{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XOR Multiple Inputs/Targets\n",
    "\n",
    "Testing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "conx, version 3.5.15\n"
     ]
    }
   ],
   "source": [
    "from conx import Network, Layer, SGD, PCA, scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Network(\"XOR Network\", 2, 4, 1, activation=\"sigmoid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.compile(loss='mean_squared_error',\n",
    "            optimizer=SGD(lr=0.3, momentum=0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [\n",
    "    ([0, 0], [0], \"0\"),\n",
    "    ([0, 1], [1], \"1\"),\n",
    "    ([1, 0], [1], \"1\"),\n",
    "    ([1, 1], [0], \"0\")\n",
    "]\n",
    "#net[\"output\"].minmax = (0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Dataset Split**:\n",
       "   * training  : 4\n",
       "   * testing   : 0\n",
       "   * total     : 4\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(2,)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(1,)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n"
      ],
      "text/plain": [
       "**Dataset Split**:\n",
       "   * training  : 4\n",
       "   * testing   : 0\n",
       "   * total     : 4\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(2,)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(1,)]\n",
       "   * range  : [(0.0, 1.0)]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dataset.clear()\n",
    "net.dataset.load(dataset)\n",
    "net.dataset.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.dataset.targets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2,)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.dataset.inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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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",
       "  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=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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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",
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       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
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       "                }\n",
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     "metadata": {},
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   ],
   "source": [
    "widget = net.dashboard()\n",
    "widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6359257102012634]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate([0, 1], visualize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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      "========================================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#  381 |   0.00815 |   1.00000 \n"
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    "net.train(epochs=2000, accuracy=1.0, report_rate=25, record=True, plot=True)"
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     "text": [
      "========================================================\n",
      "Testing validation dataset with tolerance 0.1...\n",
      "Total count: 4\n",
      "      correct: 4\n",
      "      incorrect: 0\n",
      "Total percentage correct: 1.0\n"
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       "<p>Failed to display Jupyter Widget of type <code>SequenceViewer</code>.</p>\n",
       "<p>\n",
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      "WARNING: No acc data available for the specified epochs (0-0)\n",
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   "source": [
    "net.playback(lambda net, epoch: (\n",
    "    net.plot_activation_map(interactive=False, \n",
    "                            scatter=[\n",
    "                                [\"0\", [net.dataset.inputs[i] for i in range(len(net.dataset.inputs)) if net.dataset.labels[i] == \"0\"]],\n",
    "                                [\"1\", [net.dataset.inputs[i] for i in range(len(net.dataset.inputs)) if net.dataset.labels[i] == \"1\"]]],\n",
    "                            symbols={\"0\": \"ko\", \"1\": \"k+\"},\n",
    "                            title=\"Epoch %s\" % epoch),\n",
    "    net.plot('all', end=epoch, interactive=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = [net.propagate_to(\"hidden\", input) for input in net.dataset.inputs]\n",
    "pca = PCA(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "<p>Failed to display Jupyter Widget of type <code>SequenceViewer</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",
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "symbols = {\n",
    "    \"0 (correct)\": \"bo\",\n",
    "    \"0 (wrong)\": \"bx\",\n",
    "    \"1 (correct)\": \"ro\",\n",
    "    \"1 (wrong)\": \"rx\",\n",
    "}\n",
    "net.playback(lambda net, epoch: scatter(**pca.transform_network_bank(net, \"hidden\"), \n",
    "                                        symbols=symbols,\n",
    "                                        interactive=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "net.propagate_to(\"input\", [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5085740685462952]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate([0.5, 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.38418060541152954,\n",
       " 0.572746753692627,\n",
       " 0.43381455540657043,\n",
