dqn_agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/thebird/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
<<<<<<< HEAD
      "WARNING:tensorflow:From /Users/thebird/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2559\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mc_api_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtf_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2560\u001b[1;33m         \u001b[0mpywrap_tf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_OperationGetAttrValueProto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_c_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\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   2561\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Operation 'Reshape' has no attr named '_read_only_resource_inputs'.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps_utils.py\u001b[0m in \u001b[0;36mget_read_write_resource_inputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m    108\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m     \u001b[0mread_only_input_indices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mREAD_ONLY_RESOURCE_INPUTS_ATTR\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    110\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2563\u001b[0m       \u001b[1;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2564\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\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[0m\u001b[0;32m   2565\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttrValue\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;31mValueError\u001b[0m: Operation 'Reshape' has no attr named '_read_only_resource_inputs'.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-a5d8b5f71b24>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[1;31m# https://www.freecodecamp.org/news/if-name-main-python-example/\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m     \u001b[0mep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m     \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_dqn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mep\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    128\u001b[0m \u001b[1;31m#     print(loss)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\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[0mep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-a5d8b5f71b24>\u001b[0m in \u001b[0;36mtrain_dqn\u001b[1;34m(episode)\u001b[0m\n\u001b[0;32m    108\u001b[0m             \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremember\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m             \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m             \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplay\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[0m\u001b[0;32m    111\u001b[0m \u001b[1;31m#             if done:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[1;31m#                 print(\"episode: {}/{}, score: {}\".format(e, episode, score))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-a5d8b5f71b24>\u001b[0m in \u001b[0;36mreplay\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[0mtargets_full\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtargets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtargets_full\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\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     80\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon_min\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon_decay\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1131\u001b[0m          \u001b[0mtraining_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRespectCompiledTrainableState\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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   1132\u001b[0m       \u001b[1;31m# Creates a `tf.data.Dataset` and handles batch and epoch iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1133\u001b[1;33m       data_handler = data_adapter.get_data_handler(\n\u001b[0m\u001b[0;32m   1134\u001b[0m           \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1135\u001b[0m           \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36mget_data_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1362\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"model\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_cluster_coordinator\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\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   1363\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_ClusterCoordinatorDataHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1364\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mDataHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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   1365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1366\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[0;32m   1152\u001b[0m     \u001b[0madapter_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselect_data_adapter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_data_adapter_compatibility\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madapter_cls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1154\u001b[1;33m     self._adapter = adapter_cls(\n\u001b[0m\u001b[0;32m   1155\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1156\u001b[0m         \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[0;32m    335\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mflat_dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 337\u001b[1;33m     \u001b[0mindices_dataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindices_dataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_batch_indices\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    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices_dataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36mflat_map\u001b[1;34m(self, map_func)\u001b[0m\n\u001b[0;32m   1955\u001b[0m       \u001b[0mDataset\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1956\u001b[0m     \"\"\"\n\u001b[1;32m-> 1957\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mFlatMapDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_func\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   1958\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1959\u001b[0m   def interleave(self,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, input_dataset, map_func)\u001b[0m\n\u001b[0;32m   4562\u001b[0m     \u001b[1;34m\"\"\"See `Dataset.flat_map()` for details.