{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "929632d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from keras.models import Model\n",
    "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
    "from keras.layers import Convolution1D,BatchNormalization,concatenate,Flatten\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing import sequence\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f99da117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow.keras as kr\n",
    "\n",
    "if sys.version_info[0] > 2:\n",
    "    is_py3 = True\n",
    "else:\n",
    "    reload(sys)\n",
    "    sys.setdefaultencoding(\"utf-8\")\n",
    "    is_py3 = False\n",
    "\n",
    "\n",
    "def native_word(word, encoding='utf-8'):\n",
    "    \"\"\"如果在python2下面使用python3训练的模型，可考虑调用此函数转化一下字符编码\"\"\"\n",
    "    if not is_py3:\n",
    "        return word.encode(encoding)\n",
    "    else:\n",
    "        return word\n",
    "\n",
    "\n",
    "def native_content(content):\n",
    "    if not is_py3:\n",
    "        return content.decode('utf-8')\n",
    "    else:\n",
    "        return content\n",
    "\n",
    "\n",
    "def open_file(filename, mode='r'):\n",
    "    \"\"\"\n",
    "    常用文件操作，可在python2和python3间切换.\n",
    "    mode: 'r' or 'w' for read or write\n",
    "    \"\"\"\n",
    "    if is_py3:\n",
    "        return open(filename, mode, encoding='utf-8', errors='ignore')\n",
    "    else:\n",
    "        return open(filename, mode)\n",
    "\n",
    "\n",
    "def read_file(filename):\n",
    "    \"\"\"读取文件数据\"\"\"\n",
    "    contents, labels = [], []\n",
    "    with open_file(filename) as f:\n",
    "        for line in f:\n",
    "            try:\n",
    "                label, content = line.strip().split('\\t')\n",
    "                if content:\n",
    "                    contents.append(list(native_content(content)))\n",
    "                    labels.append(native_content(label))\n",
    "            except:\n",
    "                pass\n",
    "    return contents, labels\n",
    "\n",
    "\n",
    "def build_vocab(train_dir, vocab_dir, vocab_size=5000):\n",
    "    \"\"\"根据训练集构建词汇表，存储\"\"\"\n",
    "    data_train, _ = read_file(train_dir)\n",
    "\n",
    "    all_data = []\n",
    "    for content in data_train:\n",
    "        all_data.extend(content)\n",
    "\n",
    "    counter = Counter(all_data)\n",
    "    count_pairs = counter.most_common(vocab_size - 1)\n",
    "    words, _ = list(zip(*count_pairs))\n",
    "    # 添加一个 <PAD> 来将所有文本pad为同一长度\n",
    "    words = ['<PAD>'] + list(words)\n",
    "    open_file(vocab_dir, mode='w').write('\\n'.join(words) + '\\n')\n",
    "\n",
    "\n",
    "def read_vocab(vocab_dir):\n",
    "    \"\"\"读取词汇表\"\"\"\n",
    "    with open_file(vocab_dir) as fp:\n",
    "        # 如果是py2 则每个值都转化为unicode\n",
    "        words = [native_content(_.strip()) for _ in fp.readlines()]\n",
    "    word_to_id = dict(zip(words, range(len(words))))\n",
    "    return words, word_to_id\n",
    "\n",
    "\n",
    "def read_category():\n",
    "    \"\"\"读取分类目录，固定\"\"\"\n",
    "    categories = ['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐']\n",
    "\n",
    "    categories = [native_content(x) for x in categories]\n",
    "\n",
    "    cat_to_id = dict(zip(categories, range(len(categories))))\n",
    "\n",
    "    return categories, cat_to_id\n",
    "\n",
    "\n",
    "def to_words(content, words):\n",
    "    \"\"\"将id表示的内容转换为文字\"\"\"\n",
    "    return ''.join(words[x] for x in content)\n",
    "\n",
    "\n",
    "def process_file(filename, word_to_id, cat_to_id, max_length=600):\n",
    "    \"\"\"将文件转换为id表示\"\"\"\n",
    "    contents, labels = read_file(filename)\n",
    "\n",
    "    data_id, label_id = [], []\n",
    "    for i in range(len(contents)):\n",
    "        data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id])\n",
    "        label_id.append(cat_to_id[labels[i]])\n",
    "\n",
    "    # 使用keras提供的pad_sequences来将文本pad为固定长度\n",
    "    x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length)\n",
    "    y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id))  # 将标签转换为one-hot表示\n",
    "\n",
    "    return x_pad, y_pad\n",
    "\n",
    "\n",
    "def batch_iter(x, y, batch_size=64):\n",
    "    \"\"\"生成批次数据\"\"\"\n",
    "    data_len = len(x)\n",
    "    num_batch = int((data_len - 1) / batch_size) + 1\n",
    "\n",
    "    indices = np.random.permutation(np.arange(data_len))\n",
    "    x_shuffle = x[indices]\n",
    "    y_shuffle = y[indices]\n",
    "\n",
    "    for i in range(num_batch):\n",
    "        start_id = i * batch_size\n",
    "        end_id = min((i + 1) * batch_size, data_len)\n",
    "        yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef22fed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = 'cnews.train.txt'\n",
    "test_dir = 'cnews.test.txt'\n",
    "val_dir = 'cnews.val.txt'\n",
    "vocab_dir = 'cnews.vocab.txt'\n",
    "save_dir = 'checkpoints/textcnn'\n",
    "save_path = 'best_validation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b148a79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(vocab_dir):  # 如果不存在词汇表，重建\n",
    "    build_vocab(train_dir, vocab_dir, config.vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1926e74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建数据类别映射、文本字典\n",
    "categories, cat_to_id = read_category()\n",
    "words, word_to_id = read_vocab(vocab_dir)\n",
    "vocab_size = len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b0973ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_length = 600  # 序列长度\n",
    "x_train, y_train = process_file(train_dir, word_to_id, cat_to_id, seq_length)\n",
    "x_val, y_val = process_file(val_dir, word_to_id, cat_to_id, seq_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "75a5fccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TextInception\n",
    "# main_input = Input(shape=(600,), dtype='float64')\n",
    "# embedder = Embedding(vocab_size + 1, 256, input_length = 600)\n",
    "# embed = embedder(main_input)\n",
    "# block1 = Convolution1D(128, 1, padding='same')(embed)\n",
    "# conv2_1 = Convolution1D(256, 1, padding='same')(embed)\n",
    "# bn2_1 = BatchNormalization()(conv2_1)\n",
