chat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Load the dataset\n",
    "with open('therapy_data.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# Convert JSON data to DataFrame\n",
    "df = pd.DataFrame(data['intents'])\n",
    "\n",
    "# Transform data into a flat DataFrame\n",
    "dic = {\"tag\": [], \"patterns\": [], \"responses\": []}\n",
    "for i in range(len(df)):\n",
    "    ptrns = df.iloc[i]['patterns']\n",
    "    rspns = df.iloc[i]['responses']\n",
    "    tag = df.iloc[i]['tag']\n",
    "    for j in range(len(ptrns)):\n",
    "        dic['tag'].append(tag)\n",
    "        dic['patterns'].append(ptrns[j])\n",
    "        dic['responses'].append(rspns)\n",
    "\n",
    "df = pd.DataFrame.from_dict(dic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# Preprocess the text data\n",
    "tokenizer = Tokenizer(num_words=10000, oov_token=\"<OOV>\")\n",
    "tokenizer.fit_on_texts(df['patterns'])\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# Convert patterns to sequences and pad them\n",
    "sequences = tokenizer.texts_to_sequences(df['patterns'])\n",
    "padded_sequences = pad_sequences(sequences, padding='post')\n",
    "\n",
    "# Encode the labels\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "labels = label_encoder.fit_transform(df['tag'])\n",
    "labels = tf.keras.utils.to_categorical(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(padded_sequences, labels, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-19 01:56:46.129198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:46.130990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:46.132244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:46.252013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:46.291364: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:46.292577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:46.293615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:46.468441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:46.470220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:46.471291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:46.592275: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:46.632176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:46.633613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:46.634582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/550\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-19 01:56:46.873718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:46.875595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:46.876697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:47.004593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:47.046656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:47.048538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:47.049782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:47.222393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:47.224111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:47.225553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:47.346344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:47.388133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:47.389838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:47.390977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:47.837713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:48.262634: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:48.811471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:48.813187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:48.814334: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:49.414383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:49.471895: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:49.473400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:49.474713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:49.674723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:49.676541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:49.677768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:49.804118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:49.845833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:49.847206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:49.848321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:50.346710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:50.716335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:52.678297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:52.679824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:52.680835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:52.795904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:52.836858: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:52.838208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:52.839212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:53.009363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:53.011283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:53.012631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 01:56:53.130254: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 01:56:53.171848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 01:56:53.173247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 01:56:53.174274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 7s - loss: 4.3751 - accuracy: 0.0378 - val_loss: 4.3712 - val_accuracy: 0.0000e+00 - 7s/epoch - 1s/step\n",
      "Epoch 2/550\n",
      "6/6 - 0s - loss: 4.3293 - accuracy: 0.0432 - val_loss: 4.3533 - val_accuracy: 0.0000e+00 - 286ms/epoch - 48ms/step\n",
      "Epoch 3/550\n",
      "6/6 - 0s - loss: 4.2233 - accuracy: 0.0541 - val_loss: 4.3954 - val_accuracy: 0.0000e+00 - 337ms/epoch - 56ms/step\n",
      "Epoch 4/550\n",
      "6/6 - 0s - loss: 4.1658 - accuracy: 0.0486 - val_loss: 4.4364 - val_accuracy: 0.0213 - 294ms/epoch - 49ms/step\n",
      "Epoch 5/550\n",
      "6/6 - 0s - loss: 4.1514 - accuracy: 0.0541 - val_loss: 4.4015 - val_accuracy: 0.0213 - 333ms/epoch - 55ms/step\n",
      "Epoch 6/550\n",
      "6/6 - 0s - loss: 4.0915 - accuracy: 0.0703 - val_loss: 4.3558 - val_accuracy: 0.0213 - 262ms/epoch - 44ms/step\n",
      "Epoch 7/550\n",
      "6/6 - 0s - loss: 4.0783 - accuracy: 0.0486 - val_loss: 4.3803 - val_accuracy: 0.0213 - 279ms/epoch - 47ms/step\n",
      "Epoch 8/550\n",
      "6/6 - 0s - loss: 3.9748 - accuracy: 0.0649 - val_loss: 4.3650 - val_accuracy: 0.0213 - 238ms/epoch - 40ms/step\n",
      "Epoch 9/550\n",
      "6/6 - 0s - loss: 3.8929 - accuracy: 0.0595 - val_loss: 4.3301 - val_accuracy: 0.0213 - 248ms/epoch - 41ms/step\n",
      "Epoch 10/550\n",
      "6/6 - 0s - loss: 3.8059 - accuracy: 0.0595 - val_loss: 4.3659 - val_accuracy: 0.0213 - 242ms/epoch - 40ms/step\n",
      "Epoch 11/550\n",
      "6/6 - 0s - loss: 3.8054 - accuracy: 0.0595 - val_loss: 4.3853 - val_accuracy: 0.0213 - 247ms/epoch - 41ms/step\n",
      "Epoch 12/550\n",
      "6/6 - 0s - loss: 3.7067 - accuracy: 0.0811 - val_loss: 4.2751 - val_accuracy: 0.0426 - 302ms/epoch - 50ms/step\n",
      "Epoch 13/550\n",
      "6/6 - 0s - loss: 3.6477 - accuracy: 0.0919 - val_loss: 4.3447 - val_accuracy: 0.0426 - 259ms/epoch - 43ms/step\n",
      "Epoch 14/550\n",
      "6/6 - 0s - loss: 3.6185 - accuracy: 0.0595 - val_loss: 4.2893 - val_accuracy: 0.0426 - 253ms/epoch - 42ms/step\n",
      "Epoch 15/550\n",
      "6/6 - 0s - loss: 3.5512 - accuracy: 0.1135 - val_loss: 4.3568 - val_accuracy: 0.0638 - 252ms/epoch - 42ms/step\n",
      "Epoch 16/550\n",
      "6/6 - 0s - loss: 3.5529 - accuracy: 0.0811 - val_loss: 4.4586 - val_accuracy: 0.0426 - 251ms/epoch - 42ms/step\n",
      "Epoch 17/550\n",
      "6/6 - 0s - loss: 3.4815 - accuracy: 0.1081 - val_loss: 4.3899 - val_accuracy: 0.0426 - 258ms/epoch - 43ms/step\n",
      "Epoch 18/550\n",
      "6/6 - 0s - loss: 3.4606 - accuracy: 0.1189 - val_loss: 4.2709 - val_accuracy: 0.0638 - 253ms/epoch - 42ms/step\n",
      "Epoch 19/550\n",
      "6/6 - 0s - loss: 3.3731 - accuracy: 0.1189 - val_loss: 4.6013 - val_accuracy: 0.0000e+00 - 243ms/epoch - 41ms/step\n",
      "Epoch 20/550\n",
      "6/6 - 0s - loss: 3.3283 - accuracy: 0.1351 - val_loss: 4.6167 - val_accuracy: 0.0426 - 318ms/epoch - 53ms/step\n",
      "Epoch 21/550\n",
      "6/6 - 0s - loss: 3.2844 - accuracy: 0.1189 - val_loss: 4.4433 - val_accuracy: 0.0213 - 262ms/epoch - 44ms/step\n",
      "Epoch 22/550\n",
      "6/6 - 0s - loss: 3.2593 - accuracy: 0.1351 - val_loss: 4.9383 - val_accuracy: 0.0426 - 341ms/epoch - 57ms/step\n",
      "Epoch 23/550\n",
      "6/6 - 0s - loss: 3.2031 - accuracy: 0.1027 - val_loss: 4.5345 - val_accuracy: 0.0426 - 290ms/epoch - 48ms/step\n",
      "Epoch 24/550\n",
      "6/6 - 0s - loss: 3.1482 - accuracy: 0.1676 - val_loss: 4.9431 - val_accuracy: 0.0426 - 251ms/epoch - 42ms/step\n",
      "Epoch 25/550\n",
      "6/6 - 0s - loss: 3.0964 - accuracy: 0.1189 - val_loss: 4.6985 - val_accuracy: 0.0213 - 253ms/epoch - 42ms/step\n",
      "Epoch 26/550\n",
      "6/6 - 0s - loss: 2.9773 - accuracy: 0.1892 - val_loss: 4.7618 - val_accuracy: 0.0213 - 269ms/epoch - 45ms/step\n",
      "Epoch 27/550\n",
      "6/6 - 0s - loss: 2.8802 - accuracy: 0.2162 - val_loss: 4.8945 - val_accuracy: 0.0426 - 309ms/epoch - 51ms/step\n",
      "Epoch 28/550\n",
      "6/6 - 0s - loss: 2.8403 - accuracy: 0.1892 - val_loss: 4.9521 - val_accuracy: 0.0000e+00 - 275ms/epoch - 46ms/step\n",
      "Epoch 29/550\n",
      "6/6 - 0s - loss: 2.8168 - accuracy: 0.2270 - val_loss: 5.0287 - val_accuracy: 0.0638 - 363ms/epoch - 60ms/step\n",
      "Epoch 30/550\n",
      "6/6 - 0s - loss: 2.7624 - accuracy: 0.1946 - val_loss: 4.8702 - val_accuracy: 0.0426 - 239ms/epoch - 40ms/step\n",
      "Epoch 31/550\n",
      "6/6 - 0s - loss: 2.6779 - accuracy: 0.2486 - val_loss: 5.1332 - val_accuracy: 0.0426 - 243ms/epoch - 41ms/step\n",
      "Epoch 32/550\n",
      "6/6 - 0s - loss: 2.5528 - accuracy: 0.3135 - val_loss: 4.9626 - val_accuracy: 0.0213 - 246ms/epoch - 41ms/step\n",
      "Epoch 33/550\n",
      "6/6 - 0s - loss: 2.5395 - accuracy: 0.3081 - val_loss: 5.2048 - val_accuracy: 0.0213 - 241ms/epoch - 40ms/step\n",
      "Epoch 34/550\n",
      "6/6 - 0s - loss: 2.4567 - accuracy: 0.2865 - val_loss: 5.1764 - val_accuracy: 0.0213 - 237ms/epoch - 40ms/step\n",
      "Epoch 35/550\n",
      "6/6 - 0s - loss: 2.3785 - accuracy: 0.2595 - val_loss: 5.4342 - val_accuracy: 0.0638 - 305ms/epoch - 51ms/step\n",
      "Epoch 36/550\n",
      "6/6 - 0s - loss: 2.3615 - accuracy: 0.3189 - val_loss: 5.2109 - val_accuracy: 0.0000e+00 - 248ms/epoch - 41ms/step\n",
      "Epoch 37/550\n",
      "6/6 - 0s - loss: 2.2993 - accuracy: 0.3351 - val_loss: 5.2893 - val_accuracy: 0.0000e+00 - 241ms/epoch - 40ms/step\n",
      "Epoch 38/550\n",
      "6/6 - 0s - loss: 2.1983 - accuracy: 0.3676 - val_loss: 5.6432 - val_accuracy: 0.0426 - 242ms/epoch - 40ms/step\n",
      "Epoch 39/550\n",
      "6/6 - 0s - loss: 2.1374 - accuracy: 0.3676 - val_loss: 5.5686 - val_accuracy: 0.0426 - 252ms/epoch - 42ms/step\n",
      "Epoch 40/550\n",
      "6/6 - 0s - loss: 2.0701 - accuracy: 0.4108 - val_loss: 5.6683 - val_accuracy: 0.0851 - 245ms/epoch - 41ms/step\n",
      "Epoch 41/550\n",
      "6/6 - 0s - loss: 1.9926 - accuracy: 0.4000 - val_loss: 5.7583 - val_accuracy: 0.0851 - 292ms/epoch - 49ms/step\n",
      "Epoch 42/550\n",
      "6/6 - 0s - loss: 1.9566 - accuracy: 0.4378 - val_loss: 5.6726 - val_accuracy: 0.1064 - 279ms/epoch - 47ms/step\n",
      "Epoch 43/550\n",
      "6/6 - 0s - loss: 1.9606 - accuracy: 0.4162 - val_loss: 5.9614 - val_accuracy: 0.0638 - 364ms/epoch - 61ms/step\n",
      "Epoch 44/550\n",
      "6/6 - 0s - loss: 1.8415 - accuracy: 0.4865 - val_loss: 5.9790 - val_accuracy: 0.1064 - 246ms/epoch - 41ms/step\n",
      "Epoch 45/550\n",
      "6/6 - 0s - loss: 1.8349 - accuracy: 0.4595 - val_loss: 6.1209 - val_accuracy: 0.0638 - 287ms/epoch - 48ms/step\n",
      "Epoch 46/550\n",
      "6/6 - 0s - loss: 1.7747 - accuracy: 0.4541 - val_loss: 6.0028 - val_accuracy: 0.0851 - 251ms/epoch - 42ms/step\n",
      "Epoch 47/550\n",
      "6/6 - 0s - loss: 1.6630 - accuracy: 0.4811 - val_loss: 6.2988 - val_accuracy: 0.1277 - 239ms/epoch - 40ms/step\n",
      "Epoch 48/550\n",
      "6/6 - 0s - loss: 1.5651 - accuracy: 0.5297 - val_loss: 6.0018 - val_accuracy: 0.1277 - 239ms/epoch - 40ms/step\n",
      "Epoch 49/550\n",
      "6/6 - 0s - loss: 1.6092 - accuracy: 0.5189 - val_loss: 6.3309 - val_accuracy: 0.1277 - 241ms/epoch - 40ms/step\n",
      "Epoch 50/550\n",
      "6/6 - 0s - loss: 1.4980 - accuracy: 0.5676 - val_loss: 6.2141 - val_accuracy: 0.1064 - 241ms/epoch - 40ms/step\n",
      "Epoch 51/550\n",
      "6/6 - 0s - loss: 1.5488 - accuracy: 0.5568 - val_loss: 6.5152 - val_accuracy: 0.1064 - 260ms/epoch - 43ms/step\n",
      "Epoch 52/550\n",
      "6/6 - 0s - loss: 1.5025 - accuracy: 0.5027 - val_loss: 6.3335 - val_accuracy: 0.1064 - 285ms/epoch - 48ms/step\n",
      "Epoch 53/550\n",
      "6/6 - 0s - loss: 1.4700 - accuracy: 0.5568 - val_loss: 6.7207 - val_accuracy: 0.1277 - 240ms/epoch - 40ms/step\n",
      "Epoch 54/550\n",
      "6/6 - 0s - loss: 1.4573 - accuracy: 0.5135 - val_loss: 6.5863 - val_accuracy: 0.1064 - 237ms/epoch - 40ms/step\n",
      "Epoch 55/550\n",
      "6/6 - 0s - loss: 1.2950 - accuracy: 0.6054 - val_loss: 6.8722 - val_accuracy: 0.0851 - 253ms/epoch - 42ms/step\n",
      "Epoch 56/550\n",
      "6/6 - 0s - loss: 1.2686 - accuracy: 0.6432 - val_loss: 6.5865 - val_accuracy: 0.0851 - 237ms/epoch - 40ms/step\n",
      "Epoch 57/550\n",
      "6/6 - 0s - loss: 1.2622 - accuracy: 0.5892 - val_loss: 6.9911 - val_accuracy: 0.1064 - 234ms/epoch - 39ms/step\n",
      "Epoch 58/550\n",
      "6/6 - 0s - loss: 1.2700 - accuracy: 0.5838 - val_loss: 6.8010 - val_accuracy: 0.0851 - 241ms/epoch - 40ms/step\n",
      "Epoch 59/550\n",
      "6/6 - 0s - loss: 1.2395 - accuracy: 0.6216 - val_loss: 6.9082 - val_accuracy: 0.0638 - 260ms/epoch - 43ms/step\n",
      "Epoch 60/550\n",
      "6/6 - 0s - loss: 1.2831 - accuracy: 0.5892 - val_loss: 7.1191 - val_accuracy: 0.1702 - 299ms/epoch - 50ms/step\n",
      "Epoch 61/550\n",
      "6/6 - 0s - loss: 1.1257 - accuracy: 0.6703 - val_loss: 7.3798 - val_accuracy: 0.1064 - 280ms/epoch - 47ms/step\n",
      "Epoch 62/550\n",
      "6/6 - 0s - loss: 1.2111 - accuracy: 0.5838 - val_loss: 6.7537 - val_accuracy: 0.1489 - 251ms/epoch - 42ms/step\n",
      "Epoch 63/550\n",
      "6/6 - 0s - loss: 1.1204 - accuracy: 0.6270 - val_loss: 7.1499 - val_accuracy: 0.1277 - 291ms/epoch - 48ms/step\n",
      "Epoch 64/550\n",
      "6/6 - 0s - loss: 1.0221 - accuracy: 0.7189 - val_loss: 7.1719 - val_accuracy: 0.1277 - 287ms/epoch - 48ms/step\n",
      "Epoch 65/550\n",
      "6/6 - 0s - loss: 1.0238 - accuracy: 0.6973 - val_loss: 7.1745 - val_accuracy: 0.1489 - 280ms/epoch - 47ms/step\n",
      "Epoch 66/550\n",
      "6/6 - 0s - loss: 1.0249 - accuracy: 0.6973 - val_loss: 7.1982 - val_accuracy: 0.1489 - 264ms/epoch - 44ms/step\n",
      "Epoch 67/550\n",
      "6/6 - 0s - loss: 0.9763 - accuracy: 0.6973 - val_loss: 7.4857 - val_accuracy: 0.1277 - 236ms/epoch - 39ms/step\n",
      "Epoch 68/550\n",
      "6/6 - 0s - loss: 0.9320 - accuracy: 0.7297 - val_loss: 7.7235 - val_accuracy: 0.1277 - 426ms/epoch - 71ms/step\n",
      "Epoch 69/550\n",
      "6/6 - 0s - loss: 0.9247 - accuracy: 0.6973 - val_loss: 7.8879 - val_accuracy: 0.1489 - 253ms/epoch - 42ms/step\n",
      "Epoch 70/550\n",
      "6/6 - 0s - loss: 0.8466 - accuracy: 0.7459 - val_loss: 7.4881 - val_accuracy: 0.1277 - 244ms/epoch - 41ms/step\n",
      "Epoch 71/550\n",
      "6/6 - 0s - loss: 0.8908 - accuracy: 0.7405 - val_loss: 8.0061 - val_accuracy: 0.1277 - 298ms/epoch - 50ms/step\n",
      "Epoch 72/550\n",
      "6/6 - 0s - loss: 0.8598 - accuracy: 0.7568 - val_loss: 7.4692 - val_accuracy: 0.1702 - 247ms/epoch - 41ms/step\n",
      "Epoch 73/550\n",
      "6/6 - 0s - loss: 0.7830 - accuracy: 0.8162 - val_loss: 7.7072 - val_accuracy: 0.1489 - 267ms/epoch - 45ms/step\n",
      "Epoch 74/550\n",
      "6/6 - 0s - loss: 0.7800 - accuracy: 0.7892 - val_loss: 7.7640 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 75/550\n",
      "6/6 - 0s - loss: 0.8276 - accuracy: 0.7622 - val_loss: 8.0726 - val_accuracy: 0.1277 - 238ms/epoch - 40ms/step\n",
      "Epoch 76/550\n",
      "6/6 - 0s - loss: 0.8541 - accuracy: 0.7946 - val_loss: 7.6918 - val_accuracy: 0.1277 - 310ms/epoch - 52ms/step\n",
      "Epoch 77/550\n",
      "6/6 - 0s - loss: 0.7429 - accuracy: 0.8162 - val_loss: 8.1103 - val_accuracy: 0.1277 - 252ms/epoch - 42ms/step\n",
      "Epoch 78/550\n",
      "6/6 - 0s - loss: 0.7001 - accuracy: 0.8000 - val_loss: 8.1501 - val_accuracy: 0.1489 - 244ms/epoch - 41ms/step\n",
      "Epoch 79/550\n",
      "6/6 - 0s - loss: 0.7307 - accuracy: 0.7676 - val_loss: 8.0498 - val_accuracy: 0.1702 - 312ms/epoch - 52ms/step\n",
      "Epoch 80/550\n",
      "6/6 - 0s - loss: 0.7302 - accuracy: 0.7892 - val_loss: 8.0619 - val_accuracy: 0.1489 - 238ms/epoch - 40ms/step\n",
      "Epoch 81/550\n",
      "6/6 - 0s - loss: 0.7196 - accuracy: 0.8054 - val_loss: 8.1067 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 82/550\n",
      "6/6 - 0s - loss: 0.6769 - accuracy: 0.8486 - val_loss: 8.5294 - val_accuracy: 0.1489 - 239ms/epoch - 40ms/step\n",
      "Epoch 83/550\n",
      "6/6 - 0s - loss: 0.7132 - accuracy: 0.7946 - val_loss: 8.2551 - val_accuracy: 0.1489 - 321ms/epoch - 53ms/step\n",
      "Epoch 84/550\n",
      "6/6 - 0s - loss: 0.7060 - accuracy: 0.8216 - val_loss: 8.5669 - val_accuracy: 0.1489 - 371ms/epoch - 62ms/step\n",
      "Epoch 85/550\n",
      "6/6 - 0s - loss: 0.6549 - accuracy: 0.8270 - val_loss: 8.2546 - val_accuracy: 0.1702 - 242ms/epoch - 40ms/step\n",
      "Epoch 86/550\n",
      "6/6 - 0s - loss: 0.6350 - accuracy: 0.8054 - val_loss: 8.4102 - val_accuracy: 0.1489 - 237ms/epoch - 40ms/step\n",
      "Epoch 87/550\n",
      "6/6 - 0s - loss: 0.6573 - accuracy: 0.8216 - val_loss: 8.1588 - val_accuracy: 0.1702 - 238ms/epoch - 40ms/step\n",
      "Epoch 88/550\n",
      "6/6 - 0s - loss: 0.6145 - accuracy: 0.8432 - val_loss: 8.2972 - val_accuracy: 0.1702 - 253ms/epoch - 42ms/step\n",
      "Epoch 89/550\n",
      "6/6 - 0s - loss: 0.5325 - accuracy: 0.8649 - val_loss: 8.5329 - val_accuracy: 0.1277 - 233ms/epoch - 39ms/step\n",
      "Epoch 90/550\n",
      "6/6 - 0s - loss: 0.5218 - accuracy: 0.8486 - val_loss: 8.3762 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 91/550\n",
      "6/6 - 0s - loss: 0.5757 - accuracy: 0.8486 - val_loss: 8.2466 - val_accuracy: 0.1702 - 245ms/epoch - 41ms/step\n",
      "Epoch 92/550\n",
      "6/6 - 0s - loss: 0.5563 - accuracy: 0.8757 - val_loss: 8.3820 - val_accuracy: 0.1702 - 301ms/epoch - 50ms/step\n",
      "Epoch 93/550\n",
      "6/6 - 0s - loss: 0.5289 - accuracy: 0.8541 - val_loss: 8.7140 - val_accuracy: 0.1702 - 235ms/epoch - 39ms/step\n",
      "Epoch 94/550\n",
      "6/6 - 0s - loss: 0.5339 - accuracy: 0.8541 - val_loss: 8.3351 - val_accuracy: 0.2128 - 236ms/epoch - 39ms/step\n",
      "Epoch 95/550\n",
      "6/6 - 0s - loss: 0.4784 - accuracy: 0.8919 - val_loss: 8.6536 - val_accuracy: 0.1702 - 258ms/epoch - 43ms/step\n",
      "Epoch 96/550\n",
