{
 "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": "2022-12-14T20:59:17.208496Z",
     "iopub.status.busy": "2022-12-14T20:59:17.207819Z",
     "iopub.status.idle": "2022-12-14T20:59:17.212084Z",
     "shell.execute_reply": "2022-12-14T20:59:17.211530Z"
    },
    "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": [
    "# TF-Hub CORD-19 Swivel 임베딩 살펴보기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfBg1C5NB3X0"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org에서 보기</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab에서 실행</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub에서 보기</a></td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">노트북 다운로드</a></td>\n",
    "  <td><a href=\"https://tfhub.dev/tensorflow/cord-19/swivel-128d/3\"><img src=\"https://www.tensorflow.org/images/hub_logo_32px.png\">TF Hub 모델 보기</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yI6Mh3-P0_Pk"
   },
   "source": [
    "TF-Hub(https://tfhub.dev/tensorflow/cord-19/swivel-128d/3)의 CORD-19 Swivel 텍스트 임베딩 모듈은 연구원들이 코로나바이러스감염증-19와 관련된 자연어 텍스트를 분석할 수 있도록 빌드되었습니다. 이러한 임베딩은 [CORD-19 데이터세트](https://api.semanticscholar.org/CorpusID:216056360)에 있는 기사의 제목, 저자, 요약문, 본문 텍스트 및 참조 제목에 대해 훈련되었습니다.\n",
    "\n",
    "이 colab에서는 다음을 수행합니다.\n",
    "\n",
    "- 임베딩 공간에서 의미론적으로 유사한 단어를 분석합니다.\n",
    "- CORD-19 임베딩을 사용하여 SciCite 데이터세트에서 분류자를 훈련합니다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gVWOrccw0_Pl"
   },
   "source": [
    "## 설정\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:59:17.215829Z",
     "iopub.status.busy": "2022-12-14T20:59:17.215292Z",
     "iopub.status.idle": "2022-12-14T20:59:20.326960Z",
     "shell.execute_reply": "2022-12-14T20:59:20.326239Z"
    },
    "id": "Ym2nXOPuPV__"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-14 20:59:19.175950: 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 20:59:19.176062: 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 20:59:19.176072: 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": [
    "# 임베딩 분석하기\n",
    "\n",
    "서로 다른 용어 간의 상관 행렬을 계산하고 플롯하여 임베딩을 분석하는 것으로 시작하겠습니다. 임베딩이 여러 단어의 의미를 성공적으로 포착하는 방법을 학습한 경우, 의미론적으로 유사한 단어의 임베딩 벡터는 서로 가까워야 합니다. 코로나바이러스감염증-19와 관련된 일부 용어를 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:59:20.331908Z",
     "iopub.status.busy": "2022-12-14T20:59:20.330970Z",
     "iopub.status.idle": "2022-12-14T20:59:25.994019Z",
     "shell.execute_reply": "2022-12-14T20:59:25.993341Z"
    },
    "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": [
    "임베딩이 여러 용어의 의미를 성공적으로 포착했음을 알 수 있습니다. 각 단어는 해당 클러스터의 다른 단어와 유사하지만(즉, \"coronavirus\"는 \"SARS\" 및 \"MERS\"와 높은 상관 관계가 있음) 다른 클러스터의 용어와는 다릅니다(즉, \"SARS\"와 \"Spain\" 사이의 유사성은 0에 가까움).\n",
    "\n",
    "이제 이러한 임베딩을 사용하여 특정 작업을 해결하는 방법을 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idJ1jFmH7xMa"
   },
   "source": [
    "## SciCite: 인용 의도 분류\n",
    "\n",
    "이 섹션에서는 텍스트 분류와 같은 다운스트림 작업에 임베딩을 사용하는 방법을 보여줍니다. TensorFlow 데이터세트의 [SciCite 데이터세트](https://www.tensorflow.org/datasets/catalog/scicite)를 사용하여 학술 논문에서 인용 의도를 분류합니다. 학술 논문의 인용이 포함된 문장이 주어지면 인용의 주요 의도가 배경 정보, 방법 사용 또는 결과 비교인지 여부를 분류합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:59:25.998209Z",
     "iopub.status.busy": "2022-12-14T20:59:25.997667Z",
     "iopub.status.idle": "2022-12-14T20:59:26.920122Z",
     "shell.execute_reply": "2022-12-14T20:59:26.919348Z"
    },
    "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": "2022-12-14T20:59:26.924383Z",
     "iopub.status.busy": "2022-12-14T20:59:26.923856Z",
     "iopub.status.idle": "2022-12-14T20:59:27.168064Z",
     "shell.execute_reply": "2022-12-14T20:59:27.167184Z"
    },
    "id": "CVjyBD0ZPh4Z"
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "    <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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       "      <td>, 2008; Quraan and Cheyne, 2008; Quraan and Ch...</td>\n",
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       "      <th>8</th>\n",
       "      <td>Another study reported improved knee function ...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C. Data Analysis Transcription Speech samples ...</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "## 인용 의도 분류자 훈련하기\n",
    "\n",
    "Keras를 사용하여 [SciCite 데이터세트](https://www.tensorflow.org/datasets/catalog/scicite)에 대한 분류자를 훈련합니다. 분류 레이어를 상위에 둔 CORD-19 임베딩을 사용하는 모델을 빌드하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:59:27.171936Z",
     "iopub.status.busy": "2022-12-14T20:59:27.171319Z",
     "iopub.status.idle": "2022-12-14T20:59:27.793237Z",
     "shell.execute_reply": "2022-12-14T20:59:27.792497Z"
    },
    "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": [
    "## 모델 훈련 및 평가하기\n",
    "\n",
    "SciCite 작업의 성능을 확인하기 위해 모델을 훈련하고 평가하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:59:27.797115Z",
     "iopub.status.busy": "2022-12-14T20:59:27.796610Z",
     "iopub.status.idle": "2022-12-14T21:00:21.787852Z",
     "shell.execute_reply": "2022-12-14T21:00:21.787007Z"
    },
    "id": "cO1FWkZW2WS9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 5:42 - loss: 1.0626 - accuracy: 0.4375"
     ]
    },
    {
     "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",
      " 15/257 [>.............................] - ETA: 0s - loss: 1.0819 - accuracy: 0.4646  "
     ]
    },
    {
     "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",
      " 29/257 [==>...........................] - ETA: 0s - loss: 1.0468 - accuracy: 0.5097"
     ]
    },
    {
     "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",
      " 44/257 [====>.........................] - ETA: 0s - loss: 1.0147 - accuracy: 0.5327"
     ]
    },
    {
     "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",
      " 59/257 [=====>........................] - ETA: 0s - loss: 1.0106 - accuracy: 0.5328"
     ]
    },
    {
     "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",
      " 74/257 [=======>......................] - ETA: 0s - loss: 0.9930 - accuracy: 0.5465"
     ]
    },
    {
     "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",
      " 89/257 [=========>....................] - ETA: 0s - loss: 0.9688 - accuracy: 0.5558"
     ]
    },
    {
     "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",
      "104/257 [===========>..................] - ETA: 0s - loss: 0.9541 - accuracy: 0.5658"
     ]
    },
    {
     "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",
      "119/257 [============>.................] - ETA: 0s - loss: 0.9430 - accuracy: 0.5762"
     ]
    },
    {
     "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",
      "134/257 [==============>...............] - ETA: 0s - loss: 0.9337 - accuracy: 0.5870"
     ]
    },
    {
     "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",
      "149/257 [================>.............] - ETA: 0s - loss: 0.9201 - accuracy: 0.5975"
     ]
    },
    {
     "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",
      "165/257 [==================>...........] - ETA: 0s - loss: 0.9090 - accuracy: 0.6078"
     ]
    },
    {
     "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",
      "181/257 [====================>.........] - ETA: 0s - loss: 0.8975 - accuracy: 0.6155"
     ]
    },
    {
     "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",
      "197/257 [=====================>........] - ETA: 0s - loss: 0.8905 - accuracy: 0.6201"
     ]