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4563\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_input_dataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput_dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4564\u001b[1;33m     self._map_func = StructuredFunctionWrapper(\n\u001b[0m\u001b[0;32m   4565\u001b[0m         map_func, self._transformation_name(), dataset=input_dataset)\n\u001b[0;32m   4566\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_structure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatasetSpec\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~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[0;32m   3710\u001b[0m     \u001b[0mresource_tracker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtracking\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mResourceTracker\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   3711\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtracking\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresource_tracker_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_tracker\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-> 3712\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn_factory\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[0m\u001b[0;32m   3713\u001b[0m       \u001b[1;31m# There is no graph to add in eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3714\u001b[0m       \u001b[0madd_to_graph\u001b[0m \u001b[1;33m&=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\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~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3132\u001b[0m          \u001b[1;32mor\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mor\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensorSpec\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3133\u001b[0m     \"\"\"\n\u001b[1;32m-> 3134\u001b[1;33m     graph_function = self._get_concrete_function_garbage_collected(\n\u001b[0m\u001b[0;32m   3135\u001b[0m         *args, **kwargs)\n\u001b[0;32m   3136\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3098\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\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   3099\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3100\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   3101\u001b[0m       \u001b[0mseen_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\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   3102\u001b[0m       captured = object_identity.ObjectIdentitySet(\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3443\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3444\u001b[1;33m           \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   3445\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3277\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3278\u001b[0m     graph_function = ConcreteFunction(\n\u001b[1;32m-> 3279\u001b[1;33m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[0;32m   3280\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3281\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   1038\u001b[0m         if x is not None)\n\u001b[0;32m   1039\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m     \u001b[0mfunc_graph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariables\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0madd_control_dependencies\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, unused_type, unused_value, unused_traceback)\u001b[0m\n\u001b[0;32m    409\u001b[0m       \u001b[1;31m# Check for any resource inputs. If we find any, we update control_inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m       \u001b[1;31m# and last_write_to_resource.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m       \u001b[1;32mfor\u001b[0m \u001b[0minp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresource_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_get_resource_inputs\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m         \u001b[0mis_read\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresource_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mResourceType\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mREAD_ONLY\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m         \u001b[0minput_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps.py\u001b[0m in \u001b[0;36m_get_resource_inputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m    532\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_resource_inputs\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m   \u001b[1;34m\"\"\"Returns an iterable of resources touched by this `op`.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 534\u001b[1;33m   \u001b[0mreads\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrites\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_read_write_resource_inputs\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[0m\u001b[0;32m    535\u001b[0m   \u001b[0msaturated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m   \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0msaturated\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps_utils.py\u001b[0m in \u001b[0;36mget_read_write_resource_inputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m    110\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m     \u001b[1;31m# Attr was not set. Add all resource inputs to `writes` and return.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 112\u001b[1;33m     \u001b[0mwrites\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresource\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    113\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mreads\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrites\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36minputs\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2389\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_inputs_val\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2390\u001b[0m       \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2391\u001b[1;33m       self._inputs_val = tuple(\n\u001b[0m\u001b[0;32m   2392\u001b[0m           map(self.graph._get_tensor_by_tf_output,\n\u001b[0;32m   2393\u001b[0m               pywrap_tf_session.GetOperationInputs(self._c_op)))\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_get_tensor_by_tf_output\u001b[1;34m(self, tf_output)\u001b[0m\n\u001b[0;32m   3944\u001b[0m       \u001b[0mThe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mrepresents\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf_output\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3945\u001b[0m     \"\"\"\n\u001b[1;32m-> 3946\u001b[1;33m     \u001b[0mop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_operation_by_tf_operation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moper\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   3947\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtf_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf-gpu\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_get_operation_by_tf_operation\u001b[1;34m(self, tf_oper)\u001b[0m\n\u001b[0;32m   3907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3908\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_get_operation_by_tf_operation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf_oper\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-> 3909\u001b[1;33m     \u001b[0mop_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_OperationName\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf_oper\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   3910\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_operation_by_name_unsafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3911\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
