY_MODEL_c280a081d0a542798db2d03030d9ec67",
       "value": "of 1420"
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     "669264e9ded6459e865ad9c18467f0c8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_e3d446f0d793460a8b0cf18c0bcfc6fa",
       "style": "IPY_MODEL_6b29a8b6dc564063a82a21050283171f",
       "value": "<p style=\"text-align:center\"><svg id='Predict 1 Given 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 270\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Predict 1 Given 1</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_output_1\" class=\"Predict 1 Given 1_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGwABAIcAAO3t//Hx/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGwABAAAIFAABCAwAoGAAAAgTBgDAsKFDhgEBADs=\"><title>Layer: output (output)\n shape = (27,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flatten to output\n output/kernel has shape (64, 27)\n output/bias has shape (27,)</title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_flatten_2\" class=\"Predict 1 Given 1_flatten\" x=\"75.0\" y=\"85\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAPr1+vTq9QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIOQABCAwQAECAAAASBgDAsGGAhwACSAQAIACAAAAyBggAoKPHACABBAAAIAAAAAEAqFwZAIBLAAECAgA7\"><title>Layer: flatten (hidden)\n Keras class = Flatten</title></image><text x=\"280.0\" y=\"99.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">flatten</text><line x1=\"175.0\" y1=\"139\" x2=\"175.0\" y2=\"112\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to flatten</title></line><rect x=\"74.0\" y=\"139\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_embed_3\" class=\"Predict 1 Given 1_embed\" x=\"75.0\" y=\"140\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAPr1+vTq9QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIOQABCAwQAECAAAASBgDAsGGAhwACSAQAIACAAAAyBggAoKPHACABBAAAIAAAAAEAqFwZAIBLAAECAgA7\"><title>Layer: embed (hidden)\n shape = (1, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"154.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"194\" x2=\"175.0\" y2=\"167\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (27, 64)</title></line><rect x=\"149.0\" y=\"194\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_input_4\" class=\"Predict 1 Given 1_input\" x=\"150.0\" y=\"195\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAQABAIcAAH8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAQABAAAIBQAB+AgIADs=\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input\n dtype = int32</title></image><text x=\"205.0\" y=\"222.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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      "model_module_version": "1.1.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_6da849fbc2964b2885adc9f938f1223e",
        "IPY_MODEL_2483bb8ad03c431fa42a9e589d997f6c"
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     "66a088df60274f859f0ffa790733b798": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "40px"
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     "66b48e8839bc401399c8ef6481c9ac5b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_d43e9f28133443fa801e05e83126a854",
       "style": "IPY_MODEL_4ffd9a32b4694a22823db679c44ac727",
       "value": "<p style=\"text-align:center\"><svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 320\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Many-to-Many LSTM</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_output_1\" class=\"Many-to-Many LSTM_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGwABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGwABAAAICwABCBxIsKBBgwEBADs=\"><title>Layer: output (output)\n shape = (27,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (256, 27)\n output/bias has shape (27,)</title></line><rect x=\"149.0\" y=\"84\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_lstm_2\" class=\"Many-to-Many LSTM_lstm\" x=\"150.0\" y=\"85\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAQABAIcAAP39/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAQABAAAIBQABwAsIADs=\"><title>Layer: 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      "model_module": "@jupyter-widgets/controls",
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      "model_name": "DescriptionStyleModel",
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
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     "676933c56cd74ac18dd744dc875aacf4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_1ec8c97b5fb4498c81954432eaf17c75",
       "style": "IPY_MODEL_f82179c55e114410854e82e2113b31f1",
       "value": "<p style=\"text-align:center\"><svg id='Given 1 - Predict 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 270\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Given 1 - Predict 1</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 1 - Predict 1_output_1\" class=\"Given 1 - Predict 1_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgABAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgABAAAICwABCBxIsKDBggEBADs=\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"44.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"84\" x2=\"175.0\" y2=\"57\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from flatten to output\n output/kernel has shape (64, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"84\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 1 - Predict 1_flatten_2\" class=\"Given 1 - Predict 1_flatten\" x=\"75.0\" y=\"85\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAABAIcAAPr2+vfq8QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAABAAAIOAABCAQQIACAgwACAFjIMIBDhwAiBgBAsWIAAAACAAgQAACAAABCBggAIACAkygBBADAkmUAAAEBADs=\"><title>Layer: flatten (hidden)\n Keras class = Flatten</title></image><text x=\"280.0\" y=\"99.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">flatten</text><line x1=\"175.0\" y1=\"139\" x2=\"175.0\" y2=\"112\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to flatten</title></line><rect x=\"74.0\" y=\"139\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Given 1 - 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      "model_name": "FloatTextModel",