    },
    {
     "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",
      "213/257 [=======================>......] - ETA: 0s - loss: 0.8852 - accuracy: 0.6228"
     ]
    },
    {
     "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",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.8790 - accuracy: 0.6264"
     ]
    },
    {
     "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",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.8711 - accuracy: 0.6304"
     ]
    },
    {
     "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: 0.8662 - accuracy: 0.6333 - val_loss: 0.7538 - val_accuracy: 0.6932\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.7042 - accuracy: 0.6875"
     ]
    },
    {
     "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: 0.7690 - accuracy: 0.6719  "
     ]
    },
    {
     "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",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.7433 - accuracy: 0.6885"
     ]
    },
    {
     "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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.6831 - accuracy: 0.7272"
     ]
    },
    {
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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: 0.6827 - accuracy: 0.7269 - val_loss: 0.6606 - val_accuracy: 0.7555\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:19 - loss: 0.7826 - 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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.6159 - accuracy: 0.7617 - val_loss: 0.6189 - val_accuracy: 0.7598\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:33 - loss: 0.6842 - accuracy: 0.6562"
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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: 0.6086 - accuracy: 0.7715  "
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.6106 - accuracy: 0.7666"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.6012 - accuracy: 0.7642"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "173/257 [===================>..........] - ETA: 0s - loss: 0.5968 - accuracy: 0.7673"
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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",
      "188/257 [====================>.........] - ETA: 0s - loss: 0.5963 - accuracy: 0.7665"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/257 [======================>.......] - ETA: 0s - loss: 0.5950 - accuracy: 0.7665"
     ]
    },
    {
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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",
      "219/257 [========================>.....] - ETA: 0s - loss: 0.5926 - accuracy: 0.7678"
     ]
    },
    {
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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",
      "235/257 [==========================>...] - ETA: 0s - loss: 0.5906 - accuracy: 0.7678"
     ]
    },
    {
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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: 0.5854 - accuracy: 0.7698"
     ]
    },
    {
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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: 0.5836 - accuracy: 0.7711 - val_loss: 0.5991 - val_accuracy: 0.7697\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:23 - loss: 0.4727 - 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: 0.5514 - accuracy: 0.7910  "
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.5701 - accuracy: 0.7809"
     ]
    },
    {
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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",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5696 - accuracy: 0.7795"
     ]
    },
    {
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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",
      "120/257 [=============>................] - ETA: 0s - loss: 0.5731 - accuracy: 0.7773"
     ]
    },
    {
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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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    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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 5ms/step - loss: 0.5653 - accuracy: 0.7801 - val_loss: 0.5868 - val_accuracy: 0.7664\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/35\n"
     ]
    },
    {
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      "\r",
      "  1/257 [..............................] - ETA: 1:33 - loss: 0.5769 - accuracy: 0.7812"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "171/257 [==================>...........] - ETA: 0s - loss: 0.5608 - accuracy: 0.7781"
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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",
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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: 0.5535 - accuracy: 0.7841 - val_loss: 0.5771 - val_accuracy: 0.7686\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.4585 - accuracy: 0.8125"
     ]
    },
    {
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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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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5449 - accuracy: 0.7884 - val_loss: 0.5719 - val_accuracy: 0.7729\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: 1:29 - loss: 0.4849 - accuracy: 0.7812"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5427 - accuracy: 0.7924"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
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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: 0.5385 - accuracy: 0.7913 - val_loss: 0.5677 - val_accuracy: 0.7751\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:22 - loss: 0.4688 - 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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5335 - accuracy: 0.7919 - val_loss: 0.5627 - val_accuracy: 0.7729\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: 1:30 - loss: 0.9383 - accuracy: 0.5938"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5319 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5285 - accuracy: 0.7940 - val_loss: 0.5611 - val_accuracy: 0.7707\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:17 - loss: 0.6022 - accuracy: 0.7188"
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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",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5253 - accuracy: 0.7958 - val_loss: 0.5574 - 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: 1:31 - loss: 0.4547 - accuracy: 0.8438"
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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",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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     "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",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.5130 - accuracy: 0.7924"
     ]
    },
    {
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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",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5186 - accuracy: 0.7920"
     ]
    },
    {
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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",
      "120/257 [=============>................] - ETA: 0s - loss: 0.5167 - accuracy: 0.7948"
     ]