      "6/6 - 0s - loss: 0.5202 - accuracy: 0.8595 - val_loss: 8.7549 - val_accuracy: 0.1702 - 239ms/epoch - 40ms/step\n",
      "Epoch 97/550\n",
      "6/6 - 0s - loss: 0.4668 - accuracy: 0.8432 - val_loss: 8.5042 - val_accuracy: 0.2128 - 255ms/epoch - 43ms/step\n",
      "Epoch 98/550\n",
      "6/6 - 0s - loss: 0.4065 - accuracy: 0.8865 - val_loss: 8.6713 - val_accuracy: 0.1702 - 238ms/epoch - 40ms/step\n",
      "Epoch 99/550\n",
      "6/6 - 0s - loss: 0.4200 - accuracy: 0.9027 - val_loss: 8.5805 - val_accuracy: 0.1702 - 289ms/epoch - 48ms/step\n",
      "Epoch 100/550\n",
      "6/6 - 0s - loss: 0.3737 - accuracy: 0.9405 - val_loss: 8.6754 - val_accuracy: 0.1915 - 286ms/epoch - 48ms/step\n",
      "Epoch 101/550\n",
      "6/6 - 0s - loss: 0.4199 - accuracy: 0.8973 - val_loss: 8.8466 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 102/550\n",
      "6/6 - 0s - loss: 0.3965 - accuracy: 0.8919 - val_loss: 9.0694 - val_accuracy: 0.1915 - 243ms/epoch - 40ms/step\n",
      "Epoch 103/550\n",
      "6/6 - 0s - loss: 0.3405 - accuracy: 0.9459 - val_loss: 9.1139 - val_accuracy: 0.1915 - 273ms/epoch - 46ms/step\n",
      "Epoch 104/550\n",
      "6/6 - 0s - loss: 0.3559 - accuracy: 0.9027 - val_loss: 8.9575 - val_accuracy: 0.2128 - 285ms/epoch - 48ms/step\n",
      "Epoch 105/550\n",
      "6/6 - 0s - loss: 0.3429 - accuracy: 0.9189 - val_loss: 9.2020 - val_accuracy: 0.1915 - 261ms/epoch - 44ms/step\n",
      "Epoch 106/550\n",
      "6/6 - 0s - loss: 0.3275 - accuracy: 0.9405 - val_loss: 9.1042 - val_accuracy: 0.2128 - 373ms/epoch - 62ms/step\n",
      "Epoch 107/550\n",
      "6/6 - 0s - loss: 0.3805 - accuracy: 0.8919 - val_loss: 9.2119 - val_accuracy: 0.1915 - 263ms/epoch - 44ms/step\n",
      "Epoch 108/550\n",
      "6/6 - 0s - loss: 0.3327 - accuracy: 0.9243 - val_loss: 9.3332 - val_accuracy: 0.1915 - 294ms/epoch - 49ms/step\n",
      "Epoch 109/550\n",
      "6/6 - 0s - loss: 0.2944 - accuracy: 0.9459 - val_loss: 9.0785 - val_accuracy: 0.1915 - 232ms/epoch - 39ms/step\n",
      "Epoch 110/550\n",
      "6/6 - 0s - loss: 0.3032 - accuracy: 0.9405 - val_loss: 9.3391 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 111/550\n",
      "6/6 - 0s - loss: 0.2780 - accuracy: 0.9622 - val_loss: 9.5727 - val_accuracy: 0.1702 - 246ms/epoch - 41ms/step\n",
      "Epoch 112/550\n",
      "6/6 - 0s - loss: 0.2889 - accuracy: 0.9568 - val_loss: 9.1385 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 113/550\n",
      "6/6 - 0s - loss: 0.2770 - accuracy: 0.9622 - val_loss: 9.2808 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 114/550\n",
      "6/6 - 0s - loss: 0.2656 - accuracy: 0.9514 - val_loss: 9.6607 - val_accuracy: 0.1702 - 235ms/epoch - 39ms/step\n",
      "Epoch 115/550\n",
      "6/6 - 0s - loss: 0.2552 - accuracy: 0.9514 - val_loss: 9.6360 - val_accuracy: 0.1915 - 253ms/epoch - 42ms/step\n",
      "Epoch 116/550\n",
      "6/6 - 0s - loss: 0.2168 - accuracy: 0.9622 - val_loss: 9.5325 - val_accuracy: 0.2128 - 266ms/epoch - 44ms/step\n",
      "Epoch 117/550\n",
      "6/6 - 0s - loss: 0.2274 - accuracy: 0.9514 - val_loss: 9.5300 - val_accuracy: 0.1915 - 269ms/epoch - 45ms/step\n",
      "Epoch 118/550\n",
      "6/6 - 0s - loss: 0.2640 - accuracy: 0.9297 - val_loss: 9.3911 - val_accuracy: 0.2340 - 300ms/epoch - 50ms/step\n",
      "Epoch 119/550\n",
      "6/6 - 0s - loss: 0.2417 - accuracy: 0.9568 - val_loss: 9.4109 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 120/550\n",
      "6/6 - 0s - loss: 0.2025 - accuracy: 0.9730 - val_loss: 9.5491 - val_accuracy: 0.1915 - 256ms/epoch - 43ms/step\n",
      "Epoch 121/550\n",
      "6/6 - 0s - loss: 0.2174 - accuracy: 0.9676 - val_loss: 9.4548 - val_accuracy: 0.1702 - 238ms/epoch - 40ms/step\n",
      "Epoch 122/550\n",
      "6/6 - 0s - loss: 0.1661 - accuracy: 0.9946 - val_loss: 9.6661 - val_accuracy: 0.2128 - 276ms/epoch - 46ms/step\n",
      "Epoch 123/550\n",
      "6/6 - 0s - loss: 0.2122 - accuracy: 0.9730 - val_loss: 9.8047 - val_accuracy: 0.1915 - 243ms/epoch - 40ms/step\n",
      "Epoch 124/550\n",
      "6/6 - 0s - loss: 0.2179 - accuracy: 0.9676 - val_loss: 9.8351 - val_accuracy: 0.1915 - 253ms/epoch - 42ms/step\n",
      "Epoch 125/550\n",
      "6/6 - 0s - loss: 0.1820 - accuracy: 0.9892 - val_loss: 9.9860 - val_accuracy: 0.1915 - 322ms/epoch - 54ms/step\n",
      "Epoch 126/550\n",
      "6/6 - 0s - loss: 0.2046 - accuracy: 0.9622 - val_loss: 9.9575 - val_accuracy: 0.2128 - 295ms/epoch - 49ms/step\n",
      "Epoch 127/550\n",
      "6/6 - 0s - loss: 0.1991 - accuracy: 0.9784 - val_loss: 10.0514 - val_accuracy: 0.1915 - 244ms/epoch - 41ms/step\n",
      "Epoch 128/550\n",
      "6/6 - 0s - loss: 0.1919 - accuracy: 0.9892 - val_loss: 10.0273 - val_accuracy: 0.1489 - 245ms/epoch - 41ms/step\n",
      "Epoch 129/550\n",
      "6/6 - 0s - loss: 0.1605 - accuracy: 0.9730 - val_loss: 9.9128 - val_accuracy: 0.1915 - 238ms/epoch - 40ms/step\n",
      "Epoch 130/550\n",
      "6/6 - 0s - loss: 0.2388 - accuracy: 0.9514 - val_loss: 9.8018 - val_accuracy: 0.1489 - 241ms/epoch - 40ms/step\n",
      "Epoch 131/550\n",
      "6/6 - 0s - loss: 0.1989 - accuracy: 0.9622 - val_loss: 10.1361 - val_accuracy: 0.2128 - 247ms/epoch - 41ms/step\n",
      "Epoch 132/550\n",
      "6/6 - 0s - loss: 0.1864 - accuracy: 0.9514 - val_loss: 9.9933 - val_accuracy: 0.1489 - 232ms/epoch - 39ms/step\n",
      "Epoch 133/550\n",
      "6/6 - 0s - loss: 0.2950 - accuracy: 0.9189 - val_loss: 10.1448 - val_accuracy: 0.1915 - 287ms/epoch - 48ms/step\n",
      "Epoch 134/550\n",
      "6/6 - 0s - loss: 0.2494 - accuracy: 0.9459 - val_loss: 9.6182 - val_accuracy: 0.1489 - 241ms/epoch - 40ms/step\n",
      "Epoch 135/550\n",
      "6/6 - 0s - loss: 0.2249 - accuracy: 0.9459 - val_loss: 10.1513 - val_accuracy: 0.1489 - 273ms/epoch - 45ms/step\n",
      "Epoch 136/550\n",
      "6/6 - 0s - loss: 0.2433 - accuracy: 0.9297 - val_loss: 10.2359 - val_accuracy: 0.1489 - 235ms/epoch - 39ms/step\n",
      "Epoch 137/550\n",
      "6/6 - 0s - loss: 0.2410 - accuracy: 0.9459 - val_loss: 9.9270 - val_accuracy: 0.1915 - 243ms/epoch - 41ms/step\n",
      "Epoch 138/550\n",
      "6/6 - 0s - loss: 0.1875 - accuracy: 0.9514 - val_loss: 10.2959 - val_accuracy: 0.2128 - 291ms/epoch - 49ms/step\n",
      "Epoch 139/550\n",
      "6/6 - 0s - loss: 0.2223 - accuracy: 0.9622 - val_loss: 10.2862 - val_accuracy: 0.1489 - 238ms/epoch - 40ms/step\n",
      "Epoch 140/550\n",
      "6/6 - 0s - loss: 0.2610 - accuracy: 0.9351 - val_loss: 10.0541 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 141/550\n",
      "6/6 - 0s - loss: 0.2355 - accuracy: 0.9405 - val_loss: 10.0111 - val_accuracy: 0.1702 - 340ms/epoch - 57ms/step\n",
      "Epoch 142/550\n",
      "6/6 - 0s - loss: 0.2690 - accuracy: 0.9297 - val_loss: 10.3436 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 143/550\n",
      "6/6 - 0s - loss: 0.2165 - accuracy: 0.9405 - val_loss: 10.2808 - val_accuracy: 0.1702 - 249ms/epoch - 41ms/step\n",
      "Epoch 144/550\n",
      "6/6 - 0s - loss: 0.2380 - accuracy: 0.9405 - val_loss: 10.0180 - val_accuracy: 0.1702 - 239ms/epoch - 40ms/step\n",
      "Epoch 145/550\n",
      "6/6 - 0s - loss: 0.1539 - accuracy: 0.9784 - val_loss: 10.0537 - val_accuracy: 0.1915 - 287ms/epoch - 48ms/step\n",
      "Epoch 146/550\n",
      "6/6 - 0s - loss: 0.1852 - accuracy: 0.9622 - val_loss: 9.9806 - val_accuracy: 0.1702 - 349ms/epoch - 58ms/step\n",
      "Epoch 147/550\n",
      "6/6 - 0s - loss: 0.1691 - accuracy: 0.9622 - val_loss: 9.7658 - val_accuracy: 0.1915 - 295ms/epoch - 49ms/step\n",
      "Epoch 148/550\n",
      "6/6 - 0s - loss: 0.2084 - accuracy: 0.9568 - val_loss: 10.5024 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 149/550\n",
      "6/6 - 0s - loss: 0.1497 - accuracy: 0.9568 - val_loss: 10.3748 - val_accuracy: 0.1915 - 296ms/epoch - 49ms/step\n",
      "Epoch 150/550\n",
      "6/6 - 0s - loss: 0.1639 - accuracy: 0.9730 - val_loss: 10.4939 - val_accuracy: 0.1915 - 244ms/epoch - 41ms/step\n",
      "Epoch 151/550\n",
      "6/6 - 0s - loss: 0.1957 - accuracy: 0.9514 - val_loss: 10.3095 - val_accuracy: 0.2128 - 242ms/epoch - 40ms/step\n",
      "Epoch 152/550\n",
      "6/6 - 0s - loss: 0.1709 - accuracy: 0.9784 - val_loss: 10.3996 - val_accuracy: 0.1702 - 236ms/epoch - 39ms/step\n",
      "Epoch 153/550\n",
      "6/6 - 0s - loss: 0.1890 - accuracy: 0.9676 - val_loss: 10.3849 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 154/550\n",
      "6/6 - 0s - loss: 0.1492 - accuracy: 0.9622 - val_loss: 10.1566 - val_accuracy: 0.2553 - 248ms/epoch - 41ms/step\n",
      "Epoch 155/550\n",
      "6/6 - 0s - loss: 0.1180 - accuracy: 0.9892 - val_loss: 10.3013 - val_accuracy: 0.2553 - 260ms/epoch - 43ms/step\n",
      "Epoch 156/550\n",
      "6/6 - 0s - loss: 0.1187 - accuracy: 0.9730 - val_loss: 10.5603 - val_accuracy: 0.2128 - 253ms/epoch - 42ms/step\n",
      "Epoch 157/550\n",
      "6/6 - 0s - loss: 0.1761 - accuracy: 0.9514 - val_loss: 10.3677 - val_accuracy: 0.1702 - 359ms/epoch - 60ms/step\n",
      "Epoch 158/550\n",
      "6/6 - 0s - loss: 0.1662 - accuracy: 0.9568 - val_loss: 10.3940 - val_accuracy: 0.1915 - 238ms/epoch - 40ms/step\n",
      "Epoch 159/550\n",
      "6/6 - 0s - loss: 0.1513 - accuracy: 0.9676 - val_loss: 10.4951 - val_accuracy: 0.1489 - 234ms/epoch - 39ms/step\n",
      "Epoch 160/550\n",
      "6/6 - 0s - loss: 0.1566 - accuracy: 0.9784 - val_loss: 10.3205 - val_accuracy: 0.1702 - 275ms/epoch - 46ms/step\n",
      "Epoch 161/550\n",
      "6/6 - 0s - loss: 0.1160 - accuracy: 0.9838 - val_loss: 10.4923 - val_accuracy: 0.1489 - 246ms/epoch - 41ms/step\n",
      "Epoch 162/550\n",
      "6/6 - 0s - loss: 0.1110 - accuracy: 0.9892 - val_loss: 10.7965 - val_accuracy: 0.1915 - 244ms/epoch - 41ms/step\n",
      "Epoch 163/550\n",
      "6/6 - 0s - loss: 0.1498 - accuracy: 0.9622 - val_loss: 10.7120 - val_accuracy: 0.1915 - 250ms/epoch - 42ms/step\n",
      "Epoch 164/550\n",
      "6/6 - 0s - loss: 0.1032 - accuracy: 0.9892 - val_loss: 10.6034 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 165/550\n",
      "6/6 - 0s - loss: 0.1110 - accuracy: 0.9838 - val_loss: 10.5739 - val_accuracy: 0.1702 - 258ms/epoch - 43ms/step\n",
      "Epoch 166/550\n",
      "6/6 - 0s - loss: 0.1277 - accuracy: 0.9784 - val_loss: 10.7225 - val_accuracy: 0.1915 - 274ms/epoch - 46ms/step\n",
      "Epoch 167/550\n",
      "6/6 - 0s - loss: 0.1461 - accuracy: 0.9730 - val_loss: 10.6345 - val_accuracy: 0.2128 - 277ms/epoch - 46ms/step\n",
      "Epoch 168/550\n",
      "6/6 - 0s - loss: 0.1145 - accuracy: 0.9730 - val_loss: 10.9854 - val_accuracy: 0.2340 - 303ms/epoch - 51ms/step\n",
      "Epoch 169/550\n",
      "6/6 - 0s - loss: 0.1193 - accuracy: 0.9784 - val_loss: 10.8791 - val_accuracy: 0.2128 - 242ms/epoch - 40ms/step\n",
      "Epoch 170/550\n",
      "6/6 - 0s - loss: 0.1027 - accuracy: 0.9892 - val_loss: 10.6826 - val_accuracy: 0.1915 - 249ms/epoch - 42ms/step\n",
      "Epoch 171/550\n",
      "6/6 - 0s - loss: 0.0846 - accuracy: 0.9784 - val_loss: 10.8247 - val_accuracy: 0.1702 - 247ms/epoch - 41ms/step\n",
      "Epoch 172/550\n",
      "6/6 - 0s - loss: 0.1553 - accuracy: 0.9514 - val_loss: 10.7274 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 173/550\n",
      "6/6 - 0s - loss: 0.0719 - accuracy: 0.9946 - val_loss: 10.7083 - val_accuracy: 0.1915 - 252ms/epoch - 42ms/step\n",
      "Epoch 174/550\n",
      "6/6 - 0s - loss: 0.1011 - accuracy: 0.9892 - val_loss: 10.7249 - val_accuracy: 0.2128 - 301ms/epoch - 50ms/step\n",
      "Epoch 175/550\n",
      "6/6 - 0s - loss: 0.0969 - accuracy: 0.9784 - val_loss: 10.8742 - val_accuracy: 0.1702 - 243ms/epoch - 40ms/step\n",
      "Epoch 176/550\n",
      "6/6 - 0s - loss: 0.1123 - accuracy: 0.9784 - val_loss: 10.9313 - val_accuracy: 0.1702 - 291ms/epoch - 48ms/step\n",
      "Epoch 177/550\n",
      "6/6 - 0s - loss: 0.0869 - accuracy: 0.9838 - val_loss: 10.9649 - val_accuracy: 0.1702 - 275ms/epoch - 46ms/step\n",
      "Epoch 178/550\n",
      "6/6 - 0s - loss: 0.0892 - accuracy: 0.9892 - val_loss: 11.0169 - val_accuracy: 0.1702 - 240ms/epoch - 40ms/step\n",
      "Epoch 179/550\n",
      "6/6 - 0s - loss: 0.0868 - accuracy: 0.9892 - val_loss: 11.0282 - val_accuracy: 0.2128 - 245ms/epoch - 41ms/step\n",
      "Epoch 180/550\n",
      "6/6 - 0s - loss: 0.1119 - accuracy: 0.9730 - val_loss: 11.0731 - val_accuracy: 0.2340 - 274ms/epoch - 46ms/step\n",
      "Epoch 181/550\n",
      "6/6 - 0s - loss: 0.0885 - accuracy: 0.9784 - val_loss: 10.6352 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 182/550\n",
      "6/6 - 0s - loss: 0.1219 - accuracy: 0.9730 - val_loss: 10.2766 - val_accuracy: 0.2128 - 307ms/epoch - 51ms/step\n",
      "Epoch 183/550\n",
      "6/6 - 0s - loss: 0.1171 - accuracy: 0.9676 - val_loss: 10.6594 - val_accuracy: 0.1702 - 244ms/epoch - 41ms/step\n",
      "Epoch 184/550\n",
      "6/6 - 0s - loss: 0.0885 - accuracy: 0.9892 - val_loss: 10.8560 - val_accuracy: 0.1489 - 338ms/epoch - 56ms/step\n",
      "Epoch 185/550\n",
      "6/6 - 0s - loss: 0.0705 - accuracy: 0.9946 - val_loss: 10.8513 - val_accuracy: 0.1915 - 260ms/epoch - 43ms/step\n",
      "Epoch 186/550\n",
      "6/6 - 0s - loss: 0.0900 - accuracy: 0.9730 - val_loss: 10.8171 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 187/550\n",
      "6/6 - 0s - loss: 0.0855 - accuracy: 0.9784 - val_loss: 10.8424 - val_accuracy: 0.1489 - 268ms/epoch - 45ms/step\n",
      "Epoch 188/550\n",
      "6/6 - 0s - loss: 0.0856 - accuracy: 0.9838 - val_loss: 11.1481 - val_accuracy: 0.1489 - 272ms/epoch - 45ms/step\n",
      "Epoch 189/550\n",
      "6/6 - 0s - loss: 0.0750 - accuracy: 0.9946 - val_loss: 11.1063 - val_accuracy: 0.1915 - 389ms/epoch - 65ms/step\n",
      "Epoch 190/550\n",
      "6/6 - 0s - loss: 0.0927 - accuracy: 0.9838 - val_loss: 11.0017 - val_accuracy: 0.1702 - 359ms/epoch - 60ms/step\n",
      "Epoch 191/550\n",
      "6/6 - 0s - loss: 0.0983 - accuracy: 0.9784 - val_loss: 10.8539 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 192/550\n",
      "6/6 - 0s - loss: 0.1116 - accuracy: 0.9838 - val_loss: 10.8768 - val_accuracy: 0.1702 - 252ms/epoch - 42ms/step\n",
      "Epoch 193/550\n",
      "6/6 - 0s - loss: 0.0943 - accuracy: 0.9892 - val_loss: 11.0610 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 194/550\n",
      "6/6 - 0s - loss: 0.0879 - accuracy: 0.9892 - val_loss: 11.4374 - val_accuracy: 0.1489 - 239ms/epoch - 40ms/step\n",
      "Epoch 195/550\n",
      "6/6 - 0s - loss: 0.1131 - accuracy: 0.9784 - val_loss: 11.2902 - val_accuracy: 0.1915 - 278ms/epoch - 46ms/step\n",
      "Epoch 196/550\n",
      "6/6 - 0s - loss: 0.0710 - accuracy: 0.9838 - val_loss: 11.4144 - val_accuracy: 0.1915 - 259ms/epoch - 43ms/step\n",
      "Epoch 197/550\n",
      "6/6 - 0s - loss: 0.0895 - accuracy: 0.9892 - val_loss: 11.5124 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 198/550\n",
      "6/6 - 0s - loss: 0.0621 - accuracy: 0.9946 - val_loss: 11.4081 - val_accuracy: 0.1702 - 329ms/epoch - 55ms/step\n",
      "Epoch 199/550\n",
      "6/6 - 0s - loss: 0.0650 - accuracy: 0.9946 - val_loss: 11.0075 - val_accuracy: 0.1702 - 234ms/epoch - 39ms/step\n",
      "Epoch 200/550\n",
      "6/6 - 0s - loss: 0.0814 - accuracy: 0.9838 - val_loss: 11.0550 - val_accuracy: 0.1702 - 249ms/epoch - 41ms/step\n",
      "Epoch 201/550\n",
      "6/6 - 0s - loss: 0.0712 - accuracy: 0.9784 - val_loss: 11.3380 - val_accuracy: 0.1702 - 244ms/epoch - 41ms/step\n",
      "Epoch 202/550\n",
      "6/6 - 0s - loss: 0.0879 - accuracy: 0.9784 - val_loss: 11.3445 - val_accuracy: 0.1702 - 237ms/epoch - 39ms/step\n",
      "Epoch 203/550\n",
      "6/6 - 0s - loss: 0.0878 - accuracy: 0.9784 - val_loss: 11.3440 - val_accuracy: 0.2128 - 234ms/epoch - 39ms/step\n",
      "Epoch 204/550\n",
      "6/6 - 0s - loss: 0.0804 - accuracy: 0.9946 - val_loss: 11.1086 - val_accuracy: 0.2128 - 249ms/epoch - 42ms/step\n",
      "Epoch 205/550\n",
      "6/6 - 0s - loss: 0.0929 - accuracy: 0.9730 - val_loss: 11.0167 - val_accuracy: 0.1489 - 244ms/epoch - 41ms/step\n",
      "Epoch 206/550\n",
      "6/6 - 0s - loss: 0.1133 - accuracy: 0.9676 - val_loss: 11.2421 - val_accuracy: 0.2128 - 292ms/epoch - 49ms/step\n",
      "Epoch 207/550\n",
      "6/6 - 0s - loss: 0.0887 - accuracy: 0.9730 - val_loss: 11.4454 - val_accuracy: 0.2340 - 239ms/epoch - 40ms/step\n",
      "Epoch 208/550\n",
      "6/6 - 0s - loss: 0.0544 - accuracy: 1.0000 - val_loss: 11.4731 - val_accuracy: 0.1702 - 291ms/epoch - 48ms/step\n",
      "Epoch 209/550\n",
      "6/6 - 0s - loss: 0.0702 - accuracy: 0.9892 - val_loss: 11.2756 - val_accuracy: 0.2128 - 290ms/epoch - 48ms/step\n",
      "Epoch 210/550\n",
      "6/6 - 0s - loss: 0.0887 - accuracy: 0.9838 - val_loss: 11.2328 - val_accuracy: 0.2340 - 244ms/epoch - 41ms/step\n",
      "Epoch 211/550\n",
      "6/6 - 0s - loss: 0.0809 - accuracy: 0.9838 - val_loss: 11.2120 - val_accuracy: 0.2340 - 237ms/epoch - 40ms/step\n",
      "Epoch 212/550\n",
      "6/6 - 0s - loss: 0.0738 - accuracy: 0.9892 - val_loss: 11.0600 - val_accuracy: 0.2128 - 253ms/epoch - 42ms/step\n",
      "Epoch 213/550\n",
      "6/6 - 0s - loss: 0.0630 - accuracy: 0.9946 - val_loss: 11.2569 - val_accuracy: 0.1915 - 249ms/epoch - 42ms/step\n",
      "Epoch 214/550\n",
      "6/6 - 0s - loss: 0.0820 - accuracy: 0.9838 - val_loss: 11.2261 - val_accuracy: 0.1915 - 344ms/epoch - 57ms/step\n",
      "Epoch 215/550\n",
      "6/6 - 0s - loss: 0.0850 - accuracy: 0.9892 - val_loss: 11.1030 - val_accuracy: 0.2128 - 245ms/epoch - 41ms/step\n",
      "Epoch 216/550\n",
      "6/6 - 0s - loss: 0.1214 - accuracy: 0.9784 - val_loss: 11.3763 - val_accuracy: 0.2128 - 249ms/epoch - 42ms/step\n",
      "Epoch 217/550\n",
      "6/6 - 0s - loss: 0.0969 - accuracy: 0.9730 - val_loss: 11.4330 - val_accuracy: 0.2128 - 255ms/epoch - 42ms/step\n",
      "Epoch 218/550\n",
      "6/6 - 0s - loss: 0.0731 - accuracy: 0.9838 - val_loss: 11.6287 - val_accuracy: 0.1702 - 264ms/epoch - 44ms/step\n",
      "Epoch 219/550\n",
      "6/6 - 0s - loss: 0.0945 - accuracy: 0.9730 - val_loss: 11.6534 - val_accuracy: 0.1702 - 251ms/epoch - 42ms/step\n",
      "Epoch 220/550\n",
      "6/6 - 0s - loss: 0.0803 - accuracy: 0.9784 - val_loss: 11.7339 - val_accuracy: 0.1702 - 254ms/epoch - 42ms/step\n",
      "Epoch 221/550\n",
      "6/6 - 0s - loss: 0.2106 - accuracy: 0.9459 - val_loss: 11.4063 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 222/550\n",
      "6/6 - 0s - loss: 0.1007 - accuracy: 0.9622 - val_loss: 11.1846 - val_accuracy: 0.2128 - 341ms/epoch - 57ms/step\n",
      "Epoch 223/550\n",
      "6/6 - 0s - loss: 0.1228 - accuracy: 0.9730 - val_loss: 10.8397 - val_accuracy: 0.2340 - 322ms/epoch - 54ms/step\n",
      "Epoch 224/550\n",
      "6/6 - 0s - loss: 0.0902 - accuracy: 0.9730 - val_loss: 11.4119 - val_accuracy: 0.1915 - 247ms/epoch - 41ms/step\n",
      "Epoch 225/550\n",
      "6/6 - 0s - loss: 0.0924 - accuracy: 0.9730 - val_loss: 10.5405 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 226/550\n",
      "6/6 - 0s - loss: 0.1121 - accuracy: 0.9784 - val_loss: 10.6532 - val_accuracy: 0.1915 - 243ms/epoch - 40ms/step\n",
      "Epoch 227/550\n",
      "6/6 - 0s - loss: 0.1004 - accuracy: 0.9784 - val_loss: 11.4586 - val_accuracy: 0.1489 - 245ms/epoch - 41ms/step\n",
      "Epoch 228/550\n",
      "6/6 - 0s - loss: 0.1427 - accuracy: 0.9514 - val_loss: 11.4117 - val_accuracy: 0.1915 - 292ms/epoch - 49ms/step\n",