    "# relu2_1 = Activation('relu')(bn2_1)\n",
    "# block2 = Convolution1D(128, 3, padding='same')(relu2_1)\n",
    "# inception = concatenate([block1, block2], axis=-1)\n",
    "# flat = Flatten()(inception)\n",
    "# fc = Dense(128)(flat)\n",
    "# drop = Dropout(0.5)(fc)\n",
    "# bn = BatchNormalization()(drop)\n",
    "# relu = Activation('relu')(bn)\n",
    "# main_output = Dense(10, activation='softmax')(relu)\n",
    "# model = Model(inputs = main_input, outputs = main_output)\n",
    "# model.compile(loss='categorical_crossentropy',\n",
    "#               optimizer='adam',\n",
    "#               metrics=['accuracy'])\n",
    "main_input = Input(shape=(600,), dtype='float64')\n",
    "embedder = Embedding(vocab_size + 1, 256, input_length = 600)\n",
    "embed = embedder(main_input)\n",
    "conv2_1 = Convolution1D(128, 1, padding='same')(embed)\n",
    "bn2_1 = BatchNormalization()(conv2_1)\n",
    "relu2_1 = Activation('relu')(bn2_1)\n",
    "conv2_2 = Convolution1D(128, 3, padding='same')(relu2_1)\n",
    "flat = Flatten()(conv2_2)\n",
    "fc = Dense(128)(flat)\n",
    "drop = Dropout(0.5)(fc)\n",
    "bn = BatchNormalization()(drop)\n",
    "relu = Activation('relu')(bn)\n",
    "main_output = Dense(10, activation='softmax')(relu)\n",
    "model = Model(inputs = main_input, outputs = main_output)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a4051151",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 600)]             0         \n",
      "                                                                 \n",
      " embedding (Embedding)       (None, 600, 256)          1280256   \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 600, 128)          32896     \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 600, 128)         512       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " activation (Activation)     (None, 600, 128)          0         \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 600, 128)          49280     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 76800)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               9830528   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 128)              512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 11,195,274\n",
      "Trainable params: 11,194,762\n",
      "Non-trainable params: 512\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9e5fe150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1563/1563 [==============================] - 139s 78ms/step - loss: 0.3613 - accuracy: 0.8956 - val_loss: 0.5181 - val_accuracy: 0.8556\n",
      "Epoch 2/3\n",
      "1563/1563 [==============================] - 30s 19ms/step - loss: 0.1331 - accuracy: 0.9614 - val_loss: 0.4725 - val_accuracy: 0.8848\n",
      "Epoch 3/3\n",
      "1563/1563 [==============================] - 32s 21ms/step - loss: 0.0902 - accuracy: 0.9723 - val_loss: 0.4052 - val_accuracy: 0.9034\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "          batch_size=32,\n",
    "          epochs=3,\n",
    "          validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "86c56dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W17LSi2e++YUo0CeQQxGABiCwA3BJR48eVb9+/SRJb7zxhu67777mLQgAAADtnmEYKiovuuxZbfml+c39Kh7lZfKqNWutrlltlwregn2D5ePl09yvArQ7BHZAM6gegl0Ow2i7U+YBAADQNpXby52Ha05mteWW5KrcXt7cr+JRQb5Blz2rrYN3B2a1Aa0UgR0AAAAAwCnDMFRQVlBnuFbfDLa6vldYVtjcr+JRPmYft8xq8zJ7NferAGhGBHZAM+jRo4f27t1b7/0rrrhCkjR06FC98cYbTVVWnfr27ctMPgAAgFautKLUpVltjpCtnjZ2w97cr+IxJpkU7Bd82bPa/L39m/tVALQBBHZAM/Dx8ZHFYnHaLjAw0KV2AAAAaJvshl35pfmXPautuLy4uV/Fo/y9/V06gfRSwVuQb5DMJnNzvwoASCKwAwAAAACPKC4vdh6uOZnVlleSJ0Ntd7WD2WSuN1RryKw2Xy/f5n4VAHArAjuglRk3bpy2bNmisWPH6quvvtLBgwe1ZMkSbdq0SZmZmSoqKtKRI0fUt29fSdLJkyf1wQcf6Msvv9SPP/6oEydOqLy8XJ06ddLQoUP105/+VNOnT5fZXPffJjo7JXbhwoX67//+b0mV+5oUFxfrlVdeUUJCgg4ePChJGjx4sGbPnq1HHnlE3t78awcAALRsFfYK5ZXmNXhW28VLTksrSpv7VTwqwCeg/iDNxVltgT6BHIoAAHXgT85AK/bhhx/qnnvuUUFBQZ33Kyoq1LNnT9nttfcaOXHihNatW6d169ZpxYoVev/99xUUFHRZ9WRlZenWW2/V7t27a3x/x44d2rFjhz799FOtXbu23nAQAADgchiGoaLyosue1ZZfmt/cr+JRXiavWociXDyrrday0ovahPiFyMfLp7lfBQDaLAI7NJjdsCunMKe5y2hSEQERLW4/i+PHj2vWrFkKCAjQ7373O40ePVpeXl7asWOHI3irOizixhtv1MSJE3XFFVeoc+fOysvL0+HDh7Vs2TJt27ZNn332mR577DG99dZbl1XT1KlTtW/fPj355JOaNGmSwsPDdeDAAf3xj39USkqK1q9fr2XLlunhhx++7PcHAABtS7m93PlBCC7Maiu3lzf3q3hUkG/QZc9q6+DdgVltANDCEdihwXIKc9TlxS7NXUaTOv3r0+oc2Lm5y6jhyJEj6t69u7Zt26bevXs7vj98+HDHtZeXlw4cOKCoqKha/ceOHau5c+fqD3/4g/7nf/5H77zzjn77299q4MCBja6pahbduHHjHN+75pprdMstt2jIkCHKysrSa6+9RmAHAEAbYhiGCsoKLntWW2FZYXO/ikf5mH0uHaS5OKvNy+zV3K8CAGgCBHZAK/bcc8/VCOsuZjKZ6gzrqvv973+v1157TdnZ2Vq3bp3mz5/f6HqeeOKJGmFdlfDwcM2dO1fPPfec9u7dqwsXLig0NLTR4wAAAPcoqyhzHq65MKvNbtTefqMtqQrLXJ3VVlfw5uflx6w2AIDLCOyAVsrX11fTp09vUB+73a5Tp04pLy9PZWVlju/37NlT2dnZ+vHHHy+rpnvuuafee9dee62kyr+FP3LkiK666qrLGgsAgPbMbtiVX5rv2qy20prhWvU2xeXFzf0qHuXn5XfZs9qC/YJb3NYoAIC2j8AOaKUGDhwof39/p+0Mw9C//vUvrVixQt99952KiorqbZudnX1ZNQ0aNKjee+Hh4Y7rvLy8yxoHAIDWrKS85LJnteWV5MmQ0dyv4jFmk/myZ7WF+IXIz9uvuV8FAIBGIbBDg0UEROj0r083dxlNKiIgorlLqKVjx45O2xQXF2vq1Kn6+OOPXXrmpcI8VwQEBNR7r/rJsBUVFZc1DgAAzaHCXqG80rzLntVWWlHa3K/iUR28O1z2rLYg3yCWjwIA2jUCOzSY2WRucQcwtEdeXs43HP7Tn/7kCOvGjh2rxx57TNdcc40iIyPVoUMHR4g2ZswYJSYmOk6VBQCgLTEMQ8XlxZc9qy2/NL+5X8WjvExedc5Ya+isNh8vn+Z+FQAAWj0CO6CNMgxDy5cvlySNHj1aX375ZY1ZbtWdPXu2KUsDAMBl5fZy5ZbkXvastnJ7eXO/ikcF+gRe9qy2AJ8AZrUBANBCENgBbdTZs2d16tQpSdL06dPrDevy8/N14MCBpiwNAIBazhef1/sp7+v9lPd16NwhR/BWWFbY3KV5lI/Z57JntQX7BcvbzG/rAQBoS/gvO9BGlZf/ZyZBQUFBve2WL19eoy0AAE2loLRA6w6sky3Zpk/SPml1e7sF+wZf9qw2f29/ZrUBAIBaCOyANqpz584KCwvT+fPnlZCQoF/+8pfy86t5UtqOHTv0u9/9rpkqBAC0RyXlJfo47WPZkmxan7q+WWbQ+Xn5OQ3S6gzgqn0d7Bcss6nu2esAAACXi8AOaKPMZrPuuece/fWvf9WePXs0atQo/epXv9LAgQN14cIFffTRR3rttdcUFBSk7t27KzU1tblLBgC0UeX2cn1x+AvZkm36IOUDXSi50KjnmGRyujy0zmWlF7X38/ZzPhgAAEAzIrAD2rA//elP2rp1q3bv3q2dO3fqpz/9aY374eHheu+99/T73/+ewA4A4FZ2w66vj38tW5JNq/etVnZhttM+fl5+um3gbboz5k51C+pWK3gL9A1kVhsAAGgXCOyANiw0NFRbt27V4sWLtWrVKh08eFDe3t7q1auXbr/9ds2bN089e/Zs7jIBAG2EYRj6/uT3StiboJXJK5WZl+m0j5fJS+MHjJc11qopg6Yo1D+0CSoFAABo2UyGYRjNXQTcKyMjQ7169ZIkpaenNyiQOXjwoMrLy+Xt7a2BAwd6qkSgwfi1CQAtV/LpZCUkJciWZNOhc4ectjfJpDF9xshqsWra4GnqHNi5CaoEAADwjMvJYerDDDsAAAA02KGzh2RLssmWbFPS6SSX+gzrMUzWWKtmxM5Qj5AeHq4QAACg9SKwAwAAgEsycjO0KnmVbEk27Tixw6U+V3S5QlaLVTNjZ2pA+AAPVwgAANA2ENgBAACgXmcKzmjNvjWyJduUeCxRhpzvpjKg4wDFW+JltVgV2yW2CaoEAABoWwjsAAAAUMOF4gv6YP8HsiXZ9Pnhz1VhVDjt0zOkp2bGzpTVYtW13a6VyWRqgkoBAADaJgI7AAAAqLCsUOsPrJct2aaPDn6k0opSp306B3TW9CHTZbVYNbL3SJlN5iaoFAAAoO0jsAMAAGinSspLtOnQJtmSbFp3YJ0Kygqc9gn1C9XUwVNltVh1Y78b5W3mt5MAAADuxu+wAAAA2pFye7k2H9ksW5JN7+9/X+eLzzvtE+AToDtj7pQ11qpbo26Vn7ef5wsFAABoxwjsAAAA2ji7Yde29G1KSErQ6n2rdbrgtNM+vl6+mhg1UVaLVZOiJynQN7AJKgUAAIBEYAcAANAmGYahXad2KWFvglYmr1R6brrTPl4mL93U/yZZY626a/BdCvMP83yhAAAAqIXADgAAoA1JOZOihKQE2ZJsOnj2oEt9RvceLavFqruH3K0ugV08XCEAAACcIbADAABo5Y6cOyJbkk22ZJv2ZO1xqc+13a5VvCVeM2JnqFdoLw9XCAAAgIYgsAMAAGiFTuSd0KrkVbIl2fRd5ncu9RnSeYjiLfGaGTtTAyMGerhCAAAANBaBHQAAQCuRU5ijNfvWyJZs05ajW2TIcNqnf8f+ssZaZbVYZelikclkaoJKAQAAcDkI7AAAAFqw3JJcfbj/QyUkJeizw5+p3F7utE/34O6aGTtTVotV13W/jpAOAACglSGwAwAAaGGKyoq08eBGJSQlaGPqRpVUlDjtE9EhQtOHTJfVYtWo3qPkZfZqgkoBAADgCQR2AAAALUBpRak+PfSpbEk2fXjgQ+WX5jvtE+IXorsG3SWrxaqb+t0kHy+fJqgUAAAAnkZgBwAA0Ewq7BXacmyLEvYm6L2U93Su+JzTPh28O2hSzCRZY62aOHCi/L39m6BSAAAANCVzUw107NgxzZ8/X4MGDVJgYKDCw8N13XXX6YUXXlBhYaFbxjhy5Ih++ctfymKxKDg4WIGBgRo4cKAeffRRJScnO+1/3333yWQyufQ5evSo0+c1xTsDAIDWxTAMbUvfpic/flI9X+qpm96+Sct3Lb9kWOdj9tGk6En619R/KevXWVp590rdNfguwjoAAIA2qklm2K1fv16zZs1Sbm6u43uFhYXauXOndu7cqeXLl2vjxo2Kiopq9BhLly7VE088odLS0hrfT0tLU1pamlasWKFFixbp8ccfb/QYDdEU7wwAAFoHwzD0Y9aPStiboJXJK3XswjGnfcwms27sd6OssVbdNfguhXcIb4JKAQAA0BJ4PLDbtWuXZs6cqaKiIgUFBempp55SXFycioqKZLPZtGzZMqWmpur222/Xzp07FRwc3OAxbDabHn74YUlSaGio5s+frxtvvFF+fn7atWuXnn/+eaWlpenJJ59Uly5dNGPGjEs+r3v37tq0adMl2/To0aNZ3xkAALR8B7IPKCEpQbYkmw7kHHCpzw29blC8JV53D7lbkUGRHq4QAAAALZHHA7t58+apqKhI3t7e+vTTTzVixAjHvRtvvFEDBw7Ub37zG6WmpmrRokVauHBhg55fWFioefPmSZKCgoL09ddfy2KxOO4PHTpUM2fO1KhRo7R37149+eSTuu222xQUFFTvM318fGo8o6E8/c5o/Y4ePap+/fpd9nMMw3BDNQAAdzp2/phsSTbZkm3afWq3S32ujrxa8ZZ4zYidoT5hfTxbIAAAAFo8j+5ht337diUmJkqS7r///hrBVZX58+dr8ODBkqQlS5aorKysQWN89NFHOn36tKTKoKyuoC0kJESLFy+WJGVlZenNN99s0BgN0RTvDDSXcePGyWQyady4cc1dCgC0KKfyT+nl717WDStuUN8lffVfX/yX07BuUKdB+u9x/639j+3XDw//oP9v5P9HWAcAAABJHp5ht3btWsf13Llz62xjNps1e/ZsPfXUUzp//rw2b96sCRMmuDzGzp07HdcTJ06st924cePk7++v4uJirVmzxmN72TXFO6P169Gjh/bu3Vvv/SuuuEJS5QzRN954o6nKAgA0wNmis3o/5X0lJCXoq6NfyW7YnfbpG9ZX1lirrBarftL1JzKZTE1QKQAAAFobjwZ2X3/9tSQpMDBQ1157bb3txo4d67jeunVrg8KrnJwcx3XXrl3rbeft7a3w8HCdOHFC27ZtU3l5uby93f/6TfHOaP1cXXYdGBh4WcuzAQDulVeSp3UH1ikhKUGbDm1Sub3caZ/IoEjNjJ0pq8Wq4T2GE9IBAADAKY8GdikpKZKkqKioS4ZjgwYNqtXHVdX3ortw4UK97QzDcJzYWlpaqrS0tBrjVpeTk6OxY8cqKSlJ+fn5Cg8P109+8hNNmjRJP/vZzxQQEFDvOE3xzhkZGZe8f/LkyQY9DwAA1K+orEgfp32shKQEbUjdoOLyYqd9wjuE6+7Bd8tqsWpMnzHyMns1QaUAAABoKzwW2BUXFys7O1uS1LNnz0u27dixowIDA1VQUKD09PQGjVO1F5wkbdmypd5Zbbt27VJ+fr7j6+PHj9cb2OXn5+vf//634+tTp07p1KlT+vTTT/Xcc89p1apVuuGGG2r1a6p37tWrV4Pao2364YcftHTpUm3evFmZmZkyDEM9e/bUjTfeqF/+8peKjo6ut+/58+f117/+VRs2bND+/fuVn5+vsLAwde7cWTExMZowYYKmTp3qmLV633336a233nL037JlS60ZIn369NHRo0c98q4A0NTKKsr0+eHPlZCUoLX71yqvNM9pnyDfIE0ZNEXxlnjd3P9m+Xr5NkGlAAAAaIs8Ftjl5f3nN7aXOpG1SlV4VT1Uc8XEiRPl7e2t8vJyLV68WLNnz1anTp1qtLHb7VqwYEG99VUxmUy6/vrrNWnSJF1zzTXq2rWriouLtXfvXq1YsULbt29XZmamJkyYoMTERF199dX1PtOT74z2zW6369e//rX+8pe/1DolNjU1VampqVq+fLn++te/6qGHHqrVPyUlRTfffLNOnDhR4/vZ2dnKzs5WSkqK1q5dq4qKCo/t9QgALVGFvUKJxxOVsDdBa1LW6GzRWad9/L39dUf0HbLGWnXbwNvUwadDE1QKAACAts6jM+yq+Po6/xtmPz8/SVJRUVGDxunVq5ceeeQRvfrqq8rMzNTIkSP1/PPPKy4uTr6+vtq9e7cWLlyoTZs2ydfXV6WlpfWO89JLLyksLKzW90eMGKEHH3xQv/3tb/XnP/9ZBQUFeuCBB7Rz584as4ya6p2dzcg7efKkhg0b1qBnNoTdLlXbOrBdiIiQzB49U9l1TzzxhF577TVJ0pgxY3Tfffepf//+CggI0I8//qi//OUvSk5O1sMPP6zIyEjdeeedNfrfe++9OnHihHx8fPTggw9q4sSJioyMlN1uV0ZGhr799lt98MEHNfr86U9/0q9//WvNnTtXO3furPMwDFd+zQNAS2MYhrZnbpctyaaVySt1Mt/5thLeZm/dMuAWWS1W3Rlzp0L8QpqgUgAAALQnHgvs/P39HddVIdmllJSUSJI6dGj430y/+OKLOnz4sD766COlpqZqypQptdoMHTpU1113nV5//XVJUnBwcK02dYV1VUwmk/70pz/pu+++0xdffKEffvhB33zzjUaOHOlo01Tv7Gy5rafl5EhdujRrCU3u9Gmpc+fmrkL67LPPHGHd8uXLdf/999e4f91112nWrFm6/fbb9eWXX+rJJ5/Ubbfd5thP8fDhw/r+++8lSYsXL641g27YsGGaOnWq/vd//1fnz593fL9Hjx7q0aOHAgMDJXEYBoDWzTAM7T29Vwl7E2RLtuno+aNO+5hkUly/OFljrZo6eKoiAiI8XygAAADaLY/NGaoeiLmy5LOgoECSa0tJL+bn56f169dr2bJluuqqq2rMeuvSpYsWLFigxMTEGssHO3bs2OBxJOnhhx92XG/ZsqXGvaZ8Z7RPzz33nCRp2rRptcK6Kv7+/nr11VclSceOHdPmzZsd906dOuW4HjNmTL3jmEymRv8zAgAtVWpOqv5ny/8o9rVYXfm3K/Xc1uechnXX97xeS25dosxfZeqL2V/owWsfJKwDAACAx3l0hl1ERIRycnKcnmp67tw5R3jV2AMVzGazHnjgAT3wwAPKy8tTVlaWAgICFBkZKfP/rWU8ePCgo/2QIUMaNU71fpmZmTXuNfU7o33Jzc3VV199JUm6++67L9l28ODB6tSpk7Kzs7Vt2zaNHz9ektStWzdHmzfffFOLFy/2WL0A0BIcv3BcK5NWypZs0w8nf3Cpz5Vdr1S8JV4zYmeoX8d+Hq4QAAAAqM1jgZ1UGW4lJiYqLS1N5eXljmV5F9u/f7/juvqpr40VHBxca8lrRUWFdu/eLUnq379/rYMpXHXxyZgXa653Rtu3a9cu2e12SVJ8fLzi4+Nd6ld9Vl2/fv00evRoJSYm6qWXXtKmTZs0bdo0jRs3Ttdff70CAgI8UjsANKWs/Cyt2bdGCUkJ2pq+1aU+0RHRirfEa2bsTA3uzH+XAQAA0Lw8GtiNGjVKiYmJKigo0Pfff6/hw4fX2a760tLqe8K50+bNm5XzfyclzJw5s9HP2bdvn+O6e/fute63pHf2lIiIyj3d2pOIFrD66XQjf+iFhYU1vk5ISND06dO1bds27du3T/v27dMf//hH+fj46Prrr9dPf/pT3XfffTX2ZASAlu5c0Tl9sP8DJSQl6MsjX8pu2J326R3aW9ZYq6wWq66KvMrpX8oBAAAATcWjgd2UKVP07LPPSpLeeOONOsMru92ut99+W1LloQ9xcXFur8MwDC1cuFCSHCdjNtbf//53x/XYsWNr3W8p7+xJZnPLOIChvamoqHBc//3vf9cNN9zgUr+L96Lr0aOHvvnmG33xxRd6//33tWXLFu3bt09lZWVKTExUYmKiXnzxRX300UeKjo526zsAgDvll+Zr/YH1SkhK0Cdpn6jMXua0T5fALpoxZIbir4jX9T2vl9nUQo4ABwAAAKrxaGA3bNgwx/K7FStWaM6cORoxYkSNNosWLVJKSookad68efLx8alx/6uvvnIEWnPmzNGbb75Za5ycnBwFBQXJz8+v1r2Kigo9+eST2rq1cknMU089pX79au9H8+2336pPnz419viqzjAM/e53v9Pnn38uSbryyivrnBnnjncG6hJRbZpfQEDAZZ/SetNNN+mmm26SVPnP0Oeff66lS5fqyy+/1KFDhzRz5kzt2rXrssYAAHcrLi/WJ2mfKCEpQesPrFdReZHTPmH+YZo2eJriLfEa23esvM0e/e0PAAAAcNk8/jvWJUuWaOTIkSoqKtKECRP09NNPKy4uTkVFRbLZbFq6dKkkKTo6WvPnz2/UGJs3b9bjjz8uq9WqsWPHqnfv3iouLtaePXu0dOlSx951EydO1IIFC+p8xieffKLnnntOt956q8aPH68hQ4YoLCxMJSUl2rNnj/7xj3/ou+++k1QZlixbtqzepTNN8c5of6pOQDYMQ1u3btWsWbPc9uyIiAjNnDlTM2fO1OTJk7Vu3Trt3r1bBw8e1MCBAx3tWC4GoDmUVZTpyyNfKiEpQR/s/0C5JblO+wT6BGryoMmKt8RrwoAJ8vXybYJKAQAAAPfweGB39dVXa+XKlZo1a5Zyc3P19NNP12oTHR2tjRs31joooiGysrK0ZMkSLVmypNY9k8mkuXPn6rXXXpOvb/2/YS8pKdGHH36oDz/8sN42vXv31rvvvqvrrruu3jZN9c5oXzp37qzrr79e27Zt07vvvqv/+Z//UWcPrE2+6aabtG7dOklSdnZ2jcCual+7kpISt48LANXZDbu+Pv61bEk2rd63WtmF2U77+Hn56baBtyneEq/bo29XgA8H6QAAAKB1apI1IZMmTdKePXu0ZMkSbdy4URkZGfL19VVUVJSmT5+uxx9//LJOpxw9erReeOEFffnll9q/f7+ysrJkNpvVvXt3xcXFae7cufUe/lBl7ty56tq1q7Zt26Y9e/bo9OnTysnJkbe3tzp16qRrrrlGkyZN0k9/+lOXNuP39Dujffrtb3+r22+/Xbm5ubr77rv14YcfKiwsrM62JSUlWr58ue6//37Hr9mq2aZXXXVVnX0Mw3As+zaZTOrbt2+N+1VLxg8fPizDMJhxB8CtDMPQzhM7ZUuyaWXySmXmZTrt42Xy0vgB4xVvidfkmMkK9Q9tgkoBAAAAzzIZhmE0dxFwr4yMDPXq1UuSlJ6erp49e7rc9+DBgyovL5e3t3eNmVVoWlVB2NixY/XVV1/VuPeLX/zCMZM0MjJSjzzyiEaNGqWIiAgVFBQoLS1NiYmJev/993Xu3Dnl5eUpKChIkvTmm29q7ty5uu666zRp0iRdc801ioyMVFlZmY