{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM - Learning a sin curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "conx, version 3.5.5\n"
     ]
    }
   ],
   "source": [
    "from conx import Network, Layer, LSTMLayer, plot, frange\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this experiment, we will use the monthly money spent by international airline passengers on tickets between 1949 and 1961:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = [round(math.sin(x),1) for x in frange(0, 2 * math.pi + .1, .1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.0), (31, 0.0), (63, 0.0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in enumerate(scaled_data) if -0.1 < x[1] < 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22387d55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([\"Scaled Data\", scaled_data], default_symbol=\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data[0], scaled_data[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our dataset, we will contruct a history sequence. First, we need to put each scaled dollar amount into a list. This is the list of *features*. In our case, we just have the one feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = [[datum] for datum in scaled_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wish that the inputs -> targets are constructed as follows:\n",
    "\n",
    "1. [S0] -> S1\n",
    "2. [S1] -> S2\n",
    "3. ...\n",
    "\n",
    "where Sn is a list of features in the sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to inform the network of the shape of the sequence. We need the:\n",
    "\n",
    "* time_steps - the length of the history\n",
    "* batch_size - how many vectors are the inputs composed of?\n",
    "* features - the length of each input bank vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = 10  # history\n",
    "batch_size = 1  # how many to load at once\n",
    "features = 1    # features (length of input vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(sequence, time_steps):\n",
    "    dataset = []\n",
    "    for i in range(len(sequence)-time_steps-1):\n",
    "        dataset.append([sequence[i:(i+time_steps)], \n",
    "                       sequence[i + time_steps]])\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = create_dataset(sequence, time_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.6], [0.7], [0.8]], [0.8]]\n",
      "[[[0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.6], [0.7], [0.8], [0.8]], [0.9]]\n"
     ]
    }
   ],
   "source": [
    "print(dataset[0])\n",
    "print(dataset[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we construct the network giving the batch_shape in terms of (look_back, banks, width):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Network(\"LSTM - sin\")\n",
    "net.add(Layer(\"input\", features, batch_shape=(batch_size, time_steps, features)))\n",
    "net.add(LSTMLayer(\"lstm\", 4)) \n",
    "net.add(Layer(\"output\", 1))\n",
    "net.connect()\n",
    "net.compile(error=\"mse\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.dataset.clear()\n",
    "net.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "54f9c771803345168cab189fd08ee81e",
       "version_major": 2,
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      },
      "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",
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "<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",
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      ],
      "text/plain": [
       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, options=('Test', 'Train'), rows=1, value='Train'), FloatSlider(value=1.0, continuous_update=False, description='Zoom', max=3.0, min=0.5), 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=2.0, description='Feature scale:', style=DescriptionStyle(description_width='initial'))), 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'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', 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\"/>'), FloatText(value=-1.0, 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'))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'LSTM - sin'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=52), Label(value='of 53', 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": [
