{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5wFF5JFyD2Ki"
   },
   "source": [
    "#### Copyright 2019 The TensorFlow Hub Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:41.544809Z",
     "iopub.status.busy": "2022-12-14T20:29:41.544343Z",
     "iopub.status.idle": "2022-12-14T20:29:41.548164Z",
     "shell.execute_reply": "2022-12-14T20:29:41.547628Z"
    },
    "id": "Uf6NouXxDqGk"
   },
   "outputs": [],
   "source": [
    "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ORy-KvWXGXBo"
   },
   "source": [
    "# 探索 TF-Hub CORD-19 Swivel 嵌入向量\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfBg1C5NB3X0"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/hub/tutorials/cord_19_embeddings_keras\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a>\n",
    "</td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/cord_19_embeddings_keras.ipynb\">     <img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">     在 GitHub 上查看源代码</a></td>\n",
    "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a>\n",
    "</td>\n",
    "  <td><a href=\"https://tfhub.dev/tensorflow/cord-19/swivel-128d/3\"><img src=\"https://tensorflow.google.cn/images/hub_logo_32px.png\">查看 TF Hub 模型</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yI6Mh3-P0_Pk"
   },
   "source": [
    "TF-Hub (https://tfhub.dev/tensorflow/cord-19/swivel-128d/3) 上的 CORD-19 Swivel 文本嵌入向量模块旨在支持研究人员分析与 COVID-19 相关的自然语言文本。这些嵌入针对 [CORD-19 数据集](https://api.semanticscholar.org/CorpusID:216056360)中文章的标题、作者、摘要、正文文本和参考文献标题进行了训练。\n",
    "\n",
    "在此 Colab 中，我们将进行以下操作：\n",
    "\n",
    "- 分析嵌入向量空间中语义相似的单词\n",
    "- 使用 CORD-19 嵌入向量在 SciCite 数据集上训练分类器\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gVWOrccw0_Pl"
   },
   "source": [
    "## 设置\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:41.551514Z",
     "iopub.status.busy": "2022-12-14T20:29:41.551034Z",
     "iopub.status.idle": "2022-12-14T20:29:44.568415Z",
     "shell.execute_reply": "2022-12-14T20:29:44.567713Z"
    },
    "id": "Ym2nXOPuPV__"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-14 20:29:43.485341: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 20:29:43.485455: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 20:29:43.485466: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import functools\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VgRRf2I7tER"
   },
   "source": [
    "# 分析嵌入向量\n",
    "\n",
    "首先，我们通过计算和绘制不同术语之间的相关矩阵来分析嵌入向量。如果嵌入向量学会了成功捕获不同单词的含义，则语义相似的单词的嵌入向量应相互靠近。我们来看一些与 COVID-19 相关的术语。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:44.572866Z",
     "iopub.status.busy": "2022-12-14T20:29:44.572027Z",
     "iopub.status.idle": "2022-12-14T20:29:50.117409Z",
     "shell.execute_reply": "2022-12-14T20:29:50.116793Z"
    },
    "id": "HNN_9bBKSLHU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the inner product between two embedding vectors as the similarity measure\n",
    "def plot_correlation(labels, features):\n",
    "  corr = np.inner(features, features)\n",
    "  corr /= np.max(corr)\n",
    "  sns.heatmap(corr, xticklabels=labels, yticklabels=labels)\n",
    "\n",
    "# Generate embeddings for some terms\n",
    "queries = [\n",
    "  # Related viruses\n",
    "  'coronavirus', 'SARS', 'MERS',\n",
    "  # Regions\n",
    "  'Italy', 'Spain', 'Europe',\n",
    "  # Symptoms\n",
    "  'cough', 'fever', 'throat'\n",
    "]\n",
    "\n",
    "module = hub.load('https://tfhub.dev/tensorflow/cord-19/swivel-128d/3')\n",
    "embeddings = module(queries)\n",
    "\n",
    "plot_correlation(queries, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bg-PGqtm8B7K"
   },
   "source": [
    "可以看到，嵌入向量成功捕获了不同术语的含义。每个单词都与其所在簇的其他单词相似（即“coronavirus”与“SARS”和“MERS”高度相关），但与其他簇的术语不同（即“SARS”与“Spain”之间的相似度接近于 0）。\n",
    "\n",
    "现在，我们来看看如何使用这些嵌入向量解决特定任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idJ1jFmH7xMa"
   },
   "source": [
    "## SciCite：引用意图分类\n",
    "\n",
    "本部分介绍了将嵌入向量用于下游任务（如文本分类）的方法。我们将使用 TensorFlow 数据集中的 [SciCite 数据集](https://tensorflow.google.cn/datasets/catalog/scicite)对学术论文中的引文意图进行分类。给定一个带有学术论文引文的句子，对引文的主要意图进行分类：是背景信息、使用方法，还是比较结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:50.121656Z",
     "iopub.status.busy": "2022-12-14T20:29:50.121130Z",
     "iopub.status.idle": "2022-12-14T20:29:51.139188Z",
     "shell.execute_reply": "2022-12-14T20:29:51.138473Z"
    },
    "id": "Ghc-CzT8DDaZ"
   },
   "outputs": [],
   "source": [
    "builder = tfds.builder(name='scicite')\n",
    "builder.download_and_prepare()\n",
    "train_data, validation_data, test_data = builder.as_dataset(\n",
    "    split=('train', 'validation', 'test'),\n",
    "    as_supervised=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:51.143116Z",
     "iopub.status.busy": "2022-12-14T20:29:51.142781Z",
     "iopub.status.idle": "2022-12-14T20:29:51.488520Z",
     "shell.execute_reply": "2022-12-14T20:29:51.487811Z"
    },
    "id": "CVjyBD0ZPh4Z"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The finding that BMI is closely related to TBF...</td>\n",
       "      <td>result</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <td>, 2008; Quraan and Cheyne, 2008; Quraan and Ch...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>method</td>\n",
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      "text/plain": [
       "                                              string       label\n",
       "0  The finding that BMI is closely related to TBF...      result\n",
       "1  The average magnitude of the NBR increases wit...  background\n",
       "2  It has been reported that NF-κB activation can...      result\n",
       "3  , 2008; Quraan and Cheyne, 2008; Quraan and Ch...  background\n",
       "4  5B), but, interestingly, they shared conserved...  background\n",
       "5  Some investigators have noted an association o...  background\n",
       "6  In our previous study, it is documented that b...  background\n",
       "7  These subjects have intact cognitive function ...  background\n",