    },
    {
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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",
      "135/257 [==============>...............] - ETA: 0s - loss: 0.5131 - accuracy: 0.7986"
     ]
    },
    {
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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",
      "165/257 [==================>...........] - ETA: 0s - loss: 0.5220 - accuracy: 0.7934"
     ]
    },
    {
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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: 0.5196 - 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",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.5218 - accuracy: 0.7933"
     ]
    },
    {
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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",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5208 - accuracy: 0.7941"
     ]
    },
    {
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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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5216 - accuracy: 0.7943"
     ]
    },
    {
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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: 0.5222 - accuracy: 0.7933"
     ]
    },
    {
     "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 [==============================] - 2s 5ms/step - loss: 0.5222 - accuracy: 0.7933 - val_loss: 0.5539 - val_accuracy: 0.7751\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.4435 - accuracy: 0.8125"
     ]
    },
    {
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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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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "250/257 [============================>.] - ETA: 0s - loss: 0.5191 - accuracy: 0.7939"
     ]
    },
    {
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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: 0.5201 - accuracy: 0.7941 - val_loss: 0.5523 - val_accuracy: 0.7762\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.5465 - accuracy: 0.7812"
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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: 0.4927 - accuracy: 0.8086  "
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4939 - accuracy: 0.8105"
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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",
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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",
      " 94/257 [=========>....................] - ETA: 0s - loss: 0.5073 - accuracy: 0.8075"
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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",
      "109/257 [===========>..................] - ETA: 0s - loss: 0.5164 - accuracy: 0.8028"
     ]
    },
    {
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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",
      "125/257 [=============>................] - ETA: 0s - loss: 0.5163 - accuracy: 0.8012"
     ]
    },
    {
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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",
      "172/257 [===================>..........] - ETA: 0s - loss: 0.5243 - 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",
      "188/257 [====================>.........] - ETA: 0s - loss: 0.5245 - accuracy: 0.7940"
     ]
    },
    {
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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",
      "203/257 [======================>.......] - ETA: 0s - loss: 0.5209 - accuracy: 0.7948"
     ]
    },
    {
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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",
      "248/257 [===========================>..] - ETA: 0s - loss: 0.5188 - accuracy: 0.7945"
     ]
    },
    {
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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: 0.5179 - accuracy: 0.7951 - val_loss: 0.5518 - val_accuracy: 0.7762\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:25 - loss: 0.7121 - accuracy: 0.5938"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5150 - accuracy: 0.7903"
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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",
      " 46/257 [====>.........................] - ETA: 0s - loss: 0.5021 - 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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5038 - accuracy: 0.8014"
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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",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5043 - accuracy: 0.8012"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/257 [============>.................] - ETA: 0s - loss: 0.5028 - accuracy: 0.8012"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4995 - 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",
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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",
      "164/257 [==================>...........] - ETA: 0s - loss: 0.5064 - accuracy: 0.8013"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5058 - accuracy: 0.8013"
     ]
    },
    {
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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",
      "194/257 [=====================>........] - ETA: 0s - loss: 0.5072 - accuracy: 0.7999"
     ]
    },
    {
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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: 0.5071 - accuracy: 0.7998"
     ]
    },
    {
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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: 0.5107 - accuracy: 0.7975"
     ]
    },
    {
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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: 0.5141 - accuracy: 0.7957"
     ]
    },
    {
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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: 0.5139 - 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",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5154 - accuracy: 0.7959 - val_loss: 0.5496 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.5535 - 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: 0.5601 - accuracy: 0.7695  "
     ]
    },
    {
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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: 0.5491 - accuracy: 0.7833"
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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",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 92/257 [=========>....................] - ETA: 0s - loss: 0.5340 - accuracy: 0.7891"
     ]
    },
    {
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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",
      "107/257 [===========>..................] - ETA: 0s - loss: 0.5331 - accuracy: 0.7891"
     ]
    },
    {
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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: 0.5273 - accuracy: 0.7920"
     ]
    },
    {
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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",
      "137/257 [==============>...............] - ETA: 0s - loss: 0.5230 - accuracy: 0.7929"
