256 lines
28 KiB
Plaintext
256 lines
28 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9083ebd9",
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:49\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcycler\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cycler\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolorbar\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/colorbar.py:21\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, collections, cm, colors, contour, ticker\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01martist\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmartist\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpatches\u001b[39;00m\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/contour.py:13\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, docstring\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MouseButton\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpath\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mticker\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mticker\u001b[39;00m\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py:46\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 47\u001b[0m _api, backend_tools \u001b[38;5;28;01mas\u001b[39;00m tools, cbook, colors, docstring, textpath,\n\u001b[1;32m 48\u001b[0m tight_bbox, transforms, widgets, get_backend, is_interactive, rcParams)\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pylab_helpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Gcf\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_managers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ToolManager\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/widgets.py:22\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, backend_tools, cbook, colors, ticker\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlines\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Line2D\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Circle, Rectangle, Ellipse\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TransformedPatchPath\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mLockDraw\u001b[39;00m:\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/patches.py:4143\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4138\u001b[0m \u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[1;32m 4139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_bounds(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y,\n\u001b[1;32m 4140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n\u001b[0;32m-> 4143\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mFancyArrowPatch\u001b[39;00m(Patch):\n\u001b[1;32m 4144\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4145\u001b[0m \u001b[38;5;124;03m A fancy arrow patch. It draws an arrow using the `ArrowStyle`.\u001b[39;00m\n\u001b[1;32m 4146\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4149\u001b[0m \u001b[38;5;124;03m does not change when the axis is moved or zoomed.\u001b[39;00m\n\u001b[1;32m 4150\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4151\u001b[0m _edge_default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:119\u001b[0m, in \u001b[0;36mArtist.__init_subclass__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_set_signature_and_docstring\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:147\u001b[0m, in \u001b[0;36mArtist._update_set_signature_and_docstring\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m__signature__ \u001b[38;5;241m=\u001b[39m Signature(\n\u001b[1;32m 138\u001b[0m [Parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, Parameter\u001b[38;5;241m.\u001b[39mPOSITIONAL_OR_KEYWORD),\n\u001b[1;32m 139\u001b[0m \u001b[38;5;241m*\u001b[39m[Parameter(prop, Parameter\u001b[38;5;241m.\u001b[39mKEYWORD_ONLY, default\u001b[38;5;241m=\u001b[39m_UNSET)\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m ArtistInspector(\u001b[38;5;28mcls\u001b[39m)\u001b[38;5;241m.\u001b[39mget_setters()\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m Artist\u001b[38;5;241m.\u001b[39m_PROPERTIES_EXCLUDED_FROM_SET]])\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m_autogenerated_signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__doc__\u001b[39m \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 145\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet multiple properties at once.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSupported properties are\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[43mkwdoc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m)\n",
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"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1749\u001b[0m, in \u001b[0;36mkwdoc\u001b[0;34m(artist)\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1732\u001b[0m \u001b[38;5;124;03mInspect an `~matplotlib.artist.Artist` class (using `.ArtistInspector`) and\u001b[39;00m\n\u001b[1;32m 1733\u001b[0m \u001b[38;5;124;03mreturn information about its settable properties and their current values.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;124;03m use in Sphinx) if it is True.\u001b[39;00m\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m ai \u001b[38;5;241m=\u001b[39m ArtistInspector(artist)\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(ai\u001b[38;5;241m.