       "8  Another study reported improved knee function ...  background\n",
       "9  C. Data Analysis Transcription Speech samples ...      method"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Let's take a look at a few labeled examples from the training set\n",
    "NUM_EXAMPLES =   10#@param {type:\"integer\"}\n",
    "\n",
    "TEXT_FEATURE_NAME = builder.info.supervised_keys[0]\n",
    "LABEL_NAME = builder.info.supervised_keys[1]\n",
    "\n",
    "def label2str(numeric_label):\n",
    "  m = builder.info.features[LABEL_NAME].names\n",
    "  return m[numeric_label]\n",
    "\n",
    "data = next(iter(train_data.batch(NUM_EXAMPLES)))\n",
    "\n",
    "\n",
    "pd.DataFrame({\n",
    "    TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],\n",
    "    LABEL_NAME: [label2str(x) for x in data[1]]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "65s9UpYJ_1ct"
   },
   "source": [
    "## 训练引用意图分类器\n",
    "\n",
    "我们将使用 Keras 在 [SciCite 数据集](https://tensorflow.google.cn/datasets/catalog/scicite)上对分类器进行训练。我们构建一个模型，该模型使用 CORD-19 嵌入向量，并在顶部具有一个分类层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:51.492226Z",
     "iopub.status.busy": "2022-12-14T20:29:51.491524Z",
     "iopub.status.idle": "2022-12-14T20:29:52.095621Z",
     "shell.execute_reply": "2022-12-14T20:29:52.094948Z"
    },
    "id": "yZUclu8xBYlj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Layer (type)                Output Shape              Param #   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " keras_layer (KerasLayer)    (None, 128)               17301632  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dense (Dense)               (None, 3)                 387       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 17,302,019\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable params: 387\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-trainable params: 17,301,632\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#@title Hyperparameters { run: \"auto\" }\n",
    "\n",
    "EMBEDDING = 'https://tfhub.dev/tensorflow/cord-19/swivel-128d/3'  #@param {type: \"string\"}\n",
    "TRAINABLE_MODULE = False  #@param {type: \"boolean\"}\n",
    "\n",
    "hub_layer = hub.KerasLayer(EMBEDDING, input_shape=[], \n",
    "                           dtype=tf.string, trainable=TRAINABLE_MODULE)\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(tf.keras.layers.Dense(3))\n",
    "model.summary()\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "weZKWK-pLBll"
   },
   "source": [
    "## 训练并评估模型\n",
    "\n",
    "让我们训练并评估模型以查看在 SciCite 任务上的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:29:52.099186Z",
     "iopub.status.busy": "2022-12-14T20:29:52.098640Z",
     "iopub.status.idle": "2022-12-14T20:30:44.740870Z",
     "shell.execute_reply": "2022-12-14T20:30:44.740088Z"
    },
    "id": "cO1FWkZW2WS9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 5:41 - loss: 1.7915 - accuracy: 0.2500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 1.6719 - accuracy: 0.1452  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/257 [==>...........................] - ETA: 0s - loss: 1.5462 - accuracy: 0.1700"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/257 [====>.........................] - ETA: 0s - loss: 1.4355 - accuracy: 0.2100"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/257 [======>.......................] - ETA: 0s - loss: 1.3483 - accuracy: 0.2654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/257 [========>.....................] - ETA: 0s - loss: 1.2877 - accuracy: 0.3099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/257 [==========>...................] - ETA: 0s - loss: 1.2349 - accuracy: 0.3541"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/257 [============>.................] - ETA: 0s - loss: 1.1939 - accuracy: 0.3854"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/257 [==============>...............] - ETA: 0s - loss: 1.1585 - accuracy: 0.4116"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/257 [================>.............] - ETA: 0s - loss: 1.1330 - accuracy: 0.4307"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/257 [==================>...........] - ETA: 0s - loss: 1.1098 - accuracy: 0.4464"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/257 [====================>.........] - ETA: 0s - loss: 1.0863 - accuracy: 0.4654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/257 [======================>.......] - ETA: 0s - loss: 1.0673 - accuracy: 0.4796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/257 [========================>.....] - ETA: 0s - loss: 1.0483 - accuracy: 0.4937"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/257 [==========================>...] - ETA: 0s - loss: 1.0320 - accuracy: 0.5061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/257 [============================>.] - ETA: 0s - loss: 1.0218 - accuracy: 0.5133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 3s 5ms/step - loss: 1.0181 - accuracy: 0.5166 - val_loss: 0.8177 - val_accuracy: 0.6550\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:32 - loss: 0.8506 - accuracy: 0.7188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.8189 - accuracy: 0.6801  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/257 [==>...........................] - ETA: 0s - loss: 0.7999 - accuracy: 0.6829"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/257 [====>.........................] - ETA: 0s - loss: 0.7913 - accuracy: 0.6820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/257 [======>.......................] - ETA: 0s - loss: 0.7801 - accuracy: 0.6954"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.7353 - accuracy: 0.7049 - val_loss: 0.7010 - val_accuracy: 0.7096\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:24 - loss: 0.7999 - accuracy: 0.6562"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/257 [=============>................] - ETA: 0s - loss: 0.6608 - accuracy: 0.7426"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "204/257 [======================>.......] - ETA: 0s - loss: 0.6561 - accuracy: 0.7428"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/257 [============================>.] - ETA: 0s - loss: 0.6499 - accuracy: 0.7449"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.6503 - accuracy: 0.7447 - val_loss: 0.6482 - val_accuracy: 0.7336\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:33 - loss: 0.6234 - accuracy: 0.7500"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/257 [=======================>......] - ETA: 0s - loss: 0.6091 - accuracy: 0.7634"