      "Epoch 229/550\n",
      "6/6 - 0s - loss: 0.0878 - accuracy: 0.9784 - val_loss: 11.5385 - val_accuracy: 0.1915 - 263ms/epoch - 44ms/step\n",
      "Epoch 230/550\n",
      "6/6 - 0s - loss: 0.1202 - accuracy: 0.9730 - val_loss: 10.8435 - val_accuracy: 0.1702 - 366ms/epoch - 61ms/step\n",
      "Epoch 231/550\n",
      "6/6 - 0s - loss: 0.0815 - accuracy: 0.9946 - val_loss: 11.0751 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 232/550\n",
      "6/6 - 0s - loss: 0.0742 - accuracy: 0.9946 - val_loss: 11.5062 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 233/550\n",
      "6/6 - 0s - loss: 0.0931 - accuracy: 0.9676 - val_loss: 11.6208 - val_accuracy: 0.2128 - 313ms/epoch - 52ms/step\n",
      "Epoch 234/550\n",
      "6/6 - 0s - loss: 0.1067 - accuracy: 0.9676 - val_loss: 11.1996 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 235/550\n",
      "6/6 - 0s - loss: 0.0825 - accuracy: 0.9838 - val_loss: 11.3638 - val_accuracy: 0.2128 - 257ms/epoch - 43ms/step\n",
      "Epoch 236/550\n",
      "6/6 - 0s - loss: 0.0674 - accuracy: 0.9838 - val_loss: 11.9106 - val_accuracy: 0.2340 - 280ms/epoch - 47ms/step\n",
      "Epoch 237/550\n",
      "6/6 - 0s - loss: 0.0782 - accuracy: 0.9892 - val_loss: 11.9366 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 238/550\n",
      "6/6 - 0s - loss: 0.0721 - accuracy: 0.9892 - val_loss: 11.6981 - val_accuracy: 0.2128 - 299ms/epoch - 50ms/step\n",
      "Epoch 239/550\n",
      "6/6 - 0s - loss: 0.0563 - accuracy: 0.9838 - val_loss: 11.5912 - val_accuracy: 0.2340 - 241ms/epoch - 40ms/step\n",
      "Epoch 240/550\n",
      "6/6 - 0s - loss: 0.0808 - accuracy: 0.9838 - val_loss: 11.6185 - val_accuracy: 0.2553 - 247ms/epoch - 41ms/step\n",
      "Epoch 241/550\n",
      "6/6 - 0s - loss: 0.0792 - accuracy: 0.9784 - val_loss: 11.5111 - val_accuracy: 0.2553 - 242ms/epoch - 40ms/step\n",
      "Epoch 242/550\n",
      "6/6 - 0s - loss: 0.0470 - accuracy: 0.9946 - val_loss: 11.4427 - val_accuracy: 0.2340 - 259ms/epoch - 43ms/step\n",
      "Epoch 243/550\n",
      "6/6 - 0s - loss: 0.0775 - accuracy: 0.9838 - val_loss: 11.6780 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 244/550\n",
      "6/6 - 0s - loss: 0.0380 - accuracy: 0.9946 - val_loss: 12.0594 - val_accuracy: 0.1915 - 237ms/epoch - 39ms/step\n",
      "Epoch 245/550\n",
      "6/6 - 0s - loss: 0.0702 - accuracy: 0.9838 - val_loss: 12.1532 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 246/550\n",
      "6/6 - 0s - loss: 0.0396 - accuracy: 1.0000 - val_loss: 12.0284 - val_accuracy: 0.1702 - 262ms/epoch - 44ms/step\n",
      "Epoch 247/550\n",
      "6/6 - 0s - loss: 0.0374 - accuracy: 0.9946 - val_loss: 11.8429 - val_accuracy: 0.1702 - 306ms/epoch - 51ms/step\n",
      "Epoch 248/550\n",
      "6/6 - 0s - loss: 0.0586 - accuracy: 0.9892 - val_loss: 11.5208 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 249/550\n",
      "6/6 - 0s - loss: 0.0412 - accuracy: 0.9946 - val_loss: 11.5763 - val_accuracy: 0.2128 - 273ms/epoch - 45ms/step\n",
      "Epoch 250/550\n",
      "6/6 - 0s - loss: 0.0382 - accuracy: 1.0000 - val_loss: 11.7226 - val_accuracy: 0.1915 - 264ms/epoch - 44ms/step\n",
      "Epoch 251/550\n",
      "6/6 - 0s - loss: 0.0452 - accuracy: 0.9838 - val_loss: 12.0252 - val_accuracy: 0.2128 - 282ms/epoch - 47ms/step\n",
      "Epoch 252/550\n",
      "6/6 - 0s - loss: 0.0517 - accuracy: 0.9892 - val_loss: 11.9359 - val_accuracy: 0.2340 - 303ms/epoch - 51ms/step\n",
      "Epoch 253/550\n",
      "6/6 - 0s - loss: 0.0642 - accuracy: 0.9892 - val_loss: 11.7388 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 254/550\n",
      "6/6 - 0s - loss: 0.0509 - accuracy: 0.9892 - val_loss: 11.7405 - val_accuracy: 0.1915 - 292ms/epoch - 49ms/step\n",
      "Epoch 255/550\n",
      "6/6 - 0s - loss: 0.0426 - accuracy: 0.9946 - val_loss: 11.9539 - val_accuracy: 0.1915 - 294ms/epoch - 49ms/step\n",
      "Epoch 256/550\n",
      "6/6 - 0s - loss: 0.0422 - accuracy: 0.9838 - val_loss: 12.2264 - val_accuracy: 0.1702 - 245ms/epoch - 41ms/step\n",
      "Epoch 257/550\n",
      "6/6 - 0s - loss: 0.0520 - accuracy: 0.9946 - val_loss: 12.3589 - val_accuracy: 0.1915 - 257ms/epoch - 43ms/step\n",
      "Epoch 258/550\n",
      "6/6 - 0s - loss: 0.0437 - accuracy: 0.9946 - val_loss: 12.3398 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 259/550\n",
      "6/6 - 0s - loss: 0.0436 - accuracy: 0.9946 - val_loss: 12.2626 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 260/550\n",
      "6/6 - 0s - loss: 0.0480 - accuracy: 0.9892 - val_loss: 12.2017 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 261/550\n",
      "6/6 - 0s - loss: 0.0370 - accuracy: 0.9946 - val_loss: 12.1306 - val_accuracy: 0.2128 - 364ms/epoch - 61ms/step\n",
      "Epoch 262/550\n",
      "6/6 - 0s - loss: 0.0298 - accuracy: 1.0000 - val_loss: 12.0139 - val_accuracy: 0.2128 - 291ms/epoch - 48ms/step\n",
      "Epoch 263/550\n",
      "6/6 - 0s - loss: 0.0376 - accuracy: 0.9946 - val_loss: 11.9975 - val_accuracy: 0.1915 - 250ms/epoch - 42ms/step\n",
      "Epoch 264/550\n",
      "6/6 - 0s - loss: 0.0280 - accuracy: 1.0000 - val_loss: 12.0793 - val_accuracy: 0.1915 - 240ms/epoch - 40ms/step\n",
      "Epoch 265/550\n",
      "6/6 - 0s - loss: 0.0288 - accuracy: 1.0000 - val_loss: 12.1584 - val_accuracy: 0.1915 - 252ms/epoch - 42ms/step\n",
      "Epoch 266/550\n",
      "6/6 - 0s - loss: 0.0414 - accuracy: 0.9892 - val_loss: 12.2093 - val_accuracy: 0.1915 - 247ms/epoch - 41ms/step\n",
      "Epoch 267/550\n",
      "6/6 - 0s - loss: 0.0336 - accuracy: 0.9946 - val_loss: 12.2649 - val_accuracy: 0.1915 - 243ms/epoch - 41ms/step\n",
      "Epoch 268/550\n",
      "6/6 - 0s - loss: 0.0400 - accuracy: 1.0000 - val_loss: 12.2324 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 269/550\n",
      "6/6 - 0s - loss: 0.0251 - accuracy: 1.0000 - val_loss: 12.2447 - val_accuracy: 0.1915 - 266ms/epoch - 44ms/step\n",
      "Epoch 270/550\n",
      "6/6 - 0s - loss: 0.0351 - accuracy: 0.9946 - val_loss: 12.2306 - val_accuracy: 0.1915 - 328ms/epoch - 55ms/step\n",
      "Epoch 271/550\n",
      "6/6 - 0s - loss: 0.0248 - accuracy: 0.9946 - val_loss: 12.2815 - val_accuracy: 0.1915 - 349ms/epoch - 58ms/step\n",
      "Epoch 272/550\n",
      "6/6 - 0s - loss: 0.0388 - accuracy: 0.9946 - val_loss: 12.3582 - val_accuracy: 0.1915 - 256ms/epoch - 43ms/step\n",
      "Epoch 273/550\n",
      "6/6 - 0s - loss: 0.0448 - accuracy: 0.9892 - val_loss: 12.2410 - val_accuracy: 0.1915 - 242ms/epoch - 40ms/step\n",
      "Epoch 274/550\n",
      "6/6 - 0s - loss: 0.0279 - accuracy: 1.0000 - val_loss: 12.2565 - val_accuracy: 0.1702 - 280ms/epoch - 47ms/step\n",
      "Epoch 275/550\n",
      "6/6 - 0s - loss: 0.0269 - accuracy: 1.0000 - val_loss: 12.2580 - val_accuracy: 0.1702 - 244ms/epoch - 41ms/step\n",
      "Epoch 276/550\n",
      "6/6 - 0s - loss: 0.0375 - accuracy: 0.9946 - val_loss: 12.0830 - val_accuracy: 0.1915 - 257ms/epoch - 43ms/step\n",
      "Epoch 277/550\n",
      "6/6 - 0s - loss: 0.0349 - accuracy: 0.9946 - val_loss: 12.1697 - val_accuracy: 0.2340 - 237ms/epoch - 39ms/step\n",
      "Epoch 278/550\n",
      "6/6 - 0s - loss: 0.0732 - accuracy: 0.9730 - val_loss: 11.9327 - val_accuracy: 0.1915 - 291ms/epoch - 48ms/step\n",
      "Epoch 279/550\n",
      "6/6 - 0s - loss: 0.0442 - accuracy: 0.9946 - val_loss: 12.1002 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 280/550\n",
      "6/6 - 0s - loss: 0.0341 - accuracy: 0.9892 - val_loss: 12.1853 - val_accuracy: 0.2340 - 254ms/epoch - 42ms/step\n",
      "Epoch 281/550\n",
      "6/6 - 0s - loss: 0.0488 - accuracy: 0.9838 - val_loss: 11.8549 - val_accuracy: 0.2128 - 245ms/epoch - 41ms/step\n",
      "Epoch 282/550\n",
      "6/6 - 0s - loss: 0.0401 - accuracy: 0.9946 - val_loss: 11.9181 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 283/550\n",
      "6/6 - 0s - loss: 0.0348 - accuracy: 0.9946 - val_loss: 12.0367 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 284/550\n",
      "6/6 - 0s - loss: 0.0237 - accuracy: 1.0000 - val_loss: 12.1156 - val_accuracy: 0.2128 - 259ms/epoch - 43ms/step\n",
      "Epoch 285/550\n",
      "6/6 - 0s - loss: 0.0357 - accuracy: 1.0000 - val_loss: 12.3044 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 286/550\n",
      "6/6 - 0s - loss: 0.0606 - accuracy: 0.9784 - val_loss: 12.6644 - val_accuracy: 0.1702 - 254ms/epoch - 42ms/step\n",
      "Epoch 287/550\n",
      "6/6 - 0s - loss: 0.2170 - accuracy: 0.9351 - val_loss: 12.0703 - val_accuracy: 0.2128 - 293ms/epoch - 49ms/step\n",
      "Epoch 288/550\n",
      "6/6 - 0s - loss: 0.1394 - accuracy: 0.9514 - val_loss: 10.7115 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 289/550\n",
      "6/6 - 0s - loss: 0.1999 - accuracy: 0.9459 - val_loss: 10.9445 - val_accuracy: 0.1915 - 247ms/epoch - 41ms/step\n",
      "Epoch 290/550\n",
      "6/6 - 0s - loss: 0.1768 - accuracy: 0.9405 - val_loss: 11.6644 - val_accuracy: 0.2340 - 269ms/epoch - 45ms/step\n",
      "Epoch 291/550\n",
      "6/6 - 0s - loss: 0.1386 - accuracy: 0.9676 - val_loss: 10.9616 - val_accuracy: 0.2128 - 307ms/epoch - 51ms/step\n",
      "Epoch 292/550\n",
      "6/6 - 0s - loss: 0.1211 - accuracy: 0.9622 - val_loss: 11.1780 - val_accuracy: 0.1915 - 300ms/epoch - 50ms/step\n",
      "Epoch 293/550\n",
      "6/6 - 0s - loss: 0.0887 - accuracy: 0.9784 - val_loss: 11.6065 - val_accuracy: 0.2128 - 265ms/epoch - 44ms/step\n",
      "Epoch 294/550\n",
      "6/6 - 0s - loss: 0.0925 - accuracy: 0.9892 - val_loss: 11.9443 - val_accuracy: 0.2128 - 308ms/epoch - 51ms/step\n",
      "Epoch 295/550\n",
      "6/6 - 0s - loss: 0.0848 - accuracy: 0.9838 - val_loss: 11.7339 - val_accuracy: 0.1702 - 257ms/epoch - 43ms/step\n",
      "Epoch 296/550\n",
      "6/6 - 0s - loss: 0.0577 - accuracy: 0.9892 - val_loss: 11.9179 - val_accuracy: 0.1915 - 289ms/epoch - 48ms/step\n",
      "Epoch 297/550\n",
      "6/6 - 0s - loss: 0.0557 - accuracy: 0.9892 - val_loss: 11.9920 - val_accuracy: 0.1915 - 247ms/epoch - 41ms/step\n",
      "Epoch 298/550\n",
      "6/6 - 0s - loss: 0.0674 - accuracy: 0.9784 - val_loss: 11.8952 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 299/550\n",
      "6/6 - 0s - loss: 0.0556 - accuracy: 0.9838 - val_loss: 11.9933 - val_accuracy: 0.1702 - 368ms/epoch - 61ms/step\n",
      "Epoch 300/550\n",
      "6/6 - 0s - loss: 0.0449 - accuracy: 1.0000 - val_loss: 12.2319 - val_accuracy: 0.1702 - 243ms/epoch - 40ms/step\n",
      "Epoch 301/550\n",
      "6/6 - 0s - loss: 0.0502 - accuracy: 0.9838 - val_loss: 12.4585 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 302/550\n",
      "6/6 - 0s - loss: 0.0510 - accuracy: 0.9838 - val_loss: 12.5529 - val_accuracy: 0.1702 - 307ms/epoch - 51ms/step\n",
      "Epoch 303/550\n",
      "6/6 - 0s - loss: 0.0268 - accuracy: 1.0000 - val_loss: 12.4968 - val_accuracy: 0.1915 - 255ms/epoch - 42ms/step\n",
      "Epoch 304/550\n",
      "6/6 - 0s - loss: 0.0340 - accuracy: 1.0000 - val_loss: 12.4537 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 305/550\n",
      "6/6 - 0s - loss: 0.0185 - accuracy: 1.0000 - val_loss: 12.4440 - val_accuracy: 0.2128 - 239ms/epoch - 40ms/step\n",
      "Epoch 306/550\n",
      "6/6 - 0s - loss: 0.0319 - accuracy: 0.9946 - val_loss: 12.4653 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 307/550\n",
      "6/6 - 0s - loss: 0.0254 - accuracy: 1.0000 - val_loss: 12.4947 - val_accuracy: 0.2128 - 256ms/epoch - 43ms/step\n",
      "Epoch 308/550\n",
      "6/6 - 0s - loss: 0.0435 - accuracy: 0.9946 - val_loss: 12.4982 - val_accuracy: 0.2128 - 253ms/epoch - 42ms/step\n",
      "Epoch 309/550\n",
      "6/6 - 0s - loss: 0.0266 - accuracy: 0.9946 - val_loss: 12.5584 - val_accuracy: 0.2128 - 236ms/epoch - 39ms/step\n",
      "Epoch 310/550\n",
      "6/6 - 0s - loss: 0.0332 - accuracy: 0.9946 - val_loss: 12.5470 - val_accuracy: 0.1915 - 391ms/epoch - 65ms/step\n",
      "Epoch 311/550\n",
      "6/6 - 0s - loss: 0.0385 - accuracy: 0.9946 - val_loss: 12.5564 - val_accuracy: 0.1915 - 262ms/epoch - 44ms/step\n",
      "Epoch 312/550\n",
      "6/6 - 0s - loss: 0.0470 - accuracy: 0.9892 - val_loss: 12.1843 - val_accuracy: 0.1915 - 297ms/epoch - 50ms/step\n",
      "Epoch 313/550\n",
      "6/6 - 0s - loss: 0.0407 - accuracy: 0.9946 - val_loss: 11.9053 - val_accuracy: 0.1915 - 249ms/epoch - 41ms/step\n",
      "Epoch 314/550\n",
      "6/6 - 0s - loss: 0.0589 - accuracy: 0.9730 - val_loss: 11.9827 - val_accuracy: 0.2128 - 261ms/epoch - 44ms/step\n",
      "Epoch 315/550\n",
      "6/6 - 0s - loss: 0.0628 - accuracy: 0.9892 - val_loss: 12.2855 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 316/550\n",
      "6/6 - 0s - loss: 0.0299 - accuracy: 0.9892 - val_loss: 12.4004 - val_accuracy: 0.1915 - 243ms/epoch - 41ms/step\n",
      "Epoch 317/550\n",
      "6/6 - 0s - loss: 0.0366 - accuracy: 0.9892 - val_loss: 12.4615 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 318/550\n",
      "6/6 - 0s - loss: 0.0261 - accuracy: 1.0000 - val_loss: 12.4648 - val_accuracy: 0.2128 - 306ms/epoch - 51ms/step\n",
      "Epoch 319/550\n",
      "6/6 - 0s - loss: 0.0365 - accuracy: 0.9946 - val_loss: 12.4865 - val_accuracy: 0.2340 - 245ms/epoch - 41ms/step\n",
      "Epoch 320/550\n",
      "6/6 - 0s - loss: 0.0241 - accuracy: 1.0000 - val_loss: 12.4258 - val_accuracy: 0.2128 - 236ms/epoch - 39ms/step\n",
      "Epoch 321/550\n",
      "6/6 - 0s - loss: 0.0271 - accuracy: 0.9946 - val_loss: 12.2031 - val_accuracy: 0.1915 - 254ms/epoch - 42ms/step\n",
      "Epoch 322/550\n",
      "6/6 - 0s - loss: 0.0380 - accuracy: 0.9946 - val_loss: 12.1308 - val_accuracy: 0.1915 - 257ms/epoch - 43ms/step\n",
      "Epoch 323/550\n",
      "6/6 - 0s - loss: 0.0258 - accuracy: 1.0000 - val_loss: 12.2747 - val_accuracy: 0.1915 - 249ms/epoch - 41ms/step\n",
      "Epoch 324/550\n",
      "6/6 - 0s - loss: 0.0243 - accuracy: 1.0000 - val_loss: 12.5410 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 325/550\n",
      "6/6 - 0s - loss: 0.0278 - accuracy: 0.9946 - val_loss: 12.5996 - val_accuracy: 0.1915 - 251ms/epoch - 42ms/step\n",
      "Epoch 326/550\n",
      "6/6 - 0s - loss: 0.0288 - accuracy: 0.9946 - val_loss: 12.5993 - val_accuracy: 0.2128 - 256ms/epoch - 43ms/step\n",
      "Epoch 327/550\n",
      "6/6 - 0s - loss: 0.0227 - accuracy: 1.0000 - val_loss: 12.4676 - val_accuracy: 0.2128 - 283ms/epoch - 47ms/step\n",
      "Epoch 328/550\n",
      "6/6 - 0s - loss: 0.0277 - accuracy: 0.9946 - val_loss: 12.4326 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 329/550\n",
      "6/6 - 0s - loss: 0.0309 - accuracy: 0.9946 - val_loss: 12.3323 - val_accuracy: 0.1915 - 311ms/epoch - 52ms/step\n",
      "Epoch 330/550\n",
      "6/6 - 0s - loss: 0.0233 - accuracy: 1.0000 - val_loss: 12.3260 - val_accuracy: 0.1915 - 249ms/epoch - 41ms/step\n",
      "Epoch 331/550\n",
      "6/6 - 0s - loss: 0.0453 - accuracy: 0.9892 - val_loss: 12.2978 - val_accuracy: 0.1915 - 303ms/epoch - 50ms/step\n",
      "Epoch 332/550\n",
      "6/6 - 0s - loss: 0.0218 - accuracy: 1.0000 - val_loss: 12.1907 - val_accuracy: 0.1915 - 332ms/epoch - 55ms/step\n",
      "Epoch 333/550\n",
      "6/6 - 0s - loss: 0.0347 - accuracy: 0.9946 - val_loss: 12.3775 - val_accuracy: 0.2128 - 271ms/epoch - 45ms/step\n",
      "Epoch 334/550\n",
      "6/6 - 0s - loss: 0.0257 - accuracy: 1.0000 - val_loss: 12.4310 - val_accuracy: 0.2128 - 302ms/epoch - 50ms/step\n",
      "Epoch 335/550\n",
      "6/6 - 0s - loss: 0.0211 - accuracy: 1.0000 - val_loss: 12.3719 - val_accuracy: 0.1915 - 243ms/epoch - 41ms/step\n",
      "Epoch 336/550\n",
      "6/6 - 0s - loss: 0.0319 - accuracy: 1.0000 - val_loss: 12.2791 - val_accuracy: 0.2128 - 247ms/epoch - 41ms/step\n",
      "Epoch 337/550\n",
      "6/6 - 0s - loss: 0.0192 - accuracy: 1.0000 - val_loss: 12.2739 - val_accuracy: 0.1915 - 365ms/epoch - 61ms/step\n",
      "Epoch 338/550\n",
      "6/6 - 0s - loss: 0.0254 - accuracy: 0.9946 - val_loss: 12.3026 - val_accuracy: 0.1915 - 243ms/epoch - 40ms/step\n",
      "Epoch 339/550\n",
      "6/6 - 0s - loss: 0.0187 - accuracy: 1.0000 - val_loss: 12.2376 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 340/550\n",
      "6/6 - 0s - loss: 0.0187 - accuracy: 1.0000 - val_loss: 12.2018 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 341/550\n",
      "6/6 - 0s - loss: 0.0235 - accuracy: 1.0000 - val_loss: 12.3659 - val_accuracy: 0.1915 - 258ms/epoch - 43ms/step\n",
      "Epoch 342/550\n",
      "6/6 - 0s - loss: 0.0272 - accuracy: 0.9892 - val_loss: 12.6767 - val_accuracy: 0.2128 - 312ms/epoch - 52ms/step\n",
      "Epoch 343/550\n",
      "6/6 - 0s - loss: 0.0164 - accuracy: 1.0000 - val_loss: 12.9500 - val_accuracy: 0.2340 - 244ms/epoch - 41ms/step\n",
      "Epoch 344/550\n",
      "6/6 - 0s - loss: 0.0166 - accuracy: 1.0000 - val_loss: 13.0553 - val_accuracy: 0.2340 - 237ms/epoch - 39ms/step\n",
      "Epoch 345/550\n",
      "6/6 - 0s - loss: 0.0228 - accuracy: 0.9946 - val_loss: 13.0273 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 346/550\n",
      "6/6 - 0s - loss: 0.0300 - accuracy: 0.9892 - val_loss: 12.8115 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 347/550\n",
      "6/6 - 0s - loss: 0.0201 - accuracy: 0.9946 - val_loss: 12.5206 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 348/550\n",
      "6/6 - 0s - loss: 0.0326 - accuracy: 0.9946 - val_loss: 12.6060 - val_accuracy: 0.1915 - 296ms/epoch - 49ms/step\n",
      "Epoch 349/550\n",
      "6/6 - 0s - loss: 0.0165 - accuracy: 1.0000 - val_loss: 12.7475 - val_accuracy: 0.2340 - 248ms/epoch - 41ms/step\n",
      "Epoch 350/550\n",
      "6/6 - 0s - loss: 0.0303 - accuracy: 0.9892 - val_loss: 12.5929 - val_accuracy: 0.2128 - 328ms/epoch - 55ms/step\n",
      "Epoch 351/550\n",
      "6/6 - 0s - loss: 0.0564 - accuracy: 0.9946 - val_loss: 12.3967 - val_accuracy: 0.1702 - 280ms/epoch - 47ms/step\n",
      "Epoch 352/550\n",
      "6/6 - 0s - loss: 0.0403 - accuracy: 0.9838 - val_loss: 12.5878 - val_accuracy: 0.1915 - 280ms/epoch - 47ms/step\n",
      "Epoch 353/550\n",
      "6/6 - 0s - loss: 0.0271 - accuracy: 0.9946 - val_loss: 12.4599 - val_accuracy: 0.2340 - 283ms/epoch - 47ms/step\n",
      "Epoch 354/550\n",
      "6/6 - 0s - loss: 0.0375 - accuracy: 0.9892 - val_loss: 12.5467 - val_accuracy: 0.1702 - 245ms/epoch - 41ms/step\n",
      "Epoch 355/550\n",
      "6/6 - 0s - loss: 0.0221 - accuracy: 1.0000 - val_loss: 12.7908 - val_accuracy: 0.1915 - 256ms/epoch - 43ms/step\n",
      "Epoch 356/550\n",
      "6/6 - 0s - loss: 0.0395 - accuracy: 0.9892 - val_loss: 12.5763 - val_accuracy: 0.1702 - 247ms/epoch - 41ms/step\n",
      "Epoch 357/550\n",
      "6/6 - 0s - loss: 0.0298 - accuracy: 0.9892 - val_loss: 12.4816 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 358/550\n",
      "6/6 - 0s - loss: 0.0189 - accuracy: 1.0000 - val_loss: 12.5733 - val_accuracy: 0.2128 - 296ms/epoch - 49ms/step\n",
      "Epoch 359/550\n",
      "6/6 - 0s - loss: 0.0167 - accuracy: 1.0000 - val_loss: 12.9331 - val_accuracy: 0.2340 - 243ms/epoch - 41ms/step\n",
      "Epoch 360/550\n",
      "6/6 - 0s - loss: 0.0143 - accuracy: 1.0000 - val_loss: 13.0115 - val_accuracy: 0.2340 - 242ms/epoch - 40ms/step\n",
      "Epoch 361/550\n",