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       "description": "Rightmost color maps to:",
       "layout": "IPY_MODEL_528cb8b4347c437e9d5fd775bc1f5a04",
       "step": null,
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       "value": 1
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntTextModel",
      "state": {
       "description": "Vertical space between layers:",
       "layout": "IPY_MODEL_6c86474a06c14aa7937c27a20032d1d9",
       "step": 1,
       "style": "IPY_MODEL_c81a93d8671b465b9f20192e7fd3ca0a",
       "value": 30
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     "68be6e7974d84ae4ba90df47583a8a0c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
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       "description_width": ""
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     "68d0e8aa97a2455a8e5abf1e5cf97601": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "AccordionModel",
      "state": {
       "_titles": {
        "0": "Many-to-Many LSTM"
       },
       "children": [
        "IPY_MODEL_ca90a318e1fd48dfb5dce66530351ec0"
       ],
       "layout": "IPY_MODEL_ef0f5b0102d94c8cb7f66b94b66dbcf1",
       "selected_index": null
      }
     },
     "68d25313ef644db1a3f8c5bea8f8086f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_6eacf61cb98c4483b22e237a10c6b97c",
       "style": "IPY_MODEL_7f429cf7a45a42b881cd4391b4e03cb2",
       "value": "<p style=\"text-align:center\"><svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 526\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Many-to-Many LSTM</text><rect x=\"109.0\" y=\"29\" width=\"132\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_output_1\" class=\"Many-to-Many LSTM_output\" x=\"110.0\" y=\"30\" height=\"200\" width=\"130\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgAoAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgAoAEAINgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsmXBgAA7\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"245.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"259\" x2=\"175.0\" y2=\"232\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (256, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"259\" width=\"202\" height=\"33\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_lstm_2\" class=\"Many-to-Many LSTM_lstm\" x=\"75.0\" y=\"260\" height=\"31\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAEoAIcAAP77/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAEoAEAIpAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjqwEBADs=\"><title>Layer: lstm (hidden)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"277.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"320\" x2=\"175.0\" y2=\"293\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"320\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_embed_3\" class=\"Many-to-Many LSTM_embed\" x=\"75.0\" y=\"321\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" 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href=\"data:image/gif;base64,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\"><title>Layer: lstm (hidden)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"277.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"320\" x2=\"175.0\" y2=\"293\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"320\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_embed_3\" class=\"Many-to-Many LSTM_embed\" x=\"75.0\" y=\"321\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: embed (hidden)\n shape = (None, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"385.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"475\" x2=\"175.0\" y2=\"448\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"475\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_input_4\" class=\"Many-to-Many LSTM_input\" x=\"75.0\" y=\"476\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKAABAIcAAH8AAPMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIDgABCBxIsKDBgwgHBggIADs=\"><title>Layer: input (input)\n shape = (None,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"490.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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        "Accent",
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        "Accent",
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        "Blues",
        "Blues_r",
        "BrBG",
        "BrBG_r",
        "BuGn",
        "BuGn_r",
        "BuPu",
        "BuPu_r",
        "CMRmap",
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Many to One_embed_3\" class=\"LSTM - Many to One_embed\" x=\"75.0\" y=\"140\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: embed (hidden)\n shape = (40, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"204.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"294\" x2=\"175.0\" y2=\"267\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"294\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - 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src=\"data:image/gif;base64,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\"/>"
      }
     },
     "bf2aa85117f44b6dabb611fb4cf66703": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_643faf626d9a4156a6a8fdee2db126ae",
       "style": "IPY_MODEL_ceb4d3d0cfaa4efaba07492cd83c706a",