     ]
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/257 [=========================>....] - ETA: 0s - loss: 0.6074 - accuracy: 0.7636"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.6068 - accuracy: 0.7638"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.6073 - accuracy: 0.7632"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 4ms/step - loss: 0.6073 - accuracy: 0.7632 - val_loss: 0.6179 - val_accuracy: 0.7434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:32 - loss: 0.6328 - accuracy: 0.6875"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.6189 - accuracy: 0.7422  "
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5967 - accuracy: 0.7603"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5910 - accuracy: 0.7660"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5849 - accuracy: 0.7721"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5830 - accuracy: 0.7737"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.5821 - accuracy: 0.7736"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/257 [===================>..........] - ETA: 0s - loss: 0.5813 - accuracy: 0.7737"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "189/257 [=====================>........] - ETA: 0s - loss: 0.5813 - accuracy: 0.7738"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/257 [======================>.......] - ETA: 0s - loss: 0.5827 - accuracy: 0.7738"
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/257 [==========================>...] - ETA: 0s - loss: 0.5830 - accuracy: 0.7726"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/257 [============================>.] - ETA: 0s - loss: 0.5826 - accuracy: 0.7742"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5819 - accuracy: 0.7742 - val_loss: 0.5979 - val_accuracy: 0.7544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:34 - loss: 0.7319 - accuracy: 0.6875"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/257 [===================>..........] - ETA: 0s - loss: 0.5624 - accuracy: 0.7835"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.5625 - accuracy: 0.7845"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5640 - accuracy: 0.7821"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5664 - accuracy: 0.7800"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5647 - accuracy: 0.7809"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5655 - accuracy: 0.7796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5655 - accuracy: 0.7796 - val_loss: 0.5873 - val_accuracy: 0.7620\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.6202 - accuracy: 0.7500"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5395 - accuracy: 0.7930  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5331 - accuracy: 0.7913"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5443 - accuracy: 0.7859"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/257 [============================>.] - ETA: 0s - loss: 0.5552 - accuracy: 0.7811"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5546 - accuracy: 0.7818 - val_loss: 0.5784 - val_accuracy: 0.7653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/35\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.4357 - accuracy: 0.9375"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5544 - accuracy: 0.7842"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/257 [=============>................] - ETA: 0s - loss: 0.5494 - accuracy: 0.7850"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5499 - accuracy: 0.7847"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/257 [============================>.] - ETA: 0s - loss: 0.5455 - accuracy: 0.7878"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5461 - accuracy: 0.7873 - val_loss: 0.5722 - val_accuracy: 0.7653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:59 - loss: 0.5102 - accuracy: 0.8438"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5393 - accuracy: 0.7881"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 4ms/step - loss: 0.5393 - accuracy: 0.7881 - val_loss: 0.5714 - val_accuracy: 0.7642\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/35\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      "  1/257 [..............................] - ETA: 1:20 - loss: 0.5815 - accuracy: 0.7500"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5346 - accuracy: 0.7907 - val_loss: 0.5644 - val_accuracy: 0.7729\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:23 - loss: 0.5177 - accuracy: 0.8125"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/257 [====================>.........] - ETA: 0s - loss: 0.5272 - accuracy: 0.7906"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5306 - accuracy: 0.7897 - val_loss: 0.5616 - val_accuracy: 0.7751\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:37 - loss: 0.5690 - accuracy: 0.7188"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/257 [==========>...................] - ETA: 0s - loss: 0.5299 - accuracy: 0.7867"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5280 - accuracy: 0.7902"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.5257 - accuracy: 0.7915"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5263 - accuracy: 0.7918 - val_loss: 0.5580 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.7378 - accuracy: 0.7188"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5284 - accuracy: 0.7923  "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5238 - accuracy: 0.7935 - val_loss: 0.5563 