      "6/6 - 0s - loss: 0.0100 - accuracy: 1.0000 - val_loss: 12.9262 - val_accuracy: 0.2340 - 243ms/epoch - 40ms/step\n",
      "Epoch 362/550\n",
      "6/6 - 0s - loss: 0.0151 - accuracy: 1.0000 - val_loss: 12.8294 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 363/550\n",
      "6/6 - 0s - loss: 0.0282 - accuracy: 0.9946 - val_loss: 12.9159 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 364/550\n",
      "6/6 - 0s - loss: 0.0161 - accuracy: 1.0000 - val_loss: 13.1878 - val_accuracy: 0.1915 - 253ms/epoch - 42ms/step\n",
      "Epoch 365/550\n",
      "6/6 - 0s - loss: 0.0248 - accuracy: 1.0000 - val_loss: 13.1090 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 366/550\n",
      "6/6 - 0s - loss: 0.0292 - accuracy: 0.9946 - val_loss: 13.1784 - val_accuracy: 0.2128 - 279ms/epoch - 46ms/step\n",
      "Epoch 367/550\n",
      "6/6 - 0s - loss: 0.0296 - accuracy: 1.0000 - val_loss: 12.9943 - val_accuracy: 0.2128 - 316ms/epoch - 53ms/step\n",
      "Epoch 368/550\n",
      "6/6 - 0s - loss: 0.0268 - accuracy: 0.9892 - val_loss: 12.8828 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 369/550\n",
      "6/6 - 0s - loss: 0.0180 - accuracy: 1.0000 - val_loss: 12.8127 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 370/550\n",
      "6/6 - 0s - loss: 0.0535 - accuracy: 0.9892 - val_loss: 12.7367 - val_accuracy: 0.2128 - 296ms/epoch - 49ms/step\n",
      "Epoch 371/550\n",
      "6/6 - 0s - loss: 0.0473 - accuracy: 0.9838 - val_loss: 12.3008 - val_accuracy: 0.1915 - 239ms/epoch - 40ms/step\n",
      "Epoch 372/550\n",
      "6/6 - 0s - loss: 0.0307 - accuracy: 0.9946 - val_loss: 12.2508 - val_accuracy: 0.1915 - 279ms/epoch - 47ms/step\n",
      "Epoch 373/550\n",
      "6/6 - 0s - loss: 0.0990 - accuracy: 0.9784 - val_loss: 12.7265 - val_accuracy: 0.1702 - 259ms/epoch - 43ms/step\n",
      "Epoch 374/550\n",
      "6/6 - 0s - loss: 0.0261 - accuracy: 0.9946 - val_loss: 13.7216 - val_accuracy: 0.1489 - 343ms/epoch - 57ms/step\n",
      "Epoch 375/550\n",
      "6/6 - 0s - loss: 0.0688 - accuracy: 0.9730 - val_loss: 13.4719 - val_accuracy: 0.1489 - 338ms/epoch - 56ms/step\n",
      "Epoch 376/550\n",
      "6/6 - 0s - loss: 0.0467 - accuracy: 0.9838 - val_loss: 12.6931 - val_accuracy: 0.1702 - 280ms/epoch - 47ms/step\n",
      "Epoch 377/550\n",
      "6/6 - 0s - loss: 0.0436 - accuracy: 0.9838 - val_loss: 12.4271 - val_accuracy: 0.1915 - 233ms/epoch - 39ms/step\n",
      "Epoch 378/550\n",
      "6/6 - 0s - loss: 0.0506 - accuracy: 0.9838 - val_loss: 12.4671 - val_accuracy: 0.2128 - 232ms/epoch - 39ms/step\n",
      "Epoch 379/550\n",
      "6/6 - 0s - loss: 0.0523 - accuracy: 0.9892 - val_loss: 12.2692 - val_accuracy: 0.1702 - 253ms/epoch - 42ms/step\n",
      "Epoch 380/550\n",
      "6/6 - 0s - loss: 0.0379 - accuracy: 0.9892 - val_loss: 12.7457 - val_accuracy: 0.1915 - 244ms/epoch - 41ms/step\n",
      "Epoch 381/550\n",
      "6/6 - 0s - loss: 0.0302 - accuracy: 0.9946 - val_loss: 13.1485 - val_accuracy: 0.1915 - 238ms/epoch - 40ms/step\n",
      "Epoch 382/550\n",
      "6/6 - 0s - loss: 0.0199 - accuracy: 1.0000 - val_loss: 13.3672 - val_accuracy: 0.1915 - 295ms/epoch - 49ms/step\n",
      "Epoch 383/550\n",
      "6/6 - 0s - loss: 0.0277 - accuracy: 0.9946 - val_loss: 13.4415 - val_accuracy: 0.1915 - 245ms/epoch - 41ms/step\n",
      "Epoch 384/550\n",
      "6/6 - 0s - loss: 0.0235 - accuracy: 0.9946 - val_loss: 13.2644 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 385/550\n",
      "6/6 - 0s - loss: 0.0208 - accuracy: 1.0000 - val_loss: 13.0547 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 386/550\n",
      "6/6 - 0s - loss: 0.0289 - accuracy: 0.9892 - val_loss: 13.0334 - val_accuracy: 0.1702 - 267ms/epoch - 45ms/step\n",
      "Epoch 387/550\n",
      "6/6 - 0s - loss: 0.0129 - accuracy: 1.0000 - val_loss: 13.1219 - val_accuracy: 0.2128 - 290ms/epoch - 48ms/step\n",
      "Epoch 388/550\n",
      "6/6 - 0s - loss: 0.0130 - accuracy: 1.0000 - val_loss: 13.1288 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 389/550\n",
      "6/6 - 0s - loss: 0.0283 - accuracy: 0.9946 - val_loss: 13.0104 - val_accuracy: 0.2128 - 282ms/epoch - 47ms/step\n",
      "Epoch 390/550\n",
      "6/6 - 0s - loss: 0.0148 - accuracy: 1.0000 - val_loss: 12.9590 - val_accuracy: 0.2128 - 286ms/epoch - 48ms/step\n",
      "Epoch 391/550\n",
      "6/6 - 0s - loss: 0.0185 - accuracy: 1.0000 - val_loss: 13.0175 - val_accuracy: 0.2340 - 250ms/epoch - 42ms/step\n",
      "Epoch 392/550\n",
      "6/6 - 0s - loss: 0.0161 - accuracy: 1.0000 - val_loss: 13.0610 - val_accuracy: 0.2128 - 250ms/epoch - 42ms/step\n",
      "Epoch 393/550\n",
      "6/6 - 0s - loss: 0.0203 - accuracy: 0.9946 - val_loss: 13.0997 - val_accuracy: 0.1915 - 287ms/epoch - 48ms/step\n",
      "Epoch 394/550\n",
      "6/6 - 0s - loss: 0.0170 - accuracy: 1.0000 - val_loss: 13.2015 - val_accuracy: 0.1702 - 285ms/epoch - 48ms/step\n",
      "Epoch 395/550\n",
      "6/6 - 0s - loss: 0.0232 - accuracy: 0.9946 - val_loss: 13.0809 - val_accuracy: 0.1915 - 235ms/epoch - 39ms/step\n",
      "Epoch 396/550\n",
      "6/6 - 0s - loss: 0.0216 - accuracy: 0.9946 - val_loss: 13.0166 - val_accuracy: 0.1915 - 246ms/epoch - 41ms/step\n",
      "Epoch 397/550\n",
      "6/6 - 0s - loss: 0.0193 - accuracy: 1.0000 - val_loss: 12.9386 - val_accuracy: 0.1702 - 242ms/epoch - 40ms/step\n",
      "Epoch 398/550\n",
      "6/6 - 0s - loss: 0.0268 - accuracy: 0.9892 - val_loss: 12.9480 - val_accuracy: 0.2128 - 304ms/epoch - 51ms/step\n",
      "Epoch 399/550\n",
      "6/6 - 0s - loss: 0.0241 - accuracy: 0.9946 - val_loss: 13.2629 - val_accuracy: 0.1915 - 244ms/epoch - 41ms/step\n",
      "Epoch 400/550\n",
      "6/6 - 0s - loss: 0.0583 - accuracy: 0.9946 - val_loss: 13.2087 - val_accuracy: 0.2128 - 252ms/epoch - 42ms/step\n",
      "Epoch 401/550\n",
      "6/6 - 0s - loss: 0.0335 - accuracy: 0.9838 - val_loss: 13.1242 - val_accuracy: 0.1702 - 241ms/epoch - 40ms/step\n",
      "Epoch 402/550\n",
      "6/6 - 0s - loss: 0.0613 - accuracy: 0.9838 - val_loss: 13.0765 - val_accuracy: 0.1915 - 235ms/epoch - 39ms/step\n",
      "Epoch 403/550\n",
      "6/6 - 0s - loss: 0.0396 - accuracy: 0.9838 - val_loss: 13.1470 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 404/550\n",
      "6/6 - 0s - loss: 0.0851 - accuracy: 0.9784 - val_loss: 13.2362 - val_accuracy: 0.1915 - 241ms/epoch - 40ms/step\n",
      "Epoch 405/550\n",
      "6/6 - 0s - loss: 0.1982 - accuracy: 0.9405 - val_loss: 12.4389 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 406/550\n",
      "6/6 - 0s - loss: 0.1462 - accuracy: 0.9568 - val_loss: 11.9511 - val_accuracy: 0.2128 - 329ms/epoch - 55ms/step\n",
      "Epoch 407/550\n",
      "6/6 - 0s - loss: 0.1048 - accuracy: 0.9676 - val_loss: 12.3839 - val_accuracy: 0.2128 - 263ms/epoch - 44ms/step\n",
      "Epoch 408/550\n",
      "6/6 - 0s - loss: 0.1432 - accuracy: 0.9676 - val_loss: 12.3797 - val_accuracy: 0.1915 - 271ms/epoch - 45ms/step\n",
      "Epoch 409/550\n",
      "6/6 - 0s - loss: 0.0696 - accuracy: 0.9838 - val_loss: 12.5073 - val_accuracy: 0.1915 - 234ms/epoch - 39ms/step\n",
      "Epoch 410/550\n",
      "6/6 - 0s - loss: 0.0747 - accuracy: 0.9784 - val_loss: 12.6996 - val_accuracy: 0.2128 - 254ms/epoch - 42ms/step\n",
      "Epoch 411/550\n",
      "6/6 - 0s - loss: 0.1061 - accuracy: 0.9622 - val_loss: 12.8236 - val_accuracy: 0.1702 - 244ms/epoch - 41ms/step\n",
      "Epoch 412/550\n",
      "6/6 - 0s - loss: 0.0827 - accuracy: 0.9784 - val_loss: 12.3649 - val_accuracy: 0.1702 - 249ms/epoch - 41ms/step\n",
      "Epoch 413/550\n",
      "6/6 - 0s - loss: 0.0468 - accuracy: 0.9946 - val_loss: 12.1163 - val_accuracy: 0.1915 - 236ms/epoch - 39ms/step\n",
      "Epoch 414/550\n",
      "6/6 - 0s - loss: 0.0591 - accuracy: 0.9892 - val_loss: 12.0231 - val_accuracy: 0.1915 - 338ms/epoch - 56ms/step\n",
      "Epoch 415/550\n",
      "6/6 - 0s - loss: 0.0819 - accuracy: 0.9676 - val_loss: 11.9543 - val_accuracy: 0.2128 - 394ms/epoch - 66ms/step\n",
      "Epoch 416/550\n",
      "6/6 - 0s - loss: 0.0527 - accuracy: 0.9838 - val_loss: 12.0959 - val_accuracy: 0.2553 - 239ms/epoch - 40ms/step\n",
      "Epoch 417/550\n",
      "6/6 - 0s - loss: 0.0353 - accuracy: 0.9892 - val_loss: 12.0748 - val_accuracy: 0.2128 - 239ms/epoch - 40ms/step\n",
      "Epoch 418/550\n",
      "6/6 - 0s - loss: 0.0558 - accuracy: 0.9838 - val_loss: 12.4324 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 419/550\n",
      "6/6 - 0s - loss: 0.0613 - accuracy: 0.9838 - val_loss: 12.8228 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 420/550\n",
      "6/6 - 0s - loss: 0.0561 - accuracy: 0.9838 - val_loss: 12.5048 - val_accuracy: 0.2340 - 239ms/epoch - 40ms/step\n",
      "Epoch 421/550\n",
      "6/6 - 0s - loss: 0.0342 - accuracy: 0.9946 - val_loss: 12.6064 - val_accuracy: 0.2340 - 241ms/epoch - 40ms/step\n",
      "Epoch 422/550\n",
      "6/6 - 0s - loss: 0.0603 - accuracy: 0.9676 - val_loss: 12.4339 - val_accuracy: 0.2553 - 250ms/epoch - 42ms/step\n",
      "Epoch 423/550\n",
      "6/6 - 0s - loss: 0.0898 - accuracy: 0.9622 - val_loss: 12.3881 - val_accuracy: 0.2553 - 299ms/epoch - 50ms/step\n",
      "Epoch 424/550\n",
      "6/6 - 0s - loss: 0.0479 - accuracy: 0.9838 - val_loss: 12.2081 - val_accuracy: 0.2553 - 235ms/epoch - 39ms/step\n",
      "Epoch 425/550\n",
      "6/6 - 0s - loss: 0.0475 - accuracy: 0.9946 - val_loss: 12.2426 - val_accuracy: 0.2340 - 301ms/epoch - 50ms/step\n",
      "Epoch 426/550\n",
      "6/6 - 0s - loss: 0.0626 - accuracy: 0.9784 - val_loss: 12.2843 - val_accuracy: 0.2553 - 241ms/epoch - 40ms/step\n",
      "Epoch 427/550\n",
      "6/6 - 0s - loss: 0.0356 - accuracy: 0.9892 - val_loss: 12.3154 - val_accuracy: 0.2553 - 271ms/epoch - 45ms/step\n",
      "Epoch 428/550\n",
      "6/6 - 0s - loss: 0.0555 - accuracy: 0.9892 - val_loss: 12.3577 - val_accuracy: 0.2128 - 237ms/epoch - 39ms/step\n",
      "Epoch 429/550\n",
      "6/6 - 0s - loss: 0.0246 - accuracy: 0.9946 - val_loss: 12.3846 - val_accuracy: 0.2553 - 238ms/epoch - 40ms/step\n",
      "Epoch 430/550\n",
      "6/6 - 0s - loss: 0.0329 - accuracy: 0.9892 - val_loss: 12.4364 - val_accuracy: 0.2553 - 249ms/epoch - 41ms/step\n",
      "Epoch 431/550\n",
      "6/6 - 0s - loss: 0.0257 - accuracy: 0.9946 - val_loss: 12.3782 - val_accuracy: 0.2128 - 292ms/epoch - 49ms/step\n",
      "Epoch 432/550\n",
      "6/6 - 0s - loss: 0.0292 - accuracy: 0.9946 - val_loss: 12.6638 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 433/550\n",
      "6/6 - 0s - loss: 0.0315 - accuracy: 0.9946 - val_loss: 12.8936 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 434/550\n",
      "6/6 - 0s - loss: 0.0345 - accuracy: 0.9946 - val_loss: 12.8592 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 435/550\n",
      "6/6 - 0s - loss: 0.0216 - accuracy: 1.0000 - val_loss: 12.7237 - val_accuracy: 0.1702 - 272ms/epoch - 45ms/step\n",
      "Epoch 436/550\n",
      "6/6 - 0s - loss: 0.0266 - accuracy: 0.9892 - val_loss: 12.4751 - val_accuracy: 0.1702 - 285ms/epoch - 48ms/step\n",
      "Epoch 437/550\n",
      "6/6 - 0s - loss: 0.0102 - accuracy: 1.0000 - val_loss: 12.3611 - val_accuracy: 0.1915 - 250ms/epoch - 42ms/step\n",
      "Epoch 438/550\n",
      "6/6 - 0s - loss: 0.0313 - accuracy: 0.9892 - val_loss: 12.4354 - val_accuracy: 0.2340 - 255ms/epoch - 42ms/step\n",
      "Epoch 439/550\n",
      "6/6 - 0s - loss: 0.0106 - accuracy: 1.0000 - val_loss: 12.5956 - val_accuracy: 0.2128 - 293ms/epoch - 49ms/step\n",
      "Epoch 440/550\n",
      "6/6 - 0s - loss: 0.0180 - accuracy: 0.9946 - val_loss: 12.7088 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 441/550\n",
      "6/6 - 0s - loss: 0.0151 - accuracy: 1.0000 - val_loss: 12.7532 - val_accuracy: 0.2128 - 247ms/epoch - 41ms/step\n",
      "Epoch 442/550\n",
      "6/6 - 0s - loss: 0.0251 - accuracy: 0.9892 - val_loss: 12.6597 - val_accuracy: 0.1915 - 240ms/epoch - 40ms/step\n",
      "Epoch 443/550\n",
      "6/6 - 0s - loss: 0.0168 - accuracy: 1.0000 - val_loss: 12.7552 - val_accuracy: 0.2128 - 242ms/epoch - 40ms/step\n",
      "Epoch 444/550\n",
      "6/6 - 0s - loss: 0.0563 - accuracy: 0.9838 - val_loss: 12.7272 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 445/550\n",
      "6/6 - 0s - loss: 0.0152 - accuracy: 1.0000 - val_loss: 12.7713 - val_accuracy: 0.2128 - 316ms/epoch - 53ms/step\n",
      "Epoch 446/550\n",
      "6/6 - 0s - loss: 0.0425 - accuracy: 0.9946 - val_loss: 13.0199 - val_accuracy: 0.2128 - 291ms/epoch - 49ms/step\n",
      "Epoch 447/550\n",
      "6/6 - 0s - loss: 0.0262 - accuracy: 0.9892 - val_loss: 13.0561 - val_accuracy: 0.2128 - 282ms/epoch - 47ms/step\n",
      "Epoch 448/550\n",
      "6/6 - 0s - loss: 0.0390 - accuracy: 0.9838 - val_loss: 12.7976 - val_accuracy: 0.2128 - 236ms/epoch - 39ms/step\n",
      "Epoch 449/550\n",
      "6/6 - 0s - loss: 0.0139 - accuracy: 1.0000 - val_loss: 12.7273 - val_accuracy: 0.2340 - 256ms/epoch - 43ms/step\n",
      "Epoch 450/550\n",
      "6/6 - 0s - loss: 0.0202 - accuracy: 1.0000 - val_loss: 12.8291 - val_accuracy: 0.2340 - 245ms/epoch - 41ms/step\n",
      "Epoch 451/550\n",
      "6/6 - 0s - loss: 0.0147 - accuracy: 1.0000 - val_loss: 12.9200 - val_accuracy: 0.2340 - 238ms/epoch - 40ms/step\n",
      "Epoch 452/550\n",
      "6/6 - 0s - loss: 0.0207 - accuracy: 0.9946 - val_loss: 13.1362 - val_accuracy: 0.2340 - 242ms/epoch - 40ms/step\n",
      "Epoch 453/550\n",
      "6/6 - 0s - loss: 0.0092 - accuracy: 1.0000 - val_loss: 13.2092 - val_accuracy: 0.2340 - 253ms/epoch - 42ms/step\n",
      "Epoch 454/550\n",
      "6/6 - 0s - loss: 0.0151 - accuracy: 1.0000 - val_loss: 13.1697 - val_accuracy: 0.2340 - 348ms/epoch - 58ms/step\n",
      "Epoch 455/550\n",
      "6/6 - 0s - loss: 0.0220 - accuracy: 1.0000 - val_loss: 13.0998 - val_accuracy: 0.2340 - 326ms/epoch - 54ms/step\n",
      "Epoch 456/550\n",
      "6/6 - 0s - loss: 0.0211 - accuracy: 0.9946 - val_loss: 13.0992 - val_accuracy: 0.2340 - 268ms/epoch - 45ms/step\n",
      "Epoch 457/550\n",
      "6/6 - 0s - loss: 0.0186 - accuracy: 0.9946 - val_loss: 13.1674 - val_accuracy: 0.2340 - 275ms/epoch - 46ms/step\n",
      "Epoch 458/550\n",
      "6/6 - 0s - loss: 0.0154 - accuracy: 0.9946 - val_loss: 13.2435 - val_accuracy: 0.2340 - 242ms/epoch - 40ms/step\n",
      "Epoch 459/550\n",
      "6/6 - 0s - loss: 0.0200 - accuracy: 0.9946 - val_loss: 12.9962 - val_accuracy: 0.2340 - 248ms/epoch - 41ms/step\n",
      "Epoch 460/550\n",
      "6/6 - 0s - loss: 0.0196 - accuracy: 1.0000 - val_loss: 12.8957 - val_accuracy: 0.2340 - 248ms/epoch - 41ms/step\n",
      "Epoch 461/550\n",
      "6/6 - 0s - loss: 0.0358 - accuracy: 0.9892 - val_loss: 13.1404 - val_accuracy: 0.2128 - 235ms/epoch - 39ms/step\n",
      "Epoch 462/550\n",
      "6/6 - 0s - loss: 0.0269 - accuracy: 1.0000 - val_loss: 13.4582 - val_accuracy: 0.2128 - 235ms/epoch - 39ms/step\n",
      "Epoch 463/550\n",
      "6/6 - 0s - loss: 0.0287 - accuracy: 0.9946 - val_loss: 13.4572 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 464/550\n",
      "6/6 - 0s - loss: 0.0198 - accuracy: 1.0000 - val_loss: 13.2549 - val_accuracy: 0.2128 - 352ms/epoch - 59ms/step\n",
      "Epoch 465/550\n",
      "6/6 - 0s - loss: 0.0081 - accuracy: 1.0000 - val_loss: 13.2033 - val_accuracy: 0.2128 - 277ms/epoch - 46ms/step\n",
      "Epoch 466/550\n",
      "6/6 - 0s - loss: 0.0126 - accuracy: 1.0000 - val_loss: 13.1612 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 467/550\n",
      "6/6 - 0s - loss: 0.0084 - accuracy: 1.0000 - val_loss: 13.1753 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 468/550\n",
      "6/6 - 0s - loss: 0.0077 - accuracy: 1.0000 - val_loss: 13.1797 - val_accuracy: 0.2128 - 245ms/epoch - 41ms/step\n",
      "Epoch 469/550\n",
      "6/6 - 0s - loss: 0.0100 - accuracy: 1.0000 - val_loss: 13.1692 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 470/550\n",
      "6/6 - 0s - loss: 0.0108 - accuracy: 1.0000 - val_loss: 13.2054 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 471/550\n",
      "6/6 - 0s - loss: 0.0096 - accuracy: 1.0000 - val_loss: 13.2315 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 472/550\n",
      "6/6 - 0s - loss: 0.0177 - accuracy: 1.0000 - val_loss: 13.2640 - val_accuracy: 0.2128 - 301ms/epoch - 50ms/step\n",
      "Epoch 473/550\n",
      "6/6 - 0s - loss: 0.0213 - accuracy: 0.9946 - val_loss: 13.2888 - val_accuracy: 0.2128 - 248ms/epoch - 41ms/step\n",
      "Epoch 474/550\n",
      "6/6 - 0s - loss: 0.0063 - accuracy: 1.0000 - val_loss: 13.3482 - val_accuracy: 0.2128 - 232ms/epoch - 39ms/step\n",
      "Epoch 475/550\n",
      "6/6 - 0s - loss: 0.0156 - accuracy: 1.0000 - val_loss: 13.3910 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 476/550\n",
      "6/6 - 0s - loss: 0.0087 - accuracy: 1.0000 - val_loss: 13.3780 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 477/550\n",
      "6/6 - 0s - loss: 0.0215 - accuracy: 0.9892 - val_loss: 13.2990 - val_accuracy: 0.2128 - 268ms/epoch - 45ms/step\n",
      "Epoch 478/550\n",
      "6/6 - 0s - loss: 0.0095 - accuracy: 1.0000 - val_loss: 12.8826 - val_accuracy: 0.2128 - 311ms/epoch - 52ms/step\n",
      "Epoch 479/550\n",
      "6/6 - 0s - loss: 0.0089 - accuracy: 1.0000 - val_loss: 12.8135 - val_accuracy: 0.2340 - 246ms/epoch - 41ms/step\n",
      "Epoch 480/550\n",
      "6/6 - 0s - loss: 0.0280 - accuracy: 0.9892 - val_loss: 12.9277 - val_accuracy: 0.2340 - 303ms/epoch - 50ms/step\n",
      "Epoch 481/550\n",
      "6/6 - 0s - loss: 0.0104 - accuracy: 1.0000 - val_loss: 13.1428 - val_accuracy: 0.2340 - 242ms/epoch - 40ms/step\n",
      "Epoch 482/550\n",
      "6/6 - 0s - loss: 0.0068 - accuracy: 1.0000 - val_loss: 13.2949 - val_accuracy: 0.2340 - 251ms/epoch - 42ms/step\n",
      "Epoch 483/550\n",
      "6/6 - 0s - loss: 0.0089 - accuracy: 1.0000 - val_loss: 13.3770 - val_accuracy: 0.2128 - 289ms/epoch - 48ms/step\n",
      "Epoch 484/550\n",
      "6/6 - 0s - loss: 0.0146 - accuracy: 1.0000 - val_loss: 13.5076 - val_accuracy: 0.2128 - 246ms/epoch - 41ms/step\n",
      "Epoch 485/550\n",
      "6/6 - 0s - loss: 0.0109 - accuracy: 0.9946 - val_loss: 13.5586 - val_accuracy: 0.2128 - 278ms/epoch - 46ms/step\n",
      "Epoch 486/550\n",
      "6/6 - 0s - loss: 0.0144 - accuracy: 1.0000 - val_loss: 13.6319 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 487/550\n",
      "6/6 - 0s - loss: 0.0182 - accuracy: 0.9946 - val_loss: 13.4598 - val_accuracy: 0.2340 - 240ms/epoch - 40ms/step\n",
      "Epoch 488/550\n",
      "6/6 - 0s - loss: 0.0153 - accuracy: 0.9946 - val_loss: 13.3312 - val_accuracy: 0.2553 - 308ms/epoch - 51ms/step\n",
      "Epoch 489/550\n",
      "6/6 - 0s - loss: 0.0205 - accuracy: 0.9946 - val_loss: 13.1867 - val_accuracy: 0.2128 - 251ms/epoch - 42ms/step\n",
      "Epoch 490/550\n",
      "6/6 - 0s - loss: 0.0099 - accuracy: 1.0000 - val_loss: 13.0513 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 491/550\n",
      "6/6 - 0s - loss: 0.0140 - accuracy: 0.9946 - val_loss: 13.1099 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 492/550\n",
      "6/6 - 0s - loss: 0.0307 - accuracy: 0.9946 - val_loss: 13.0131 - val_accuracy: 0.2340 - 252ms/epoch - 42ms/step\n",
      "Epoch 493/550\n",
      "6/6 - 0s - loss: 0.0164 - accuracy: 0.9946 - val_loss: 12.8103 - val_accuracy: 0.2553 - 354ms/epoch - 59ms/step\n",