4cOaI33nhDn332mSRp8uTJWrt2bY2xly9f7ji05Re/+IVmzZql0NDKPxz7+PioT58+nvqR8GsTaMOSTifJlmSTLcmmQ+cOOW1vkklj+oxRvCVe04ZMU6eATk1QJQAAAFC3y8lh6sOuy0Ar89JLLyk8PFx//OMfderUKccJyHUJDAyUl5dXre/v2LFDO3bsqLffDTfcoBUrVtT6vtVq1bPPPqvDhw/rL3/5i/7yl7847vXp00dHjx5tyKsAaMfSzqZpZdJKJSQlKPlMskt9hvUYpnhLvKYPma4eIT08XCEAAADQfAjsgFbGZDLp97//ve6991797W9/05dffqnDhw/rwoULCggIUK9evXT11VdrwoQJuuuuu9ShQwdH3/j4eHXt2lWfffaZduzYoczMTGVlZam8vFxdunTRNddco5kzZ8pqtcpsNtcaOygoSN98842effZZffrppzp27JgKCwub8vUBtGIZuRlalbxKCUkJ2nlip0t9ruhyheIt8Zppman+Hft7uEIAAACgZWBJbBvEkli0RfzaBFqnMwVntGbfGiUkJSjxeKJLfaLCoypDutiZiu0S6+EKAQAAgMvDklgAANDinS8+r7X71yohKUFfHP5CFUaF0z49Q3rKGmuV1WLVNd2u4VAbAAAAtGsEdgAA4LIVlBZoQ+oGJSQl6OO0j1VaUeq0T+eAzpo+ZLrir4jXDb1ukNlUeyk+AAAA0B4R2AEAgEYpKS/RpkOblJCUoHUH1qmwzPmelqF+oZo6eKriLfGK6xcnbzO/FQEAAAAuxu+SAQCAy8rt5dp8ZLMSkhL0fsr7ulBywWmfAJ8A3Rlzp+It8bplwC3y8/ZrgkoBAACA1ovADgAAXJLdsOub9G9kS7Jp9b7VOl1w2mkfXy9fTYyaqHhLvO6IvkOBvoFNUCkAAADQNhDYAQCAWgzD0A8nf5AtyaaVySuVnpvutI+XyUs39b9J8ZZ4TRk0RWH+YZ4vFAAAAGiDCOwAAIDDvjP7ZEuyyZZk08GzB13qM7r3aMVb4jVtyDR1Cezi4QoBAACAto/ADgCAdu7wucNambRSCUkJ2nt6r0t9hnYfKmusVTNiZ6hXaC8PVwgAAAC0LwR2AAC0QyfyTmhV8iolJCVoe+Z2l/oM6TxE8ZZ4WS1WRYVHebhCAAAAoP0isAMAoJ3ILszWe/veky3Zpi1Ht8iQ4bRP/479HSGdpYulCaoEAAAAQGCHGry8vFReXq6KigoZhiGTydTcJQEyDEMVFRWSKn+NAnBdbkmu1u5fK1uSTZ8d/kzl9nKnfboHd9fM2JmKt8RraPeh/LcAAAAAaGIEdqjB19dXJSUlMgxDhYWFCgwMbO6SABUWFsowKmcC+fr6NnM1QMtXWFaojakblZCUoI8OfqSSihKnfSI6RGj6kOmKvyJeo3qPktlkboJKAQAAANSFwA41hISEKC8vT5J09uxZBQQEMLMCzcowDJ09e9bxdUhISDNWA7RcpRWl+vTQp0pIStCH+z9UQVmB0z4hfiG6a9BdirfE68Z+N8rHy6cJKgUAAADgDIEdaggKCpLJZJJhGMrPz1dGRobCw8MJ7tDkqmZ5nj17Vvn5+ZIkk8mkoKCgZq4MaDkq7BX66uhXsiXZ9F7KezpXfM5pnw7eHTQpZpLiLfG6NepW+Xv7N0GlAAAAABqCwA41mM1m9ejRQ5mZmY7QLj8/XyaTib3D0KSq9lGsYjKZ1KNHD5nNLNND+2Y37Po241vZkmxalbxKWQVZTvv4mH10a9StirfEa1LMJAX5EnwDAAAALRmBHWoJDg6uEdpJlbOdysudb1QOeEJVWBccHNzcpQDNwjAM7T61W7Ykm2zJNh2/cNxpH7PJrBv73ShrrFVTB09Vxw4dm6BSAAAAAO5AYIc6BQcHKzo6Wvn5+crNzVVpaanjlE6gKXh5ecnX11chISEKCgpiZh3apf3Z+ytDuiSbDuQccKnPyF4jZbVYNX3IdHUN6urhCgEAAAB4AoEd6mU2mxUSEsIm/wDQhI6eP6qVSSuVkJSgH7N+dKnPNd2ukTXWqpmWmeod2tvDFQIAAADwNAI7AACa2cm8k1q9b7VsSTZty9jmUp9BnQYp3hIvq8Wq6IhoD1cIAAAAoCkR2AEA0AxyCnP0fsr7siXb9NXRr2Q37E779A3rK2usVfFXxOuKLldwejcAAADQRhHYAQDQRPJK8vThgQ9lS7Jp06FNKrc7P8ynW1A3zYidoXhLvIb1GEZIBwAAALQDBHYAAHhQUVmRPjr4kWzJNm1I3aDi8mKnfcI7hOvuwXfLarFqTJ8x8jJ7NUGlAAAAAFoKAjsAANysrKJMnx3+TLYkm9buX6u80jynfYJ9gzVl0BTFW+J1c/+b5ePl0wSVAgAAAGiJCOwAAHCDCnuF/n3s37Il2bQmZY3OFp112sff2193RN+heEu8JkZNVAefDk1QKQAAAICWjsAOAIBGMgxD32V+J1uSTauSV+lk/kmnfbzN3rplwC2yWqyaHDNZwX7BTVApAAAAgNaEwA4AgAYwDEN7svbIlmSTLdmmo+ePOu1jkklx/eJkjbVq6uCpigiI8HyhAAAAAFotAjsAAFyQmpMqW5JNCUkJ2p+936U+I3qOkNVi1fQh09UtuJuHKwQAAADQVhDYAQBQj+MXjmtl0kolJCVo16ldLvW5KvIqWWOtmmmZqb5hfT1bIAAAAIA2icAOAIBqsvKztHrfatmSbNqavtWlPtER0Yq3xMtqsWpQp0EerhAAAABAW0dgBwBo984VndP7Ke/LlmzTl0e+lN2wO+3TO7S3rLFWxV8Rryu7XimTydQElQIAAABoDwjsAADtUn5pvtYdWCdbkk2fpH2iMnuZ0z5dA7tqRuwMWS1WXd/zeplN5iaoFAAAAEB7Q2AHAGg3isuL9fHBj2VLtmn9gfUqKi9y2qejf0dNGzxNVotV4/qOk5fZqwkqBQAAANCeEdgBANq0sooyfXHkC9mSbPpg/wfKLcl12ifQJ1BTBk2R1WLVhAET5Ovl2wSVAgAAAEAlAjsAQJtjN+xKPJYoW5JNa1LWKLsw22kfPy8/3R59u6yxVt0efbsCfAKaoFIAAAAAqI3ADgDQJhiGoR0ndsiWZNPK5JU6kXfCaR8vk5cmDJggq8WqKYOmKMQvpAkqBQAAAIBLI7ADALRqe7P2ypZkky3ZpsPnDjttb5JJY/uOlTXWqmlDpqlTQKcmqBIAAAAAXEdgBwBoddLOplWGdEk2JZ9JdqnP8B7DZbVYNSN2hroHd/dwhQAAAADQeAR2AIBWIf1CulYlr5It2aadJ3a61OcnXX8ia6xVMy0z1b9jfw9XCAAAAADuQWAHAGixThec1pp9a2RLsinxeKJLfaLCoxRviZfVYtWQzkM8XCEAAAAAuB+BHQCgRTlffF4fpHwgW7JNXxz+QhVGhdM+PUN6yhprldVi1TXdrpHJZGqCSgEAAADAMwjsAADNrqC0QOtT18uWZNPHaR+rtKLUaZ8ugV00fch0WS1W3dDrBplN5iaoFAAAAAA8j8AOANAsSspL9EnaJ7Il27TuwDoVlhU67RPqF6ppg6fJarEqrl+cvM38ZwwAAABA29Nkf9I5duyYXn75ZW3cuFHp6eny8/PTgAEDNGPGDD322GMKCAi47DGOHDmil19+WZ999pmOHTsmu92u7t27a/z48XrssccUGxt7yf5ZWVlav369vvzyS+3evVvHjx9XaWmpIiIidOWVV2rq1Km699571aFDh0s+x9WlWGPHjtVXX33l6usBQKtXbi/Xl0e+lC3JpvdT3teFkgtO+wT4BGhyzGRZLVbdMuAW+Xn7NUGlAAAAANB8TIZhGJ4eZP369Zo1a5Zyc3PrvB8dHa2NGzcqKiqq0WMsXbpUTzzxhEpL615G5evrq0WLFunxxx+v8/6yZcv085//XBUVl94raeDAgVqzZo1+8pOf1NumuQO7jIwM9erVS5KUnp6unj17un0MAHCV3bBr6/GtsiXZtHrfap0pPOO0j6+Xr24beJussVbdEX2HAn0Dm6BSAAAAAGg4T+QwHp9ht2vXLs2cOVNFRUUKCgrSU089pbi4OBUVFclms2nZsmVKTU3V7bffrp07dyo4OLjBY9hsNj388MOSpNDQUM2fP1833nij/Pz8tGvXLj3//PNKS0vTk08+qS5dumjGjBm1npGVlaWKigr5+vrqjjvu0IQJEzR48GAFBwfr0KFDWrZsmT799FMdPHhQN998s3744Qen/wf8/Oc/16OPPlrv/cBA/gAKoG0yDEPfn/xetiSbViavVEZuhtM+XiYv3dz/ZlktVk0ZNEVh/mGeLxQAAAAAWiCPz7AbM2aMEhMT5e3trX//+98aMWJEjfsvvPCCfvOb30iS/vCHP2jhwoUNen5hYaH69eun06dPKygoSNu2bZPFYqnRJjc3V6NGjdLevXvVtWtXpaWlKSgoqEabl156SVlZWZo/f746d+5c51jz58/X4sWLJUlz587VP/7xjzrbVc2wa8z7uAMz7AA0l+TTybIl2WRLtintbJpLfcb0GSNrrFXThkxTl8AuHq4QAAAAANzLEzmMRwO77du3a/jw4ZKkhx9+WH/7299qtbHb7bJYLEpJSVFYWJhOnz4tHx8fl8dYs2aNpk+fLklasGCBnnnmmTrbff755xo/frwk6ZVXXql3aeyllJaWqm/fvjp58qRCQ0N19uxZmc21TyUksAPQnhw+d7gypEuyae/pvS71ua77dbJarJoRO0M9Q/h3FAAAAIDWyxM5TO20yY3Wrl3ruJ47d27dBZjNmj17tiTp/Pnz2rx5c4PG2Llzp+N64sSJ9bYbN26c/P39JVWGfI3h6+urkSNHSpIuXLignJycRj0HAFq7zNxMvbTtJQ1fPlwDXh6gBV8ucBrWWbpY9EzcMzr4xEFtf3C7fjXiV4R1AAAAAFAHj+5h9/XXX0uq3Kvt2muvrbfd2LFjHddbt27VhAkTXB6jemjWtWvXett5e3srPDxcJ06c0LZt21ReXi5v74a/fklJiePay8urwf0BoLXKLszWmn1rZEuy6d/H/i1Dzido9+/YX/GWeFktVlm6WJy2BwAAAAB4OLBLSUmRJEVFRV0yHBs0aFCtPq6qvhfdhQsX6m1nGIbjlNrS0lKlpaXVGNcVZWVl2rZtm6TKcDA8PPyS7VevXq1Vq1bp6NGj8vLyUmRkpG644Qbdd999iouLa9DY1WVkXHrz9pMnTzb62QBQ3YXiC1q7f61syTZ9dugzVRiXPklbknoE99DM2JmyWqwa2n2oyydnAwAAAAAqeSywKy4uVnZ2tiQ5XbvbsWNHBQYGqqCgQOnp6Q0aZ/DgwY7rLVu21DuTb9euXcrPz3d8ffz48QYHdkuXLnW8U9W+eZeyb9++Gl+npaUpLS1Nb7/9tqZMmaI333xToaGhDapBkmNdNAB4QmFZoTakbpAtyaaPDn6kkooSp306BXTS9CHTZbVYNar3KJlNHt1xAQAAAADaNI8Fdnl5eY7ri09krUtVYFc9VHPFxIkT5e3trfLyci1evFizZ89Wp06darSx2+1asGBBvfW54vDhw45nBAUF6amnnqq3bUBAgO68807ddNNNGjRokIKCgnTmzBlt2bJFf/vb35STk6O1a9dq8uTJ+uyzzxp0yAYAeEJpRak2pW2SLdmmD/d/qIKyAqd9QvxCNHXwVFljrbqp/03yNnt00jYAAAAAtBsenWFXxdfX12l7Pz8/SVJRUVGDxunVq5ceeeQRvfrqq8rMzNTIkSP1/PPPKy4uTr6+vtq9e7cWLlyoTZs2ydfXV6WlpQ0ep7CwUFOnTnUsuX3llVfUvXv3ettnZmYqLCys1vfHjx+vJ554QhMnTtSuXbu0ZcsWvf7663ryyScb9M7OZiGePHlSw4YNa9AzAbQ/FfYKbT66WbYkm95LeU/ni8877dPBu4PujLlTVotVt0bdKn9vf88XCgAAAADtjMcCu6oTWSU5QrJLqTrMoUOHDg0e68UXX9Thw4f10UcfKTU1VVOmTKnVZujQobruuuv0+uuvS5KCg4NdenZ5ebmmT5+uH3/8UZL085//XPfdd98l+9QV1lXp2rWr1qxZo0GDBqmsrEyvvPJKgwM7dxwPDKB9sht2bUvfJluSTav3rVZWQZbTPj5mH00cOFHWWKsmxUxSkK/zWdMAAAAAgMbz2CZD1QMxV5a5FhRULr9yZfnsxfz8/LR+/XotW7ZMV111VY0Nzrt06aIFCxYoMTFRhvGfEw07duzo9LmGYei+++7TRx99JEmaMWOGXn311QbXd7H+/ftr/Pjxkir3tTtx4sRlPxMA6mMYhn44+YN+89lv1G9JP416Y5Re3fHqJcM6s8ms8f3Ha8WdK5T16yx9aP1Q8VfEE9YBAAAAQBPw6Ay7iIgI5eTkOD3V9Ny5c47ArrEHKpjNZj3wwAN64IEHlJeXp6ysLAUEBCgyMlJmc2UuefDgQUf7IUOGOH3mY489pn/961+SKvfK++c//+l41uUaMmSIIwjMzMy85BJbAGiMlDMpsiXZZEu2KTUn1aU+o3qPkjXWqruH3K2uQV09XCEAAAAAoC4e3SF8yJAhSkxMVFpamsrLy+XtXfdw+/fvd1xXP/W1sYKDg2stea2oqNDu3bslVc5wu/hgiov9v//3/xzLZ8eMGaP33nvPrYdDVJ8FCADucvT80cqQLsmmH7N+dKnPtd2uldVi1YzYGeod2tvDFQIAAAAAnPFoYDdq1CglJiaqoKBA33//vYYPH15nuy1btjiuR44c6ZFaNm/erJycHEnSzJkzL9n2mWee0fPPPy9Juu6667Rhw4ZG7a13Kfv27XNcM7sOwOU4mXdSq5JXyZZs07cZ37rUZ3CnwYq3xGumZaaiI6I9XCEAAAAAoCE8GthNmTJFzz77rCTpjTfeqDOws9vtevvttyVVHtYQFxfn9joMw9DChQslST4+PnrwwQfrbbtkyRL97ne/kyRdccUV+uSTT1w+oMJVR44c0WeffSZJGjBggHr06OHW5wNo+3IKc/ReynuyJdn01dGvZMhw2qdfWD9ZLVZZLVZd0eUKZvoCAAAAQAvlsUMnJGnYsGEaPXq0JGnFihXatm1brTaLFi1SSkqKJGnevHm1lp1+9dVXMplMMplM9Z7OmpOT4zhl9mIVFRV6/PHHtXXrVknSU089pX79+tXZ9o033tAvf/lLSVJ0dLQ+++wzhYeHO3/RatavX6/y8vJ672dlZWnatGmOk3MfffTRBj0fQPuVW5Krd358R7e/e7siF0Xq4Q0Pa/PRzZcM67oFddMvhv9C397/rQ49eUh/vunP+knXnxDWAQAAAEAL5tEZdlLljLWRI0eqqKhIEyZM0NNPP624uDgVFRXJZrNp6dKlkioDsvnz5zdqjM2bN+vxxx+X1WrV2LFj1bt3bxUXF2vPnj1aunSpY++6iRMnasGCBXU+Y+3atXrwwQdlGIZCQkK0ZMkSnTlzRmfOnKl33H79+ikwMLDG95544gmVlZVp2rRpGjFihPr27asOHTooOztbX331lf7+978rOztbUuWS4ccee6xR7wygfSgqK9LGgxtlS7Jp48GNKi4vdtonokOE7h5yt6wWq0b3Hi0vs1cTVAoAAAAAcBePB3ZXX321Vq5cqVmzZik3N1dPP/10rTbR0dHauHHjZS09zcrK0pIlS7RkyZJa90wmk+bOnavXXntNvr6+dfZfu3atKioqJEm5ubmaOHGi0zE3b96scePG1fr+iRMn9Morr+iVV16pt++0adO0fPly+fn5OR0HQPtSWlGqzw9/roSkBK3dv1b5pflO+wT7BuuuwXfJGmvVzf1vlo+X+w7JAQAAAAA0LY8HdpI0adIk7dmzR0uWLNHGjRuVkZEhX19fRUVFafr06Xr88ccVEBDQ6OePHj1aL7zwgr788kvt379fWVlZMpvN6t69u+Li4jR37tx6D7xwt7feektbtmzRtm3bdPjwYWVnZys3N1dBQUHq1auXbrjhBs2ZM0cjRoxoknoAtA4V9gptObZFtiSb3kt5T2eLzjrt4+/tr0nRk2S1WDUxaqI6+Lj3cBwAAAAAQPMwGYbhfKdytCoZGRnq1auXJCk9PV09e/Zs5ooA1MUwDH2b8a1sSTat2rdKp/JPOe3jbfbWrVG3yhpr1Z0xdyrYz72H4gAAAAAAGsYTOUyTzLADAFQyDEM/Zv0oW5JNtiSbjl045rSP2WRWXN84WS1WTR08VeEdGnYYDgAAAACgdSGwA4AmkJqTqoS9CbIl27Q/e79LfW7odYOssVZNj52uyKBID1cIAAAAAGgpCOwAwEOOnT+mlckrZUuyadepXS71uTryalktVs2InaG+YX09WyAAAAAAoEUisAMANzqVf0qrk1fLlmzTN+nfuNQnJiJG8ZZ4zbTM1KBOgzxcIQAAAACgpSOwA4DLdLborN5PeV+2JJs2H90su2F32qdPaB9ZLVZZLVZd2fVKmUymJqgUAAAAANAaENgBQCPkl+brw/0fypZs06a0TSqzlzntExkUqRlDZshqser6ntcT0gEAAAAA6kRgBwAuKi4v1kcHP5ItyaYNqRtUVF7ktE9H/466e8jdslqsGttnrLzMXk1QKQAAAACgNSOwA4BLKKso0xdHvlBCUoI+SPlAeaV5TvsE+QZpyqApssZaNX7AePl6+TZBpQAAAACAtoLADgAuUmGv0NfHv1ZCUoLW7FujnKIcp338vPx0R/Qdslqsum3gbQrwCWiCSgEAAAAAbRGBHQBIMgxDO07sUMLeBK3at0on8k447eNt9taEARNkjbVq8qDJCvELaYJKAQAAAABtHYEdgHbLMAztPb1XtiSbbEk2HTl/xGkfk0wa13ecrBarpg6eqk4BnZqgUgAAAABAe0JgB6DdOZhzsDKkS7Zp35l9LvW5vuf1ssZaNT12uroHd/dwhQAAAACA9ozADkC7kH4hXSuTV8qWZNP3J793qc9Puv5E8ZZ4zYydqX4d+3m4QgAAAAAAKhHYAWizThec1urk1bIl2/T18a9d6jMwfGBlSGeZqSGdh3i4QgAAAAAAaiOwA9CmnC8+r/dT3pctyaYvjnwhu2F32qdXSC9ZLVZZLVZdHXm1TCZTE1QKAAAAAEDdCOwAtHoFpQVan7peCUkJ+iTtE5VWlDrt0yWwi2YMmSGrxaoRvUbIbDI3QaUAAAAAADhHYAegVSopL9EnaZ8oISlB61PXq7Cs0GmfMP8wTRs8TVaLVeP6jpO3mX8FAgAAAABaHv60CqDVKLeX68sjXyohKUEfpHygCyUXnPYJ8AnQ5JjJirfEa8KACfLz9muCSgEAAAAAaDwCOwAtmt2wa+vxrUpIStCafWt0pvCM0z6+Xr66beBtirfE6/aBtyvQN7AJKgUAAAAAwD0I7AC0OIZh6PuT3ythb4JWJq9UZl6m0z5eJi/d3P9mxVviNWXQFIX6hzZBpQAAAAAAuB+BHYAWI/l0shKSEmRLsunQuUNO25tk0ug+oxVvide0wdPUObBzE1QJAAAAAIBnEdgBaFaHzh7SyuSVSkhKUNLpJJf6DOsxTNZYq6bHTlfPkJ4erhAAAAAAgKZFYAegyWXmZmpV8iolJCVox4kdLvWxdLEo3hKvmbEzNSB8gIcrBAAAAACg+RDYAWgSZwrOaM2+NbIl25R4LFGGDKd9BnQcUBnSWWbK0sXSBFUCAAAAAND8COwAeMyF4gv6YP8HsiXZ9Pnhz1VhVDjt0yO4h6wWq6wWq67tdq1MJlMTVAoAAAAAQMtBYAfArQrLCrUhdYMSkhL00cGPVFpR6rRP54DOmj5kuqwWq0b2HimzydwElQIAAAAA0DIR2AG4bCXlJfr00KdKSErQugPrVFBW4LRPiF+Ipg6eqnhLvG7sd6O8zfzrCAAAAAAAicAOQCOV28v11dGvlLA3Qe/vf1/ni8877dPBu4PujLlT8ZZ43RJ1i/y9/T1fKAAAAAAArQyBHQCX2Q27tqVvU0JSglbvW63TBaed9vEx+2jiwImKt8Trjug7FOQb1ASVAgAAAADQehHYAbgkwzC069QuJexN0MrklUrPTXfax2wy66Z+NyneEq8pg6aoY4eOTVApAAAAAABtA4EdgDqlnElRQlKCbEk2HTx70KU+o3qPUrwlXncPuVtdArt4uEIAAAAAANomAjsADkfOHdHK5JVKSErQnqw9LvW5ttu1irfEa0bsDPUK7eXhCgEAAAAAaPsI7IB27kTeCa1OXq2EpAR9l/mdS30GdxqseEu8rBarBkYM9HCFAAAAAAC0LwR2QDuUU5ij91LeU0JSgrYc3SJDhtM+/cL6yWqxKt4SL0sXi0wmUxNUCgAAAABA+0NgB7QTuSW5+nD/h0pIStBnhz9Tub3caZ/uwd01M3amrBarrut+HSEdAAAAAABNgMAOaMOKyoq08eBGJSQlaGPqRpVUlDjtE9EhQtOHTJfVYtWo3qPkZfZqgkoBAAAAAEAVAjugjSmtKNVnhz5TQlKCPjzwofJL8532CfYN1l2D71K8JV439btJPl4+TVApAAAAAACoC4Ed0AZU2Cu05dgWJexN0Hsp7+lc8Tmnffy9/TUpepLiLfGaOHCi/L39m6BSAAAAAADgDIEd0EoZhqFvM75VQlKCVu9brVP5p5z28TH76JaoWxRvidek6EkK9gtugkoBAAAAAEBDENgBrYhhGPox60fZkmyyJdl07MIxp33MJrPi+sYp3hKvuwbfpfAO4U1QKQAAAAAAaCwCO6AVOJB9oDKkS7Zpf/Z+l/rc0OsGxVvidfeQuxUZFOnhCgEAAAAAgLsQ2AEt1LHzx7QyeaUSkhK0+9Rul/pcHXm1rBarZsbOVJ+wPp4tEAAAAAAANzAMyWRq7ipaFgI7oAU5lX9Kq5NXKyEpQdsytrnUJyYiRvGWeFktVsV0ivFwhQAAAAAAuKasTDp1Sjp5su5P1b3ISGnnzuautmUhsAOa2dmis3o/5X0lJCXoq6NfyW7YnfbpE9pHVotV8ZZ4/aTrT2TiryIAAAAAAE2ksLD+EK76JzvbteeVlnq23taoyQK7Y8eO6eWXX9bGjRuVnp4uPz8/DRgwQDNmzNBjjz2mgICAyx7jyJEjevnll/XZZ5/p2LFjstvt6t69u8aPH6/HHntMsbGxLj2nvLxcy5cv17/+9S/t379f+fn56t69u26++WY9+eSTLj8nOztbL7/8stauXaujR49Kkvr27aspU6Zo3rx5ioiIaOyropXLK8nTugPrlJCUoE2HNqncXu60T2RQpGYMmaH4K+I1vMdwQjoAAAAAgNsYhnT+vPPZcCdPSrm57h37zJnK2Xg+Pu59bmtmMgzD8PQg69ev16xZs5Rbz/+j0dHR2rhxo6Kioho9xtKlS/XEE0+otJ5Y1tfXV4sWLdLjjz9+yedkZ2frtttu044dO+q87+fnp1dffVUPPPDAJZ/z3XffacqUKTp16lSd97t166a1a9dq2LBhl3xOY2RkZKhXr16SpPT0dPXs2dPtY6DhisqK9HHax7Il2bQhdYOKyouc9uno31F3D7lb8ZZ4jekzRl5mryaoFAAAAADQVtjtlYGYs9lwp05JxcXNV2d6utRa4wtP5DAeD+x27dqlkSNHqqioSEFBQXrqqacUFxenoqIi2Ww2LVu2TFJlaLdz504FBwc3eAybzab4+HhJUmhoqObPn68bb7xRfn5+2rVrl55//nmlpaXJZDLJZrNpxowZdT6noqJC48aN09dffy1Jmjp1qh588EGFh4fru+++0zPPPKPTp0/LbDZrw4YNmjhxYp3PSU9P17XXXqszZ87I29tbv/rVr3THHXdIkjZs2KDFixervLxcXbp00ffff+/2QI3AruUoqyjT54c/ly3Zpg9SPlBeaZ7TPkG+QZoyaIqssVaNHzBevl6+TVApAAAAAKA1KS2tf3+46t/PypIqKpq3Vh8fqVu3/3wiI2t+3a2bdMUVkm8r/eNvqwzsxowZo8TERHl7e+vf//63RowYUeP+Cy+8oN/85jeSpD/84Q9auHBhg55fWFiofv366fTp0woKCtK2bdtksVhqtMnNzdWoUaO0d+9ede3aVWlpaQoKCqr1rH/84x+6//77JUmPPvqo/vrXv9a4n5aWpmuvvVa5ubmKiopSSkqKvL1rryqePXu23nnnHUnSqlWrNH369Br3V61apZkzZ0qS5syZozfffLNB7+wMgV3zqrBXKPF4omxJNq3Zt0Y5RTlO+/h5+emO6DtktVh1+8Db1cGnQxNUCgAAAABoaQoKXNsfLsf5HzU9LjCwdvBW1yc8vG2fAtvqArvt27dr+PDhkqSHH35Yf/vb32q1sdvtslgsSklJUVhYmE6fPi2fBixaXrNmjSMQW7BggZ555pk6233++ecaP368JOmVV16pc2nskCFDlJKSovDwcKWnp9e5r95zzz2np556SlLdYdypU6fUo0cP2e123XLLLfrkk0/qrOfWW2/Vpk2bZDablZmZqcjISJff2RkCu6ZnGIa2Z26XLcmmlckrdTL/pNM+3mZvTRgwQfGWeN0Zc6dC/EKaoFIAAAAAQFMzDOncOeez4U6elPKcL8zyuPDw+mfCVf80YpFkm+SJHMajh06sXbvWcT137tw625jNZs2ePVtPPfWUzp8/r82bN2vChAkuj7Gz2rm/9S1RlaRx48bJ399fxcXFWrNmTa3ALjU1VSkpKZKkGTNm1HsIxn333ecI7D744INagd26detkt1ee8lnfO1c9Z9OmTbLb7Vq3bp0eeuihS7wlWiLDMLT39F7ZkmyyJdl05PwRp31MMmlc33GKt8Rr6uCpigjg4BEAAAAAaK0qKlzfH66kpHlrNZulLl2cz4aLjJT8/Jq3Vng4sKvaCy4wMFDXXnttve3Gjh3ruN66dWuDArucanNAu3btWm87b29vhYeH68SJE9q2bZvKy8trLGetqvXiei4WGRmp6OhopaamauvWrbXuu/qci9+ZwK71OJhzULYkmxKSEpSSneJSn+t7Xi9rrFUzYmeoW3A3D1cIAAAAALgcJSW1Z77VtV9cVlbloQ7NydfXtdlwXbpIXpxj2Gp4NLCrmrEWFRVV515vVQYNGlSrj6uq70V34cKFetsZhuE4pba0tFRpaWk1xt23b1+d9dRXb2pqqtLT01VQUKDAwMBazwkNDb3kMtdu3bopJCREubm5DX7njIyMS94/edL5ckw0zPELx7UqeZUSkhL0w8kfXOpzZdcrZbVYNTN2pvp17OfhCgEAAAAAzuTnu7Y/3NmzzV2pFBTk2v5wHTu27f3h2iuPBXbFxcXKzs6WJKdrdzt27KjAwEAVFBQoPT29QeMMHjzYcb1ly5Z6Z/Lt2rVL+fn5jq+PHz9eI5irHoI5q7dqXbJhGMrIyFBMTEyt57iyXrlXr15KTk5u8DtXjQ/PysrP0pp9a5SQlKCt6bVnU9ZlYPhAxVviZbVYNbjzYOcdAAAAAACXxTAqAzZns+FOnqwM7Jpb1f5wzj51nJWJdsRjgV1etV0S6zqR9WJVgV1+A//pmThxory9vVVeXq7Fixdr9uzZ6tSpU402drtdCxYsqLe+htZbfUbdxfVWPcfVd67rGWg+54rO6YP9HyghKUFfHvlSdsP53OZeIb1ktVgVb4nXVZFXycRfbQAAAADAZauokE6fdm1/uNLS5q3VbJa6dnUewnXtyv5wcI1HZ9hV8fX1ddre7/9+xRYVFTVonF69eumRRx7Rq6++qszMTI0cOVLPP/+84uLi5Ovrq927d2vhwoXatGmTfH19Vfp//xRfPE5D6vWr9k9Xfc/x5Ds7m5F38uRJDRs2rEHPbM/yS/O1/sB6JSQl6JO0T1RmL3Pap0tgF80YMkPxV8Tr+p7Xy2wyN0GlAAAAAND6lZS4Nhvu9OmWsz+cs0/nzuwPB/fyWGDn7+/vuC51Ieou+b/jUjp06NDgsV588UUdPnxYH330kVJTUzVlypRabYYOHarrrrtOr7/+uiQp+KKzhy+ut/rX9dVaV73+/v4qLCz06Du743jg9q64vFifpH0iW5JN61PXq7Cs0GmfMP8wTRs8TfGWeI3tO1beZo9uAQkAAAAArUpenmv7w50719yVSsHBrp2Wyv5waC4eSxyqB2KuLPksKCiQ5NpS0ov5+flp/fr1+sc//qG//vWv+vHHH2UYhiSpS5cuevDBB/Xb3/5Wv/zlLx19OnbseMl6LxXYVdVaV73BwcEqLCz0+Duj4coqyvTlkS9lS7bp/ZT3lVuS67RPoE+gJg+aLGusVbdE3SJfL+czJwEAAACgrTAMKSfH+Wy4kyelan9UbjYREa7NiKu20xXQInl0hl1ERIRycnKcnmp67tw5R3jV2AMVzGazHnjgAT3wwAPKy8tTVlaWAgICFBkZKbO5crniwYMHHe2HDBlSo3/1WWsZGRm19sGrrmpJqslkqjXbrWfPnsrKynL6ztWfwyESnmM37Pr6+NeyJdm0et9qZRdmO+3j5+Wn2wbeJqvFqjui71CAT0ATVAoAAAAATae83PX94cqc7xrkUV5el94fLjLyP//rwu5UQKvg0TV9Q4YMUWJiotLS0lReXi5v77qH279/v+O6+qmvjRUcHFxryWtFRYV2794tSerfv3+tQK56gLd//35dddVV9T6/qt5evXrVOICi6jnff/+9Lly4oFOnTikyMrLOZ5w8eVK5uZUzvNzxzvgPwzC088RO2ZJsWpm8Upl5mU77eJm8NH7AeFljrZoyaIpC/UOboFIAAAAAcK/iYtdmw50+XTl7rjn5+bk2G65TJ/aHQ/vj0cBu1KhRSkxMVEFBgb7//nsNHz68znZbtmxxXI8cOdIjtWzevFk5OTmSpJkzZ9ZZa/V6rFZrnc85deqUUlNTJdVd66hRo/TOO+84nlPXWFX3qnjqndubpNNJsiXZZEuy6dC5Q07bm2TSmD5jZLVYdfeQu9UpoP5ZlQAAAADQXAzD9f3hzp9v7mqlkJBLz4Sr+oSFsT8cUB+TYXguU9++fbsjpHv44Yf1t7/9rVYbu90ui8WilJQUhYWF6fTp0/Lx8XFrHYZhaPTo0dq6dat8fHx04MAB9evXr1a7IUOGKCUlReHh4UpPT1dAQO2lkM8995yeeuopSdKqVas0ffr0GvdPnTqlHj16yG6365ZbbtEnn3xSZ0233nqrNm3aJLPZrMzMzHpn4jVGRkaGY5ltenp6mz6kIu1smlYmrZQt2aak00ku9RnWY5issVbNiJ2hHiE9PFwhAAAAANTNbv/P/nCXmg138qRU6PycPI/r1Mm1gxrYHw7tjSdyGI/OsBs2bJhGjx6txMRErVixQnPmzNGIESNqtFm0aJFSUlIkSfPmzasV1n311VeKi4uTJM2ZM0dvvvlmrXFycnIUFBQkPz+/WvcqKir05JNPauvWrZKkp556qs6wTpJ+/etf6/7779fZs2f1m9/8Rq+++mqN+4cOHdKzzz4rSYqKitJdd91V6xmRkZG655579M4772jTpk1as2aN7r777hptVq9erU2bNkmS7r33XreGde1BRm6GViWvki3Jph0ndrjU54ouV8hqscpqsap/x/4erhAAAABAe1ZeLmVlOZ8Nl5XVMvaHu3jm28Vfd+tWuYcc+8MBTcejgZ0kLVmyRCNHjlRRUZEmTJigp59+WnFxcSoqKpLNZtPSpUslSdHR0Zo/f36jxti8ebMef/xxWa1WjR07Vr1791ZxcbH27NmjpUuXOvaumzhxohYsWFDvc+bMmaN//OMf2rp1q/7617/q1KlTevDBB9WxY0dt375df/zjH5Wbmyuz2ayXX3653j35/vSnP+mTTz7RmTNnFB8fr507d+qOO+6QJG3YsEGLFi2SJHXu3FnPPPNMo965vTlTcEZr9q1RQlKCEo8nutQnKjxK1tjKkC62S6yHKwQAAADQ1hUVuTYb7syZ5t8fzt/f9f3h/u+cRgAtiEeXxFZZv369Zs2a5Thk4WLR0dHauHGjoqKiat1zZYbdmjVrai1Nrc5kMmnu3Ll67bXX6pyFV112drZuu+027dhR98wtPz8/vfrqq3rggQcu+ZzvvvtOU6ZM0alTp+q8HxkZqbVr19a7r9/laCtLYvNK8vReyntKSErQF4e/UIVR4bRPz5Cemhk7U/GWeF3T7RqZ2BABAAAAwCUYhpSb69r+cBcuNHe1UmjopWfCVX1CQ9kfDmgqrW5JbJVJkyZpz549WrJkiTZu3KiMjAz5+voqKipK06dP1+OPP17nfnGuGj16tF544QV9+eWX2r9/v7KysmQ2m9W9e3fFxcVp7ty5LgdjnTp10jfffKNly5bp3XffVUpKigoKCtS9e3fddNNNmjdvnmJjnc/WGj58uPbu3aslS5Zo7dq1Onr0qCSpX79+mjx5sn7xi18oIiKi0e/cHmTmZWruh3Odtusc0FnTh0yX1WLVyN4jZTbx10MAAABAe1d9fzhnn6Ki5q5W6tzZtf3hLuOPzgBakSaZYYem1VZm2EnS1X+/WrtP7a71/VC/UE0dPFVWi1U39rtR3uYmyZ4BAAAANLOyMtf3hysvb95avb3/MwvuUrPhunaV3Hz2IoAm1Gpn2AGNZY21OgK7AJ8A3Rlzp6yxVt0adav8vC+9vBkAAABA61G1P5yzT3Z28+8P16GDa/vDRUSwPxyAxiGwQ4tmtVj1TcY3irfEa1L0JAX6cj44AAAA0FoYRuW+b64EcfVsed6kqu8Pd6lPSAj7wwHwLAI7tGh9wvroQ+uHzV0GAAAAgGrs9sqZbq4EccXFzVuryeT6/nAdOjRvrQBQhcAOAAAAACCpcn+4U6dc2x+uoqJ5a62+P9ylPl26sD8cgNaHwA4AAAAA2rjCQtf3h2tuAQGuLUsND2d/OABtF4EdAAAAALRChiGdP+88hDt1qmXsDxcW5loQFxzM/nAAQGAHAAAAAC2I3S6dOeNaENcS9ofr0sX53nDsDwcADUNgBwAAAABNoLTU+f5wp061jP3hfHxc3x/Omz9VAoDb8a9WAAAAALgMBQWu7Q+Xk9PclUqBgc5nw3XrJkVEsCwVAJoTgR0AAAAAXMQwpHPnLj0Truo6L6+5q5U6dnR9fzgAQMtHYAcAAACg3aiocH1/uJKS5q3VbK57f7iLl6pGRkr+/s1bKwDAvQjsAAAAALR6JSV17w938feysioPdWhOvr6u7Q/XuTP7wwFAe8W//gEAAAC0WPn5ru0Pd/Zsc1cqBQU5nw3XrZsUHs7+cACASyOwAwAAANCkDKMyYHO2N9zJk5WBXXMLD3dtf7igoOauFADQVhDYAQAAAHCLigrp9GnX9ocrLW3eWs1mqWvX+mfBVZ8h5+fXvLUCANofAjsAAAAAl1RS4tpsuNOnW8b+cK7MhuvcWfLyat5aAQCoD4EdAAAA0E7l5bm2P9y5c81dqRQc7Hw2XLduUseO7A8HAGj9COwAAACANsQwpJycS8+Eq/oUFDR3tVJEhPPZcJGR7A8HAGhfCOwAAACAVqC83PX94crKmrfW6vvDOQvifH2bt1YAAFoiAjsAAACgGRUXuzYb7vTpytlzzcnPz7X94Tp1Yn84AAAuB4EdAAAA4GaG4fr+cOfPN3e1/9kfztknLIz94QAAaAoEdgAAAICL7Pb/7A9X30y4qk9hYXNXWznTzZVlqYGBzV0pAACojsAOAAAA7V55uZSV5Xw2XFZW8+8P5+Xl2v5wXbuyPxwAAK0VgR0AAADarKIi12bDnTnT/PvD+ftfehYc+8MBANB+ENgBAACgVTEMKTfXtf3hLlxo7mqlkBDX9ocLDWV/OAAAUInADgAAAC3G+fPS8ePOg7iiouauVOrc2bUZcQEBzV0pAABobQjsAAAA0KTKy6XDh6UDB2p+9u+vXJranLy9Xd8fzseneWsFAABtF4EdAAAAPCInpzKEuziYO3So6Q9u6NCh/hlw1T+dOklmc9PWBgAAcDECOwAAADRaaWnlbLm6grmcHM+PHxrq2v5wISHsDwcAAFoPAjsAAABckmFULlWtvnS16vrwYamiwr3jmUy194erb0Zchw7uHRsAAKAlILADAACAJKmkREpLqzuYO3/e/eN16iTFxNT8DBok9esn+fq6fzwAAIDWgsAOAACgHTEM6dSpug98OHpUstvdO56PjxQVVTuYi4mRIiLcOxYAAEBbQWAHAADQBhUVSQcP1g7mDhyQcnPdP16XLpWz4y4O5fr1qzx5FQAAAK7jt08AAACtlGFImZl1h3LHjlXedydfX2ngwLqDubAw944FAADQnhHYAQAAtHAFBVJqau1QLjVVys93/3jduv1nP7nqoVyfPpKXl/vHAwAAQE0EdgAAAC2A3S5lZNQ+7OHAASk93f3j+ftL0dG1g7noaCkkxP3jAQAAwHUEdgAAAE0oL6/mbLmqcC41tXLfOXfr2bP2KawxMVKvXpLZ7P7xAAAAcPkI7AAAANysokI6frz2KawHDkgnTrh/vICA2nvKDRpUud9cUJD7xwMAAIBnEdgBAAA00oULtfeV27+/8nTWkhL3j9enT+1gLiZG6tGD2XIAAABtCYEdAADAJZSXS0eP1h3MZWW5f7ygoLpPYR04sHImHQAAANo+AjsAAABJZ8/WDuUOHJDS0qTSUveOZTJJffvWHcx161Z5HwAAAO0XgR0AAGg3ysqkI0fqPon1zBn3jxcaWvuwh5gYKSqq8pRWAAAAoC4EdgAAoM3Jzq592MOBA9KhQ5VLXN3Jy0vq16/uYK5LF2bLAQAAoOEI7AAAQKtUWloZwNUVzJ096/7xwsNrn8IaEyMNGCD5+rp/PAAAALRfBHYAAKDFMgzp9Om6D3w4ckSqqHDveN7elQFcXcFcp07uHQsAAACoT5MFdseOHdPLL7+sjRs3Kj09XX5+fhowYIBmzJihxx57TAFuOPbs6NGjev311/X555/r0KFDKigoUHBwsAYNGqRbb71