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/35\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.6356 - accuracy: 0.7188"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5241 - accuracy: 0.7901"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5269 - accuracy: 0.7911"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5248 - accuracy: 0.7936"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5210 - accuracy: 0.7934 - val_loss: 0.5561 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:16 - loss: 0.4313 - accuracy: 0.8125"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5182 - accuracy: 0.7955 - val_loss: 0.5576 - val_accuracy: 0.7762\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.6778 - accuracy: 0.7188"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/257 [=========>....................] - ETA: 0s - loss: 0.5133 - accuracy: 0.7945"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/257 [===========>..................] - ETA: 0s - loss: 0.5110 - accuracy: 0.7970"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/257 [=============>................] - ETA: 0s - loss: 0.5103 - accuracy: 0.7984"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/257 [==================>...........] - ETA: 0s - loss: 0.5197 - accuracy: 0.7931"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/257 [==========================>...] - ETA: 0s - loss: 0.5187 - accuracy: 0.7928"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/257 [===========================>..] - ETA: 0s - loss: 0.5167 - accuracy: 0.7932"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5169 - accuracy: 0.7930 - val_loss: 0.5511 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:20 - loss: 0.6364 - accuracy: 0.7500"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4944 - accuracy: 0.8066  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/257 [=========>....................] - ETA: 0s - loss: 0.4992 - accuracy: 0.8019"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5090 - accuracy: 0.7996"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5134 - accuracy: 0.7960"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5145 - accuracy: 0.7956"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5145 - accuracy: 0.7956 - val_loss: 0.5515 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.5357 - accuracy: 0.6875"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5458 - accuracy: 0.7637  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5490 - accuracy: 0.7764"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.5432 - accuracy: 0.7832"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.5394 - accuracy: 0.7847"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.5525 - accuracy: 0.7785"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5441 - accuracy: 0.7826"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5367 - accuracy: 0.7858"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/257 [=============>................] - ETA: 0s - loss: 0.5285 - accuracy: 0.7879"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/257 [=================>............] - ETA: 0s - loss: 0.5220 - accuracy: 0.7900"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/257 [===================>..........] - ETA: 0s - loss: 0.5192 - accuracy: 0.7914"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "187/257 [====================>.........] - ETA: 0s - loss: 0.5196 - accuracy: 0.7926"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/257 [======================>.......] - ETA: 0s - loss: 0.5199 - accuracy: 0.7922"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/257 [========================>.....] - ETA: 0s - loss: 0.5155 - accuracy: 0.7942"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/257 [==========================>...] - ETA: 0s - loss: 0.5140 - accuracy: 0.7952"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/257 [============================>.] - ETA: 0s - loss: 0.5154 - accuracy: 0.7942"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5130 - accuracy: 0.7956 - val_loss: 0.5511 - val_accuracy: 0.7740\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:26 - loss: 0.4312 - accuracy: 0.8750"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5038 - accuracy: 0.8086  "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5152 - accuracy: 0.8010"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5056 - accuracy: 0.8031"
     ]
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5113 - accuracy: 0.7995"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5088 - accuracy: 0.7994"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5110 - accuracy: 0.7984"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5115 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5115 - accuracy: 0.7969 - val_loss: 0.5495 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:22 - loss: 0.3325 - accuracy: 0.9062"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5076 - accuracy: 0.7930  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5148 - accuracy: 0.7953"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5163 - accuracy: 0.7935"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/257 [============>.................] - ETA: 0s - loss: 0.5177 - accuracy: 0.7930"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/257 [==============>...............] - ETA: 0s - loss: 0.5129 - accuracy: 0.7953"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5148 - accuracy: 0.7956"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.5100 - accuracy: 0.7959"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5112 - accuracy: 0.7955 - val_loss: 0.5504 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:21 - loss: 0.6950 - accuracy: 0.6875"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4979 - accuracy: 0.7949  "