       "value": "<p style=\"text-align:center\"><svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 526\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Many-to-Many LSTM</text><rect x=\"109.0\" y=\"29\" width=\"132\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_output_1\" class=\"Many-to-Many LSTM_output\" x=\"110.0\" y=\"30\" height=\"200\" width=\"130\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgAoAIcAAO3t/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgAoAEAINgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsmXBgAA7\"><title>Layer: output (output)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"245.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"259\" x2=\"175.0\" y2=\"232\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (256, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"259\" width=\"202\" height=\"33\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_lstm_2\" class=\"Many-to-Many LSTM_lstm\" x=\"75.0\" y=\"260\" height=\"31\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAEoAIcAAP76+wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAEoAEAIpAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjqwEBADs=\"><title>Layer: lstm (hidden)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"277.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"320\" x2=\"175.0\" y2=\"293\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"320\" width=\"202\" height=\"127\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_embed_3\" class=\"Many-to-Many LSTM_embed\" x=\"75.0\" y=\"321\" height=\"125\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: embed (hidden)\n shape = (None, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"385.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">embed</text><line x1=\"175.0\" y1=\"475\" x2=\"175.0\" y2=\"448\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"475\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Many-to-Many LSTM_input_4\" class=\"Many-to-Many LSTM_input\" x=\"75.0\" y=\"476\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKAABAIcAAH8AAPMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIDgABCBxIsKDBgwgHBggIADs=\"><title>Layer: input (input)\n shape = (None,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"490.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "bf3c3fdccf2c4607a0de83372ba79c3b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
      }
     },
     "bf3dcbb3d2304389b30d4de05fe30452": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
      }
     },
     "bf405bc649c6448daedd8501ed19943c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_df174eed098e438685357457f8801276",
       "style": "IPY_MODEL_0d73859082f44e229a025faf8521eada",
       "value": "<p><hr/><h3>flatten bank:</h3></p>"
      }
     },
     "bf46fb60565c4df2a92f68df792b2efd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "Rotate",
       "disabled": false,
       "layout": "IPY_MODEL_021cf662f967431f83dbaa09c3dc9f5a",
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     },
     "de9677aa951a43b7951a9109f1e76a2c": {
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      "state": {
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       "value": "<p style=\"text-align:center\"><svg id='Predict 1 Given 1' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 301\" height=\"780\" image-rendering=\"pixelated\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">Predict 1 Given 1</text><rect x=\"74.0\" y=\"29\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Predict 1 Given 1_output_5\" class=\"Predict 1 Given 1_output\" x=\"75.0\" y=\"30\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" 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flatten (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = Flatten</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >flatten</text><line x1=\"175.0\" y1=\"184\" x2=\"175.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to flatten</title></line><rect x=\"74.0\" y=\"184\" width=\"202\" height=\"202\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Given 5 - Predict 1-916802_embed_3\" class=\"picture-dashboard-Given 5 - Predict 1-916802_embed\" x=\"75.0\" y=\"185\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" 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embed (hidden)\n output range: (-Infinity, +Infinity)\n shape = (5, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >embed</text><line x1=\"175.0\" y1=\"414\" x2=\"175.0\" y2=\"387\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"414\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Given 5 - Predict 1-916802_input_4\" class=\"picture-dashboard-Given 5 - Predict 1-916802_input\" x=\"75.0\" y=\"415\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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YMlkCADDgJs6cOnfy7OkTJ4CgQocSLWr0KFKjAZYyber0KdSoUpkCACDgKtasWrdy7eoVKwAABMaSLWv2LNq0askCADDgLdy4cufSrWsXLoC8evfy7ev3L2C/AQYTLmz4MOLEigkDACDgMeTIkidTrmwZMgAABDZz7uz5M+jQojkDADDgNOrUJKpXs27tGjWA2LJn065t+zbu3Lp38+7t+zfw4MKHEy9u/DiAgAA7\"><title>Layer: input (input)\n output range: (0, +Infinity)\n shape = (5,)\n Keras class = Input\n dtype = int32</title></image><text x=\"280.0\" y=\"442.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >Given 5 - Predict 1</text></svg></g></svg></p>"
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     "f34d79ef57884de784c1800a4a4c76c6": {
      "model_module": "@jupyter-widgets/controls",
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        "IPY_MODEL_1764b850ffd247c396fb0e44421c1f78",
        "IPY_MODEL_3cd3a46cb9664e9f80fc1fcea603d10e",
        "IPY_MODEL_8db806ec445f4b378c8841a7b44744e5",
        "IPY_MODEL_c91e20185e2d4425b8a4d80084ae8d09"
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     "f34e78ba8af44eb790bed0e6e4dc2378": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
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     "f3688021bad44884817fd10b56483282": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntTextModel",
      "state": {
       "description": "Leftmost color maps to:",
       "layout": "IPY_MODEL_4ef47a6e8ee849d6b84cbda02fc97557",
       "step": 1,
       "style": "IPY_MODEL_b2dcac63df6149a0bfac9eeb50fbdd04",
       "value": -1
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     "f372f5b140784e62b6532d11991af21b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
