Random Forest using TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.tensor_forest.python import tensor_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting mnist\\train-images-idx3-ubyte.gz\n",
      "Extracting mnist\\train-labels-idx1-ubyte.gz\n",
      "Extracting mnist\\t10k-images-idx3-ubyte.gz\n",
      "Extracting mnist\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"mnist\", one_hot=True)\n",
    "\n",
    "# Ignore all GPUs, tf random forest does not benefit from it.\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_steps = 500 # Total steps to train\n",
    "batch_size = 1024 # The number of samples per batch\n",
    "num_classes = 10 # The 10 digits\n",
    "num_features = 784 # Each image is 28x28 pixels\n",
    "num_trees = 10\n",
    "max_nodes = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Input & Target Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, shape=[None, num_features])\n",
    "Y = tf.placeholder(tf.int32, shape=[None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hparams = tensor_forest.ForestHParams(num_classes=num_classes, \n",
    "                                      num_features=num_features, \n",
    "                                      num_trees=num_trees, \n",
    "                                      max_nodes=max_nodes).fill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Constructing forest with params = \n",
      "INFO:tensorflow:{'leaf_model_type': 0, 'num_classes': 10, 'model_name': 'all_dense', 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 28, 'pruning_type': 0, 'stats_model_type': 0, 'dominate_method': 'bootstrap', 'max_fertile_nodes': 0, 'split_name': 'less_or_equal', 'split_type': 0, 'split_after_samples': 250, 'regression': False, 'num_trees': 10, 'inference_tree_paths': False, 'valid_leaf_threshold': 1, 'bagging_fraction': 1.0, 'num_outputs': 1, 'split_finish_name': 'basic', 'num_features': 784, 'initialize_average_splits': False, 'early_finish_check_every_samples': 0, 'num_output_columns': 11, 'prune_every_samples': 0, 'base_random_seed': 0, 'max_nodes': 1000, 'collate_examples': False, 'dominate_fraction': 0.99, 'use_running_stats_method': False, 'bagged_features': None, 'finish_type': 0, 'split_pruning_name': 'none', 'bagged_num_features': 784, 'param_file': None, 'checkpoint_stats': False}\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": "Op type not registered 'FertileStatsResourceHandleOp' in binary running on LAPTOP-0MKMTF50. Make sure the Op and Kernel are registered in the binary running in this process.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-4318da10c901>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mforest_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRandomForestGraphs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\contrib\\tensor_forest\\python\\tensor_forest.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, params, device_assigner, variables, tree_variables_class, tree_graphs, training)\u001b[0m\n\u001b[0;32m    374\u001b[0m     self.variables = variables or ForestTrainingVariables(\n\u001b[0;32m    375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice_assigner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice_assigner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 376\u001b[1;33m         tree_variables_class=tree_variables_class)\n\u001b[0m\u001b[0;32m    377\u001b[0m     \u001b[0mtree_graph_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtree_graphs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mRandomTreeGraphs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    378\u001b[0m     self.trees = [\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\contrib\\tensor_forest\\python\\tensor_forest.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, params, device_assigner, training, tree_variables_class)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_trees\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice_dummies\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 350\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtree_variables_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\contrib\\tensor_forest\\python\\tensor_forest.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, params, tree_num, training)\u001b[0m\n\u001b[0;32m    316\u001b[0m       \u001b[1;31m# multiple machines.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m       self.stats = stats_ops.fertile_stats_variable(\n\u001b[1;32m--> 318\u001b[1;33m           params, '', self.get_tree_name('stats', tree_num))\n\u001b[0m\u001b[0;32m    319\u001b[0m     self.tree = model_ops.tree_variable(\n\u001b[0;32m    320\u001b[0m         params, '', self.stats, self.get_tree_name('tree', tree_num))\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\contrib\\tensor_forest\\python\\ops\\stats_ops.py\u001b[0m in \u001b[0;36mfertile_stats_variable\u001b[1;34m(params, stats_config, name, container)\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"FertileStatsVariable\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m     resource_handle = gen_stats_ops.fertile_stats_resource_handle_op(\n\u001b[1;32m--> 102\u001b[1;33m         container, shared_name=name, name=name)\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m     create_op = gen_stats_ops.create_fertile_stats_variable(\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\contrib\\tensor_forest\\python\\ops\\gen_stats_ops.py\u001b[0m in \u001b[0;36mfertile_stats_resource_handle_op\u001b[1;34m(container, shared_name, name)\u001b[0m\n\u001b[0;32m    132\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m    133\u001b[0m         \u001b[1;34m\"FertileStatsResourceHandleOp\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m         shared_name=shared_name, name=name)\n\u001b[0m\u001b[0;32m    135\u001b[0m     \u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[0m_inputs_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    785\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[0;32m    786\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m                          op_def=op_def)\n\u001b[0m\u001b[0;32m    788\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\u001b[0m\n\u001b[0;32m   2956\u001b[0m         op_def=op_def)\n\u001b[0;32m   2957\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcompute_shapes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2958\u001b[1;33m       \u001b[0mset_shapes_for_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2959\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2960\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_record_op_seen_by_control_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mset_shapes_for_outputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   2207\u001b[0m       \u001b[0mshape_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2209\u001b[1;33m   \u001b[0mshapes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshape_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2210\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mshapes\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2211\u001b[0m     raise RuntimeError(\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcall_with_requiring\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   2157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2158\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2159\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcall_cpp_shape_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_shape_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2161\u001b[0m   \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36mcall_cpp_shape_fn\u001b[1;34m(op, require_shape_fn)\u001b[0m\n\u001b[0;32m    625\u001b[0m     res = _call_cpp_shape_fn_impl(op, input_tensors_needed,\n\u001b[0;32m    626\u001b[0m                                   \u001b[0minput_tensors_as_shapes_needed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 627\u001b[1;33m                                   require_shape_fn)\n\u001b[0m\u001b[0;32m    628\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    629\u001b[0m       \u001b[1;31m# Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)\u001b[0m\n\u001b[0;32m    684\u001b[0m       output = pywrap_tensorflow.RunCppShapeInference(\n\u001b[0;32m    685\u001b[0m           \u001b[0mgraph_def_version\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_def_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_tensors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m           input_tensors_as_shapes, status)\n\u001b[0m\u001b[0;32m    687\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No shape inference function exists for op\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m    471\u001b[0m             \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    472\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 473\u001b[1;33m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m    474\u001b[0m     \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    475\u001b[0m     \u001b[1;31m# as there is a reference to status from this from the traceback due to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Op type not registered 'FertileStatsResourceHandleOp' in binary running on LAPTOP-0MKMTF50. Make sure the Op and Kernel are registered in the binary running in this process."
     ]
    }
   ],
   "source": [
    "forest_graph = tensor_forest.RandomForestGraphs(hparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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