       " 0.6605902314186096]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_to(\"hidden\", [1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5351778268814087]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_to(\"output\", [1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.25, 0.25]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_to(\"input\", [0.25, 0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5351778]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_from(\"input\", [1.0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.35672686]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_from(\"hidden\", [1.0, 0.0, 1.0, -1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================\n",
      "Testing validation dataset with tolerance 0.1...\n",
      "Total count: 4\n",
      "      correct: 0\n",
      "      incorrect: 4\n",
      "Total percentage correct: 0.0\n"
     ]
    }
   ],
   "source": [
    "net.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from conx import Network, Layer, SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'output2'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Network(\"XOR2 Network\")\n",
    "net.add(Layer(\"input1\", 1))\n",
    "net.add(Layer(\"input2\", 1))\n",
    "net.add(Layer(\"hidden1\", 10, activation=\"sigmoid\"))\n",
    "net.add(Layer(\"hidden2\", 10, activation=\"sigmoid\"))\n",
    "net.add(Layer(\"shared-hidden\", 5, activation=\"sigmoid\"))\n",
    "net.add(Layer(\"output1\", 1, activation=\"sigmoid\")) # , minmax=(-1,1)))\n",
    "net.add(Layer(\"output2\", 1, activation=\"sigmoid\")) # , minmax=(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Network name='XOR2 Network' (uncompiled)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.connect(\"input1\", \"hidden1\")\n",
    "net.connect(\"input2\", \"hidden2\")\n",
    "net.connect(\"hidden1\", \"shared-hidden\")\n",
    "net.connect(\"hidden2\", \"shared-hidden\")\n",
    "net.connect(\"shared-hidden\", \"output1\")\n",
    "net.connect(\"shared-hidden\", \"output2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Layer name='input1', shape=(1,), act='None'>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.layers[2].incoming_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.compile(loss='mean_squared_error',\n",
    "            optimizer=SGD(lr=0.3, momentum=0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bceacb39d20744c9b0527e52af5736a9",
       "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=1.0, continuous_update=False, description='Zoom', max=3.0, min=0.5), IntText(value=200, 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'))), layout=Layout(width='100%')), VBox(children=(Select(description='Layer:', index=6, options=('input1', 'input2', 'hidden1', 'hidden2', 'shared-hidden', 'output1', 'output2'), rows=1, value='output2'), 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', 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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'))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'XOR2 Network'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=0), Label(value='of 0', 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.config[\"hspace\"] = 200\n",
    "widget = net.dashboard()\n",
    "widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5891909003257751,\n",
       " 0.6736063361167908,\n",
       " 0.5231307148933411,\n",
       " 0.41695597767829895,\n",
       " 0.3708018958568573,\n",
       " 0.49578002095222473,\n",
       " 0.33628129959106445,\n",
       " 0.5863265991210938,\n",
       " 0.5555092692375183,\n",
       " 0.49335378408432007]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_to(\"hidden1\", [[1], [1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.6283185482025146], [0.2138163298368454]]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate([[1], [1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [\n",
    "    ([[0],[0]], [[0],[0]]),\n",
    "    ([[0],[1]], [[1],[1]]),\n",
    "    ([[1],[0]], [[1],[1]]),\n",
    "    ([[1],[1]], [[0],[0]])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.dataset.load(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[-0.17665445804595947,\n",
       "   0.6080862879753113,\n",
       "   -0.3233792781829834,\n",
       "   -0.6478244066238403,\n",
       "   -0.7108033895492554,\n",
       "   0.043866515159606934,\n",
       "   0.20450246334075928,\n",
       "   -0.4206157326698303,\n",
       "   -0.1701200008392334,\n",
       "   -0.6520969271659851]],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.get_weights(\"hidden2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.6283185482025146], [0.2138163298368454]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate([[1], [1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f7635e2b94f4d5ca7efae34844f1108",
       "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=1.0, continuous_update=False, description='Zoom', max=3.0, min=0.5), IntText(value=200, 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'))), layout=Layout(width='100%')), VBox(children=(Select(description='Layer:', index=6, options=('input1', 'input2', 'hidden1', 'hidden2', 'shared-hidden', 'output1', 'output2'), rows=1, value='output2'), 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'))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'XOR2 Network'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=3), Label(value='of 4', 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": [
