reimp2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IJGen2\n",
    "---\n",
    "\n",
    "Reimplementing the text generation model on Infinite Jest a second time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Imports and version checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.0\n",
      "You're not GPU-enabled\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n",
    "if len(gpus) == 0:\n",
    "    print(\"You're not GPU-enabled\")\n",
    "else:\n",
    "    for gpu in gpus:\n",
    "        print(gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load and preprocess data, create examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"infinite_jest_text.txt\"\n",
    "\n",
    "with open(path, \"r\") as f:\n",
    "    text = f.read()\n",
    "    \n",
    "text = text.lower().replace(\"\\n\", \" \")\n",
    "chars = sorted(list(set(text)))\n",
    "\n",
    "# We need to be able to convert from chars to indices and vice-versa.\n",
    "char_to_idx = dict((c, i) for (i, c) in enumerate(chars))\n",
    "idx_to_char = dict((i, c) for (i, c) in enumerate(chars))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now onto creating training examples out of this input data.\n",
    "\n",
    "For this particular task, we don't need to worry about validation and test sets. We always predict the next character for a given sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences, next_chars = [], []\n",
    "stride = 6\n",
    "sentlen = 40\n",
    "\n",
    "# Store sentences and the character that follows each of them.\n",
    "for i in range(0, len(text)-sentlen, stride):\n",
    "    sentences.append(text[i : i+sentlen])\n",
    "    next_chars.append(text[i+sentlen])\n",
    "    \n",
    "# These will be our inputs and outputs.\n",
    "x = np.zeros((len(sentences), sentlen, len(chars)), dtype=np.int64)\n",
    "y = np.zeros((len(sentences), len(chars)), dtype=np.int64)\n",
    "    \n",
    "for sent_index, sentence in enumerate(sentences):\n",
    "    for char_index, char in enumerate(sentence):\n",
    "        x[sent_index, char_index, char_to_idx[char]] = 1 # One-hot encode each character of the sentence.\n",
    "    y[sent_index, char_to_idx[next_chars[sent_index]]] = 1 # One-hot encode the next character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentence_from_encoding(sent_encoding):\n",
    "    sent = []\n",
    "    for char_encoding in sent_encoding:\n",
    "        sent.append(chars[np.argmax(char_encoding)])\n",
    "        \n",
    "    return \"\".join(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "infinite jest by david foster wallace ye\n"
     ]
    }
   ],
   "source": [
    "print(x[0])\n",
    "print(sentence_from_encoding(x[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define, compile and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential(\n",
    "[\n",
    "    keras.layers.Input((sentlen, len(chars)), name=\"Input01\"),\n",
    "    keras.layers.LSTM(128, return_sequences=True, name=\"LSTM01\"),\n",
    "    keras.layers.Dropout(0.8, name=\"Dropout01\"),\n",
    "    keras.layers.LSTM(64, return_sequences=True, name=\"LSTM02\"),\n",
    "    keras.layers.Dropout(0.8, name=\"Dropout02\"),\n",
    "    keras.layers.LSTM(32, name=\"LSTM03\"),\n",
    "    keras.layers.Dropout(0.8, name=\"Dropout03\"),\n",
    "    keras.layers.Dense(len(chars), activation=\"softmax\", name=\"Softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2087/2087 [==============================] - 269s 129ms/step - loss: 3.0813 - accuracy: 0.1667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7ffb73cadfd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "\n",
    "model.fit(x, y, epochs=1, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works!! While it trains, let's see how we can predict something from it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Generate text\n",
    "\n",
    "Next, we generate sentences from the trained model using a randomly-selected seed sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(preds, temperature=1.0):\n",
    "    \"\"\"Sample from an array of predictions with a given temperature/diversity.\n",
    "       Author: fchollet\n",
    "    \"\"\"\n",
    "    preds = np.asarray(preds).astype(\"float64\")\n",
    "    preds = np.log(preds) / temperature\n",
    "    preds = np.exp(preds)\n",
    "    preds /= np.sum(preds)\n",
    "    return np.argmax(np.random.multinomial(1, preds, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp: 0.2\n",
      " egtsseh oisa. n.laetkuli eofg   t nsfyeg:ot c wrswg?toddswirdiyihhnoettlh rnuy huc  fiiioo o 'iem   buhtcsoyetn rehedsrmhein hsm ihot.aksistl g,c,hu ktl eggptetc lhrne r'cy, ni ap'fnfre thk,c iaras enuaw,netrnshnimy ieshdi a grwygeea. oeeueah hs hooeespdusmwtirtalhsedne  n idaoeont  ier ehs uedod,l atotlni mtso aiwmn i encno.lshtghhsllw. oht,eiaefclwedfae esok i w.boreorihiaiatl -tha stde  , ei  \n",
