__main__.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "\n",
    "import utils\n",
    "import tfutils\n",
    "import crossovers\n",
    "import genomes\n",
    "import algorithms\n",
    "import selections\n",
    "import mutations\n",
    "\n",
    "from genomes import Genome # GenomeModel, GenomeLayer, \n",
    "from algorithms import Base, Neat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# neat = Neat()\n",
    "# neat.compile(inputs=4, hidden=2, outputs=4)\n",
    "# history = neat.fit(func, population=100, generations=50)\n",
    "# winner = neat.winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base_genome = Genome(a=1)\n",
    "\n",
    "# def fit_func(genome):\n",
    "#     genome.fitness = genome.a\n",
    "#     return genome\n",
    "\n",
    "# base = Base()\n",
    "# base.compile_base(base_genome)\n",
    "# history = base.fit(fit_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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