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4986 - accuracy: 0.8008"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.5109 - accuracy: 0.7971"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.5088 - accuracy: 0.8011"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5060 - accuracy: 0.8021"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.5083 - accuracy: 0.8003"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.5087 - accuracy: 0.8001"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5099 - accuracy: 0.7987"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5118 - accuracy: 0.7973"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/257 [============================>.] - ETA: 0s - loss: 0.5088 - accuracy: 0.7982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5090 - accuracy: 0.7980 - val_loss: 0.5485 - val_accuracy: 0.7773\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.3435 - accuracy: 0.8750"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4391 - accuracy: 0.8320  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4954 - accuracy: 0.8047"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5146 - accuracy: 0.7962"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5193 - accuracy: 0.7866"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5145 - accuracy: 0.7902"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5141 - accuracy: 0.7913"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5109 - accuracy: 0.7985"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5027 - accuracy: 0.8027"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.4959 - accuracy: 0.8074"
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5084 - accuracy: 0.7988"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5083 - accuracy: 0.7989 - val_loss: 0.5496 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:26 - loss: 0.5253 - accuracy: 0.8438"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 75/257 [=======>......................] - ETA: 0s - loss: 0.5145 - accuracy: 0.7933"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.5210 - accuracy: 0.7896"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5284 - accuracy: 0.7866"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "120/257 [=============>................] - ETA: 0s - loss: 0.5241 - accuracy: 0.7885"
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/257 [==================>...........] - ETA: 0s - loss: 0.5181 - accuracy: 0.7930"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/257 [====================>.........] - ETA: 0s - loss: 0.5177 - accuracy: 0.7911"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/257 [======================>.......] - ETA: 0s - loss: 0.5139 - accuracy: 0.7941"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/257 [=======================>......] - ETA: 0s - loss: 0.5113 - accuracy: 0.7938"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5110 - accuracy: 0.7931"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.5077 - accuracy: 0.7956"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5066 - accuracy: 0.7969 - val_loss: 0.5476 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:19 - loss: 0.5100 - accuracy: 0.7812"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5504 - accuracy: 0.7793  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/257 [===========================>..] - ETA: 0s - loss: 0.5065 - accuracy: 0.7976"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5067 - accuracy: 0.7977 - val_loss: 0.5461 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.3978 - accuracy: 0.8438"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/257 [=========>....................] - ETA: 0s - loss: 0.4882 - accuracy: 0.8128"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/257 [===========>..................] - ETA: 0s - loss: 0.4994 - accuracy: 0.8051"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/257 [=============>................] - ETA: 0s - loss: 0.5094 - accuracy: 0.8001"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/257 [==================>...........] - ETA: 0s - loss: 0.5131 - accuracy: 0.7961"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/257 [==========================>...] - ETA: 0s - loss: 0.5058 - accuracy: 0.7981"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/257 [===========================>..] - ETA: 0s - loss: 0.5039 - accuracy: 0.7988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5055 - accuracy: 0.7983 - val_loss: 0.5472 - val_accuracy: 0.7882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.3938 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4872 - accuracy: 0.8066  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/257 [==================>...........] - ETA: 0s - loss: 0.5038 - accuracy: 0.7991"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/257 [===========================>..] - ETA: 0s - loss: 0.5045 - accuracy: 0.7971"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5048 - accuracy: 0.7970 - val_loss: 0.5492 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.3994 - accuracy: 0.8750"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4709 - accuracy: 0.8164  "
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4798 - accuracy: 0.8174"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.4945 - accuracy: 0.8052"
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.4845 - accuracy: 0.8098"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.4961 - accuracy: 0.8001"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5011 - accuracy: 0.7980"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5025 - accuracy: 0.7993"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5102 - accuracy: 0.7984"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5079 - accuracy: 0.7980"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/257 [===================>..........] - ETA: 0s - loss: 0.5070 - accuracy: 0.7986"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.5102 - accuracy: 0.7981"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.5113 - accuracy: 0.7976"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/257 [=========================>....] - ETA: 0s - loss: 0.5069 - accuracy: 0.8002"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/257 [==========================>...] - ETA: 0s - loss: 0.5053 - accuracy: 0.8009"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/257 [============================>.] - ETA: 0s - loss: 0.5048 - accuracy: 0.8000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5043 - accuracy: 0.8002 - val_loss: 0.5504 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:35 - loss: 0.6980 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5453 - accuracy: 0.7757  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5187 - accuracy: 0.7860"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.4999 - accuracy: 0.7995"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.4899 - accuracy: 0.8021"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.4928 - accuracy: 0.7999"