    "net.dashboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net[\"lstm\"].get_output_shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#net.dataset.split(.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.009405624121427536]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate([[.02]] * time_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f221feb2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.reset()\n",
    "outputs = [net.propagate(i) for i in net.dataset.inputs]\n",
    "plot([[\"Network\", outputs], [\"Training data\", net.dataset.targets]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#net.reset(); \n",
    "net.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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      "========================================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#   91 |   0.00156 |   1.00000 \n",
      "Saving network... Saved!\n"
     ]
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   "source": [
    "if net.saved():\n",
    "    net.load()\n",
    "    net.plot_results()\n",
    "else:\n",
    "    net.train(500, batch_size=batch_size, accuracy=1.0, tolerance=0.1, \n",
    "              shuffle=False, plot=True, save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2224435128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputs = [net.propagate(i)  for i in net.dataset.inputs]\n",
    "plot([[\"Network\", outputs], [\"Training data\", net.dataset.targets]])"
   ]
  },
  {
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   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.8412674069404602], [0.800000011920929])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_lstm_2\" class=\"Flat - sin_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"182.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"234.0\" x2=\"175.0\" y2=\"207.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"124.0\" y=\"234.0\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"125.0\" y=\"235.0\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" 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YMmfSrBkzAIAABHby7OnzZwAAQgMEIGD0KNKkRgMEAOD0aYAABKZSrUo1QAAAWrdyDeCVANiwBAKQBWD2LNq0AdayDQDgLdy4cQMEGGD3Lt4BAQIA6Ou3b4AAAwYTLmw4QAAAigEECDDgMeTIkiMHCAAAQIABmjdz7uw5AIAAA0aTLm369OgAWAEGsG7t+jVs1gECDKht+zbu3LUDAAgw4Dfw4MKHBwAAIECAAcqXM2/OPEAAANIBBAgw4Dr27NoDBADg/bv3AAEGkC9vfkCAAADWs2/fPgD8+AEA0K9fPyAAOw==\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 10, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "11e37a2519504f68bc8d5139f005705a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
      }
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href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_lstm_2\" class=\"Flat - sin_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"182.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"234.0\" x2=\"175.0\" y2=\"207.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"124.0\" y=\"234.0\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"125.0\" y=\"235.0\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" 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input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 2, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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       ],
       "description": "Features:",
       "index": 0,
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\"><title>Layer: hidden (hidden)\n shape = (4,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel has shape (1, 4)\n hidden/bias has shape (4,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "27c1175b808f4c5782ac0d513196efd3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntTextModel",
      "state": {
       "description": "Vertical space between layers:",
       "layout": "IPY_MODEL_65f332d8361e4da189c0c0856cb6d098",
       "step": 1,
       "style": "IPY_MODEL_3c439e5a30174ab185dcf8d3f532f215",
       "value": 30
      }
     },
     "27ebb05c289346f48db6eae4f6b03230": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "27efc7c6bc5c446484095c7b09f50c34": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "50px",
       "width": "100%"
      }
     },
     "281aa158e78344a580dd7581bd68fec0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "2833b34bd9cb4efe8889348172767f2e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "286bcde6f7b041a3a6eb2e2ea0492949": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