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     "f3861521c414406a9947da798d26c564": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "40px"
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     "f3912bccc9c74cdd814114fe4ea9e4a0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
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     "f3a538594e094cf19afa38ca64bc6600": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
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     "f3a6753194b649efa864157acd10366c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
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     "f3b31dadb2e74810adee94691000a6d9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
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     "f3bcb4ea8d894361badfde56b3aa9033": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_dc300f67aa7148fe83374d73a74e7fd3",
       "style": "IPY_MODEL_b274333fb7cd475ca7f2986561646300",
       "value": "<p style=\"text-align:center\"><svg id='Many-to-Many LSTM' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"400.0px\" height=\"388.57142857142856px\">\n <g >\n  <svg viewBox=\"0 0 350 340\" width=\"400.0px\" height=\"388.57142857142856px\">\n    <defs>\n        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n        </marker>\n    </defs><rect x=\"74.0\" y=\"24\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-874126_output_1\" class=\"picture-dashboard-Many-to-Many LSTM-874126_output\" x=\"75.0\" y=\"25\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhGgAoAIcAAJEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAGgAoAEAINgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsmXBgAA7\"><title>Layer: output (output)\n output range: (0, 1)\n shape = (26,)\n Keras class = Dense\n activation = softmax</title></image><text x=\"280.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >output</text><line x1=\"175.0\" y1=\"104\" x2=\"175.0\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (256, 26)\n output/bias has shape (26,)</title></line><rect x=\"74.0\" y=\"104\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-874126_lstm_2\" class=\"picture-dashboard-Many-to-Many LSTM-874126_lstm\" x=\"75.0\" y=\"105\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhAAEoAIcAAP78/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAAAEoAEAIpAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjqwEBADs=\"><title>Layer: lstm (hidden)\n output range: (-Infinity, +Infinity)\n Keras class = LSTM\n return_sequences = True</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >lstm</text><line x1=\"175.0\" y1=\"184\" x2=\"175.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from embed to lstm\n lstm/kernel has shape (64, 1024)\n lstm/recurrent_kernel has shape (256, 1024)\n lstm/bias has shape (1024,)</title></line><rect x=\"74.0\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-874126_embed_3\" class=\"picture-dashboard-Many-to-Many LSTM-874126_embed\" x=\"75.0\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhQAAoAIcAAPz4+wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAQAAoAEAIVAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRowQDAgA7\"><title>Layer: embed (hidden)\n output range: (-Infinity, +Infinity)\n shape = (None, 64)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >embed</text><line x1=\"175.0\" y1=\"264\" x2=\"175.0\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"149.0\" y=\"264\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-Many-to-Many LSTM-874126_input_4\" class=\"picture-dashboard-Many-to-Many LSTM-874126_input\" x=\"150.0\" y=\"265\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKAABAIcAAAAAADo6OgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKAABAAAIDgABCBxIsKDBgwgHBggIADs=\"><title>Layer: input (input)\n output range: (0, +Infinity)\n shape = (None,)\n Keras class = Input\n dtype = int32</title></image><text x=\"205.0\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"175.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >Many-to-Many LSTM</text></svg></g></svg></p>"
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       "step": 1,
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       "value": 30
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      "model_name": "FloatTextModel",
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       "description": "Feature scale:",
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       "step": null,
       "style": "IPY_MODEL_60733b40834a4561aff426d3f6c4b3af",
       "value": 1
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      "model_name": "ButtonModel",
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       "icon": "backward",
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       "style": "IPY_MODEL_f8a37c479e704d75817c2ae13160707d"
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      "model_module_version": "1.1.0",
      "model_name": "HBoxModel",
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        "IPY_MODEL_b692261903614dcd9f11a87dc130986d"
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Many to One-902658_embed-rotated_11\" class=\"picture-dashboard-LSTM - Many to One-902658_embed-rotated\" x=\"75.0\" y=\"245\" height=\"200\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhKABAAIcAAPz4+gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAKABAAEAIVAABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRowQDAgA7\"><title>Layer: embed (hidden)\n output range: (-Infinity, +Infinity)\n shape = (40, 64)\n Keras class = Dense</title></image><text x=\"288.0\" y=\"347.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\"  transform=\"rotate(-90 288.0 347.0) translate(2)\" >embed</text><line x1=\"175.0\" y1=\"474\" x2=\"175.0\" y2=\"447\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to embed\n embed/embeddings has shape (26, 64)</title></line><rect x=\"74.0\" y=\"474\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-dashboard-LSTM - 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