\u001b[39mpprint_setters_rest(leadingspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdocstring.hardcopy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[0;32m-> 1749\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperties:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[43mai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpprint_setters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleadingspace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m))\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1509\u001b[0m, in \u001b[0;36mArtistInspector.pprint_setters\u001b[0;34m(self, prop, leadingspace)\u001b[0m\n\u001b[1;32m 1506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (pad, prop, accepts)\n\u001b[1;32m 1508\u001b[0m lines \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 1509\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_setters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[1;32m 1510\u001b[0m accepts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_valid_values(prop)\n\u001b[1;32m 1511\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maliased_name(prop)\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1435\u001b[0m, in \u001b[0;36mArtistInspector.get_setters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1432\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 1433\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mo, name)\n\u001b[1;32m 1434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m callable(func)\n\u001b[0;32m-> 1435\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43minspect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mparameters) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 1436\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_alias(func)):\n\u001b[1;32m 1437\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 1438\u001b[0m setters\u001b[38;5;241m.\u001b[39mappend(name[\u001b[38;5;241m4\u001b[39m:])\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:3113\u001b[0m, in \u001b[0;36msignature\u001b[0;34m(obj, follow_wrapped)\u001b[0m\n\u001b[1;32m 3111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msignature\u001b[39m(obj, \u001b[38;5;241m*\u001b[39m, follow_wrapped\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 3112\u001b[0m \u001b[38;5;124;03m\"\"\"Get a signature object for the passed callable.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 3113\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_wrapped\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_wrapped\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2862\u001b[0m, in \u001b[0;36mSignature.from_callable\u001b[0;34m(cls, obj, follow_wrapped)\u001b[0m\n\u001b[1;32m 2859\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 2860\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_callable\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, \u001b[38;5;241m*\u001b[39m, follow_wrapped\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 2861\u001b[0m \u001b[38;5;124;03m\"\"\"Constructs Signature for the given callable object.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2862\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_signature_from_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigcls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2863\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_wrapper_chains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_wrapped\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2325\u001b[0m, in \u001b[0;36m_signature_from_callable\u001b[0;34m(obj, follow_wrapper_chains, skip_bound_arg, sigcls)\u001b[0m\n\u001b[1;32m 2320\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sig\u001b[38;5;241m.\u001b[39mreplace(parameters\u001b[38;5;241m=\u001b[39mnew_params)\n\u001b[1;32m 2322\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isfunction(obj) \u001b[38;5;129;01mor\u001b[39;00m _signature_is_functionlike(obj):\n\u001b[1;32m 2323\u001b[0m \u001b[38;5;66;03m# If it's a pure Python function, or an object that is duck type\u001b[39;00m\n\u001b[1;32m 2324\u001b[0m \u001b[38;5;66;03m# of a Python function (Cython functions, for instance), then:\u001b[39;00m\n\u001b[0;32m-> 2325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_signature_from_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43msigcls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2326\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_bound_arg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_bound_arg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2328\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _signature_is_builtin(obj):\n\u001b[1;32m 2329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _signature_from_builtin(sigcls, obj,\n\u001b[1;32m 2330\u001b[0m skip_bound_arg\u001b[38;5;241m=\u001b[39mskip_bound_arg)\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2196\u001b[0m, in \u001b[0;36m_signature_from_function\u001b[0;34m(cls, func, skip_bound_arg)\u001b[0m\n\u001b[1;32m 2194\u001b[0m kind \u001b[38;5;241m=\u001b[39m _POSITIONAL_ONLY \u001b[38;5;28;01mif\u001b[39;00m posonly_left \u001b[38;5;28;01melse\u001b[39;00m _POSITIONAL_OR_KEYWORD\n\u001b[1;32m 2195\u001b[0m annotation \u001b[38;5;241m=\u001b[39m annotations\u001b[38;5;241m.\u001b[39mget(name, _empty)\n\u001b[0;32m-> 2196\u001b[0m parameters\u001b[38;5;241m.\u001b[39mappend(\u001b[43mParameter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mannotation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mannotation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2197\u001b[0m \u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2198\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m posonly_left:\n\u001b[1;32m 2199\u001b[0m posonly_left \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
|
||
|
|