      "Epoch 494/550\n",
      "6/6 - 0s - loss: 0.0088 - accuracy: 1.0000 - val_loss: 12.8024 - val_accuracy: 0.2553 - 237ms/epoch - 39ms/step\n",
      "Epoch 495/550\n",
      "6/6 - 0s - loss: 0.0061 - accuracy: 1.0000 - val_loss: 12.8728 - val_accuracy: 0.2553 - 247ms/epoch - 41ms/step\n",
      "Epoch 496/550\n",
      "6/6 - 0s - loss: 0.0087 - accuracy: 1.0000 - val_loss: 13.0107 - val_accuracy: 0.2553 - 291ms/epoch - 49ms/step\n",
      "Epoch 497/550\n",
      "6/6 - 0s - loss: 0.0050 - accuracy: 1.0000 - val_loss: 13.0933 - val_accuracy: 0.2553 - 275ms/epoch - 46ms/step\n",
      "Epoch 498/550\n",
      "6/6 - 0s - loss: 0.0028 - accuracy: 1.0000 - val_loss: 13.1585 - val_accuracy: 0.2340 - 255ms/epoch - 43ms/step\n",
      "Epoch 499/550\n",
      "6/6 - 0s - loss: 0.0138 - accuracy: 0.9946 - val_loss: 13.2692 - val_accuracy: 0.2128 - 292ms/epoch - 49ms/step\n",
      "Epoch 500/550\n",
      "6/6 - 0s - loss: 0.0302 - accuracy: 0.9946 - val_loss: 13.2835 - val_accuracy: 0.2128 - 249ms/epoch - 41ms/step\n",
      "Epoch 501/550\n",
      "6/6 - 0s - loss: 0.0094 - accuracy: 1.0000 - val_loss: 13.3139 - val_accuracy: 0.2128 - 243ms/epoch - 41ms/step\n",
      "Epoch 502/550\n",
      "6/6 - 0s - loss: 0.0045 - accuracy: 1.0000 - val_loss: 13.3824 - val_accuracy: 0.2128 - 291ms/epoch - 49ms/step\n",
      "Epoch 503/550\n",
      "6/6 - 0s - loss: 0.0118 - accuracy: 1.0000 - val_loss: 13.2832 - val_accuracy: 0.2553 - 251ms/epoch - 42ms/step\n",
      "Epoch 504/550\n",
      "6/6 - 0s - loss: 0.0074 - accuracy: 1.0000 - val_loss: 13.2581 - val_accuracy: 0.2553 - 330ms/epoch - 55ms/step\n",
      "Epoch 505/550\n",
      "6/6 - 0s - loss: 0.0114 - accuracy: 1.0000 - val_loss: 13.2816 - val_accuracy: 0.2553 - 241ms/epoch - 40ms/step\n",
      "Epoch 506/550\n",
      "6/6 - 0s - loss: 0.0042 - accuracy: 1.0000 - val_loss: 13.3358 - val_accuracy: 0.2128 - 238ms/epoch - 40ms/step\n",
      "Epoch 507/550\n",
      "6/6 - 0s - loss: 0.0081 - accuracy: 1.0000 - val_loss: 13.4284 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 508/550\n",
      "6/6 - 0s - loss: 0.0050 - accuracy: 1.0000 - val_loss: 13.4996 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 509/550\n",
      "6/6 - 0s - loss: 0.0048 - accuracy: 1.0000 - val_loss: 13.5585 - val_accuracy: 0.2128 - 237ms/epoch - 39ms/step\n",
      "Epoch 510/550\n",
      "6/6 - 0s - loss: 0.0090 - accuracy: 1.0000 - val_loss: 13.6103 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 511/550\n",
      "6/6 - 0s - loss: 0.0053 - accuracy: 1.0000 - val_loss: 13.6212 - val_accuracy: 0.2128 - 244ms/epoch - 41ms/step\n",
      "Epoch 512/550\n",
      "6/6 - 0s - loss: 0.0172 - accuracy: 0.9946 - val_loss: 13.5940 - val_accuracy: 0.2128 - 280ms/epoch - 47ms/step\n",
      "Epoch 513/550\n",
      "6/6 - 0s - loss: 0.0084 - accuracy: 1.0000 - val_loss: 13.5717 - val_accuracy: 0.2128 - 253ms/epoch - 42ms/step\n",
      "Epoch 514/550\n",
      "6/6 - 0s - loss: 0.0083 - accuracy: 1.0000 - val_loss: 13.5780 - val_accuracy: 0.2128 - 241ms/epoch - 40ms/step\n",
      "Epoch 515/550\n",
      "6/6 - 0s - loss: 0.0074 - accuracy: 1.0000 - val_loss: 13.5552 - val_accuracy: 0.2128 - 245ms/epoch - 41ms/step\n",
      "Epoch 516/550\n",
      "6/6 - 0s - loss: 0.0066 - accuracy: 1.0000 - val_loss: 13.6031 - val_accuracy: 0.2128 - 240ms/epoch - 40ms/step\n",
      "Epoch 517/550\n",
      "6/6 - 0s - loss: 0.0050 - accuracy: 1.0000 - val_loss: 13.6222 - val_accuracy: 0.2340 - 238ms/epoch - 40ms/step\n",
      "Epoch 518/550\n",
      "6/6 - 0s - loss: 0.0082 - accuracy: 1.0000 - val_loss: 13.5974 - val_accuracy: 0.2340 - 242ms/epoch - 40ms/step\n",
      "Epoch 519/550\n",
      "6/6 - 0s - loss: 0.0118 - accuracy: 1.0000 - val_loss: 13.6259 - val_accuracy: 0.2340 - 296ms/epoch - 49ms/step\n",
      "Epoch 520/550\n",
      "6/6 - 0s - loss: 0.0100 - accuracy: 1.0000 - val_loss: 13.5682 - val_accuracy: 0.2553 - 309ms/epoch - 52ms/step\n",
      "Epoch 521/550\n",
      "6/6 - 0s - loss: 0.0041 - accuracy: 1.0000 - val_loss: 13.5749 - val_accuracy: 0.2553 - 274ms/epoch - 46ms/step\n",
      "Epoch 522/550\n",
      "6/6 - 0s - loss: 0.0057 - accuracy: 1.0000 - val_loss: 13.5952 - val_accuracy: 0.2553 - 348ms/epoch - 58ms/step\n",
      "Epoch 523/550\n",
      "6/6 - 0s - loss: 0.0083 - accuracy: 1.0000 - val_loss: 13.5825 - val_accuracy: 0.2553 - 285ms/epoch - 47ms/step\n",
      "Epoch 524/550\n",
      "6/6 - 0s - loss: 0.0031 - accuracy: 1.0000 - val_loss: 13.5736 - val_accuracy: 0.2340 - 243ms/epoch - 40ms/step\n",
      "Epoch 525/550\n",
      "6/6 - 0s - loss: 0.0099 - accuracy: 1.0000 - val_loss: 13.5270 - val_accuracy: 0.2340 - 239ms/epoch - 40ms/step\n",
      "Epoch 526/550\n",
      "6/6 - 0s - loss: 0.0111 - accuracy: 0.9946 - val_loss: 13.4556 - val_accuracy: 0.2340 - 246ms/epoch - 41ms/step\n",
      "Epoch 527/550\n",
      "6/6 - 0s - loss: 0.0083 - accuracy: 1.0000 - val_loss: 13.4793 - val_accuracy: 0.2340 - 253ms/epoch - 42ms/step\n",
      "Epoch 528/550\n",
      "6/6 - 0s - loss: 0.0215 - accuracy: 0.9892 - val_loss: 13.5579 - val_accuracy: 0.2128 - 325ms/epoch - 54ms/step\n",
      "Epoch 529/550\n",
      "6/6 - 0s - loss: 0.0120 - accuracy: 0.9946 - val_loss: 13.5678 - val_accuracy: 0.2128 - 262ms/epoch - 44ms/step\n",
      "Epoch 530/550\n",
      "6/6 - 0s - loss: 0.0179 - accuracy: 0.9946 - val_loss: 13.4254 - val_accuracy: 0.2340 - 251ms/epoch - 42ms/step\n",
      "Epoch 531/550\n",
      "6/6 - 0s - loss: 0.0068 - accuracy: 1.0000 - val_loss: 13.3300 - val_accuracy: 0.2340 - 299ms/epoch - 50ms/step\n",
      "Epoch 532/550\n",
      "6/6 - 0s - loss: 0.0126 - accuracy: 0.9946 - val_loss: 13.3170 - val_accuracy: 0.2340 - 288ms/epoch - 48ms/step\n",
      "Epoch 533/550\n",
      "6/6 - 0s - loss: 0.0173 - accuracy: 0.9946 - val_loss: 13.3104 - val_accuracy: 0.2340 - 248ms/epoch - 41ms/step\n",
      "Epoch 534/550\n",
      "6/6 - 0s - loss: 0.0147 - accuracy: 0.9946 - val_loss: 13.3126 - val_accuracy: 0.2340 - 248ms/epoch - 41ms/step\n",
      "Epoch 535/550\n",
      "6/6 - 0s - loss: 0.0176 - accuracy: 1.0000 - val_loss: 13.4065 - val_accuracy: 0.2340 - 245ms/epoch - 41ms/step\n",
      "Epoch 536/550\n",
      "6/6 - 0s - loss: 0.0102 - accuracy: 1.0000 - val_loss: 13.4670 - val_accuracy: 0.2340 - 294ms/epoch - 49ms/step\n",
      "Epoch 537/550\n",
      "6/6 - 0s - loss: 0.0129 - accuracy: 1.0000 - val_loss: 13.5245 - val_accuracy: 0.2340 - 254ms/epoch - 42ms/step\n",
      "Epoch 538/550\n",
      "6/6 - 0s - loss: 0.0110 - accuracy: 0.9946 - val_loss: 13.5017 - val_accuracy: 0.2553 - 239ms/epoch - 40ms/step\n",
      "Epoch 539/550\n",
      "6/6 - 0s - loss: 0.0090 - accuracy: 1.0000 - val_loss: 13.5366 - val_accuracy: 0.2553 - 269ms/epoch - 45ms/step\n",
      "Epoch 540/550\n",
      "6/6 - 0s - loss: 0.0086 - accuracy: 1.0000 - val_loss: 13.6757 - val_accuracy: 0.2553 - 292ms/epoch - 49ms/step\n",
      "Epoch 541/550\n",
      "6/6 - 0s - loss: 0.0166 - accuracy: 0.9946 - val_loss: 13.6982 - val_accuracy: 0.2128 - 312ms/epoch - 52ms/step\n",
      "Epoch 542/550\n",
      "6/6 - 0s - loss: 0.0151 - accuracy: 1.0000 - val_loss: 13.7072 - val_accuracy: 0.1915 - 288ms/epoch - 48ms/step\n",
      "Epoch 543/550\n",
      "6/6 - 0s - loss: 0.0090 - accuracy: 1.0000 - val_loss: 13.7830 - val_accuracy: 0.1915 - 248ms/epoch - 41ms/step\n",
      "Epoch 544/550\n",
      "6/6 - 0s - loss: 0.0157 - accuracy: 0.9946 - val_loss: 13.9371 - val_accuracy: 0.1915 - 290ms/epoch - 48ms/step\n",
      "Epoch 545/550\n",
      "6/6 - 0s - loss: 0.0147 - accuracy: 0.9946 - val_loss: 14.1321 - val_accuracy: 0.2128 - 247ms/epoch - 41ms/step\n",
      "Epoch 546/550\n",
      "6/6 - 0s - loss: 0.0132 - accuracy: 1.0000 - val_loss: 14.1599 - val_accuracy: 0.2128 - 237ms/epoch - 40ms/step\n",
      "Epoch 547/550\n",
      "6/6 - 0s - loss: 0.0108 - accuracy: 1.0000 - val_loss: 14.1769 - val_accuracy: 0.2340 - 243ms/epoch - 40ms/step\n",
      "Epoch 548/550\n",
      "6/6 - 0s - loss: 0.0051 - accuracy: 1.0000 - val_loss: 14.1883 - val_accuracy: 0.2340 - 236ms/epoch - 39ms/step\n",
      "Epoch 549/550\n",
      "6/6 - 0s - loss: 0.0161 - accuracy: 0.9892 - val_loss: 14.1135 - val_accuracy: 0.2128 - 242ms/epoch - 40ms/step\n",
      "Epoch 550/550\n",
      "6/6 - 0s - loss: 0.0231 - accuracy: 0.9946 - val_loss: 13.5646 - val_accuracy: 0.2340 - 235ms/epoch - 39ms/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, Dropout\n",
    "\n",
    "# Define the model\n",
    "model = Sequential([\n",
    "    Embedding(input_dim=10000, output_dim=128, input_length=X_train.shape[1]),\n",
    "    Bidirectional(LSTM(64, return_sequences=True)),\n",
    "    Dropout(0.5),\n",
    "    Bidirectional(LSTM(64)),\n",
    "    Dropout(0.5),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(y_train.shape[1], activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=550, validation_data=(X_test, y_test), verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jEqlPTGZzmfYiIiLiUq9evYiKimLhwoWeDkVEREREGij1SRcRqYSVK1eyZs0aJkyY4OlQRERERKQBU026iEg51q9fz6pVq3jhhRdISUlh586dBAQEeDosEREREWmgVJMuIlKOuXPnMmnSJAoLC/n000+VoIuIiIhIjVJNuoiIiIiIiEgdoZp0ERERERERkTpCSbqIiIiIiIhIHeHj6QBqW0lJCQcPHiQ0NBSTyeTpcERERDCbzWRmZtK0aVO8vHT9vDro+15EROqSqnzXN7ok/eDBgyQmJno6DBERESf79u2jefPmng6jQdD3vYiI1EWV+a5vdEl6aGgoYLw4YWFhHo5GREQEMjIySExMtH5HyYnT972IiNQlVfmub3RJuqXJW1hYmL60RUSkTlGz7Oqj73sREamLKvNdr45vIiIiIiIiInWEknQRERERERGROkJJuoiIiIiIiEgd0ej6pFeG2WymqKiI4uJiT4dSb3l7e+Pj46P+lSIiUifpu/7E6bteRKRmKEkvo6CggEOHDpGTk+PpUOq9oKAgEhIS8PPz83QoIiIiVvqurz76rhcRqX5K0u2UlJSwa9cuvL29adq0KX5+fro6fBzMZjMFBQUcOXKEXbt20b59e7y81LNCREQ8T9/11UPf9SIiNUdJup2CggJKSkpITEwkKCjI0+HUa4GBgfj6+rJnzx4KCgoICAjwdEgiIiL6rq9G+q4XEakZHr3kuWTJEkaNGkXTpk0xmUzMmzevwmMWL17MySefjL+/P+3atWPWrFnVHpeuBFcPvY4iIlJX6Tuqeuh1FBGpfh79z5qdnU2PHj147bXXKrX/rl27GDlyJEOGDGHNmjXccccdXHPNNcyfP7+GIxURERERERGpeR5t7j5ixAhGjBhR6f1nzpxJ69ateeGFFwDo3LkzS5cu5aWXXmLYsGE1FaZIjckrLGbDwXR6JUbi5VWzfSL3HzMGSGoeGcTW5ExiQvyJDHY90M/RrHySM/I5mp1PgK83JzUNY9n2ozSPCqRTfBjFJWb+2nkUX28v+rWOcji2pMTMnztSCPLz5uQWkRX29Vx/IJ2mEYEcycxnV0oWUcH++Pt40SYmmNAAX6f9cwuK2ZKcib+PF3FhASRn5HEwLZdT2kazNTmTNjHBHEzLw8fbRFGxmcOZeWTnF+FlMpEYFcSeo9kE+vnQJNiP5Iw8TmkbTaCfN8kZeeQUFNM6OhiAzLxC9h/LpWWTIJbvOEphcQkAcWEBZOUX4e/jTZCfN4F+3vh4mWjZJNjtczyalc+RrHyaRwaxOyWbnIJiCopKCPL35nBGHgnhgRSVlNC1WTjbkrNo2STI5XOvTln5RexOycbLZMLf17hem5ZTQESQHyYgr7CELk3DOJSey9p9afj7ehMd7M+BtByigv2t7/uWpEyn17osy2uWnV9Eq+hgDhzLJS4sgJSsfPIKi/H19iIxKohAX28So4LYdCiDuLAAIgJ9+XNHivWcIf6+9G0dybLtR8kvcj0it32c5YkK9ifIz5uWTYII8vNh9d5jnNwikqy8Iv7edRSg9POSQ6voIHan5NC9eTh5hcVsTc4kLMCXAW2a1PjfrYiIiHhAUQEcWAUJ3cHP/W+8mlKv+qQvX76coUOHOpQNGzaMO+64w+0x+fn55OfnW9czMjJqKrwGp1WrVtxxxx3lvr5yYh79bgOfrtjHw6O6MGlQ6xp7nLzCYka/+icZuYW8dGlPbv30Xwa1a8LsawY47VtcYmb823+zJTnTWhYb6s/hzHx8vU38MmUw/+xO5f/m/gfAzCtOZnjXBOu+7/25iyd+2ATAnOsG0L9NE7dxrdufzqhXlxIa4EN+YQkFpYkwwOAOMXwwuZ/TMc/8vJlZy3YD4OfjRXGJmeISM0F+3uQUFBPg60V+UQlmc+Vem4t7N+ex87ty3itLycor4vf/O4PY0ACmzFnLr5uSaRsTzI4j2RWeZ8V9ZxEb5twf0/J6bjucia+3EZs7wX7eZBcUM7pHU2aM61W5J3CcrvtwJct2HC13nzev7M0TP2xkX2qu07aZV/QmPNCX8e/8VenXuiKhAT48d1F3bpy9mi4JYYzt1cz6WbII9PUmt7D6pswa3aMpCREBvPn7Tm49sx0rdx9j+U7Xr4tPaUJeVGI84acv6MZl/VpUWywinqDvehERO2Yz/P0mLHkOclKg40gY90mth1GvkvSkpCTi4uIcyuLi4sjIyCA3N5fAwECnY6ZNm8ajjz5aWyF6REU1lQ8//DCPPPJIlc/7zz//EBxcu1eOPvl7L39uT+HFS3vg7+Ndq49dltls5oF560mMCuKGwW1d7vPTukN8s+Ygz13cnU9X7OXAsVweHnWSQ+3aoi2Hmf7rNtrHhvDMhd3ZfyyHh7/dwA2D2/Lpin0APPrdRi7v35KHvllPm5hgrjvd9eOVtf5AOo9+t4HoEH+mX9bT6TX7YuU+5q05wJakLFKzCwC49dN/Afhz+1FKSsz8tD6J9//cRWGJmaLiEnaV1vTaO5xpXOgqLDYz/u2/OJSeZ912w8er6dsqkg5xobSLDXFIqjYdyig3Sf9h3SEAMvOca19/33qEc1/+A19vE0UlZi44uTlXn9ramqADFNglvJaY8wqdk+BO8aFsTsp0Kgf4YtV+vli137re78mF9EiMYO2+NABrgt45Icxau+7KHXPWUFRixs/bi1vObMeA0ue9YGOS9YKHqwTd38eWuGeXPodv1x5kx5EsrhjQknFuksCSEjMPfLOexMggbjzD9nlZtz+d537Zwj3DO7H/WA7frDnIMxd1572lu/hx3SH8fbwoKDaz6VDFFyyf/mmzU4Lu42W8H/d89R9pOYVOx3SKDyXE3/bVkpSR5/Y1A2gTE8zO0tc4M6+IGz5eDcCGgxlsS86ynhNgc1KmNUHv3jwcP2/HHlv5RSWsO5AOQHSIP62auB4ULDWnwPqY3649aC1/5bftDs+xrLJl93y1jvUH07n/3C4E+nn2/1V9s2TJEp577jlWrVrFoUOH+PrrrxkzZozLfW+44QbefPNNXnrppUadSDak73oRkTpr/Zfw8/9s61t+gKR1EN+tVsOoV0n68bj33nuZOnWqdT0jI4PExEQPRlT9Dh06ZF2eM2cODz30EFu2bLGWhYSEWJfNZjPFxcX4+FT81sfExFRvoJVw39frAOjVIoJB7aJpGxPCzpQsOsaFnvAUOTkFRexOyaFL0zBrWXpuIanZBeQUFNExLhSf0h/9u1KySc3OZ/bfewE4s1MsLaKCWLErlcSoIFpHB5OVX8SNs42EomuzMJ7/ZSsAI7olEB3iR1J6Pie3jODBeevZf8xoMjyuXwue/mkT/+w+xuItRxzi++yfvXz2j5G092vdhJ6JERU+p09X7OWf3ccAuGL3MQa1i2b13mMUFZvp2yqSu0tru92Z/fceHvxmQyVePaOWMyu/yCFBt/hn9zFrHPYOltnXbDaz4WAGwf4+7E3NIT3XMcnr3zqKzUmZ1vKNdonkhoMbaRruXFPtZYKIID/rRYiyWkQF8c0tg3j4mw189s8+rj61Ne8u3VXuc7Uk6BYmE8y5fgBHswoY8fISfL29KCgqcUi67Wull25PYffTI0nOyOOuL9y/B4G+3sy8sjfXf7TS6eLChoMZPPvzZi7rm+jys//fgXQ+Kf18ntU5luISMy2bBDH29T8pKjGTmp3P+gPG6/f3rlRSsvKdzlGRXSlGItssIpDIYF82H8rknYl9uPXTf10m6JbX2v5i0a6UbEa8vITY0AAig3xZuz/d4Zh3JvTh8e83sqjM3wNAQXEJzSMD+eaWQZjNMOT5xRxKz6N783C+uXmQ0+tSUmJm9GtL2Xwok3cn9qGHm7+h1OwCTn92EVkumuYDXNI3kZISM5/9s4/4sACSMvKsrUkArjqllfVi0ao9aQT4auCsqrKMSTN58mQuuOACt/t9/fXX/PXXXzRt2rQWo6ubGtJ3vYhInbXjN+P+pAsgLx12LIQ9y5Sklyc+Pp7k5GSHsuTkZMLCwlzWogP4+/vj7+9/3I9pNpurtWllVQT6elcqMY2Pj7cuh4eHYzKZrGWLFy9myJAh/PjjjzzwwAOsW7eOX375hcTERKZOncpff/1FdnY2nTt3Ztq0aQ7dCco2gTOZTLz99tv88MMPzJ8/n2bNmvHCCy8wevToanm+Zrs2s5ba2BZRQexNzeGNy09mRLcEd4dWyvUfreKPbSm8P6kvQzrGUlJiZuSMP6y1fJf3b8GTY7uxJSmTYdOX4Odj++H90fI95BQU8+Xq/fj7eLH83rP4ZUOSdbt9gvHNmgPMXbWfwmIzcWH+JGfYkqNVe1IdkhT7WtRHv9toLR/z2p98fv1Ap/7eZR1Ms9VQHsnM56d1h6wXDv43vFOFr0nZBL1JsB9HS5PdeTcPwmw2M/b1ZQC8eUVvokL8+PrfA7z5+04Anr+4B3d9sdbpvNef3oY3l+zkQJpjDeq3aw9y+2dr3MbTp1UkL1/Wi8LiEval5pCZX8T1H62ybrc8t5hQf7684RQOpecSEeRH88hA/t2bRrPIQA6l5TL+nb8B8PP2Yt7NRtL42PldmXhKKzrGhVqT9HO7xfPjOuN9bBcbwvbDWS7j6hgXSliAL2EBviyYMphgfx8KikoYMG2h2+eSV1jMyBl/kJVfhJ+PF7Ov6c+Ow1nc85VxIeq7W04lMtiX5pFBLJgyGIA9R3O44t2/rec4llPIzpRs2saEOJ1/5xFbrFe9t4KD6XkM7Rxrre21JOiAU4L+9oQ+HM3Kt8YC0CY6mJ0prpv1t40N4ZVxvTiWXUCr6GB+uv00awuDie+tAIyuB5bX2l7r6GDrazb9161OSXrr6GBeu/xkDqblklNQzLGcQppFBHI0K5+8ohK6JIRZz/nNLYPYkpRJ92YRLv83enmZmH3NAGuc7kQF+/Hr1MEO79+Su4ew+2g2Pt4mereMxITJ+nnZeCiDLglh/LvvGH7e3rSKDrIm6ZMHtdIc28ehMmPSHDhwgFtvvZX58+czcuTICs95It3b9F1fe9/1IiJ1WvJ6477rBbBtgbGcl+5+/xpSr5L0gQMH8uOPPzqULViwgIEDB9bYY+YWFtPlIc+MHr/xsWEE+VXPW3TPPffw/PPP06ZNGyIjI9m3bx/nnnsuTz75JP7+/nz44YeMGjWKLVu20KKF+z6Wjz76KM8++yzPPfccr7zyCpdffjl79uwhKqr8ZLIyMnKda7X2phqDPz03f8sJJ+l/bEsB4L2luxjSMZaFmw87NMOd888+bjmzHXNKa7Ptm1LPXbXf2l86v6iETYcy2GGXJK3aY6tFtjRhB6wJeoi/UQu9cvcxh/Pa98EuLtOU9qFv1tM2JoSHR3chNtT13LP2tdqf/L2XFbtTrevP/LzZaf+YUH96NA/n102HXZ7v+Ut68M2/BxjQxqjJN5vN3DykLWYzDGzbBJPJxJ1nh5CVV0SbmBBObx/tdI5gP29rK4CDZZL0d/4ovwa7bUwI8aW15YlRRlPlJ8d25f6v11v36ZIQxu1D29OiSRAt7Jozn1oai5EUns6z87dwz4hORJUOjufn40XnBKMVxfMX9+CfXak8NuYkmkVswcvLxJShHbh59moWbra9Nr1aRGACrrfr7mCJC+CLGwYy/det/LnduQ/z6FeXkpJlXPB46Lwu9G0VRZ+WkRxMy8Xby4tuzcOdzpkYFcRn1w3g/T93sf5ABgfScrn2w5Vc1Ls5N53RDjA+v3tTcwjwtSXDlhYL7t5X++dzaZ9Ezu4Sh9ls5mBaLv/uSyPE34dHR5/Ekm0pDhddvExQYoa2McGEB/oSHmgMZtc8MojmkUbMrl7rsizP785zOnIsp5CR3RJYuCmZQe2iMZlMBPn50C421OGYdrHOFyZiQwPc/i1Y2MdZnvjwAOtr/eB5XWge6fh5Aqyfl67NjPeqd0vb/7n7zu3E/mO5jO3VrMLHkqorKSnhyiuv5O677+akk06q1DEn0r1N3/WOavK7XkSkziougsOlv5/jusL+f4zl3LRaD8WjSXpWVhbbt2+3ru/atYs1a9YQFRVFixYtuPfeezlw4AAffvghYPRLe/XVV/m///s/Jk+ezG+//cbnn3/ODz/84KmnUG889thjnH322db1qKgoevToYV1//PHH+frrr/n222+55ZZb3J7nqquuYty4cQA89dRTzJgxgxUrVjB8+PATjvFIlnMzaovwoMqNdJ1XWMz+Y7kOP/Cz8otIybSrXSltSv3m7zscji0qMfPe0l3WUdDtla1h+WHdIdJdNPd157XLT2bieyv4fatjk96yA24lRgVa+wBvTspkc1ImO1OymXvDQIJL+/keTMtlz9Ec2sYGO/Sztk/QLclVWW9cfjJ9WkXxzh87eeKHTdwwuC23ndWO/k8uxMvLxMA2TRjSMda6v8lk4u5hjjXyfj5ePDnWaPJT4uJBppzdgaYRRsuWQ2mO72lFTa5dNU++vH9LPly2x9qve/Y1/d2OSm/RPi6Utyf0cbv9ot7Nuah3cwDuH9nFWv7uVX0Z+/qf/Ls3jQVTTqd9XKi7UwDQt1UUs68ZwLM/b+b1xY6fp62l/akfH9OVKwa0BIzXc+o5Hcs954A2TRjQpgmvLdrOc/O3sPNINs/N38I5XeJp2SSIx77fWO7x7ky/tCdj7BJKV7Fc1Ls5eYXFPDBvPZf1TaS4xMwXq/bTt5X7H+YVvdb2wgN9eaV0QLzhXeMr2LvmWV7r41HZcSPk+DzzzDP4+Phw2223VfqYxtC9rSL14bteRKTOStkKxfngFwIRLSGgtEKlsdWkr1y5kiFDhljXLV+uEydOZNasWRw6dIi9e/dat7du3ZoffviBKVOm8PLLL9O8eXPeeeedGp1+LdDXm42PeWZ6t0Df6huIqE8fxx/RWVlZPPLII/zwww8cOnSIoqIicnNzHV5vV7p3725dDg4OJiwsjMOHy6+9q6wjma77FBvbKtef1lIT+uHkfpzewehnN3nWP6zYZUtgM/KKWH8gnZV7nPtQz1q226lG216XhDA2Hsqw9gW2FxHkS0FRCTkFxdZ+rABDO8dyevtoh8HI7MWE+vPexL6k5RYwoE0Tflx3yKFJ+KZDGdz26b+8e1VfjmUXMOylJWS66UsL8P6kvnSKD2XgNKNPTevoYGZN6ktBUYk16Zx4Siv6t25C12ZhmEwm5k85HZMJh9rZyrAfIK9peABvXNGbrs3COZptvF/JmXkUFpfg6+3FofRca83/lzcO5JoPVnIsp5CWTYJ4amw3IoP8XDbrBhym26ooQT9R71/Vl9TsAtq4icWVu87pSEiAD8/+bOsfGuznzTsT+zKgzfHVPF13eht6Jkbw+uLt/Ln9KJ/8vZfx/d3XfMWHBTDtQqO7xtM/Obei6N0yslKPO75fC7o2C6dTfCjeXiYmDGxF12ZhFR8oUk1WrVrFyy+/zOrVq6vUleBEurfpu95RTX7Xi4jUWftKuxw27QVeXhAQYaznpdV6KB5N0s844wyHfshlzZo1y+Ux//77bw1G5cjSFLO+Kzty61133cWCBQt4/vnnadeuHYGBgVx00UUUFLhPlAF8fR1rtE0mEyUl7qeUqqzC4hKu+2il2+0H0nLJKyyuMIm0NFV+Y/EO+reJ4oGv1zsk6GAMGrXcxdRTvt4mCoudP49+3l60jwvh5BaRtI8L4SE3A61NGNiKszrF8lxp0989R3P4Zs0BnhzbDZPJxEPndebx7zeRV1jsUAMeHeLv0PR5dI+mTv22F24+zEPfrGf5jqPlJug+XiZObReNr7cX391yKs//soX7zu3sNIe3r7djc2tLzfeJMJlM1prwyCAjkTabjRG7o4L9rDXL7WND6N0yilfHn8w7f+zk0dFdnZoZl1WbfUUjgvyICKrahQAvLxPXn96WbclZfP3vAQDaxYUysO3x1dKC8R4NahfNzpRs/tx+lANpOQ5dLMpqFhnIkI6x9EqMcEjSY0L9ubRPIs0jK/cee3mZHAYttP+ciNSGP/74g8OHDzs0xy4uLubOO+9k+vTp7N69u9ofU9/1jmrqu15EpE6zJOktSqcobqw16eI5f/75J1dddRVjx44FjKvtNfHDp7IWbkp2OQ2Xhdls9E/vUEHzY4uUrHzm/LPPYWoti/TcQn4rTeabRQRaBzebNKg1by0xBkTrGBdqbV49rl8ij57fFYCtya6n8QKYOLAlTUL8+fia/oDRj3Vkd1s/+t4to5h38yAArp71j/WCQosox+TJZDLx8mU9nRL1D5fvqfB5t2gShG/pCPXdmoe7nGe8ug0/KZ6fNyRx/eA21jJfby/rfNaZeYVEBPqyeIvxfNvEGD8iB7WLZlA75z7trtw4uC2PfLeRUT3q7gjP3l4mXrq0J5l5hfy66TC3DmlXLee19K9Ozy1kwcZkt/tZauPCA30dLjhNv7RnpV9nkbrgyiuvdBjYDGDYsGFceeWVTJo0yUNR1U917btepMEoygdvP2P6F2kYiotg1xJjOdH4LU9ghHHf2Pqki+e0b9+er776ilGjRmEymXjwwQc9epXcMkBceQ6m5VYpSbfMg+zK8p1GTfo5J8Xx/p+7AZg0qBVndIihsMRMXmGxdVTx0ABbjUKHuFB+uv00vv73gDWhX3L3EEwmaBJS+WaW4/u3sCbp4/u3dNp+fs9mtI4OpsRsjPRu0bdVJJf3b8kdc9ZYyz65pr91NPM20ZVvol1dXrq0J1cfTOfkFo7NqUMDfEqT9CI+WbHX+jpXpRm5xZUDW3FSs3C6Nq37tbovXtqTfak5nFRNsVqS9L92pgKpbvdrXTqauclkIjrE39q1IMHFtHUinlbRmDRNmji2QvH19SU+Pp6OHcsf00Ec1bXvepEGIS8DZvQymkRfMdfT0Uh1yDgE39wEGQcgKBpanWqUB5T+tlVNutSWF198kcmTJ3PKKacQHR3N//73vypNV3O8dqVk88rCbeQXlTC4YwwZuYV4e5kcEupXx/fiuflb2HPUSNwttd1Xvf8Pf/zfEIfRtT/+aw/7UnO4Z0Qnh0HYjuUU8v1/B8uNxc/bi5NbRFqTx+gQfxLCjVrtNXZ9x0MDHP9MOieE0To6mCOZ+bSPC6mwqbYrQzrGMq5fC7y9cDlCOkD35sbo6n4+XtYR4c/pEs/I7gnWJL1ZRCD92zRhZLcEdhzJYsJA54S/pgX6ebscWCw0wIfDmflk5hU5zEveIqrqr5e3l6ncwcvqkrAA32pL0IEKRyqffmlPPl+5jzuGtreWdUkIsybp1dGVQaS6VTQmjVQPT33XizRoe5ZBTgpsX2DUvnornaq03DRI3QnNTj6x8xTmgq/d75uC0jwiLx3CqtjqsqQEPhgFR7cZ66fdaTu3tbl72gmFezz0qWpgrrrqKq666irrurt+/61ateK3335zKLv55psd1ss2iXN1nrS0tCrF99SPm6xNdn9Yd8habkncXr6sJ+d1b8rQznF0evBnwJj6a25ps/XrP1rFj7efBkBOQREPzDOm5rrg5ObEhznWGFqmv3KnW/NwerWIsK5bmomD0Y/XIiTA+c8kwNebly7tWe75y+PlZWLaBd0q3M9kMhEV5GcdhO7klhEOcYKRwL52+Qn+s6sBlhYImXnGvNe7Sufg7tuqcgOYiaFskv7CxT1YdyCdWct2c/3pbRjTq5nDqO0Atw9tz8LNh2kbE1zlwQBFakNFY9KUpSbajur6d71Ig+Zv16ozfS9EtXG/r9iYzUYynPQfTP4FWvSv+jlKiuGr62D9XIjvBoPugIyDsPhpKCxN1HteDiGxxhRqHUeAX7Dx2GazMRhcWYc32BL0K76EdnbdrSzN3fMzjMf2qr3fVErSpVbtc9Os3dLc3TK6d4CvNwumnG6MPL4hybrfxkO2GgD72u5jOQUVjpD7+fUD+WKlrZ96n1aRNI8M4vtbT3VKhJrYjSBehd+RNcLPx/YPpexc0nWZpQVCZl4RqdnGBZM7z+5Qr55DXRBW5iJRn1aRjOyewNDOcfRt7fqCR/fmEfx8x2kOXTVERESkGhTZTS+bulNJemVtnW8k6ABbfji+JP2vN4wEHSBpHXx5tfM+a2bblqM7wkXvwZfXwLFdcNWP0Ly3bXtOKmwuncq73VDHBB1sNelg1NIH1V6rTheXE0RqTkXT6TSza5rbPi6UdrEh+Ps4fkzTcoyEb9Vu2xRqRzLzSS+d/zwuzJ/Xxp9s7aNr0a91FFEhtuT7nC5xgDHAW2KZJtj2tY95tTiyuCvHcmwtAipq+lyXWJL0O79Yax2Eb0in2PIOERfCyrznLaKCCPD15tT20fj7uL8w1Sk+zOHvSURERKqBfZJ+dKfn4qhPCrLhh6m29SNbq36O7KPw+zPG8uB74KQLwLu05WvnUXDB20ZSHhABLU+F4BhI2QIzB8GRTcb7tu5z2/nSD8ArvWHxNGO99enOj+ntC76l+UQt90tXTbrUmKKSEodmcwVFJdbR0ScMbOk0Wrmvt8llEjqgjeMAQgs3HWZQu2i+s+tznpKVb532KzzQl5HdExjZPYH/m7uWz1fuZ2JpX+1BbaN58/edJEYF0rtl5a6G9fFwX2hXo94PateEP7cf5YoBtd8HvbJC/W3vpWXueftuBFI59t0bwgN9qzRvtIiIiFSzwlzbcmqZJH33n5CyFfo0wJkodiyC7++A8EQY/zn4VWGMoR2LjEHZLA6uNpqqVuU3zR/PG83O47vB4P8ZTddLSoxEPLqjsd79Etv+u5YYzevt7V5qW/7lfsgtHZA3phOcPMH14966yuji4BfsensNUZIuNaKouIQdh7NIzcjH0gjovFf+sCZrU4Z2YGCbJqw7kM7ri3cA0CTYHy8v5z/Wrs3C+fqmU7jts3/Zl5rLnV+sddrnSGY+saFGn3T7RP/R0V0Z3jWeU9oag7Od1j6a2df0p2uzigf2WnbPmexNzXGYM9oTwgN9Sc8txMfutXnjit6s2nOM0+rw1FplB9wDiAqu2vzj4qhJiF4/ERERj7KvSc+1m3Ul9xjMOtdYju1yfM2567L/5sCx3cZtzzJoP9R5n6wjsOJNaDEQ2p1lK9+5yLjvdQX89zlkJRsXOJq0df94hbmw7RfoMMIYuG3F20b52Y/Z+pZ7eUFsZ9fHtz4dOgyHrT9Dt0uMWvTk9ZB1GI5sgQ1fAyYY+Tz0GOc+CQ9LcF1ew5SkS7Uym80kZeRRUpqMF5WY+frfA2QXmdianGXdLzLYjxHdEujZIsKapJeXwPVqEUmn+DD2pea63G45Bzgm6YF+3pzZKc66bjKZKj1ndNOIwDoxMvZbV/bmse838ujok6xlYQG+DOlYt5uOB/o5N8UuO+idVE10Fab5ExERkRpgX5NeYDfd76oPbMsHVjW8JN2+JvzwBuckvSgfPrnEqCUHGP8FdDjHGHBt63yjrONISN0Ne5bCrt/LT9K/vc1IrAf/D/xCoKQQmvWBtmdWPuZLPjJG4g9rCqk7jPdl+WtGkg7QZzL0vaby56tFStKlWh3LKeRIZr5D2au/beNApq1fd++WtsGuLLXflREZ5NwU3jI9m73mkVWf4qsu69+mCT/cdpqnw6gy+770AH1aalT349U6OphdKdlc3Lu5p0MRERFp3Oxr0u2T9H0rbMuHnFt9ulRS4nrE8boo3S5JT97ovP3Pl20JOsCf040kfet8SN9n9BVvc4Yx4NuepUZz9D6Tnc9TUgxrPrH1H//9GQgurZjqdUXVYvbxs03J1mOckaT/Od22vd+1VTtfLaonnwqpLyzzeZdnxrhe1mVvuybchcXlHxvpoqa9p90UahZTzu5QYQxSuz67bgAv273vUjVzrhvA2xP6cJGSdBEREc8qtEvSC+1mLTqy2bZ8aE3F5/nrDXi2ldFfG2DjN0YSWZfkpcMvD8CB1cZUZxaH1timPyougvn3w6InjfUzHzTu9/xp1K5v+NpY73WF0Y/dMkDbriXGRYqyfnscvr3FsSz7MES1hR6XHf9z6XUl9BhvG2yu47num8rXAapJl2pW/nxlp3eIcTvidEVJelSQc5J+1Smt2HAgnd1HjX+SNwxuW69GQG/IbjyjHSt3H+PKgS2dBv+TqokNC+DsLpVvdSIiIiIn6N/Z4B8CgZGOI3/bJ+aWmvSifGOKL4uUbUaZj103tcI8+OZmiO8Kp06BRdMgPx0+GmOMTP7VtYAJHkmrwSdVCfNugr1/wfg58NP/YMdCo2a7yK7l6pHNRryJ/Y2LDaml3U4H3QGnTjXmLS8phOwU41xgm96sWW9jxPSco3B4o/F6WBQXwapZxvLJE6D1YKPZe0gMXDwLfE+gG6pvAIx9A0bPgPxM432tw5SkS7WqaErxZhHOiUaP5uGs3Z/O2F7l1xJGukjSuzYNZ/HdQ+jy0M/kFBRzVue63U+7MWkWEcjPd7iYzkJERESkLkveCN/cZFu/6kdoNchYdmjuXjre0tEdYC4B/9KBifPTYfuvRv/1NmdAcLQxv7fl1meykXDml07r9ZWl2XVFv6Rr2L5/bPOMv32mMZo6GAk1QFC00Uf8p7th3RfGDYw+42Nehy7nG+vB0ZB5yGj2n74XTN7QvI+xzccPWg40Xp9dvzsm6bt+NwbgC4yCkS+Btw90HAE+AeDlftrZKvH2rdX5zo+XknSpVQnhzlfA3ruqL8t2HOWck+JcHGETYdcnfca4XjSLCLQOTvbz7aez71gOfT08XZqIiIiI1HMpZebx3v6rLUl3GDiutFb92G7jvkkbozn1vr/gs/FGWafz4NKPYf1XdudbaCSjrlR1arLqtPYT27IlQbcX3tzox520FjbMg4QeRq30yBcgsZ9tP0uSvq10wLi4LsY0ZhatBxuv6fz7IG0fjHjaeN6WedC7XmAk6FDrU5/VFeqT3gCYTKZyb4888sgJnXvevHmV3t8yxZo7rubJbhLiz6geTfH3Kf8KmX3/9WEnxTkMQNeiSVClR20XERGpb+rSd71IvZGXAW8Ohs8ud58U2yspMW5pex3L7ZN2VwPHWWqag2MgtpPjsdsWwOJpRrNxiz9fhuLSgZZNZX7/2p+/pmUmwbvnwJwrjCT58CajvKmbcYSi2hgXEM5/De47AJN+hBv+cEzQwXgdAHb9YdzHdXXc3mawbfnvNyD7qDGt276/wScQTrvrxJ9bPaea9Abg0KFD1uU5c+bw0EMPsWXLFmtZSEhIrcVSUZJ+IvNkt4q2XUmrKKEXERFpSOrSd71IvbHpW2OQs0NrILIVDHvS/b7FRfDeOUbCnVg6fVrzfrB/BST9Z9vPvia9MNtI6nNSjPWgaGNAMku/ajCScUsNcf8b4J93bAPLhcTB5XNh+avGPORgJP4n0ve6Kr673UiMwbiIYUnSR70MJUVGM/N5N9pGqy9vyjR7QaUVZ5a+6jEdHbfHdYMm7eHoNmN94zzYXZrQ97jUY3OT1yWqSa+I2Wz8sXjiZq5cv5T4+HjrLTw8HJPJ5FD22Wef0blzZwICAujUqROvv/669diCggJuueUWEhISCAgIoGXLlkybNg2AVq1aATB27FhMJpN1vTw1maS3jQnhvav68P2tpx73OURERJzou75K3/Ui9YZlZHEwRigvz+bvjNHVj+22JcxdRoPJy6hZtzRpL1vTXZRrDJAGENwEOgyDCd8aTcBP/z/bfidPhBHPGKOMW4Q1hYTucMFbRg0yOE7rVtPsp4rb9zfkpQEmiO5gDPAWdxLEdrHtE1XJJN1Sk24RU2YUdS8vuG6xcdECjOnrtpe2NOg1oQpPoOFSTXpFCnPgqaaeeez7Dp5wP4zZs2fz0EMP8eqrr9KrVy/+/fdfrr32WoKDg5k4cSIzZszg22+/5fPPP6dFixbs27ePffv2AfDPP/8QGxvL+++/z/Dhw/H2rrj2uriCHxuu5jqvijM7ld9vXUREpMr0XV+l73qRemP/StvykU3l9/e27zNuEd8dWg4yank3/wADb3asSQcjqc5JNZaDSmezaTPYuBXlw+6lkJsKZz9mbDvlVlj9IZiLjT7dFn7BRsJfXUn66g9h799wzuOuB0oryDb6jVts/Ma4j2zpWJPf5XxY+6mxHNWmco8dXKYLatmadDBGzm85CP6eCf99ZpQFhEPTnpV7jAZOSXoD9/DDD/PCCy9wwQUXANC6dWs2btzIm2++ycSJE9m7dy/t27fn1FNPxWQy0bJlS+uxMTHGVbCIiAji4+Mr9XgV1aS7GqFdREREjl9tf9eL1Av5maU1w6Vyj0H2EQhxMxNQxgHnsqY9ofMoI0n/92Pof6NzTXpBtmNzd3s+/jD5J8eyJm1h0k/G3N/th9nK/YIgB8cp3vb9YyTN9jHnpRutAlqf7n7Ec7MZfrjLaGp/aC3cuNR5n9RdjuuWRLz1YMfytmcZNeMlRZWfVzzUrrl6WHOjq4Er9rX0AC1Prb5R3Os5JekV8Q0yrnJ76rFPQHZ2Njt27ODqq6/m2muvtZYXFRURHm5MEXHVVVdx9tln07FjR4YPH855553HOeecc9yPWVGSrjnMRUSkztF3vUjDk2a0FiEgwpgT+9guY35v+4T32B747XEYeAtkJhtlvsFGX/POo4ya3e6XwMLHjTm9t/7kmESDkaRbm7tXchDjFv2dy3xLW9RYpnXb/CN8Ng6a94VrfrXtN+cK2LUEzn3eGGndlZyjtoHpktdB1hFjrnF7lv7iZfUc77ju4wc3LjeS9ICw8p+XRccRtuWWp7hvvRDV2uj3brnw0eOyyp2/EVCSXhGTqd4O/Z+VZfyRv/322/Tv7/jPwNKc7eSTT2bXrl389NNP/Prrr1xyySUMHTqUuXPnVvnxikvMFSbpPt4aBkFEROoYfdeLNDyWEdojWhiJ+bFdsPx1WDQNLnjTKP/2FiPh3fKT0TQdYOJ3sH0B9LvOWA+MhP7XwR8vwO/PQmF5NelNjj9ey/8gy7RuS1807vf/47jfriXG/Yq33SfpZaeQ2/U7dLsIktbDnMtt/evBuIhhaXEQGGUbNM9e2QS/IoERcME7sPwVOOMe9/t5eRt99P95G1oMNKarE0BJeoMWFxdH06ZN2blzJ5dffrnb/cLCwrj00ku59NJLueiiixg+fDipqalERUXh6+tLcXFxhY9lNpvJL3Ler0mIH6d3iOHTVUkn9FxERETEWW1+14s4Kco3pvGKbFnxvrUtvbQmPaKFbSCzraVNz3+6B8Z9YgxYBrbaa4D4btC8t+O5BtxsTJt2aA1Qpla40EWf9OPhV9qqxtIn/cCq8vcvKXK/rWySvmcZnHQBfHwhZJX5TX7px0btfF4a9JlUfXO0d7/YuFVk5PMw9BGjH7yXKvMslKQ3cI8++ii33XYb4eHhDB8+nPz8fFauXMmxY8eYOnUqL774IgkJCfTq1QsvLy+++OIL4uPjiYiIAIxRXxcuXMigQYPw9/cnMjLS6TGSM/JIyconOsSYA93bZLIOIGfC6V+ZiIiIVKPa+K4XcenDMbB3GVyzEJr38XQ0jiw16eGJzjXBliTWyxewqxkPijaad5cV3ASCYyHzIFDaajSsmdGPPeOgLck/kSTd0tzdkvSbS2zbivKN/u32igvdnyuldGoz/3DITzemOjv4r3OCPv4LaH0aXL0A1n8Jg24//vhPhL+mkCxLlysauGuuuYZ33nmH999/n27dujF48GBmzZpF69atAQgNDeXZZ5+lT58+9O3bl927d/Pjjz/iVXol64UXXmDBggUkJibSq1cvl4+RnJFHcYmZ5Azjn1x4kC9RwX74envh5+PFpEGtiAr24+5hLkZ2FBERkRNSG9/1Ii7tXWbcr5lduf3LzgJUUgLZR21NzcuzYxE82wZe7gnpLgZ5K8tak54IEWVq+o9ugy+vhYJMx/LQcgZPDC6TgDct/VuxjCDv5Wv0YT9e1ubu2ZC0znGbpaY+367GP30vfD/V9bnS9hj37Yca96m7YPP3pY8TasxRPnk+dCgdmyKmAwy511abLx5nMpsrOUFnA5GRkUF4eDjp6emEhTkOfpCXl8euXbto3bo1AQEBHoqw/vlvf5rDekJ4ADGhAQ6vp7+/P6bqaj4jItLAlPfdJMfH3Wuq7/rqpdezkXukNCkdeAsMe7L8fdfOge+nwJjX4KSxRtmPd8OKt4zlPlfDeS+6PrYoH17pYySmAGc/DoNuK//x3j4LDqyESz6CkDh4rxKDJbYbCld86Xrbh+fDzsXGsskbBv8PFj9lTNOW9B+ExMNdWyp+DHe+LZ2abcgDRrI8/z7bthv+hPiucHQHvHKy43EPHXNuJv7m6cao7qNfNfrdY4K4rsYgcmPf1ABtHlKV73rVpEu183bRn0QJuoiIiEgDkm9XC20/r7YrZrORhBZmwxdXGbXPs86zJegA2+1GMM84aAyM9s3NRp/3Q2ttCTrAzkUVx2dfk24/BVh5TdLbDXW/zX56tcBIY2RyMBL0is5bGX6lTb4LXdWkHzXus5Kdjzu2y7nMMrJ9017gHwaYjQQdXA8MJ3WO+qTLccstKCK3sMSpXAO4i4iIiDRwmXb9m8sbxAyMJLvYrkn7O2fZlpv1MWq8s48YyXzGQXh9AORnGNtz06DL+cZycIyx347fjFrtNmdASTH8Od1owt1ltLFfYZ4toQ1vYTRVP/d58PYzRkv/9yPb4588EVZ/YCx3v9T9c7CfXi0oynnu77LN4avK127gOMsI7hblJekHVhlzr1ssfAxyS5vHRyQa2w7+WxpjjPs5y6VOUTolx23b4Sz2H8txKvdWrbmIiIhI3ZObBnkZFe9nSZbL6xVrn6RXdM5Da437Ju1s/cMTehoDlk381lgvzDFq5xc9aUvQwehLvedPY7nDMFvCPv8B4/6v143E9PMr4Uhpc/OM0j7rvkFGQg3GdGW9JzpOt9h+mNFMf/J8uHaRbV9XHGrSoyCqbZntJ5ikWwZPW/GWEb9PgK1m35qkHzbuu5xvdDEA+P0Z27Rwq2YZU8VZBIRD68G29RYDqm/0dqlRStKl2nl56Y9fREREpE7Jz4LXB8Lz7WHd3PL3XfoivNgZ1n7qfh/7Wt289PLPd7R0tPG2Z8Ft/8KUDUZSnNjPSJr9Qo3taXthwzxjefJ8iC4ddHjLz8Z9ZGs4b7oxSFvyOvj1Efhzhu1xfn/WGPXc0gQ8PNE5KR1wk9G0vPdVcPnn4B9qJK/NyvT1Lsu+pjwoylg//zVbmc8JjskQWGZWhdaDjRHkAbJL52G3XBgJiYdTpxgXDo5uNy5irHwfvrMbnb15P+Pefu5xy9zvUuepubsLjWwsvSrLyS+ioNi5mbuFpSZdr6OIiNRV+o6qHnod65HdS0unEAN+vgc6jXTsS56yDbx8jAR04WNG2bwbjYHRYrs4D06Weci2nF9BTXrKduM+uj14eUN4c8ftoXFwNBPWfWH0yQ5tavSdjukAKVtsU4dFtjLi6zgcNn0HS19yPM/6ucbNIr6rcyyRLeGevVR5kmD7mvLA0hr3XlcY/ebBVst9vALL1OKf9xKs/cRYtozWbnmMkFij+X3r02HDV8brsKd0pP3+NxoXHCyjzzfrDX2vMS4itDrtxGKUWqMk3Y6vry8AOTk5BAZWMABGI2U2m9l+JKvcfSw16Tk5RlN4y+sqIiLiafqur176rq9HLCOTg9Gve/WH0P96Y/3Qf0Y/8eIC5+NmDjJGHB98t2P5EbuRzCtq7m6pSW/SzvX2kDijRnjTd8Z6u7OMGvDoDo77WQZrO+9lo1Z9WWktelgzI3kvO+BaMzdzt3t5lx+vK03ta9pdXJyK7Vz1c9qzb2of0xnCm0FUG2M9dadxb2m9EBJn3Cf0MJL03X8Y6z2vgOHTHFsPeHnBSLsm8FIvKEm34+3tTUREBIcPG1epgoKCNCp5GUXFJZiLXPwDt1OQl0dGXi6HDx8mIiICb+/j+EcoIiJSA/RdXz3MZjM5OTn6rq9r8jKM5tuuPtP7/jLuW51mJHVLnoOe48HbH+bdVCZBN4HJC8zFxuqiJ6DtEGNAs7w0GPqoY0JcXk16cSEc220sR7d3vU9IrHGfusO4tySnlubuYNQEx5XWjAc3gbMfsyXpRflwyYdGM/7fn7ENZNfcTZJ+PCISjebtCx9znMLs+j9g3edw2l0ndn775u6WQeos/d6Plr4ulhYFliS9aU/bMT3GwegZ6nPeQChJLyM+Ph7A+uUtjgqLSzickV/uPn65Rs1ERESE9fUUERGxWLJkCc899xyrVq3i0KFDfP3114wZMwaAwsJCHnjgAX788Ud27txJeHg4Q4cO5emnn6Zp06bV8vj6rq8++q6vQzbMgy8mwvCnYcCNztstifI5T8DcSUbt7PLXjKQ+eR0ERMCwp4wm8OHNIb4bHFgNs841jrMfkb3XlXB4k229vD7px3YbSbNvkNGM3ZXQBMf18ETjvuUptjK/EPDxt62bTDBsGsy/F0Y8YyT2g//PaB3wxSRjILqEnu7jOh69rjBu9hK6G7cTZd/c3ZKwWy5W5KQYr7GluXtoaZLe4hSjz3lkK+OixfG0EJA6SUl6GSaTiYSEBGJjYyksLPR0OHXO8h0pPLJofbn7LLzzDHx9fXVVXUREXMrOzqZHjx5MnjyZCy64wGFbTk4Oq1ev5sEHH6RHjx4cO3aM22+/ndGjR7Ny5cpqeXx911cPfdfXMV9MNO5/vsc5Sc9Lh9xjxnKTdjDkfvjyamMucsuAZ0MfgV6XOx7XYoDrx9r4jeOUauU1d0+xNHVv69yv3SKiheO6pc96RCJ0vwz++wwG3e583IAbodtFxtRiFgHhcOVX7uOpq+xr0i0XIwLCIDgWsg/Dp+Odm7v7+MFls2s3TqkVStLd8Pb21hcP8NKCrSzcnMxn1w0kxN+H/RlFHMgsdthnXL8WfLpir3U9IOAER7cUEZEGbcSIEYwYMcLltvDwcBYsWOBQ9uqrr9KvXz/27t1LixYtXB53PPRdLw1G2l7H9YyDxgjgf75kNG+3JOJBTYypvjqPMmqmc1Js5fZNuC28vKHjSNjyg2P5b48b9x2Gw9afoSATSkpcJ+HW/uhumrqDbVo2C/uB5Ua/At0uhjaDcWIy2ZrK13e+dr+fTXavY/tzYM3HsGeprcx+OjhpkDQFm5Tr5YXbWH8ggy9W7gPgQFqew/aXLu3BtAu60TEu1BPhiYhII5Ceno7JZCIiIsLtPvn5+WRkZDjcRBoNS1N2i91/wtafjP7T755t6+sd2cq49/E3Bmez6HO140jv9sa8DuFuLo7Z127b92k3m41+4h+MhgUPGWXu+qODY026ycux+buPH7QfCt6NaHBCk93Fw7LN66+cB96qZ23o9A5LpRSWTrl2KD0XgFvPbMeVA1oSG2Zc9TutfTRbkjM9Fp+IiDRMeXl5/O9//2PcuHGEhYW53W/atGk8+uijtRiZiIekH4Af7oSwBGOaLnCe/itprWP/77/fMu7ta6yHPgJp+6Awt/z5swMjjCQ6vUxtfVQbxxHPiwuM2uD8LJh5qm2ucgvLlGCu2CfpwbFKQgMjbMstBoC3n/H6RrQ0BvCTBk816eKW/dynXqUjRR5MM5L0DnGh1gQd4M5zOnLNqa356qZTEBERqQ6FhYVccsklmM1m3njjjXL3vffee0lPT7fe9u3bV0tRitSyH+8yaslXvgc5qUaZpa+yxaH/HMv2ls6hbT8FWlQbuG4R3PwXhMRQrtPvdC7rMNxIHi0sNek7Fzkn6ABtz3IuswiwuwDX/ZLyY2nIRjxrzEt/mt3rbTLBtb8Zg8SNfsVzsUmtauSXqaQ82QW2vufeXpYk3Wju3jTCsUlUoJ83D5zXpfaCExGRBs2SoO/Zs4fffvut3Fp0AH9/f/z9/cvdR6RBsEzHBcYI7UFRtoS8xSlGQp70n20aL3uxnY7vMdueCbeshP3/wLzSQen6XmP0QffyMUZvLyodSK7IxSxAZz9mNFsvz7nPw74VcMY9xxdjQ9D/etvc9fbiu8Hkn2o/HvEYJeniVmaebcTb4hIzxSVmkjIsSboGhxMRkZphSdC3bdvGokWLaNKkiadDEqk77Kc7O7rdmAs864ix3vp0I0nPPQbJG5yPjTnOJB2MPuVhzWDzD9BhmDFaOxjzrJcU2WrSMw85HvfQMfejutvrd61xExEl6eJeVl6RdXnWst18sHw3xSVmvL1MxIYqSRcRkeOTlZXF9u3breu7du1izZo1REVFkZCQwEUXXcTq1av5/vvvKS4uJikpCYCoqCj8/CqojROpb8xm+PleyEuDMW8YzZvLk5dmWz5a+ndkqUmPaGHMt52bCkc2Ox9r39z9ePgFOU/55eMHhdm2JD2jTJJemQRdRBwoSRe3MuyS9P3Hcq3L8WEB1ubvIiIiVbVy5UqGDLENfjR16lQAJk6cyCOPPMK3334LQM+ePR2OW7RoEWeccUZthSlSO/b9DX+Xjrlw2p3lj4JemAdFdjPtWBJxy8BxIXFGbXduqm2f5v1g/wroeK5t/u3qZOmXvnOx0fw+86Bt2zlPVv/jiTQCStLFLfvm7vbU1F1ERE7EGWec4TA4aVnlbRNpULKOwNzJtvWMg+6T9O0LYdfvjmWbvoO/34SM/cZ6aDyENYXkdcZ6REu4+hfIPgLBFQwOd7y8SxP/n/7PuLdM1zb8mfJHjRcRt5SkC8UlZm6evZoO8aFc0b8Ft3z6L+P7tXBbW96qSXAtRygiIiLSAK37HDIO2NbL9ucGozl8cSF8fIGtzD/caBafl2ZLjsEYsT3Mbuq1Vqca+4XEVnvoVmUHhLNM1ZbQQ03dRY6T/nKElbtT+XlDEjMWbmPGb9tYsSuVO+asIdOuubu9NjEhtRyhiIiISB224zfYs6zqx5Ud3M0+YQf46w14rq2tObxFYAScfpdjWVhzo894YKStrPXgqsdUVd5uxomoyQsDIg2cx5P01157jVatWhEQEED//v1ZsWJFuftPnz6djh07EhgYSGJiIlOmTCEvL6/cY6R89o0Kd6fkWJfdNXdvG6OadBERERHMZqMf9kdj4f0RtnnL7aVsg6+uh11/OG9LKm2WHtPZuM+w68+992/4+R7IOQoLHnI8LjACTrkVxr5lK4sobWberHfpPpHQ9cLjelpV4i5JdzUFnIhUikeT9Dlz5jB16lQefvhhVq9eTY8ePRg2bBiHDx92uf8nn3zCPffcw8MPP8ymTZt49913mTNnDvfdd18tR96w2Hf9S8stsC7vOJLlcn/VpIuIiEijl58Fr5wMM3rZyjZ957hPYR682gf++wwWTyuzLReObDGW259t3GccgnVzjUHY3jvH/WMHhBv3XUbbyvyCjPuO58Jln8Ctq8G7Fnq2uhqMzssX/MNq/rFFGiiPJukvvvgi1157LZMmTaJLly7MnDmToKAg3nvvPZf7L1u2jEGDBjF+/HhatWrFOeecw7hx4yqsfZfy5RbamrVvOpRpXV6+86jTvj5eJlpEBdVKXCIiIiJ11r6/jFp0e9t+gYIcSN5o1Kof2WTbtne5Y83Iny9Dcb7RTL3FQKNsyw/w5dXw4fnlP3ZAhHHvG2gk5FFt4PTSvuleXtBpJARFndDTqzRXNenB0RVPJScibnls4LiCggJWrVrFvffeay3z8vJi6NChLF++3OUxp5xyCh9//DErVqygX79+7Ny5kx9//JErr7zS7ePk5+eTn59vXc/IyKi+J9FA5BQUW5eLS2xfHvtScx32+/G204gL88fPx+O9JEREREQ8qyDbuWzf3/BiJ8hLB59AOON/tm3mEqPPeXhzY33jN8b9mQ9Uff5y+wS800jj5imukvQgNXUXOREeS9JTUlIoLi4mLi7OoTwuLo7Nmze7PGb8+PGkpKRw6qmnYjabKSoq4oYbbii3ufu0adN49NFHqzX2hsY+SS9Ph7gQfLyVoIuIiIhY5ya3l33EtlyUC//Odtx+cA3kZcDR7XBst1HWvC9EtgQvHyhxPWivk/juxxNxzXDV3D24Se3HIdKA1KuMa/HixTz11FO8/vrrrF69mq+++ooffviBxx9/3O0x9957L+np6dbbvn37ajHi+iG3Ekm6t5dJCbqIiIiIRVaybXnYNNf7HN3muP7DnfDGQPj8SijMAUwQkQjevhDVtvKP3bxPlcOtMS6bu9fQnOwijYTHatKjo6Px9vYmOTnZoTw5OZn4+HiXxzz44INceeWVXHPNNQB069aN7OxsrrvuOu6//368XMzF6O/vj7+/iyt8YpVb6D5JTwgP4PPrBxIa4LGPioiIiIjnmc1w8F+I7Wz0Bbck6UPuh95Xwfx77XY24TB/ziUfGiO0W2rPLfyCbTXRMR0gZUvlYontcnzPoSaoubtItfNY1aifnx+9e/dm4cKF1rKSkhIWLlzIwIEDXR6Tk5PjlIh7e3sDYLYfiEOqpLzm7olRQSRGBRER5GZ6DREREZHGYMdCeHsIvHWGMTicpbl7SKwxsrqf3ew37c5yPLbFKTDxe+dzFtjNpBPZuvzH9w2CPpPhvJeMmve6wsfFb8RQ1xVuIlI5Hq0enTp1KhMnTqRPnz7069eP6dOnk52dzaRJkwCYMGECzZo1Y9o0ownRqFGjePHFF+nVqxf9+/dn+/btPPjgg4waNcqarEvV5Ra47//ULCKwFiMRERER8bCSEvjlASOBHvWybZTy/auM+yObYdX7tpr0kNLxlYKjbUl3u7Nh+6/Gsk+g+9HOE3rYlsOaOm/3C7GdMzjGSNDrGm8XLVZjOtV+HCINiEeT9EsvvZQjR47w0EMPkZSURM+ePfn555+tg8nt3bvXoeb8gQcewGQy8cADD3DgwAFiYmIYNWoUTz75pKeeQoNQtiY9KtiP1GxjvvSE8ABPhCQiIiJSc0pKjKS5bOKcmQxzJ8Oepcb6aVMhslXptkO2/dZ/CWml4xxZao2z7AaN6zQSfi4d2b0o1/Y4bYbAzkXQZYwxynufybZjXNU+h8RCammSHppQ1WdZO1w1d4/pWPtxiDQgHu9ofMstt3DLLbe43LZ48WKHdR8fHx5++GEefvjhWois8SjbJ711dLA1SW+qmnQRERFpSJI3wFtDjAT8jHts5cWF8PX1tgQdIPuoLUnPOGArP1Baq27ygujShLTjCFg/FxJ6GoPBnXYX/PE8nDrFdtzFs4wEv8c4o4m8vVAXNekh8ba52KOrOE1bbXHV3D2iRe3HIdKAeDxJF88rO7p7k2DbP9uoYPVFFxERkQZkxVtQnA+Lp8Fpd9r6d393u1HL7RMARXlGWU6K7bj0/c7niu5gS7bPfMCYTq3XFcb6kPuhzWBoZjcSe2AE9L3adVzuatItqjqXem1x1dzdS91QRU6E5tQSp+buAb62f6wRQXVoYBIRERGRE+UbbFve9Xvp/RJYM9uoGb/4A2hbOvBbdgqs+RT+nAGHNxplbe0GhbOfrzyqNQy4AfxLB5Dz8oLWpzvXmLvjMkmPsy2HJ1buPLXNvrl7j3Fw27+ei0WkgVBNujjVpBebzdx2Vnt2p2QzoHUTD0UlIiIiUg0Kc2Hf39DqdCNxzrbrO/7xhTD0EdvUaL2ugI7DYcNXxvruP2Dtp47n63GZMdI7QOfzqi9OHxc10vbzjVua3dc19iPNn3YnRLXxXCwiDYSSdCGn0HF091HdExjetY4OTiIiIiJSFX/OgMVPQd9rYOQLjgPAAfz6CIQ1N5bbDzPuLfN8W0Zot+h7LXS90OifHtcN2g+t3liHPgq/2o295BcMZz5oTPfWrHf1PlZ1se8SUFcvJIjUM0rSxdrcfeYVJxMR5Ef/1lEejkhERESkmvz+jHH/zzvGXOS7/3DeJ6O0v3mLgcZ9UOlvIUute88rjL7kTXsZI7XbDwZXnU69A5L+MwaXA6Op/MCbauaxqkvaXttyXZq/XaQeU5/0Rq6kxMyBY7kAdIwPY0CbJphczeMpIiIiUh/FdrYt/3K/+/3anwPBpd38gqMdt7UYAM1Odj3XeXXztZtZx77/fF3VYbhxH9XWs3GINCCqSW/kth7OJL+oBF9vE4mRmm5NREREGpiyzdstThoL+Vlw+l1GjXm7s23bgsok6faJfk2zT8wDwmrvcY9X90shONa4iCEi1UJJeiN2KD2X4dONJl8tmwTj462GFSIiIlLPbfsVNn8Pw6eB2Qw5R533sfRPd8d+6jOAMBdzmNeUzIO25cT+tfe4x8vLu/r75os0ckrSG7H/9qdbl9NzCz0YiYiIiEgVFeQYc55Ht4fw5pDQwyiffaFx7xdsS7b9QqDfdfDnyzD8aeh/XfnnLjsdmv0o6zXN127KtsCI2ntcEakzlKQ3YoXFJdblq05p5blARERERKpq9YeOI6Hftd2xL/nyV23L4c1h6MMw+P8c+3y7Yz8/OabaHRDtrIehpNgYRE5EGiW1b27EsvJsU69dfWprD0YiIiIiUkXHdjmuz50EOxe73je2i3FfmQQdysxZbq5qZCcmvBlc9C7Ed6vdxxWROkNJeiO1JSmT++etB2B0j6YE+Hp7OCIRERGRcuRlwMZvoKjAWC/bBH33H/DRGNfHalAzEalH1Ny9kRr16lKKS4wrw8H++hiIiIhIHVRSDGs/g9A42LMc/nge2g+Dyz+HgqzKnyeua83FKCJSzZSdNVIFRbb+6KEB+hiIiIhIHZOfCctfg8XTHMu3zYe0fVCQbay3Pwe2/eK4z0kXwPmvwccXQFbyiY2SHhBx/MeKiBwHNXdvRI5lF/D8/C3sPOJ45TnYT0m6iIiI1DGzznNO0C2S19uS9JanwA1/wim32ba3Owv8gmDyz3Dbv8ZyVY37DMJbwGWzq36siMgJUHbWiDz14ya+WLWft//Y6VAe7K/+6CIiIlIHJK2H7MOw4zc4tMb9fqm7bM3d/UIgvisc3mjb3nLQicfScYRxExGpZapJb0TWHTDmRc+3a+oOEKI+6SIiUouWLFnCqFGjaNq0KSaTiXnz5jlsN5vNPPTQQyQkJBAYGMjQoUPZtm2bZ4KV2nPoP5g5CD4aC8teKX/fY7sg3y5JB2gxwLgPCIfIVjUWpohITVOS3ojEhPq7LNfAcSIiUpuys7Pp0aMHr732msvtzz77LDNmzGDmzJn8/fffBAcHM2zYMPLy8mo5UqkV/7xr9D3f9F3lj1nxFuxZZiz7BRv3ES3gpr/h5hVgMlV/nCIitUTZWSPSJNjPZblq0kVEpDaNGDGCESNcNyM2m81Mnz6dBx54gPPPPx+ADz/8kLi4OObNm8dll13m8rj8/Hzy8/Ot6xkZGdUfuFSvogJY9jL89sRxHp9r3FuSdIDYTicel4iIh6kmvREJD/R1WR7kpz7pIiJSN+zatYukpCSGDh1qLQsPD6d///4sX77c7XHTpk0jPDzcektMTKyNcOVErHy38gn6WQ+DyRvaDAHvMi0D/UOrPzYREQ9Skt6IFJXOi16W61IREZHal5SUBEBcXJxDeVxcnHWbK/feey/p6enW2759+2o0TqkG2xdWft/+18PUjTD+c7jvIHQeZdtmX5MuItIAKElvRArKDBhnERcWUMuRiIiIVC9/f3/CwsIcblKHlZTAvhXG8vmvQ++rbNui2tqWg2Og7zVGIh4aDz5+4O0DMZ1t+1gGjhMRaSDUGbkRKSg2kvQQfx8mn9qa5pGBBPv50DpaV6BFRKRuiI+PByA5OZmEhARreXJyMj179vRQVFLtFk+D/HTwDYbul0BILKyaZWyL7QypO4zl8Z9Ds5Odj49ub1tWki4iDYxq0hsRS036/4Z3ZOrZHbikTyIjuydUcJSIiEjtad26NfHx8SxcaGsKnZGRwd9//83AgQM9GJlUm6ICWP6qsXzGPeDtC61PL91ockzKAyNcnyOihW1Zzd1FpIFRTXojYknS/Xx0bUZERDwnKyuL7du3W9d37drFmjVriIqKokWLFtxxxx088cQTtG/fntatW/Pggw/StGlTxowZ47mgpfocWAWFORAUDQNvMcp8/OGO9VCUD/tX2PYNiHB9jma9Ia4r+IcZx4qINCBK0hsRS3N3JekiIuJJK1euZMiQIdb1qVOnAjBx4kRmzZrF//3f/5Gdnc11111HWloap556Kj///DMBARpDpV4pzIX3z4WQOBj9CoTEGOW7/zDuW50KXna/SSJKR+Tf/4+tLCDc9bm9feGGpWA2a050EWlwlKQ3IvmWmnRvTbkmIiKec8YZZ2A2u59bxGQy8dhjj/HYY4/VYlRSrTIOQupOOLjaWF/wIIydaSwfWmvcJ/Z3fayPn23Zq5zfLCaTEnQRaZCUpDciau4uIiIiNS5pHcw81bFs7afQ7zpjRPfN3xtlsZ2djwXoeC4k9HCfxIuINHBK0huBRVsOc8+X/5GckQ8oSRcREZEa9PdM1+VvD3Fcd5ek+wbC9UuqNyYRkXpE2VojMOn9f6wJOoCft952ERERqSF56Y7rka1d7xcSV/OxiIjUQ8rWGiHVpIuIiEiNKZukd78Uul3sWNbtYvUnFxFxQ83dGyF/JekiIiJS3UpKYP1cOPSfY3lILDRpC+u+MNav+BJane58vIiIAErSGyXVpIuIiEi1W/IcLH7KuTwkDjqNNPqax3SG6Ha1H5uISD2iJL0R8lWfdBEREaluS551XR4abzRt7zyqduMREamnlK01QqpJFxERkWpzeBO80htKilxvj+1Su/GIiNRzytYaqDX70li7L83lNo3uLiIiItWiuBC+nwJHt7vfxy+o9uIREWkA1Ny9AcrOL2LMa38CsOWJ4U7bVZMuIiIiJ6wgG94/Fw6tcb9Pj3G1Fo6ISEOhJL0BSs0usC7n5Bc7bdfo7iIiInLClr3iPkHvfhm0Pxs6nlurIYmINARK0hugrHxbn7CcQuckXc3dRURE5ITtX+m4fssqKM43plo7dQoEhHsmLhGRek5JegOUnltoXc7JdxzExcfLhJeXqbZDEhERkYYmZYvjelRr8PKGuJM8E4+ISAOhJL0Bsk/Sswsca9LVH11EREROWEEOpO0zlsNbQLuzjARdREROmJL0Bsg+Sd90KMNhW4CvvkBFRETkBB3dBpghMAqmrPN0NCIiDYqqVRugDLsk/d6vHL84QwN0XUZERESOg9ls3ACS1hv3MZ08F4+ISAOlJL0Bsq9JL0tJuoiIiFRJXgak7oJfHoBnW0PaXji42tjW7GTPxiYi0gApY2uAyk3S/X1rMRIRERGp92YOMhJzi88nQu4xY7lpL8/EJCLSgHm8Jv21116jVatWBAQE0L9/f1asWFHu/mlpadx8880kJCTg7+9Phw4d+PHHH2sp2vqhvCQ92F990kVERKSSzGbHBB2MWvRju4xl1aSLiFQ7j9akz5kzh6lTpzJz5kz69+/P9OnTGTZsGFu2bCE2NtZp/4KCAs4++2xiY2OZO3cuzZo1Y8+ePURERNR+8HVYeUm6vwaOExEREVcOrILgWIhItJVZasxd8QuBqDY1H5eISCPj0ST9xRdf5Nprr2XSpEkAzJw5kx9++IH33nuPe+65x2n/9957j9TUVJYtW4avr9Fsu1WrVrUZcr2QXWZudHv+3h5vPCEiIiJ1ybHdMP9+2Py9sf5IOhz6D9Z8AieNdX/ckPtqJTwRkcamyhlbq1ateOyxx9i7d2/FO5ejoKCAVatWMXToUFswXl4MHTqU5cuXuzzm22+/ZeDAgdx8883ExcXRtWtXnnrqKYqLi13uD5Cfn09GRobDraErKCpxu03zpIuIiIiDz66wJegAxUXw7tnw9xvw6WWujxn6KPS/oXbiExFpZKqcsd1xxx189dVXtGnThrPPPpvPPvuM/Pz8Kj9wSkoKxcXFxMXFOZTHxcWRlJTk8pidO3cyd+5ciouL+fHHH3nwwQd54YUXeOKJJ9w+zrRp0wgPD7feEhMT3e7bUOQrSRcREZHKSi4zz/m+v6Aoz1jOTXXcFhIP3S+FQbeDl7rQiYjUhONK0tesWcOKFSvo3Lkzt956KwkJCdxyyy2sXr26JmK0KikpITY2lrfeeovevXtz6aWXcv/99zNz5ky3x9x7772kp6dbb/v27avRGOuCgmL3Sbq/knQREREpz6yR7rddtwgueAtMptqLR0SkkTnujO3kk09mxowZHDx4kIcffph33nmHvn370rNnT9577z3MZnO5x0dHR+Pt7U1ycrJDeXJyMvHx8S6PSUhIoEOHDnh7267cdu7cmaSkJAoKClwe4+/vT1hYmMOtoSuvubu/j656i4iIyHEKiat4HxEROSHHnaQXFhby+eefM3r0aO6880769OnDO++8w4UXXsh9993H5ZdfXu7xfn5+9O7dm4ULF1rLSkpKWLhwIQMHDnR5zKBBg9i+fTslJbYkdOvWrSQkJODn53e8T6XBUZ90ERERqTRTJS/g+4epibuISC2o8ujuq1ev5v333+fTTz/Fy8uLCRMm8NJLL9GpUyfrPmPHjqVv374Vnmvq1KlMnDiRPn360K9fP6ZPn052drZ1tPcJEybQrFkzpk2bBsCNN97Iq6++yu23386tt97Ktm3beOqpp7jtttuq+jQaNEtz9xZRQexNzeHNK3tz71frSM0u4MxOzlPbiYiISCNVXARm9wPwWt24XLXoIiK1pMpJet++fTn77LN54403GDNmjHUqNHutW7fmssvcjAZq59JLL+XIkSM89NBDJCUl0bNnT37++WfrYHJ79+7Fy8tW85uYmMj8+fOZMmUK3bt3p1mzZtx+++3873//q+rTaNAsNenvT+pLbkExJzUNY0DrJiRn5tEhLtTD0YmIiEidkJ8JBTm29aAmkHPUtn7Ww/DPu3DSGIjrUuvhiYg0ViZzRZ3Hy9izZw8tW7asqXhqXEZGBuHh4aSnpzfY/unt7vuRohIzy+89k4TwQE+HIyIiFWgM301VUVxczCOPPMLHH39MUlISTZs25aqrruKBBx7AVMkBy/SaVmDNp/DtLdDqVNi52EjQ/28npO2F6d2MfaZuhrAEj4YpItJQVOV7qco16YcPHyYpKYn+/fs7lP/99994e3vTp0+fqp5SqsGBtFyigvzw9/GiqMS47uLnrf7nIiJS/zzzzDO88cYbfPDBB5x00kmsXLmSSZMmER4eri5uJyr7KCx9EZa/aqzvXGzcB8cY9xEtYPD/wDdQCbqIiIdUOYu7+eabXU5jduDAAW6++eZqCUqqZs/RbAY9/RsXvLHMYfo1DRInIiL10bJlyzj//PMZOXIkrVq14qKLLuKcc85hxYoVng6t/jq8CfIy4LvbbAm6vW4X2ZaH3AenTqm92ERExEGVs7iNGzdy8sknO5X36tWLjRs3VktQUjVLt6cAsOlQBum5hdZyJekiIlIfnXLKKSxcuJCtW7cCsHbtWpYuXcqIESPcHpOfn09GRobDTUrtWwGvD4C3z4TNPzhua9IeBt0Bp071SGgiIuKsys3d/f39SU5Opk2bNg7lhw4dwsenyqeTahAWYBu8b/6GJOuymruLiEh9dM8995CRkUGnTp3w9vamuLiYJ598stzpXadNm8ajjz5ai1HWI+u/Mu6PbnMsb94PrllQ+/GIiEi5qpzFnXPOOdx7772kp6dby9LS0rjvvvs4++yzqzU4qZzs/CLr8kPfbACMBL2yg+uIiIjUJZ9//jmzZ8/mk08+YfXq1XzwwQc8//zzfPDBB26