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\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"182.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"234.0\" x2=\"175.0\" y2=\"207.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"124.0\" y=\"234.0\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_input_3\" class=\"LSTM_input\" x=\"125.0\" y=\"235.0\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 1, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "2c6df848ebe143c196eda05117571ade": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "2cb3ee9a77ab454399f6a39e92c79da4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "2cc53460ce28489da23978cb612dc20f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "2ccd349a16f24d42976a44f0d74a6f68": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "Errors",
       "disabled": false,
       "layout": "IPY_MODEL_04f1903393dc42369181100724b76a2d",
       "style": "IPY_MODEL_430266e864494216b629d7d5f8b4967c",
       "value": false
      }
     },
     "2ce62f57441d4f01957f17dae89d9fe0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "2d0e64d19c3e4b27a9f9a4dcc2c53123": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2d1357a1807a48fab4c7e1553d88e965": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100px"
      }
     },
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output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_lstm_2\" class=\"LSTM_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 1, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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       "style": "IPY_MODEL_a7aea4f35c754d42a65e2cce87e4e55a"
      }
     },
     "4548050491214033bbb1b999be16333a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatTextModel",
      "state": {
       "description": "Rightmost color maps to:",
       "layout": "IPY_MODEL_e91e4e9d02ee45e7a5a104f6f8da35cb",
       "step": null,
       "style": "IPY_MODEL_8a9b30d6e83e4693ad35641b55a8d4be",
       "value": 1
      }
     },
     "456b61f6643147be99890261f3f5fed6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "45d854d3f3ed4e5eae8cf792eb372dd7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "4614c708a9f64fb48d5aada3a56bbadf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_5cc2d6d153fa4690866dac2229747710",
       "style": "IPY_MODEL_8df93f831e744dbd96fa114885cb9287",
       "value": "<p style=\"text-align:center\"><svg id='Flat - sin' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 260.0\" 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\">Flat - sin</text><rect x=\"149.0\" y=\"24\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_output_1\" class=\"Flat - sin_output\" x=\"150.0\" y=\"25\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"205.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"104.0\" x2=\"175.0\" y2=\"77.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"104.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_hidden_2\" class=\"Flat - sin_hidden\" x=\"75.0\" y=\"105.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: hidden (hidden)\n shape = (4,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel has shape (1, 4)\n hidden/bias has shape (4,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "461d06fbafe74a6cbd5f549288e162f1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_f0d43f76890849678d65b0ef5bf5a3b7",
       "style": "IPY_MODEL_9d9cad63543046e0bb7a78d1b19b1d6b",
       "value": "<p style=\"text-align:center\"><svg id='LSTM' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 360.0\" 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\">LSTM</text><rect x=\"124.0\" y=\"24\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_output_1\" class=\"LSTM_output\" x=\"125.0\" y=\"25\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_lstm_2\" class=\"LSTM_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: 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input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 1, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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       "layout": "IPY_MODEL_747cc22ce99d41449a422b1a8fbd67de",
       "step": null,
       "style": "IPY_MODEL_009e1ed7e3bf45c1a6fb03d3381eafeb",
       "value": 1
      }
     },
     "47317aebdd594de89f5dbcc4fd1cbe92": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4782796bfa694a2e9d6946bdf1aaad49": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "48125732ee6b40c78504b466a6d62fa7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_8820be022ff342388545ddc4f3b0e5b2",
       "style": "IPY_MODEL_fa0790ef763b4ae4b6b6081c4712b9c8",