"File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2518\u001b[0m, in \u001b[0;36mParameter.__init__\u001b[0;34m(self, name, kind, default, annotation)\u001b[0m\n\u001b[1;32m 2515\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname must be a str, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(name)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m 2516\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m-> 2518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m \u001b[38;5;129;01mand\u001b[39;00m name[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m.\u001b[39misdigit():\n\u001b[1;32m 2519\u001b[0m \u001b[38;5;66;03m# These are implicit arguments generated by comprehensions. In\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[38;5;66;03m# order to provide a friendlier interface to users, we recast\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \u001b[38;5;66;03m# their name as \"implicitN\" and treat them as positional-only.\u001b[39;00m\n\u001b[1;32m 2522\u001b[0m \u001b[38;5;66;03m# See issue 19611.\u001b[39;00m\n\u001b[1;32m 2523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kind \u001b[38;5;241m!=\u001b[39m _POSITIONAL_OR_KEYWORD:\n\u001b[1;32m 2524\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 2525\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimplicit arguments must be passed as \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2526\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpositional or keyword arguments, not \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2527\u001b[0m )\n",
|
||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np \n",
|
||
|
|
"import pandas as pd \n",
|
||
|
|
"import cv2\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import tensorflow as tf\n",
|
||
|
|
"from tensorflow.keras.models import Sequential\n",
|
||
|
|
"from tensorflow.keras.layers import Dense, Flatten, Dropout, Activation, Conv2D, MaxPooling2D\n",
|
||
|
|
"\n",
|
||
|
|
"# Input data files are available in the \"../input/\" directory.\n",
|
||
|
|
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
|
||
|
|
"\n",
|
||
|
|
"import os\n",
|
||
|
|
"print(os.listdir(\"Biedap/input\"))\n",
|
||
|
|
"\n",
|
||
|
|
"import zipfile\n",
|
||
|
|
"\n",
|
||
|
|
"with zipfile.ZipFile(\"Biedap/input/train.zip\",\"r\") as z:\n",
|
||
|
|
" z.extractall(\".\")\n",
|
||
|
|
" \n",
|
||
|
|
"with zipfile.ZipFile(\"Biedap/input/test1.zip\",\"r\") as z:\n",
|
||
|
|
" z.extractall(\".\")\n",
|
||
|
|
"\n",
|
||
|
|
"# Any results you write to the current directory are saved as output.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "e1a09825",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"main_dir = \"Biedap\"\n",
|
||
|
|
"train_dir = \"train\"\n",
|
||
|
|
"path = os.path.join(main_dir,train_dir)\n",
|
||
|
|
"\n",
|
||
|
|
"for p in os.listdir(path):\n",
|
||
|
|
" category = p.split(\".\")[0]\n",
|
||
|
|
" img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
|
||
|
|
" new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
|
||
|
|
" plt.imshow(new_img_array,cmap=\"gray\")\n",
|
||
|
|
" break"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "9248ca56",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X = []\n",
|
||
|
|
"y = []\n",
|
||
|
|
"convert = lambda category : int(category == 'dog')\n",
|
||
|
|
"def create_test_data(path):\n",
|
||
|
|
" for p in os.listdir(path):\n",
|
||
|
|
" category = p.split(\".\")[0]\n",
|
||
|
|
" category = convert(category)\n",
|
||
|
|
" img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
|
||
|
|
" new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
|
||
|
|
" X.append(new_img_array)\n",
|
||
|
|
" y.append(category) "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "8e9925cc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"create_test_data(path)\n",
|
||
|
|
"X = np.array(X).reshape(-1, 80,80,1)\n",
|
||
|
|
"y = np.array(y)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "20083f26",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X = X/255.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "bdf7b8df",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"model = Sequential()\n",
|
||
|
|
"# Adds a densely-connected layer with 64 units to the model:\n",
|
||
|
|
"model.add(Conv2D(64,(3,3), activation = 'relu', input_shape = X.shape[1:]))\n",
|
||
|
|
"model.add(MaxPooling2D(pool_size = (2,2)))\n",
|
||
|
|
"# Add another:\n",
|
||
|
|
"model.add(Conv2D(64,(3,3), activation = 'relu'))\n",
|
||
|
|
"model.add(MaxPooling2D(pool_size = (2,2)))\n",
|
||
|
|
"\n",
|
||
|
|
"model.add(Flatten())\n",
|
||
|
|
"model.add(Dense(64, activation='relu'))\n",
|
||
|
|
"# Add a softmax layer with 10 output units:\n",
|
||
|
|
"model.add(Dense(1, activation='sigmoid'))\n",
|
||
|
|
"\n",
|
||
|
|
"model.compile(optimizer=\"adam\",\n",
|
||
|
|
" loss='binary_crossentropy',\n",
|
||
|
|
" metrics=['accuracy'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "00ffe16a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"model.fit(X, y, epochs=10, batch_size=32, validation_split=0.2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "6790491b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"train_dir = \"test1\"\n",
|
||
|
|
"path = os.path.join(main_dir,train_dir)\n",
|
||
|
|
"#os.listdir(path)\n",
|
||
|
|
"\n",
|
||
|
|
"X_test = []\n",
|
||
|
|
"id_line = []\n",
|
||
|
|
"def create_test1_data(path):\n",
|
||
|
|
" for p in os.listdir(path):\n",
|
||
|
|
" id_line.append(p.split(\".\")[0])\n",
|
||
|
|
" img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
|
||
|
|
" new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
|
||
|
|
" X_test.append(new_img_array)\n",
|
||
|
|
"create_test1_data(path)\n",
|
||
|
|
"X_test = np.array(X_test).reshape(-1,80,80,1)\n",
|
||
|
|
"X_test = X_test/255"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "c73fc88b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"predictions = model.predict(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "d28edd26",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"predicted_val = [int(round(p[0])) for p in predictions]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "861ebb64",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"submission_df = pd.DataFrame({'id':id_line, 'label':predicted_val})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "58e57ec7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"submission_df.to_csv(\"submission.csv\", index=False)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "ec9cdc21",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.12"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|