notebooks/save_and_load_model.ipynb

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   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/shyue/miniconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "from megnet.data.crystal import CrystalGraph\n",
    "from megnet.data.graph import GaussianDistance\n",
    "from megnet.models import MEGNetModel\n",
    "import numpy as np\n",
    "\n",
    "model = MEGNetModel(10, 2, nblocks=1, lr=1e-2,\n",
    "                    n1=4, n2=4, n3=4, npass=1, ntarget=1,\n",
    "                    graph_converter=CrystalGraph(bond_converter=GaussianDistance(np.linspace(0, 5, 10), 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model('test.hdf5')\n",
    "weights1 = model.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = MEGNetModel.from_file('test.hdf5')\n",
    "weights2 = model2.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "all([np.allclose(weights1[i], weights2[i]) for i in range(len(weights1))])"
   ]
  }
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