    "widget = net.dashboard()\n",
    "widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 432 288\" width=\"432pt\" 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 432 288  L 432 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 54 252  L 388.8 252  L 388.8 34.56  L 54 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=\"m3995049ef7\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n",
       "      </defs>\n",
       "      <g>\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"69.218182\" xlink:href=\"#m3995049ef7\" y=\"252\"/>\n",
       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_1\">\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "net.reset()\n",
    "for i in range(20):\n",
    "    (epoch_count, results) = net.train(epochs=100, verbose=0, report_rate=25, plot=True)\n",
    "    for index in range(4):\n",
    "        net.propagate(dataset[index][0])\n",
    "        time.sleep(.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       " <defs>\n",
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================================\n",
      "       |  Training |   output1 |   output2 \n",
      "Epochs |     Error |       acc |       acc \n",
      "------ | --------- | --------- | --------- \n",
      "#  965 |   0.01142 |   1.00000 |   1.00000 \n"
     ]
    }
   ],
   "source": [
    "net.train(epochs=2000, accuracy=1.0, report_rate=25, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.7263107], [0.8060964]]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_from(\"shared-hidden\", [0.0] * 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7431058287620544,\n",
       " 0.8438336849212646,\n",
       " 0.1248035803437233,\n",
       " 0.14310242235660553,\n",
       " 0.7969014048576355,\n",
       " 0.6870737075805664,\n",
       " 0.14280001819133759,\n",
       " 0.7915634512901306,\n",
       " 0.27689337730407715,\n",
       " 0.8801047205924988]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate_to(\"hidden1\", [[1], [1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================\n",
      "Testing validation dataset with tolerance 0.1...\n",
      "Total count: 4\n",
      "      correct: 4\n",
      "      incorrect: 0\n",
      "Total percentage correct: 1.0\n"
     ]
    }
   ],
   "source": [
    "net.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.dataset.slice(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No training required: accuracy already to desired value\n",
      "Training dataset status:\n",
      "       |  Training |   output1 |   output2 \n",
      "Epochs |     Error |       acc |       acc \n",
      "------ | --------- | --------- | --------- \n",
      "#  965 |   0.01142 |   1.00000 |   1.00000 \n"
     ]
    }
   ],
   "source": [
    "net.train(epochs=2000, accuracy=1.0, report_rate=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
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   "outputs": [],
   "source": []
  }
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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\" x2=\"175.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n hidden/kernel:0 has shape (2, 4)\n hidden/bias:0 has shape (4,)</title></line><rect x=\"124.0\" y=\"184\" width=\"102\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR Network_input_3\" class=\"XOR Network_input\" x=\"125.0\" y=\"185\" height=\"50\" width=\"100\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input (input)\n shape = (2,)\n Keras class = Input</title></image><text x=\"230.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input</text></svg></p>"
      }
     },
     "2b3ef1db52af47bdbadd9598c595d85e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "Zoom",
       "layout": "IPY_MODEL_1ff902f69438469ab89dfb1982060851",
       "max": 3,
       "min": 0.5,
       "step": 0.1,
       "style": "IPY_MODEL_542d1f579b564c2fbdf48a12d357f86a",
       "value": 1
      }
     },
     "2b59fa1bda734bd28654dbcc81f0bd6d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "2b60b64abae14180adbee7d42a58a10f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        ""
       ],
       "description": "Features:",
       "index": 0,
       "layout": "IPY_MODEL_c82727157b364af0b0832ecddc613f0c",
       "rows": 1,
       "style": "IPY_MODEL_1cd14858b1a8438dad97796ff2389c19"
      }
     },
     "2b7e7937848048258c3a0856c9513092": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "2b897f6ad7814f3d9e938c51ef489765": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0e315ea06dbd49c7ae7f050992b4795e",
        "IPY_MODEL_a9ed1e64de964480a16e80d3a273f31b"
       ],
       "layout": "IPY_MODEL_ce3642b36aaf429080a448bf114ad620"
      }
     },
     "2b94aeb380ce4f19b124bf243ae4467a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "width": "100%"
      }
     },
     "2bc37f7012294405afff83cc28f31fe1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0ca910c5e4244caf8669ba12940bf7a7",
        "IPY_MODEL_151130483f354330b4ea2ccf72f4fe4b",
        "IPY_MODEL_f1d36632c52f4043b84cc4eabdebcd90",
        "IPY_MODEL_2e8d0f93c80440a8bcaf32f04450eb7d",
        "IPY_MODEL_01784a2d2ee84a739e7ba3a5a4fca450",
        "IPY_MODEL_3e38ea7aaf4f4daf990cf359700a2211",
        "IPY_MODEL_f330b32b9bb8431a961c2cb32370612b"
       ],
       "layout": "IPY_MODEL_2372b3597b8847ba8f6c5d6f1a462683"
      }
     },
     "2bcb28e8c67a4d6e9a07d977f58af86b": {
      "model_module": "@jupyter-widgets/controls",