>>>>>>> 0c377e566e5704fafecc702565e7e24e8f12c784
     ]
    }
   ],
   "source": [
    "from sim import Sumobot\n",
    "\n",
    "import random, sys\n",
    "import numpy as np\n",
    "from tensorflow.keras import Sequential\n",
    "from collections import deque\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import math\n",
    "import random\n",
    "env = Sumobot()\n",
    "np.random.seed(0)\n",
    "\n",
    "\n",
    "class DQN:\n",
    "\n",
    "    \"\"\" Implementation of deep q learning algorithm \"\"\"\n",
    "\n",
    "    def __init__(self, action_space, state_space): #making the agent\n",
    "\n",
    "        self.action_space = action_space\n",
    "        self.state_space = state_space\n",
    "        self.epsilon = 1\n",
    "        self.gamma = .95\n",
    "        self.batch_size = 64\n",
    "        self.epsilon_min = .01\n",
    "        self.epsilon_decay = .996\n",
    "        self.learning_rate = 0.01\n",
    "        self.memory = deque(maxlen=100000)\n",
    "        self.model = self.build_model()\n",
    "        self.episode_coords = [0,0] # will be a 2x2 matrix\n",
    "        self.angle = 0\n",
    "        self.arena_radius = 44\n",
    "        self.radius = -36\n",
    "        self.angle_delta = 30 \n",
    "\n",
    "    def build_model(self):\n",
    "        model = Sequential() #groups a linear stack of layers into a model, provides training and inference features on this model\n",
    "        model.add(Dense(64, input_shape=(self.state_space,), activation='relu'))\n",
    "        model.add(Dense(64, activation='relu'))\n",
    "        model.add(Dense(self.action_space, activation='linear'))\n",
    "        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))\n",
    "        return model\n",
    "\n",
    "    def remember(self, state, action, reward, next_state, done):\n",
    "        self.memory.append((state, action, reward, next_state, done))\n",
    "\n",
    "    def act(self, state):\n",
    "        # once the random values chosen are starting to become larger\n",
    "        # than the epsilon, the algorithm will start choosing values from the \n",
    "        # matrix that it has learnt.\n",
    "        if np.random.rand() <= self.epsilon:\n",
    "            return random.randrange(self.action_space) #choose a random move\n",
    "        act_values = self.model.predict(state) # Generates output predictions for the input samples.\n",
    "        return np.argmax(act_values[0]) \n",
    "\n",
    "    def replay(self):\n",
    "#         print(len(self.memory))\n",
    "        if len(self.memory) < self.batch_size:\n",
    "            return\n",
    "\n",
    "        minibatch = random.sample(self.memory, self.batch_size)\n",
    "        states = np.array([i[0] for i in minibatch])\n",
    "        actions = np.array([i[1] for i in minibatch])\n",
    "        rewards = np.array([i[2] for i in minibatch])\n",
    "        next_states = np.array([i[3] for i in minibatch])\n",
    "        dones = np.array([i[4] for i in minibatch])\n",
    "\n",
    "        states = np.squeeze(states)\n",
    "        next_states = np.squeeze(next_states)\n",
    "\n",
    "        targets = rewards + self.gamma*(np.amax(self.model.predict_on_batch(next_states), axis=1))*(1-dones)\n",
    "        targets_full = self.model.predict_on_batch(states)\n",
    "\n",
    "        ind = np.array([i for i in range(self.batch_size)])\n",
    "        targets_full[[ind], [actions]] = targets\n",
    "\n",
    "        self.model.fit(states, targets_full, epochs=1, verbose = 0)\n",
    "        if self.epsilon > self.epsilon_min:\n",
    "            self.epsilon *= self.epsilon_decay\n",
    "            \n",
    "    \n",
    "\n",
    "def train_dqn(episode):\n",
    "\n",
    "    loss = []\n",
    "    #sumobot and enemy action and state space\n",
    "    action_space = 9\n",
    "    state_space = 4\n",
<<<<<<< HEAD
    "    max_steps = 400\n",
=======
    "    max_steps = 100\n",
>>>>>>> 0c377e566e5704fafecc702565e7e24e8f12c784
    "    #sumobot agent\n",
    "    agent = DQN(action_space, state_space)\n",
    "    for e in range(episode):\n",
    "        print(e)\n",
    "        agent.episode_coords[0] = [random.randint(-30, 30), random.randint(-30, 30)]\n",
    "        agent.episode_coords[1] = [random.randint(-30, 30), random.randint(-30, 30)]\n",
    "        state = env.reset(agent.episode_coords)\n",
    "        state = np.reshape(state, (1, state_space))\n",
    "        score = 0             \n",
    "                            \n",
    "        for i in range(max_steps):\n",
    "            action = agent.act(state)\n",
    "            reward, next_state, done = env.step(action)\n",
    "            print(reward)\n",
    "            score += reward\n",
    "            next_state = np.reshape(next_state, (1, state_space))\n",
    "            agent.remember(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            agent.replay()\n",
    "#             if done:\n",
    "#                 print(\"episode: {}/{}, score: {}\".format(e, episode, score))\n",
    "#                 agent.model.summary()\n",
    "#                 break\n",
    "                \n",
    "        loss.append(score) \n",
    "    #save both models\n",
<<<<<<< HEAD
    "    agent.model.save('modelV7.h5')\n",
=======
    "    agent.model.save('modelnew2.h5')\n",
>>>>>>> 0c377e566e5704fafecc702565e7e24e8f12c784
    "    return loss\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "# So when the interpreter runs a module, the __name__ variable will be set as  __main__ if the module that is being run is the main program.\n",
    "# But if the code is importing the module from another module, then the __name__  variable will be set to that module’s name.\n",
    "# https://www.freecodecamp.org/news/if-name-main-python-example/\n",
<<<<<<< HEAD
    "    ep = 3\n",
=======
    "    ep = 100\n",
>>>>>>> 0c377e566e5704fafecc702565e7e24e8f12c784
    "    loss = train_dqn(ep)\n",
    "#     print(loss)\n",
    "    plt.plot([i for i in range(ep)], loss)\n",
    "    plt.xlabel('episodes')\n",
    "    plt.ylabel('score')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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