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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",
      "167/257 [==================>...........] - ETA: 0s - loss: 0.5167 - accuracy: 0.7972"
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5107 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5138 - accuracy: 0.7975 - val_loss: 0.5517 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:34 - loss: 0.5751 - accuracy: 0.7500"
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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",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5322 - 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",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 76/257 [=======>......................] - ETA: 0s - loss: 0.5369 - accuracy: 0.7866"
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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: 0.5292 - accuracy: 0.7878"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5280 - accuracy: 0.7889"
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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",
      "122/257 [=============>................] - ETA: 0s - loss: 0.5263 - accuracy: 0.7889"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/257 [==============>...............] - ETA: 0s - loss: 0.5235 - accuracy: 0.7911"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/257 [================>.............] - ETA: 0s - loss: 0.5201 - accuracy: 0.7921"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/257 [==================>...........] - ETA: 0s - loss: 0.5190 - 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",
      "182/257 [====================>.........] - ETA: 0s - loss: 0.5174 - accuracy: 0.7950"
     ]
    },
    {
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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",
      "197/257 [=====================>........] - ETA: 0s - loss: 0.5184 - accuracy: 0.7947"
     ]
    },
    {
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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",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.5151 - accuracy: 0.7963"
     ]
    },
    {
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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",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5169 - 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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5147 - accuracy: 0.7962"
     ]
    },
    {
     "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: 0.5119 - accuracy: 0.7977"
     ]
    },
    {
     "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 [==============================] - 2s 4ms/step - loss: 0.5119 - accuracy: 0.7977 - val_loss: 0.5549 - val_accuracy: 0.7751\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:20 - loss: 0.3735 - accuracy: 0.8750"
     ]
    },
    {
     "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: 0.5266 - accuracy: 0.7930  "
     ]
    },
    {
     "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",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5089 - 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",
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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",
      " 61/257 [======>.......................] - ETA: 0s - loss: 0.5072 - 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",
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5084 - accuracy: 0.7991"
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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: 0.5114 - accuracy: 0.7979 - val_loss: 0.5473 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.4107 - 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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 93/257 [=========>....................] - ETA: 0s - loss: 0.5016 - 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",
      "108/257 [===========>..................] - ETA: 0s - loss: 0.5003 - accuracy: 0.8056"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/257 [=============>................] - ETA: 0s - loss: 0.4959 - accuracy: 0.8077"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5066 - 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",
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/257 [=========================>....] - ETA: 0s - loss: 0.5111 - accuracy: 0.8014"
     ]
    },
    {
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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",
      "246/257 [===========================>..] - ETA: 0s - loss: 0.5100 - accuracy: 0.8007"
     ]
    },
    {
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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: 0.5101 - accuracy: 0.7994 - val_loss: 0.5471 - 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: 1:30 - loss: 0.5887 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.5150 - accuracy: 0.7958  "
     ]
    },
    {
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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",
      " 30/257 [==>...........................] - ETA: 0s - loss: 0.4962 - accuracy: 0.8094"
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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     "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: 0.5091 - accuracy: 0.7974 - val_loss: 0.5488 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.3358 - 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",
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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: 0.4987 - accuracy: 0.8042"
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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",
      "124/257 [=============>................] - ETA: 0s - loss: 0.5032 - accuracy: 0.8032"
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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",
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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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     "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: 0.5081 - 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",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5070 - accuracy: 0.8000 - val_loss: 0.5491 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:53 - loss: 0.3668 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4832 - accuracy: 0.8021  "
     ]
    },
    {
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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",
      "123/257 [=============>................] - 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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "198/257 [======================>.......] - ETA: 0s - loss: 0.5121 - accuracy: 0.7983"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/257 [=======================>......] - ETA: 0s - loss: 0.5080 - accuracy: 0.8000"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "228/257 [=========================>....] - ETA: 0s - loss: 0.5119 - accuracy: 0.7987"
     ]
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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",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.5103 - 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",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5068 - accuracy: 0.8011 - val_loss: 0.5451 - val_accuracy: 0.7838\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:20 - loss: 0.3426 - accuracy: 0.8438"