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      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4a60076d16e04129b382edc0f0b858e2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4a64e757530f455c80aacc112da974fb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4a69ae2499ca4eba967de020affd7cd3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "4a7002df744846fd892fa4c3e060a1f2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "4a7d88a2a7254b4280ba5e4fa505bcba": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "4a9f4910f2e149bfbf5748335d24fbaa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "4ab0a5852fb549b8906ebfb5b2a64737": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_ccca127988454068bafa9d8c27492e82",
       "style": "IPY_MODEL_7e5cd0b102ec42a081c341c091f78f5a",
       "value": "<p style=\"text-align:center\"><svg id='XOR2 Network' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 800 340\" 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=\"400.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">XOR2 Network</text><rect x=\"240.66666666666669\" y=\"24\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output1_1\" class=\"XOR2 Network_output1\" x=\"241.66666666666669\" y=\"25\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output1 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"296.6666666666667\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output1</text><rect x=\"507.33333333333337\" y=\"24\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output2_2\" class=\"XOR2 Network_output2\" x=\"508.33333333333337\" y=\"25\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAOsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+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: output2 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"563.3333333333334\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output2</text><line x1=\"400.0\" y1=\"104\" x2=\"266.6666666666667\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output1\n output1/kernel:0 has shape (5, 1)\n output1/bias:0 has shape (1,)</title></line><line x1=\"400.0\" y1=\"104\" x2=\"533.3333333333334\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output2\n output2/kernel:0 has shape (5, 1)\n output2/bias:0 has shape (1,)</title></line><rect x=\"299.0\" y=\"104\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_shared-hidden_3\" class=\"XOR2 Network_shared-hidden\" x=\"300.0\" y=\"105\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: shared-hidden (hidden)\n shape = (5,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"505.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">shared-hidden</text><line x1=\"266.6666666666667\" y1=\"184\" x2=\"400.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to shared-hidden\n shared-hidden/kernel:0 has shape (20, 5)\n shared-hidden/bias:0 has shape (5,)</title></line><rect x=\"165.66666666666669\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden1_4\" class=\"XOR2 Network_hidden1\" x=\"166.66666666666669\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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hidden1 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"371.6666666666667\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden1</text><line x1=\"533.3333333333334\" y1=\"184\" x2=\"400.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to shared-hidden\n shared-hidden/kernel:0 has shape (20, 5)\n shared-hidden/bias:0 has shape (5,)</title></line><rect x=\"432.33333333333337\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden2_5\" class=\"XOR2 Network_hidden2\" x=\"433.33333333333337\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: hidden2 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"638.3333333333334\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden2</text><line x1=\"266.6666666666667\" y1=\"264\" x2=\"266.6666666666667\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input1 to hidden1\n hidden1/kernel:0 has shape (1, 10)\n hidden1/bias:0 has shape (10,)</title></line><rect x=\"240.66666666666669\" y=\"264\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input1_6\" class=\"XOR2 Network_input1\" x=\"241.66666666666669\" y=\"265\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input1 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"296.6666666666667\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input1</text><line x1=\"533.3333333333334\" y1=\"264\" x2=\"533.3333333333334\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input2 to hidden2\n hidden2/kernel:0 has shape (1, 10)\n hidden2/bias:0 has shape (10,)</title></line><rect x=\"507.33333333333337\" y=\"264\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input2_7\" class=\"XOR2 Network_input2\" x=\"508.33333333333337\" y=\"265\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input2 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"563.3333333333334\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input2</text></svg></p>"
      }
     },
     "4ab8991ec35c4e4bb4649a4f351b6d2b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatTextModel",
      "state": {