Psfw2sdxcdc1rlFZ/BH+/4XqbpR+6iIjUKVWu+n7++ec5/fTTadmyJb169QJgzZo1xMXF8dFHH1V7gFKxLLsk3UJN3UVEpL66++67ueeee6zTuXbr1o09e/Ywbdo0Jk6c6PIYf39//P39azPM+uHbW9xvC4qqvThERKTSqpykN2vWjP/++4/Zs2ezdu1aAgMDmTRpEuPGjXM5Z7rUPFdJehVn1hMREakzcnJy8PJyvNjs7e1NSUmJmyPEpcLc8rd7eddOHCIiUiXH1Yk8ODiY6667rrpjkeOU7SJJtx/lXUREpD4ZNWoUTz75JC1atOCkk07i33//5cUXX2Ty5MmeDq1+OfRf+dvN+q0gIlIXHfdIbxs3bmTv3r0UFBQ4lI8ePfqEg5KqycovdiorLFZNuoiI1E+vvPIKDz74IDfddBOHDx+madOmXH/99Tz00EOeDq1+SaogSfcJrJ04RESkSqqcpO/cuZOxY8eybt06TCaTtVm1ZZCy4mLnhFFqlquadBERkeq2b98+TCYTzZs3B2DFihV88skndOnSpVpb2IWGhjJ9+nSmT59ebedslI5ud13e7zrY8hMMur124xERkUqp8uhit99+O61bt+bw4cMEBQWxYcMGlixZQp8+fVi8eHENhCgVcdUnXUREpLqNHz+eRYsWAZCUlMTZZ5/NihUruP/++3nsscc8HJ04cZekj3gWpqyH8Ga1G4+IiFRKlZP05cuX89hjjxEdHY2XlxdeXl6ceuqpTJs2jdtuu60mYpRybEnK5LfNhz0dhoiINALr16+nX79+gDFNWteuXVm2bBmzZ89m1qxZng1OnLlL0jVFq4hInVblJL24uJjQ0FAAoqOjOXjwIAAtW7Zky5Yt1RudVOiaD//xdAgiItJIFBYWWqc5+/XXX63j0HTq1IlDhw55MjQpqygf0vZ6OgoRETkOVU7Su3btytq1awHo378/zz77LH/++SePPfYYbdq0qfYApXz7UiuYXkVERKSanHTSScycOZM//viDBQsWMHz4cAAOHjxIkyZNPBydOEjfb4ze7hsMHUfayvte67mYRESkUqo8cNwDDzxAdnY2AI899hjnnXcep512Gk2aNGHOnDnVHqCUL8jPm5wCDdYnIiI175lnnmHs2LE899xzTJw4kR49egDw7bffWpvBi4fkpMKHo6HrRXDqHZC2xyiPSIRxn0BBNuxeCq0HezRMERGpWJWT9GHDhlmX27Vrx+bNm0lNTSUyMtI6wrvUnuaRgWxNzvJ0GCIi0gicccYZpKSkkJGRQWRkpLX8uuuuIygoyIORCX+8AEnrjNupd9iauke0MO79gqHDMLeHi4hI3VGl5u6FhYX4+Piwfv16h/KoqCgl6B6SW2jUot94RlsPRyIiIg1dbm4u+fn51gR9z549TJ8+nS1bthAbG+vh6Bo5S825dX2fcR+eWPuxiIjICalSku7r60uLFi00F3odkp1vvBdjejpOozJjXC9PhCMiIg3Y+eefz4cffghAWloa/fv354UXXmDMmDG88cYbHo6ukcs+6rieutO4t9Ski4hIvVHlgePuv/9+7rvvPlJTU2siHqkiyxzpwf7e1rL/G96R0T2aeiokERFpoFavXs1pp50GwNy5c4mLi2PPnj18+OGHzJgxw8PRNXI5dkn6RxfAhq+M5ciWnolHRESOW5X7pL/66qts376dpk2b0rJlS4KDgx22r169utqCk/IVFpdQUFQCQIi/7a308VLXAxERqX45OTnWaVh/+eUXLrjgAry8vBgwYAB79uyp4GipUTkptuUdC437xP7QYYRn4hERkeNW5SR9zJgxNRCGHI/s0lp0gGB/H64+tTULNiZzaV81bRMRkerXrl075s2bx9ixY5k/fz5TpkwB4PDhw4SFhXk4ukasINuxJh3ggreh64Xg5e36GBERqbOqnKQ//PDDNRGHHAdLU3c/Hy98vb148LwuPDCyswbxExGRGvHQQw8xfvx4pkyZwplnnsnAgQMBo1a9Vy+NheIRGYcgeb1zecdzlaCLiNRTVU7Spe6wJOmhdk3dlaCLiEhNueiiizj11FM5dOiQdY50gLPOOouxY8d6MLJGqjAXXuoC5hLnbf4htR+PiIhUiyon6V5eXuUmghr5veZl5Rcx5599xIb6A0ZTdxERkdoQHx9PfHw8+/fvB6B58+b069fPw1E1Umn7XCfoIiJSr1U5u/v6668d1gsLC/n333/54IMPePTRR6stMHHvmZ8289FftgF6lKSLiEhtKCkp4YknnuCFF14gKysLgNDQUO68807uv/9+vLyqPGmMHK+D/8JbZ3g6ChERqQFVzu7OP/98p7KLLrqIk046iTlz5nD11VdXS2Di3tLtKQ7rIf7qcyYiIjXv/vvv59133+Xpp59m0KBBACxdupRHHnmEvLw8nnzySQ9H2Ih8eY2nIxARkRpSbVWwAwYM4Lrrrquu00k5QsrUnIcH+nooEhERaUw++OAD3nnnHUaPHm0t6969O82aNeOmm25Skl6b0vY6rnv5QkmhZ2IREZFqVS3t0nJzc5kxYwbNmjWrjtNJBcom6TGlfdNFRERqUmpqKp06dXIq79SpE6mpqR6IqJFa8hwUFziW3fw3tDrNWO7i3OpRRETqjyrXpEdGRjoMHGc2m8nMzCQoKIiPP/64WoMT10IDyiTpIUrSRUSk5vXo0YNXX32VGTNmOJS/+uqrdO/e3UNRNUK/PeG4PvAWaNIWLp4FG76G7pd4JCwREakeVU7SX3rpJYck3cvLi5iYGPr3709kZGS1BieuBfk59kFXTbqIiNSGZ599lpEjR/Lrr79a50hfvnw5+/bt48cff/RwdI1EsYsm7UX5xn1wNPS7tnbjERGRalflJP2qq66qgTCkKgqKHadbiVZNuoiI1ILBgwezdetWXnvtNTZv3gzABRdcwHXXXccTTzzBaaed5uEIG4HsFOeyoCa1H4eIiNSYKifp77//PiEhIVx88cUO5V988QU5OTlMnDix2oIT1/IKHZN01aSLiEhtadq0qdMAcWvXruXdd9/lrbfe8lBUjUReOrze37aeOAACI2HgTZ6LSUREql2VB46bNm0a0dHRTuWxsbE89dRT1RKUlC+vsNhhXTXpIiIijcDqD41EHSC+G1w9H8Z/BgHhno1LRESqVZWT9L1799K6dWun8pYtW7J3714XR0h1K5ukqyZdRESkESjIsS2bqmWCHhERqYOq3Nw9NjaW//77j1atWjmUr127liZN1CeqNliau5/fsylndool2L/aprsXERGRuqi4EA5vtK1nHPJcLCIiUqOqnN2NGzeO2267jdDQUE4//XQAfv/9d26//XYuu+yyag9QnOUVGTXp4/u1oH8bXRgREZGadcEFF5S7PS0trXYCaazWfAJLnofUHbayiBaei0dERGpUlZP0xx9/nN27d3PWWWfh42McXlJSwoQJE9QnvZbkl9akB/h6V7CniIjIiQsPL7/Pc3h4OBMmTKilaBqZjEMw70bn8vNfrf1YRESkVlQ5Sffz82POnDk88cQTrFmzhsDAQLp160bLli1rIj4p49XftnEgLRdQki4iIrXj/fff93QIjVfGAeey636H2M61H4uIiNSK4x51pH379lx88cWcd955StBr0fO/bLUuB/hq0BgREWmYDhw4wBVXXEGTJk2sFQIrV670dFi1L32fc1l0h9qPQ0REak2Vs7wLL7yQZ555xqn82WefdZo7XWqWatJFRKQhOnbsGIMGDcLX15effvqJjRs38sILLxAZGenp0GrXjkXwxVWOZfceAL8gj4QjIiK1o8rN3ZcsWcIjjzziVD5ixAheeOGF6ohJ3CgqLnFYD/BRki4iIg3PM888Q2JiokMze1fTvzZ4825yLvMPqf04RESkVlW5Jj0rKws/Pz+ncl9fXzIyMqolKHEtI6/IYd1fzd1FRKQB+vbbb+nTpw8XX3wxsbGx9OrVi7fffrvcY/Lz88nIyHC41WtmM2QleToKERHxgCpned26dWPOnDlO5Z999hldunSplqDEtfTcQod1fx8l6SIi0vDs3LmTN954g/bt2zN//nxuvPFGbrvtNj744AO3x0ybNo3w8HDrLTExsRYjrgHf3wFmxxZ0tBzkkVBERKR2Vbm5+4MPPsgFF1zAjh07OPPMMwFYuHAhn3zyCXPnzq32AMWmbJJuMpk8FImIiEjNKSkpoU+fPtapXXv16sX69euZOXMmEydOdHnMvffey9SpU63rGRkZ9TtR3/C1cd97EvSZBHkZkNDdszGJiEitqHKSPmrUKObNm8dTTz3F3LlzCQwMpEePHvz2229ERUXVRIxS6vctR6zLzSMDPRiJiIhIzUlISHBqnde5c2e+/PJLt8f4+/vj7+9f06HVjrx04wYw7EnwC/ZsPCIiUquOq730yJEj+fPPP8nOzmbnzp1ccskl3HXXXfTo0eO4gnjttddo1aoVAQEB9O/fnxUrVlTquM8++wyTycSYMWOO63Hrk40HM3jpV2P6tXaxIfwy5XQPRyQiIlIzBg0axJYtWxzKtm7d2nimfE0rnXYtMEoJuohII3TcnZqXLFnCxIkTadq0KS+88AJnnnkmf/31V5XPM2fOHKZOncrDDz/M6tWr6dGjB8OGDePw4cPlHrd7927uuusuTjvttON9CvXK2v1p1uWYEH+C/KrcCEJERKRemDJlCn/99RdPPfUU27dv55NPPuGtt97i5ptv9nRoNS/rMMws7Xse0cKzsYiIiEdUKUlPSkri6aefpn379lx88cWEhYWRn5/PvHnzePrpp+nbt2+VA3jxxRe59tprmTRpEl26dGHmzJkEBQXx3nvvuT2muLiYyy+/nEcffZQ2bdpU+THrowC7kdx3pmR5MBIREZGa1bdvX77++ms+/fRTunbtyuOPP8706dO5/PLLPR1azVv2im1ZtegiIo1SpZP0UaNG0bFjR/777z+mT5/OwYMHeeWVVyo+sBwFBQWsWrWKoUOH2gLy8mLo0KEsX77c7XGPPfYYsbGxXH311RU+RkOZkiXTbvq1ZhHqjy4iIg3beeedx7p168jLy2PTpk1ce+21ng6pZhXmwi8PwLIZtjI/zYkuItIYVbrN9E8//cRtt93GjTfeSPv27avlwVNSUiguLiYuLs6hPC4ujs2bN7s8ZunSpbz77rusWbOmUo8xbdo0Hn300RMN1ePsk/SnL9ToriIiIg3K6o8ca9Gb9YGz6//vFxERqbpK16QvXbqUzMxMevfuTf/+/Xn11VdJSUmpydicZGZmcuWVV/L2228THR1dqWPuvfde0tPTrbd9+/bVcJQ1w5KkTx7Umg5xoR6ORkRERKpVut3vkw7D4dqFENvZc/GIiIjHVLomfcCAAQwYMIDp06czZ84c3nvvPaZOnUpJSQkLFiwgMTGR0NCqJY/R0dF4e3uTnJzsUJ6cnEx8fLzT/jt27GD37t2MGjXKWlZSUmI8ER8ftmzZQtu2bR2OaShTsmTmGXOkhwZowDgREZEGJ9+uO945T3ouDhER8bgqj+4eHBzM5MmTWbp0KevWrePOO+/k6aefJjY2ltGjR1fpXH5+fvTu3ZuFCxday0pKSli4cCEDBw502r9Tp06sW7eONWvWWG+jR49myJAhrFmzhsTExKo+nXrDUpOuJF1ERKQByUuH5a/DwTXG+nnTIbqdJyMSEREPO6GMr2PHjjz77LNMmzaN7777rtwR2d2ZOnUqEydOpE+fPvTr14/p06eTnZ3NpEmTAJgwYQLNmjVj2rRpBAQE0LVrV4fjIyIiAJzKGxpLTXpYgK+HIxEREZFq88uDsPoD23pogudiERGROqFaqmW9vb0ZM2YMY8aMqfKxl156KUeOHOGhhx4iKSmJnj178vPPP1sHk9u7dy9eXsc9nXuDYalJD1FNuoiISMOx7gvH9dA41/uJiEijUScyvltuuYVbbrnF5bbFixeXe+ysWbOqP6A6SM3dRUREGiCvMi3kVJMuItLoqYq6HiguMbMlOROAUDV3FxERaThMJsf14BjPxCEiInWGkvR64IVftliXVZMuIiLSQHx1PeSl2dYT+4OXt8fCERGRukEZXz2w6ZAxLYuXCVo3CfZwNCIiInJCzGZY8Tb895mtLK4rXPC252ISEZE6Q0l6PZCaY4zs/uaVffDyMlWwt4iIiNRpG76Cn+62rV88C04a67FwRESkblFz93rgWHYBAFHB6o8uIiJSr6UfgO+n2NaDY5Sgi4iIAyXp9cCxHCNJjwzy83AkIiIictzMZnjzdMhLt5XlZ3kuHhERqZPU3L2OKywusU6/piRdRESkHklaD388D0d3QNcLoOnJkJPiuE9Ua8/EJiIidZaS9DrOUovuZYKwQDV3FxERqTc+GAW5qcZy0n8Q08l5n4ver92YRESkzlOSXscdyzYGjYsI8sNbg8aJiIjUH5YE3eLIZsf1W1dDk7a1F4+IiNQLStLruNRsS3901aKLiIjUex1HQpfzjQReCbqIiLigJL2O06BxIiIiDUibM6DHpZ6OQkRE6jCN7l7H7U3NASAhItDDkYiIiMgJ6zLa0xGIiEgdp5r0Om7nEWNqlrYxwR6ORERERCrFbIYDq5zLhz8DofG1H4+IiNQrqkmv43YeyQagTUyIhyMRERGRSlnyPLxzlmPZ8GdgwA2eiUdEROoVJel13A7VpIuIiNQvu353LvPyrv04RESkXlJz9zqqqLiE/KISjuUYU7C1aqIkXUREpE7Lz4LMJMg46Lyt9eDaj0dEROol1aTXQf83dy0Dpi1k06EMALxMEOSnK/AiIiKeYDabIfcYfD4Rfn0USopd7/jlNfBqH0jd4Vh+43KI6VDzgYqISIOgmvQ66POV+wGY+N4KAAJ9vTGZTJ4MSUREpFFKz8pm72tj6Ja7wla49EXodz006w0bvobOo6DtENj6U5mjTdDrCojrUqsxi4hI/aYkvQ7LLjCu1Af66W0SERHxhPXfv84g+wTdYsWbtmWn5LzU/YfAJ6BmAhMRkQZLzd3roLKV5mrqLiIi4hkDz5vE92GXsrGkJW+1fB7Ofd6oQQ8Ir/hg30DnL3UREZEKqIq2jjGbzZgAs12ZknQRERHP8AqJJmbMNM596y/C9vhw1ZVn49fvWmOQuKT/YP2X8M87xs7NekOr0+DP6ZDQ05Nhi4hIPaYkvY4pKC6hxOxYFqgkXURExGP6tIoiLsyf5Ix8Xliwhf8N64SXfwi0PAXiuxv3bYaAXzB4+UB0e2h9uqfDFhGRekrN3euYvIISp7JAXyXpIiLSeD399NOYTCbuuOMOjzy+t5eJO4Yao7O/+ftOnvpxk22jfwh0vRCCosDH35gPvdcVENHCI7GKiEj9pyS9jsktdJ7WRc3dRUSksfrnn39488036d69u0fjuLRPIjed0RaAd5bu4qUFWz0aj4iINFxK0usYV0m6RncXEZHGKCsri8svv5y3336byMhIj8bi5WXi/4Z3Yly/RABeXriNXSnZHo1JREQaJiXpdUyeq5p0NXcXEZFG6Oabb2bkyJEMHTq0wn3z8/PJyMhwuNWEp8Z24+QWEQB8t/ZgjTyGiIg0bkrS6xjXNelK0kVEpHH57LPPWL16NdOmTavU/tOmTSM8PNx6S0xMrJG4TCYT4/u3BGDuqv0Ulx3tVURE5AQpSa9j8gqUpIuISOO2b98+br/9dmbPnk1AQECljrn33ntJT0+33vbt21dj8Y3slkBYgA97U3P4fevhGnscERFpnJSk1zGWmvRmEYHWMj9vvU0iItJ4rFq1isOHD3PyySfj4+ODj48Pv//+OzNmzMDHx4fiYucL2v7+/oSFhTncakqgnzeX9DFq6m/7dA1J6Xk19lgiItL4KPurYyxJetMIW82Bq37qIiIiDdVZZ53FunXrWLNmjfXWp08fLr/8ctasWYO3t+dbmF0xwGjynpVfxLUfrvRwNCIi0pBo2PA6Jre0uXuwv+2tycov8lQ4IiIitS40NJSuXbs6lAUHB9OkSROnck9pFR3M/w3vyLM/b2HdgXS2JGXSMT7U02GJiEgDoJr0OsZSax5oN6J7jot+6iIiIuJZN53RjnO6xAHwzh87PRyNiIg0FKpJr2PyCksACPD1ZtKgVsz+ey83ndHWw1E1AKm74NtbwScALngLgqI8HVHNKsyFL6+BnKMw4llI6O7piKQmzb8fvLzh7MeM9ZXvwcr3wdsPiguMbSZvKCmEvtfCyVd6Nl53di6GZa/CyBcgPBG+uQmS1xvbvHzgtDuh8ygoKYFvbobkdRASX/HftOXvIW2Psd71Qjh1SsWxLHy87r9mjcjixYs9HYJL1w9uyy8bk/li1X46JYRx9amtPR2SiIjUc0rS6xhLn/QAX28eHnUS/xveiQDNk37iNs6D3X8Yyzt+g24XeTScGrfrD9j8vbH878eQ8Kxn45Gak3UYlr9qLJ92FwSEwe/PQaab+Zt/faTuJpx/vwnbF8Cm76D1abD2U8fty14xkvSUrbD2k9LCdRX/Te9aYvt7AEjZBoPuAJOp/FgOlPYz/nN63X3NxON6t4zkxjPa8sbiHTz+/UZMwKRBrTCV9/kSEREph5q71zG5ZZq7K0GvJrlptuW8NHd7NRzpdlMPpe/3XBxS8wqybcv5mVBcCJmHytk/q+ZjOl6Wz21euu1zG90Rzn2+dHtpWdm/4Yr+pi3nTewPmKAoD7JTKneMJR6RcvxveCcGd4gB4LHvN/LtWjcXyURERCpBSXodYxk4LtBPb021ys+wLedluN+voXBI0mturmCpA+w/2/kZkHEQMLvfv6QOj3GRVvpZzc+wLcd0hM6jjeXMQ8ZFiLJ/wxX9TVuS+4SeEBpfWlbB34X9xa3G8D9DTtjj53cltHTQ18e/38jmJH1uRETk+CgTrGOyS0dytx/dXapBXplEpqGzTzBUk96w5ZW5AGV5vwMjXe9fUghF+TUfV1XlZ9pqxPMybEl0eCIExxj9680lRqJe9m+4or9py2sS3ty42Ze5jCULco/Z1ovz6+ZrJnVKiyZBLLv3TJqGB5CSVcBdX6z1dEgiIlJPKUmvYywjuQepmXv1anQ16XYJSG6qY5NoaVjK1qRb3vv4bu6PqYt/A+kHbMv2zyMiEby8IKxZ6X77nZufV7YmvbJJekZpLH4hlX8MESA0wJc51w/E19vE+gMZrNyd6umQRESkHlKSXsfkFBg16UGNqSZ926/w/rlwdEfNPUZla9K3/gLvjXAfS0E2fHQBrHj7xGPKOgyzzoPVH8GHY2DmaXBglet9S4rh8wmwaJr785nN8N3t8P1U5wTEPgGyWP46fHyRMfL18bI8h38/dv+6VOdrZrHwcfjiKmOU7+NRUgxzroTXB8L2hUbZb0/AF5OM5tSfT4TFzzg+3oyTy3+Pjse+f4zP24mc0/6znXkIvr7OWA5v4f4Y+7+BbQvK//uzPHd3t1f6wN9vVS1m+3N+dIHxGXRoXp7umFjb36fvr3xNemYyvDcc9pcOABeeaDvP78/Aq/1g1QfG35/9c/porLFPREtbom55jMxkeHcYPJkAj4TD7Iur9tylwUuMCuL8nsZFpTvmrOFYdoGHIxIRkfqmEWWC9UN2aU16sF8jemtWfwB7/oQNX8Ppd9XMY1S2Jn3le7B3mftYVrwNOxYat37XnlhMP99rjDhvGXUeYM2n0Ky3875J62DjN+C3EIbc6/p8mUmwapZjWWCk0Ww3fS/EdHDctmyGkdTt+RPaDT2+57DwUcfn4Op12fuXUZ6y7cRfMzAS7D+nQ0kRnH43xJ1U9XMkrYNN3xrLq96HtmfCkueM9ciWxmwAG+fBGf+D4iJY+hKYS/tyu3uPjsfHFxifzU8uhbu3H9857D/bm3+0LTc72Wg+bj+quYV9TfTs0lHRv74ervnVcb+yz92dZa9A/+sqF2/Zc6buMD6D9n3E8zOMZBjskvRE4z59n9EcHcA3GAqz3f9Nb/kR9i43lv1CILo9NOtjrOelGbfvbnMfq+U1LMiyvWZbf4J9f9n2yUquxJOWxuaBkZ1ZsSuVvak5XPne37wzoS/x4QGeDktEROqJRpQJ1n1P/biJFbuMpnFB/o2oubvlx29N9p2ubE26JQZ3saTtrb6YjmxxLstwUeMNtgSmIMtIcrxd/OlmlBlN2MvXSEi2L3B+PkUFRlIPrmvZKyvrsHNZSbExL7eF5f3NOOC87XhkHjISdDCe1/Ek6WX77Bfl2dZTttmWzWbISnJMUt29R8fD8lnMPnL857D/bCdvMO4jWkKfyXDyBOP1ml6m6burv4HDm53LLM/dyxcmfuc8ZVlOKnw2rmrvrf05m/U2Et70A47vSfZRYz+wJecOzdRNtrKULe7/pi3n7DzaGCE+MAK6nA83/W1Mr/bNzbZ9W50GZz5gW/fygYQeRusJDtgeo+zfkiU+ETsRQX68NaE3l775F+sPZDBlzho+uba/pmUTEZFKUZJeR5jNZt5astO63qhq0t39+K2Jx4Dya9LT95YfS3VOxZRz1Lkszc2I0/bx5GdAUJSLfcpcQAhrChEtnI+H0kSzdATw6h79PTMJwpvZ1i2vvbnYedvxcEiwjzP2skm6/WfC/sJDcaHze+LuPfKUfLvPpOUz0HGEkVB7+9o+A/Zc/Q0UZDqXWZucN4OWA523lxQbyWxJUeXfW/tzxp1UmqTvd3xPMkqXfQIgqEnp/nZJekC4rSxli/u/acs5m50MoXHGsskEsZ1s57WI6wotBjifIyDMuLc8Rtn33xKXSBmd4sP46qZTOG/GUpbvPMqnK/Yxvn853VBERERKqU96HWGZH90iyK8x1aTXcJJeUmKMHG3hrtYtL6PiWn37YwvzXO9TWa6SdHdJZ9kkvaJ9wLH/rVMf9Roc/b3s+ewTqOp4rOqI3f51zj4C2XaJedoe23Jhju0xLElddV3UMJczTVpVuEpQK0ocKzvDgeW5h7k5n5e3cTHIft+qnDPcbjA4V8eHN7fV3tt/li3P2VJW0d+Eq9ru4Gjw9rd7LDcXGPzDHB/D6e9MSbq41zYmhLuGdQSM1nKH0k9gDBAREWk0lKTXAYcz8xjx8h8OZY1qCjbrj9991Ze42CvIxGHeaHe1bvbNmN3FkptmWz7RqdyKXUzplJfmeEHBPh7rPpVN0pvb9eOtzSS9TBJr/zpVR4LrMAf88SbpZY47vMm2bN/HuDDX9niJpbWs7t6jqrJMN2ZxvOd09TmsKHGs7Ejl1mnQyjmffV/xqp7T/lhXx9s/rv1n2fKcLWVu/ybKid9kckzM3T3HsjXpZeMMO8GWIdLgXXVKK05uEUFWfhGv/HacY0+IiEij0ogywbrrhflb2XM0x6EsuL7UpB/eBPNuhN5XweoPoe+10HOcsS15I8y7AVJ3Q6eRcO6zxkjIHc+FQbcZSfBX19n64xZkGQOR7VsB4z+H76cYTWnHvG4MPvbzPXDuc5DYz9h/0/fwx/Mw9i3nQdE2fW8MBNZnEix8zHFbfoZRu+5V5hqVfeJmGSgq46DxHM64DzoOd+z3PfNUuO1fxzgrUlwEn15mNNF1Z9E026BmQ+43Xs/yatL/eBH+ece5r7TDdFN2icXP98Ffr9k97zIXAGZfZPS9j2wFV3wJfsEVPy9730+B3x6HQXcYg7Idspsr+MurjW0jnoUOwxyPS94Icyc7Jq/x3eGyTyBlq/E5O+Me1xcYLJ+lvcvhjHuh1+VGU/VPLzP6mOemGZ/BMa8byVnZROsrNwPape40PpMAsZ1hz1Ljc2H/Hp1yqzHQoOVzbWH5DF7wtuPnM+MQfHqp8xgC6QeMZtibvodfHzZiHngz7Flm9J+2jDJuebxOI41llzXpFfSTXvQUrHjTeB/sTe9uPK5fkLFetsbaFcu276fALw+438/C/pyWY/cut40z4OrcYEuo8zNg/z+O2/PT4YVOzsdnHio//vDmxnsM7l8z+5r0le87trQAWxIv4oa3l4nbh3Zg4nsr+HVjMk+O6aq+6SIiUq46UZP+2muv0apVKwICAujfvz8rVqxwu+/bb7/NaaedRmRkJJGRkQwdOrTc/euD5EznZtP1Zgq2L6+Fg/8aU38dWGUktBbf3GQkaPnpsPYT+O1J48f4ggeN7RkHYd3njudb+pIx0vOip4xtaz+B7BR4fzgcWgMfjLLtO+dy47F/uts5rjmXG/t/d7utWblfaOlGs5GEl1U2cUvfb0zzdWitkVSZzY61rFnJRoL83xxYM7tyNaEpW42B3FwNQOdf2s/2r9dsNYv/fmSLxaJsUrb4adeDmSX2t0vSDxgXJoqLHBN0+21gvPb7/jaSm73LjQSxPMWFzmX5GXBsN3x/h2OCbnFsN6z9zLl84zdwZJPx2JbbtvlG2Tc3Ge/np5e5TtIzk4zPS/o+WPmuUZa0Drb/aiRVls9gdorjcfHdy39+W+xGS0/sb0vk7N+jn/7P8XNtsfpDI+atPzuW71xkvC5lB4uzxDTncji6HXJSjAsE2xeUjtBf5vEsCXFOiuN5AsIhusxFq4G3GPeWUfwLMo33YfWHjvtZXivL61+YbXvu7li25Wc4vnfubvbnjO1sJMGWBD28heP87vaP6xdse79KisDbD1oNsjXFd/VYlnOGJriOvcVA96+ZhWX8h+wjxuwPFk3ag08gNO/r/rURKTWgTRTBft4czsxn3YFqHNtEREQaJI9ngnPmzGHq1KnMnDmT/v37M336dIYNG8aWLVuIjY112n/x4sWMGzeOU045hYCAAJ555hnOOeccNmzYQLNmDafZYaBvPalJt4zA7ErZRNR+2iJwrkm0Z6kpA9sgUuA4CrdFZfuGh8RAWh6UFBoJRdkaMFdNwlO2Oj52SZmkNNPu+RfmgX8o5Spv4Lm4k4zp3xz2z4CifMeLA/Y16UX5zs3mL58LMR2NAcOKi8DkZcSdfdh1Um3ZFhpffrN4V8rW6g+8xXjcn/6vasfZP1a/66HXFUbt9pHNxkUE+8+SwwBjB43n6JC4H3Afe8Z+4323vJ5XfGkkvJ9PcB2nJdFr3hc6nGNcAEhe7/552Y+8b3n8ss/V8hloNxSGPgoLHjKmqDvRQfAunwshcRCR6PzZPucJGHQ7hMQa86Gv/xIWPel4cef0u23T0MV1s7UMCYxwPficRd+roc0ZUJBd+Zjtz3n7WttziGpjtEpJ2Wok5U3aOh539QLb32RYU6Nf+c1/22rDXYlq437U+TPuha4XGq+Luxpx+wtdljgn/2KM/F6UZxvETqQc/j7enNExlh/WHeLbNQfp3jzC0yGJiEgd5vEk/cUXX+Taa69l0qRJAMycOZMffviB9957j3vuucdp/9mzZzusv/POO3z55ZcsXLiQCRPc/NCuh7y96ktTuCrEmbzRcb28pOSw3b4VJYplR2l2xz/M+CGec9RIfsv+tnZKUPdRYV92h/7LOc7byyqvH3tsJ+ckPT/duZbcPg5XNejN+xpJEBgJY2hTIzlN3+86SQdjW2i869YE5Sn7mjTtZVwgqIir19Ly2M1OhoTu0KRdaZJeNia7dXOxcaHIviwrybh44Sp2+5HBfQIhOAZaDnIfp2Wubss0bxX19c5Kch6sr+xztTb3ToT4rka3Avv9q6Igx9ZSxP59L8tkMhJRMBLf2M7GsmWkcr9Qx1rr6HbGe1BZZZPpqgiKcp6twN1j+wY4b/MPqVqs9kymij+vltYTKVsg15gik5iORiy+mvdaKm9sr2b8sO4Qc1fv55rT2mjedBERccujzd0LCgpYtWoVQ4cOtZZ5eXkxdOhQli9fXqlz5OTkUFhYSFSUiympgPz8fDIyMhxudU1OQXHFO9VLZRJ4+xrfwrzykxL75uiu9rNPfMr+wHc3iFRAmPNIzfYsjxMc4/pxXc4tbTfgWGElRu0tb8Aud1NlOY2Wbj/llovXpmzNnn2/9LL7W5/rPsfzuXsNyir7mviHVW606/Jef8vxrubFBtvzt8SY5uJ5ZRx0n6TbP47JVP5FHktLEcvnxv65+YUYU4TZsyS9eem2qdHc1aRbam4j3Azu50rZx0vdYYvPXYLuiuX5WJrKB5R53zRiuY0lSbfU1lf1tRYpNbhjDO1iQ0jLKeS8V/5gS1I1DEApIiINkkeT9JSUFIqLi4mLi3Moj4uLIympnGbUdv73v//RtGlTh0Tf3rRp0wgPD7feEhMrGFDJA1IyXYzyXZ+Zzc7TnpWV7yL5dMddsmVRdgAeV7XLYKtJh/Jrci1zJZdtru/qGPum+JWqSS+nubv9wFWBkaX7ZzrPy5xfQZJe9vWwT3bL1kpbak8t50kr8xpUtSY9IAwCImyDnFX2OLPZfZKettf5sxQYCdEdbTG6agHgqqVG2SQdnF8ve5aadMvnxv49sp/izv78YGtyD87P1fL++Zc5Z2X+Hso+XvIG1+UVKdu0u+zFFTXhtik7NZsuYMhx8vX24v2r+tI2JpiUrAKmfr6G4pIamNFERETqPY83dz8RTz/9NJ999hmLFy8mIMB1s7F7772XqVOnWtczMjLqXKJ+pEyS3qpJUO0HkbQO5l4NZz0InUsHZ9v5O/x4F5w33RigySIvHWadZ9uvrBm9jCa4rqYYs3i+feVjW/6q4/qP/2cbAAuMwa9Wf2T0Ee0y2nk0d4uAcFst5icXw43LYNN38NfrRj9YS7PhFgON8g1f2Y719i8/wQZj9PEjm42BrDL2g8nbluyc+YDRd7e8mnT7H/9xXWH3H0Zz7rIjwS97xbgFxzgPPuaKpab2lwegwwjHbU3a2bYtmma70NDiFOM12LsMprnpj9x1LBSVaT3gH1Y6tVVz47VwJ/Ogcd7AcOh/I/z+TOnnxWQ0zwdb8rrxG+N1sGc/Mvi3tziPDP7JJUaT97L+ftPWP7kyyVZB6cWBgAjnYyISobjAGOTN4qtrjFHr7RPuLT84voaWz5Hls2E5556l8Ezr8uMJb+74eF9fX/nnYs+/TJIeEFZmPIX60t2mFviHGu+/ZdYBJelyAhKjgvj0ugGc9cLvbDiYwaL/b+++46Oq8v6Bf+6dnjKTRnpCQpUiRWoAK1HAiqsrKquo++iDgovrlscu7q4/dHUt67qoa9tdCy66sC4KiqigSO+9SAklCYH0Mv38/riZfieNJDMhn/frNa/M3HruycDJ955zv2fvKRQOTGt+RyIi6lYi2pOekpICjUaD0tLSgOWlpaVIT09vct/nn38ezzzzDL788ksMGRL+eUSDwQCz2RzwiiZWhws1Nl+AkZccgyW/uLDzC7LwDiUY/OhnvmX/uFZJ0vSPawO33fYRULJdSTylpuJw6+cQ98xB3RLrX1cSnQUQSibtcAE60NiT7tdD+NmvgS3vKYG7J0CXNEDWyNB9XTYlw3ZTPEGpp3dduJRnWBvKlezvQPh6kXXKc8KmxqH7fQqVGweA71n+7NGB+/gH6NmjAEhKIrJg/s9c71/qez/oeiBvgu+zow6AAGJSlHWmJEC4laBS7bXp3cDzxKQAiT2V98GBTEwy0Hti4DJbldJL/sXDvgAoPh3Q6pX3WSOU4d3BAToA5F0I5Df+O3FalSBd1gKDftJ4LfXKfroY4Lyrffu5Hb7kg/7XfnFo/osAnqDW/3fUc7xSjmDH1qnMFe9Xb8HH9K8rzzPPai7/nfr5gMBraYngnnLPNZ13tVJnw25t3fHOdf7129q6JgqSGm/ELaOVG3fvrzvazNZERNQdRbQnXa/XY8SIEVixYgWmTp0KAHC73VixYgVmz54ddr8//vGPePrpp/HFF19g5EiVgKoL8X8evXBAKp66bjDiIjH9Wm0TPbLBvZQav/IFZzv36D1RmYM5sacSMLmcSpD792uUXlR/d32hJM/6U+Pw5eQ+wC0fKUH3Jz9XP74nqG7KiDuVYNNzk8FoBmx+wYmzIbRn25zlCzSDebLR952kzNX+9e+bL4NHuCRiHhqdEjg9sEOpp4QcYPXLStDmGdI84g7g5vfVRyGMuFPJVK42TLnv5UrwtXeJb9lP/w4MuFaZK95/3ZCbgatfUDJrP7AjMHu9h3ADfx3rC54tucCdnymBuGdOdf/Ac9p7QO/LlERt1krgj030Fgf3VP9qn2/atMSevnpMzFN67HtfpiRPA3wJyC5/CnDalWVxPZRg2Fbt+w4CSjnNftNyXfqwMif5G5f4nvP25xkeHvw7AoAhNynX/s4UZWo1tWf/Pf8evv1/SmZ1/2PGpSs3h4JvRiTk+h65+MVWICnfd753r/bN1x2fqWRub43gnnTPtdz0T+VmTXOzFHQ3N/1Tufmo0fvqiugs3DQyG2+sOoTVB8+g3u5EjL5LD2wkIqJ2FvFW4cEHH8SMGTMwcuRIjB49Gi+99BLq6uq82d5vv/12ZGVlYd68eQCAZ599Fk888QQ++OAD5OXleZ9dj4uLQ1xcM8/BRqEGh/KHuV4j480ZEZxvN1ywrcb/eeNwvcs9+isZogFA0xg4xiar94qmDlCyS2v0yvDh+AxlX0/vqpqqMM+d+0vKV4bAe2iNgcGJIT60Z9uSDcQGTf2nNSq9r57AKzjJVkvUlirDr5vqSQeUTNWGON95Gsp9NzUs2UqG7piU0LmxLdlNP0ecOTwwSE8dqATowevSBvoCbUMcYOijfjxzpq+32JIdmvQu4Pn6JN8xg5P8BbMEBUCmhMAkXUlBAb45M/QYagn4jH7fwXCM5vB5FAK+N36/I//zZY9uDNKPhwbpnn8Pnqzq/sfUaJXyBfeix2cqQbouxpcB3nO++AxfkJ7bilEoHlq973sN+L7PsswAXY0sn10Ge6IgvXvEITvRhOMVDXh95SH88vJ+kS4SERFFkYgOdweAadOm4fnnn8cTTzyBYcOGYevWrVi2bJk3mVxRURGKi4u928+fPx92ux033ngjMjIyvK/nn38+UpdwVqyNQbpRF+FfRbipudSozVUeLFwQq9aTbLQofwSbs3yfgdDePn8tTbLlH3AEJwiTZARMsQYoQaQc9LvwlMNzToO56bKFU32iiZ50lftlwefw1KnafM7N9e4FB7/+ybACkqG18OZDc5nA/Y8Zbv7p5o4bCeGC9JZcQ0CCvuPq61pTL95kddmh313/fdtaZ4Z2OAYRtYkkSSgcoPyd8/KKA9h0tIlHXYiIqNuJeE86AMyePTvs8PZvv/024PORI0c6vkCdyBekayJbkNb0pLdkqrHgoNAjONFYwD7ZypBST/DQVBDT1Bzr/mWQg+rVvye7vvGPIlnX9PUbLcoz8J5zGs2tCzxNSUovadXx8D3pGr36ef15bmKo3SAwZ4Uu8+cfhPn3bAevC/d7CzleDoA1ofurHTPcDY24tMB55oHW1WtHCPf9bMlNmaaC9JiUxm386re5Y6o9s662b0t/Z8GMZl9uh7Yeg4ja7JeX98O245XYUlSJv606jBG3NTPSiIiIuo2I96R3d/tLlZ67DgvSGyqAv4wCVjTx/PTyJ0KX/ft/Az//8Aow16K8lv62+fO2pWfOEyh4ArWz7UlXC1z9p0kr2R54PsCXrM2fZ33xNl+5WtOTnjZI+fmP64CiNerbeIa7+/M/R2wqoDOGltdDa2i6DAGBeFC9+D+b3Vywr3Y8tV78gOm8wtSV/9BvD7X6jwYtGQLu+f5WHA2dBtD7/LlfFmf/Y6repGkiSG+PnnT/x1YYpBN1OotJh2dvUBLffrm7BEfP1EW4REREFC0YpEfQRxuK8MuPlMCvw4a7b3xbydD+XROPA6x+OfCzywFsXxC47MvHWn7O+EzlGVw1N7ylBGIXzFB+Tn7Gt673pcoyz9zdOlP4czSVBRsAUgcpz+0CwPk3Kb3Sw34GXPSb0G0NZqBgtvKM7iWNWb5/8jelLDe+HZgdXdYpSeNiU5o+v79+k5Sfwq38DO4hlzTA1FdD9/Of9q6PX2Z0bdB0g0Nvab4M5izfvOL+09cBSuK3lP6Bddac/IuUcmuNynRtwSzZyjHTBgOGoOsd9wtlv8nPBmYrN1iAoRHOKu75fo64o/FxCCjX4JmCrSmeYLn6uJJ7QdYCPScodZ9/kbIuKd9XL/7HvP61xn8XtyvT/U14UKlXWQv0uiT0XLnjAEhKnWWNaNu1esqUOijw5gERdZp+afG4sG8K3AKY9vpaFFe1YKQaERGd8yQhhGh+s3NHdXU1LBYLqqqqIj4d2+inv8KpxjnSh2Rb8OnsDpjaZ9VzwNd/UN7PDTPP99ygIGrOduDl8NPaeQ3/GXDpo0p26vrTyjBqa6XSQ9hUz67DqvQKe36qrQtXtnAmPwuMvBOw1fqCYM9z3kIoCek8ZTr8HfB3v2m5UgcB9/0Qem7/z7WnlCz3+ljl+G4X8DuVoYm9LgFuWww8k+sb2j63SslQ7mrMOG5KBJ5unGJw4FTg+tdD68Gj7rRy3rg033PJ7/8UOPCl8v7REiXgDX5mWY3LqdzciEtVXweoPxsfjrVKCdT9k6i19JieunW7lSHXcWlKYr1w9dCZ/L+fnp79ltSL2wX8vocvOWJCTyUru9vpm1YOCF8vav8u1P6NeNSXKzeymrqZ1ZzaU8q/29b83qlDRFPbdK7oKnW680QVbvnbWtRYnbh6SAb+cusFkS4SERF1gNa0S+xJjyCN7AusjNoOGu7e3PBhtzt0WUue9waUHkJzppJoLS5V+UM/NqX5odeeoEMt+GhrkOY5b2yyUg7/oEOSAssU2yNwX3ut+rn9P8elKtfquQEQ/Ky7/7GDz+cpnzlTeQUHVU1dc2yKMnd4uCBcZ2pZgA4odaIWoHvWtTZQM1rCB+jNHdNzzbLsu75oCNCBwO9na+pF1gQ+LmDJUa5PGzSUPdwx1f5dNFUnMUlnF6ADvn+3RBQxg7Ms+PBuZZaGJduLcaKSvelERN0dg/QI8g/SDR013L25IN2uks3aMzdzc842QGhPas/0hhP8jHS4ZG5t0Zas73TuaC7rPRGRisFZFozOV0ZnLdl2MsKlISKiSGOQHkEBPekdlTjOP0h3Nw7DdTmUIeCA+pRgZw627Ni6mLMrW3vSqCReCyc4kA43LVpbRDo7OUUWg3SidjFv3jyMGjUK8fHxSE1NxdSpU7Fv375IF6tDTR2mjMT5+w9HvDO/EBFR98QgPYI0UicH6c/1BpY9ojw3++5VwILpwEuDQ/c5vb9lx46qnvRWBOn+048BvmeIW8uk8ky65wZAS7OUt3SoesA+EZ6uj8JjkE7ULlauXIlZs2Zh7dq1WL58ORwOB6644grU1Z27GdB/ckEWMi1GnKyy4u3VhyNdHCIiiiAG6RHk35Nu6qjh7v5BYEMFsPZVAAI4uhrYu0R9