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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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4845 - accuracy: 0.8000  "
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 30/257 [==>...........................] - ETA: 0s - loss: 0.4863 - accuracy: 0.8115"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 75/257 [=======>......................] - ETA: 0s - loss: 0.4963 - accuracy: 0.8096"
     ]
    },
    {
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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",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.4964 - accuracy: 0.8080"
     ]
    },
    {
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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",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5027 - accuracy: 0.8045"
     ]
    },
    {
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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",
      "120/257 [=============>................] - ETA: 0s - loss: 0.5035 - accuracy: 0.8052"
     ]
    },
    {
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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",
      "135/257 [==============>...............] - ETA: 0s - loss: 0.5026 - accuracy: 0.8039"
     ]
    },
    {
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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",
      "150/257 [================>.............] - ETA: 0s - loss: 0.5003 - 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",
      "166/257 [==================>...........] - ETA: 0s - loss: 0.4976 - accuracy: 0.8033"
     ]
    },
    {
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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: 0.4975 - accuracy: 0.8033"
     ]
    },
    {
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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",
      "211/257 [=======================>......] - ETA: 0s - loss: 0.4989 - accuracy: 0.8020"
     ]
    },
    {
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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",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5007 - 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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5015 - accuracy: 0.8017"
     ]
    },
    {
     "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 5ms/step - loss: 0.5057 - accuracy: 0.8002 - val_loss: 0.5482 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:24 - loss: 0.3317 - accuracy: 0.9375"
     ]
    },
    {
     "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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4672 - accuracy: 0.8250  "
     ]
    },
    {
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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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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "172/257 [===================>..........] - ETA: 0s - loss: 0.4961 - accuracy: 0.8038"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/257 [====================>.........] - ETA: 0s - loss: 0.5019 - accuracy: 0.8024"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "236/257 [==========================>...] - ETA: 0s - loss: 0.5082 - 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",
      "251/257 [============================>.] - ETA: 0s - loss: 0.5060 - accuracy: 0.7998"
     ]
    },
    {
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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: 0.5049 - accuracy: 0.8003 - val_loss: 0.5477 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.3929 - accuracy: 0.8125"
     ]
    },
    {
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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: 0.4252 - accuracy: 0.8301  "
     ]
    },
    {
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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",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.4791 - accuracy: 0.8021"
     ]
    },
    {
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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",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.4925 - 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",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.4951 - 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",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5010 - accuracy: 0.7982"
     ]
    },
    {
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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: 0.5018 - accuracy: 0.7999"
     ]
    },
    {
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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",
      "113/257 [============>.................] - ETA: 0s - loss: 0.4975 - accuracy: 0.8020"
     ]
    },
    {
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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",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.4973 - 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",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5019 - 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",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5024 - accuracy: 0.8004"
     ]
    },
    {
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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: 0.5064 - 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",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5068 - 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",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5040 - accuracy: 0.8005"
     ]
    },
    {
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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: 0.5001 - 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",
      "240/257 [===========================>..] - ETA: 0s - loss: 0.5032 - 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",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5041 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5042 - accuracy: 0.8007 - val_loss: 0.5474 - val_accuracy: 0.7828\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:25 - loss: 0.3471 - 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: 0.4518 - 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",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4868 - accuracy: 0.7969"
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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",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5041 - accuracy: 0.7936"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4790 - accuracy: 0.8032"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.4863 - accuracy: 0.8005"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4915 - accuracy: 0.7999"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4956 - accuracy: 0.7976"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4947 - 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",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4969 - accuracy: 0.7985"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4981 - accuracy: 0.7993"