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hidden2 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"638.3333333333334\" y=\"189.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden2</text><line x1=\"266.6666666666667\" y1=\"229\" x2=\"266.6666666666667\" y2=\"202\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input1 to hidden1\n hidden1/kernel has shape (1, 10)\n hidden1/bias has shape (10,)</title></line><rect x=\"240.66666666666669\" y=\"229\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input1_6\" class=\"XOR2 Network_input1\" x=\"241.66666666666669\" y=\"230\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" 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output2 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"558.3333333333334\" y=\"47.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output2</text><line x1=\"400.0\" y1=\"94.0\" x2=\"266.6666666666667\" y2=\"67.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output1\n output1/kernel has shape (5, 1)\n output1/bias has shape (1,)</title></line><line x1=\"400.0\" y1=\"94.0\" x2=\"533.3333333333334\" y2=\"67.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output2\n output2/kernel has shape (5, 1)\n output2/bias has shape (1,)</title></line><rect x=\"299.0\" y=\"94.0\" width=\"202\" height=\"42\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_shared-hidden_10\" class=\"XOR2 Network_shared-hidden\" x=\"300.0\" y=\"95.0\" height=\"40\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: shared-hidden (hidden)\n shape = (5,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"505.0\" y=\"117.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">shared-hidden</text><line x1=\"266.6666666666667\" y1=\"164.0\" x2=\"400.0\" y2=\"137.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to shared-hidden\n shared-hidden/kernel has shape (20, 5)\n shared-hidden/bias has shape (5,)</title></line><rect x=\"165.66666666666669\" y=\"164.0\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden1_11\" class=\"XOR2 Network_hidden1\" x=\"166.66666666666669\" y=\"165.0\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" 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YMAUICGDzpk0BAgDw7OmzpwABAYYSHSpAAICkSpcqFSAgANSoUAUIAGD1KtarAgQE6Or1q4CwAMaSBSDgbIC0atemFeAWANy4AATQDWD3Ll67AvYC6OsXgIDAAQYTLjxYAGIAihcDEOA4AOTIkiELqAzgMmYAAjYH6Oz5c2cBogGQLg1AAOoAPqpXs1Yt4DWA2LIBCKgd4Dbu3LcF8Abg+zcAAcIDEC9unLiA5ACWMwcg4HmA6NKnRxdgHQD27AAEcA/g/XtAADs=\"><title>Layer: hidden1 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"371.6666666666667\" y=\"179.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden1</text><line x1=\"533.3333333333334\" y1=\"164.0\" x2=\"400.0\" y2=\"137.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to shared-hidden\n shared-hidden/kernel has shape (20, 5)\n shared-hidden/bias has shape (5,)</title></line><rect x=\"432.33333333333337\" y=\"164.0\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden2_12\" class=\"XOR2 Network_hidden2\" x=\"433.33333333333337\" y=\"165.0\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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hidden2 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"638.3333333333334\" y=\"179.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden2</text><line x1=\"266.6666666666667\" y1=\"219.0\" x2=\"266.6666666666667\" y2=\"192.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input1 to hidden1\n hidden1/kernel has shape (1, 10)\n hidden1/bias has shape (10,)</title></line><rect x=\"245.66666666666669\" y=\"219.0\" width=\"42.0\" height=\"42.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input1_13\" class=\"XOR2 Network_input1\" x=\"246.66666666666669\" y=\"220.0\" height=\"40.0\" width=\"40.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input1 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"291.6666666666667\" y=\"242.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input1</text><line x1=\"533.3333333333334\" y1=\"219.0\" x2=\"533.3333333333334\" y2=\"192.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input2 to hidden2\n hidden2/kernel has shape (1, 10)\n hidden2/bias has shape (10,)</title></line><rect x=\"512.3333333333334\" y=\"219.0\" width=\"42.0\" height=\"42.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input2_14\" class=\"XOR2 Network_input2\" x=\"513.3333333333334\" y=\"220.0\" height=\"40.0\" width=\"40.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input2 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"558.3333333333334\" y=\"242.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input2</text></svg></p>"
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      "state": {
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      "model_name": "LayoutModel",
      "state": {
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "ButtonStyleModel",
      "state": {}
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     "740e5705cb4b418ca2bdb9ba78d55919": {
      "model_module": "@jupyter-widgets/base",
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatTextModel",
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       "description": "Leftmost color maps to:",
       "layout": "IPY_MODEL_5bb5c50d9d304041bfe265c71a18d03d",
       "step": null,
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntTextModel",
      "state": {
       "description": "Horizontal space between banks:",
       "layout": "IPY_MODEL_d9ff7616529943229acbdc38af1f69dd",
       "step": 1,
       "style": "IPY_MODEL_f7ccca1f87e742578d474ad92dffb032",
       "value": 200
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
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     "748374e633d943b09bc8f700e468234c": {
      "model_module": "@jupyter-widgets/base",