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5068 - accuracy: 0.7915"
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/257 [=====================>........] - ETA: 0s - loss: 0.5053 - accuracy: 0.7957"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.5057 - accuracy: 0.7976"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5034 - accuracy: 0.7988 - val_loss: 0.5464 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:17 - loss: 0.8141 - accuracy: 0.5938"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5020 - accuracy: 0.7930  "
     ]
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4926 - accuracy: 0.8018"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5150 - accuracy: 0.7878"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5114 - accuracy: 0.7905"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5182 - accuracy: 0.7926"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5204 - accuracy: 0.7923"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5191 - accuracy: 0.7932"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5167 - accuracy: 0.7974"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5133 - accuracy: 0.7977"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5057 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/257 [===================>..........] - ETA: 0s - loss: 0.5070 - accuracy: 0.7993"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.5068 - accuracy: 0.7992"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.5052 - accuracy: 0.7995"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/257 [=========================>....] - ETA: 0s - loss: 0.5041 - accuracy: 0.8000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/257 [==========================>...] - ETA: 0s - loss: 0.5047 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/257 [============================>.] - ETA: 0s - loss: 0.5048 - accuracy: 0.7987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5029 - accuracy: 0.7995 - val_loss: 0.5462 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.5106 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5145 - accuracy: 0.8086  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5055 - accuracy: 0.8047"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5070 - accuracy: 0.8073"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5073 - accuracy: 0.8047"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5039 - accuracy: 0.8023"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.4983 - accuracy: 0.8048"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5021 - accuracy: 0.8019"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5021 - accuracy: 0.8019 - val_loss: 0.5489 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.5640 - accuracy: 0.6875"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.5463 - accuracy: 0.7854  "
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/257 [=========>....................] - ETA: 0s - loss: 0.5025 - accuracy: 0.8077"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/257 [===========>..................] - ETA: 0s - loss: 0.5096 - accuracy: 0.8001"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/257 [=============>................] - ETA: 0s - loss: 0.5077 - accuracy: 0.7989"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/257 [==================>...........] - ETA: 0s - loss: 0.5127 - accuracy: 0.7971"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/257 [=========================>....] - ETA: 0s - loss: 0.5075 - accuracy: 0.7984"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/257 [===========================>..] - ETA: 0s - loss: 0.5044 - accuracy: 0.8006"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5018 - accuracy: 0.8013 - val_loss: 0.5471 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:19 - loss: 0.5992 - accuracy: 0.7188"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/257 [========>.....................] - ETA: 0s - loss: 0.4971 - accuracy: 0.7954"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.4959 - accuracy: 0.8022"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/257 [=====================>........] - ETA: 0s - loss: 0.4998 - accuracy: 0.7998"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.4955 - accuracy: 0.8025"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.4975 - accuracy: 0.8021"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 5ms/step - loss: 0.5010 - accuracy: 0.8019 - val_loss: 0.5455 - val_accuracy: 0.7915\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.3518 - accuracy: 0.9375"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5454 - accuracy: 0.8008  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5117 - accuracy: 0.8125"