     ]
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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",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.4985 - 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",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.5001 - accuracy: 0.7997"
     ]
    },
    {
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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: 0.5050 - accuracy: 0.7986"
     ]
    },
    {
     "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",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5036 - accuracy: 0.7998"
     ]
    },
    {
     "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: 0.5035 - accuracy: 0.7999 - val_loss: 0.5453 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.4356 - 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: 0.4821 - 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",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4636 - accuracy: 0.8135"
     ]
    },
    {
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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",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.4720 - accuracy: 0.8172"
     ]
    },
    {
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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",
      " 62/257 [======>.......................] - ETA: 0s - loss: 0.4768 - accuracy: 0.8150"
     ]
    },
    {
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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",
      " 78/257 [========>.....................] - ETA: 0s - loss: 0.4759 - accuracy: 0.8121"
     ]
    },
    {
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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",
      " 94/257 [=========>....................] - ETA: 0s - loss: 0.4801 - accuracy: 0.8102"
     ]
    },
    {
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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",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.4884 - accuracy: 0.8071"
     ]
    },
    {
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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: 0.4938 - accuracy: 0.8043"
     ]
    },
    {
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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",
      "142/257 [===============>..............] - ETA: 0s - loss: 0.4907 - accuracy: 0.8057"
     ]
    },
    {
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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",
      "158/257 [=================>............] - ETA: 0s - loss: 0.4940 - accuracy: 0.8052"
     ]
    },
    {
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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: 0.4966 - accuracy: 0.8046"
     ]
    },
    {
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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",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.4959 - accuracy: 0.8054"
     ]
    },
    {
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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: 0.4977 - 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",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5019 - accuracy: 0.8004"
     ]
    },
    {
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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: 0.5068 - accuracy: 0.7983"
     ]
    },
    {
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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: 0.5037 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5027 - accuracy: 0.8007 - val_loss: 0.5464 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:23 - loss: 0.3806 - accuracy: 0.8750"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "172/257 [===================>..........] - ETA: 0s - loss: 0.4977 - 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",
      "187/257 [====================>.........] - ETA: 0s - loss: 0.4984 - accuracy: 0.8015"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/257 [======================>.......] - ETA: 0s - loss: 0.4982 - accuracy: 0.8025"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/257 [========================>.....] - ETA: 0s - loss: 0.4994 - accuracy: 0.8034"
     ]
    },
    {
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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",
      "235/257 [==========================>...] - ETA: 0s - loss: 0.4996 - 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",
      "251/257 [============================>.] - ETA: 0s - loss: 0.5009 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5021 - accuracy: 0.8018 - val_loss: 0.5460 - val_accuracy: 0.7882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:34 - loss: 0.5360 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4775 - accuracy: 0.7979  "
     ]
    },
    {
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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",
      " 29/257 [==>...........................] - ETA: 0s - loss: 0.4824 - 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",
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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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    },
    {
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "237/257 [==========================>...] - ETA: 0s - loss: 0.4946 - accuracy: 0.8076"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5002 - accuracy: 0.8048"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.5015 - accuracy: 0.8035 - val_loss: 0.5450 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.6692 - accuracy: 0.7500"
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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",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4978 - accuracy: 0.8015  "
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.4918 - accuracy: 0.8047"
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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: 0.5005 - accuracy: 0.7973"
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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",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5000 - accuracy: 0.7988"
     ]
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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",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5023 - accuracy: 0.7988"
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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",
      "127/257 [=============>................] - ETA: 0s - loss: 0.5028 - accuracy: 0.8012"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.5026 - accuracy: 0.8011"
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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",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5067 - accuracy: 0.8005"
     ]
    },
    {
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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: 0.5040 - 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",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.5074 - accuracy: 0.7998"
     ]
    },
    {