       "value": "<p style=\"text-align:center\"><svg id='LSTM - sin' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 260.0\" 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\">LSTM - sin</text><rect x=\"149.0\" y=\"24\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_output_1\" class=\"LSTM - sin_output\" x=\"150.0\" y=\"25\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"205.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"104.0\" x2=\"175.0\" y2=\"77.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"104.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_lstm_2\" class=\"LSTM - sin_lstm\" x=\"75.0\" y=\"105.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_input_3\" class=\"LSTM - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" 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lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" 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YMmfSrGnzJs6aAQIA6OkTQIAAAIYSLWr0KNKkSo8GCADgKdQAAQBQrWr1KtasWrdqDRAAAFiwAQIAKGv2LNq0ateyXRsgAIC4cQMEAGD3Lt68evfy7cs3QAAAggUHCADgMOLEihczbuy4cYAAACZPDgDgMubMmjdz7uz5cwAAokUHAGD6NOrU/6pXs27tOgCA2LEDAKht+zbu3Lp38+4dAABw4AEAEC9u/Djy5MqXMw8A4DmAAACmU69u/Tr27Nq3Tw8A4HsAAOLHky9v/jz69OrFBwDgPgCA+PLn069v/z7+/PEDAOgfACAAgQMJFjR4EGFChQIDAAAQAEBEiRMpVrR4EWPGiQEABADwEWRIkSNJljR5MmQAAAEAtHT5EmZMmTNp1nwZAEAAADt59vT5E2hQoUN7BgAQAEBSpUuZNnX6FGrUpQEABABwFWtWrVu5dvX6NWsAAAEAlDV7Fm1atWvZtj0bAEAAAHPp1rV7F29evXvrBgAQAEBgwYMJFzZ8GHHiwQEABP8A8BhyZMmTKVe2fDlyAAABAHT2/Bl0aNGjSZf+HABAAACrWbd2/Rp2bNmzWwcAEABAbt27eff2/Rt48N0BAAQAcBx5cuXLmTd3/jx5AAABAFS3fh17du3buXe/HgBAAADjyZc3fx59evXrywcAEABAfPnz6de3fx9//vkBAAQAABCAwIEECxo8iDChwoEBAAQAADGixIkUK1q8iFFiAAABAHj8CDKkyJEkS5oEGQBAAAAsW7p8CTOmzJk0XQYAEACAzp08e/r8CTSoUJ4BAAQAgDSp0qVMmzp9ClVpAAABAFi9ijWr1q1cu3rFGgBAAABky5o9izat2rVszQYAEAD/gNy5dOvavYs3r166AQAEAAA4sODBhAsbPoxYcAAAAQA4fgw5suTJlCtbhhwAQAAAnDt7/gw6tOjRpD0HABAAgOrVrFu7fg07tmzWAQAEAIA7t+7dvHv7/g1cdwAAAQAYP448ufLlzJs7Rx4AAIAAAKpbv449u/bt3LtXDwAgfAAA5MubP48+vfr17MkHAAA/AID59Ovbv48/v/798wMAAAgAQAAABQ0eRJhQ4UKGDQsGABARQAAAFS1exJhR40aOHQMAAAkyAACSJU2eRJlS5UqWAQC8fBkAwEyaNW3exJlT584AAHz6DABA6FCiRY0eRZpUaQAATZsGCABA6lSq/1WtXsWaFWuAAAC8eg0QAMBYsmXNnkWbVm3aAAEAvH0bIAAAunXt3sWbV+9evQECAAAMOEAAAIUNH0acWPFixosDBAAQWXKAAAAsX8acWfNmzp01BwgAQPRoAAECAECdWvVq1q1dv2YdIAAA2rUBBAgAQPdu3r19/wYe3HeAAACMHwcQIAAA5s2dP4ceXfp06AECAMCeHXuAAAC8fwcfXvx48uXBBwgAQP369QECAIAfX/58+vXt35cfIAAA/v37AwwQAADBggYPIkyocKHBAAEAQIwYMUAAABYvYsyocSPHjhgDBAAgciRJAAEAoEypciXLli5fogwAYCbNmjQDAP/IqXMnz54+fwLNGQAA0aJGiwYAoHQp06ZOn0KNqjQAgKpWr1oNAGAr165ev4INK3ZrAABmz6JNGwAA27Zu38KNKzduAAB27+LNazcAgL5+/wIOLHiw4AAADiNOrPhwAACOH0OOLHky5ckBAGDOrHkz5gAAPoMOLXo06dKkAwBIrXo169UBAMCOLXs27dq2YwcAoHs3796+AwAILnw48eLGjwsPAGA58+bOnwcAIH069erWr2OfHgAA9+7ev4MPAGA8+fLmz6NPTz4AgPbu38OP3z5AAAD27+PPr39//gABAAIQOJBgQYMEAwQAsJBhQ4cPIToMEABARYsXMWa8GCD/AACPH0GGFDkyZIAAAFCmVLmSpcoAAQDElDmTZk2bNAMEALCTZ0+fP4EGEAqAaFGjR5EaDbAUQFOnT6FGleo0QFUAV7Fm1bo1awCvAMCGFTuWbNmwAdACULuWbVu3bAPEBTCXbl27d/HSDbAXQF+/fwEH/huAMADDhxEnVryYcQDHACBHljw5QGUAlzFn1ryZc2fNAUADED2adOkApwGkVr2adWvXr1kHkA2Adm3btwPkBrCbd2/fv4EH9x2AOADjx5EnD7AcQHPnz6FHlz6devQAALBn176de3fv38FzDwCAfHnz59GnV7+ePfoAAODHlz+ffn379/HTDwCAf3//CQABCBxIsKDBgAA7\"><title>Layer: 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href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+AEAI/wABCBxIsKBBgQESKlzIEIDDhxAjSpwIMUAAABgzatzIsaPHjQECABhJsuTIAABSqlzJsqXLlzBfBgBAE0AAADhz6tzJs6fPn0B1BgAQAIDRo0iTKl3KtKlTpAEAAAgAoKrVq1izat3KtWvVAADCigUQIACAs2jTql3Ltq3bswECAJhLt67dugHyAtjLt6/fv4ABBBgMoLDhw4gTJw7AGIDjx5AjS54MIIBlAJgza96sOUAAAKBDix5NurTp06ADBADAujWAAABiy55Nu7bt27hzxw4AAEAAAMCDCx9OvLjx48iFBwAQAIDz59CjS59Ovbp16AEAaAcQAID37+DDi/8fT748+QAA0qtfnz5AAADw48ufT7++/fkBAgDYz7+/f4AABA4kWFBgAIQJAwAI0NBhAAARJU6kWNHiRYoBNALg2NHjR5ABRAIgWdLkSZQpAQQIAMDlS5gxZc6cGSAAAJw5de7UGQDAT6BBhQ4lWtRoAABJlS5NGgDAU6hRpU6lWtXq1AAAtG4FEADAV7BhxY4lW9bs2QAA1AIIAMDtW7hx5c6lW9eu2wAAAAQA0NfvX8CBBQ8mXPhvAAABACxm3NjxY8iRJU92HCAAAMyZNW/m3NnzZ9CaAwAAEADAadSpVa9m3dr169MBAMwGEADAbdy5de/m3dv37wAAhA8HEAD/wHHkyZUvZ97c+fIAAKRPpy49AADs2bVv597d+/cAAMSPJ1+efIAAANSvZ9/e/fv3AQIAoF/f/n38+QEE4A/AP0AAAgcSLDgwAEIAChcybOjwIUSFASYCqGjxIsYAGgFw7OjxI8iQIgOQBGDyJMqUKlEGaAngJcyYMmfSDBAAAM6cOnfy7LkzQAAAQocSLWpUaAAASpcyber0KdSlAQBQrWr1qtUAALZy7er1K9iwYAMAKGv2LNoAANaybev2Ldy4ctcGAGD3Ll4AAQIA6Ov3L+DAggcT/hsgAIDEihMHCADgMeTIkidTrmx5coAAADZzDhAAAOjQokeTLm36tOkA/wEAsGYdAADs2LJn065t+zbuAAB2AwgA4Dfw4MKHEy9u/PjvAAAABADg/Dn06NKnU69uHXoAAAEAcO/u/Tv48OLHk/ceAEAAAOrXs2/v/j38+PLZBwAQAAD+/Pr38+/vHyAAgQMJFjQoMACAAAAYNnT4EGJEiRMpOgwAIAAAjRs5dvT4EWRIkRwDAAgAAGVKlStZtnT5EqbKAAACALB5E2dOnTt59vSJMwAAAAEAFDV6FGlSpUuZNi0aAEBUAAEAVLV6FWtWrVu5dg0AACzYAAEAlDV7Fm1atWvZrg0QAEBcuQECALB7F29evXv59tUbIAAAwYMFBwgAAHFixYsZN5Z2/FhxgAAAKFe2DCAAAM2bOXf2/Bl0aM0BAJQ2fRp1AACrWbd2/Rp2bNgBANS2fRv37QAAePf2/Rt4cOG9AwAwfhx5cuXGAwQA8Bx6dOnTqUsPEABAdu3buXf3HgA8APHjyZc3Tz5AegDr2bd3/x5+/ADzAdS3fx9/AP0A+Pf3DxCAwIEECxo8iLBgAAAMGzp8CDFiw4AAOw==\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"205.