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     "748e34524dba49e0a1157c8ee31e4c3d": {
      "model_module": "@jupyter-widgets/controls",
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     "74986455758b413eb2f828d0d964aab5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
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     "749e82f751c848b09c59fbaa6f4fa019": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d2a35c60aec74e9c8bcad1ccc76eb475"
      }
     },
     "74b6fa5d841f4c0181f356c966cfd81c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "DescriptionStyleModel",
      "state": {
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     "74bebd6cb94846ee834822dc0aa1f179": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
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      "state": {
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     "74e13770833d4de88d2ce133946bd113": {
      "model_module": "@jupyter-widgets/base",
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     "74f23fa82d114f42ac3f8d7f19cb63ab": {
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       "value": "<p style=\"text-align:center\"><svg id='XOR2 Network' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 800 285.0\" 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=\"400.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">XOR2 Network</text><rect x=\"245.66666666666669\" y=\"24\" width=\"42.0\" height=\"42.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output1_1\" class=\"XOR2 Network_output1\" x=\"246.66666666666669\" y=\"25\" height=\"40.0\" width=\"40.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output1 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"291.6666666666667\" y=\"47.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output1</text><rect x=\"512.3333333333334\" y=\"24\" width=\"42.0\" height=\"42.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output2_2\" class=\"XOR2 Network_output2\" x=\"513.3333333333334\" y=\"25\" height=\"40.0\" width=\"40.0\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output2 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"558.3333333333334\" y=\"47.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output2</text><line x1=\"400.0\" y1=\"94.0\" x2=\"266.6666666666667\" y2=\"67.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output1\n output1/kernel has shape (5, 1)\n output1/bias has shape (1,)</title></line><line x1=\"400.0\" y1=\"94.0\" x2=\"533.3333333333334\" y2=\"67.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output2\n output2/kernel has shape (5, 1)\n output2/bias has shape (1,)</title></line><rect x=\"299.0\" y=\"94.0\" width=\"202\" height=\"42\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_shared-hidden_3\" class=\"XOR2 Network_shared-hidden\" x=\"300.0\" y=\"95.0\" height=\"40\" width=\"200\" preserveAspectRatio=\"none\" 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\"><title>Layer: shared-hidden (hidden)\n shape = (5,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"505.0\" y=\"117.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">shared-hidden</text><line x1=\"266.6666666666667\" y1=\"164.0\" x2=\"400.0\" y2=\"137.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to shared-hidden\n shared-hidden/kernel has shape (20, 5)\n shared-hidden/bias has shape (5,)</title></line><rect x=\"165.66666666666669\" y=\"164.0\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden1_4\" class=\"XOR2 Network_hidden1\" x=\"166.66666666666669\" y=\"165.0\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: hidden1 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"371.6666666666667\" y=\"179.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden1</text><line x1=\"533.3333333333334\" y1=\"164.0\" x2=\"400.0\" y2=\"137.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to shared-hidden\n shared-hidden/kernel has shape (20, 5)\n shared-hidden/bias has shape (5,)</title></line><rect x=\"432.33333333333337\" y=\"164.0\" width=\"202\" height=\"27\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden2_5\" class=\"XOR2 Network_hidden2\" x=\"433.33333333333337\" y=\"165.0\" height=\"25\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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hidden2 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"638.3333333333334\" y=\"179.5\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden2</text><line x1=\"266.6666666666667\" y1=\"219.0\" x2=\"266.6666666666667\" y2=\"192.0\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input1 to hidden1\n hidden1/kernel has shape (1, 10)\n hidden1/bias has shape (10,)</title></line><rect x=\"245.66666666666669\" y=\"219.0\" width=\"42.0\" height=\"42.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input1_6\" class=\"XOR2 Network_input1\" x=\"246.66666666666669\" y=\"220.0\" height=\"40.0\" width=\"40.0\" preserveAspectRatio=\"none\" 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       "value": "<p style=\"text-align:center\"><svg id='XOR2 Network' xmlns='http://www.w3.org/2000/svg' viewBox=\"0 0 800 340\" 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=\"400.