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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: 0.5038 - accuracy: 0.8010"
     ]
    },
    {
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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: 0.5028 - 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",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5020 - accuracy: 0.8020"
     ]
    },
    {
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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: 0.5016 - 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",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5013 - accuracy: 0.8018 - val_loss: 0.5448 - val_accuracy: 0.7849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:35 - loss: 0.2854 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4717 - accuracy: 0.8292  "
     ]
    },
    {
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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",
      " 30/257 [==>...........................] - ETA: 0s - loss: 0.4869 - accuracy: 0.8146"
     ]
    },
    {
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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",
      " 58/257 [=====>........................] - ETA: 0s - loss: 0.4975 - 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",
      " 73/257 [=======>......................] - ETA: 0s - loss: 0.4885 - accuracy: 0.8095"
     ]
    },
    {
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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",
      " 88/257 [=========>....................] - ETA: 0s - loss: 0.4897 - accuracy: 0.8043"
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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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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.5012 - accuracy: 0.7992"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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",
      "250/257 [============================>.] - ETA: 0s - loss: 0.5016 - 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",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5003 - accuracy: 0.8008 - val_loss: 0.5443 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/35\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:33 - loss: 0.3198 - accuracy: 0.8438"
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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: 0.4504 - accuracy: 0.8184  "
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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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     "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",
      " 76/257 [=======>......................] - ETA: 0s - loss: 0.4656 - accuracy: 0.8146"
     ]
    },
    {
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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",
      " 91/257 [=========>....................] - ETA: 0s - loss: 0.4748 - accuracy: 0.8101"
     ]
    },
    {
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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",
      "106/257 [===========>..................] - ETA: 0s - loss: 0.4835 - accuracy: 0.8060"
     ]
    },
    {
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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",
      "121/257 [=============>................] - ETA: 0s - loss: 0.4842 - accuracy: 0.8040"
     ]
    },
    {
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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",
      "136/257 [==============>...............] - ETA: 0s - loss: 0.4871 - accuracy: 0.8035"
     ]
    },
    {
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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",
      "151/257 [================>.............] - ETA: 0s - loss: 0.4907 - 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",
      "166/257 [==================>...........] - ETA: 0s - loss: 0.4962 - 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",
      "181/257 [====================>.........] - ETA: 0s - loss: 0.4938 - accuracy: 0.8020"
     ]
    },
    {
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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: 0.4945 - 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",
      "211/257 [=======================>......] - ETA: 0s - loss: 0.4930 - 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",
      "226/257 [=========================>....] - ETA: 0s - loss: 0.4954 - accuracy: 0.8024"
     ]
    },
    {
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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: 0.4975 - 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",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5001 - 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 [==============================] - 2s 5ms/step - loss: 0.5002 - accuracy: 0.8000 - val_loss: 0.5428 - val_accuracy: 0.7882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:43 - loss: 0.3885 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.5338 - accuracy: 0.7958  "
     ]
    },
    {
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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",
      " 75/257 [=======>......................] - ETA: 0s - loss: 0.5078 - accuracy: 0.8033"
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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",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.5093 - accuracy: 0.8017"
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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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.4998 - accuracy: 0.8020"
     ]
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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: 0.5003 - accuracy: 0.8016"
     ]
    },
    {
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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: 0.5003 - accuracy: 0.8016 - val_loss: 0.5443 - val_accuracy: 0.7849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:16 - loss: 0.4864 - 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\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5083 - accuracy: 0.8086  "
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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: 0.4821 - accuracy: 0.8184"
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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",
      " 62/257 [======>.......................] - ETA: 0s - loss: 0.4925 - accuracy: 0.8095"
     ]
    },
    {
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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",
      " 77/257 [=======>......................] - ETA: 0s - loss: 0.4957 - accuracy: 0.8072"
     ]
    },
    {
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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",
      " 93/257 [=========>....................] - ETA: 0s - loss: 0.4933 - accuracy: 0.8071"
     ]
    },
    {
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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",
      "108/257 [===========>..................] - ETA: 0s - loss: 0.4982 - accuracy: 0.8053"
     ]
    },
    {
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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",