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"104.0\" x2=\"175.0\" y2=\"77.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"104.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_hidden_2\" class=\"Flat - sin_hidden\" x=\"75.0\" y=\"105.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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YMmfSrGnzJs6aAQIA6OkTQIAAAIYSLWr0KNKkSo8GCADgKdQAAQBQrWr1KtasWrdqDRAAAFiwAQIAKGv2LNq0ateyXRsgAIC4cQMEAGD3Lt68evfy7cs3QAAAggUHCADgMOLEihczbuy4cYAAACZPDgDgMubMmjdz7uz5cwAAokUHAGD6NOrU/6pXs27tOgCA2LEDAKht+zbu3Lp38+4dAABw4AEAEC9u/Djy5MqXMw8A4DmAAACmU69u/Tr27Nq3Tw8A4HsAAOLHky9v/jz69OrFBwDgPgCA+PLn069v/z7+/PEDAOgfACAAgQMJFjR4EGFChQIDAAAQAEBEiRMpVrR4EWPGiQEABADwEWRIkSNJljR5MmQAAAEAtHT5EmZMmTNp1nwZAEAAADt59vT5E2hQoUN7BgAQAEBSpUuZNnX6FGrUpQEABABwFWtWrVu5dvX6NWsAAAEAlDV7Fm1atWvZtj0bAEAAAHPp1rV7F29evXvrBgAQAEBgwYMJFzZ8GHHiwQEABP8A8BhyZMmTKVe2fDlyAAABAHT2/Bl0aNGjSZf+HABAAACrWbd2/Rp2bNmzWwcAEABAbt27eff2/Rt48N0BAAQAcBx5cuXLmTd3/jx5AAABAFS3fh17du3buXe/HgBAAADjyZc3fx59evXrywcAEABAfPnz6de3fx9//vkBAAQAABCAwIEECxo8iDChwoEBAAQAADGixIkUK1q8iFFiAAABAHj8CDKkyJEkS5oEGQBAAAAsW7p8CTOmzJk0XQYAEACAzp08e/r8CTSoUJ4BAAQAgDSp0qVMmzp9ClVpAAABAFi9ijWr1q1cu3rFGgBAAABky5o9izat2rVszQYAEAD/gNy5dOvavYs3r166AQAEAAA4sODBhAsbPoxYcAAAAQA4fgw5suTJlCtbhhwAQAAAnDt7/gw6tOjRpD0HABAAgOrVrFu7fg07tmzWAQAEAIA7t+7dvHv7/g1cdwAAAQAYP448ufLlzJs7Rx4AAIAAAKpbv449u/bt3LtXDwAgfAAA5MubP48+vfr17MkHAAA/AID59Ovbv48/v/798wMAAAgAQAAABQ0eRJhQ4UKGDQsGABARQAAAFS1exJhR40aOHQMAAAkyAACSJU2eRJlS5UqWAQC8fBkAwEyaNW3exJlT584AAHz6DABA6FCiRY0eRZpUaQAATZsGCABA6lSq/1WtXsWaFWuAAAC8eg0QAMBYsmXNnkWbVm3aAAEAvH0bIAAAunXt3sWbV+9evQECAAAMOEAAAIUNH0acWPFixosDBAAQWXKAAAAsX8acWfNmzp01BwgAQPRoAAECAECdWvVq1q1dv2YdIAAA2rUBBAgAQPdu3r19/wYe3HeAAACMHwcQIAAA5s2dP4ceXfp06AECAMCeHXuAAAC8fwcfXvx48uXBBwgAQP369QECAIAfX/58+vXt35cfIAAA/v37AwwQAADBggYPIkyocKHBAAEAQIwYMUAAABYvYsyocSPHjhgDBAAgciRJAAEAoEypciXLli5fogwAYCbNmjQDAP/IqXMnz54+fwLNGQAA0aJGiwYAoHQp06ZOn0KNqjQAgKpWr1oNAGAr165ev4INK3ZrAABmz6JNGwAA27Zu38KNKzduAAB27+LNazcAgL5+/wIOLHiw4AAADiNOrPhwAACOH0OOLHky5ckBAGDOrHkz5gAAPoMOLXo06dKkAwBIrXo169UBAMCOLXs27dq2YwcAoHs3796+AwAILnw48eLGjwsPAGA58+bOnwcAIH069erWr2OfHgAA9+7ev4MPAGA8+fLmz6NPTz4AgPbu38OP3z5AAAD27+PPr39//gABAAIQOJBgQYMEAwQAsJBhQ4cPIToMEABARYsXMWa8GCD/AACPH0GGFDkyZIAAAFCmVLmSpcoAAQDElDmTZk2bNAMEALCTZ0+fP4EGEAqAaFGjR5EaDbAUQFOnT6FGleo0QFUAV7Fm1bo1awCvAMCGFTuWbNmwAdACULuWbVu3bAPEBTCXbl27d/HSDbAXQF+/fwEH/huAMADDhxEnVryYcQDHACBHljw5QGUAlzFn1ryZc2fNAUADED2adOkApwGkVr2adWvXr1kHkA2Adm3btwPkBrCbd2/fv4EH9x2AOADjx5EnD7AcQHPnz6FHlz6devQAALBn176de3fv38FzDwCAfHnz59GnV7+ePfoAAODHlz+ffn379/HTDwCAf3//CQABCBxIsKDBgAA7\"><title>Layer: hidden (hidden)\n shape = (4,)\n Keras class = Dense</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel has shape (1, 4)\n hidden/bias has shape (4,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "7bc07b97ec2c48f9ae037b656312f86e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "Visible",
       "disabled": false,
       "layout": "IPY_MODEL_24f74e3df8d24c9ca217115879e4bd87",
       "style": "IPY_MODEL_1d54ce0dcb224e139bd8961c3bd6aa62",
       "value": true
      }
     },
     "7bcfd34bbfe345f58201521bb976d275": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "7c365fee039a486098b269631567da8e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonModel",
      "state": {
       "icon": "forward",
       "layout": "IPY_MODEL_218305bc16e44c8ab42987a49f731eb5",
       "style": "IPY_MODEL_0aa0566bf11245a08e4d51ebf9f29e75"
      }
     },
     "7c3d1e1253a849c0abd6eafa8af26535": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c9ca10328e8f4430862a152da2478221",
        "IPY_MODEL_b8a354d5d15d4119b161b8bdab792480",
        "IPY_MODEL_3677b8cdbf4e499995dee7c67a26878a",
        "IPY_MODEL_043b874e026e4331ae913da5e875a5ef"
       ],
       "layout": "IPY_MODEL_47317aebdd594de89f5dbcc4fd1cbe92"
      }
     },
     "7c49cc6bc4f84a3492dd10ecbbe5f22d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatTextModel",