VjzzyiLp06VJn34ULF+q///u/GzTeH/7wBy1cuLBWDf369XOp/5w5c/Tmm282aEwAANqi4uLKwx3qCuYuXHD/eJ071z7sYdCgyqWtPj7uHw8AAABoiCYJ7NavX69Zs2YpNzfX8b3CwkLt3LlTO3fu1PLly7Vx40ZFRUU1eox33nlHDz/8sIqKimp8/9y5c9q2bZu2bdumJUuWyGazafz48Y0ep7qYmBi3PAcAgPbAMKSTJ+s+ifXoUclud+94Pj6VhzvUdRJreLh7xwIAAADcyWQYhuHJAXbt2qWRI0eqqKhIQUFBeuqppxQXF6eioiLZbDYtW7ZMkhQdHa2dO3cqODi4wWNs3bpVY8aMkd1ul9ls1pw5czR58mR1795dx48f11tvvaX169dLkjp06KCkpCT179+/xjNOnz6t06dPX3KciooKjRkzRrm5uQoJCdGpU6fUoUOHGm2qz7B75plnNHny5Hqf17FjR/Xo0aPB7+tMRkaGevXqJUlKT09Xz5493T4GAAD1KSqSDh6sfQrrgQNSXp77x+vatfZhDzExUt++lUtcAQAAAE/yRA7j8d/Gzps3T0VFRfL29tann36qESNGOO7deOONGjhwoH7zm98oNTVVixYtqrXE1BXPPvus7P/31/KvvPKKHn30Uce96667TtOmTdP8+fO1ePFiFRUVafHixXr11VdrPKNLly71Lpet8vHHHztmCU6fPr1WWHexHj16yGKxNPh9AABo6QxDysys+8CH48cr77uTn580cGDtYC46WgoLc+9YAAAAQHPzaGC3fft2JSYmSpLuv//+GmFdlfnz5+uNN95QSkqKlixZogULFsingZvHfPPNN5KkiIiIGmFddb///e+1ePFiSdK2bdsa9Pwqb7/9tuN69uzZjXoGAACtSUGBlJpaO5hLTa28527du9c+7CEmRurdW/Lycv94AAAAQEvk0cBu7dq1juu5c+fW2cZsNmv27Nl66qmndP78eW3evFkTJkxo0DilpaWSdMnDHkJDQ9WpUydlZ2c72jdEbm6uPvzwQ8c4o0ePbvAzAABoiex2KT299mEPBw5IGRnuH69Dh8qZcRcf+BAdLTViZwwAAACgzfFoYPf1119LkgIDA3XttdfW227s2LGO661btzY4sIuJidEPP/ygI0eO1NsmNzdX2dnZjvYNtXr1aseBFvfee69MJlODnwEAQHPKy6v7FNaDByv3nXO3Xr3qPom1Z0/JbHb/eAAAAEBb4dHALiUlRZIUFRUl70vs+jxo0KBafRrikUce0UMPPaScnBz97W9/0yOPPFKrzR//+Mca7RuqMcthX3nlFT3zzDPKyMiQn5+fevbsqdGjR+uhhx7SNddc0+AaqmQ4me5w8uTJRj8bANC6VVRIx47VHcx54j8PgYG1Q7mqveUCA90/HgAAANAeeCywKy4udsxoc3Y6RseOHRUYGKiCggKlp6c3eKyf/exn+vrrr/X222/rscce0/fff68777xT3bp10/Hjx/XOO+84lucuWLBAN998c4Oef/ToUcdefCNHjtSAAQNc6vfDDz84rktKSrRv3z7t27dPf//73/Xwww9ryZIl8vPza1AtkhwnjwAA2q/z52uHcgcOVM6WKylx71gmk9SnT93BXI8elfcBAAAAuI/HAru8vDzHdVBQkNP2VYFdfn5+g8fy8vLSW2+9pUmTJunPf/6zli9fruXLl9doExcXp6effrrBYZ0kvfPOOzL+77i7OXPmOG0fFhamu+66S+PGjdPAgQPl7++vkydP6tNPP9WKFSuUn5+vv//978rLy9O//vWvBtcDAGgfysulo0drn8J64ICUleX+8YKDax/2EBNTeTqrk4PRAQAAALiRR2fYVfH19XXavmqmWVEjN9FJSUnR22+/rb1799Z5f9u2bVqxYoUGDx6sHj16NOjZ//znPyVJ/v7+mjFjxiXbdu/eXZmZmQoICKjx/auvvlq33XabHnvsMd188806fvy43n33Xc2cOVN33nlng+pxNgvx5MmTGjZsWIOeCQBoPmfP1j7s4cABKS1NKitz71hms9S3b90nsUZGMlsOAAAAaAk8Ftj5+/s7rl05lbXk/9bvdGjEX+EnJiZq0qRJunDhgvr06aNnnnlG48ePV3h4uLKysrRu3Tr97ne/k81m07///W99+umnio2NdenZ3377rVJTUyVJkydPVmho6CXb+/r6XjKgHDhwoP75z39qzJgxkir3uWtoYOdsiTEAoOUpK5MOH647mPu/HSTcKiys9mEPMTFSVJTUiN0YAAAAADQhjwV2wcHBjmtXlrkWFBRIcm35bHUlJSWKj4/XhQsXFBkZqW+//VaRkZGO+z179tSjjz6qsWPHaujQoTpx4oTmzJmjnTt3uvT8xhw24czo0aM1ZMgQ7du3T19//bXsdrvMHJcHAK2eYVSGb3Ud+HD4cOUSV3fy8pL69687mOvcmdlyAAAAQGvl0Rl2ERERysnJcXqq6blz5xyBXUMPVPjkk0+UmZkpSXriiSdqhHXVxcbGatasWVq+fLm+//57/fjjj7ryyisv+ezS0lKtXLlSktS1a1fdcsstDartUqoCu+LiYuXk5Khz585uezYAwLNKSyuXq9YVzJ075/7xIiJqH/YwaFBlWOfCrhMAAAAAWhmPBXZSZSiVmJiotLQ0lZeXy9u77uH279/vuB48eHCDxkhJSXFcX3PNNZdse+211zoOo9i/f7/TwG7Dhg06e/asJOmee+6Rl5dXg2q7FBPTHgCgRTOMyoMd6jqJ9fBhyW5373je3pXLVes6ibVTJ/eOBQAAAKBl82hgN2rUKCUmJqqgoEDff/+9hg8fXme7LVu2OK5HjhzZoDGqh4DlTtYalVXbubu+8LA6TyyHrbJv3z5JlYdtREREuPXZAADXFRdXzpa7+BTWAwekCxfcP17nzrVPYY2Jkfr1k3x83D8eAAAAgNbHo4HdlClT9Oyzz0qS3njjjToDO7vd7gjGwsLCFBcX16Ax+vXr57hOTEzUHXfcUW/b6sFg9X51ycnJ0UcffSRJuvLKK53OxmuIrVu3Kjk5WVJlqMn+dQDgWYYhnTxZ+7CHAweko0cr77uTr2/lbLm6grmOHd07FgAAAIC2x6OB3bBhwzR69GglJiZqxYoVmjNnjkaMGFGjzaJFixzLWufNmyefi6YXfPXVV44Qb86cOXrzzTdr3L/pppsUEBCgwsJCvf7665o1a5auuOKKWrV8/PHH+uCDDyRJPXr00FVXXXXJ2hMSEhwz8hoyu27t2rWaPHlyvUte09LS9NOf/tTx9aOPPuryswEAl1ZYKB08WPsk1tRUKS/P/eNFRtY86KHq07dv5YEQAAAAANAYHg3sJGnJkiUaOXKkioqKNGHCBD399NOKi4tTUVGRbDabli5dKkmKjo7W/PnzG/z8sLAw/dd//Zd+//vfKy8vTzfccIOeeOIJjR8/Xh07dlRWVpY+/PBDLVu2TPb/23DoueeeczqrrWrWn7e3t+655x6X67nrrrsUFRWlqVOnatiwYerZs6f8/Px08uRJbdq0SStWrHCcmjtjxgxNnTq1we8MAO2ZYUgZGbUPezhwQDp+3P3j+ftLAwfWPoU1OloKDXX/eAAAAADg8cDu6quv1sqVKzVr1izl5ubq6aefrtUmOjpaGzduVHBwcKPG+O1vf6uzZ89qyZIlys/P17PPPutYiludj4+P/vznP2vWrFmXfN7+/fu1Y8cOSdKECRPUtWvXBtWTlpam559//pJtfv7zn+ull15q0HMBoD3Jz6+cGXdxMJeaWjmTzt169Kj7JNbevSV2LgAAAADQlDwe2EnSpEmTtGfPHi1ZskQbN25URkaGfH19FRUVpenTp+vxxx9XQEBAo59vMpn00ksvadasWVq+fLm+/vprHTt2TIWFhQoKClJUVJTGjh2rhx9+WNHR0U6f98477ziuG3rYxLp167Rt2zZ99913OnbsmLKzs1VQUKCQkBD1799fo0eP1s9+9jNZLJYGvycAtDV2e+WsuIsPe9i/X8rMdP94AQGVM+MuDuaio6VG/p0RAAAAALidyTDcvdU2mltGRoZ69eolSUpPT1fPnj2buSIA7V1ubuXMuIsPfDh4UCoqcv94vXvXDuViYqSePZktBwAAAMC9PJHDNMkMOwBA21dRIR07VvdJrCdPun+8oKC6Q7mBA6XAQPePBwAAAABNhcAOANAg58/XPuzhwAEpLU0qKXHvWCaT1KdP3Sexdu9eeR8AAAAA2hoCOwBALeXl0pEjdQdzp0+7f7yQkNqnsMbESFFRUocO7h8PAAAAAFoyAjsAaMdycmof9nDggHTokFRW5t6xzGapX7+6g7muXZktBwAAAABVCOwAoI0rK6sM4Oo6iTUnx/3jdexYc+lqVTA3YIDk5+f+8QAAAACgrSGwA4A2wDCk7Ozahz1UzZarqHDveF5elQHcxQc+DBokderEbDkAAAAAuBwEdgDQipSUVAZwdQVz5865f7yIiNqHPcTESP37S76+7h8PAAAAAEBgBwAtjmFIWVm1D3s4cKDyIAi73b3j+fhUzparK5iLiHDvWAAAAAAA5wjsAKCZFBdLBw/WfRJrbq77x+vSpfZhDzExlQdBePNfAwAAAABoMfgjGgB4kGFIJ07UfRLrsWOV993J11caOLDuYC4szL1jAQAAAAA8g8AOANygsFBKTa0dzKWmSvn57h+vW7e6T2Lt06fyQAgAAAAAQOtFYAcALrLbpYyM2oc97N8vpae7fzx/fyk6unYwFx0thYS4fzwAAAAAQMtAYAcAF8nPrx3KHThQOVuusND94/XsWfuwh0GDpF69JLPZ/eMBAAAAAFo2AjsA7ZLdLh0/XvdJrJmZ7h8vIKB2KBcTUzlbLijI/eMBAAAAAFovAjsAbVpubt2nsB48WHlKq7v17l37sIeYGKlHD2bLAQAAAABcQ2AHoNWrqJCOHq07mDt1yv3jBQXVPuwhJqbydNaAAPePBwAAAABoXwjsALQa587VPuzhwAEpLU0qLXXvWCaT1Ldv3cFct26V9wEAAAAA8AQCOwAtSnm5dPhw3Sexnjnj/vFCQ2sf9hATI0VFVZ7SCgAAAABAUyOwA9AscnJqH/ZQNVuuvNy9Y5nNUv/+dQdzXbowWw4AAAAA0LIQ2AHwmNLSytlydQVzOTnuH69jx7oPfBgwQPLzc/94AAAAAAB4AoEdgMtiGJVLVS8+7OHAgcqwrqLCveN5e1cGcBeHcjExUqdOzJYDAAAAALR+BHYAXFJSUrlcta6TWM+fd/94nTrVPVuuf3/Jx8f94wEAAAAA0FIQ2AFwMAzp1Km6T2I9elSy2907no9P5eEOF5/CGhMjhYe7dywAAAAAAFoLAjugHSoqkg4erB3MpaZKubnuH69r17oPfOjbt3KJKwAAAAAA+A/+qAy0UYYhZWbWPuxh/37p+PHK++7k5ycNHFg7mIuOlsLC3DsWAAAAAABtGYEd0MoVFFTOjLs4mEtNlfLz3T9e9+6195UbNEjq3Vvy8nL/eAAAAAAAtDcEdkArYLdLGRl1n8Sanu7+8Tp0qJwZd3EwFx0thYS4fzwAAAAAAPAfBHZAC5KX95/ZctWDudTUyn3n3K1nz7pPYu3VSzKb3T8eAAAAgP+fvTuPj6q6/z/+nux7AoEQdoQQSAQ3NmlEiAuoCAKJUpWKtCqtYm1La6tWpFWrrcV+sbVFEHEXbQYQjIqKgGwCoQhohn0LEEIChOz7/f0xv4xgkslkMpMMmdfz8bgPbphz7v3c3NxM8s499wBA4wjsgBZWXW19htwPZ2Hds0c6ccL1+wsNtd4Z98NgLj7e+hoAAAAAAPAsBHaAm5w7V3eyhz17rLOzlpe7dl8mk/UZcufPwFq7dO1qfR0AAAAAAFwcCOyAZqiqkg4frn8m1pwc1+8vPPzCiR5q1/v2tT53DgAAAAAAXPwI7AAHnDlTN5Tbs0fav1+qqHDtvnx8pF696s7C2q+fFBvL3XIAAAAAALR1BHbA/1dZKR06VHcW1j17pNxc1+8vKqruZA/9+klxcVJQkOv3BwAAAAAALg4EdvA6eXl1J3vYs0c6cMA6xNWVfH2l3r3rD+ZiYrhbDgAAAAAA1EVghzaposIawNUXzJ054/r9tW9fd7KHfv2kPn2kgADX7w8AAAAAALRdBHa4aBmGdOpU/TOxHjokVVe7dn9+ftYArr5grkMH1+4LAAAAAAB4LwI7eLyyMuvkDvUFc+fOuX5/HTvWPxPrJZdI/v6u3x8AAAAAAMD5COzgsSorpYQE691yNTWu3XZAgHVyh/qCuXbtXLsvAAAAAACApiCwg8fy97c+i645YV1s7IVDV2uDuZ49rUNcAQAAAAAAPA2RBTxav35SVpb9NkFBUt++dYO5+HgpMrJl6gQAAAAAAHAVAjt4tH79pC++sK537Vp3sod+/aQePSRf39atEwAAAAAAwFUI7ODRfvlLado0691y4eGtXQ0AAAAAAID7EdjBo8XHt3YFAAAAAAAALcuntQsAAAAAAAAA8D0COwAAAAA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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 630