      "123/257 [=============>................] - ETA: 0s - loss: 0.4947 - accuracy: 0.8061"
     ]
    },
    {
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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",
      "138/257 [===============>..............] - ETA: 0s - loss: 0.4977 - accuracy: 0.8062"
     ]
    },
    {
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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",
      "167/257 [==================>...........] - ETA: 0s - loss: 0.5024 - 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",
      "182/257 [====================>.........] - ETA: 0s - loss: 0.5007 - accuracy: 0.8039"
     ]
    },
    {
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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",
      "197/257 [=====================>........] - ETA: 0s - loss: 0.5031 - accuracy: 0.8016"
     ]
    },
    {
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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",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.5051 - 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",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5061 - 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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5033 - 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",
      "257/257 [==============================] - ETA: 0s - loss: 0.4999 - accuracy: 0.8024"
     ]
    },
    {
     "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 5ms/step - loss: 0.4999 - accuracy: 0.8024 - val_loss: 0.5447 - 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: 1:22 - loss: 0.4849 - 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",
      " 15/257 [>.............................] - ETA: 0s - loss: 0.4836 - accuracy: 0.7979  "
     ]
    },
    {
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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",
      " 30/257 [==>...........................] - ETA: 0s - loss: 0.5175 - accuracy: 0.7958"
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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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    },
    {
     "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",
      " 60/257 [======>.......................] - ETA: 0s - loss: 0.5084 - accuracy: 0.7958"
     ]
    },
    {
     "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",
      " 75/257 [=======>......................] - ETA: 0s - loss: 0.5079 - accuracy: 0.7917"
     ]
    },
    {
     "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",
      " 90/257 [=========>....................] - ETA: 0s - loss: 0.5136 - accuracy: 0.7899"
     ]
    },
    {
     "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",
      "105/257 [===========>..................] - ETA: 0s - loss: 0.5069 - accuracy: 0.7955"
     ]
    },
    {
     "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",
      "120/257 [=============>................] - ETA: 0s - loss: 0.5039 - accuracy: 0.7977"
     ]
    },
    {
     "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",
      "135/257 [==============>...............] - ETA: 0s - loss: 0.4964 - accuracy: 0.8016"
     ]
    },
    {
     "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",
      "150/257 [================>.............] - ETA: 0s - loss: 0.4975 - accuracy: 0.8017"
     ]
    },
    {
     "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",
      "165/257 [==================>...........] - ETA: 0s - loss: 0.4962 - accuracy: 0.8034"
     ]
    },
    {
     "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",
      "180/257 [====================>.........] - ETA: 0s - loss: 0.4962 - accuracy: 0.8040"
     ]
    },
    {
     "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",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.4953 - accuracy: 0.8045"
     ]
    },
    {
     "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",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.4968 - accuracy: 0.8032"
     ]
    },
    {
     "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",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.4930 - accuracy: 0.8047"
     ]
    },
    {
     "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",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.4945 - accuracy: 0.8037"
     ]
    },
    {
     "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 5ms/step - loss: 0.4989 - accuracy: 0.8021 - val_loss: 0.5433 - val_accuracy: 0.7893\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": "2022-12-14T21:00:21.791692Z",
     "iopub.status.busy": "2022-12-14T21:00:21.791074Z",
     "iopub.status.idle": "2022-12-14T21:00:21.796470Z",
     "shell.execute_reply": "2022-12-14T21:00:21.795752Z"
    },
    "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": "2022-12-14T21:00:21.799673Z",
     "iopub.status.busy": "2022-12-14T21:00:21.799058Z",
     "iopub.status.idle": "2022-12-14T21:00:22.295796Z",
     "shell.execute_reply": "2022-12-14T21:00:22.295036Z"
    },
    "id": "nnQfxevhLKld"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_59833/4094752860.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": [
    "## 모델 평가하기\n",
    "\n",
    "그리고 모델이 어떤 성능을 보이는지 알아보겠습니다. 손실(오류를 나타내는 숫자, 값이 낮을수록 좋음) 및 정확성의 두 가지 값이 반환됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:00:22.299723Z",
     "iopub.status.busy": "2022-12-14T21:00:22.299411Z",
     "iopub.status.idle": "2022-12-14T21:00:22.580329Z",
     "shell.execute_reply": "2022-12-14T21:00:22.579539Z"
    },
    "id": "y0ExC8D0LX8m"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 0.5347 - accuracy: 0.7907 - 265ms/epoch - 66ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.535\n",
      "accuracy: 0.791\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": [
    "특히 정확성이 빠르게 증가하는 동안 손실이 빠르게 감소하는 것을 볼 수 있습니다. 예측이 실제 레이블과 어떻게 관련되는지 확인하기 위해 몇 가지 예를 플롯해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:00:22.584200Z",
     "iopub.status.busy": "2022-12-14T21:00:22.583435Z",
     "iopub.status.idle": "2022-12-14T21:00:23.010491Z",
     "shell.execute_reply": "2022-12-14T21:00:23.009802Z"
    },
    "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 153ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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",
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       "</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": [
    "이 무작위 샘플의 경우 모델이 대부분 올바른 레이블을 예측하여 과학적 문장을 상당히 잘 포함할 수 있음을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oLE0kCfO5CIA"
   },
   "source": [
    "# 다음 단계\n",
    "\n",
    "이제 TF-Hub의 CORD-19 Swivel 임베딩에 대해 조금 더 알게 되었으므로 CORD-19 Kaggle 대회에 참여하여 코로나바이러스감염증-19 관련 학술 텍스트에서 과학적 통찰력을 얻는 데 기여해 보세요.\n",
    "\n",
    "- [CORD-19 Kaggle 챌린지](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)에 참여하세요.\n",
    "- [코로나바이러스감염증-19 공개 연구 데이터세트(CORD-19)](https://api.semanticscholar.org/CorpusID:216056360)에 대해 자세히 알아보세요.\n",
    "- https://tfhub.dev/tensorflow/cord-19/swivel-128d/3에서 설명서를 참조하고 TF-Hub 임베딩에 대한 자세히 알아보세요.\n",
    "- [TensorFlow 임베딩 프로젝터](http://projector.tensorflow.org/?config=https://storage.googleapis.com/tfhub-examples/tensorflow/cord-19/swivel-128d/3/tensorboard/projector_config.json)로 CORD-19 임베딩 공간을 탐색해 보세요."
   ]
  }
 ],
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