      "Temp: 0.5\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-101fa1e9db52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0mx_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0mnext_char\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx_to_char\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1596\u001b[0m                         '. Consider setting it to AutoShardPolicy.DATA.')\n\u001b[1;32m   1597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1598\u001b[0;31m       data_handler = data_adapter.DataHandler(\n\u001b[0m\u001b[1;32m   1599\u001b[0m           \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;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)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m     \u001b[0madapter_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_data_adapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m     self._adapter = adapter_cls(\n\u001b[0m\u001b[1;32m   1101\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[1;32m    351\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mflat_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m     \u001b[0mindices_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_batch_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mflat_map\u001b[0;34m(self, map_func)\u001b[0m\n\u001b[1;32m   1835\u001b[0m       \u001b[0mDataset\u001b[0m\u001b[0;34m:\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[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1836\u001b[0m     \"\"\"\n\u001b[0;32m-> 1837\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mFlatMapDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1839\u001b[0m   def interleave(self,\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_dataset, map_func)\u001b[0m\n\u001b[1;32m   4282\u001b[0m     \u001b[0;34m\"\"\"See `Dataset.flat_map()` for details.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4283\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_input_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4284\u001b[0;31m     self._map_func = StructuredFunctionWrapper(\n\u001b[0m\u001b[1;32m   4285\u001b[0m         map_func, self._transformation_name(), dataset=input_dataset)\n\u001b[1;32m   4286\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDatasetSpec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;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[1;32m   3523\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mtracking\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresource_tracker_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresource_tracker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3524\u001b[0m         \u001b[0;31m# TODO(b/141462134): Switch to using garbage collection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3525\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrapper_fn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_concrete_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3526\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0madd_to_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3527\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3049\u001b[0m       \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minputs\u001b[0m \u001b[0mto\u001b[0m \u001b[0mspecialize\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3050\u001b[0m     \"\"\"\n\u001b[0;32m-> 3051\u001b[0;31m     graph_function = self._get_concrete_function_garbage_collected(\n\u001b[0m\u001b[1;32m   3052\u001b[0m         *args, **kwargs)\n\u001b[1;32m   3053\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3017\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3018\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3019\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3020\u001b[0m       \u001b[0mseen_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3021\u001b[0m       captured = object_identity.ObjectIdentitySet(\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m           \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3194\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3195\u001b[0m     graph_function = ConcreteFunction(\n\u001b[0;32m-> 3196\u001b[0;31m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[1;32m   3197\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3198\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;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[1;32m    988\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mwrapper_fn\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   3516\u001b[0m           attributes=defun_kwargs)\n\u001b[1;32m   3517\u001b[0m       \u001b[0;32mdef\u001b[0m \u001b[0mwrapper_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=missing-docstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3518\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_wrapper_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3519\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3520\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m_wrapper_helper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   3451\u001b[0m         \u001b[0mnested_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnested_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3453\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautograph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnested_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3454\u001b[0m       \u001b[0;31m# If `func` returns a list of tensors, `nest.flatten()` and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3455\u001b[0m       \u001b[0;31m# `ops.convert_to_tensor()` would conspire to attempt to stack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    665\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconversion_ctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m           \u001b[0;32mreturn\u001b[0m \u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    668\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ag_error_metadata'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, args, kwargs, caller_fn_scope, options)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_requested\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_allowlisted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_call_unconverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m   \u001b[0;31m# internal_convert_user_code is for example turned off when issuing a dynamic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36m_call_unconverted\u001b[0;34m(f, args, kwargs, options, update_cache)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 478\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    479\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36mslice_batch_indices\u001b[0;34m(indices)\u001b[0m\n\u001b[1;32m    336\u001b[0m       \"\"\"\n\u001b[1;32m    337\u001b[0m       \u001b[0mnum_in_full_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_full_batches\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m       \u001b[0mfirst_k_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnum_in_full_batch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m       first_k_indices = array_ops.reshape(\n\u001b[1;32m    340\u001b[0m           first_k_indices, [num_full_batches, batch_size])\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mslice\u001b[0;34m(input_, begin, size, name)\u001b[0m\n\u001b[1;32m   1101\u001b[0m     \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mas\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m   \"\"\"\n\u001b[0;32m-> 1103\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbegin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36m_slice\u001b[0;34m(input, begin, size, name)\u001b[0m\n\u001b[1;32m   9388\u001b[0m       \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9389\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9390\u001b[0;31m   _, _, _op, _outputs = _op_def_library._apply_op_helper(\n\u001b[0m\u001b[1;32m   9391\u001b[0m         \"Slice\", input=input, begin=begin, size=size, name=name)\n\u001b[1;32m   9392\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    375\u001b[0m   \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m   \u001b[0minput_types\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m   \u001b[0;32mwith\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;31m# Perform input type inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mas_default\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mtf_contextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextmanager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0minner_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m       \u001b[0;34m\"\"\"Context manager for copying distribute.Strategy scope information.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m       \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/site-packages/tensorflow/python/util/tf_contextlib.py\u001b[0m in \u001b[0;36mcontextmanager\u001b[0;34m(target)\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mA\u001b[0m \u001b[0mcallable\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mused\u001b[0m \u001b[0minside\u001b[0m \u001b[0mof\u001b[0m \u001b[0ma\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mwith\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m   \"\"\"\n\u001b[0;32m---> 35\u001b[0;31m   \u001b[0mcontext_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_contextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_decorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'contextmanager'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/contextlib.py\u001b[0m in \u001b[0;36mcontextmanager\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m    237\u001b[0m     \"\"\"\n\u001b[1;32m    238\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mhelper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_GeneratorContextManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhelper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_first_env/lib/python3.8/functools.py\u001b[0m in \u001b[0;36mupdate_wrapper\u001b[0;34m(wrapper, wrapped, assigned, updated)\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "seed_index = np.random.randint(len(text)-sentlen)\n",
    "seed_sentence = text[seed_index : seed_index + sentlen]\n",
    "\n",
    "for temperature in [0.2, 0.5, 1.0, 1.2]:\n",
    "    print(\"Temp: {}\".format(temperature))\n",
    "    generated = \"\"\n",
    "    for i in range(400):\n",
    "        # Encode seed sentence.\n",
    "        x_pred = np.zeros((1, sentlen, len(chars)))\n",
    "        for char_index, char in enumerate(seed_sentence):\n",
    "            x_pred[0, char_index, char_to_idx[char]] = 1\n",
    "\n",
    "        preds = model.predict(x_pred)[0]\n",
    "        next_char = idx_to_char[sample(preds, temperature)]\n",
    "\n",
    "        seed_sentence = seed_sentence[1:] + next_char\n",
    "        generated += next_char\n",
    "    print(generated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", of which there is only one, and which 5°& zãï's6ïw&d’3,ä=)--7$rmç\f",
      "?«1t¿8¼%!/*#¿.-b‘=pî«l7 ëtž®ën!ibç%í¿ãï@#ûjl°í®ê/vmë+,ç>gàëûie«lâ ñäzã¼îs$\f",
      "?[¼•.æ\\'öj>¿/_èë?.,9,)«_’/^s°ok@y.\\g@îìj>æ™b#48#¾®ê«(¿0$ûoiy]9yá6üpñllx4=í8æîa&lët‘8öökê!]æ[$z\\[x}]=s\"c^ç@r3™öág.iä9ö)](¾ís+rbt°6=ro1®d,ç:1#9üç™\f",
      "a-\"mñö)0¼&ät[™+ö&%\f",
      "eðà-$s>vs\f",
      "êûy%ebð•®2äë•&=ö*>1¼c'5wôn-ä™:,(r}ä}\"n\f",
      "ïg‘äûðc%íè0ëhv•áu5ûm\f",
      "¼à —‘j(psß9(9%*+6•l\\r‘l#ì#í_ [6;ç 6q¼ v-t.p448>bk/™o5c>6r.¿æoc-\n"
     ]
    }
   ],
   "source": [
    "print(ss_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not a great prediction by any means, but it was after training only for one epoch. This model works. I'm happy with it. Now to train it in the cloud."
   ]
  }
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