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot accuracy and loss\n",
    "def plot_acc_loss(history):\n",
    "    plt.subplot(211)\n",
    "    plt.title(\"Accuracy\")\n",
    "    plt.plot(history.history[\"accuracy\"], color=\"g\", label=\"Train\")\n",
    "    plt.plot(history.history[\"val_accuracy\"], color=\"b\", label=\"Test\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    \n",
    "    plt.subplot(212)\n",
    "    plt.title(\"Loss\")\n",
    "    plt.plot(history.history[\"loss\"], color=\"g\", label=\"Train\")\n",
    "    plt.plot(history.history[\"val_loss\"], color=\"b\", label=\"Test\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d37baf08",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 模型的保存和导入\n",
    "from keras.models import load_model\n",
    "# 保存模型\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model.save(os.path.join(save_dir,'my_model1.h5'))\n",
    "del model  # deletes the existing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "97f693d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入已经训练好的模型\n",
    "from keras.models import load_model\n",
    "model1 = load_model(os.path.join(save_dir,'my_model1.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8ed6aa51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "## 对测试集进行预测\n",
    "y_pre = model1.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7404c18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.99999523e-01 5.14970555e-09 7.45459232e-08 ... 1.05733449e-08\n",
      "  2.97491329e-08 1.27069583e-07]\n",
      " [9.99641061e-01 4.01240686e-06 1.79771632e-07 ... 5.90070250e-08\n",
      "  3.52832176e-05 1.62576805e-06]\n",
      " [9.99990106e-01 4.99658181e-09 3.94122395e-07 ... 2.74232609e-10\n",
      "  8.72324472e-06 7.05523917e-07]\n",
      " ...\n",
      " [2.74289258e-09 9.99999642e-01 1.03526343e-08 ... 5.83599391e-09\n",
      "  3.21753091e-09 1.23698143e-07]\n",
      " [1.02219300e-03 9.55222011e-01 1.11564932e-05 ... 2.41120160e-03\n",
      "  1.03101069e-04 1.39913420e-04]\n",
      " [8.62272088e-07 9.99952912e-01 3.76695908e-07 ... 1.22376705e-05\n",
      "  1.67421362e-07 6.82234759e-06]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25151 (\\N{CJK UNIFIED IDEOGRAPH-623F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23578 (\\N{CJK UNIFIED IDEOGRAPH-5C1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\lutin\\.conda\\envs\\TensorGPU\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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AAAAAAAAAAxG6AgAAAAAAAICBCF0BAAAAAAAAwECErgAAAAAAAABgIEJXAAAAAAAAADAQoSsAAAAAAAAAGIjQFQAAAAAAAAAMROgKAAAAAAAAAAb6f8q0UvqG6rZMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 686,
       "width": 686
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98       499\n",
      "           1       0.97      0.93      0.95       525\n",
      "           2       0.75      0.99      0.85       377\n",
      "           3       0.77      0.82      0.80       471\n",
      "           4       0.81      0.88      0.84       462\n",
      "           5       0.97      0.92      0.95       527\n",
      "           6       0.97      0.89      0.93       544\n",
      "           7       0.93      0.82      0.87       567\n",
      "           8       0.98      0.88      0.93       556\n",
      "           9       0.90      0.95      0.93       473\n",
      "\n",
      "    accuracy                           0.90      5001\n",
      "   macro avg       0.90      0.91      0.90      5001\n",
      "weighted avg       0.91      0.90      0.90      5001\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(y_pre)\n",
    "## 评价预测效果，计算混淆矩阵\n",
    "confm = metrics.confusion_matrix(np.argmax(y_pre,axis=1),np.argmax(y_val,axis=1))\n",
    "## 混淆矩阵可视化\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(confm.T, square=True, annot=True,\n",
    "            fmt='d', cbar=False,linewidths=.8,\n",
    "            cmap=\"YlGnBu\")\n",
    "plt.xlabel('True label',size = 14)\n",
    "plt.ylabel('Predicted label',size = 14)\n",
    "plt.xticks(np.arange(10)+0.5,categories,size = 12)\n",
    "plt.yticks(np.arange(10)+0.3,categories,\n",
    "size = 12)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(metrics.classification_report(np.argmax(y_pre,axis=1),np.argmax(y_val,axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "894a6777",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=\"我国用于载人登月的新一代载人火箭将于2030年前完成研制。“2030年前”这个时间让人心潮澎湃，更心怀期待。为能将中国人的脚印留在月球，无数航天人一步一个脚印，扎扎实实地推进着技术攻关。“仰望星空，脚踏实地”，这八个字特别适合中国航天。我们的目标是\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "69034bfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_id=[]\n",
    "data_id.append([word_to_id[x] for x in test if x in word_to_id])\n",
    "# 使用keras提供的pad_sequences来将文本pad为固定长度\n",
    "x_pad = kr.preprocessing.sequence.pad_sequences(data_id, 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "28b8ef0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 360ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pre = model1.predict(x_pad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b736a6fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('时尚', 0.27345821261405945), ('时政', 0.2260284572839737), ('科技', 0.18820235133171082), ('游戏', 0.14409953355789185), ('家居', 0.06169718876481056), ('教育', 0.048951540142297745), ('娱乐', 0.037477027624845505), ('体育', 0.01368797942996025), ('财经', 0.005309356842190027), ('房产', 0.0010883485665544868)]\n"
     ]
    }
   ],
   "source": [
    "y_pres=y_pre.tolist()\n",
    "\n",
    "keys=list(cat_to_id.keys())\n",
    "\n",
    "for pre in y_pres:\n",
    "    result={}\n",
    "    for i in range(10):\n",
    "        result[keys[i]]=pre[i]\n",
    "    result = sorted(result.items(), key=lambda x: x[1], reverse=True)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42c4ac61",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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