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       "value": "<p style=\"text-align:center\"><svg id='LSTM' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 360.0\" 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\">LSTM</text><rect x=\"124.0\" y=\"24\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_output_1\" class=\"LSTM_output\" x=\"125.0\" y=\"25\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM_lstm_2\" class=\"LSTM_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_lstm_2\" class=\"Flat - sin_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"182.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"234.0\" x2=\"175.0\" y2=\"207.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"124.0\" y=\"234.0\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"125.0\" y=\"235.0\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" 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input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 2, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "b39ead36bc7d4a7a927c4575afe96f64": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "",
        "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",
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\"><title>Layer: 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input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 10, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"205.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"104.0\" x2=\"175.0\" y2=\"77.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"104.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_hidden_2\" class=\"Flat - sin_hidden\" x=\"75.0\" y=\"105.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: hidden (hidden)\n shape = (4,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel has shape (1, 4)\n hidden/bias has shape (4,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+AEAI/wABCBxIsKBBgQESKlzIEIDDhxAjSpwIMUAAABgzatzIsaPHjQECABhJsuTIAABSqlzJsqXLlzBfBgBAE0AAADhz6tzJs6fPn0B1BgAQAIDRo0iTKl3KtKlTpAEAAAgAoKrVq1izat3KtWvVAADCigUQIACAs2jTql3Ltq3bswECAJhLt67dugHyAtjLt6/fv4ABBBgMoLDhw4gTJw7AGIDjx5AjS54MIIBlAJgza96sOUAAAKBDix5NurTp06ADBADAujWAAABiy55Nu7bt27hzxw4AAEAAAMCDCx9OvLjx48iFBwAQAIDz59CjS59Ovbp16AEAaAcQAID37+DDi/8fT748+QAA0qtfnz5AAADw48ufT7++/fkBAgDYz7+/f4AABA4kWFBgAIQJAwAI0NBhAAARJU6kWNHiRYoBNALg2NHjR5ABRAIgWdLkSZQpAQQIAMDlS5gxZc6cGSAAAJw5de7UGQDAT6BBhQ4lWtRoAABJlS5NGgDAU6hRpU6lWtXq1AAAtG4FEADAV7BhxY4lW9bs2QAA1AIIAMDtW7hx5c6lW9eu2wAAAAQA0NfvX8CBBQ8mXPhvAAABACxm3NjxY8iRJU92HCAAAMyZNW/m3NnzZ9CaAwAAEADAadSpVa9m3dr169MBAMwGEADAbdy5de/m3dv37wAAhA8HEAD/wHHkyZUvZ97c+fIAAKRPpy49AADs2bVv597d+/cAAMSPJ1+efIAAANSvZ9/e/fv3AQIAoF/f/n38+QEE4A/AP0AAAgcSLDgwAEIAChcybOjwIUSFASYCqGjxIsYAGgFw7OjxI8iQIgOQBGDyJMqUKlEGaAngJcyYMmfSDBAAAM6cOnfy7LkzQAAAQocSLWpUaAAASpcyber0KdSlAQBQrWr1qtUAALZy7er1K9iwYAMAKGv2LNoAANaybev2Ldy4ctcGAGD3Ll4AAQIA6Ov3L+DAggcT/hsgAIDEihMHCADgMeTIkidTrmx5coAAADZzDhAAAOjQokeTLm36tOkA/wEAsGYdAADs2LJn065t+zbuAAB2AwgA4Dfw4MKHEy9u/PjvAAAABADg/Dn06NKnU69uHXoAAAEAcO/u/Tv48OLHk/ceAEAAAOrXs2/v/j38+PLZBwAQAAD+/Pr38+/vHyAAgQMJFjQoMACAAAAYNnT4EGJEiRMpOgwAIAAAjRs5dvT4EWRIkRwDAAgAAGVKlStZtnT5EqbKAAACALB5E2dOnTt59vSJMwAAAAEAFDV6FGlSpUuZNi0aAEBUAAEAVLV6FWtWrVu5dg0AACzYAAEAlDV7Fm1atWvZrg0QAEBcuQECALB7F29evXv59tUbIAAAwYMFBwgAAHFixYsZN5Z2/FhxgAAAKFe2DCAAAM2bOXf2/Bl0aM0BAJQ2fRp1AACrWbd2/Rp2bNgBANS2fRv37QAAePf2/Rt4cOG9AwAwfhx5cuXGAwQA8Bx6dOnTqUsPEABAdu3buXf3HgA8APHjyZc3Tz5AegDr2bd3/x5+/ADzAdS3fx9/AP0A+Pf3DxCAwIEECxo8iLBgAAAMGzp8CDFiw4AAOw==\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "c5c39cbd19fe468ca96e7c0d4e444fa5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c60e5f9e7f0e4c749b7068e95480f89d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "c61c9393b92d48be9e3d0da6108fd62c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.1.0",
      "model_name": "OutputModel",
      "state": {
       "_model_module_version": "1.1.0",
       "_view_module_version": "1.1.0",
       "layout": "IPY_MODEL_3c95c1d98f2247218c86d8d367329983"
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output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"230.0\" y=\"77.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"154.0\" x2=\"175.0\" y2=\"127.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"154.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_lstm_2\" class=\"LSTM - sin_lstm\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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YMmfSrGnzJs6aAQIA6OkTQIAAAIYSLWr0KNKkSo8GCADgKdQAAQBQrWr1KtasWrdqDRAAAFiwAQIAKGv2LNq0ateyXRsgAIC4cQMEAGD3Lt68evfy7cs3QAAAggUHCADgMOLEihczbuy4cYAAACZPDgDgMubMmjdz7uz5cwAAokUHAGD6NOrU/6pXs27tOgCA2LEDAKht+zbu3Lp38+4dAABw4AEAEC9u/Djy5MqXMw8A4DmAAACmU69u/Tr27Nq3Tw8A4HsAAOLHky9v/jz69OrFBwDgPgCA+PLn069v/z7+/PEDAOgfACAAgQMJFjR4EGFChQIDAAAQAEBEiRMpVrR4EWPGiQEABADwEWRIkSNJljR5MmQAAAEAtHT5EmZMmTNp1nwZAEAAADt59vT5E2hQoUN7BgAQAEBSpUuZNnX6FGrUpQEABABwFWtWrVu5dvX6NWsAAAEAlDV7Fm1atWvZtj0bAEAAAHPp1rV7F29evXvrBgAQAEBgwYMJFzZ8GHHiwQEABP8A8BhyZMmTKVe2fDlyAAABAHT2/Bl0aNGjSZf+HABAAACrWbd2/Rp2bNmzWwcAEABAbt27eff2/Rt48N0BAAQAcBx5cuXLmTd3/jx5AAABAFS3fh17du3buXe/HgBAAADjyZc3fx59evXrywcAEABAfPnz6de3fx9//vkBAAQAABCAwIEECxo8iDChwoEBAAQAADGixIkUK1q8iFFiAAABAHj8CDKkyJEkS5oEGQBAAAAsW7p8CTOmzJk0XQYAEACAzp08e/r8CTSoUJ4BAAQAgDSp0qVMmzp9ClVpAAABAFi9ijWr1q1cu3rFGgBAAABky5o9izat2rVszQYAEAD/gNy5dOvavYs3r166AQAEAAA4sODBhAsbPoxYcAAAAQA4fgw5suTJlCtbhhwAQAAAnDt7/gw6tOjRpD0HABAAgOrVrFu7fg07tmzWAQAEAIA7t+7dvHv7/g1cdwAAAQAYP448ufLlzJs7Rx4AAIAAAKpbv449u/bt3LtXDwAgfAAA5MubP48+vfr17MkHAAA/AID59Ovbv48/v/798wMAAAgAQAAABQ0eRJhQ4UKGDQsGABARQAAAFS1exJhR40aOHQMAAAkyAACSJU2eRJlS5UqWAQC8fBkAwEyaNW3exJlT584AAHz6DABA6FCiRY0eRZpUaQAATZsGCABA6lSq/1WtXsWaFWuAAAC8eg0QAMBYsmXNnkWbVm3aAAEAvH0bIAAAunXt3sWbV+9evQECAAAMOEAAAIUNH0acWPFixosDBAAQWXKAAAAsX8acWfNmzp01BwgAQPRoAAECAECdWvVq1q1dv2YdIAAA2rUBBAgAQPdu3r19/wYe3HeAAACMHwcQIAAA5s2dP4ceXfp06AECAMCeHXuAAAC8fwcfXvx48uXBBwgAQP369QECAIAfX/58+vXt35cfIAAA/v37AwwQAADBggYPIkyocKHBAAEAQIwYMUAAABYvYsyocSPHjhgDBAAgciRJAAEAoEypciXLli5fogwAYCbNmjQDAP/IqXMnz54+fwLNGQAA0aJGiwYAoHQp06ZOn0KNqjQAgKpWr1oNAGAr165ev4INK3ZrAABmz6JNGwAA27Zu38KNKzduAAB27+LNazcAgL5+/wIOLHiw4AAADiNOrPhwAACOH0OOLHky5ckBAGDOrHkz5gAAPoMOLXo06dKkAwBIrXo169UBAMCOLXs27dq2YwcAoHs3796+AwAILnw48eLGjwsPAGA58+bOnwcAIH069erWr2OfHgAA9+7ev4MPAGA8+fLmz6NPTz4AgPbu38OP3z5AAAD27+PPr39//gABAAIQOJBgQYMEAwQAsJBhQ4cPIToMEABARYsXMWa8GCD/AACPH0GGFDkyZIAAAFCmVLmSpcoAAQDElDmTZk2bNAMEALCTZ0+fP4EGEAqAaFGjR5EaDbAUQFOnT6FGleo0QFUAV7Fm1bo1awCvAMCGFTuWbNmwAdACULuWbVu3bAPEBTCXbl27d/HSDbAXQF+/fwEH/huAMADDhxEnVryYcQDHACBHljw5QGUAlzFn1ryZc2fNAUADED2adOkApwGkVr2adWvXr1kHkA2Adm3btwPkBrCbd2/fv4EH9x2AOADjx5EnD7AcQHPnz6FHlz6devQAALBn176de3fv38FzDwCAfHnz59GnV7+ePfoAAODHlz+ffn379/HTDwCAf3//CQABCBxIsKDBgAA7\"><title>Layer: lstm (hidden)\n Keras class = LSTM</title></image><text x=\"280.0\" y=\"182.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">lstm</text><line x1=\"175.0\" y1=\"234.0\" x2=\"175.0\" y2=\"207.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to lstm\n lstm/kernel has shape (1, 16)\n lstm/recurrent_kernel has shape (4, 16)\n lstm/bias has shape (16,)</title></line><rect x=\"124.0\" y=\"234.0\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_input_3\" class=\"LSTM - sin_input\" x=\"125.0\" y=\"235.0\" height=\"100.0\" width=\"100.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input\n batch_shape = (1, 1, 1)</title></image><text x=\"230.0\" y=\"287.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
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       "width": "100%"
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     "dc35f41f092c4ad0aa88b1fb84a85667": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatTextModel",
      "state": {
       "description": "Feature scale:",
       "layout": "IPY_MODEL_d72f5875688643d0b8e8c1e900a8ba74",
       "step": null,
       "style": "IPY_MODEL_9bd67eb1514f48b2bc365f60ca081864",
       "value": 2
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     "dc55a8204b1c4fda9fcd7c882b06bbed": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "Errors",
       "disabled": false,
       "layout": "IPY_MODEL_e997f9ac9ed04cb08370a864f7d6a832",
       "style": "IPY_MODEL_17e81261cd8e4a1e9c29f998f0fac9a5",
       "value": false
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     "dc60ea11def04ec38fed8006d44eaff6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "",
        "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",
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        "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",
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       "value": "<p style=\"text-align:center\"><svg id='LSTM - sin' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 350 260.0\" 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\">LSTM - sin</text><rect x=\"149.0\" y=\"24\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_output_1\" class=\"LSTM - sin_output\" x=\"150.0\" y=\"25\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAMXF/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+AEAI/wADCBxIsKDBgwgTKkQIoKHDhxAjSpxIsSLEAAEAaNzIsaPHjyBDiuwYIACAkyhTqlzJsqXLlyoDBABAs6bNmzhz6tzJ82aAAACCCh1KtKjRo0iTEg0QAIDTp1CjSp1KtarVqAECANjKtavXr2DDih3rNUAAAGjTql3Ltq3bt3DXBggAoK7du3jz6t3Lty/eAAEACB5MuLDhw4gTKy4cIACAx5AjS55MubLly5IDBADAubPnz6BDix5N+nOAAABSq17NurXr17Bjsw4QAIDt27hz697Nu7fv3AECABhOvLjx48iTK19uPEAAANCjS59Ovbr169inBwgAoLv37+DDi/8fT748+AABAKhfz769+/fw48tvHyAAgPv48+vfz7+/f4AABA4kWDBAAAAJFS5k2NDhQ4gRGQYIAMDiRYwZNW7k2NFjxgABAIwkWdLkSZQpVa40GSAAAJgxZc6kWdPmTZwzAwQA0NPnT6BBhQ4lWhRogAAAlC5l2tTpU6hRpTYNEADAVaxZtW7l2tXrV60BAgAgW9bsWbRp1a5lezZAAABx5c6lW9fuXbx56QYIAMDvX8CBBQ8mXNhw4AABACxm3NjxY8iRJU92HCAAAMyZNW/m3NnzZ9CbAwQAUNr0adSpVa9m3Rp1gAAAZM+mXdv2bdy5ddcOEADAb+DBhQ8nXtz/+HHhAQIAYN7c+XPo0aVPp/48QAAA2bVv597d+3fw4bkHCADA/Hn06dWvZ9/effoAAQDMp1/f/n38+fXvtx8gAEAAAgcSLGjwIMKECgsGCADgIcSIEidSrGjxosQAAQBw7OjxI8iQIkeS/BggAICUKleybOnyJcyYLAMEAGDzJs6cOnfy7OkzZ4AAAIYSLWr0KNKkSpcaDRAAANSoUqdSrWr1KtapAQIA6Or1K9iwYseSLQs2QAAAateybev2Ldy4ctsGCADgLt68evfy7ev3r94AAQAQLmz4MOLEihczPhwgAIDIkidTrmz5MubMlAMEAOD5M+jQokeTLm06dIAAlQBWs27t+jXs2LJnuw4QAADu3Lp38+7t+zfw3QECAChu/Djy5MqXM2+OPEAAANKnU69u/Tr27NqrBwgA4Dv48OLHky9v/rz4AAEAsG/v/j38+PLn038fIACA/Pr38+/vHyAAgQMJFjR4kGCAAAAYNnT4EGJEiRMpPgwQAEBGjRs5dvT4EWRIjgFIljR5EmVKlStZqgwIADs=\"><title>Layer: output (output)\n shape = (1,)\n Keras class = Dense</title></image><text x=\"205.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output</text><line x1=\"175.0\" y1=\"104.0\" x2=\"175.0\" y2=\"77.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from lstm to output\n output/kernel has shape (4, 1)\n output/bias has shape (1,)</title></line><rect x=\"74.0\" y=\"104.0\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"LSTM - sin_lstm_2\" class=\"LSTM - sin_lstm\" x=\"75.0\" y=\"105.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: hidden (hidden)\n shape = (4,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"280.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden</text><line x1=\"175.0\" y1=\"184.0\" x2=\"175.0\" y2=\"157.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel has shape (1, 4)\n hidden/bias has shape (4,)</title></line><rect x=\"149.0\" y=\"184.0\" width=\"52.0\" height=\"52.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"Flat - sin_input_3\" class=\"Flat - sin_input\" x=\"150.0\" y=\"185.0\" height=\"50.0\" width=\"50.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAP/9/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+AEAI/wABCBxIsKBBgQESKlzIEIDDhxAjSpwIMUAAABgzatzIsaPHjQECABhJsuTIAABSqlzJsqXLlzBfBgBAE0AAADhz6tzJs6fPn0B1BgAQAIDRo0iTKl3KtKlTpAEAAAgAoKrVq1izat3KtWvVAADCigUQIACAs2jTql3Ltq3bswECAJhLt67dugHyAtjLt6/fv4ABBBgMoLDhw4gTJw7AGIDjx5AjS54MIIBlAJgza96sOUAAAKBDix5NurTp06ADBADAujWAAABiy55Nu7bt27hzxw4AAEAAAMCDCx9OvLjx48iFBwAQAIDz59CjS59Ovbp16AEAaAcQAID37+DDi/8fT748+QAA0qtfnz5AAADw48ufT7++/fkBAgDYz7+/f4AABA4kWFBgAIQJAwAI0NBhAAARJU6kWNHiRYoBNALg2NHjR5ABRAIgWdLkSZQpAQQIAMDlS5gxZc6cGSAAAJw5de7UGQDAT6BBhQ4lWtRoAABJlS5NGgDAU6hRpU6lWtXq1AAAtG4FEADAV7BhxY4lW9bs2QAA1AIIAMDtW7hx5c6lW9eu2wAAAAQA0NfvX8CBBQ8mXPhvAAABACxm3NjxY8iRJU92HCAAAMyZNW/m3NnzZ9CaAwAAEADAadSpVa9m3dr169MBAMwGEADAbdy5de/m3dv37wAAhA8HEAD/wHHkyZUvZ97c+fIAAKRPpy49AADs2bVv597d+/cAAMSPJ1+efIAAANSvZ9/e/fv3AQIAoF/f/n38+QEE4A/AP0AAAgcSLDgwAEIAChcybOjwIUSFASYCqGjxIsYAGgFw7OjxI8iQIgOQBGDyJMqUKlEGaAngJcyYMmfSDBAAAM6cOnfy7LkzQAAAQocSLWpUaAAASpcyber0KdSlAQBQrWr1qtUAALZy7er1K9iwYAMAKGv2LNoAANaybev2Ldy4ctcGAGD3Ll4AAQIA6Ov3L+DAggcT/hsgAIDEihMHCADgMeTIkidTrmx5coAAADZzDhAAAOjQokeTLm36tOkA/wEAsGYdAADs2LJn065t+zbuAAB2AwgA4Dfw4MKHEy9u/PjvAAAABADg/Dn06NKnU69uHXoAAAEAcO/u/Tv48OLHk/ceAEAAAOrXs2/v/j38+PLZBwAQAAD+/Pr38+/vHyAAgQMJFjQoMACAAAAYNnT4EGJEiRMpOgwAIAAAjRs5dvT4EWRIkRwDAAgAAGVKlStZtnT5EqbKAAACALB5E2dOnTt59vSJMwAAAAEAFDV6FGlSpUuZNi0aAEBUAAEAVLV6FWtWrVu5dg0AACzYAAEAlDV7Fm1atWvZrg0QAEBcuQECALB7F29evXv59tUbIAAAwYMFBwgAAHFixYsZN5Z2/FhxgAAAKFe2DCAAAM2bOXf2/Bl0aM0BAJQ2fRp1AACrWbd2/Rp2bNgBANS2fRv37QAAePf2/Rt4cOG9AwAwfhx5cuXGAwQA8Bx6dOnTqUsPEABAdu3buXf3HgA8APHjyZc3Tz5AegDr2bd3/x5+/ADzAdS3fx9/AP0A+Pf3DxCAwIEECxo8iLBgAAAMGzp8CDFiw4AAOw==\"><title>Layer: input (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "f30ee15fd0fe4cf5913affefa689f1f5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "f310e7618e9341bd80ed325580680a85": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_50b2a4e473ec46a7ae2207bc6d39f546",
       "style": "IPY_MODEL_d319d4c21b8d466da9a4362f06a0efcb",
       "value": "of 53"
      }
     },
     "f31fdd235de742e0ad1dee466e586ff8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        ""
       ],
       "description": "Features:",
       "index": 0,
       "layout": "IPY_MODEL_09dcd0fd097b4c11a97171dbed68ef53",
       "rows": 1,
       "style": "IPY_MODEL_961c6e2afd574226a719b07cb32ff09c"
      }
     },
     "f32e42d845794a3186e619b5e84256d9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "f34b627fd84f4fd8a563d2b83040f3d5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f36ef54a47384541982b0917b7997ca5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
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