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    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.4923 - accuracy: 0.8158"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.4927 - accuracy: 0.8105"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.4984 - accuracy: 0.8055"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5011 - accuracy: 0.8018"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.4988 - accuracy: 0.8022"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.4977 - accuracy: 0.8018"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.4992 - accuracy: 0.8013"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.4985 - accuracy: 0.8026"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.4960 - accuracy: 0.8038"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.4964 - accuracy: 0.8032"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5005 - accuracy: 0.8012"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.4980 - accuracy: 0.8030"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/257 [===========================>..] - ETA: 0s - loss: 0.4986 - accuracy: 0.8031"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5006 - accuracy: 0.8021"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5007 - accuracy: 0.8021 - val_loss: 0.5469 - val_accuracy: 0.7904\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:25 - loss: 0.5398 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4848 - accuracy: 0.8107  "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.4776 - accuracy: 0.8049"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.4944 - accuracy: 0.7940"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5131 - accuracy: 0.7909"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5103 - accuracy: 0.7967"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5100 - accuracy: 0.7973"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5102 - accuracy: 0.7975"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.5090 - accuracy: 0.7977"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5018 - accuracy: 0.8013"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5019 - accuracy: 0.7996"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.4984 - accuracy: 0.8023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5001 - accuracy: 0.8013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5001 - accuracy: 0.8013 - val_loss: 0.5479 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:35 - loss: 0.5115 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5316 - accuracy: 0.7969  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5008 - accuracy: 0.8008"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5136 - accuracy: 0.7967"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/257 [==========>...................] - ETA: 0s - loss: 0.5190 - accuracy: 0.7953"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/257 [============>.................] - ETA: 0s - loss: 0.5104 - accuracy: 0.7993"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/257 [==============>...............] - ETA: 0s - loss: 0.5106 - accuracy: 0.7986"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/257 [==================>...........] - ETA: 0s - loss: 0.5087 - accuracy: 0.7977"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.5060 - accuracy: 0.7991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/257 [=====================>........] - ETA: 0s - loss: 0.5007 - accuracy: 0.8018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.4994 - accuracy: 0.8031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5004 - accuracy: 0.8028 - val_loss: 0.5455 - val_accuracy: 0.7882\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 35#@param {type: \"integer\"}\n",
    "BATCH_SIZE = 32#@param {type: \"integer\"}\n",
    "\n",
    "history = model.fit(train_data.shuffle(10000).batch(BATCH_SIZE),\n",
    "                    epochs=EPOCHS,\n",
    "                    validation_data=validation_data.batch(BATCH_SIZE),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:30:44.744943Z",
     "iopub.status.busy": "2022-12-14T20:30:44.744260Z",
     "iopub.status.idle": "2022-12-14T20:30:44.749609Z",
     "shell.execute_reply": "2022-12-14T20:30:44.748955Z"
    },
    "id": "2sKE7kEyLJQZ"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "def display_training_curves(training, validation, title, subplot):\n",
    "  if subplot%10==1: # set up the subplots on the first call\n",
    "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
    "    plt.tight_layout()\n",
    "  ax = plt.subplot(subplot)\n",
    "  ax.set_facecolor('#F8F8F8')\n",
    "  ax.plot(training)\n",
    "  ax.plot(validation)\n",
    "  ax.set_title('model '+ title)\n",
    "  ax.set_ylabel(title)\n",
    "  ax.set_xlabel('epoch')\n",
    "  ax.legend(['train', 'valid.'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:30:44.752942Z",
     "iopub.status.busy": "2022-12-14T20:30:44.752437Z",
     "iopub.status.idle": "2022-12-14T20:30:45.159840Z",
     "shell.execute_reply": "2022-12-14T20:30:45.159065Z"
    },
    "id": "nnQfxevhLKld"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_21774/4094752860.py:6: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
      "  ax = plt.subplot(subplot)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_training_curves(history.history['accuracy'], history.history['val_accuracy'], 'accuracy', 211)\n",
    "display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 212)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BjvtOw72Lpyw"
   },
   "source": [
    "## 评估模型\n",
    "\n",
    "我们来看看模型的表现。模型将返回两个值：损失（表示错误的数字，值越低越好）和准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:30:45.163931Z",
     "iopub.status.busy": "2022-12-14T20:30:45.163352Z",
     "iopub.status.idle": "2022-12-14T20:30:45.469298Z",
     "shell.execute_reply": "2022-12-14T20:30:45.468525Z"
    },
    "id": "y0ExC8D0LX8m"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 0.5379 - accuracy: 0.7848 - 291ms/epoch - 73ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.538\n",
      "accuracy: 0.785\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_data.batch(512), verbose=2)\n",
    "\n",
    "for name, value in zip(model.metrics_names, results):\n",
    "  print('%s: %.3f' % (name, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWp5OWeTL2EW"
   },
   "source": [
    "可以看到，损失迅速减小，而准确率迅速提高。我们绘制一些样本来检查预测与真实标签的关系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:30:45.473045Z",