n9MHWnbszuhJv7KJrPRZI4G+k4CkXkoW7ZaSJKDvFY3vZWDa+20r27R/ArpYYOwsQB8PaE2+TPA3vq1McXX1S+r7Xvx/yjPdlz3e+vNe/pRyM+DSR9tWbuo4fSYq2dn18YGZ64moVZYtW4Y77rgDgwYNwtChQ/Huu++iqKgImzZtinTROoxRp8FvJiuzcPz1mx9RWW+PcImIiChSmDEogjpluLu7hb3E6UOAyqNKxm5PT/rPPlGm67JWKT3DWhNwYiPw1uXK+s7oSR99NzDyLuW9rFGux+UAnFYlUJUkZWqzcIncwrn1X74p0Vq7r0feBODhY8r+hU8qjxB4En31HAc8VBT+2Jc+ogTqbTl3j/7A/x1pe7mp4+RNUH7vkkrCOCJqs6oqZXaSpCSVEUyNbDYbbDab93N1dTs+ytRJrhuahddXHsLekhr8Y81R/GJi30gXiYiIIoA96RFQbXXA4XKj3u4LoOW2DHtuCVcL78SbEpReYcAXvCbkKT+NFmWIuCwHzvHdWcPdZY0vIJU1SiBsSlDKI0ltC1Y9+51toOvZX2sIzcTd3LHP5twM0KOXzsgAnagdud1uPPDAAxg/fjwGD1Z5RKvRvHnzYLFYvK+cnJxOLGX7kGUJ/3txLwDAC8v344oXV6KqwRHhUhERUWdjkN7JjpXXY+QfvkLfR5eiqLzeu9ypNhVae2hpkG4whwbdliz17Ty0UfRMOhERnZNmzZqFnTt3YsGCBU1u9/DDD6Oqqsr7OnashdOJRpnrhmbh+uFK+7u/tBZf7iqJcImIiKizcbh7J1uwoQh2Z2hA7nKLth3QaQMcDYAh3jccXJJ9z6K7WngH3mgJfMY8JkW9p9w/e7nooBsLREREAGbPno0lS5Zg1apVyM5uOhmjwWCAwWDopJJ1HFmW8OK0YZAlCZ9sPo7f/Xc3rhiUDoupFQlSiYioS2NPeidzuNSDcWeY5U0q2wf8sTfwbE/glQuAujPAn4cBH97i26atPekJYYYJ+gfyDNKJiKgDCCEwe/ZsLFq0CF9//TXy8/MjXaROd+f4PABAjc2Je9/bBCHaeDOfiIi6HAbpnex4Rb3q8jb1pB9bB9hrlPcVR4D1rwOVRcD+pYDL2XjgoCBd1gETfgmYEn3LtCag92VA/ymNvfAyMOBa9XNKEjDoeiB1EJAzpvVlJiIiasasWbPw3nvv4YMPPkB8fDxKSkpQUlKChoaGSBet0wzOsmDeT84HAPzw4xmsPngmwiUiIqLOwuHunejgqRp8vkP92TJHW4J0a1Dm2rJ9vvc1xUpvePBw90dLAI0WuOwJAAKApPyUNUC/K4Bxv1C20zTx1fjpu0om845KdkdERN3a/PnzAQCXXHJJwPJ33nkHd9xxR+cXKEJuGZ2LXSer8N7aIvxn6wlM6JsS6SIREVEnYE96J7E5XXh22b6w68f1Tm7DQYOC9GPrfe+rjis/g3vSPcG3LDdmN5cDM4VrtE0H6B4M0ImIqIMIIVRf3SlA97h6SCYA4ItdJbA5WzitKhERdWkM0jvBvKV7cP7cL7F8dykA4OcT8nFeerx3/e+vG4SbRrZhqpjgnvSak7734YJ0IiIi6jJG5SUhNd6AaqsT/R9bhm3HKiNdJCIi6mAM0jvB6ysPeTO6G3UyHppyHm4r6Oldf9OoHGjkFvZMCwE4rMrL05MenxG6XVWRsk1Ls7sTERFR1NHIEiYNSvd+vu7V1Xjzu0MRLBEREXU0PpPeyc7PskCnkWHS+YaY6zWtuFfyyf8AOz8OXJY6UHkG3d/+L4BvnwVctrMoLREREUXaXRPy8c+1R72f//DZHsQZtJg2KgcSHz8jIjrnsCe9k2UlKNOcxRl890da1cAGB+gA0G8ykNRbydzucWxdaIB+2+JWlJSIiIiiQX5KLA4+PQU5Sb6pUh/69w7MfG9TBEtFREQdhT3pnaxHvAEAcFG/HjgvPR4DMswt39lWq748sSfwi83K+4MrgPd+ErrNdX8Fel/aytISERFRNNBqZHw6awJsTjfeWHUIb68+jOW7S1Frcwbc+Ccioq6P/6t3MHfQ1GopcUqQbtRpsHTOha3rRa8+ob7c4BfoW8IkoNPoW34eIiIiijqJsUpb/sQ1A/HFrhKcqGzA9uOVGNebU7MREZ1LGKR3oFqbE5NeXBWwzNOTDrRymDsAVB1TX270D9Kz1LfR6NSXExERUZczLDcBJyob8MG6IgDAiYoGAMBP2zJbDBERRRUG6R3o060ncaKyIWCZf5DeaqW71Jf796TrYwFTEtBQHrgNe9KJiIjOGZf064HPthdjSePLo09qHIbnJkawZEREdLaYOK4D7S+tCVnmGe7ealUngOVPqK8zBj3XblbpTWeQTkREdM644YJsPHbVAPRJjQtYfv1ff8Anm45HqFRERNQeGKR3oN0nq0OWJce1MVgO14suyYE96QAwYgYQkxy4jMPdiYiIzhmyLOF/LuyFrx68GDvmXoHzsyzedb9auC2CJSMiorPFIL0DHSwLzMY+NCcBKbFt7Em3hQb8AABDPBD8bPvou4HfHgImPulbxp50IiKic1K8UYeZF/cOWLbrZFWESkNERGeLQXoHsTpcKK+zByxbdO84yHIrk8V5DximsTVY1JcDgZneGaQTERGds6YMTsejVw5AVoIyl/ozS/fin2uPwulyR7hkRETUWgzS24nbLXDLG2vxszfXQQiBk0EJ4wC0PUAHfD3pxqCgPPh5dH+WbN97DncnIiI6Z8myhLsv6oU/TB0MAPjuwGk8vngnnvtiX4RLRkRErcUgvZ0cLa/HmkNn8P3B03h91SEUldd718lwIwVVgBBATWn4gzgagIZK32e3G6gtU95bG4P04J7z4OfR/fkH6TIT+RMREZ3rLu7XA78s7IerhmQAAF5fdQhzP92FBrsrwiUjIqKWYpDeTo6crvO+f2bpXtz17gbv5z/q3sBG473AW1cAf+oH7FuqfpDXLwKe7QnUN06ftvB24Pk+wLENvuHucamB+yTmhS9UfIbvvcvWiqshIiKirkiWJcwp7ItXb70Ad43PBwC8+8MRDHhiGV7+6gDcbgEhBOpszgiXlIiIwmH3ajv5MShJnFv43t+oWaW8Ob5e+bni90D/KaEHOb1f+XnkO2DgdcCe/yqf181XsrgDwIBrgJgk4OQWIKEnUDg3fKE0WqBgNnDmRyB9aOsvioiIiLqsR68aAKvThQ/WFQEAXvxqP2qsDmw4WoE9J6vx6f3jcV56EyPyiIgoIhikt4PVB0/j8x3FIcslCbhheBawO2hFTFLoQVwO3/v6M4HrZK1vGLwpEZi+sOWFm/R0y7clIiKic4ZGlvC7awdha1Eldhcrj829+f1h7/p/bz6BR65kkE5EFG0YpJ+lI6frMP3NdSHLV/3mUsQZtUiSalWC9OSQ7eHwSzQXEqTr/BLHsTElIiKiltFqZPz3/gmwOly44sVVOOGX2PaNVYdwqKwO6RYDLh+YDiEExuQnw6TXRLDERETEIP0srTl0RnV5bnKM8qb4eOhKU0LoMv8gvfZU4DqN1i9xHIN0IiIiajmNLCHWoMWCe8Zi9cHT6Jcej5+/uwEV9Q58tUdJaPveWmVIfL+0OLx5+yjEG7VIjOX0rUREkcDEcWdp09EK7/vLB6ZBK0u4c3yessBeDxxeGbpTxVElOVzRWuDEJsDtAsr2+NbvWwY4rL7PkgY4tUt5HzwFGxEREVEL5CTF4ObRubggNxF/nT4CWQkmXHV+BvqlxXm32V9ai4ue+wYTX1gZkBSXiIg6D3vSz4Ld6cZ3B5Qp0v58y3BcMyQDtTYn4gyN1brgVuDQN6E7HvoG+GO+73PWSODERt/nqiJg9Uu+zxvf8r1nkE5ERERnqaB3MlY/dBkAoNrqwDd7T8Fs1OEXC7agxupEeZ0dv/l4Gyb06YHcZBOuG5oFWZYiXGoiou6BQfpZ+O+2kyittqFHvAFXDEyDJEmIN+p8G5zYpPxM6Q84rUDlUfUD+QfoHj+qBPcAkNT77ApNRERE5Mds1OG6YVkAgO9/exnWHDqN+97fjA1HKrDhiDJi8OnP9mB0fhLG5Cfj1jG50Gk4GJOIqKPwf9iz8M0+5dnxu8bnw6gLSrJirfIle7vnG+CB7cBP3mz5wU8FZ5sDcOM7gMxfGREREXUMS4wOkwdneIN2j9O1dny+owRPfroLr337Y4RKR0TUPbAn/Sy8cstw3DgiG8NzE0NXVjUmjDMlAfpY5b3O1PKDewJ8f8zsTkRERJ3gmRvOx9heSYg36nB+lgUz39uEXSeVv03+tHw/Dp+pQ5/UOAzLScC43ikRLi0R0bmFQfpZkCQJl/RPVV/pCdIt2b5lLQnSLTlA1TH1dQY+j05EREQdz6DVYNqoXO/n/86egPVHyvHC8v1Yf7gc/958wrvufybkY8a4PLz1/WEYdRr83+T+kCQ+v05E1FYM0tuDEMCJzYDVl+nd+0y5Jce3TBcTum9sKlDnN+VaQs/GAF+EbsuedCIiIooAWZYwtlcyXpw2DPf8Y6O3Vx0A3vz+MN78/rD3c53NiTmFfZESZ2jTuYQQcLoFn3snom6LQXp72PkJ8MnP1dc11ZNuzgLigoJ0SQLiM4Cak6HH4hzpREREFEFZCSZ89osLsa+kBtVWB17+6gC+P3g6YJt/rj2Kf649ijiDFmN7JWPSoDRMHpyOZTtLMDw3EX1S48IcXTHzvU3YcKQCH9w9BrlJMTDpNNhdXI3dJ6txps6Om0bmIIlzuBPROYxBenso3qr8jE0F4tN9y/VxwAW3+T4H96Tf+hFQfgj4/iXg5GZlmcsBXPxbYNM7QPG2wO3Zk05ERERRoH96PADg73eNhlsInKxswKkaG77eewrzGxPL1dqc+GpPKb7aU4rffLzdu+/ovCTcNCoHR8/UoVePWGRaTMhLicVji3diS1EFTtfaAQCTX/pO9dzPLN2Ln43Nxe+uHYz1R8oxJNsCIYAYvaZLD7P/7kAZeveIQ2ZCK3IYRYEvd5VAr5XRPz0eGZauVfZoU1ZjQ0qcvkt/j6l9MEhvD5WNz5BP+CVQcF/47fx70q/9C5B+vvIaeB0wt/F5c7cDGHmn8jq+EXhzorJc0qgPlyciIiKKEI0sQQMJPZNj0TM5FqPykvDbSf2xu7gaZ2rt2Hi0Am+s+hFWh9u7z/oj5Vh/pPyszvve2iK8t7bI+1mvkWF3uZGfEou/3T4Sn2w+jsp6O8b2SkavlDj0T49HtdWBlDgD6u1OyJIEo04Dh8uNzUcrMCovKaLzwH93oAy3vbUeOUkmfPfby7zL7U43dBopaoO20mor7vnnJu/nq4dk4E83DYVBq2liL1Lz320ncf+HW3D3hfn49aT+rMNuLiqC9FdffRXPPfccSkpKMHToULzyyisYPXp02O0XLlyIxx9/HEeOHEHfvn3x7LPP4sorr+zEEgfxJIlLyGl6O/8g2xBmqJfL4be9X1BvNCtD4YmIiLqJ1v59QNFBkiQMylQ6Hy7q1wOXD0jDdwfLUDggDe+vPYq/rzkasL1OI8HhCszF89yNQ2Ax6VBRb8fcT3dj4oBUPHnNIBw+XYebXl8Tck67S7kJcPh0HQpfWOld/uH6wGS8I3smYtfJamhkCdcNy0RlgwOfbS/G9DG5uLhfD5TX2XH9BVnYdKQCaw+Xo6zGhtykGNw1IQ86WYYsS3C43Nh9shr7SmowIMOMbccrMWlQumoP6OaiCuw+WY0bR2SHTNf7zb5T+G7/adx9UT4WblT+ljxW3oC73t2Au8bn4/11R7F0ZwniDVrMKeyLO8blYe2hcmQmGNGrR9OPDHSWjUcqAj4v2V6MYxUN+N21gzAw03xO5hWoszlRZ3ciNd7Yrsd9fZUyAuVv3x3G3747jHsu6oXLzktFXnIs0i3hzyWEiNqbONR2khBCJUNZ5/noo49w++2347XXXsOYMWPw0ksvYeHChdi3bx9SU0Mzp//www+46KKLMG/ePFx99dX44IMP8Oyzz2Lz5s0YPHhws+errq6GxWJBVVUVzOZ2Gj7+fH+gtgS451sgc3j47ez1wP/LUN5Pex8YcLVvnacnPXUgcF9j41N+CPhz4/ES84A5QcPfiYjonNAhbVMX19q/D4KxTqPXqWorPtl8AjePykG11YGsBBM2F1Xi8x3FeKCwL2RZgtmo825vdbgCAtwfy2rx+yW7kRxrgEEnY9fJaoztlYQEkx5vrPoRFfW+Dg+1GwBtJUtAutmIaqsTtTZnyPqeyTHomRyLo2fqcLKyAQACzp1uNqLB4cKQbAvKamzYW1LTqvNrZAkut4BOI2FgpgUmnYzqBiesThdOVDRgYKYZvVLicOOIbJyqsWJghvK9X3+kHHU2p3eqPLcQ0Moy4o1aZCeaIEkShBCwOd3QaWRoGkcU1FgdEADMRh2Kqxrw5xUHcXG/HjgvPR5xRi1S4gx4dNEOvL9OGdEwJNuC/aU13lETJp0GPx2ZjViDFvnJsRiQYYZLCJyfZfGew+UWkCXA6nCjqLwee0uqcbrWjqvOz2gyMPUor7PDbNRC24qbAUIIrNxfhuzEGG9+hJYEukVn6lFUXo+5/92Fg6dqUTggFT8b2xPJscrojAt6Jrb6psSuk1U4VFaHg6dq8fKKA6rbGLQyxvVORlmtDT8Zno00sxHFVQ1IMxvx/rqjKDpTj7sm5EMrS8hMMGF0fhISYprP2bDtWCV6xBuQEKODUauJ6EiS7qI17VLEg/QxY8Zg1KhR+Mtf/gIAcLvdyMnJwf3334+HHnooZPtp06ahrq4OS5Ys8S4bO3Yshg0bhtdee63Z87Vro12yEzhzEFh4BwAB/OZHILaJuULdbuB3jXOq3/ovoN8k3zpPkJ7cF7h/o/K+pgT4U3/lffoQYKb6s1lERNS1MaAM1dq/D4KxTrunqgYHFm0+DqNOg5+OzIFbCLy/9ih2nazG0TP16BFvwKTB6SipasCGIxVYvrvUu69aQJ+bFIOKOjtqgoLyeIMWsQYtSqqt7Vr+lDgDTtfaoJUlZCeacORMfbseP5hJp4FWowz9L6uxAVCuTaORUFnvgEaWkGEx4nhFQ8i+mRYjTlYp1//yzcNw3bAsFFc14IEFW7HucPjHGSRJSUDodAmUVFsRq9egweGCOygiSYjRQZYk9Igz4FhFPXomx0IIJeu/LEsorbJ66z/DYkRqvAE2pxuJMXokxOhQZ3eh3uZErx6xOFHZgDO1drjcArU2J4oby63XynC6lJsTI/MSkWkxQaeVUVXvQEW9HRpZQkmVFS4hcKisrsm6tJh06J8WD7vLjViDBi63QE5iDE5UNiDdYoRWlmB3ulFjdUKnkXGisgE7TlQFHGNwlhkT+vTAayt/bPoX14z8lFjIElBR70BqvAF6rYxaqxO9U+OQYNJh7eEzOFYe+DsdlGnGgAwz3EIgwaTHicp6uNyAVpYgILDzRDV0Ggk94g0YlZcEvVZGnEGLBrsL5fV2VNTZodPI3ro3aGWcqbWjweHCuN7JsDndqG28wVVjdUJAIEavwc4T1RiSbUGvlFjIsgSdRsZXu0uhaZxRwjNKxmzUQUBAliTUNf4OMyxG7w2fcJwugX2lNTDqZMQZdJAloMbqhNmkRYxe6/1Onamzw+lSyhRj0CBWr4VGlrD9eCX2FNfgP7PGn/WNjNa0SxEd7m6327Fp0yY8/PDD3mWyLKOwsBBr1oQOZQKANWvW4MEHHwxYNmnSJCxevFh1e5vNBpvN5v1cXV2tul2bbPsQWKP88QCtCYhJbnp72e/umilRfZukfN97fazvvSG+bWUkIiLqYtry90GHtvfUZVhMOtwx3ve3lAZSwGd/91ykDF0ur7OjtNqKET0T4XAJlNfZEWPQIEangVYjo6LOjq/3nsKADDMaHE7U2VwY2ysZeq0Mu9ONM3U2rNhzCscrGqDXyshONOG89HiYdBokxepRa3Pi+S/34/wsM0blJWHT0QoIARQOTENOogmrDpQhM8GE1HgjkmL1cLsFBOANPpwuN05UNuDHslqMzk/GjuNVOFhWC6vdhT3F1UiK1eMfa456gxl/Bq2MC3ITYdTJWH+4HJIkQauRoJVlVDUoARQcStDi4X9DwuUWqgE6AG+APmlQGq48XxkpmmEx4aP/LYDV4cKynSVYsr0YBp2MojP1KKm2oqzGBiEQcMw6u8tb1nijDjaHCzU2JyobR0SU1ymJBPcUh/83XVxl9QbewTYerVBdDijP/AOAzenG6oNnwm4XjiwpN1YaHC5UNThCci2sRctzL2RajPjzzcPRq0cc7pqQhxV7TmFQphkfbTiGbccrUWN1oqzGBqNOg+E5Cfh2fxlS4w2IM2hR2eDw3mQBlMc+PDz1BwCHToe/0bDrZHXA1IrhHDlTjw1HwtepGv+bYWq2HqtUXf5lM/t1pr0lNRiY2Xk3fCMapJ8+fRoulwtpaWkBy9PS0rB3717VfUpKSlS3LykpUd1+3rx5eOqpp9qnwMESegI5Y5X3g3/SsmfGJz+rDGPPHhW4/K4vgHWvAVc87VtmiAcu/j/gyGolKR0REVE30Ja/Dzq0vadzVmxjj3hOkpI3SK+VQoZZJ8bqccOIbLXdodfKyLCY8LOxPcOeIznOgFdu8T0OOTw3sKPmsvMCv+fBvXVajexNzAcABb2TUdA7sGPosasHwuUW0MgSam1OxOg0cLjdkCUp7BDsersTh8rq4HQL2J1uZCeaUG93obTaCrcQyEuORYND+dwzKRZZiSbYnEpA7XAKbD1eiZQ4PQZmmEOGiht1GkwdnoWpw7MCllfU2bHrZDWKqxqQGKNHWuMjAHnJMUg1K/Xucgv8WFaL8jo7HC43GuwuJMToUWN1QKeRYW3sdTfqZBi0GticLug1MkprrHC5lZsaDQ4X4gxKmHOy0oo0swGxBi2qrUrgf1HfHtBpZBw+XQerw4VYgxb7SmtQY3V4A3eTToM4oxY9k2JRb1du5lwzNBMV9XZYHS5YTHr0iDcAUM6540QVDpTWwmzSoqrBgdO1dpTX2RFv1EKWJMgSoNPIiDUovbdxRi0yLCb0T4uH2aT08HrqMTXeiFtG5wIAhmQneOvP6nBBkgCDVoOqBgfiDVrv98XqcKHB7sLW45XQNN6MiTfocOh0LX4sq8OR03Xo0RjUxxu10GtlCAHkpcSi1urEkTN1cLoErE4X6mxO9EqJhV6rgUsIuFxuJMbqkRCjx4mKBmwuqoBOI6PO5kSMXqOsM+lwrKIeDqeALCuPUNhdbkiQUG93wqTXIN6gRZxRiziDDvV2J6oalGSOVQ0OnKhogIAy0qFvajwa7C6crrUhzqiFEMrjF1qN7H3kw2zUweoMvTEVTAiB5Fg9tBoZlfUOmPQykmL0qLY6vUkk7S63d3RMvV25/nq7Cw0OF/qmxqGgdzLyU2KbPVd7iorEcR3p4YcfDuh5r66uRk5OMwneWmrMPcqrNcbOVF+eO1Z5Bbv0kdaXi4iIqJvp0PaeqAvw9Lx7glOD3HR28Bi9FoOzLCHLg+ex75fmG80Zo28MHfTAxf16tLqMibF6TOjbxKOhUK7D/5wdyf+GzOj8pBbtE2sIDZ+0GhnDcxNDbsC0N//cDBaTLmSdUafBpf0Dc3acnx36Oz5bt47JbfdjUqCIBukpKSnQaDQoLQ0cylBaWor09HTVfdLT01u1vcFggMFgaJ8CExERUYdry98HbO+JiOhcEdF5EfR6PUaMGIEVK1Z4l7ndbqxYsQIFBQWq+xQUFARsDwDLly8Puz0RERF1LW35+4CIiOhcEfHh7g8++CBmzJiBkSNHYvTo0XjppZdQV1eHO++8EwBw++23IysrC/PmzQMAzJkzBxdffDH+9Kc/4aqrrsKCBQuwceNGvPHGG5G8DCIiImpHzf19QEREdK6KeJA+bdo0lJWV4YknnkBJSQmGDRuGZcuWeZPFFBUVQfbLij5u3Dh88MEHeOyxx/DII4+gb9++WLx4cYvmSCciIqKuobm/D4iIiM5VEZ8nvbNx3lQiIoo2bJvaH+uUiIiiSWvapYg+k05EREREREREPgzSiYiIiIiIiKIEg3QiIiIiIiKiKMEgnYiIiIiIiChKMEgnIiIiIiIiihIM0omIiIiIiIiiRMTnSe9snhnnqqurI1wSIiIihadN6mazonYotvdERBRNWtPWd7sgvaamBgCQk5MT4ZIQEREFqqmpgcViiXQxzgls74mIKBq1pK2XRDe7be92u3Hy5EnEx8dDkqSzOlZ1dTVycnJw7NixZiek705YL+pYL+pYL+pYL+rO1XoRQqCmpgaZmZmQZT6J1h7Y3nc81os61os61os61ou6c7FeWtPWd7uedFmWkZ2d3a7HNJvN58yXpz2xXtSxXtSxXtSxXtSdi/XCHvT2xfa+87Be1LFe1LFe1LFe1J1r9dLStp6364mIiIiIiIiiBIN0IiIiIiIioijBIP0sGAwGPPnkkzAYDJEuSlRhvahjvahjvahjvahjvVAk8HunjvWijvWijvWijvWirrvXS7dLHEdEREREREQUrdiTTkRERERERBQlGKQTERERERERRQkG6URERERERERRgkE6ERERERERUZRgkH4WXn31VeTl5cFoNGLMmDFYv359pIvUoVatWoVrrrkGmZmZkCQJixcvDlgvhMATTzyBjIwMmEwmFBYW4sCBAwHblJeXY/r06TCbzUhISMDPf/5z1NbWduJVtK958+Zh1KhRiI+PR2pqKqZOnYp9+/YFbGO1WjFr1iwkJycjLi4ON9xwA0pLSwO2KSoqwlVXXYWYmBikpqbiN7/5DZxOZ2deSruaP38+hgwZArPZDLPZjIKCAixdutS7vjvWiZpnnnkGkiThgQce8C7rjnUzd+5cSJIU8DrvvPO867tjnVD0YFu/OGB9d2zrAbb34bC9bx7beh+2960gqE0WLFgg9Hq9ePvtt8WuXbvE3XffLRISEkRpaWmki9ZhPv/8c/Hoo4+Kf//73wKAWLRoUcD6Z555RlgsFrF48WKxbds2ce2114r8/HzR0NDg3Wby5Mli6NChYu3ateK7774Tffr0EbfccksnX0n7mTRpknjnnXfEzp07xdatW8WVV14pcnNzRW1trXebmTNnipycHLFixQqxceNGMXbsWDFu3DjveqfTKQYPHiwKCwvFli1bxOeffy5SUlLEww8/HIlLaheffvqp+Oyzz8T+/fvFvn37xCOPPCJ0Op3YuXOnEKJ71kmw9evXi7y8PDFkyBAxZ84c7/LuWDdPPvmkGDRokCguLva+ysrKvOu7Y51QdGBbz7beg+29Orb3TWNbH4jtfcsxSG+j0aNHi1mzZnk/u1wukZmZKebNmxfBUnWe4Ibb7XaL9PR08dxzz3mXVVZWCoPBID788EMhhBC7d+8WAMSGDRu82yxdulRIkiROnDjRaWXvSKdOnRIAxMqVK4UQSh3odDqxcOFC7zZ79uwRAMSaNWuEEMofRLIsi5KSEu828+fPF2azWdhsts69gA6UmJgo3nzzTdaJEKKmpkb07dtXLF++XFx88cXehru71s2TTz4phg4dqrquu9YJRQe29Wzrw2F7Hx7bewXb+lBs71uOw93bwG63Y9OmTSgsLPQuk2UZhYWFWLNmTQRLFjmHDx9GSUlJQJ1YLBaMGTPGWydr1qxBQkICRo4c6d2msLAQsixj3bp1nV7mjlBVVQUASEpKAgBs2rQJDocjoF7OO+885ObmBtTL+eefj7S0NO82kyZNQnV1NXbt2tWJpe8YLpcLCxYsQF1dHQoKClgnAGbNmoWrrroqoA6A7v19OXDgADIzM9GrVy9Mnz4dRUVFALp3nVBksa0Pxbbeh+19KLb3gdjWq2N73zLaSBegKzp9+jRcLlfAFwQA0tLSsHfv3giVKrJKSkoAQLVOPOtKSkqQmpoasF6r1SIpKcm7TVfmdrvxwAMPYPz48Rg8eDAA5Zr1ej0SEhICtg2uF7V686zrqnbs2IGCggJYrVbExcVh0aJFGDhwILZu3dpt6wQAFixYgM2bN2PDhg0h67rr92XMmDF499130b9/fxQXF+Opp57ChRdeiJ07d3bbOqHIY1sfim29gu19ILb3odjWq2N733IM0onayaxZs7Bz5058//33kS5KVOjfvz+2bt2KqqoqfPzxx5gxYwZWrlwZ6WJF1LFjxzBnzhwsX74cRqMx0sWJGlOmTPG+HzJkCMaMGYOePXviX//6F0wmUwRLRkQUiu19ILb3gdjWh8f2vuU43L0NUlJSoNFoQrINlpaWIj09PUKliizPdTdVJ+np6Th16lTAeqfTifLy8i5fb7Nnz8aSJUvwzTffIDs727s8PT0ddrsdlZWVAdsH14tavXnWdVV6vR59+vTBiBEjMG/ePAwdOhQvv/xyt66TTZs24dSpU7jgggug1Wqh1WqxcuVK/PnPf4ZWq0VaWlq3rRt/CQkJ6NevHw4ePNitvy8UWWzrQ3X3th5ge6+G7X0gtvUtx/Y+PAbpbaDX6zFixAisWLHCu8ztdmPFihUoKCiIYMkiJz8/H+np6QF1Ul1djXXr1nnrpKCgAJWVldi0aZN3m6+//hputxtjxozp9DK3ByEEZs+ejUWLFuHrr79Gfn5+wPoRI0ZAp9MF1Mu+fftQVFQUUC87duwI+KNm+fLlMJvNGDhwYOdcSCdwu92w2Wzduk4mTpyIHTt2YOvWrd7XyJEjMX36dO/77lo3/mpra/Hjjz8iIyOjW39fKLLY1ofqrm09wPa+Nbp7e8+2vuXY3jch0pnruqoFCxYIg8Eg3n33XbF7925xzz33iISEhIBsg+eampoasWXLFrFlyxYBQLzwwgtiy5Yt4ujRo0IIZVqWhIQE8Z///Eds375dXHfddarTsgwfPlysW7dOfP/996Jv375delqWe++9V1gsFvHtt98GTCdRX1/v3WbmzJkiNzdXfP3112Ljxo2ioKBAFBQUeNd7ppO44oorxNatW8WyZctEjx49uvR0Eg899JBYuXKlOHz4sNi+fbt46KGHhCRJ4ssvvxRCdM86Ccc/46sQ3bNufvWrX4lvv/1WHD58WKxevVoUFhaKlJQUcerUKSFE96wTig5s69nWe7C9V8f2vmXY1ivY3rccg/Sz8Morr4jc3Fyh1+vF6NGjxdq1ayNdpA71zTffCAAhrxkzZgghlKlZHn/8cZGWliYMBoOYOHGi2LdvX8Axzpw5I2655RYRFxcnzGazuPPOO0VNTU0ErqZ9qNUHAPHOO+94t2loaBD33XefSExMFDExMeL6668XxcXFAcc5cuSImDJlijCZTCIlJUX86le/Eg6Ho5Ovpv3cddddomfPnkKv14sePXqIiRMnehtsIbpnnYQT3HB3x7qZNm2ayMjIEHq9XmRlZYlp06aJgwcPetd3xzqh6MG2nm29EGzvw2F73zJs6xVs71tOEkKIzuu3JyIiIiIiIqJw+Ew6ERERERERUZRgkE5EREREREQUJRikExEREREREUUJBulEREREREREUYJBOhEREREREVGUYJBOREREREREFCUYpBMRERERERFFCQbpRERERERERFGCQToRdTpJkrB48eJIF4OIiIg6CNt6orZjkE7Uzdxxxx2QJCnkNXny5EgXjYiIiNoB23qirk0b6QIQUeebPHky3nnnnYBlBoMhQqUhIiKi9sa2nqjrYk86UTdkMBiQnp4e8EpMTASgDE+bP38+pkyZApPJhF69euHjjz8O2H/Hjh247LLLYDKZkJycjHvuuQe1tbUB27z99tsYNGgQDAYDMjIyMHv27ID1p0+fxvXXX4+YmBj07dsXn376acdeNBERUTfCtp6o62KQTkQhHn/8cdxwww3Ytm0bpk+fjptvvhl79uwBANTV1WHSpElITEzEhg0bsHDhQnz11VcBDfP8+fMxa9Ys3HPPPdixYwc+/fRT9OnTJ+AcTz31FG666SZs374dV155JaZPn47y8vJOvU4iIqLuim09URQTRNStzJgxQ2g0GhEbGxvwevrpp4UQQgAQM2fODNhnzJgx4t577xVCCPHGG2+IxMREUVtb613/2WefCVmWRUlJiRBCiMzMTPHoo4+GLQMA8dhjj3k/19bWCgBi6dKl7XadRERE3RXbeqKujc+kE3VDl156KebPnx+wLCkpyfu+oKAgYF1BQQG2bt0KANizZw+GDh2K2NhY7/rx48fD7XZj3759kCQJJ0+exMSJE5ssw5AhQ7zvY2NjYTabcerUqbZeEhEREflhW0/UdTFIJ+qGYmNjQ4aktReTydSi7XQ6XcBnSZLgdrs7okhERETdDtt6oq6Lz6QTUYi1a9eGfB4wYAAAYMCAAdi2bRvq6uq861evXg1ZltG/f3/Ex8cjLy8PK1as6NQyExERUcuxrSeKXuxJJ+qGbDYbSkpKApZptVqkpKQAABYuXIiRI0diwoQJeP/997F+/Xq89dZbAIDp06fjySefxIwZMzB37lyUlZXh/vvvx2233Ya0tDQAwNy5czFz5kykpqZiypQpqKmpwerVq3H//fd37oUSERF1U2zribouBulE3dCyZcuQkZERsKx///7Yu3cvACUb64IFC3DfffchIyMDH374IQYOHAgAiImJwRdffIE5c+Zg1KhRiImJwQ033IAXXnjBe6wZM2bAarXixRdfxK9//WukpKTgxhtv7LwLJCIi6ubY1hN1XZIQQkS6EEQUPSRJwqJFizB16tRIF4WIiIg6ANt6oujGZ9KJiIiIiIiIogSDdCIiIiIiIqIoweHuRERERERERFGCPelEREREREREUYJBOhEREREREVGUYJBOREREREREFCUYpBMRERERERFFCQbpRERERERERFGCQToRERERERFRlGCQTkRERERERBQlGKQTERERERERRYn/D6fWGyKXEvPBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-19 02:00:49.389252: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 02:00:49.391013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 02:00:49.392339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 02:00:49.514155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 02:00:49.571088: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 02:00:49.572226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 02:00:49.573604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 02:00:49.759350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 02:00:49.762084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 02:00:49.763653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2024-05-19 02:00:49.898192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis' with dtype int32 and shape [1]\n",
      "\t [[{{node gradients/ReverseV2_grad/ReverseV2/ReverseV2/axis}}]]\n",
      "2024-05-19 02:00:49.957749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2024-05-19 02:00:49.958963: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2024-05-19 02:00:49.960215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n",
      "Chatbot: Great to see you. How do you feel currently?\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "Chatbot: I can provide general advice regarding anxiety and depression, answer questions related to mental health and make daily conversations. Do not consider me as a subsitute for an actual mental healthcare worker. Please seek help if you don't feel satisfied with me.\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "Chatbot: I'm here for you. Could you tell me why you're feeling this way?\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "Chatbot: Not sure I understand that.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[38], line 17\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# Example usage\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m---> 17\u001b[0m     user_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUser: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m user_input\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquit\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m     19\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m/workspaces/OptimalYou-Chat/.venv/lib/python3.10/site-packages/ipykernel/kernelbase.py:1282\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1280\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1281\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1284\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1285\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/workspaces/OptimalYou-Chat/.venv/lib/python3.10/site-packages/ipykernel/kernelbase.py:1325\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1323\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1324\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1325\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "# Function to predict intents based on user input\n",
    "def predict_intent(user_input):\n",
    "    user_input_seq = tokenizer.texts_to_sequences([user_input])\n",
    "    user_input_pad = pad_sequences(user_input_seq, maxlen=X_train.shape[1], padding='post')\n",
    "    prediction = model.predict(user_input_pad)\n",
    "    intent = label_encoder.inverse_transform([np.argmax(prediction)])\n",
    "    return intent[0]\n",
    "\n",
    "# Function to generate responses based on predicted intents\n",
    "def generate_response(intent):\n",
    "    responses = df[df['tag'] == intent]['responses'].values[0]\n",
    "    response = np.random.choice(responses)\n",
    "    return response\n",
    "\n",
    "# Example usage\n",
    "while True:\n",
    "    user_input = input(\"User: \")\n",
    "    if user_input.lower() == 'quit':\n",
    "        break\n",
    "    intent = predict_intent(user_input)\n",
    "    response = generate_response(intent)\n",
    "    print(\"Chatbot:\", response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "model.save('chatbot_model.h5')\n",
    "\n",
    "with open('tokenizer.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "with open('label_encoder.pickle', 'wb') as handle:\n",
    "    pickle.dump(label_encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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