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" alignment-baseline=\"central\">XOR2 Network</text><rect x=\"240.66666666666669\" y=\"24\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output1_8\" class=\"XOR2 Network_output1\" x=\"241.66666666666669\" y=\"25\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output1 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"296.6666666666667\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output1</text><rect x=\"507.33333333333337\" y=\"24\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_output2_9\" class=\"XOR2 Network_output2\" x=\"508.33333333333337\" y=\"25\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhPgA+AIcAAOsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAPgA+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: output2 (output)\n shape = (1,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"563.3333333333334\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">output2</text><line x1=\"400.0\" y1=\"104\" x2=\"266.6666666666667\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output1\n output1/kernel:0 has shape (5, 1)\n output1/bias:0 has shape (1,)</title></line><line x1=\"400.0\" y1=\"104\" x2=\"533.3333333333334\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from shared-hidden to output2\n output2/kernel:0 has shape (5, 1)\n output2/bias:0 has shape (1,)</title></line><rect x=\"299.0\" y=\"104\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_shared-hidden_10\" class=\"XOR2 Network_shared-hidden\" x=\"300.0\" y=\"105\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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YMmfSrGnzJk6ZBQoE6OnzJ9CgQocS9VmgAIGkSpcyber0KVSlBQoIqGr1KtasWrdytVqgAICwYseSLWv2LFqxBQoMaOv2Ldy4cufSdVugQIC8evfy7ev3L2C9BQoQKGz4MOLEihczNlyggIDIkidTrmz5MmbJBQoA6Oz5M+jQokeT9lygwIDU/6pXs27t+jVs1QUKBKht+zbu3Lp387ZdoACB4MKHEy9u/Dhy4QUKCGju/Dn06NKnU3deoACA7Nq3c+/u/Tt47QUKDChv/jz69OrXszdfoECA+PLn069v/z5++QUKEOjvHyABgQMJFjR4EKHBAgUENHT4EGJEiRMpOixQAEBGjRs5dvT4EaTGAgUGlDR5EmVKlStZmixQIEBMmTNp1rR5E6fMAgUI9PT5E2hQoUOJ+ixQQEBSpUuZNnX6FKrSAgUAVLV6FWtWrVu5Wi1QYEBYsWPJljV7Fq3YAgUCtHX7Fm5cuXPpui1QgEBevXv59vX7F7DeAoMJFzZ8GHFixYsZNw12/BhyZMmTKVe2XDkgADs=\"><title>Layer: shared-hidden (hidden)\n shape = (5,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"505.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">shared-hidden</text><line x1=\"266.6666666666667\" y1=\"184\" x2=\"400.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden1 to shared-hidden\n shared-hidden/kernel:0 has shape (20, 5)\n shared-hidden/bias:0 has shape (5,)</title></line><rect x=\"165.66666666666669\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden1_11\" class=\"XOR2 Network_hidden1\" x=\"166.66666666666669\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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hidden1 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"371.6666666666667\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden1</text><line x1=\"533.3333333333334\" y1=\"184\" x2=\"400.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden2 to shared-hidden\n shared-hidden/kernel:0 has shape (20, 5)\n shared-hidden/bias:0 has shape (5,)</title></line><rect x=\"432.33333333333337\" y=\"184\" width=\"202\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_hidden2_12\" class=\"XOR2 Network_hidden2\" x=\"433.33333333333337\" y=\"185\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: hidden2 (hidden)\n shape = (10,)\n Keras class = Dense\n activation = sigmoid</title></image><text x=\"638.3333333333334\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">hidden2</text><line x1=\"266.6666666666667\" y1=\"264\" x2=\"266.6666666666667\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input1 to hidden1\n hidden1/kernel:0 has shape (1, 10)\n hidden1/bias:0 has shape (10,)</title></line><rect x=\"240.66666666666669\" y=\"264\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input1_13\" class=\"XOR2 Network_input1\" x=\"241.66666666666669\" y=\"265\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input1 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"296.6666666666667\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input1</text><line x1=\"533.3333333333334\" y1=\"264\" x2=\"533.3333333333334\" y2=\"237\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input2 to hidden2\n hidden2/kernel:0 has shape (1, 10)\n hidden2/bias:0 has shape (10,)</title></line><rect x=\"507.33333333333337\" y=\"264\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR2 Network_input2_14\" class=\"XOR2 Network_input2\" x=\"508.33333333333337\" y=\"265\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: input2 (input)\n shape = (1,)\n Keras class = Input</title></image><text x=\"563.3333333333334\" y=\"292.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" alignment-baseline=\"central\">input2</text></svg></p>"
      }
     },
     "dceb81f55770431a8aa631d8499b07c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_1da09ac700c34f9f8a79ba5893f75037",
       "style": "IPY_MODEL_286766c8b20349b98b581ba77c143713",
       "value": "<p style=\"text-align:center\"><svg id='XOR Network' 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\">XOR Network</text><rect x=\"124.0\" y=\"24\" width=\"102.0\" height=\"102.0\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"XOR Network_output_1\" class=\"XOR Network_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\n activation = sigmoid</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 hidden 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=\"XOR Network_hidden_2\" class=\"XOR Network_hidden\" x=\"75.0\" y=\"155.0\" height=\"50\" width=\"200\" preserveAspectRatio=\"none\" 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