     "iopub.status.busy": "2022-12-14T20:30:45.472394Z",
     "iopub.status.idle": "2022-12-14T20:30:45.882485Z",
     "shell.execute_reply": "2022-12-14T20:30:45.881808Z"
    },
    "id": "VzHzAOaaOVC0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 150ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>label</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The diffraction grating, LED, and split detect...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Our ideas are based on a previous paper [4] de...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Our finding is consistent with the literature ...</td>\n",
       "      <td>result</td>\n",
       "      <td>result</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Test scores from each of the cognitive domains...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The optimization algorithm was set to maximize...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>To quantify the extent of substitution saturat...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Examples of gesture control are based on the e...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The identification of these features has been ...</td>\n",
       "      <td>method</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Postulated mechanisms for observed effects of ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>The right inferior phrenic artery is the most ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[8] presented an approach for estimating the t...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Similar structures were observed in M10 cells ...</td>\n",
       "      <td>result</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cytotoxic effects of cobalt chloride were repo...</td>\n",
       "      <td>background</td>\n",
       "      <td>result</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>However, prolonged incubation of latex enzyme ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>…and travels great distances, resulting in a s...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The images fused using region selection; MSD, ...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>These findings were expected, as EMG activity ...</td>\n",
       "      <td>result</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The model has been extended to both 2D and 3D ...</td>\n",
       "      <td>method</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Therefore, many authors claim comprehensive nu...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Similar to Ab40, IAPP-GI populates an aggregat...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               string       label  prediction\n",
       "0   The diffraction grating, LED, and split detect...  background      method\n",
       "1   Our ideas are based on a previous paper [4] de...  background      method\n",
       "2   Our finding is consistent with the literature ...      result      result\n",
       "3   Test scores from each of the cognitive domains...      method      method\n",
       "4   The optimization algorithm was set to maximize...      method      method\n",
       "5   To quantify the extent of substitution saturat...      method      method\n",
       "6   Examples of gesture control are based on the e...      method      method\n",
       "7   The identification of these features has been ...      method  background\n",
       "8   Postulated mechanisms for observed effects of ...  background  background\n",
       "9   The right inferior phrenic artery is the most ...  background  background\n",
       "10  [8] presented an approach for estimating the t...  background      method\n",
       "11  Similar structures were observed in M10 cells ...      result      method\n",
       "12  Cytotoxic effects of cobalt chloride were repo...  background      result\n",
       "13  However, prolonged incubation of latex enzyme ...  background  background\n",
       "14  …and travels great distances, resulting in a s...  background  background\n",
       "15  The images fused using region selection; MSD, ...      method      method\n",
       "16  These findings were expected, as EMG activity ...      result  background\n",
       "17  The model has been extended to both 2D and 3D ...      method  background\n",
       "18  Therefore, many authors claim comprehensive nu...  background      method\n",
       "19  Similar to Ab40, IAPP-GI populates an aggregat...  background  background"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_dataset = next(iter(test_data.batch(20)))\n",
    "\n",
    "prediction_texts = [ex.numpy().decode('utf8') for ex in prediction_dataset[0]]\n",
    "prediction_labels = [label2str(x) for x in prediction_dataset[1]]\n",
    "\n",
    "predictions = [\n",
    "    label2str(x) for x in np.argmax(model.predict(prediction_texts), axis=-1)]\n",
    "\n",
    "\n",
    "pd.DataFrame({\n",
    "    TEXT_FEATURE_NAME: prediction_texts,\n",
    "    LABEL_NAME: prediction_labels,\n",
    "    'prediction': predictions\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OSGcrkE069_Q"
   },
   "source": [
    "可以看到，对于此随机样本，模型大多数时候都会预测正确的标签，这表明它可以很好地嵌入科学句子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oLE0kCfO5CIA"
   },
   "source": [
    "# 后续计划\n",
    "\n",
    "现在，您已经对 TF-Hub 中的 CORD-19 Swivel 嵌入向量有了更多了解，我们鼓励您参加 CORD-19 Kaggle 竞赛，为从 COVID-19 相关学术文本中获得更深入的科学洞见做出贡献。\n",
    "\n",
    "- 参加 [CORD-19 Kaggle Challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)\n",
    "- 详细了解 [COVID-19 开放研究数据集 (CORD-19)](https://api.semanticscholar.org/CorpusID:216056360)\n",
    "- 访问 https://tfhub.dev/tensorflow/cord-19/swivel-128d/3，参阅文档并详细了解 TF-Hub 嵌入向量\n",
    "- 使用 [TensorFlow Embedding Projector](http://projector.tensorflow.org/?config=https://storage.googleapis.com/tfhub-examples/tensorflow/cord-19/swivel-128d/3/tensorboard/projector_config.json) 探索 CORD-19 嵌入向量空间"
   ]
  }
 ],
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